Search Results (1,870)

Rapid urban growth in many Global South cities strains waste systems and slows the shift to circular economy (CE) practice. Colombo, Sri Lanka, exemplifies this challenge, where overstretched state-led services coexist with neighborhood groups, NGOs, and informal collectors driving circular activities. This study adopts a layered social network diagnostic framework to examine how community-based waste management networks operate and how they might be reshaped to enable a city-wide CE. Using survey and interview data from 185 actors, information-sharing, collaboration, and resource-exchange networks are analyzed separately and in combination. The results reveal three principal findings: (i) Social-capital forms operate largely in parallel, with limited conversion between information, collaboration, and material exchange; (ii) the network exhibits “thin bridges and thick clusters,” in which a small number of NGO hubs mediate most cross-cluster connectivity; (iii) layers operate with mismatched coordination logics, producing gaps between awareness, collective action, and resource mobilization. As a result, ideas circulate widely but rarely translate into joint projects, local teams coordinate effectively yet remain isolated, and material flows depend on a narrow and fragile logistics spine. By diagnosing these structural misalignments, this study demonstrates a key novelty: scalable circular economy adoption depends not only on technology and policy but also on the design and alignment of underlying coordination networks. Full article

Human–machine teaming (HMT) in air traffic management (ATM) promises safer, more efficient operations by combining human expertise in decision-making with machine efficiency in data processing, where traffic pair crossing identification is crucial for effective conflict detection and resolution by recognizing aircraft pairs that may lead to conflict. To facilitate this goal, this paper presents a four-phase cognitive framework to enhance HMT for monitoring traffic pairs at crossing points through a human-centered, visual-based approach. The visual cognitive framework integrates three data streams—eye-tracking metrics, mouse-over actions, and issued radar commands—to capture the traffic context from the controller’s perspective. A target pair identification method is designed to generate potential conflict pairs. Controller behavior is then modeled using a sighting timeline, yielding insights to develop the cognitive mechanism. Using air traffic crossing-conflict monitoring in en route airspace as a case study, the framework successfully captures the state of controllers’ monitoring and awareness behavior through tests on five target flight pairs under various crossing conditions. Specifically, aware monitoring activities are characterized by higher fixation count on either flight across a 10 min window, with 53% to 100% of visual input activities occurring between 8 to 7 and 3 to 2 min before crossing, ensuring timely conflict management. Furthermore, the study quantifies the effect of crossing geometry, whereby narrow-angle crossings (21 degrees) require significantly higher monitoring intensity (15 paired sightings) compared to wide or moderate angle crossings. These results indicate that controllers exhibit distinct monitoring and awareness behaviors when identifying and managing conflicts across the different test pairs, demonstrating the effectiveness and applicability of the proposed visual cognitive framework. Full article

Nature-based health interventions (NBHIs) are increasingly used in the healthcare system to support people with anxiety, depression and/or stress, highlighting the need for systematic development and evaluation. This study aims to identify target group, professionals, mechanisms, and outcomes of NBHIs for people with mild to moderate anxiety, depression, and/or stress. A Delphi-based study was conducted to explore core components of NBHIs in healthcare settings. Thirteen vs. eleven researchers with expertise related to the target group responded in two rounds. Respondents rated statements on a 7-point Likert scale and prioritised core components regarding target group, professionals, mechanisms, and outcomes. A thematic analysis was applied to synthesise qualitative responses. Consensus was achieved on 12 of 21 items across the four domains. Highest agreement concerned core mechanisms (nature interaction, social community, and physical activity), outcome priorities (mental wellbeing and quality of life), and professional competencies. Greater variation was observed regarding group composition and team delivery. Analysis of qualitative expert responses highlighted four key themes: (1) Balancing Group Composition, (2) Adapting Competencies to Context, (3) Core Mechanisms for Change, and (4) Weighing Perspectives in Outcome Selection. By setting out guiding principles for a programme theory, the study lays the foundation for the design and implementation of context-adapted NBHIs. The study underscores the need to approach NBHIs as complex interventions, thus contributing to a paradigm shift towards a new era of a bio-psycho-social health perspective. Full article

Background: Aripiprazole is an atypical antipsychotic that acts as a partial agonist on the dopamine receptor D2 while also displaying agonistic activity on the 5-HT1A and antagonistic activity on the 5-HT2A receptors. As a partial agonist, aripiprazole stabilizes the activity of the D2 receptor, preventing overactivation. Case presentation: Within our deprescribing activity, we came across the case of a 30-year-old antipsychotic-naïve patient treated with the depot formulation of aripiprazole for bipolar disorder and acute mania, possibly developing hypersexuality due to an overdose that impacted negatively and heavily on his personal life. Results: The patient developed a peculiar subset of hypersexuality, changing his sexual orientation. Of interest, one month after discontinuing aripiprazole and switching to paliperidone, all the sexual-related symptoms and impulse control disorders resolved. Conclusions: We suggest stronger communication among the clinical teams involved in the patient’s care and screening patients for impulse control disorder prior to the administration of aripiprazole and monitoring them during treatment. Full article

