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Artificial intelligence, innovation and the new architecture of exploitation: Towards reconfiguring humanness in the age of algorithmic labour
No full textPurpose This conceptual study explores how artificial intelligence (AI) is transforming the nature of work and reconfiguring the experience of humanness, particularly among low-skilled and informal workers. Method Using an integrative literature review methodology, the study synthesises interdisciplinary research from organisational studies, sociology, and AI ethics to examine the mechanisms through which AI-driven labour displacement, algorithmic management, and structural precarity contribute to new forms of exploitation. Findings The study develops a novel conceptual framework that links technological transformation to the erosion of the relational, moral, and emotional dimensions of work conditions, resulting in conditions increasingly resembling modern slavery. Originality the study’s novelty lies in its reframing of AI as a socio-technical actor with ontological consequences for worker identity, autonomy, and dignity. The findings underscore the need for ethical AI design, inclusive policy frameworks, and human-centred organisational practices. Practical implications This paper offers practical implications for policymakers, technologists, and business leaders seeking to align innovation with social justice and sustainable labour futures. Plain summary Artificial intelligence (AI) is reshaping the nature of work and disrupting the human experience, especially for low-skilled and informal workers, highlighting the urgency and complexity of this research. AI-driven labour displacement and algorithmic management contribute to new forms of exploitation that echo modern slavery. The erosion of humanness at work is linked to reduced autonomy, empathy, and moral agency under opaque algorithmic systems. A socio-technical framework is needed to address AI’s impact on dignity and agency, with ethical design and inclusive governance at its core. JEL Code O330, O31, O3
Clinical and MRI variables in decision support systems for prostate MRI: A systematic review of decision support tools, nomograms, and risk models
No full textIntroduction Workforce shortages and rising demand for MRI have increased interest in clinical decision support systems (CDSSs) to standardise imaging workflows. The Prostate Imaging–Reporting and Data System (PI-RADS) committee recommends real-time radiologist input to guide MRI pathways, but workforce constraints may limit consistent implementation. This systematic review examined CDSSs used in prostate MRI, including decision tools, nomograms, and risk calculators, to identify the clinical and MRI-derived variables they incorporate and assess their relevance for future development. Methods A systematic search of Medline, Cochrane, CINAHL, Web of Science, and ProQuest was conducted in June 2025. Eligible studies were original research published in English since January 2015 describing development, validation, or clinical use of a CDSS using structured clinical and MRI-derived variables for prostate cancer diagnosis, pre-biopsy risk stratification, or staging. Exclusion criteria included radiomics-only studies, non-primary research, studies without MRI variables, and those lacking external validation. Two reviewers independently screened studies, extracted data, and assessed risk of bias using PROBAST. Certainty of evidence was appraised using the GRADE framework. Results Twenty-two studies met inclusion criteria: fifteen evaluated nomograms, five described risk calculators, and two reported predictive models. None assessed a fully implemented CDSS. Common predictors included PI-RADS (82 %), prostate-specific antigen density (64 %), age (64 %), prostate-specific antigen (41 %), and prostate volume (23 %). Most tools showed strong discriminative accuracy (AUC >0.80), though calibration and decision curve analysis were inconsistently reported. Conclusion Validated clinical and MRI predictors support robust CDSSs, but heterogeneity and lack of implementation limit evidence. Prospective multicentre validation is needed. Implications for practice Radiographer-facing tools integrating key predictors could guide contrast use, staging, and workflow decisions, improve diagnostic accuracy and reduce unnecessary contrast administration
Servant leadership and members' well-beingin China's village committees: A multilevelanalysis of the serial mediating roles of serving culture and organization-based selfesteem
