Search Results (15,502)

Cognitive rehabilitation represents a cornerstone of disease management in multiple sclerosis (MS), targeting the progressive cognitive decline that affects a significant proportion of patients. Despite growing evidence supporting its clinical utility, rehabilitation outcomes remain variable, and identifying modifiable factors that limit its efficacy has become a research priority. Sleep disorders are common in MS and have been increasingly linked to cognitive impairment, yet evidence based on objective polysomnographic assessment remains limited, and the specific parameters that influence cognitive function are poorly understood. This review synthesizes evidence from polysomnographic studies examining how sleep disturbances influence cognitive performance in MS patients. Following a systematic search of PubMed, EMBASE, and the Cochrane Library, 488 patients were included. Sleep fragmentation, reduced sleep efficiency, and oxygen desaturation indices were associated with impairments in attention, information processing speed, and verbal memory, with nocturnal hypoxia emerging as a potentially important mechanism of cognitive impairment. These findings suggest that identifying and treating sleep disorders may be essential for optimizing cognitive rehabilitation outcomes in MS. Further longitudinal studies are needed to determine whether addressing sleep pathology can enhance rehabilitation efficacy and preserve cognitive function over time. Full article

Developing countries participating in the Warsaw Framework for Reducing Emissions from Deforestation and Forest Degradation Plus (REDD+) (WFR) are eligible to receive financial incentives linked to verified reductions in greenhouse gas emissions from forest-related activities. It is necessary to strategically select priority countries among the WFR participants to achieve REDD+ cooperation and mutual benefits between recipient and donor countries. This study evaluates the mitigation potential of 71 developing countries registered under the WFR (December 2025) using two dimensions: national measurement, reporting, and verification (MRV) capacity and the need-effectiveness of official development assistance (ODA) in strengthening MRV capacity. Countries were ranked and classified into six typological groups based on MRV capacity and ODA need-effectiveness. The results show that countries with an intermediate MRV implementation capacity and high ODA need-effectiveness can transition to the MRV implementation phase through policy and financial interventions, suggesting high potential to achieve emission reductions and become priority countries for cooperation. Meanwhile, those with an intermediate MRV implementation capacity but low ODA need-effectiveness were interpreted as types where medium- to long-term cooperation possibilities can be reviewed based on improvements to MRV components. Our findings suggest a two-stage cooperation strategy that integrates short-term MRV-based engagement with long-term ODA-driven capacity-building to expand REDD+ mitigation outcomes under the WFR. Full article

Rural territories characterised by dispersed settlement systems face mounting challenges related to demographic decline, economic fragility, ecological degradation, and the erosion of local knowledge systems. In this context, rural self-sufficiency has re-emerged as a strategic objective; yet it remains inadequately operationalised within spatial planning and territorial assessment practices. This paper proposes a bioregional framework for operationalising rural self-sufficiency in dispersed territories, integrating ecological, morphological, socio-productive, cultural, and governance dimensions across multiple spatial scales. The framework is structured around a tiered system of 108 indicators, hierarchised into priority, secondary, and aspirational levels, combined with a multi-scalar territorial reading articulated through five nested frames—ranging from municipal systems to local productive units. Rather than constituting a mere checklist for immediate quantitative evaluation, the indicator system functions as a structured diagnostic universe, enabling progressive operationalisation based on data availability and governance capacity. To bridge the gap between diagnosis and action, the framework introduces 34 strategic drivers and 28 spatial artefacts, conceived as reversible and context-sensitive interventions. The framework is demonstrated through the case of Arbo (Galicia, Spain), illustrating its capacity to structure territorial diagnosis and articulate coherent pathways from analytical interpretation to strategic spatial intervention. The proposed approach contributes a replicable methodological tool for bioregional and rural planning in dispersed settlement systems. The study contributes to advancing bioregional planning by demonstrating how extensive indicator universes can be rendered operational through selective tiering and multi-scalar deployment. Full article

