Search Results (1,989)

Leaf biochemical traits are crucial for understanding plant physiological status and ecological dynamics. Partial least squares regression (PLSR) models have been widely used to estimate leaf biochemical traits from spectral reflectance information. However, variations in sun–sensor geometry, the sensor field of view, and the random orientation of leaves can introduce multi-angular reflection properties that differ between leaf sides. In this context, the transferability of PLSR models across different leaf sides and viewing zenith angles (VZAs) remains unclear. This study investigated the potential of multi-angular spectral reflection from dorsiventral leaves to improve the transferability of PLSR models for estimating the leaf chlorophyll content (LCC) and equivalent water thickness (EWT). We compared models trained using multi-angular data from both leaf sides with models trained using nadir data (from the adaxial side, abaxial side, or their combination). The results show that the PLSR models trained with multi-angular data from both leaf sides outperformed the models trained with nadir data, achieving the highest accuracy in estimating biochemical traits (LCC: R² = 0.87, RMSE = 7.17 μg/cm², NRMSE = 10.71%; EWT: R² = 0.86, RMSE = 0.0015 g/cm², NRMSE = 10.00%). In contrast, the PLSR models trained using single-angle reflection from either the adaxial or abaxial side showed a lower estimation accuracy and greater variability across leaf sides and VZAs. The superior performance across datasets obtained under different measurement conditions (e.g., integrating spheres and leaf clips) further confirmed the improved generalizability of the PLSR model trained with multi-angular data from dorsiventral leaves. These findings highlight the potential of the multi-angular spectral reflection of dorsiventral leaves to enhance the estimation of biochemical traits across various leaf sides, viewing angles, and measurement conditions. They also underscore the importance of incorporating spectral diversity into model training for improved transferability. Full article

Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporated into the initialization phase to produce high-quality initial solutions. Subsequently, a nonlinear inertia weight factor is integrated into the exploration phase to bolster the algorithm’s global search capabilities and accelerate convergence. Finally, the coati vigilante mechanism is introduced in the exploitation phase to improve the algorithm’s capacity to escape local optima. Comparative experiments with many existing algorithms are conducted using the CEC2017 test functions, and the proposed algorithm is applied to seven representative engineering design problems. MACOA’s average rankings in the three dimensions (30, 50, and 100) were 2.172, 1.897, and 1.759, respectively. The results show improved optimization speed and better performance. Full article

Developing accurate predictive models in statistical analysis presents significant challenges, especially in domains with limited routine assessments. This study aims to advance the theoretical underpinnings of longitudinal logistic and zero-inflated Poisson (ZIP) models in the context of small area estimation (SAE). Utilizing data from the Canadian Healthy Infant Longitudinal Development (CHILD) study as a case study, we explore the use of individual- and area-level random effects to enhance model precision and reliability. The study evaluates various covariates’ impact (such as mother’s asthma, mother wheezed, mother smoked) on model performance to predict child’s wheezing, emphasizing the role of location within Manitoba. Our main findings contribute to the literature by providing insights into the development and refinement of small area models, emphasizing the significance of advancing theoretical frameworks in statistical modeling. Full article

Cloud computing is now established as a viable alternative to on-premise infrastructure from both a system administration and cost perspective. This research provides insight into cluster computing performance and variability in cloud-provisioned infrastructure from two popular public cloud providers, Amazon Web Services (AWS) and Google Cloud Platform (GCP). In order to evaluate the perforance variability between these two providers, synthetic benchmarks including Memory bandwidth (STREAM), Interleave or Random (IoR) performance, and Computational CPU performance by NAS Parallel Benchmarks-Embarrassingly Parallel (NPB-EP) were used. A comparative examination of the two cloud platforms is provided in the context of our research methodology and design. We conclude with a discussion of the results of the experiment and an assessment of the suitability of public cloud platforms for certain types of computing workloads. Both AWS and GCP have their strong points, and this study provides recommendations depending on user needs for high throughput and/or performance predictability across CPU, memory, and Input/Output (I/O). In addition, the study discusses other factors to help users decide between cloud vendors such as ease of use, documentation, and types of instances offered. Full article

