Search Results (12,360)

Fig tree growth and development are highly susceptible to variations influenced by abiotic factors and management practices, including fertilization and training systems. This study aimed to evaluate the effect of organic and mineral fertilization on the photosynthetic, nutritional, and productive efficiency of fig trees subjected to different training systems in semi-arid regions. The experimental design was randomized blocks in a 5 × 4 factorial scheme, with three blocks and three plants per plot. The treatments consisted of five fertilizer sources (mineral fertilizer (NPK) applied at a dose of 126 g N, 90 g P, and 90 g K per plant (M); and four organic sources—cattle manure (CM), organic compost (OC), chicken litter (CL), and sheep manure (SM), all applied at a dose of 10 kg per plant); and four types of training systems (plants with two branches (2B), three branches (3B), four branches (4B), and espalier). Our results demonstrated that the mineral fertilizer (M) and chicken litter (CL) treatments yielded the highest results, particularly in photosynthetic performance. Fig trees fertilized with mineral fertilizer and subjected to the 3B system showed enhanced net photosynthesis (36.96 µmol m⁻² s⁻¹) and, consequently, higher productivity of 21.28 t ha⁻¹. Similarly, plants fertilized with chicken litter (CL) under the 4B system produced comparable results. These findings demonstrate that the use of mineral and organic fertilizers, combined with an appropriate training system, is a viable strategy for optimizing fig productivity and profitability in semi-arid conditions. Full article

Background and Objectives: Neuron-specific enolase (NSE) is an isoenzyme of enolase, of which the γ isoform is expressed in nerve cells. The activity of NSE occurs during late neuronal differentiation, which determines the specificity of the enzyme for neurodevelopmental cells. The activity of NSE is also observed in processes associated with neuronal damage. The aim of this study was to present the state of the art related to the knowledge, advances, and possible developmental directions in terms of the use of NSE as a biomarker in the diagnosis of selected neurological and mental disorders (NDs, MDs), with particular emphasis on ischemic stroke (IS) and psychotic disorders (PSDs). Materials and Methods: A literature review was performed using the PubMed, Embase, and Scopus databases. Keywords such as “neuron-specific enolase”, “neuron-specific enolase in schizophrenia”, “neuron-specific enolase in ischemic stroke”, “neuron-specific enolase in psychiatric disorders”, and “neuron-specific enolase in neurological diseases” were used during the literature search. A total of 11,350 items were found. However, 188 papers were finally selected after applying the filters (“clinical trial”, “meta-analysis”, “randomized control trial”, and “systematic review”). Results: The literature was analyzed and 67 items relevant to the subject of this study were selected. This article points out the differences in NSE levels in different clinical groups, such as patients after an incident of hypoxic/ischemic encephalopathy (HIE), neuroinfection, or particular inflammatory processes in the nervous system region, as well as central nervous system (CNS) injury, selected MD, neurodegenerative disorders (NGDs), headaches, or epilepsy (EP). Conclusions: In the future, they may serve to support further work on the use of enolase as a potential biomarker of the described diseases. Full article

Dietary fiber plays an important role in animal nutrition by influencing gut health, feed intake, and metabolism. In swine production, studies suggest that fibers may also affect reproductive traits, but findings remain inconsistent, especially in adult boars. This study evaluated the effects of partially fermentable insoluble fiber (PFIF) on semen quality, behavior, and general health of adult boars. Thirty animals were assigned to a completely randomized design with two treatments: (1) CON: no fiber supplementation, and (2) PFIF: fiber supplementation (35 g/animal/day). Fiber was provided once daily for 120 consecutive days. During the period, semen was collected weekly and analyzed macroscopically and microscopically using the Computer-Assisted Sperm Analysis (CASA) system. Behavior was recorded weekly, one and three hours after feeding, based on a pre-established ethogram. Feed intake, perineal, and fecal scores were also evaluated. Fiber supplementation did not affect total motility, progressive motility, sperm concentration, fecal or perineal scores, or behavior. However, improvements were observed in sperm kinematics, with higher straight-line distance (DSL), linearity (LIN), and straightness (STR), as well as a tendency for increased straight-line velocity (VSL) and wobble (WOB). Conversely, a higher incidence of proximal cytoplasmic droplets was recorded in the fiber group, indicating more sperm maturation defects. Supplemented animals also showed reduced feed intake compared with controls, suggesting a satiety effect of the fiber. In conclusion, PFIF supplementation (35 g/animal/day offered once daily) in adult boars produced mixed outcomes, with improved sperm kinematics but increased maturation defects and only minor changes in feeding behavior, indicating a limited and inconsistent physiological response. Full article

