Disability rating scales play a pivotal role in clinical trials, but there is a notable lack of guidance on how to analyze these scales. Using amyotrophic lateral sclerosis as a case study, our aim was to explore how disability rating scales have been analyzed in completed clinical trials and to assess how these different approaches influence both the risk of false-positive findings and the statistical power to detect true treatment effects. We searched PubMed and Embase to systematically identify randomized, placebo-controlled clinical trials using the revised ALS functional rating scale (ALSFRS-R) as primary end point, with ≥20 randomly assigned patients and ≥12-weeks of follow-up. Data were extracted on the statistical analysis approaches and strategies for handling missing data. Variability in statistical methods was mapped to the various research questions that the trials aimed to address. A simulation study assessed how each statistical method influenced validity (false-positive rate) and precision (statistical power), using the Ceftriaxone trial data set to model a realistic trial scenario. Our analysis included 45 randomized clinical trials, comprising a total sample size of 7,338 patients, and identified 39 distinct statistical methods using a mixture of longitudinal and cross-sectional techniques. Most trials (55.6%) did not use all available (longitudinal) ALSFRS-R measurements, resulting in suboptimal utilization of patient data and reduced statistical precision. Applying the different statistical methods to the same trial data set resulted in large differences in the estimated treatment effect size, ranging from a negative 1.33 to a positive 2.33 SD difference. Among the methods used, 38.9% (95% CI 24.8%-55.1%) were at risk of increasing false-positive rates, potentially contributing to the erroneous advancement of ineffective treatments. Statistical power of valid strategies varied widely, ranging from 17.9% to 78.2%. Our results demonstrate considerable variability in statistical methods, with the choice of method able to influence the estimated treatment effects, potentially resulting in misleading conclusions and uncertainty about treatment effects. This limits the interpretability and comparability of clinical trials and influences clinical decision-making and drug development. Establishing statistical consensus recommendations could improve the utility of disability scales in clinical trials and accelerate progress toward effective therapies for neurodegenerative diseases.

Low levels of financial literacy and poor financial management practices are still the main problems faced by micro, small and medium enterprises that are based on community and religious organizations. Problems with unorganized financial recording systems, in addition to the inability to separate business from personal finances, are obstacles to obtaining financing as well as making appropriate business decisions. The community service program is intended to improve the financial management capacity of micro, small and medium enterprises based at the Al-Muhajirat Mosque Taklim Assembly in Biring Romang Village, Makassar City through practical training on basic accounting. The method used is lectures, tutorials, and discussions tailored to the needs of partners with a participatory approach. The evaluation was carried out using participatory observation, case studies as well as post-training assessments to measure the effectiveness of the program. The results showed a significant increase in participants' ability to record transactions, implement a simple accounting cycle, and separate business from personal finances. Participants can also prepare basic financial reports for decision-making purposes. This program strengthens MSMEs' readiness for more accountable, transparent, and sustainable financial management.

While industrialization and urbanization patterns occur following economic and demographic factors, development control instruments such as permitting are used to channel growth to avoid adverse environmental impacts. Using three years of Environmental Clearance Records (2022-2024), the siting trends of upcoming industrial projects and large residential complexes in West Bengal, India, were studied against the backdrop of existing laws for development control. It was found that more than fifty percent of industrial projects would be coming at locations in a non-industrial neighborhood. About one-third of projects for large residential complexes would be coming up in areas under rural administration lacking supporting civic infrastructure. Thus, the study revealed that while project-level development control instruments (environmental clearance, consent with respect to pollution control, building permit) are functioning as designed, the absence of enforceable spatial plans and concurrent infrastructure development are producing environmental risks. Possibilities for improvement in regulatory architecture were explored. These findings can be generalized to rapidly urbanizing regions where proposed projects outpace civic infrastructure, illustrating that environmental protection requires aligning project approval with location-based, climate-aware spatial planning.

ABSTRACT As climate resilience becomes central to urban planning, cities increasingly use urban greening to mitigate climate risks. Integrating nature into urban environments – especially through public green spaces – provides substantial environmental and health benefits. However, these interventions also reshape urban space and property values, often reinforcing socio-spatial inequalities by favouring affluent areas or displacing vulnerable communities. While scholars highlight the role of real estate dynamics in these processes, little is known about how local governments address them. This study examines how municipalities account for real estate market dynamics in the planning and management of green spaces, drawing on case studies from three Spanish cities: Bilbao, Valladolid, and Málaga. Using policy document analysis and interviews with urban planners and policymakers, we identify three key challenges to just urban greening: (1) siloed and reactive planning focused narrowly on redistributing green cover (organizational challenge); (2) funding models dependent on adjacent real estate development, tying green space provision to market dynamics (financial challenge); and (3) the alignment of greening initiatives with urban competitiveness agendas, often prioritizing economic gains over citizen well-being (strategic challenge). By situating urban greening within critical political ecology debates, this study highlights governance gaps undermining socially just climate adaptation and outlines pathways toward more equitable urban greening practices.