This research aims to explore the interdisciplinary connection between the field of neurology and artificial intelligence (AI) through machine learning (ML) algorithms. The central objective is to evaluate the current state of research in the Neuro-ML field and identify gaps in the literature that require additional approaches. To achieve this objective, 10 analyses were introduced that analyze the distribution of articles based on keywords, countries, years, publishers, and ML algorithms used in the context of neurological diseases. Surveys were also conducted to identify the diseases most frequently studied through ML algorithms. Thus, it was found that Alzheimer’s disease (37 articles for Support Vector Regression—SVR; 31 for Random Forest—RF), Parkinson’s disease (46 articles for SVM and 48 for RF), and multiple sclerosis (9 articles for SVM) are the most studied diseases in the field of Neuro-ML. The study analyzes Alzheimer’s, Parkinson’s, and multiple sclerosis in detail by focusing on diagnosis. The overall results highlight an increase in researchers’ interest in applying ML in neurology, with models such as SVM (597 articles), Artificial Neural Network (525 articles), and RF (457 articles) being the most used. The results highlighted three major gaps: the underrepresentation of rare diseases, the lack of standardization in evaluating the performance of ML models, and the lack of exploration of algorithms with greater implementation difficulty, such as Extreme Gradient Boosting and Multilayer Perceptron. The value analysis of the performance metrics of ML models demonstrates the ability to correctly classify neuro-degenerative diseases, with high accuracy in some cases (for example, 97.46% accuracy in Alzheimer’s diagnosis), but there may still be improvements. Future directions include exploring rare diseases, investigating underutilized algorithms, and developing standardized protocols for evaluating the performance of ML models, which will facilitate the comparison of results across different studies. Full article

Background: Depression is one of the leading causes of disability worldwide. Among pharmacological treatments, fluvoxamine—an early SSRI with a distinct pharmacological profile—has been recently reappraised for its broader clinical relevance. Objective: To assess the efficacy of fluvoxamine in the treatment of depression compared to placebo and other antidepressants through a comprehensive overview of systematic reviews and meta-analyses. Methods: A systematic search was conducted in MEDLINE and the Cochrane Central Register of Controlled Trials, including systematic reviews and meta-analyses of randomized controlled trials evaluating fluvoxamine’s efficacy. Reviews were eligible if they included adults diagnosed with depressive disorders based on the DSM or ICD criteria. Reviews focusing on other psychiatric disorders, comorbidities, tolerability, or economic evaluations were excluded. Data extraction included effect size measures and methodological quality assessments using the AMSTAR-2 tool. Results were synthesized by comparing fluvoxamine to placebo, tricyclic antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and other antidepressants. Results: A total of 74 reviews were identified, of which 14 systematic reviews met the inclusion criteria after screening and full-text analysis. These reviews, published between 1994 and 2021, predominantly involved nine pairwise meta-analyses and five network meta-analyses, comparing fluvoxamine with placebo and various antidepressants. Fluvoxamine demonstrated consistent superiority over placebo in achieving treatment response and remission outcomes. Comparisons with imipramine, clomipramine, amitriptyline, dothiepin, paroxetine, fluoxetine, citalopram, mianserin, nortriptyline, and moclobemide generally revealed no significant differences in efficacy. However, some reviews indicated that venlafaxine and mirtazapine were superior to fluvoxamine in certain outcomes, while fluvoxamine demonstrated greater efficacy than desipramine in one review. Sertraline and milnacipran showed mixed or review-quality-dependent results, with one low-quality review favoring milnacipran. Most reviews assessed outcomes over a median follow-up of six weeks using standardized depression rating scales. Conclusions: Fluvoxamine is a robust and effective antidepressant, demonstrating consistent efficacy comparable to other antidepressants and superior to placebo. While no single antidepressant was universally superior, fluvoxamine’s unique pharmacological profile and favourable safety characteristics support its clinical utility. Further research is needed to explore its role in personalized treatment strategies and emerging therapeutic contexts, such as comorbid anxiety and post-traumatic stress disorder. Full article