No full textThis study investigates the impact of servant leadership on the well-being of village committee members within China's rural communities, examining the mediating roles of serving culture and organization-based self-esteem. Drawing on social identity theory and employing a multilevel research design, the research analyzes two-wave survey data collected from 263 members of village committees, the grassroots rural public organizations in China. The results suggest a positive correlation between servant leadership and village committee members' well-being, with serving culture and organization-based self-esteem acting as key mediators in this relationship. Furthermore, these mediating effects occur sequentially: servant leadership fosters a serving culture, which in turn boosts organization-based self-esteem, ultimately enhancing member well-being. These findings offer new insights into the role of servant leadership in effective public management in rural communities
Toward agentic AI: User acceptance of a deeply personalized AI super assistant (AISA)
No full textRecent scholarship underscores the transformative potential of generative AI in shaping consumer decision-making, preferences, and overall brand satisfaction. Among these technologies, chatbots and AI voice assistants are increasingly deployed in marketing to influence consumer behavior. A critical question, however, is whether consumers are willing to accept a new generation of such technologies. In July 2025, OpenAI introduced the agent mode of ChatGPT, which represents a shift toward highly personalized, multimodal, and autonomous systems. This study defines these systems as AI super assistants (AISA). Informed by the broader literature on AI adoption and consumer behavior, an adapted AIDUA model with perceived risk is proposed. Survey data from the Philippines (N = 407) was analyzed using combined PLS-SEM and NCA methods. The results show that users appear increasingly confident in their ability to engage with new AI technologies, indicating that they do not feel overwhelmed but instead perceive AISA's new features as manageable. Hedonic motivation, novelty value, performance expectancy, and effort expectancy were identified as necessary conditions for user acceptance, while perceived risk is a necessary condition for objection. These findings offer new insights into user perception toward AISA, with implications for responsible AI design and deployment
What patient reported outcome measures are used in clinical trials in hip fracture? A systematic mapping review
No full textBackground: Patient-reported outcome measures (PROMs) are increasingly used to assess treatment effectiveness in various domains from the patients’ perspective. This systematic review aimed to identify what PROMs have been used in hip fracture clinical trials, whether they are used as the primary outcome, whether validity evidence is referenced and how their use has changed over time. Methods: Studies obtained from PubMed, Embase, and Web of Science published between 01/01/2010 and 29/09/2025 were assessed. Eligible studies were controlled trials on hip fracture interventions in adult populations published in English. We checked the reference for validity evidence of PROMs used in included studies. Characteristics of each study were extracted, and PROMs usage was summarised by year of publication. Results: A total of 28 different PROMs were used in 189 trials, with each covering different outcome domains. The most used PROMs were Harris hip score, EuroQoL-5D and pain visual analogue scale. A predominant proportion of studies (n = 162, 85.7%) utilised at least one PROMs, including 65 studies used multiple PROMs. There is an increasing trend of PROMs usage in trials and the number of papers using a PROM as a primary outcome over time. However, 95 studies did not reference any validity evidence for PROMs used. Conclusion: The frequent usage of PROMs in trials, and often as a primary outcome, suggests patient perspective is valued when evaluating hip fracture intervention. However, the lack of a single PROM covering all outcome domains necessitates using more than one PROMs in the included trials. A more comprehensive PROM or a core set of PROMs that measures all patient-related outcomes would achieve a holistic assessment and the ability to make direct comparisons between different interventions
Stop-motion anatomy and digitally reversed dissection of a human hand
No full textThis paper and accompanying video present stop-motion and digitally reversed dissection as contemporary methods of exhibiting cadaveric anatomy. Stop-motion techniques can give the impression of an ‘invisible dissector’, providing a unique and intriguing view of the body disassembling itself. Digitally reversed dissection then allows reversal of stop-motion footage, enabling the viewer to witness the body being reassembled, thus highlighting structural relationships from a new perspective. This alternate form of time-lapse video was established as an effective method of recording small and complex body regions without the dissector obstructing the field of view. Implementation of these techniques is showcased and described here with reference to dissection of a soft-fix cadaveric hand. Both technical and pedagogical strengths and limitations of these techniques are discussed and some advice on production of dissection videos has been offered
Motor imagery EEG signal classification using minimally random convolutional kernel transform and hybrid deep learning.
