Search Results (1,128)

Corn stunt complex, transmitted by the corn leafhopper (Dalbulus maidis), poses significant yield risks to corn production. This study evaluated the effects of two corn hybrids and top-dressed nitrogen (N) and potassium (K) fertilization on D. maidis incidence and corn stunt symptom expression under field conditions. Eighteen treatments were tested in a randomized complete block design with six replications over two seasons. Leafhopper populations were monitored using yellow sticky traps, and symptom incidence and severity were assessed at R₁ and R₃ stages, respectively. While D. maidis populations varied substantially between seasons, neither N nor K fertilization, nor hybrid selection, significantly affected vector abundance. Importantly, symptom frequency and severity were not directly proportional to leafhopper density. Top-dressed fertilization, particularly with K, reduced the visual expression of corn stunt symptoms although it did not prevent infection. Hybrid responses to fertilization varied, with a genotype exhibiting greater symptom mitigation. Grain yield was not significantly influenced by nutrient rates or hybrid choice. These findings suggest that balanced N and K fertilization enhances crop resilience to corn stunt disease without directly suppressing vector populations. Integrating nutritional management with hybrid selection presents a promising strategy to add in corn stunt control and deepens our understanding of the environmental factors that mitigate severe symptoms. Full article

In our study, we examine how the temporal window shift—the step between consecutive analysis windows—affects EEG-based cognitive fatigue detection while keeping the window length fixed. Using a reference workload dataset and a pipeline that includes preprocessing and feature extraction, we vary the shift to control segment overlap and, consequently, the number and independence of training samples. We evaluate six machine-learning models (decision tree, random forest, SVM, kNN, MLP, and a transformer). Across the models, smaller shifts generally increase accuracy and F1 score, consistent with the larger sample count; however, they also reduce sample independence and can inflate performance if evaluation splits are not sufficiently stringent. Class-wise analyses reveal persistent confusion for the moderate-fatigue class, the severity of which depends on the chosen shift. We discuss the methodological trade-offs, provide practical recommendations for choosing and reporting shift parameters, and argue that temporal segmentation decisions should be treated as first-class design choices in EEG classification. Our findings highlight the need for transparent reporting of window length, shift/overlap, and subject-wise evaluation protocols to ensure reliable and reproducible results in cognitive fatigue detection. Our conclusions pertain to subject-wise generalization on the STEW dataset; cross-dataset validation is an important next step. Full article

Surgical site infections (SSIs) remain a significant complication in spine surgery, especially in instrumented procedures with long operative times. Although guidelines recommend cefazolin as the first-line agent due to its efficacy against Staphylococcus aureus, predictable pharmacokinetics, and safety, its real-world practice is highly variable, with inappropriate and prolonged regimens reported across Europe. Vancomycin is often used as the first choice of therapy empirically and without screening, exposing patients to risks such as delayed infusion, nephrotoxicity, and the emergence of vancomycin-resistant enterococci (VRE).This review assesses the present function of vancomycin in relation to cefazolin for spinal prophylaxis and examines wider trends in the misuse of surgical antibiotic prophylaxis, which were identified through PubMed and Scopus searches. Evidence from randomized and prospective studies consistently supports cefazolin as the preferred prophylactic agent in clean spinal surgery. Observational data suggest that adjunctive or topical vancomycin may reduce infection rates in selected high-risk or revision cases, though the results are inconsistent and frequently limited by retrospective designs and heterogeneous outcome reporting. Importantly, the most rigorous randomized controlled trial found no benefit of intrawound vancomycin over the placebo. A small number of available investigations in vancomycin use with major design limitations have resulted in no significant VRE emergency. Unexpectedly, widespread use of vancomycin was followed by a notable transition toward Gram-negative and opportunistic organisms. In summary, vancomycin may only be considered in patients with documented MRSA colonization, β-lactam allergy, or selected revision procedures, but its widespread empirical use as a first-choice therapy is not supported. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

