Search Results (1,771)

This review focuses on hydrogel-based systems specifically designed for non-opioid analgesics, aiming to improve efficacy, safety, and translational applicability. The opioid crisis has intensified the need for safer and more effective alternatives in pain management. Non-opioid analgesics including NSAIDs, acetaminophen, gabapentinoids, antidepressants, anticonvulsants, NMDA receptor antagonists, topical agents, and cannabinoids offer promising options but are limited by rapid clearance, short half-lives, and off-target effects. Hydrogel-based drug delivery systems present a novel solution by enabling controlled, localized, and sustained release of analgesics, thus improving therapeutic efficacy and minimizing systemic toxicity. Advances in stimulus-responsive, self-healing, mechanically robust, and hybrid or nanocomposite hydrogels have broadened their biomedical applications and clinical relevance. This narrative review summarizes key hydrogel technologies and their integration with non-opioid analgesic agents. We explore encapsulation strategies, drug release mechanisms, and emerging clinical data, while also addressing critical challenges such as biocompatibility, mechanical durability, and translational scalability. Interdisciplinary collaboration between material scientists, clinicians, and regulatory experts is essential to advance hydrogel-based therapies from bench to bedside. Overall, hydrogel platforms hold transformative potential in optimizing non-opioid analgesic delivery and redefining the future of pain management. Full article

Objectives: The present study analyzes the effects of a high-intensity interval training (HIIT) program based on the Tabata method on physiological and psychological variables in contemporary dancers (n = 10) and sedentary individuals (n = 8), who performed a 10-week protocol, with sessions of self-loading exercises structured in intervals of 20 s of effort and 10 s of rest three times a week. Methods: Parameters of body composition, muscle strength, aerobic and anaerobic capacity, heart rate variability, as well as perceptions of health, anxiety, stress, sleep quality, and levels of physical activity and sedentary lifestyle were evaluated. Results: The results showed that no significant changes occurred in most body composition variables, except for visceral fat, where group differences were observed (F = 5.66, p = 0.030, η²ₚ = 0.261). In the indicators of strength and power, the dancers improved the height and relative power of the jump (F = 5.996, p = 0.026, η²ₚ = 0.273), while the sedentary ones increased the strength of the handgrip (p = 0.023). In terms of functional performance, both groups significantly increased anaerobic endurance (F = 10.374, p = 0.005, η²ₚ = 0.393), although no changes were recorded in maximal oxygen consumption or heart rate variability (p > 0.05). On a psychological level, improvements in healthy lifestyle habits and a decrease in the trait anxiety variable were evidenced in dancers (p = 0.023), while in sedentary participants no relevant effects were found. Conclusions: In conclusion, the Tabata protocol may represent an efficient and complementary strategy to enhance strength, anaerobic power, and psychological well-being, particularly among dancers. The observed improvements suggest potential benefits related to movement quality, injury prevention, and general physical conditioning. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental factors, and frequent missing data in multi-source observations. To address these issues, we propose DAHG, a novel long-term precipitation forecasting framework based on dynamic augmented heterogeneous graphs with reinforced graph generation, contrastive representation learning, and long short-term memory (LSTM) networks. Specifically, DAHG constructs a temporal heterogeneous graph to model the complex interactions among multiple meteorological variables (e.g., precipitation, humidity, wind) and remote sensing indicators (e.g., NDVI). The forecasting task is formulated as a dynamic spatiotemporal regression problem, where predicting future precipitation values corresponds to inferring attributes of target nodes in the evolving graph sequence. To handle missing data, we present a reinforced dynamic graph generation module that leverages reinforcement learning to complete incomplete graph sequences, enhancing the consistency of long-range forecasting. Additionally, a self-supervised contrastive learning strategy is employed to extract robust representations of multi-view graph snapshots (i.e., temporally adjacent frames and stochastically augmented graph views). Finally, DAHG integrates temporal dependency through long short-term memory (LSTM) networks to capture the evolving precipitation patterns and outputs future precipitation estimations. Experimental evaluations on multiple real-world meteorological datasets show that DAHG reduces MAE by $\stackrel{\sim }{3}$% and improves R² by $\stackrel{\sim }{0}$.02 over state-of-the-art baselines (p < 0.01), confirming significant gains in accuracy and robustness, particularly in scenarios with partially missing observations (e.g., due to sensor outages or cloud-covered satellite readings). Full article