Climate change and overgrazing significantly constrain the sustainability of meadow land and vegetation in the livestock industry on the Tibetan–Plateau ecosystem. In context of climate change mitigation, grassland soil C sequestration and forage sustainability, it is important to understand how manure regimes influence SOC stability, grassland soil, forage structure and nutritional quality. However, the responses of SOC fractions, soil and forage structure and quality to the influence of manure gradient practices remain unclear, particularly at Tianzhu belt, and require further investigation. A field study was undertaken to evaluate the soil bulk density, aggregate fractions and dynamics in SOC concentration, permanganate oxidizable SOC fractions, SOC stabilization and soil nutrients at the soil aggregate level under manure gradient practices. Moreover, the forage biodiversity, aboveground biomass and nutritional quality of alpine meadow plant communities were also explored. Four treatments, i.e., control (CK), sole sheep manure (SM), cow dung alone (CD) and a mixture of sheep manure and cow dung (SMCD) under five input rates, i.e., 0.54, 1.08, 1.62, 2.16 and 2.70 kg m⁻², were employed under randomized complete block design with four replications. Our analysis confirmed the maximum soil bulk density (BD) (0.80 ± 0.05 g cm⁻³) and micro-aggregate fraction (45.27 ± 0.77%) under CK, whilst the maximum macro-aggregate fraction (40.12 ± 0.54%) was documented under 2.70 kg m⁻² of SMCD. The SOC, very-labile C fraction (Cfrac₁), labile C fraction (Cfrac₂) and non-labile/recalcitrant C fraction (Cfrac₄) increased with manure input levels, being the highest in 2.16 kg m⁻² and 2.70 kg m⁻² applications of sole SM and the integration of 50% SM and 50% CD (SMCD), whereas the less-labile fraction (Cfrac₃) was highest under CK across aggregate fractions. However, manures under varying gradients improved SOC pools and stabilization for both macro- and micro-aggregates. A negative response of the carbon management index (CMI) in macro-aggregates was observed, whilst CMI in the micro-aggregate fraction depicted a positive response to manure addition with input rates, being the maximum under sole SM addition averaged across gradients. Higher SOC pools and CMI under the SM, CD and SMCD might be owing to the higher level of soil organic matter inputs under higher doses of manures. Moreover, the highest accumulation of soil nutrients,, for instance, TN, AN, TP, AP, TK, AK, DTPA extractable Zn, Cu, Fe and Mn, was recorded in SM, CD and SMCD under varying gradients over CK at both aggregate fractions. More nutrient accumulation was found in macro-aggregates over micro-aggregates, which might be credited to the physical protection of macro-aggregates. Overall, manure addition under varying input rates improved the plant community structure and enhanced meadow yield, plant community diversity and nutritional quality more than CK. Therefore, alpine meadows should be managed sustainably via the adoption of sole SM practice under a 2.16 kg m⁻² input rate for the ecological utilization of the meadow ecosystem. The results of this study deliver an innovative perspective in understanding the response of alpine meadows’ SOC pools, SOC stabilization and nutrients at the aggregate level, as well as vegetation structure, productivity and forage nutritional quality to manure input rate practices. Moreover, this research offers valuable information for ensuring climate change mitigation and the clean production of alpine meadows in the Qinghai–Tibetan Plateau area of China. Full article

Tourism urbanization has become an important pathway for promoting regional economic growth, optimizing urban spatial structures and enhancing residents’ quality of life, especially in the context of sustainable development. Balancing the relationship between tourism urbanization and residents’ well-being in China’s Central Plains Urban Agglomeration is a key objective for the promotion of sustainable regional development in the context of rapid tourism development. However, few studies have quantitatively explored the spatiotemporal coupling dynamics between tourism urbanization and residents’ well-being at the urban agglomeration scale, leaving a significant gap in understanding their integrated evolution. Therefore, in this study, we constructed an evaluation index system for tourism urbanization and residents’ well-being. Next, we explored the coupling relationship between tourism urbanization and residents’ well-being and its influencing factors in the Central Plains Urban Agglomeration from 2005 to 2022 via the coupling coordination degree and random forest approaches. The study’s three major findings are as follows: (1) First, in terms of development level, the tourism urbanization of the Central Plains Urban Agglomeration from 2005 to 2019 generally showed a steady upwards trend, and the well-being of residents as a whole showed a steady development trend; however, there were significant regional differences in the level of development. The spatial differentiation between tourism urbanization and residents’ well-being was characterized by “high in the west and low in the east” and “high in the middle and low in the surroundings”, and the degree of spatial differentiation tended to gradually narrow over time. (2) In terms of the level of coupling coordination, the overall coordination between tourism urbanization and residents’ well-being in the Central Plains Urban Agglomeration increased annually and reached the stages of running-in and high coordination. (3) The key factors affecting the coupled coordination of tourism urbanization and residents’ well-being in the Central Plains Urban Agglomeration differed significantly over time. The importance of the number of tourists, policy support, and fiscal balance ratio increased significantly over time, whereas the importance of the vegetation index and the distance to the nearest provincial capital city decreased. These findings have valuable implications for urban planning, governance optimization, and the formulation of sustainable development strategies, highlighting the need to strengthen resilience and promote synergistic growth between tourism development and residents’ well-being. Full article