Background/Objectives: The GCS is widely used to assess a patient’s level of consciousness after trauma. Although not a diagnostic tool for traumatic brain injury (TBI), prehospital clinicians frequently rely on GCS findings—along with pupil exam, mechanism of injury, and clinical presentation, to estimate the likelihood that TBI may be present before imaging is available. However, the GCS has known limitations and fails to identify a significant proportion of TBI patients. This study aimed to evaluate the association between GCS scores and the presence of TBI, and whether additional clinical variables improve its discriminatory value. Methods: This retrospective cohort study analyzed data from trauma patients registered in the TraumaRegister DGU^® between 2015 and 2017. TBI was defined as a head injury with an Abbreviated Injury Scale (AISHead) score of ≥2. Inclusion criteria consisted of trauma team activations with a maximum AIS ≥ 3 and/or the need for intensive care. Prognostic values were assessed using multivariable logistic regression analysis. Results: 40,216 patients were included of which 17,205 (42.8%) were diagnosed with TBI and 23,011 (57.2%) were non-TBI patients. In the TBI group, 36.4% (n = 6216) presented with an initial GCS of 15 prehospitally. 17.8% (n = 3059) of TBI patients had anisocoric or bilaterally dilated pupils, 22.1% (n = 3799) had sluggish or fixed light reactivity and 17% (n = 2934) had no motoric response in Eppendorf-Cologne Scale (ECS) motor component. GCS score by itself showed better TBI prediction value than pupil size or reactivity or motor component alone. Nevertheless, substantial misclassification was observed when using GCS alone: 25.7% of patients with a normal GCS (15) had TBI (AIS Head ≥ 2), while 19.1% of patients with GCS 3 had no TBI. In the non-TBI group, 2.7% (n = 622) had a GCS of 3, 2.9% (n = 685) had anisocoric or bilaterally dilated pupils, 4.2% (n = 960) had sluggish or fixed light reactivity and 3.3% (n = 751) had no motoric response. Even at the lowest GCS score of 3, 19.1% of patients did not have TBI, while a normal GCS of 15 still included 25.7% of patients with TBI. Conclusions: The expanded model combining GCS with pupillary assessment and the ECS motor component demonstrated superior performance in prehospital TBI detection compared with the GCS alone. Implementing an extended GCS incorporating pupillary and ECS assessment may facilitate earlier recognition of TBI and support timely triage decisions; however, potential effects on patient outcomes require confirmation in prospective studies. Full article

Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article

This paper presents a study of the tonality of sound emitted by a wind farm into the surrounding environment. The wind turbines installed at the site have a rated power of 3.0 MW. The aim of the study was to analyse the tonality of sounds in the environment at the nearest residential area. The issue of tonal noise near the wind farm was identified during routine periodic noise monitoring. An experienced survey team identified the phenomenon and carried out preliminary field analyses. Detailed studies were then carried out to identify the environmental hazard and failure-free operation of the turbines. The recorded acoustic events are described in detail and an in-depth analysis is carried out. An action plan has been implemented in consultation with the wind farm operator to reduce tonal sound emissions to the surrounding environment. As a result of these interventions, tonal noise from the wind turbines was successfully reduced. It was determined that the detection of the potential tonality of the sounds emitted by wind turbines should take place during the analysis (active listening) of the .wav file, synchronised with Fast Fourier Transform (FFT) analysis. Conducting tonality assessments solely during field measurements may lead to incorrect identification of tonal sources. Full article

Studies have rarely examined the effects of changes in legal anti-doping knowledge (LADK) on doping tendencies in athletes. This study aimed to evaluate the effectiveness of a structured educational intervention focused on LADK and to analyze how LADK changes affect elite athletes’ doping tendency. The participants were athletes (n = 310; 156 females; 24.1 ± 4.2 years of age), all actively competing at the senior national or international level in either individual (N = 119) or team sports (N = 191), tested on sociodemographic-, sport-, doping-factors (including doping tendency—DT), and LADK. Participants were randomly divided into an experimental group (E: N = 140) and a control group (C: N = 170). The E group participated in a structured educational program on LADK. A pre- and posttest design was used to evaluate changes in LADK (dependent variable). Logistic regression was calculated to evaluate the association between LADK and binarized DT (negative vs. neutral/positive DT). Factorial ANOVA for repeated measurements revealed significant improvement in LADK in the E group, with significant ANOVA effects for time (F test = 35.8, p < 0.05) and time × group interaction (F test = 12.27, p < 0.05). The logistic regression did not reveal significant correlations between LADK and DT. Further studies exploring younger athletes, as well as long-term, multidimensional interventions, are warranted. Full article

