Search Results (16,698)

This research investigates the problem of robust control of a nonlinear, fast-dynamics, high-precision planar manipulator with a limited workspace. Starting from a mathematical model formulated in terms of Euler-Lagrange dynamics, the proposed approach considers, given the limitations of the physical system, that some states are unavailable for direct measurement, and the system is perturbed by external disturbances. Consequently, a reduced-order state observer and an estimator with time-varying gains are proposed. Using these estimates in the control stage, four types of control laws are synthesized. Two are derived from super-twisting control theory (STC), the third is an integer-order PID (IOPID) controller, and the fourth is based on a fractional-order PID (FOPID) approach with optimization-based tuning. The first three strategies require accurate information about the mathematical model of the system. All the considered control strategies address external perturbations to ensure the robustness of the system. Several tests have been conducted to validate the overall estimation and control scheme. Based on the dynamic evolution of the manipulator tip and the analyzed performance metrics, the numerical results show that all the proposed controllers provide suitable solutions to robust tracking problems. Full article

The paper presents a novel large language model-based neighbourhood search method for production scheduling using discrete-event simulation (DES) as the modelling and evaluation engine. The combination of large language models (LLMs) and DES represents a relatively new area of research with significant potential. The current study aims to demonstrate the applicability of LLMs for the automated closed-loop optimisation of production systems, using the DES environment as a configurable modelling tool and an evaluation engine at the same time, from which the results and the related contextual information are extracted at the end of each simulation run to provide feedback and context for the LLM in its autonomous search for improved solutions. The architecture was implemented with the use of Mistral Small 3.2 (24B) as the LLM and Siemens Tecnomatix Plant Simulation as the DES environment. The results from the proposed approach were compared with those of a genetic algorithm (GA) native to the applied DES environment, and with those achieved using simulated annealing (SA). While the proposed method was generally outperformed by the GA and the SA in terms of final solution quality and computational cost, it demonstrated advantages in terms of simulation demand while also providing a highly adaptable optimisation framework with significant potential for improvement and wider generalisation in the future. Full article

Climate-Neutral Districts (CNDs) and Positive Energy Districts (PEDs) have become central concepts in European strategies for climate-neutral cities. Existing research, however, has predominantly interpreted these models from technological and energy-system perspectives, while their governance implications and effects on real-estate investment remain comparatively underexplored. This paper develops a conceptual and interpretative framework that also considers CNDs and PEDs as governance and investment environments shaped by different stakeholder configurations. Methodologically, the study combines an evidence-informed stakeholder and governance framework developed within CapaCITIES 2.0 with an interdisciplinary conceptual interpretation. The framework derives from comparative analysis of Countries’ Self-Assessments and National Action Plans involving 33 consortium participants representing 14 Member States, complemented by interviews, focus-group exchanges, collegial reconciliation, and participant validation. The unit of analysis is the stakeholder configuration associated with a dominant district-transition logic. The analysis positions PEDs as an energy-system-led, rather than energy-system-exclusive, configuration characterised by high infrastructural integration and comparatively strong dependence on specialised technical and regulatory actors. This perspective also highlights the structurally central, yet often under-leveraged, role of real estate actors. The paper concludes that recognising the plurality of district-transition logics provides a more differentiated basis for examining governance arrangements and the conditions under which real-estate stakeholders may move from reactive implementation roles towards greater strategic agency in urban climate transitions. Full article

Image segmentation enables accurate object identification, a key requirement in computer vision applications. In aquaculture, this technology is essential for monitoring and management of species such as Oncorhynchus mykiss. In the Peruvian highlands, where trout farming is a vital economic activity, robust computational models are needed to automate the estimation of fish size and weight and to optimize the sustainability of production systems. This research proposes ATrUNet, a U-Net-based architecture optimized for accurate segmentation of Oncorhynchus mykiss. ATrUNet improves the information flow between the encoding and decoding layers by incorporating convolutional layers, batch normalization, and activation functions. A dataset of 1166 images was constructed, processed using LabelMe with JSON annotations, and converted into binary masks. Evaluation was conducted using loss, accuracy, and IoU. As a result, ATrUNet showed higher performance than U-Net, achieving a 27.55% reduction in loss, a 0.40% increase in accuracy (0.992), and improvements in overlap metrics such as IoU and GIoU by 2.70% and 8.75%, respectively. Future work includes expanding dataset diversity, exploring instance segmentation, and optimizing for embedded deployment. This research contributes to the application of computer vision to aquaculture, with the possibility of extending it to different species and various research contexts. Full article