To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physiological measures for monitoring work-related stress, electrooculography (EOG) remains underexplored in this context. Although less extensively studied, EOG shows significant promise for comparable applications. Furthermore, the realm of human factors and ergonomics lacks sufficient research on the integration of wearable sensors, particularly in the evaluation of human work. This article aims to bridge these gaps by examining the potential of EOG signals, captured through smart eyewear, as indicators of stress. The study involved twelve subjects in a controlled environment, engaging in four stress-inducing tasks interspersed with two-minute relaxation intervals. Emotional responses were categorized both into two classes (relaxed and stressed) and three classes (relaxed, slightly stressed, and stressed). Employing supervised machine learning (ML) algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)—the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. The proposed wearable system shows promise in monitoring workers’ well-being, especially during visual activities. Full article

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs. Full article

It is known that bio-physical alterations in ecosystems change the relationships between people and their environments. The urban ecosystems cannot be managed without considering the role of green spaces. In Pakistan, many such eco-systems exist, regulated and monitored by well-established authorities. However, they do not have practical frameworks to manage them. In this context, this research examines the decline of the natural ecosystems of Lahore and the roles and responsibilities of the authorities towards this decline. This research employs both qualitative and quantitative methods to gather data: questionnaires, interviews and satellite observations. Questionnaires administered by the researchers gathered information from the people taking care of these places and interviews with the people responsible for planning and managing the city ascertained the issues related to monitoring and maintenance of the green spaces. Satellite data provided information related to the changes in land use from 2010–2018, which indicated diminishing green spaces. The findings reveal extensive transformations in land use and a significant increase in built-up land, resulting from irregular and unmonitored expansion of the city. These indicate that the decline of the natural ecosystems of Lahore is a result of two failures of the authorities: ineffective implementation of policies and poor coordination among stakeholders. Weaknesses in the maintenance of the eco-systems and negligence in the monitoring systems have also contributed. This research therefore concludes that the poor monitoring system has led to the decline of the natural ecosystems and an increase in random and synthetic growth of the city of Lahore despite it having a well-established network and laws. Full article

Substantial in vitro experimental data have been produced about the safety, antioxidant, neuro- and hepatoprotective effects of a series of recently synthesized N-pyrrolyl hydrazide-hydrazones (compounds 5, 5a–5g). However, compound activity across multiple assays varies and it is challenging to elucidate the favorable physicochemical characteristics of the studied compounds and guide further lead optimization. The aim of the current study is to apply exploratory data analysis in order to profile the biological effects of the novel hydrazide-hydrazones, gain insights related to their mechanisms of action in the context of in silico predictions and identify key predictor–outcome relationships. We collected a dataset from available in vitro studies of compounds 5, 5a–5g. It included cytotoxicity values, protection against hydrogen peroxide-induced damage in HepG2 and SH-SY5Y cells, two radical scavenging assays and a hemolysis assay across a range of treatment concentrations. SwissADME-based predictions of chemometric and ADME parameters and pro-oxidant enzyme docking data were generated to provide context for the interpretation of in vitro outcome patterns and identify causal relationships. Multiple factor analysis (MFA), followed by hierarchical clustering on principal components (HCPC), was applied to profile compounds’ biological behavior. This revealed that differences in the number of H-bond donors, in the permeability coefficient and in the docking scores to two pro-oxidant enzymes could aid in explaining the effects of compounds with similar in vitro profiles. HCPC differentiated 5a as mostly neuroprotective, 5 and 5d as hepatoprotective radical scavengers, 5g with higher docking affinity to 5-lipoxygenase (5-LOX) and myeloperoxidase (MPO) and 5b, 5c and 5f as having less H-bond donors and variable in vitro activity. The consensus application of three variable selection approaches based on standard lasso regression, robust penalized regression and random forest confirmed the relationships between some in vitro outcomes and LogP, pan-assay interference (PAINS) alerts, 5-LOX allosteric site docking and H-bond donor numbers. The exploratory analysis of the combined in vitro and in silico dataset provides useful insights which could help explain the major drivers behind the experimental results. It can be informative in the design of new, improved members of the series of novel N-pyrrolyl hydrazide-hydrazones with better neuroprotective potential and less side effects. Full article