Islanded microgrids face significant frequency stability challenges due to limited system capacity, low inertia levels, and the strong variability in renewable energy sources. Traditional reliability assessment methods, often based on static power balance, struggle to comprehensively reflect frequency dynamic characteristics and their impact on power supply reliability. To address this issue, this paper proposes a sequential Monte Carlo reliability assessment method integrated with a system frequency response model. First, an SFR model for the isolated microgrid, incorporating diesel generators, gas turbines, energy storage, and wind turbines, is established. For synchronous units, a frequency deviation-based failure rate correction mechanism is introduced to characterize the impact of frequency fluctuations on equipment reliability. State transitions are achieved by integrating failure and repair rates to reach threshold values. Second, sequential Monte Carlo simulation is employed to conduct time-series simulations of annual operation. Random sampling of unit failure and repair times is used to calculate reliability metrics. MATLAB/Simulink simulation results demonstrate that system frequency fluctuations caused by power imbalance worsen unit failure rates, leading to microgrid reliability values lower than static calculations. This provides reference for planning, design, and operational scheduling of isolated microgrids. Full article

While prior studies on LULC change in the Ewa region of O’ahu Hawai’i have explored the policy implications and the rapid infrastructure changes on land use, very few studies have attempted to fully integrate both of these changes in a comprehensive, long-term study of island geographies. Most of the past work has focused on general trends or short-term fluctuations, without considering the play of nuanced interactions between urbanization policies, transit-oriented development, and constraints of Hawai’i’s finite land resources. To fill these gaps, this study examines LULC changes in Ewa, Honolulu between 2002 and 2022, which emphasizes the impacts of strategic urban policies and infrastructure development, such as the Honolulu Skyline Rail Transit System. Using Landsat 7 satellite imagery and random forest machine learning classifier, in Google Earth Engine, LULC is classified into urban, forest, vegetation, barren, and water with classification accuracy of over 85%. The results highlight trends of significant urban growth especially after 2010, and highlight key issues of tension between housing demands and environmental sustainability in O’ahu. This study highlights the potential of integrated remote sensing and policy analysis for informing sustainable development in land-constrained island settings, and advocates for planning frameworks that more effectively balance growth, ecosystem stewardship, and community welfare. Full article

Understanding the mechanisms that maintain biodiversity is crucial for effective conservation in riverine ecosystems. However, the direct and indirect mechanisms by which land use patterns and water quality parameters influence plankton α- and β-diversity remain poorly elucidated. Here, we undertook a comprehensive survey of plankton communities across the Jialing River basin. Our results showed that Bacillariophyta and Chlorophyta were the dominant phytoplankton groups, whereas Protozoa and Copepoda predominated among zooplankton. Redundancy analysis identified dissolved oxygen and total phosphorus as key environmental factors shaping plankton community structure. Additionally, random forest models indicated that anthropogenic stressors exerted consistent effects on both α- and β-diversity of phytoplankton. Importantly, the decomposition of β-diversity revealed that species turnover constituted the major component, underscoring the importance of basin-scale management approaches. Structural equation modeling further demonstrated that land use practices predominantly affected phytoplankton β-diversity indirectly via water quality alterations, with a relatively weak direct effect. In contrast, neither the direct nor indirect effects of land use were significant for zooplankton communities. These findings suggest that phytoplankton may serve as more reliable bioindicators of anthropogenic disturbance than zooplankton in this freshwater system. Moreover, our findings highlight the central role of water quality in regulating phytoplankton diversity responses to environmental change. Consequently, we recommend that conservation strategies in the Jialing River basin focus on water quality monitoring and the mitigation of its ecological effects. Full article