This work suggests a new use for the Non-Monopolize Optimization (NO) method to improve the dynamic stability and robustness of PID and PIDA controllers in Automatic Voltage Regulator (AVR) systems when there are load disruptions. The NO algorithm is a new search method that does not use metaphors and only looks for one answer. It utilizes adaptive dimension modifications to strike a balance between exploration and exploitation. Its addition to AVR control makes parameter tweaking more efficient, without relying on random metaphors or population-based heuristics. MATLAB/Simulink R2025a runs full simulations to check how well the system works in both the time domain (step response, root locus) and the frequency domain (Bode plot). We compare the results to those of well-known optimizers like WOA, TLBO, ARO, GOA, and GA. The suggested NO-based PID and PIDA controllers always show less overshoot, faster rise and settling periods, and higher phase and gain margins, which proves that they are more stable and responsive. A robustness test with a load change of ±50% shows that NO-tuned controllers are even more reliable. The results show that using NO to tune different controllers could be a good choice for real-time AVR controller tuning in modern power systems because it is lightweight and works well. Full article

Effective operation of any service system requires optimal organization of the sharing of resources between the users (customers). To this end, it is necessary to elaborate on the mechanisms that allow for the mitigation of congestion, i.e., the accumulation of many users requiring service. Due to the randomness of the user’s arrival process, congestions can occur even when an arrival rate is constant, e.g., the arrivals are described by the stationary Poisson process, which is assumed in the majority of existing papers. However, congestions can be more severe if the possibility of fluctuation of the instantaneous arrival rate exists. Such a possibility is an inherent feature of many systems and can be taken into account via the description of arrivals by the Markov arrival process (MAP). This makes the problem of congestion avoidance drastically more challenging. In many real-world systems, there exists the possibility of customer admission control via dynamic pricing. We propose a novel predictive mechanism of dynamic pricing. Decision moments coincide with the transition moments of the underlying process of the MAP. A customer may join or balk the system or postpone joining the system depending on the current cost. We illustrate the application of this mechanism in a multi-server retrial queueing model with dynamic service pricing. The behavior of the system is described by a multidimensional Markov chain with state-inhomogeneous transitions. Its stationary distribution is computed and may be used for solving the various problems of system revenue maximization via the choice of the proper pricing strategy. Full article

The integration of renewable energy into the grid has led to problems such as low utilization rate of energy storage resources (“underutilization after construction”) and insufficient system stability. This paper studied the co-evolution mechanism of power market entities empowered by shared energy storage. Based on the interaction among power generation enterprises, power grid operators, and government regulatory agencies, this paper constructed a three-party evolutionary game model. The model introduced a dynamic reward and punishment mechanism as well as a random interference mechanism, which makes it more in line with the actual situation. The stability conditions of the game players were analyzed by using stochastic differential equations, and the influences of key parameters and incentive mechanisms on the stability of the game players were investigated through numerical simulation. The main research results showed the following: (1) The benefits of shared energy storage and opportunistic gains had a significant impact on the strategic choices of power generation companies and grid operators. (2) The regulatory efficiency had significantly promoted the long-term stable maintenance of the system. (3) Dynamic incentives were superior to static incentives in promoting cooperation, while the deterrent effect of static penalties is stronger than that of dynamic penalties. (4) The increase in the intensity of random disturbances led to strategy oscillation. This study suggested that the government implement gradient-based dynamic incentives, maintain strict static penalties to curb opportunism, and enhance regulatory robustness against uncertainty. This research provided theoretical and practical inspirations for optimizing energy storage incentive policies and promoting multi-subject coordination in the power market. Full article