Generative artificial intelligence (AI) has evolved from a passive information tool into a responsive conversational system. For individuals with obsessive–compulsive and related disorders (OCRDs) that are, in part, maintained by safety behaviors and other negative reinforcement loops, large language models (LLMs) can function as highly available, potentially sycophantic, and readily repeatable sources of reassurance that may undermine the principles of exposure and response prevention (ERP). Measurement paradigms inherited from the early digital era (e.g., screen time and problematic internet use) were not designed to capture these relational dynamics. In this perspective, we propose a cognitive-behavioral framework for conceptualizing and assessing patient–AI relationships in the OCRDs. We argue that generative AI may interfere with exposure-based treatment (by eroding therapeutic friction), the discomfort, delay, and uncertainty on which corrective learning depends. We suggest that this occurs through three interlocking mechanisms: supernormal stimuli, algorithmic sycophancy, and digital accommodation. We propose an assessment architecture that records the context of AI use and rates five functional domains: Emotional Reliance, Reassurance and Checking, Anthropomorphic Attribution, Epistemic Trust, and Displacement. Adjunct ratings capture adaptive benefit, global interference, concealment, and safety-relevant exchanges. Hypothesized digital phenotypes may be derived from the domain-level profiles. We conclude by outlining a cognitive-behavioral approach to treatment planning for AI-facilitated reassurance seeking and discussing implications for safeguarding internal validity in research. We argue that structured inquiry into the patient–AI relationship belongs in OCRD assessment and research design, and we offer the present framework as a testable approach whose measurement properties remain to be established. Full article

Past field experiments have demonstrated multiple cloud seeding techniques for modifying the electrical characteristics of developing thunderstorms with the purpose of reducing or eliminating lightning strikes. One motivation for these weather modification research programs was the prospect of preventing wildfires in inaccessible locations or particularly hazardous conditions by temporarily suppressing a prolific ignition source: lightning. It has been nearly 50 years since the last large-scale field effort in lightning suppression, and the ensuing five decades of technological innovation hold the promise of supporting this novel hazard mitigation approach. This study outlines observational and forecasting requirements, as well as an associated remote sensing architecture, that would support an operational wildfire prevention program based on lightning suppression by chaff seeding clouds. Two capabilities enabled by remote sensing observations are highlighted: (1) identifying potential regions of extreme wildfire behavior based on wildland fuels, topography, and weather, and (2) predicting regions where dry lightning strikes are probable. In combination, the proposed nowcasting system would predict the most likely areas of lightning-initiated wildfire danger. Dependent on land management strategies, surrounding infrastructure, firefighting capacity, and risk to human wellbeing, this information could be used to deploy lightning suppression technology to reduce or temporarily prevent wildfire ignitions in these high-risk situations. Full article

Despite extensive research on the abrasive properties of lunar regolith, the use of in-process tribological signals for wear assessment remains insufficiently studied. The aim of this work is to evaluate the relationship between torque response and abrasive wear severity in rotating shaft–seal systems exposed to lunar regolith simulants. Rotating EN 1.4404 stainless-steel shafts and spring-loaded natural polytetrafluoroethylene (PTFE) lip seals were tested under three-body abrasive wear conditions. Five particle-size fractions of LX-M100 Lunar Mare and LX-TH100 simulants were investigated at test durations of 15 min, 30 min and 1440 min. Frictional torque was continuously recorded, while post-test shaft surface roughness was used to characterize wear severity and Scanning Electron Microscope (SEM) analysis of the PTFE counterface was used to identify wear mechanisms. The results showed that torque response systematically depended on particle-size fraction and simulant type. Furthermore, the torque-response descriptors provide in-process information related to abrasive interaction severity, with peak torque events ($T_{max}$) showing the strongest relationship with abrasive surface modification. The statistical analysis was based on 27 complete observations, and considering the prematurely terminated test runs and the resulting limitations of the dataset, the obtained relationships are interpreted as exploratory. These findings provide a physical basis for torque-based wear monitoring in sealed rotating mechanisms for future lunar applications. Full article

