A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different (...) class='Hi'>control stages. Lastly, the proposed fuzzy DDMFAC approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations. (shrink)

In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. (...) The simulation results testify the effectiveness of the presented control algorithm. (shrink)

The proliferation of big data analytics in criminal justice suggests that there are positive frames and imaginaries legitimising them and depicting them as the panacea for efficient crime control. Criminological and criminal justice scholarship has paid insufficient attention to these frames and their accompanying narratives. To address the gap created by the lack of theoretical and empirical insight in this area, this article draws on a study that systematically reviewed and compared multidisciplinary academic abstracts on the data- (...) class='Hi'>driven tools now shaping decision-making across several justice systems. Using insights distilled from the study, the article proposes three frames for understanding how the technologies are portrayed. Inherent in the frames are a set of narratives emphasising their ostensible status as vital crime control mechanisms. These narratives obfuscate the harms of data-driven technologies and evince idealistic imaginaries of their capabilities. The narratives are bolstered by unequal structural arrangements, specifically the unevenly distributed digital capital with which some are empowered to participate in technology development for criminal justice application and other forms of penal governance. In unravelling these issues, the article advances current understanding of the dynamics that sustain the depiction of data-driven technologies as prime crime prevention and law enforcement tools. (shrink)

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of (...) this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of (...) this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, (...) conservative ethos, creating technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration. (shrink)

This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could—especially considering current developments in big data and artificial intelligence—compromise the (...) right by leading to injustices and affecting concrete modes of individual self-determination. In order to address this potential tension, two key elements for ethical reflection on data-driven medicine are proposed: the controllability of information flows, including technical infrastructures that are conducive towards controllability, and a paradigm shift towards output-orientation in governance and policy. (shrink)

Research shows that exploring language corpora through data-driven learning plays a significant role in language learning. Nevertheless, it is not clear if using concordancing as an application of DDL affects the learners’ second language motivation. To address this gap, the current study adopted a triangulation design, validating quantitative data model, and a quasi-experimental design. Ninety English-major university students with an intermediate level of English language proficiency, divided into control and experimental groups, took part in the study. (...) Drawing on a second language motivational self-system questionnaire, the findings of the study did not result in any statistically significant differences between the students in their second language motivational self-system. Altogether, the students found learning English through a DDL approach with concordancing less motivating than receiving explicit instruction. The study has implications for language teaching and learning. (shrink)

The present research investigates the role of voluntary, conscious processing in strategy change. In 2 experiments, we address whether the switch to a new strategy is the result of data - driven, automatic processes or of voluntary processes. Experiment 1 demonstrates that participants performing an alphabet verification task are able to transfer a newly adopted strategy to dissimilar information never encountered before, verbally describe the task regularity that allows for the generation and application of the new strategy immediately (...) after the strategy was adopted. Using the same experimental task, Experiment 2 shows that participants, and decide against adopting a new strategy when the available evidence suggests that the new strategy cannot be used for the entire range of problems encountered. Overall, the obtained results support the view that strategy change is mediated by voluntary controlled processing. They do not support the view that strategy change is an inevitable, automatic consequence of task practice. The present research thus highlights a potential function of conscious human processing. (shrink)

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from (...) studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare. (shrink)

Implications of algorithmic mediation can be studied through the artefact itself, peoples’ practices, and the social/political/economical arrangements that affect and are affected by such interactions. Most studies in Academic social media (ASM) focus on one of these elements at a time, either examining design elements or the users’ behaviour on and perceptions of such platforms. We take a multi-faceted approach using affordances as a lens to analyze practices and arrangements traversed by algorithmic mediation. Following our earlier studies that examined the (...) artefact, this study’s aim is to understand how algorithmic mediation in ASM may shape researchers’ perceptions. We conducted online in-depth interviews with show and tell (n = 11) with ASM users from four countries, about their use of ASM. Data were analyzed using thematic analysis, and the codes were clustered in themes and validated via peer debriefing process. Four themes—related to exposure to content, visibility, ethical or legal infringements, and engagement—are discussed considering implications of datafication and findability. We conclude that algorithmic mediation constructs a narration of the relevant other in ASM, configuring the other as participatory and productive. (shrink)

New data-driven technologies yield benefits and potentials, but also confront different agents and stakeholders with challenges in retaining control over their data. Our goal in this study is to arrive at a clear picture of what is meant by data sovereignty in such problem settings. To this end, we review 341 publications and analyze the frequency of different notions such as data sovereignty, digital sovereignty, and cyber sovereignty. We go on to map agents they (...) concern, in which context they appear, and which values they allude to. While our sample reveals a considerable degree of divergence and an occasional lack of clarity about intended meanings of data sovereignty, we propose a conceptual grid to systematize different dimensions and connotations. Each of them relates in some way to meaningful control, ownership, and other claims to data articulated by a variety of agents ranging from individuals to countries. Data sovereignty alludes to a nuanced mixture of normative concepts such as inclusive deliberation and recognition of the fundamental rights of data subjects. (shrink)