Background: Life insurance markets are experiencing unprecedented transformation in the wake of economic disruption, evolving consumer expectations, and behavioral shifts following the COVID-19 pandemic. Traditional economic models often fail to capture the complex interplay of attitudinal, and cognitive factors that now shape insurance demand and premium selection. Methods: This study analyzes nationally representative survey data from over 3600 U.S. adults (2024 NAIC Financial Inclusion Survey). It uses a weighted full maximum likelihood Heckman selection model to identify determinants of life insurance uptake and premiums. The main innovation is modeling psychological price, a composite of perceived affordability, with higher-order polynomials. The design integrates psychometrically validated measures of financial knowledge and risk tolerance. Political ideology, race and ethnicity, and sources of financial advice serve as exclusion restrictions in the selection equation. Results: Psychological price shows an inverse-U relation with term outcomes: uptake rises at low to moderate affordability and declines at high affordability; among purchasers, term premiums rise at low to mid affordability and decline at high levels. For cash value policies, premiums decrease as psychological price increases. Financial knowledge and risk tolerance increase term uptake; financial knowledge reduces cash premiums. Education and income increase term uptake and term premiums. Compared with respondents reporting no ideology, conservative and centrist respondents have lower term uptake and higher cash uptake; using a professional advisor is associated with higher cash uptake. The selection correlation is positive for term ($\rho \approx 0.98$) and negative for cash ($\rho \approx -0.38$), indicating non-random selection in both markets. Implications: In order to reduce disparities, insurers should target the mid-affordability threshold with term offerings, streamline options for high-affordability consumers, offer pricing support and guidance for low-affordability households, increase uptake through advice channels and financial education, and address affordability barriers. Conclusions: Nonlinear affordability effects shape both market entry and pricing choices. Modeling psychological price with higher-order polynomials identifies thresholds and turning points that linear specifications miss. The results support targeted product design and outreach when perceived affordability drives insurance participation and premium choices. Full article

Background/Objectives: Ιnflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is increasingly associated with cardiovascular (CV) complications, such as heart failure (HF), arrhythmias, and acute coronary syndromes (ACSs). As the therapeutic landscape of IBD evolves, with the introduction of newer biologics and small molecules, their CV safety warrants critical evaluation. The objective of this review is to provide an update on the current evidence of CV risks associated with IBD treatments. Methods: A comprehensive literature search from inception to April 2025 was conducted using PubMed and Medline to identify randomized controlled trials, observational studies, systematic reviews, as well as pharmacovigilance data reporting CV safety outcomes of biologic and small-molecule drugs approved for IBD. Additionally, analysis of the European Summary of Product Characteristics for each agent was also performed. Results: Anti-TNF agents, particularly infliximab, have been associated with increased reporting of HF and arrhythmias, particularly in patients with pre-existing cardiac disease. Ustekinumab and vedolizumab show consistently favorable CV safety profiles across trials and real-world studies. IL-23p19 inhibitors demonstrate low CV event rates overall, although signals for atrial fibrillation have emerged with risankizumab. Janus kinase inhibitors and sphingosine-1-phosphate receptor modulators carry class-specific CV warnings, due to signals mainly on non-IBD populations, and require careful use in high-risk individuals. Conclusions: Although most IBD therapies are generally safe from a CV perspective, certain agents may pose risks in vulnerable patients. Individualized CV risk assessment and ongoing post-marketing surveillance are essential to guide therapeutic choices and ensure patient safety. Full article

Background/Objectives: Dietary microstructure affects energy intake. Traditional Universal Eating Monitors (UEMs) offer accuracy but are limited for monitoring diverse diets. We developed the ‘Feeding Table’, a novel UEM that simultaneously tracks intake of up to 12 foods, enabling high-resolution monitoring of eating microstructure for multiple foods simultaneously. Methods: Forty-nine healthy volunteers participated: 15 (10 male, 8 female) in a location preference experiment and 31 (15 male, 16 female) in a standard meal test. The location preference study involved four weekly sessions. Participants received a standardized breakfast based on individual energy needs; lunch intake was measured 3 h later with food items in pseudo-randomized positions. The standard meal test occurred over two consecutive days to assess the Feeding Table’s performance in monitoring eating behavior under standardized conditions. Results: In two consecutive days of standard meal tests, the Feeding Table showed reasonable day-to-day repeatability for energy and macronutrient intake (energy: r = 0.82; fat: r = 0.86; carbohydrate: r = 0.86; protein: r = 0.58). Among the four repeated intake measurements, the results demonstrated high intra-class correlation coefficients (ICCs: energy 0.94, protein 0.90, fat 0.90, and carbohydrate 0.93). No significant positional bias was observed (energy: p = 0.07; macronutrients: p = 0.70). Conclusions: The Feeding Table maintains UEM accuracy while enabling multi-food, real-time monitoring of dietary microstructure and food choice, offering enhanced precision for studying eating behaviors. Full article

