Search Results (1,045)

The cherry fruit fly (Rhagoletis cerasi L.) poses a major threat to global cherry production, with significant economic implications. This study presents an innovative approach to assist pest control strategies by classifying cherry fruit samples based on pomological data using evolutionary rule-based classification algorithms. A unique dataset comprising 396 samples from five different coloring periods was collected, focusing particularly on the second pomological period when pest activity peaks. Three evolutionary algorithms, CORE (Evolutionary Rule Extractor for Classification), DMEL (Data Mining with Evolutionary Learning for Classification) and OCEC (Organizational Evolutionary Classification), were applied to find interpretable classification rules that find whether an incoming cherry sample belongs to the second pomological period or other periods. Two distinct fitness functions were used to evaluate the algorithms’ performance. The results of the algorithms are compared with various visual graphs and the metric values are compared with visual graphs in a similar fashion. The findings highlight the potential of explainable AI models in enhancing agricultural decision-making and offer a novel, data-based methodology for integrated pest management in cherry production for the prediction of cherry fruit phenology class. Full article

Accurate forecasts of the U.S. renewable energy consumption mix are essential for planning transmission upgrades, sizing storage, and setting balancing market rules. We introduce a Bayesian Dirichlet ARMA model (BDARMA) tailored to monthly shares of hydro, geothermal, solar, wind, wood, municipal waste, and biofuels from January 2010 through January 2025. The mean vector is modeled with a parsimonious VAR(2) in additive log ratio space, while the Dirichlet concentration parameter follows an intercept plus five Fourier harmonics, allowing for seasonal widening and narrowing of predictive dispersion. Forecast performance is assessed with a 61-split rolling origin experiment that issues twelve month density forecasts from January 2019 to January 2024. Compared with three alternatives (a Gaussian VAR(2) fitted in transform space, a seasonal naive approach that repeats last year’s proportions, and a drift-free ALR random walk), BDARMA lowers the mean continuous ranked probability score by 15 to 60 percent, achieves componentwise 90 percent interval coverage near nominal, and maintains point accuracy (Aitchison RMSE) on par with the Gaussian VAR through eight months and within 0.02 units afterward. These results highlight BDARMA’s ability to deliver sharp and well-calibrated probabilistic forecasts for multivariate renewable energy shares without sacrificing point precision. Full article

Educational strategies based on technological models that integrate the dimensions of prevention, screening, and treatment of diabetic foot ulcers are emerging as promising methods to improve nurses’ knowledge, skills, and clinical competencies in primary care. In this investigation, we evaluated the effectiveness of a digital education program, mediated by a virtual learning environment, in enhancing nurses’ clinical knowledge about diabetic foot ulcers. This quasi-experimental intervention study was conducted with 114 nurses, selected for convenience, from the five health districts that make up primary care in the municipality of Teresina, Brazil. Two stages, separated by the educational intervention, allowed us to measure their knowledge levels before and after the implementation of the digital technology. A characterization form and the Nurse Knowledge Assessment Questionnaire on Diabetic Foot were used to evaluate the outcomes. The McNemar test compared the pre- and post-intervention knowledge levels, while accuracy rate-based parameters allowed for the classification of results into performance categories. The intervention effect size was estimated using Cohen’s d test. Results showed substantial improvements in knowledge, particularly in domains related to definition (p = 0.002), risk factors (p < 0.001), associated complications (p < 0.001), signs and symptoms of neuropathies (p < 0.001), application of tests to assess protective sensation (p < 0.001) and foot biomechanics (p < 0.001), risk classification (p < 0.001), and prevention strategies (p < 0.001), with performance ratings predominantly “good” or “excellent” after the intervention. The effect size for paired samples was large (Cohen’s dz = 1.82), based on the total knowledge scores. Findings support the effectiveness signal of the virtual learning environment for knowledge improvement; however, without a control group, we cannot rule out testing effects. Controlled or stepped-wedge trials should confirm causality. Full article

The gut microbiota of social bees is vital for host health, yet pesticide exposure can disrupt these communities. Because most active ingredients are poorly soluble, toxicological tests often use cosolvents, but their effects remain unclear. We assessed four common cosolvents (DMSO, DMF, acetone, and Tween 80) at laboratory-relevant concentrations on honeybee survival, pollen consumption, body weight, and gut microbiota. In parallel, in vitro assays tested their impact on five dominant gut symbionts. The results showed no significant changes in survival, feeding, body weight, bacterial load, community composition, or core taxa abundance. Similarly, cosolvents did not inhibit bacterial growth in vitro. These findings demonstrate that commonly used cosolvents exert no detectable influence on honeybee physiology or gut microbiota. Although negative, this evidence is critical: it rules out cosolvents as hidden confounders, improving confidence in pesticide toxicology studies and providing essential reference data for pollinator risk assessment. Full article