With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account. Full article

Post-disaster underground (UG) mine environments are characterized by complex and rapidly changing conditions, adding extra attenuation to propagating electromagnetic (EM) waves. One such complex condition is the extreme generation of dust and sudden rise in humidity contributing to extra attenuation effects to propagating waves, especially under varying airborne humidity and dust levels. The existing wave propagation prediction models, especially those that factor in the effect of dust particles, are deterministic in nature, limiting their ability to account for uncertainties, especially during emergency conditions. In this work, the vector parabolic equation (VPE) model is modified to include dust attenuation effects. Using the complex permittivity of dust as a random variable, the Karhunen–Loève (KL) expansion is used to generate random samples of permittivity along the drifts for which each realization is solved using deterministic VPE method. The model is validated using a modified Friis method and experimentally obtained data from literature. The findings show that accounting for dust and humidity effects stochastically captures the extra losses that would have otherwise been lost using deterministic methods. The proposed framework offers key insights for designing resilient underground wireless systems, strengthening miner tracking, and improving safety during emergencies. Full article

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

Background: Despite chemotherapy-related toxicities being more likely in older patients, no routine prediction tool has been validated for the UK population. Previous research within the TOASTIE (tolerance of anti-cancer systemic therapy in the elderly) study found a low predictive performance of the Cancer and Aging Research Group (CARG) score for severe chemotherapy-related toxicities. Building on this, the TOASTIE study dataset was used to assess the viability of developing a predictive model with baseline variables and frailty scores for severe chemotherapy-related toxicities in older patients. Methods: All patients from the TOASTIE dataset were included, with the inclusion/exclusion criteria detailed in the TOASTIE protocol. Demographic factors, self-assessment scores, Rockwood Clinical Frailty Score and researcher’s estimated risks of toxicity were assessed for their association with severe chemotherapy-related toxicities. After data partition into 70:15:15 train/validation/test, models were built on the training dataset using logistic regression (LR), LASSO and random forest (RF). Models were optimized with a validation set with LR and LASSO; cross-validation was used with RF. Model performance was assessed with balanced accuracy, NPV and AUC. Results: Of the 322 patients included, the incidence of severe toxicities was 22% (n = 71). Ten variables were statistically significant, albeit weakly associated with severe toxicities: primarily patient-reported factors, Performance Status and high baseline neutrophil count. LR models gave the best balanced accuracies of 0.6382 (AUC 0.6950, NPV 0.8696) and 0.6469 (AUC 0.6469, NPV 0.4286) with LASSO, and 0.6294 (AUC 0.6557, NPV 0.6557) with RF. Conclusions: Models lack sufficiently robust results for clinical utility. However, a high NPV in predicting no toxicity could help identify lower-risk patients who may not require dose reductions, potentially improving overall outcomes. Full article

Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud patterns. A dataset comprising inpatient claims, outpatient claims, and beneficiary details was used to train and evaluate five ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. Data preprocessing techniques included resampling SMOTE method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and procedural codes. Random Forest emerged as the best-performing model, achieving a training accuracy of 99.2% and validation accuracy of 98.8%, and F1-score (98.4%). The Decision Tree also performed well, achieving a validation accuracy of 96.3%. KNN and AdaBoost demonstrated moderate performance, with validation accuracies of 79.2% and 81.1%, respectively, while LDA struggled with a validation accuracy of 63.3% and a low recall of 16.6%. The results highlight the importance of advanced resampling techniques, feature engineering, and adaptive learning in detecting Medicare fraud effectively. This study underscores the potential of machine learning in addressing the complexities of fraud detection. Future work should explore explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection systems that protect healthcare resources and beneficiaries. Full article

Reliability assessment of power systems is essential for ensuring the secure and stable operation of power grids, and identifying weak links constitutes a critical step in enhancing system reliability. Traditional deterministic methods are limited in their ability to capture the complex, nonlinear relationships between component failures and overall system risk. To overcome this limitation, this paper proposes an explainable machine learning-based approach for identifying weak components in power systems. Specifically, a set of contingency scenarios is constructed through enumeration, and a random forest regression model is trained to map transmission line outage events to the amount of system load curtailment. The trained model is then interpreted using SHapley Additive exPlanations (SHAP) values. By aggregating these values, the global reliability contribution of each component is quantified. The proposed method is validated on the IEEE 57-bus system, and the results demonstrate its effectiveness and feasibility. This research offers a data-driven framework for translating system-level reliability metrics into device-level quantitative attributions, thereby enabling interpretable identification of weak links. Full article