This study proposes a conceptual framework to operationalize the transition from conventional, technical, and material-centric circularity to an inclusive socio-technical paradigm through the deployment of a Co-Creative Digital Process Twin (DPT) framework in Progreso, Hidalgo, Mexico. The primary objective is to bridge the gap between technocentric industrial applications and the socio-technical needs of developing regions, specifically by integrating informal waste pickers (pepenadores) into a formal circular economy ecosystem. The methodology is structured into three distinct stages: a systematic scoping review following PRISMA-ScR protocols to identify architectural gaps in the current literature; the generation of a conceptual framework and Business Model Canvas grounded in participatory design; and the formulation of Key Performance Indicators for future longitudinal testing. The results indicate a profound bias in existing digital twin research toward industrial automation, with a near-total absence of social inclusion mechanisms. In response, this study presents a multidimensional business model featuring service-oriented cooperative architecture. This system utilizes open standards (FIWARE, BPMN 2.0) and crowdsourcing platforms (Ushahidi) to maximize material recovery while ensuring socio-economic equity. This will need to be verified in future studies. A limitation of this study is its specificity to a single municipality with certain characteristics, which could hinder its immediate generalization to metropolitan areas. Furthermore, the success of the model could depend on basic digital literacy and a stable telecommunications infrastructure. Full article

The remarkable success of biomedical HIV prevention has transformed the global response to HIV through the widespread implementation of oral pre-exposure prophylaxis (PrEP), long-acting antiretroviral agents and HIV self-testing. However, ensuring equitable access to these interventions remains a major challenge. Although more than one billion people worldwide live with a disability, disability has received surprisingly little attention within contemporary HIV prevention research and implementation. This narrative review examines the intersection between disability and modern HIV prevention, integrating evidence on sexual and reproductive health, structural barriers to healthcare and the evolving landscape of biomedical prevention. The available literature suggests that people with disabilities experience persistent inequalities in access to sexuality education, HIV information, testing and preventive services, largely due to physical, communication and systemic barriers rather than biological vulnerability. Despite these well-documented disparities, disability is rarely incorporated into PrEP implementation studies, HIV prevention policies or differentiated service delivery models. We argue that disability should be considered an implementation and health equity challenge rather than a pharmacological one. The effectiveness of biomedical prevention ultimately depends on healthcare systems capable of delivering accessible, person-centred and inclusive services. Integrating disability into implementation science, clinical practice and public health policy represents an essential step toward achieving equitable HIV prevention. As HIV prevention continues to evolve, accessibility should be recognised not as an optional adaptation but as a fundamental component of high-quality prevention programmes. Full article

Background: Nonfatal strangulation (NFS) is one of the most lethal forms of intimate partner violence (IPV) that is associated with increased risk of future homicide, yet it frequently goes unrecognized within healthcare settings. Black women experience disproportionately high rates of IPV while simultaneously facing structural barriers to healthcare access. Little research has examined how Black women survivors experience strangulation and navigate decisions about seeking care following NFS and other potentially lethal forms of IPV. Methods: This secondary qualitative analysis draws on interview data from 28 Black women survivors of IPV who participated in a larger community-engaged mixed-methods study conducted during the COVID-19 pandemic in an Upper Midwestern state. Of these, 17 (60.7%) both reported experiencing strangulation on the parent-study survey and described experiences consistent with nonfatal strangulation during their qualitative interviews and comprised the analytic sample for the present analysis. Guided by Black Feminist Thought, semi-structured interviews examined survivors’ experiences with violence, safety, help-seeking, and interactions with healthcare and social service systems. Data were analyzed using thematic analysis following Schensul and Schensul. Results: Three interconnected themes were identified. First, survivors described racism and anticipated discrimination as significant barriers to seeking healthcare following strangulation. Second, shame, stigma, and fear of judgment contributed to self-silencing and avoidance of formal services. Third, survivors described strangulation as a terrifying and potentially lethal form of violence occurring within broader histories of polyvictimization and cumulative trauma. Despite experiencing serious symptoms, many participants avoided healthcare because they feared retaliation from abusive partners, anticipated dismissive treatment, or believed healthcare systems would be unable to protect them. Together, these experiences illustrate how structural racism, institutional mistrust, and gendered violence intersect to shape healthcare decision-making. Conclusions: Findings suggest that barriers to care following strangulation extend beyond individual help-seeking behaviors and are deeply rooted in structural inequities. Improving outcomes for Black women survivors requires enhanced provider training on strangulation assessment, trauma- and violence-informed approaches, culturally responsive care, and healthcare system reforms that address racism, mistrust, and survivor safety. These findings have important implications for reducing disparities in IPV-related health outcomes and improving responses to high-lethality violence. Full article