As the global imperative to decarbonize infrastructure intensifies, wastewater treatment plants (WWTPs) are emerging as critical nodes for implementing circular and energy-positive solutions. Among these, thermal energy recovery from sewage sludge presents a transformative opportunity to reduce greenhouse gas (GHG) emissions, enhance energy self-sufficiency, and valorize waste streams. While anaerobic digestion remains the dominant stabilization method in large-scale WWTPs, it often underutilizes the full energy potential of sludge. Recent advancements in thermal processing, including pyrolysis, gasification, hydrothermal carbonization, and incineration with energy recovery, offer innovative pathways for extracting energy in the form of biogas, bio-oil, syngas, and thermal heat, with minimal carbon footprint. This review explores the physicochemical variability of sewage sludge in relation to treatment processes, highlighting how these characteristics influence thermal conversion efficiency and emissions. It also compares conventional and emerging thermal technologies, emphasizing energy yield, scalability, environmental trade-offs, and integration with combined heat and power (CHP) systems. Furthermore, the paper identifies current research gaps and outlines future directions for optimizing sludge-to-energy systems as part of net-zero strategies in the water–energy nexus. This paper contributes to a paradigm shift toward sustainable, decarbonized wastewater management systems by reframing sewage sludge from a disposal challenge to a strategic energy resource. Full article

Background: Physical activity (PA) is essential for the overall physical, mental, and psychological health of university students. However, participation rates remain low, particularly in developing regions such as Libya. Materials and Methods: This cross-sectional study involved 768 university students who completed self-administered questionnaires assessing PA engagement, motivations, and barriers to participation. Results: The findings revealed that 60.5% of participants were physically inactive, highlighting a significant public health concern. Among inactive participants, the most prevalent external barriers were time constraints (3.45 ± 1.07), limited resources (3.22 ± 0.97), and lack of support (2.96 ± 1.01). values are presented as mean ± standard deviation (SD). The primary motivating factors for PA were health improvement (3.95 ± 0.96) and revitalization (3.93 ± 1.01). Notably, gender differences emerged: male students reported higher motivation for competition and enjoyment, while female students were primarily motivated by weight management. Furthermore, female students reported significantly higher scores for both internal and external barriers than their male counterparts, indicating greater challenges faced in engaging with PA. The study also found a pronounced decrease in PA levels during the college years, suggesting that the transition to university life contributes to reduced activity. Conclusions: This study highlights the critical need for focused strategies to increase student participation in PA and address the identified barriers. Understanding both motivational aspects and barriers is essential for promoting healthier lifestyles among university students in Libya, which could ultimately lead to better health outcomes and fostering a more physically active student community. Full article

This rapid review and knowledge synthesis study presents a multicomponent model for adolescent reading instruction aimed at improving Tier 1 classroom practices across content areas. The authors conducted a rapid review of literature (RRL) on recent research on effective reading instruction and identified six evidence-based components necessary in reading instruction for students in middle and high school classrooms. The proposed Reading Circuit model integrates evidence-based strategies that address word recognition, word knowledge, sentence analysis, and verbal reasoning, while also emphasizing the role of self-efficacy and background knowledge in reading development. By combining cognitive and motivational components, the framework supports content area teachers in delivering instruction that fosters reading proficiency among all students, particularly those reading at basic or below-basic levels. This model aims to bridge the gap between elementary and secondary reading instruction, providing a scalable, whole-class solution for improving adolescent reading. Full article