Background: Initial fluid therapy in acute pancreatitis is critical for modulating the systemic inflammatory response. The choice between Lactated Ringer and normal saline remains debated, given their potentially divergent impacts on disease progression and clinically relevant outcomes. The objective of this meta-analysis is to determine the effectiveness of one solution versus the other in patients with AP. Methods: A systematic review of randomized clinical trials published between 2000 and 2024 was conducted through an exhaustive search in databases such as PubMed, ScienceDirect, LILACS, SCOPUS, Web of Science, Springer, Scielo, and Cochrane. The review protocol adhered to the recommendations established by PRISMA. The methodological quality of the selected studies was assessed using the Jadad scale, while statistical analyses were performed with RevMan 5.4^® and Jamovi 2.3.28^® software. Results: Five trials with 299 patients showed that, in patients with AP, Lactated Ringer significantly reduced ICU admission (RR: 0.39; 95% CI: 0.18–0.85; p = 0.02) and the progression of pancreatitis (RR: 0.63; 95% CI: 0.40–0.98; p = 0.04). There was no significant difference in mortality or hospital stay (SMD: −0.89; 95% CI: −2.26 to 0.48; p = 0.23). No clear effects were observed on SIRS at 24, 48, and 72 h. CRP at 48 h was significantly lower with lactate (SMD: −3.91; 95% CI: −4.66 to −3.17; p < 0.00001), but not at 72 h. Conclusions: The administration of Lactated Ringer in acute pancreatitis shows clinical and anti-inflammatory benefits, but the evidence is mostly of low quality. Full article

Background: Gamification may enhance engagement and higher-order learning in health-care profession education, but evidence from undergraduate nursing programs—particularly in the Middle East—is limited. We evaluated whether integrating structured gamified activities into an anatomy and physiology course improves class engagement and knowledge-based critical thinking. Methods: In this pragmatic, nonrandomized, section-allocated quasi-experimental study at a single Saudi institution, 121 first-year female nursing students were assigned by existing cohorts to traditional instruction (control; n = 61) or instruction enhanced with gamified elements (intervention; n = 60) groups. The intervention (introduced mid-semester) comprised time-limited competitive quizzing with immediate feedback and aligned puzzle tasks. Outcomes were measured at baseline, mid-semester, and end-semester using a four-item Class Engagement Rubric (CER; scale 1–5) and a 40-item high-cognitive multiple-choice (MCQ) assessment mapped to course objectives. Analyses used paired and independent t-tests with effect sizes and 95% confidence intervals. Results: No attrition occurred. From baseline to end-semester, the intervention group had a mean CER increase of 0.59 points (95% CI, 0.42 to 0.76; p < 0.001)—approximately a 15% relative gain—and a mean MCQ increase of 0.30 points (95% CI, 0.18 to 0.42; p < 0.001), an ~8% relative gain. The control group showed no material change over the same interval. Between-group differences in change favored the intervention across CER items and for the MCQ outcome. Semester grade-point average did not differ significantly between groups (p = 0.055). Conclusions: Embedding a brief, structured gamification package within an undergraduate nursing anatomy and physiology course was associated with measurable improvements in classroom engagement and modest gains in knowledge-based critical thinking, with no detectable effect on overall semester GPA. Given the nonrandomized, single-site design, causal inference is limited. Multi-site randomized trials using validated critical-thinking instruments are warranted to confirm effectiveness and define dose, durability, and generalizability. Full article