Massive random access (MRA) involves massive devices sporadically and randomly sending short-packet messages through a shared wireless channel. It is a crucial scenario in 6G communications to support the Internet-of-Things. Grant-free access, where devices complete transmission without grants, is a promising scheme for MRA. In grant-free access, the design of pilot sequences has a significant effect on joint activity detection and channel estimation (JADCE) and, consequently, system performance. Inter-user interference (IUI), caused by non-orthogonal pilots, is random owing to the random set of active users, and existing studies on pilot design for grant-free access often attempt to reduce the mean IUI. However, the performance of JADCE is affected not only by the mean IUI but also by the tail behavior of the IUI distribution. In this paper, we propose a metric for pilot design, exploiting the distribution of IUI to reflect the impact of pilots on JADCE more precisely. We further develop a pilot design algorithm based on the proposed metric, with modified approximate message passing (AMP) adopted as the JADCE algorithm. Simulation results demonstrate that the proposed pilot design reduces the probability of missed detection of active users and channel estimation error, compared with existing pilot designs. Full article

In this paper, the iterative Gerchberg–Saxton (GS) phase-retrieval algorithm is employed to reconstruct the amplitude spread function (ASF) of hololenses (HLs) recorded on a sustainable PVA/acrylate-based photopolymer, Biophotopol, when working with a CCD sensor. The main objective of this work is to characterize the spatial resolution of HLs, which are key components in a wide range of optical systems, including augmented reality (AR) glasses, combined information displays, and holographic solar concentrators. The GS algorithm, known for its efficiency in phase retrieval without prior knowledge of the phase of the optical system, is used to reconstruct the ASF, which is critical for mitigating information loss during imaging. Spatial resolution is quantified by convolving the ASFs obtained with two resolution tests (objective and subjective) and analyzing the resulting image using a CCD sensor. The convolution process allows an accurate assessment of lens performance, highlighting the resolution limits of manufactured lenses. The results show that the iterative GS algorithm provides a reliable method to improve image quality by recovering phase and amplitude information that might otherwise be lost, especially when using CCD or CMOS sensors. In addition, the recorded hololenses exhibit a spatial resolution of 8.9 lp/mm when evaluated with the objective Siemens star chart, and 30 cycles/degree when evaluated with the subjective Random E visual acuity test, underscoring the ability of Biophotopol-based HLs to meet the performance requirements of advanced optical applications. This work contributes to the development of sustainable high-resolution holographic lenses for modern imaging technologies, offering a promising alternative for future optical systems. Full article

Accurate prediction of geopolymer concrete compressive strength is vital for sustainable construction. Traditional experiments are time-consuming and costly; therefore, computer-aided systems enable rapid and accurate estimation. This study evaluates three ensemble learning algorithms (Extreme Gradient Boosting (XGB), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), as well as two baseline models (Support Vector Regression (SVR) and Artificial Neural Network (ANN)), for this task. To improve performance, hyperparameter tuning was conducted using Bayesian Optimization (BO). Model accuracy was measured using R², RMSE, MAE, and MAPE. The results demonstrate that the XGB model outperforms others under both default and optimized settings. In particular, the XGB-BO model achieved high accuracy, with RMSE of 0.3100 ± 0.0616 and R² of 0.9997 ± 0.0001. Furthermore, Shapley Additive Explanations (SHAP) analysis was used to interpret the decision-making of the XGB model. SHAP results revealed the most influential features for compressive strength of geopolymer concrete were, in order, coarse aggregate, curing time, and NaOH molar concentration. The graphical user interface (GUI) developed for compressive strength prediction demonstrates the practical potential of this research. It contributes to integrating the approach into construction practices. This study highlights the effectiveness of explainable machine learning in understanding complex material behaviors and emphasizes the importance of model optimization for making sustainable and accurate engineering predictions. Full article