No full textThe brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. Electroencephalography (EEG) is a popular non-invasive technique for recording brain signals. It is critical to process and comprehend the hidden patterns linked to a specific cognitive or motor task, for instance, measured through the motor imagery brain-computer interface (MI-BCI). A significant challenge is presented by classifying motor imagery-based electroencephalogram (MI-EEG) tasks, given that EEG signals exhibit nonstationarity, time-variance, and individual diversity. Achieving good classification accuracy is also challenging due to the increasing number of classes and the inherent variability among individuals. To overcome these issues, this paper proposes a novel method for classifying EEG motor imagery signals that efficiently extracts features using the Minimally Random Convolutional Kernel Transform (MiniRocket). A linear classifier then utilises the extracted features for activity recognition. Furthermore, a novel deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture was proposed and demonstrated to serve as a baseline. The classification via MiniRocket's features achieved higher performance than the best deep learning models at a lower computational cost. PhysioNet and BCI Comp IV 2a datasets were used to evaluate the performance of the proposed approaches. Using PhysioNet, the proposed models achieved mean accuracy values of 98.63% and 98.06%, respectively, for the MiniRocket and CNN-LSTM. With the BCI-CompIV-2a dataset, proposed models achieved mean accuracy values of 92.57% and 92.32%, respectively. The findings demonstrate that the proposed approach can significantly enhance motor imagery EEG accuracy and provide new insights into the feature extraction and classification of MI-EEG. An additional future direction is non-additive electrode-source fusion (Choquet-integral/coalition formulations) to improve robustness under low-SNR EEG and inter-subject variability. [Abstract copyright: Copyright © 2026. Published by Elsevier Inc.
Evening salivary cortisone for evaluation of hypercortisolaemia: evaluation and implications.
Full text linkObservational constraints on tidal orbital decay in short-period giant extrasolar planets using transit timing variations
Full text linkHot Jupiters are the most massive extrasolar planets with the shortest orbital periods, two qualities that make them some of the best laboratories for observing tidal evolution. Amongst recent research in astronomy, there has been a particular interest in observing the tidal decay of a planetary orbit to place constraints on the tidal dissipation mechanisms within the stellar host. Orbital decay presents itself observationally through variations in transit timing and a decrease in orbital period. I present 78 new ground-based transit light curves of nine transiting planetary systems: HATS-18, HIP 65 A, K2-31, NGTS-6, NGTS-10, NGTS-28 A, WASP-32, WASP-145 A, WASP173 A. I measure the times of transit, combine these with published measurements, and construct linear and quadratic models to fit for a constant-period orbit and decaying orbit respectively. The findings of this study support several of these systems being good candidates for observing orbital decay in future with additional data, although all analyses resulted in non-detection. This work will act as a baseline for future studies which can incorporate the timing data from this study with new observations, such as those from the upcoming PLATO mission
The Columbus Model: Crowd Psychology, Dialogue Policing and Protest Management in the U.S.A
Full text linkProtest policing in the United States remains contentious, with many law enforcement agencies continuing to rely on coercive tactics rooted in outdated classical models of crowd psychology. This article presents the first systematic empirical analysis of a theory-informed, facilitation-led, and communication-led protest policing model implemented by a U.S. police department. In the aftermath of the 2020 social justice unrest-and a federal court order restricting the use of force-the Columbus Division of Police (CPD) introduced a novel operational framework: Public Order and Public Safety (POPS), shaped by the Elaborated Social Identity Model (ESIM) and designed around principles of dialogue, de-escalation, and graded tactical deployment. Adopting a participatory action research framework and an embedded ethnographic methodology, the study constructs an interpretation of how this model was developed, applied, and adapted over time. The analysis offers a theoretically informed, empirically grounded, longitudinal account of the co-production of protest policing strategies across a series of real-world contexts involving more than sixty events. It explores how legitimacy-focused approaches contributed to the containment of conflict, the marginalisation of confrontational actors, and the emergence of self-regulation within protest crowds-particularly in complex, high-risk environments. The study also foregrounds the tensions and resistances inherent in organisational change, both within policing institutions and protest communities. By documenting this shift from force-led to dialogue-led practices, the article makes a significant and original contribution to policing scholarship and practice, illustrating how dialogue-led, rights-focused and psychologically informed models of protest policing can be successfully implemented in the U.S. context