Multi-view Synthetic Aperture Radar (SAR) provides rich information for target recognition. However, fusing features from unaligned multi-view images presents challenges for existing methods. Conventional early fusion methods often rely on image registration, a process that is computationally intensive and can introduce feature distortions. More recent registration-free approaches based on the Transformer architecture are constrained by standard position encodings, which were not designed to represent the rotational relationships among multi-view SAR data and thus can cause spatial ambiguity. To address this specific limitation of position encodings, we propose a registration-free fusion framework based on a spatially aware Transformer. The framework includes two key components: (1) a multi-view polar coordinate position encoding that models the geometric relationships of patches both within and across views in a unified coordinate system; and (2) a spatially aware self-attention mechanism that injects this geometric information as a learnable inductive bias. Experiments were conducted on our self-developed FAST-Vehicle dataset, which provides full 360° azimuthal coverage. The results show that our method outperforms both registration-based strategies and Transformer baselines that use conventional position encodings. This work indicates that for multi-view SAR fusion, explicitly modeling the underlying geometric relationships with a suitable position encoding is an effective alternative to physical image registration or the use of generic, single-image position encodings. Full article

Background and Clinical Significance: Hell’s Itch is a rare, intensely uncomfortable post-sunburn condition with burning pruritus emerging 24–72 h after UV exposure. This condition often goes unrecognized and is frequently misdiagnosed by healthcare providers due to a lack of knowledge and familiarity. Standard antipruritic measures are often ineffective, and patients frequently rely on anecdotal self-management. Case Presentation: Three healthy adult males between 23 and 28 years old experienced multiple episodes of delayed-onset intense pruritus following moderate-to-severe sun exposure. The patients experienced a burning or stinging pain which they described as “fire ants” or “thumbtacks,“ and their symptoms started between 24 and 72 h after sun exposure without any rash or fever symptoms. The patients did not achieve symptom relief from standard treatments which included oral antihistamines and topical lidocaine, NSAIDs, aloe vera, and cold compresses. The patients received β-alanine treatment through pre-workout supplements or pure powder after consulting non-clinical sources. Each patient ingested β-alanine and reported rapid relief (itch 8–10/10 → 1–2/10) lasting 2–3 h. The only adverse effect reported by one patient was mild paresthesia. Conclusions: This case introduces β-alanine as a potential off-label therapy for Hell’s Itch and emphasizes the psychological burden and clinical complexity of the condition. While anecdotal, further study is needed to elucidate the mechanism of action of β-alanine in relieving symptoms of Hell’s Itch, as well as assess safety and efficacy in controlled settings. Increased clinical awareness of Hell’s Itch may reduce patient distress and improve management strategies. Full article

Ladle slag (LF slag) is a by-product of secondary steelmaking that presents unique valorization challenges compared to BOF or EAF slags due to its distinctive chemical composition (high Al₂O₃ and CaO content) and uncontrolled hydraulic activity. While other steelmaking slags can be reused as supplementary cementitious materials or aggregates, LF slag is predominantly landfilled, with over 2 million tons discarded annually in Europe alone. This study introduces a novel pyrometallurgical valorization strategy that, unlike conventional approaches focused solely on mineral recovery, simultaneously recovers both metallic and mineral value through aluminothermic reduction. This process utilizes end-of-waste aluminum scrap rather than virgin materials to reduce Fe and Si oxides, creating a circular economy solution that addresses two waste streams simultaneously. The process generates two valuable products with low liquidus temperatures: a ferrosilicon alloy (FeSi15-50 grade) and a residual oxide rich in calcium and magnesium aluminates suitable for cementitious or ceramic applications. Through the integration of FactSage thermodynamic simulations with experimental validation, it is possible to predict and control phase evolution during equilibrium cooling, an approach not previously applied to LF slag valorization. Experimental validation using industrial slags confirms the theoretical predictions and demonstrates the process operates in a near-energy-neutral, self-sustaining mode by recovering both chemical and sensible thermal energy (50–100 kWh per ton of slag). This represents approximately 90% lower energy consumption compared to conventional ferrosilicon production. The work provides a comprehensive and scalable approach to transform a problematic waste material into valuable products, supporting circular economy principles and low-carbon metallurgy objectives. Full article