Background: Age-related hearing loss (ARHL) is the most common sensory disorder in older adults and has been identified as a potentially modifiable risk factor for cognitive decline and dementia. Increasing evidence suggests that auditory dysfunction may contribute to adverse cognitive trajectories through multiple interacting pathways. This narrative review examines the mechanisms underlying the association between ARHL and cognitive decline, evaluates the impact of hearing rehabilitation, and discusses future research priorities. Methods: A narrative synthesis of epidemiological, neurobiological, and interventional studies was conducted, with emphasis on longitudinal cohort studies, neuroimaging research, and clinical investigations of hearing aids (HAs) and cochlear implants (CIs). Results: ARHL is consistently associated with accelerated cognitive decline and increased dementia risk. Proposed mechanisms include sensory deprivation-related cortical reorganization, increased cognitive load during effortful listening, shared neuropathological processes, and social disengagement. Neuroimaging studies demonstrate structural and functional alterations in auditory and associative brain regions in individuals with hearing loss. Emerging evidence suggests that HA and CI may improve cognitive performance and potentially attenuate decline, although long-term randomized data remain limited. Conclusions: Current evidence supports ARHL as a clinically relevant and potentially modifiable contributor to cognitive decline. Clarifying causal pathways and optimizing early hearing rehabilitation strategies represent key priorities for future dementia prevention research. Full article

Urban seismic vulnerability poses severe challenges for disaster preparedness in Lima, Peru, where a long-standing seismic gap increases risk to a metropolitan population of approximately ten million residents. This study presents an adaptive multi-objective optimization framework that dynamically adjusts shelter allocation priorities across earthquake intensity scenarios. The methodology integrates spatial data on population distribution, infrastructure vulnerability, and seismic hazard zones to optimize three competing objectives through the NSGA-III algorithm: inter-shelter spacing, population coverage, and safety. Model parameters were calibrated using controlled synthetic scenarios and subsequently validated with real-world data from Lima. Under the high-impact scenario used by the Municipality of Lima, the official set of 356 designated shelters was compared with an optimized configuration selected from 5855 potential sites under identical hazard and demand conditions. The optimized solution increased population coverage by 66.82% and reduced the average distance to critical resources by 24.55%, while reducing service gaps in peripheral districts. Scenario-adaptive optimization improved the robustness of shelter planning by producing configurations that were better aligned with operational priorities as hazard severity escalated, supporting more equitable access in a resource-constrained urban context. This research contributes an evidence-based decision-support tool for emergency management, translating multi-objective trade-offs into actionable shelter layouts for Lima. Full article

Solitary living is an evolutionarily widespread yet comparatively under-theorized social system, despite its occurrence across diverse animal taxa. Shrikes (family Laniidae) are small predatory passerines that combine raptorial behavior, strong territoriality, and predominantly solitary space use, making them a powerful model for examining the ecology and evolution of solitary living. Here, I synthesize published work on shrike behavioral ecology and explicitly link these traits to the costs and benefits of a solitary lifestyle. I argue that shrikes exemplify how solitary species can offset the absence of social buffering through cognitive innovation, finetuned habitat selection, and flexible yet tightly bounded sociality. I then compare shrike ecology to solitary mammals and reptiles, highlighting convergent patterns in resource dispersion, spatial memory, risk management, and juvenile dispersal. I further examine how anthropogenic pressures, such as habitat fragmentation, climatic instability, and urbanization, interact with solitary life histories and review evidence from management interventions in both European farmland and North American systems that demographic recovery is achievable but remains contingent on addressing broader land-use conflicts and sources of adult mortality. Finally, I outline five interconnected research priorities—spanning cognitive ecology, trophic interactions, movement ecology, genomics, and formal comparative analyses—that would move shrike research from its current observational foundation toward a more experimental, mechanistic, and phylogenetically informed programme. By reframing shrikes as a model taxon for solitary living, this review aims to integrate avian behavioral ecology into broader comparative frameworks of social organization, cognition, and resilience under global change. Full article

Carbapenem-resistant Acinetobacter baumannii infections pose a significant challenge due to their severity and the poor prognoses they often result in, particularly in cases where there are risk factors present. The United States (US) Centers for Disease Control and Prevention (CDC) identified carbapenem-resistant Acinetobacter baumannii (CRAB) infections as a threat to human health. The World Health Organization (WHO) has classified it as a top priority for research. In 2023, the US FDA approved sulbactam–durlobactam for treating certain A. baumannii infections. As of 2024, this combination is designated as the preferred treatment strategy by the Infectious Diseases Society of America (IDSA) for infections due to carbapenem-resistant A. baumannii. In this therapeutic review, the preclinical and clinical data relevant to this regulatory decision were analyzed. This in-depth analysis will provide a comprehensive overview of the complex subject matter. It should be observed that carbapenem-based combination therapy is indicated for carbapenem-resistant A. baumannii. Full article