As ESG disclosure and climate governance requirements become increasingly institutionalized, understanding how firms respond to external sustainability pressures through specific governance mechanisms and how these responses relate to carbon performance has become an important research issue. Moving beyond aggregate ESG ratings, this study examines the informational value of specific ESG-related factors for corporate carbon management, including energy management systems, innovation capability, human capital, market valuation, and board governance. Using data on Taiwanese listed and over-the-counter companies from the ESG database of the Financial Supervisory Commission, the Taiwan Economic Journal, and the Leadership ISO Survey, this study employs a time-lagged design linking 2023 firm characteristics to 2024 carbon emission intensity. Multiple regression analysis is the primary method, with firm size, leverage, capital intensity, profitability, and firm age included as firm-level controls in extended models; exploratory data analysis (EDA) serves as a supplementary diagnostic for data distribution, nonlinearity, and variable operationalization. Cross-year, alternative dependent-variable, and supplementary analyses are used to assess the stability of the estimates. The results show that ISO 50001 certification is positively associated with subsequent carbon emission intensity, whereas the structural characteristics of R&D investment and independent director governance show more stable negative associations. Average salary, female director representation, and Tobin’s Q yield inconsistent results. Proportion-based board measures outperform director-count and threshold-based measures, but no clear critical-mass threshold is supported. Results also vary under alternative carbon-performance measures, suggesting that carbon intensity and absolute emissions capture different dimensions of environmental performance. These findings indicate conditional statistical associations rather than causal effects. Full article

Background: Conversational artificial intelligence (AI), including chatbots, large language models, virtual agents, and embodied conversational systems, is increasingly being introduced into healthcare, rehabilitation, education, and professional practice. In pediatric occupational therapy, these technologies could potentially support clinical documentation, professional reasoning, parent coaching, therapeutic engagement, home-program implementation, and access to information. However, the profession-specific evidence remains fragmented, and the implications for children, families, and occupational therapists have not been comprehensively examined. Objective: This comprehensive narrative review aims to critically examine the current and emerging applications of conversational AI in pediatric occupational therapy, integrate direct and transferable evidence, evaluate its alignment with occupation- and family-centered practice, and identify priorities for responsible future development. Methods: A structured multidisciplinary literature search was conducted across health, rehabilitation, allied-health, education, and computer-science databases. The narrative synthesis was guided by the quality domains of the Scale for the Assessment of Narrative Review Articles. Evidence was organized across four interconnected levels: direct pediatric occupational therapy research, broader occupational therapy applications, transferable pediatric evidence, and foundational literature concerning conversational-AI design, implementation, and governance. Results: Direct pediatric occupational therapy evidence remains limited but demonstrates two emerging directions: therapist-facing large language models supporting clinical documentation and child-facing embodied conversational systems intended to facilitate therapeutic engagement. The wider literature indicates potential applications in clinical reasoning, professional education, reflective practice, caregiver coaching, adherence, home routines, waiting-list support, and personalized communication. Nevertheless, most evidence is preliminary, heterogeneous, and based on small samples, technical prototypes, simulations, or indirect pediatric applications. Major unresolved concerns include developmental appropriateness, hallucinated information, algorithmic bias, privacy, safeguarding, emotional attachment, automation bias, professional accountability, and limited evaluation of participation and occupational outcomes. Conclusions: Conversational AI may become a valuable augmentative tool in pediatric occupational therapy, but it should not replace occupational therapists, contextual clinical reasoning, or therapeutic relationships. Future systems should be occupation-centered, family-centered, developmentally appropriate, transparent, context-sensitive, and governed through professional oversight. Co-design with children, caregivers, and occupational therapists, together with rigorous real-world evaluation, will be important before routine clinical use can be considered. Full article