The Snowden leaks, first published in June 2013, provided unprecedented insights into the operations of state-corporate surveillance, highlighting the extent to which everyday communication is integrated into an extensive regime of control that relies on the ‘datafication’ of social life. Whilst such data-driven forms of governance have significant implications for citizenship and society, resistance to surveillance in the wake of the Snowden leaks has predominantly centred on techno-legal responses relating to the development and use of encryption and (...) policy advocacy around privacy and data protection. Based on in-depth interviews with a range of social justice activists, we argue that there is a significant level of ambiguity around this kind of anti-surveillance resistance in relation to broader activist practices, and critical responses to the Snowden leaks have been confined within particular expert communities. Introducing the notion of ‘data justice’, we therefore go on to make the case that resistance to surveillance needs to be conceptualized on terms that can address the implications of this data-driven form of governance in relation to broader social justice agendas. Such an approach is needed, we suggest, in light of a shift to surveillance capitalism in which the collection, use and analysis of our data increasingly comes to shape the opportunities and possibilities available to us and the kind of society we live in. (shrink)

Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of (...) the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort. (shrink)

Pneumatic muscle actuators own excellent compliance and a high power-to-weight ratio and have been widely used in bionic robots and rehabilitated robots. However, the high nonlinear characteristics of PMAs due to inherent construction and pneumatic driving principle bring great challenges in applications acquired accurately modeling and controlling. To tackle the tricky problem, a single PMA mass setup is constructed, and a back propagation neural network is employed to identify the dynamics of the setup. An offline model is built up using (...) sampled data, and online modifications are performed to further improve the quality of the model. An adaptive controller based on BPNN is designed using gradient descent information of the built-up model. Experiments of identifying the PMA setup using BPNN and position tracking by adaptive BPNN controller are performed, and results demonstrate the good capacity in accurate controlling of the PMA setup. (shrink)

Buildings account for a significant portion of global energy consumption and carbon emissions, making energy efficiency a critical focus for urban sustainability. Traditional building management systems often lack the adaptability and precision needed to optimize energy usage dynamically. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance energy efficiency in smart buildings by enabling real-time monitoring, predictive maintenance, and adaptive control systems. By integrating data from smart meters, occupancy sensors, and environmental monitors, cities can reduce (...) energy waste, lower carbon footprints, and improve occupant comfort. Experimental results demonstrate significant improvements in energy savings, operational costs, and environmental impact, offering a sustainable blueprint for smart building management. (shrink)

Background Sharing anonymized/de-identified clinical trial data and publishing research outcomes in scientific journals, or presenting them at conferences, is key to data-driven scientific exchange. However, when data from scientific publications are linked to other publicly available personal information, the risk of reidentification of trial participants increases, raising privacy concerns. Therefore, we defined a set of criteria allowing us to determine and minimize the risk of data reidentification. We also implemented a review process at Takeda for (...) clinical publications prior to submission for publication in journals or presentation at medical conferences. Methods Abstracts, manuscripts, posters, and oral presentations containing study participant information were reviewed and the potential impact on study participant privacy was assessed. Our focus was on direct and indirect identifiers, such as sex, age or geographical indicators in rare disease studies or studies with small sample size treatment groups. Risk minimization was sought using a generalized presentation of identifier-relevant information and decision-making on data sharing for further research. Additional risk identification was performed based on study participant/personnel parameters present in materials destined for the public domain. The potential for participant/personnel identification was then calculated to facilitate presentation of meaningful but de-identified information. Results The potential for reidentification was calculated using a risk ratio of the exposed versus available individuals, with a value above the threshold of 0.09 deemed an unacceptable level of reidentification risk. We found that in 13% of Takeda clinical trial publications reviewed, either individuals could potentially be reidentified or inappropriate data sharing plans could pose a data privacy risk to study participants. In 1/110 abstracts, 58/275 manuscripts, 5/87 posters and 3/58 presentations, changes were necessary due to data privacy concerns/rules. Despite the implementation of risk-minimization measures prior to release, direct and indirect identifiers were found in 11% and 34% of the analysed documents, respectively. Conclusions Risk minimization using de-identification of clinical trial data presented in scientific publications and controlled data sharing conditions improved privacy protection for study participants. Our results also suggest that additional safeguards should be implemented to ensure that higher data privacy standards are met. (shrink)

This article describes how the City of Los Angeles is showcasing data-driven services to the public through dynamic visualisations of open data. I frame an analysis of this aspect of datafication in local government through linguistics and cultural theory; drawing on this set of literature I theorise the use of public data as both a performative tool and a performance of data-driven city services. I then discuss examples of interactive maps on the City of (...) Los Angeles’ open data websites, produced by the Bureau of Sanitation, the Controller’s Office, and the Department of Transportation. The article concludes with some general thoughts on how open data maps are a new form of impression management; they set up a relationship in which government and citizens are in alignment, sharing an orientation towards administrative problems that begins at the same point, framed by an administration’s datasets. (shrink)

Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds (...) that the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context. (shrink)

While the control of cell migration by biochemical and biophysical factors is largely documented, a precise quantification of cell migration parameters in different experimental contexts is still questionable. Indeed, these phenomenological parameters can be evaluated from data obtained either at the cell population level or at the individual cell level. However, the range within which both characterizations of cell migration are equivalent remains unclear. We analyse here to which extent both sources of data could be integrated within (...) a unified description of cell migration by considering the motility of the endothelial cell line EAhy926. Using time-lapse video-microscopy and associated analysis of digital image time series, we quantified EAhy926 random motility coefficient, migration speed and trajectory persistence time in two different migration assays: the in vitro wound healing assay, and the cell-populated agarose drop assay. In order to analyse the agreement between independent quantifications of cell motility based either on individual cell analysis or cell population dynamic analysis, a theoretical multi-agents cellular model was developed and discussed as a possible theoretical framework able to unify these multi-scale data. Model simulations especially reveal the potential bias induced by cell proliferation and cell-cell adhesion when cell migration parameters are estimated from the extensively used in vitro wound healing assay. (shrink)

Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are (...) absent from these debates. This paper addresses this gap, reporting on a survey of the public on their views of data management approaches, undertaken by the authors and administered in the UK, where departure from the EU makes future data legislation uncertain. The survey found that respondents dislike the current approach in which commercial organizations control their personal data and prefer approaches that give them control over their data, that include oversight from regulatory bodies or that enable them to opt out of data gathering. Variations of data trusts – that is, structures that provide independent stewardship of data – were also preferable to the current approach, but not as widely preferred as control, oversight and opt out options. These features therefore constitute ‘good data management’ for survey respondents. These findings align only in part with principles of good data identified by policy experts and researchers. Our findings nuance understandings of good data as a concept and of good data management as a practice and point to where further research and policy action are needed. (shrink)

Cognitive control refers to the regulation of mental activity to support flexible cognition across different domains. Cragg and Nation (2010) propose that the development of cognitive control in children parallels the development of language abilities, particularly inner speech. We suggest that children’s late development of cognitive control also mirrors their limited ability to revise misinterpretations of sentence meaning. Moreover, we argue that for certain tasks, a tradeoff between bottom-up (data-driven) and top-down (rule-based) thinking may actually (...) benefit performance in both children and adults. Specifically, we propose that a lack of cognitive control may promote important aspects of cognitive development, like language acquisition and creativity. (shrink)

This study investigates audience perceptions of AI-generated news across ten African countries, focusing on trust, bias, and transparency. Using a non-probability cross-sectional online survey, data were collected from 1960 participants between May and July 2024. The sample encompassed diverse demographics, leveraging social media for broad reach. The study revealed that trust in AI-generated news is generally neutral, with significant variations influenced by demographic factors, particularly age. A moderate positive correlation between perceived bias and trust suggests that awareness of potential (...) biases does not necessarily erode trust. Younger participants showed greater receptiveness to AI-generated content, especially when transparency and readability were prioritized. The findings underscore the need to address bias, transparency, and digital literacy to build trust in AI-generated news, particularly among older audiences. The study advances theoretical frameworks, including uses and gratifications, digital divide, and cultivation theory, while highlighting the importance of digital literacy in shaping media consumption patterns. Practical implications emphasize the need for culturally relevant AI applications in journalism and digital literacy programs to bridge the trust gap in AI-driven news across Africa. These insights are crucial for news organizations, policymakers, and educators aiming to understand the interaction between AI and Journalism in Africa. (shrink)

The use of transcranial Electrical Stimulation in the modulation of cognitive brain functions to improve neuropsychiatric conditions has extensively increased over the decades. tES techniques have also raised new challenges associated with study design, stimulation protocol, functional specificity, and dose-response relationship. In this paper, we addressed challenges through the emerging methodology to investigate the dose-response relationship of High Definition-transcranial Direct Current Stimulation, identifying the role of negative valence in tinnitus perception. In light of the neurofunctional testable framework and tES application, (...) hypotheses were formulated to measure clinical and surrogate endpoints. We posited that conscious pairing adequately pleasant stimuli with tinnitus perception results in correction of the loudness misperception and would be reinforced by concurrent active HD-tDCS on the left Dorsolateral Prefrontal Cortex. The dose-response relationship between HD-tDCS specificity and the loudness perception is also modeled. We conducted a double-blind, randomized crossover pilot study with six recruited tinnitus patients. Accrued data was utilized to design a well-controlled adaptive seamless Bayesian dose-response study. The sample size and optimum interims were anticipated for adaptive decision-making about efficacy, safety, and single session dose parameters. Furthermore, preliminary pilot study results were sufficient to show a significant difference within the longitudinally detected self-report tinnitus loudness between before and under positive emotion induction. This study demonstrated a research methodology used to improve emotion regulation in tinnitus patients. In the projected method, positive emotion induction is essential for promoting functional targeting under HD-tDCS anatomical specificity to indicate the efficacy and facilitate the dose-finding process. The continuous updating of prior knowledge about efficacy and dose during the exploratory stage adapts the anticipated dose-response model. Consequently, the effective dose range to make superiority neuromodulation in correcting loudness misperception of tinnitus will be redefined. Highly effective dose adapts the study to a standard randomized trial and transforms it into the confirmatory stage in which active HD-tDCS protocol is compared with a sham trial. Establishing the HD-tDCS intervention protocols relying on this novel method provides reliable evidence for regulatory agencies to approve or reject the efficacy and safety. Furthermore, this paper supports a technical report for designing multimodality data-driven complementary investigations in emotion regulation, including EEG-driven neuro markers, Stroop-driven attention biases, and neuroimaging-driven brain network dynamics. (shrink)