Background/Objectives: Children with cerebral palsy (CP) commonly present impairments in gross motor function and limitations in activities of daily living (ADLs), which negatively impact independence and quality of life. Identifying effective rehabilitation strategies is essential to promote functional development. To evaluate the effectiveness of occupational therapy (OT) interventions on gross motor function and independence in ADLs among children with CP. Methods: Seven electronic databases were searched through August 2025. The review protocol was registered in PROSPERO (CRD42025634706) and conducted in accordance with PRISMA guidelines. Methodological quality and certainty of evidence were assessed using the Oxford Centre for Evidence-Based Medicine scale, the Risk of Bias 2 (RoB 2) tool, and GRADEpro. Randomized controlled trials reporting OT interventions targeting gross motor and ADL outcomes were included. Results: Of 594 identified records, 14 studies met the inclusion criteria. Meta-analysis indicated that OT interventions significantly improved gross motor function (GMFM-66; ES = 0.32 [0.01–0.63], p = 0.04), mobility (PEDI-Mobility; ES = 0.46 [0.05–0.87], p = 0.02), and occupational performance (COPM-Performance; ES = 2.63 [1.14–4.11], p = 0.001) and satisfaction (COPM-Satisfaction; ES = 2.17 [0.82–3.51], p = 0.002). No significant changes were observed in self-care (PEDI-Self-Care; ES = 0.19 [−0.14–0.53], p = 0.26). Conclusions: Evidence suggests that OT interventions effectively enhance gross motor function, mobility, and occupational performance in children with CP. These results support the integration of OT within pediatric rehabilitation programs to optimize functional outcomes. Full article

Background/Objectives: Patients in palliative care often experience multifaceted forms of suffering that extend beyond physical symptoms, including existential distress, loss of meaning, and emotional pain. Ketamine-assisted psychotherapy (KAP) has emerged as a promising intervention for alleviating such complex forms of suffering, yet models specifically tailored to palliative populations remain scarce. This narrative review synthesizes current evidence on ketamine’s neurobiological, psychological, and experiential effects relevant to end-of-life care, and presents a novel, time-limited KAP model designed for use in palliative settings. Methods: Drawing from both biochemical and psychedelic paradigms, the review integrates findings from neuroscience, phenomenology, and clinical practice. In particular, it incorporates a dual-level experiential framework informed by recent models distinguishing ketamine’s differential effects on self-processing networks: the Salience Network (SN), related to embodied self-awareness, and the Default Mode Network (DMN), associated with narrative self-construction. This neurophenomenological perspective underpins the rationale for using two distinct dosing sessions. Results: The article proposes a short-course, time-limited KAP model that integrates preparatory and integrative psychotherapy, two ketamine dosing sessions (one low-dose and one moderate-dose), concurrent psychotherapy, goals of care discussion (GOCD), and optional pharmacological optimization. The model emphasizes psychological safety, meaning-making, and patient-centered care. The sequential dosing strategy leverages ketamine’s unique pharmacology and experiential profile to address both bodily and narrative dimensions of end-of-life distress. Conclusions: This dissociative-psychedelic model offers a compassionate, pragmatic, and theoretically grounded approach to relieving psychological and existential suffering in palliative care. By integrating neurobiological insights with psychotherapeutic processes, it provides a flexible and patient-centered framework for enhancing meaning, emotional resolution, and quality of life at the end of life. Further research is needed to evaluate its clinical feasibility, safety, and therapeutic efficacy. Full article