Upper-limb motor impairment after stroke often limits independence and quality of life, while immersive virtual reality (IVR) offers a promising technology-assisted rehabilitation approach. However, the growing availability of rehabilitation-specific systems and commercial VR games complicates application selection for usability research involving individuals with motor, cognitive, and perceptual impairments. This study aimed to develop a preliminary, literature-informed multi-criteria framework to guide IVR application selection for usability evaluation in upper-limb stroke rehabilitation. Using a narrative synthesis approach, 22 peer-reviewed studies were identified through PubMed, Scopus, IEEE Xplore, and ACM Digital Library, supplemented by reference-list screening and continuing literature tracking, and were included in the framework synthesis. Rehabilitation-oriented applications and commercial IVR games were examined, resulting in six evaluation domains: usability, safety and tolerability, engagement and motivation, cognitive load, feasibility, and clinical relevance. A worked demonstration applied the framework to eight representative applications, comprising four rehabilitation-oriented and four commercial applications. Domain scores ranged from 0 to 3, with application-level domain profiles demonstrating distinct trade-offs. Overall, the findings support multi-criteria application selection rather than selection based solely on therapeutic purpose or entertainment value. The framework is preliminary and requires validation of its content validity, reliability, and relationship with user outcomes. Full article

This research investigates the security of large language models (LLMs) with the aim of identifying key security and safety threats, control measures, and governance considerations that are relevant to their trustworthy adoption in the Extended Reality (XR) domain. To achieve this goal, the research combines an extensive narrative literature review with a workshop with 21 participants who have an AI background and are early-stage technical professionals. The workshop was designed to capture informed perspectives and critical reflections on this behalf. The findings show that LLMs security is understood as a lifecycle-wide, socio-technical challenge in which data quality, privacy, model robustness, adversarial resilience, human misuse, monitoring, and regulatory compliance are deeply interconnected. Building on these insights, this research proposes a set of recommendations for the design and deployment of secure LLM-enabled XR systems, emphasizing strategies such as security defense in depth, privacy-aware data practices, layered technical safeguards, continuous monitoring, and governance mechanisms that align security controls with the specific risks of immersive and interactive environments. Full article

High-value greenhouse crops require quality-sensitive selective harvesting under labor shortages and variable crop conditions. This review synthesizes 188 unique primary studies and examines robotic harvesting as a complete task chain rather than a set of isolated sensing or manipulation modules. Embodied intelligence is used as an analytical lens to connect perception, harvestability assessment, decision making, manipulation, feedback, failure propagation, and recovery. A greenhouse-cucumber case study illustrates how errors propagate across these stages. The synthesis shows that the dominant bottleneck is system integration: incomplete observability, short-lived scene states, contact uncertainty, and weak outcome verification or recovery cause cumulative losses in complete-task success, effective throughput, and product quality. Most reported systems remain at closed-loop automation or early embodied interaction, with limited evidence of interaction-driven learning or cross-context generalization. We therefore frame evaluation around six production-relevant dimensions: complete-task success, effective cycle time and retry cost, product quality, failure detection and recovery, human intervention, and continuous operation. Near-term priorities are reliable, recoverable operation in defined crop–facility systems and standardized reporting; adaptation across cultivars and greenhouse configurations is a medium-term objective, whereas embodied learning and broad cross-platform transfer remain longer-term research directions. This system-level perspective reframes smart robotic harvesting around information continuity, fault containment, and deployable greenhouse performance. Full article