Multi-agent large language models (LLMs) were evaluated for Process Systems Engineering (PSE) tasks. Two multi-agent systems (Dyad 2.1 and Claude Opus 4.6) were compared with two single-agent baselines (ChatGPT 5.2 and Google Gemini Pro 3) in three crystallization-centered case studies spanning soft sensing, mechanistic modeling, and nonlinear model predictive control (NMPC). In Case Study 1, ATR–FTIR calibration models were built to predict paracetamol mole fraction from spectra, temperature, and solvent composition. All LLMs converged to latent-variable linear chemometric models and achieved near-linear parity with R 2 close to unity across training, validation, and test sets. In Case Study 2, all systems reconstructed a moment-based population balance model (PBM) coupled to a solute mass balance for potassium sulfate dissolution and seeded crystallization, but robustness differed. For dissolution, multi-agent workflows maintained stronger validation performance, while the single-agent baseline showed the largest degradation, including strongly reduced R 2 for the moments of crystal size distribution (CSD). For crystallization, only the multi-agent PBMs reproduced the dynamics consistently and were retained for model-updating tests under dataset shift. In Case Study 3, LLMs proposed NMPC formulations for batch crystallization of potassium sulfate, which temperature was manipulated to regulate mean size L ̄ 10 and crystal mass m under constraints. All NMPCs were implementable at a 1 min sampling time, achieved near set-point tracking with moderate control effort over five scenarios, and Dyad provided the best overall closed-loop performance. • Specialized multi-agent LLMs were evaluated on chemical engineering tasks. • LLMs were applied to develop solutions for sensing, modeling, and nonlinear control. • ATR-FTIR calibration models achieve R 2 > 0 . 97 with automated feature selection. • Population balance models are iteratively refined and recover correct equilibrium behavior. • NMPC formulations reach set-points with computation times below 20 s per control move.

Evidence integration in TSCA risk evaluation: The value of tiered risk assessment - A case study using HHCB.

Extended reality head-mounted displays as measuring systems: Conceptualization and measurement uncertainty evaluation

• Advanced estimation framework for on-board comfort from incomplete APC data. • Real-time performance of complex multi-line public transport networks. • Case study on Helsinki commuter train network with high estimation accuracy. • Reliable comfort estimates even at low APC coverage levels. • Guidance for optimal APC deployment and service quality monitoring. Comfort on-board public transport vehicles is a critical metric of user experience and service performance. The quantification of this metric requires knowledge of the number of passengers on-board every time a vehicle arrives at or departs from a stop or station. Automatic Passenger Counting (APC) systems allow obtaining such knowledge in real-time, but the information is often incomplete due to system malfunctions, or, more commonly, a lack of the relevant equipment in some vehicles. This study develops an advanced method for passenger estimation that fills gaps in incomplete APC datasets, with computational performance allowing real-time application, and calculates comfort levels on-board public transport vehicles in complex networks where stations are served by multiple lines. The proposed method is tested on a case study considering the Helsinki commuter train network, comprising 6 service lines and 20 stations. The results indicate that the proposed framework can achieve comfort level estimations with high precision across the different cases evaluated. Furthermore, the study provides insight into the key practical question of the number of vehicles that need to be equipped with APC devices in order to obtain sufficiently accurate on-board passenger comfort estimates, and it is shown that it is possible to obtain these estimates even when only a small subset of the runs of any single day are performed by equipped vehicles. Finally, the proposed estimation approach is a valuable tool for operators to obtain a better understanding of daily mobility patterns, evaluate their services through quantifying user experience, and enhance their operations.

The integration of virtual reality (VR) into clinical sciences education offers transformative potential for enhancing experiential learning and clinical training. This paper presents a comprehensive framework for integrating VR into clinical sciences education, using a case study from a speech-language pathology (SLP) program. It provides a roadmap for aligning VR integration with program and institutional goals, ensuring sustainability, and fostering stakeholder collaboration. Key components of the integration process are identified, and a case study on the implementation of the Bodyswaps VR platform for training active listening skills in SLP students is presented to illustrate the practical application and benefits of VR in clinical education. Preliminary findings indicate that VR integration increased student engagement and self-efficacy and improved clinical competencies. This paper concludes with reflections on the challenges and future directions of VR adoption in higher education. The roadmap presented serves as a scalable model for other programs seeking to leverage VR to enhance educational outcomes, boost student engagement, and prepare students for a technologically advanced workforce.