We comment on the proposition “that lower temperatures and especially greater seasonal variation in temperature call for individuals and societies to adopt … a greater degree of self-control” (Van Lange et al., sect. 3, para. 4) for which we cannot find empirical support in a large data set with data-driven analyses. After providing greater nuance in our theoretical review, we suggest that Van Lange et al. revisit their model with an eye toward the social determinants of (...) self-control. (shrink)

Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we term “carceral algorithms” that are used during incarceration. This paper is focused on the Pennsylvania Additive Classification Tool used to classify prisoners’ custody levels while they are incarcerated. Algorithms that are used during incarceration warrant deeper attention by scholars because they have the power to enact the lived reality of the prisoner. The algorithm in this case determines the likelihood a person would endure (...) additional disciplinary actions, can complete required programming, and gain experiences that, among other things, are distilled into variables feeding into the parole algorithm. Given such power, examining algorithms used on people currently incarcerated offers a unique analytic view to think about the dialectic relationship between data and algorithms. Our examination of the PACT is two-fold and complementary. First, our qualitative overview of the historical context surrounding PACT reveals that it is designed to prioritize incapacitation and control over rehabilitation. While it closely informs prisoner rehabilitation plans and parole considerations, it is rooted in population management for prison securitization. Second, on analyzing data for 146,793 incarcerated people in PA, along with associated metadata related to the PACT, we find it is replete with racial bias as well as errors, omissions, and inaccuracies. Our findings to date further caution against data-driven criminal justice reforms that rely on pre-existing data infrastructures and expansive, uncritical, data-collection routines. (shrink)

In recent years, the green aviation technology draws more attention, and more hybrid power units have been applied to the aerial vehicles. To achieve the high performance and long lifetime of components during varied working conditions, the effective regulation of the energy management is necessary for the vehicles with hybrid power unit. In this paper, power prediction-based model predictive control for energy management strategy is proposed for the vehicle equipped with HPU based on turboshaft engine in order to maintain (...) proper battery’s state of charge and decrease turboshaft engine’s exhaust gas temperature. First, a modeling approach based on data-driven method is adopted to obtain the mathematical model of turboshaft engine considering time delay and inertial of states. An integrated power predictor consisting of the classification of input status and the subpredictors are developed based on the deep learning method to improve the accuracy of the prediction model of the model predictive control. Subsequently, an EMS based on MPC using the proposed power predictor is introduced to regulate the SOC of battery and the EGT of turboshaft engine. The comparison with experimental results shows the high accuracy of mathematical model of turboshaft engine. The simulation results show the effectiveness of the proposed EMS for the vehicle, and the effects of different weight coefficients of objective function on the proposed EMS are discussed. (shrink)

Policy and technological transformation have coalesced to usher in massive changes to educational systems over the past two decades. Teachers’ roles, subjectivities and professional identities have been subject to sweeping changes enabled by sophisticated forms of governance. Simultaneously, students have been recast as ‘learners’; like teachers, learners have become subject to new forms of governance, through technological surveillance and datafication. This paper focuses on the intersection of the metrics driven approach to education and the political as a way to (...) re-think the future of schooling in more explicitly philosophical terms. This exploration starts with a critical examination of constructions of teachers, learners and the digital data-driven educational culture in order to explicate the futures being generated. The trajectory of this future is explored through reference to the techno-educational models currently being developed in Silicon Valley. Drawing on Deleuze’s notion of control societies we contribute to the ongoing philosophical investigation of the datafication of education; a necessary discussion if we are to explore the future implications of schooling in a technologically saturated world. We present consideration of the past, present and future, as three ways of considering alternatives to a datafied education system. Alternative conceptualisations of the future of schooling are possible which offer ways of understanding and politicising what happens when we impose data-driven accountabilities into people’s lives. (shrink)