Single-use plastics (SUPs) are deeply embedded in everyday consumption in rapidly developing Asian cities, yet their widespread use contributes to marine debris, microplastic pollution, and health risks. This study aimed to inform evidence-based policymaking to mitigate marine plastic pollution in the ASEAN+3 region. Stratified random sampling surveys (n = 1492) were conducted both face to face and online across five representative cities between September 2022 and February 2023. We quantified and compared the consumption and disposal patterns across nine SUP categories, assessed demographic influences, evaluated the impact of COVID-19, and derived insights for targeted policy interventions. Non-parametric tests were used to evaluate the differences. The results reveal significant inter-city variation: Shanghai and Harbin reported high overall SUP use despite a lower consumption of plastic shopping bags; Hanoi and Depok showed lower overall use but distinct preferences for plastic shopping bags and party cups; and Phnom Penh had the highest consumption of plastic shopping bags, bottles, and straws. Plastic shopping bags were the most used item in all cities (18–34 bags per week), with no significant differences between urban and rural areas, ages, or genders. In contrast, urban residents reported a higher use of plastic takeout containers, cutlery, coffee cups, and party cups. The COVID-19 pandemic notably reshaped SUP consumption patterns. Additionally, over half of SUPs were disposed of without proper separation. These findings underscore the need for flexible, phased, and context-specific interventions to support a resilient circular economy. Full article

The frequency and magnitude of natural hazards have been steadily increasing, largely due to extreme weather events driven by climate change. These hazards pose significant global challenges, underscoring the need for accurate prediction models and systematic preparedness. This study aimed to predict multiple natural hazards in South Korea using various machine learning algorithms. The study area, South Korea (100,210 km²), was divided into a grid system with a 0.01° resolution. Meteorological, climatic, topographical, and remotely sensed data were interpolated into each grid cell for analysis. The study focused on three major natural hazards: drought, flood, and wildfire. Predictive models were developed using two machine learning algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGB). The analysis showed that XGB performed exceptionally well in predicting droughts and floods, achieving ROC scores of 0.9998 and 0.9999, respectively. For wildfire prediction, RF achieved a high ROC score of 0.9583. The results were integrated to generate a multi-hazard susceptibility map. This study provides foundational data for the development of hazard management and response strategies in the context of climate change. Furthermore, it offers a basis for future research exploring the interaction effects of multi-hazards. Full article

Background/Objectives: Structured medium- and long-chain triglycerides (sMLCT) may be a superior vehicle for medium-chain fatty acid delivery to peripheral tissues, such as skeletal muscle. Limited information is available concerning the effect of sMLCT on muscular performance or recovery after a muscle-damaging exercise protocol. The purpose of this study was to establish the effect of a novel formulation of sMLCT on muscular performance and recovery. Methods: Forty female adults (mean ± SD age = 22 ± 3 years; body mass index = 23.5 ± 3.4 kg/m²) were randomized into one of two study groups, placebo control [CON; n = 20] or sMLCT [n = 20], and completed five total visits to the laboratory. The baseline (i.e., pre-exercise) assessments of muscle performance, size, and soreness were compared to assessments immediately following exercise and 24, 48, and 72 h post-exercise. Results: No statistically significant condition × time interactions were noted for strength outcomes, although trends for condition × time interactions were present for torque over 25 ms (p = 0.06) and peak torque (p = 0.05). Similarly, no condition x time interactions were present for ultrasound echo intensity, the subjective ratings of soreness and pain, thigh circumference, leg volume, and vertical jump performance. Conclusions: Within the context of the current study, the ingestion of sMLCT did not significantly influence the rate of muscle strength recovery following muscle damaging resistance exercise. Full article