In their final semester, nursing students write a bachelor's thesis, aiming to master academic writing and data analysis. Although supervision has been well researched, little evidence is available on how nursing students experience learning qualitative analysis by participating in digital workshops. To explore and describe the experiences of nursing students as they learn qualitative analysis in a digital workshop setting. This study employed a case study design. Data were collected through surveys and field notes and analyzed using thematic analysis based on Braun and Clarke's methodology. The study involved 53 bachelor's students and took place at a mid-sized Norwegian university between 2021 and 2023. Based on the students' experiences with preparation, content, and the learning environment, the results of the current study reveal aspects of their 'learning path.' The students' own preparations and efforts facilitated the acquisition and sharing of knowledge, which in turn motivated progress in both their own and their peers' writing processes. The students' 'learning path' is characterized by a starting point, progression, learning process, and motivation. This study highlights how digital group supervision supports bachelor's degree students' learning of qualitative analysis, emphasizing the importance of preparation, peer collaboration, and supervisor guidance. Structured, flexible digital workshops are found to enhance analytical skills and confidence, underscoring the importance of integrating research methodology into nursing education.

Introduce a case study for Federated Learning (FL) in healthcare, addressing challenges posed by patient privacy and limited large-scale datasets. Our goal is to assess the features learned by FL methods in a simulated, diverse setting that emphasizes realistic data heterogeneity, and to analyze the learned representations for their medical relevance using both local and global explainability techniques. Six fundus oculi datasets were combined to simulate a diverse federated learning environment, representing heterogeneous data conditions. We evaluated three established FL methods against centrally trained models, assessing both predictive performance and the learned representations. Specifically, explainability techniques were employed to examine the features learned by the models, and local explanations were evaluated against attention maps annotated by ophthalmologists. Robustness against common biases in fundus datasets was also assessed. Our study found improvements in model utility (up to 9.97%) with FL methods compared to isolated training. Analysis of learned representations revealed that federated models predominantly learn the vertical cup-to-disc ratio, a crucial feature for glaucoma diagnosis, and demonstrated robustness against common biases. High agreement was observed between local explanations and ophthalmologist-annotated attention maps. This study demonstrates the benefits of FL systems in a healthcare scenario, providing a case study for evaluating federated systems beyond idealized benchmarks. Our findings highlight the potential of FL to not only improve model utility in privacy-sensitive medical domains but also to learn medically relevant features instead of spurious correlations.

Following previous failures to predict drug-induced adverse immune reactions in clinical trials, for example in cases with preclinical species differences or poorly indicative in vitro assays, there has been an emphasis on developing improved preclinical hazard identification tools. Concurrently, there is a regulatory agency-backed responsibility to reduce reliance on preclinical animal models, highlighted by the Food and Drug Administration (FDA) Modernization Act 2.0 and the FDA's 2025 announcement to phase out animal testing for specific compounds. Traditional in vitro cytokine release assays utilize plastic-based formats of antibody presentation to blood cell fractions, and, although biologically simple to run, they do not accurately recapitulate in vivo blood vessel physiology. Including endothelial cells improves physiological relevance by representing the internal vascular wall, enabling cell-cell interactions, compound presentation, and cellular responses from endothelial cells alongside blood cells. Here, endothelial cells outgrown from healthy donors were cocultured with their blood cells to model the immune response to compounds. Building on existing endothelial assays cocultured with blood cell fractions, we established the model using whole blood as an alternative format. We then transferred both formats from 2-dimensional (2D) 96-well plates into a 3D microfluidics system, further mimicking the dynamics and structural microenvironment of a blood vessel. We used these human vasculature models to recapitulate the expected cytokine response to existing compounds and highlight the additional preclinical safety end points that can be investigated by using a 3D vessel, such as vascular leak. This proof-of-concept study demonstrates foundations for a scalable, physiologically relevant method for preclinical testing while reducing reliance on animal models.

Mechanistic models in systems biology enable biophysically-backed testing of hypothesised mechanisms. However, determination of their parameter values is highly challenging, and the data available for calibration is frequently qualitative in nature. Acknowledging this, many approaches abandon mechanistic description, avoiding parameterisation and simulating biological network behaviours in a qualitative fashion. Appealing are the methods that capture some of the best of both types of approach, maintaining a qualitative perspective while using mechanistic models that naturally generalise to quantitative data and carry biochemical implications. Here, using a pea branching network model as an exemplar, we demonstrate the conversion of biological hypotheses into simplified, parameter-free mathematical models, elucidating the biophysical assumptions implicitly made by this approach and analysing the exemplar model's behaviour. Using likelihood-free Bayesian calibration, we compare the parameter-free model to the set of plausible calibrations of its parameterised analog, hence demonstrating that almost all of the qualitative conclusions given data - including both suitability of a hypothesised network structure, and sensitivity analysis - are obtained by the parameter-free paradigm. Altogether, our findings highlight the usefulness of parameter-free treatments of quantitative models, and also deepen understanding of branching network function across mutant and grafted plants.