Ever since Big Data became a mot du jour across social fields, optical metaphors such as the microscope began to surface in popular discourse to describe and qualify its epistemological impact. While the persistence of optics seems to be at odds with the datafication of vision, this article suggests that the optical metaphor offers an opportunity to reflect about the material consequences of the modes of seeing and knowing that currently shape datafied worlds. Drawing on feminist new materialism, the (...) article investigates the optical metaphor as a material-discursive practice that actively constitutes the world, as metaphors imply modes of thinking, knowing and doing that have material enactions. Expanding visual culture theories, the notion of ‘optical unconscious’ is taken up to discuss the tensions between displacement and persistence of optics within datafied worlds, that is, how optical vision is displaced but also mobilised and repurposed by data-driven knowledge. In dialogue with feminist science and technology studies and speculative ethics, I suggest that the datafication of vision offers a chance to reconceptualize the sense of sight towards a sensorial engagement with Big Data premised on responsibility, care, and an ethics of unknowability. Within this framework, vision may be conceived differently, perhaps not only as enhancement and control, but as generator of new possibilities. Ultimately, the article proposes that the visual theories after which Big Data is being imagined matter not only for our understanding of Big Data's epistemic potential, but also for the possibility of shaping emerging data worlds. (shrink)

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS (...) should deal with data responsibly, guaranteeing nondiscrimination with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed. (shrink)

The ‘smart city’ has been driven by advances in information and communication technologies, with the aim of integrating these technologies with urban infrastructures for improved optimisation, automation and control. Smart cities have emerged as a response to the challenges faced by megacities and are likely to manifest the ‘datafying’ society in the public space. However, the pervasive nature of data collection, continuous analysis and inference, and long-term data storage result in a potentially problematic reconfiguration of society (...) that undermines individual and collective autonomy. This article proposes the concept of ‘data autonomy’ as a guiding principle for the development of smart cities, addressing challenges related to the potential misuse of personal data, sphere transgression, and datafied gentrification. Section II. explores concerns related to data protection law, which can be understood as a proxy for the relationship between the individual and society, highlighting power dynamics and the autonomy of individuals and communities. Section III. discusses the concept of sphere transgression, where tech giants expand their influence in society without adequate expertise or accountability. Section IV. examines the potential impact of datafied gentrification on individual and collective autonomy. Building on the challenges identified throughout these sections, this paper proposes ‘data autonomy’ as a guiding principle for smart city development in section V., promoting a value-based approach centred on human dignity. To make it practicable, increased interdisciplinary collaboration in the development of smart cities is needed, with a particular focus on improved design and impact assessment methodologies. (shrink)

This study investigates the intersection of existing computational frameworks for zoonotic disease control within the emerging societal paradigm, Society 5.0. Technologies in human-centric computing can facilitate real-time data collection and analysis, enabling early detection and rapid response to zoonotic disease outbreaks, thereby enhancing surveillance and containment efforts for public health protection. It aims to explore challenges and opportunities within these frameworks and delineate future research directions to serve as a benchmark. Conducting a three-layered analysis, the study identifies high-level (...) technologies, second-layer technologies within those brackets, and minutely analyzes each technology in the second layer. The focus includes a comprehensive examination of the evolving landscape of zoonotic disease control and the influence of computational tools in public health. The findings highlight the potential of leveraging computational frameworks to predict, monitor, and control zoonotic diseases more effectively within Society 5.0. The major identified thrust areas include One Health and governmental integration, digital and data-driven solutions, healthcare and medical technologies, technologies for communication and information dissemination, and socio-cultural and ethical considerations. The integration of large-scale healthcare services offer a robust data source for AI-driven models, enabling early detection and dynamic response to zoonotic threats using data acquired from IoT surveillance and data acquisition infrastructures. However, challenges such as data privacy and equitable healthcare access need addressing. The study emphasizes the opportunities arising from the integration of computational frameworks with Society 5.0 for zoonotic disease control, proposing potential benefits in terms of improved disease surveillance and response. To fully realize this potential, the study suggests exploring specific technological landscapes for further investigation in the context of zoonotic disease control. (shrink)

This study investigates the intersection of existing computational frameworks for zoonotic disease control within the emerging societal paradigm, Society 5.0. Technologies in human-centric computing can facilitate real-time data collection and analysis, enabling early detection and rapid response to zoonotic disease outbreaks, thereby enhancing surveillance and containment efforts for public health protection. It aims to explore challenges and opportunities within these frameworks and delineate future research directions to serve as a benchmark. Conducting a three-layered analysis, the study identifies high-level (...) technologies, second-layer technologies within those brackets, and minutely analyzes each technology in the second layer. The focus includes a comprehensive examination of the evolving landscape of zoonotic disease control and the influence of computational tools in public health. The findings highlight the potential of leveraging computational frameworks to predict, monitor, and control zoonotic diseases more effectively within Society 5.0. The major identified thrust areas include One Health and governmental integration, digital and data-driven solutions, healthcare and medical technologies, technologies for communication and information dissemination, and socio-cultural and ethical considerations. The integration of large-scale healthcare services offer a robust data source for AI-driven models, enabling early detection and dynamic response to zoonotic threats using data acquired from IoT surveillance and data acquisition infrastructures. However, challenges such as data privacy and equitable healthcare access need addressing. The study emphasizes the opportunities arising from the integration of computational frameworks with Society 5.0 for zoonotic disease control, proposing potential benefits in terms of improved disease surveillance and response. To fully realize this potential, the study suggests exploring specific technological landscapes for further investigation in the context of zoonotic disease control. (shrink)

Research in Artificial Intelligence (AI) and the Law has maintained an emphasis on knowledge representation and formal reasoning during a period when statistical, data-driven approaches have ascended to dominance within AI as a whole. Electronic discovery is a legal application area, with substantial commercial and research interest, where there are compelling arguments in favor of both empirical and knowledge-based approaches. We discuss the cases for both perspectives, as well as the opportunities for beneficial synergies.