Microalgae are increasingly recognized as renewable biofactories for producing high-value bioactive molecules. However, their industrial exploitation is limited by their rigid cell walls, metabolite heterogeneity, and the energy-intensive nature of the extraction processes. Recent advances in process-intensification technologies, including microwave-assisted, ultrasound-assisted, enzymatic, pressurized liquid, and supercritical CO₂-based methods, have significantly improved extraction efficiency and selectivity, with reported lipid recoveries exceeding 40–50% in some microalgal systems and carotenoid recoveries approaching 90% under optimized conditions. NADES-assisted systems further enhance mass transfer and solubilization through tailored hydrogen-bonding interactions, enabling selective extraction of polar and semi-polar metabolites under mild conditions. However, limitations remain, including high viscosity, variability in extraction performance, and challenges in solvent recovery and scale-up. This review critically evaluates the extraction efficiency, mechanistic basis, and sustainability of NADES-assisted processes, highlighting key limitations and identifying research priorities for their integration into scalable microalgal biorefinery systems. Full article

This study estimates China’s methane (CH₄) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model methane framework, incorporating updated province-level activity data to capture the pronounced regional heterogeneity inherent in emission profiles and mitigation capacities. The results reveal a national CH₄ budget of 1114 MtCO₂e in 2020, with the energy sector (59%) and agriculture (28%) emerging as the primary contributors. A substantial technical mitigation potential is identified; by 2050, emissions could be curtailed by up to 48% relative to the CLE scenario, representing a 46% reduction from 2020 levels. The energy and waste sectors emerge as the primary contributors to this potential. Specifically, coal mining CH₄ abatement constitutes 58% of the energy sector’s total reduction potential, while enhanced solid waste management accounts for 97% of the mitigation within the waste sector. Key measures include ventilation air methane (VAM) oxidation and pre-mining degasification, as well as anaerobic digestion and recovery and utilization for energy use. Owing to regional disparities in hydrothermal conditions (representing the combined influence of temperature and moisture), demographic status, economic development, the most effective mitigation strategies vary across provinces. For example, pre-mining degasification and VAM oxidation are most impactful in major coal-producing regions such as Shanxi, Inner Mongolia, and Shaanxi. In contrast, anaerobic digestion, recovery and utilization, and waste incineration play a dominant role in more economically developed and densely populated provinces such as Jiangsu, Shandong and Zhejiang. By delineating region-specific technological priorities, this study quantifies the maximum technical mitigation potential for China and offers guidance for other nations facing similar mitigation challenges. Full article

Aqueous organic flow batteries are a promising technology for large-scale energy storage, owing to their safety, low cost, and tunable molecular properties. Battery performance is critically governed by the redox potential, solubility, and stability of organic active species, making molecular design a central research priority. Yet, many current systems still rely on inorganic metal-based materials, which face challenges such as high cost and sluggish kinetics. This review outlines a systematic molecular-engineering framework for designing novel redox species, offering strategies to tailor solubility, redox potential, and molecular size in both organic compounds. Recent advances in mechanistic insight, functionalization, and structure-dependent electrochemical performance are summarized. Computational chemistry and machine learning are highlighted for accelerating high-throughput screening and property prediction, speeding up molecular optimization. Small molecules (1–4 rings), including quinones (C=O), alloxazines, phenazines, and indigo derivatives, which undergo reversible redox reactions involving nitrogen and/or carbonyl groups, have been explored as anolytes and/or catholytes in aqueous redox flow batteries. Key challenges remain, including limited electrochemical stability windows, insufficient solubility, and poor molecular stability, leading to low energy density and cycling degradation. Improving anolyte performance by simultaneously lowering redox potential and enhancing solubility and stability is therefore crucial for advancing both organic and broader redox-active battery systems. Computational and machine learning approaches for identifying and refining electrolyte molecules are also addressed, enabling efficient screening and molecular modification toward high-performance flow batteries. Full article