Artificial intelligence in pharmaceutical manufacturing: Applications, case studies, and GxP implementation considerations.

Integrated techno-economic and environmental evaluation of fast pyrolysis of raw and defatted spent coffee grounds: A case study in Brazil

Comparative life cycle assessment of paper and cardboard based packaging solutions for e-commerce: A case study on a book application.

• Monte Carlo simulations capture uncertainty in EV fast-charging station demand. • Charging protocols are compared under realistic power and queuing constraints. • The optimized OPT-FDP protocol achieves shorter charging times. • Optimal station sizes: 200 kW (urban) and 900 kW (highway) The paper reports a comparative study on the performance of fast-charging protocols for electric vehicles (EVs), evaluated through a stochastic simulation framework, representing both urban and highway charging stations. Three charging protocols are modeled and compared: two derived from commercially available vehicles (Nio and Tesla Model 3) and one optimization-based protocol (OPT-FDP), developed by the Authors, that balances charging speed and battery degradation. The framework accounts for queue management and power-allocation strategies, and Monte Carlo simulations reproduce variability in daily traffic and initial state-of-charges for realistic operating conditions. Key performance indicators include total delivered energy, number of vehicles served, and median station time, representing both user −and operator- oriented perspectives. Two case studies have been developed. In urban contexts, station power capacity is the dominant variable affecting throughput, with 200 kW identified as the minimum rating to ensure user-perceived fast charging, keeping median station time below 30 min and serving approximately 100% of daily arrivals. In highway scenarios, power and sockets number jointly determine service rate, with 900 kW and 10 sockets identified as the ideal size for 200 EVs per day, delivering about 9000 kWh/day and serving approximately 99% of vehicles. Among the tested protocols, OPT-FDP consistently minimizes charging time, achieving median station times of approximately 15 min in properly sized highway configurations, compared to about 20–25 min for Tesla and Nio protocols, without compromising energy delivery. These findings provide quantitative insight into planning future high-power charging infrastructure, highlighting ideal station sizing and management strategies for large-scale EV adoption.

Mass gatherings at sporting events pose critical risk for crowd safety, especially in venues with limited history of hosting high-demand events or insufficient data to inform evidence-based interventions. Ahead of the UEFA Europa League (UEL) Final 2024–2025, San Mamés Stadium implemented staged access protocols across league matches. These real-world experiences provide a timely case study on crowd management strategies and operational planning in urban environments. This work presents a simulation-based study of the pedestrian ingress dynamics during fan entry to the stadium under high-attendance settings. The agent-based modeling framework integrates a Social Force Model that considers the pedestrian’s limited visual range, queuing behavior, and where path-finding is computed from the numerical solution of the Laplace equation. Using empirical data of the geometrical constraints of the stadium and its surrounding areas and timestamped turnstile access records from pre-final league matches, the model reproduces the pedestrian flow under every access protocol implemented during the tournament. Quantitative analysis of ingress efficiency, access rates, and pedestrian flow patterns reveals specific stadium-adjacent zones susceptible to undesired counterflow and overcrowding. Simulations indicate that the suggested access guidelines can streamline crowd movement and lower density levels by roughly 20%, while delaying individual screening at the fenced security perimeter by 2.5 s can decrease the maximum average local density by nearly 80%. These findings emphasize the role of computational modeling as a decision-support tool, allowing the evaluation of alternative crowd management strategies before their implementation in real-world events.

Recent studies have shown that essential oils (EOs) from spices are effective in reducing high blood glucose levels, with β-caryophyllene (BCP) emerging as one of the most promising bioactive compounds. Despite the increasing interest in this field, few studies in the literature have elucidated the link between sample components and their resulting biological activities. In this paper, a detailed investigation was carried out, using a black pepper EO as a case study, and α-glucosidase as biological target. Initial biological assays showed very similar activities between the entire EO and the corresponding amount of BCP in the EO on α-glucosidase. These outcomes confirmed the manifest role of BCP, and further suggested that EO's fractionation could help to unravel the complex interactions among terpene families, BCP, and the biological target. As a first step, a suitable preparative GC method was developed to enable the effective and rapid isolation of the separated monoterpene and sesquiterpene families for subsequent biological assays. The biological outcomes bio-guided consequent fractionation procedures, which were aimed at understanding the role of BCP in the overall sesquiterpene family. Consequently, the use of multidimensional preparative GC guaranteed the effective isolation of the sesquiterpene family without BCP, underscoring its relevant role in this fraction. In a complementary approach, the sesquiterpene family was further fractionated to understand potential enhancing/inhibitory effects with BCP. To the best of authors' knowledge, this paper represents the first instance in the literature where preparative gas chromatography has been employed as an analytical approach to carry out a bio-guided fractionation of EOs.

CL-MHAD: Contrastive Learning-based Multi-Hypergraph Aggregation and Diffusion model for prescription recommendation.