This paper introduces the concept of Operator Contestability in AI systems: the principle that those overseeing AI systems (operators) must have the necessary control to be accountable for the decisions made by these algorithms. We argue that designers have a duty to ensure operator contestability. We demonstrate how this duty can be fulfilled by applying the'Design for Defeaters' framework, which provides strategies to embed tools within AI systems that enable operators to challenge decisions. Defeaters are designed to contest either (...) the justification for the AI’s data inputs (undercutting defeaters) or the validity of the conclusions drawn from that data (rebutting defeaters). To illustrate the necessity and application of this framework, we examine case studies such as AI-driven recruitment processes, where operators need tools and authority to uncover and address potential biases, and autonomous driving systems, where real-time decision-making is crucial. The paper argues that operator contestability requires ensuring that operators have (1) epistemic access to the relevant normative reasons and (2) the authority and cognitive capacity to act on these defeaters. By addressing these challenges, the paper emphasizes the importance of designing AI systems in a way that enables operators to effectively contest AI decisions, thereby ensuring that the appropriate individuals can take responsibility for the outcomes of human-AI interactions. (shrink)

Under the innovation-driven development strategy, the improvement of the core competitiveness of enterprises demonstrates increasing dependence on the ability of technological innovation. In this article, data of A-share listed companies in Shanghai and Shenzhen stock markets from 2008 to 2018 were selected as research samples for the analysis of the influencing factors and mechanism of enterprise technological innovation from the dual perspectives of the external economic environment and internal management system based on the use of the fixed-effect model. (...) The results show that government intervention significantly hinders enterprises' investment in resources for technological innovation, and less government intervention can improve the innovation investment of enterprises. The intervention of internal control fails to bring institutional advantages; rather, it aggravates the negative effect of government intervention on enterprise technological innovation. The research enriches the existing academic research results on government intervention, internal control quality, and enterprise technological innovation. The findings provide experience for accelerating the marketization process in China, enterprise governance, and improving the level of enterprise technological innovation. (shrink)

Odor baited methods of controlling tsetse have received considerable attention as ecologically friendly ways for African farmers to reduce their levels of livestock trypanosomosis. Over the last decade, a number of tsetse control projects have been set up in East Africa using these methods. Although much has been written, few hard data are available regarding their ongoing success, problems, and sustainability. To evaluate the situation on the ground, the authors conducted a series of site visits to a number (...) of such tsetse control projects in Kenya. A comparison of these projects with others across the region identified the possible constraints to a wider uptake of these methods. Poor information, coupled with inappropriate research and development policies, were found to be the key constraints. These could be overcome with a farmer-based approach to control, with a better application of existing techniques, and with a greater role for veterinarians. Tsetse control needs to become demand rather than supply driven, if it is to bean important component of livestock disease control in Africa. (shrink)

The 2019 coronavirus (COVID‐19), also known as SARS‐CoV‐2, is highly pathogenic and virulent, and it spreads very quickly through human‐to‐human contact. In response to the growing number of cases, governments across the spectrum of affected countries have adopted different strategies in implementing control measures, in a hope to reduce the number of new cases. However, 5 months after the first confirmed case, countries like the United States of America (US) seems to be heading towards a trajectory that indicates a (...) health care crisis. This is in stark contrast to the downward trajectory in Europe, China, and elsewhere in Asia, where the number of new cases has seen a decline ahead of an anticipated second wave. A data‐driven approach reveals three key strategies in tackling COVID‐19. Our work here has definitively evaluated these strategies and serves as a warning to the US, and more importantly, a guide for tackling future pandemics. Also see the video abstract here https://youtu.be/gPkCi2_7tWo. (shrink)

The attempt by the digital forces to ‘naturalize’ the digital and thus to make it one with our ontology raises a whole host of issues about how to identify the Self. The multi-pronged process of naturalization are driven by a particular dynamic: the ‘more’ of data. Data is not a static pile of information, but only works within strategies of accumulation. Businesses and academe have bought into this strategy – addicted to its potential for control – (...) in ways that make it impossible to see ‘an outside’. This ‘more’ is, however, hardly foolproof, and is in fact designed around a wide range of fallibilities – some visible, but most not - that are also now part of the new natural. The resultant dialectic is unstable and as it operates to re-engineer our sense of Self it faces its own destiny. (shrink)