Over the past decades, plastics have been increasingly employed to package foods and beverages. Furthermore, foods, nowadays, are kept in contact with plastics for far longer periods than ever before. A number of conventional polymers, i.e., polyethylene (PE), Polypropylene (PP), Ethylene Vinyl Acetate (EVA), Εthylene vinyl alcohol (EVOH) polystyrene (PS), Polyvinyl chloride (PVC), Polyvinylidene chloride (PVDC), polyethylene terephthalate (PET), Polycarbonate (PC), polyethylene naphthalate (PEN), Polyamides (PAs), Polyacrylonitrile (PAN) as well as biodegradable polymers-[Polylactide (PLA)] are used commercially in food packaging applications. Potential interaction of food with the packaging container includes: permeation, migration and flavor scalping. Most food and beverage containers are lined with plastics mainly polyolefins, which due to their low polarity tend to absorb volatile compounds of similar polarity. Absorption of flavor compounds by polymers involves both partitioning and diffusion through the plastic. Absorption is influenced by (i) polymer properties such as polarity, morphology, glass transition temperature, density, free volume, crystallinity and surface area, (ii) flavor compound properties such as structure, concentration, and polarity, and (iii) external factors such as temperature, time of contact, relative humidity and the proximity of other compounds. Based on the above, it is apparent that flavor scalping should be among one of the food packaging industry priorities in order to efficiently preserve the quality of packaged food flavor. This review highlights the various factors affecting the scalping process, as well as the consequences of flavor scalping in various food and beverage commodities. The review covers the period 1990–2925 and used the LitChemPlast data base for literature search. Full article

Scientifically assessing the spatiotemporal evolution of regional carbon storage is of great significance for achieving the “dual carbon” goals and optimizing territorial spatial patterns. This study integrated the PLUS and InVEST models to systematically reconstruct the spatiotemporal pattern of carbon storage in Hebei Province from 2000 to 2020, simulate its evolution trajectory under different scenarios in 2030, and identify its driving mechanisms using the GeoDetector model. The main findings are as follows: (1) From 2000 to 2020, cropland was the dominant land use type in Hebei Province, and carbon storage exhibited a spatial pattern of “high in the northwest, low in the southeast.” Carbon storage increased from 16.23 × 10⁸ t to 16.31 × 10⁸ t, with a significantly slowed growth rate after 2010. (2) Multi-scenario simulations for 2030 indicate that under the natural development and economic priority scenarios, construction land expands significantly while cropland and grassland continue to decrease. In contrast, carbon storage shows an increasing trend under the ecological protection and cropland protection scenarios. (3) Driving factor analysis reveals that the spatial differentiation of carbon storage is primarily controlled by natural factors such as slope, elevation, and NDVI, while the explanatory power of anthropogenic factors, particularly population density, has significantly increased. The interaction between NDVI and slope exhibits a synergistic enhancement effect. This study elucidates the coupling mechanisms between land use change and carbon storage under different policy orientations, providing a scientific basis for territorial spatial optimization and the formulation of differentiated carbon neutrality pathways in Hebei Province. Full article

Sustainable Development Goal 7 (SDG 7) aims to ensure access to affordable, reliable, sustainable, and modern energy for all. Evaluating the effectiveness of national energy policies in achieving this goal requires comprehensive and quantitative assessment frameworks. Countries’ performance toward SDG 7 is influenced by various structural factors, including energy demand growth, dependency on energy imports, renewable energy potential, and policy priorities. Therefore, systematic performance evaluation is essential for understanding policy effectiveness and identifying areas requiring improvement. This study evaluates Türkiye’s SDG 7 energy policy performance on a yearly basis over the period 2014–2023. A multi-criteria decision-making (MCDM) framework combining the Stepwise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is employed to quantify and compare performance across years. The proposed approach allows for the determination of indicator weights and the ranking of yearly performance levels based on multiple energy sustainability criteria. The results reveal an overall upward trend in Türkiye’s SDG 7 performance during the study period, although notable fluctuations are observed. A significant decline occurs in 2017, followed by a rapid recovery in subsequent years. Another temporary downturn is identified in 2021, while a remarkable improvement emerges in 2023. A sensitivity analysis based on multiple weighting scenarios was also conducted to examine the robustness of the obtained rankings, and the results confirm the stability of the overall ranking structure, with 2023 consistently maintaining the top position across most scenarios. These findings provide insights into how policy measures, market dynamics, and energy system developments influence the country’s progress toward sustainable energy goals. By incorporating a time-based evaluation framework, this study contributes to the SDG 7 literature by offering a quantitative and policy-oriented assessment of national energy performance. The proposed framework also provides a practical analytical tool for policymakers and energy regulators to monitor progress, identify vulnerable areas, and support evidence-based decision-making in the transition toward sustainable and clean energy systems. Full article

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article