Large Language Models (LLMs) promise to enhance clinical decision-making, yet empirical studies reveal a paradox: physician performance with LLM assistance shows minimal improvement or even deterioration. This failure stems from an “acquiescence problem”: current LLMs passively confirm rather than challenge clinicians’ hypotheses, reinforcing cognitive biases such as anchoring and premature closure. To address these limitations, we propose a Dialogic Reasoning Framework that operationalizes Dialogical AI principles through a prototype implementation named “Diagnostic Dialogue” (DiDi). This framework operationalizes LLMs into three user-controlled roles: the Framework Coach (guiding structured reasoning), the Socratic Guide (asking probing questions), and the Red Team Partner (presenting evidence-based alternatives). Built upon Retrieval-Augmented Generation (RAG) architecture for factual grounding and traceability, this framework transforms LLMs from passive information providers into active reasoning partners that systematically mitigate cognitive bias. We evaluate the feasibility and qualitative impact of this framework through a pilot study (DiDi) deployed at Centro Chirurgico Toscano (CCT). Through purposive sampling of complex clinical scenarios, we present comparative case studies illustrating how the dialogic approach generates necessary cognitive friction to overcome acquiescence observed in standard LLM interactions. While rigorous clinical validation through randomized controlled trials remains necessary, this work establishes a methodological foundation for designing LLM-based clinical decision support systems that genuinely augment human clinical reasoning. Full article

Background: Patient-centered care (PCC) is a cornerstone of quality, yet its translation into managerial decision-making remains underexplored. Middle nurse managers (MNMs) play a pivotal role in enabling patient-centeredness, but their perspectives on PCC decisions are rarely investigated. Aim: This study explored MNMs’ perceptions of what constitutes a patient-centered decision in hospital settings and identified the essential dimensions underpinning such decisions. Methods: A qualitative descriptive design was adopted using semi-structured interviews. Thirty-eight MNMs from three hospitals in central Italy were included. Data were analyzed using Elo and Kyngäs’ content analysis approach. Results: Two overarching themes emerged as central to patient-centered managerial decision-making (PCMDM): “Meaning and definition of PCMDM,” and “Influencing dimensions of PCMDM”. MNMs described PCMDM as an evolving and adaptable process shaped by patient needs and organizational constraints and unfolding across distinct phases. Key influencing dimensions included the manager’s role, organizational environment, human resource management and knowledge of the patient. Conclusions: PCMDM is a continuous, ethical, and reflective process mediated by MNMs, who reconcile institutional priorities, team dynamics, and patient needs to create conditions for high-quality PCC. Implications for Practice: Strengthening PCMDM requires coordinated action aimed at equipping nurse managers with advanced leadership capabilities, building organizational structures that sustain patient-centered decisions, and empowering patients to actively co-shape the care process. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article

Schools focus on the education of students, but it is recognised that student engagement and educational achievement are enhanced where student wellbeing is considered. Student wellbeing can be supported both in school and through connections to the school and broader community. While teachers seek to support student wellbeing, they are often ill-equipped, given workload and educational focus, limiting their capacity to address student wellbeing needs, particularly those linked to social or community issues. School social workers provide a valuable adjunct to the work of educators, enabling a greater focus on wellbeing through the provision of targeted psychosocial support and community engagement that recognises and responds to broader factors impacting education achievement. In schools without social workers, social work student placements can provide opportunities to introduce school communities to the value and benefits social workers offer. This practice paper explores examples of school-based social work student placements offered through the social work field education program at one regional Australian University, including activities, strategies undertaken, and identified benefits of social work student placements. With indicated benefits, it is argued that the inclusion of social workers in schools adds value to the educational team, supporting children’s wellbeing and thereby contributing to improved educational engagement and achievement. Full article