In this paper, it is argued that although data mining (DM) is being touted as a solution to many business problems and is basking in the glory of electronic business environments of today, as practiced currently, it reflects a preoccupation with short-run commercial applications and a neglect of the underlying theoretical issues. Although an argument can be made that theoretical precedence is not a necessary prerequisite for practical application or for commercial success, it can also be argued that an (...) exclusive reliance on data-driven and exploratory components of pattern recognition without a corresponding attention to the causal schemas underlying patterns is destined to limit the potential for DM to evolve into a long-term solution to business problems or into an intellectual discipline in its own right. This paper presents an overview of key features and assumptions in DM and examines some of the key practical, philosophical, and foundational issues in DM. (shrink)

In the wake of the current financial crises triggered by risky mortgage-backed securities, the question of ethics and risk-taking is once again at the front and center for both practitioners and academics. Although risk-taking is considered an integral part of strategic decision-making, sometimes firms could be propelled to take risks driven by reasons other than calculated strategic choices. The authors argue that a firm's risk-taking propensity is impacted by its ethical climate (egoistic or benevolent) and its emphasis on output (...) control to manage its marketing function. The firm's long-term orientation is argued to moderate the control–risk propensity relationship. The authors also extend research on risk and performance and argue that the association of risk-taking propensity and firm performance is contingent on the ownership (publicly traded versus privately held) structure of the firm. Based on survey data from a sample of manufacturing industries in the United States, the results show significant impact of ethical climate and marketing output control on a firm's risk-taking propensity; also risk-taking propensity shows a stronger association with firm performance in privately held firms than in publicly traded firms. (shrink)

To maximize brain plasticity after stroke, several rehabilitation strategies have been explored, including the use of intensive motor training, motor imagery, and action observation. Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by (...) utilizing motor-imagery (MI) based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing, and very little is known about how to optimally integrate BCI and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of a BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence as more data are needed on the effect of this type of interventions not only on functional improvement but also through brain imaging to study the effect of the intervention on plasticity. (shrink)

Complementary and alternative medicine is becoming increasingly popular in western countries, with estimates of CAM usage as high as 40%. This has prompted a change of attitude of the medical establishment: the initial dismissal of CAM is being replaced by a drive to integrate CAM into the mainstream. Two possible explanations for this integration thrust are considered. Firstly, integration could be motivated largely by cognitive interest in CAM. Secondly, integration could be mainly power-driven, aimed at controlling the alternative movement (...) and exploiting its capacity to make money. The second explanation seems more plausible, and is supported by data showing the paucity of research on CAM. The medical establishment does not sufficiently appreciate the challenge CAM poses, and does not consider its scientific appraisal to be a high priority. Instead, the allopathic community seems preoccupied with the opportunistic desire to protect its authority and social status. Such a discourse can become dogmatic, reluctant to revise critically the ‘accepted’ body of knowledge. Popper's critical rationalism could help to undercut dogmatism and encourage a critical, open-minded orientation of medical science. (shrink)

BackgroundThe public and healthcare workers have a high expectation of animal research which they perceive as necessary to predict the safety and efficacy of drugs before testing in clinical trials. However, the expectation is not always realised and there is evidence that the research often fails to stand up to scientific scrutiny and its 'predictive value' is either weak or absent.DiscussionProblems with the use of animals as models of humans arise from a variety of biases and systemic failures including: 1) (...) bias and poor practice in research methodology and data analysis ; 2) lack of transparency in scientific assessment and regulation of the research; 3) long-term denial of weaknesses in cross-species translation; 4) profit-driven motives overriding patient interests; 5) lack of accountability of expenditure on animal research; 6) reductionist-materialism in science which tends to dictate scientific inquiry and control the direction of funding in biomedical research.SummaryBias in animal research needs to be addressed before medical research and healthcare decision-making can be more evidence-based. Research funding may be misdirected on studying 'disease mechanisms' in animals that cannot be replicated outside tightly controlled laboratory conditions, and without sufficient critical evaluation animal research may divert attention away from avenues of research that hold promise for human health. The potential for harm to patients and trial volunteers from reliance on biased animal data1 requires measures to improve its conduct, regulation and analysis. This article draws attention to a few of the many forms of bias in animal research that have come to light in the last decade and offers a strategy incorporating ten recommendations stated at the end of each section on bias. The proposals need development through open debate and subsequent rigorous implementation so that reviewers may determine the value of animal research to human health. The 10Rs + are protected by a Creative Commons Attribution 3.0 Unported License and therefore may be 'shared, remixed or built on, even commercially, so long as attributed by giving appropriate credit with a link to the license, and indicate if changes were made.’. (shrink)