Accurate malware family classification from dynamic sandbox reports continues to be a fundamental cybersecurity challenge. Most prior works depend on random splits that tend to overestimate accuracy, whereas deployment requires robustness under temporal drift as well as changing behaviors. We present a leakage-aware pipeline that transforms CAPEv2 sandbox JSON reports into structured visual heatmaps and evaluate models under stratified and chronological splits. The pipeline rigorously flattens behavioral keys, builds normalized representations, and benchmarks Random Forest, MLP, CNN64, HybridNet, and a modern ResNeXt-50 backbone. On the Avast–CTU CAPEv2 dataset containing ten malware families, Random Forest achieves nearly state-of-the-art accuracy (97.2% accuracy, 0.993 AUC) with high efficiency on CPUs, making it attractive for triage. ResNeXt-50 achieves the best overall performance (98.4% accuracy, 0.998 AUC) and provides visual interpretability via Grad-CAM, enabling analysts to verify predictions. We further quantify efficiency trade-offs (inference throughput and GPU memory) and report ablation studies on vocabulary size and keyset choices. These results affirm that though ensemble methods are still robust, heatmap-based CNNs provide better accuracy, interpretability, and robustness against drift. Full article

Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, including alkali-hydrolyzable nitrogen (AN), total nitrogen (TN), total phosphorus (TP), and organic matter (OM). The framework systematically integrates three typical spectral preprocessing methods (Standard Normal Variate transformation (SNV), Savitzky–Golay first derivative (SG_D1), and wavelet transform (Wavelet)), three feature selection strategies (Recursive Feature Elimination, XGBoost importance, and Random Forest importance), and 14 mainstream regression models to construct a multi-combination modeling system. Model performance was evaluated using five-fold cross-validation, with 80% of samples used for training and 20% for validation in each fold. Preprocessed Raman spectral features served as input variables, while the corresponding nutrient contents were used as outputs. Results showed significant differences in prediction performance across various combinations of preprocessing methods and regression algorithms for the four soil nutrient indicators. For AN prediction, the combination of Raw_SNV preprocessing with ElasticNet and BayesianRidge models achieved the best performance, with Test R² values of 0.713 and 0.721, and corresponding Test NRMSE as low as 0.092. For OM prediction, the same Raw_SNV preprocessing with ElasticNet and BayesianRidge also performed well, yielding Test R² values of 0.825 and 0.832, and Test NRMSE of 0.100 and 0.098, respectively. In TN prediction, both ElasticNet and BayesianRidge under Raw_SNV preprocessing achieved consistent Test R² of 0.74 and Test NRMSE around 0.20, indicating stable reliability. For TP prediction, the BayesianRidge model with Raw_SNV preprocessing outperformed all others with a Test R² of 0.71 and Test NRMSE of just 0.089, followed closely by ElasticNet (Test R² = 0.70, Test NRMSE = 0.092). Overall, the Raw_SNV preprocessing method demonstrated superior performance compared to SG_D1_SNV and Wavelet_SNV. Both BayesianRidge and ElasticNet consistently achieved high R² and low NRMSE across multiple targets, showcasing strong generalization and robustness, making them optimal model choices for Raman spectroscopy-based soil nutrient prediction. This study demonstrates that Raman spectroscopy, when combined with appropriate preprocessing and modeling techniques, can effectively predict soil organic matter and nitrogen in specific soil types under laboratory conditions. These results provide initial methodological insights for future development of intelligent soil nutrient diagnostics. Full article

On the spectrum of complexity for general surgery operations, umbilical hernia repair (UHR) is on the light side. After inguinal hernias, they are the most commonly repaired hernias and, as such, umbilical hernias are an important component of a general surgery practice. Since the time at which WJ Mayo published his seminal technique on the repair of umbilical hernias, multiple strategies for the management of umbilical hernias have emerged ranging from watchful waiting to open repair, as well as minimally invasive approaches. The present perspective maintains that each approach has its merits depending on the patient, surgeon, and institution. However, randomized controlled trials and clinical practice guidelines have favored some approaches over others. Similarly, recommendations have been developed regarding body mass index classification as well as hernia size for mesh placement. Other factors important to UHR are the choice of anesthesia and smoking cessation for elective repair. Though we do not contest well-designed randomized controlled trials (RTCs), or clinical guidelines, we offer our perspective on the care of these common hernias. Full article