(1) Background: Zinc is an essential micronutrient involved in immune regulation, epithelial barrier integrity, and the host response to bacterial infections. However, the clinical benefits of zinc supplementation across different age groups remain uncertain, with heterogeneous findings and variable dosing strategies reported in the literature. (2) Objectives: To map and summarize randomized controlled trials (RCTs) evaluating zinc supplementation (either as treatment or prophylaxis) for bacterial infection outcomes in neonates, children, and adults, and to identify gaps requiring further research, including the use of zinc-based nanoparticles. (3) Eligibility Criteria: We included English-language RCTs that evaluated zinc supplementation and reported clinical outcomes related to bacterial infections. Observational studies, trials without infection-related outcomes, and studies not involving human participants were excluded. (4) Sources of Evidence: A MEDLINE (PubMed) search was conducted from 2000 to 1 November 2025 using predefined keywords related to zinc supplementation, neonates, children, adults, and bacterial infections. Reference lists of eligible articles were screened to identify additional studies. (5) Charting Methods: Data were charted for each included study, including population characteristics, zinc dosing and regimen, type of supplementation (therapeutic or prophylactic), main infection-related outcomes, and key findings. Data charting was performed independently and verified within the research team. (6) Results: A total of 51 RCTs were included: 10 in neonates, 32 in children, and 9 in adults. In neonates, therapeutic zinc supplementation as an adjunct to antibiotics showed heterogeneous results, with some studies reporting reductions in morbidity, inflammatory markers or mortality, while others found no significant differences in clinical outcomes. In children, zinc supplementation consistently reduced the duration and severity of diarrheal episodes and, in several trials, improved the resolution of respiratory infections. In adults, the evidence was limited but suggested potential benefits in selected populations, such as burn patients or those with zinc deficiency or immunologic dysfunction. Variability in zinc dosage, treatment duration, and outcome definitions limits direct comparison across studies. (7) Conclusions: Zinc supplementation appears to provide benefits in neonates and children, whereas evidence in adults remains mixed and inconclusive. Standardized, well-powered RCTs are needed to define optimal dosing strategies, identify populations most likely to benefit, and clarify the mechanisms underlying zinc’s anti-infective effects. Future research should consider the use of zinc oxide nanoparticles (ZnO-NPs) demonstrated broad-spectrum antimicrobial activity and potential synergy with antibiotics, although clinical data remain still limited. Full article

Research laboratories in universities face a complex challenge: they must manage multiple projects, diverse teams, and tight deadlines, often with limited resources. While the business world has long used agile and quality management tools to navigate such complexity, these methods are surprisingly rare in academic research. In this study, we set out to bridge this gap. We implemented a combined management model, blending agile Scrum practices with proven quality tools like the Ishikawa diagram and PDCA cycle, within a pharmaceutical sciences research lab. Over a six-month period, we diagnosed key issues, created a structured action plan, and introduced an online platform to monitor progress continuously. Our approach led to a significant increase in productivity, with 65% of targeted articles being published or submitted and 75% of general lab activities completed. Perhaps just as importantly, communication improved dramatically, and the lab successfully met all its institutional deadlines. We conclude that this hybrid framework is not just a theoretical idea but a practical and powerful innovation. It provides a tangible blueprint for other research groups looking to enhance their productivity, streamline communication, and build a more adaptive and effective research culture in the face of academic complexity. Full article

The parameter extraction of proton exchange membrane fuel cells (PEMFCs) has been an active area of study over the past few years, relying on metaheuristic optimizers and experimental datasets to achieve accurate current/voltage (I/V) curves. This work develops a mirage search optimizer (MSO) to precisely estimate the PEMFC model parameters. The MSO employs two search techniques based on the physical phenomena of light bending caused by atmospheric refractive index gradients: a superior mirage for global exploration and an inferior mirage for local exploitation. The MSO employs optical physics to direct search behavior, in contrast to conventional optimization approaches, allowing for a dynamic balance between exploration and exploitation. Convergence efficiency is increased by its iteration-dependent control and fitness-based influence. Using two common PEMFC modules, a comparison study with previously published methodologies and new, recently developed optimizers—the Educational Competition Optimizer (ECO), basketball team optimization (BTO), the fungal growth optimizer (FGO), and the naked mole rat optimizer (NMRO)—was conducted to evaluate the proposed MSO for parameter identification. Furthermore, the two models were tested under various temperatures and pressures. For the three examples studied, the MSO achieved the best sum of squared errors (SSE) values with an intriguing overall standard deviation (STD). It is undeniable that the STD and cropped SSE values, among other difficult techniques, are quite competitive and display the fastest convergence. According to the MSO, the BCS 500W, Ballard Mark V, and Modular SR-12 each have MSO values of 0.011697781, 0.852056, and 1.42098181379214 × 10⁻⁴, respectively. Additionally, the comparison results demonstrate that the proposed MSO can be successfully used to quickly and accurately define the PEMFC model. Full article