Background/Objectives: The prison environment presents a unique context for examining the impact of addiction on physical and psychological functioning. Individuals with substance use disorders (SUDs) are overrepresented in correctional facilities and often experience greater emotional difficulties and impaired physical capacity. This study aimed to conduct a comparative analysis of psychological and functional profiles between addicted and non-addicted male inmates in a semi-open correctional facility. Methods: The study included 47 male prisoners (19 addicted, 28 non-addicted). Physical performance was assessed using the Countermovement Jump (CMJ), handgrip strength, the Functional Movement Screen (FMS), and the FitLight reaction time test. Psychological functioning was evaluated using six standardized questionnaires: problem-focused, emotion-focused, and avoidant coping strategies, depression (PHQ-9), perceived stress (PSS-10), and self-compassion (SCS). Results: No statistically significant differences (p > 0.05) were found between addicted and non-addicted inmates in physical performance parameters. Addicted individuals demonstrated slightly higher handgrip strength with lower variability, while non-addicted inmates showed slightly better lower-body power in the CMJ test. Functional movement quality and reaction speed were similar between groups. Psychological assessments also revealed no significant differences between the groups. Coping styles, depressive symptoms, perceived stress levels, and self-criticism scores were comparable in both populations. In the addicted group, deeper squats correlated with lower stress (rho = −0.46, p = 0.047), and better hurdle step performance correlated with emotion-focused coping (rho = 0.46, p = 0.048). Conclusions: Although no statistically significant differences were found between addicted and non-addicted male inmates in the assessed physical and psychological outcomes, the limited sample size and context-specific nature of this pilot study suggest that these findings should be viewed as preliminary and interpreted with caution. Nonetheless, the observed associations between physical performance and psychological variables indicate subtle interconnections between motor capacity, stress perception, and coping mechanisms that merit further investigation in larger, longitudinal studies. Full article

Global electric vehicle sales are growing exponentially, with the European Union actively promoting the adoption of electric vehicles to significantly reduce mobility-related emissions. Concurrently, research efforts are increasingly directed toward optimizing vehicle charging strategies for the effective integration of renewable energy sources. Nevertheless, despite extensive theoretical studies, few practical implementations have been carried out. In response, this paper presents a digital twin of a microgrid designed specifically for optimizing the charging schedules of an electric vehicle fleet, with the goal of maximizing photovoltaic self-consumption. Machine learning algorithms are utilized to forecast vehicle energy consumption, and various heuristic optimization methods are applied to determine optimal charging schedules. The system incorporates an interactive dashboard, enabling users to input specific preferences or delegate charging decisions to a real-time optimizer. Additionally, a user-centric decision support system was developed to provide recommendations on optimal vehicle connection timings and heat pump setpoints. Certain algorithms failed to converge on a feasible optimal solution, even after 340 s and over 500 generations, particularly within high-production scenarios. Conversely, using the GWO-WOA algorithm, optimal charging schedules are computed in less than 25 s, balancing photovoltaic power exports under varying weather conditions. Furthermore, K-Means was identified as the most effective clustering technique, achieving a Silhouette Score of up to 0.57 with four clusters. This configuration resulted in four distinct velocity ranges, within which energy consumption varied by up to 5.8 kWh/100 km, depending on the vehicle’s velocity. Finally, the facility managers positively assessed the usability of the DT dashboard and the effectiveness of the decision support system. Full article

Understanding the degradation behavior of lithium-ion batteries under realistic application conditions is critical for the design and operation of Battery Energy Storage Systems (BESS). This research presents a modular, cell-level simulation framework that integrates electrical, thermal, and aging models to evaluate system performance in representative utility and residential scenarios. The framework is implemented using Python and allows time-series simulations to be performed under different state of charge (SOC), depth of discharge (DOD), C-rate, and ambient temperature conditions. Simulation results reveal that high-SOC windows, deep cycling, and elevated temperatures significantly accelerate capacity fade, with distinct aging behavior observed between residential and utility profiles. In particular, frequency modulation and deep-cycle self-consumption use cases impose more severe aging stress compared to microgrid or medium-cycle conditions. The study provides interpretable degradation metrics and visualizations, enabling targeted aging analysis under different load conditions. The results highlight the importance of thermal effects and cell-level stress variability, offering insights for lifetime-aware BESS control strategies. This framework serves as a practical tool to support the aging-resilient design and operation of grid-connected storage systems. Full article