BackgroundRandomized controlled trials (RCTs) are often complex and expensive to perform. Less than one third achieve planned recruitment targets, follow-up can be labor-intensive, and many have limited real-world generalizability. Designs for RCTs conducted using cohorts and routinely collected health data, including registries, electronic health records, and administrative databases, have been proposed to address these challenges and are being rapidly adopted. These designs, however, are relatively recent innovations, and published RCT reports often do not describe important aspects of their methodology (...) in a standardized way. Our objective is to extend the Consolidated Standards of Reporting Trials (CONSORT) statement with a consensus-driven reporting guideline for RCTs using cohorts and routinely collected health data.MethodsThe development of this CONSORT extension will consist of five phases. Phase 1 (completed) consisted of the project launch, including fundraising, the establishment of a research team, and development of a conceptual framework. In phase 2, a systematic review will be performed to identify publications (1) that describe methods or reporting considerations for RCTs conducted using cohorts and routinely collected health data or (2) that are protocols or report results from such RCTs. An initial “long list” of possible modifications to CONSORT checklist items and possible new items for the reporting guideline will be generated based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statements. Additional possible modifications and new items will be identified based on the results of the systematic review. Phase 3 will consist of a three-round Delphi exercise with methods and content experts to evaluate the “long list” and generate a “short list” of key items. In phase 4, these items will serve as the basis for an in-person consensus meeting to finalize a core set of items to be included in the reporting guideline and checklist. Phase 5 will involve drafting the checklist and elaboration-explanation documents, and dissemination and implementation of the guideline.DiscussionDevelopment of this CONSORT extension will contribute to more transparent reporting of RCTs conducted using cohorts and routinely collected health data. (shrink)

In various official documents, the European Union has declared its goal to pursue a citizen-centric governance of digital transformation. Through a critical review of several of these documents, here we show how “citizen-centric” is more a glamouring than a driving concept. De facto, the EU is enabling a federated data system that is corporate-driven, economic-oriented, and GDPR-compliant; in other words, a Digital Single Market (DSM). This leaves out societal and collective-level dimensions of digital transformation—such as social inclusion, digital (...) sovereignty, and environmental sustainability—which are acknowledged, but not operationalized, by the EU as pillars of a citizen-centric governance. Hence, the door is open to a complementary approach to the governance of digital transformation. We argue that, while a federated data model can constitute the tech-legal backbone of the emerging DSM, a commoning of data, as an ecosystemic approach that maintains a societal and collective outlook by default, can represent a complement to enact a truly citizen-centric governance. (shrink)

This contribution offers reflections about Chilean Cybersyn, 50 years ago. In recent years, Cybersyn, has received significant attention. It was the brainchild of Stafford Beer, who conceived it to support the transformation of the Chilean economy from its bureaucratic history to hopefully create a vibrant and modern society, driven by cybernetic tools. These aspects have received much attention in recent times; however, in this contribution, I want to discuss how working in Cybersyn influenced my work after the coup of (...) 1973. Perhaps, for me, its major influence was in the management of complexity, through what I refer here as variety engineering and through the Viable System Model VSM as a paradigm to the management of relationships with implications to enterprises, society and the economy. After the 1973 coup major interest was in technological aspects of Cybersyn such as real-time management and its contribution to decision support and executive information systems. In the late 70s I was personally influenced by information management, but by the early 1980s my work moved towards methodological aspects of how to use the VSM. By 1989 I had created the VIPLAN method. Key questions I attempted to answer were, how to model the complexity of enterprises and their interactions with environmental agents. Later on, in the 1990s and 2000s, the main direction of my work was epistemological, ontological and methodological towards second-order cybernetics and relationships. Only in recent decades the political transformations proposed by Cybersyn have captured the imagination of many commentators. The confluence of social and cultural changes with information technology, data models, artificial intelligence, algorithms and several additional technological developments have challenged the excesses of capitalism, particularly after the banking crisis of 2008–2009. The purpose in this paper is discussing this evolution in the light of those early days in Chile. (shrink)

Two facets of diagnostic reasoning related to scientific thinking are recognizing the difference between confounded and unconfounded evidence and selecting appropriate interventions that could provide learners the evidence necessary to make an appropriate causal conclusion (i.e., the control-of-variables strategy). The present study investigates both these abilities in 3- to 6-year-old children (N= 57). We found both competence and developmental progress in the capacity to recognize that evidence is confounded. Similarly, children performed above chance in some tasks testing for the (...) selection of a controlled test of a hypothesis. However, these capacities were unrelated, suggesting that preschoolers’ nascent understanding of the control-of-variables strategy may not be driven by a metacognitive understanding that confounded evidence does not support a unique causal conclusion, and requires further investigation. (shrink)

Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we (...) conducted semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings. (shrink)

In recent years, data-driven approaches to chronic pain care have increased dramatically. However, people living with chronic pain are ambivalent about datafication practices. Drawing on in-depth interviews with individuals living with chronic pain, I discuss and analyze this ambivalence. On the one hand, participants imbibe the promissory rhetoric of data as that which may organize and control the body in pain. On the other hand, they dismiss and critique the type of data collected. This micro-level (...) analysis of the pain tracking experience illuminates a tension between datafication and chronic pain. Datafication demands that the patient relay information about their body that is free of ambiguity. However, chronic pain is ambiguous and full of paradox. This article illuminates the emotional chasm between datafication enthusiasts and chronic pain patients who track their pain and suggests that such enthusiasm may lead to bad faith. (shrink)