Pandemics place extraordinary pressure on healthcare systems, particularly in identifying and prioritizing high-risk groups such as the elderly, pregnant women, and individuals with chronic diseases. Existing Artificial Intelligence models often fall short, focusing on single diseases, lacking interpretability, and overlooking patient-specific vulnerabilities. To address these gaps, we propose an Explainable Deep Learning approach for identifying at-risk patients during pandemics (AI-RiskX). AI-RiskX performs risk classification of patients diagnosed with COVID-19 or related infections to support timely intervention and resource allocation. Unlike previous models, AI-RiskX integrates five public datasets (asthma, diabetes, heart, kidney, and thyroid), employs the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing, and uses a hybrid convolutional neural network–long short-term memory model (CNN–LSTM) for robust disease classification. SHAP (SHapley Additive exPlanations) enables both individual and population-level interpretability, while a post-prediction rule-based module stratifies patients by age and pregnancy status. Achieving 98.78% accuracy, AI-RiskX offers a scalable, interpretable solution for equitable classification and decision support in public health emergencies. Full article

Automated safety monitoring on construction sites requires precise helmet-status detection and robust multi-object tracking in long, occlusion-rich video sequences. This study proposes a two-stage framework: (i) a YOLOv5 model enhanced with self-adaptive coordinate attention (SACA), which incorporates coordinate-aware contextual information and reweights spatial–channel responses to emphasize head-region cues—SACA modules are integrated into the backbone to improve small-object discrimination while maintaining computational efficiency; and (ii) a DeepSORT tracker equipped with fuzzy-logic gating and temporally consistent update rules that fuse short-term historical information to stabilize trajectories and suppress identity fragmentation. On challenging real-world video footage, the proposed detector achieved a mAP@0.5 of 0.940, surpassing YOLOv8 (0.919) and YOLOv9 (0.924). The tracker attained a MOTA of 90.5% and an IDF1 of 84.2%, with only five identity switches, outperforming YOLOv8 + StrongSORT (85.2%, 80.3%, 12) and YOLOv9 + BoT-SORT (88.1%, 83.0%, 10). Ablation experiments attribute the detection gains primarily to SACA and demonstrate that the temporal consistency rules effectively bridge short-term dropouts, reducing missed detections and identity fragmentation under severe occlusion, varied illumination, and camera motion. The proposed system thus provides accurate, low-switch helmet monitoring suitable for real-time deployment in complex construction environments. Full article

Nineteen previously and newly synthesized amphoteric 8-[(dialkylamino)methyl]-7-hydroxy-4-(2-oxo-2H-chromen-3-yl)-2H-chromen-2-ones were assayed as inhibitors of monoamine oxidases (MAO-A and B) and cholinesterases (AChE and BChE). Five of the tested compounds (2b, 2c, 3c, 5b, and 5c), namely those bearing the less bulky alkyls in the Mannich base 8-CH₂NR₂ (R = Me, Et) and the halogens (Cl, Br) at C6 of the 4-coumarin-3-yl moiety, showed moderate inhibitory potencies toward human MAO-A in the single-digit micromolar range (IC₅₀s from 1.49 to 3.04 µM). In particular, the 6′-Cl derivatives 2b and 5b proved to be reversible competitive inhibitors of human MAO-A with K_i values of 0.272 and 0.326 µM. Among the tested compounds, 3c proved to also be a moderate inhibitor of human AChE (IC₅₀ 4.27 µM). Molecular docking calculations suggested binding modes of the most active compounds to MAO-A and AChE binding sites consistent enough with the experimental data. Chemoinformatic tools suggest for the most active compounds, including the dual MAO-A/AChE inhibitor 3c, full compliance with Lipinski’s rule of five, high probability of gastrointestinal absorption, but low blood–brain barrier (BBB) permeability. While further efforts are required to improve their CNS distribution, herein new phenolic Mannich bases have been identified that may have potential for treating neurodegenerative syndromes. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, with significant resistance to conventional therapies, highlighting the urgent need for novel therapeutic strategies. Moringa oleifera (M. oleifera), a medicinal plant rich in diverse bioactive compounds, has shown promising potential for anti-lung carcinoma activity. This study investigates the molecular mechanisms underlying the therapeutic effects of M. oleifera bioactive compounds for their anti-lung cancer activities through an integrated network modeling and molecular docking approach. By constructing comprehensive compound–target–lung cancer pathway networks, we aim to elucidate the multitarget pharmacology of M. oleifera compounds, providing valuable insights into their potential as therapeutic candidates. Computational pipeline was applied to identify 180 phytochemicals from M. oleifera, filtered using Lipinski’s Rule of Five and ADMET properties, resulting in 10 lead compounds followed by their potential biological target proteins in regulating lung cancer progression. We identified 80 targeted proteins involved in lung cancer, with EGFR being the most enriched in pathway enrichment analysis. In the molecular docking analysis, caffeic acid showed the highest binding score (−28.97 kcal/mol) with EGFR forming stable complex during molecular dynamics simulations compared to the known EGFR inhibitor ‘erlotinib’. The overall results suggest that caffeic acid, a key bioactive compound in M. oleifera, is an EGFR-mediated oncogenic signaling inhibitor for lung cancer therapy, warranting further experimental validation to translate these findings into clinical applications. Full article

Background: Exposure therapy is an established intervention for treating specific phobias. This study evaluates a Virtual Exposure Therapist (VET), a virtual reality (VR)-based system enhanced with artificial intelligence (AI), designed to reduce spider fear symptoms. Methods: The VET system delivers three progressive exposure scenarios involving interactive 3D spider models and features an adaptive relaxation mode triggered when physiological stress exceeds preset thresholds. AI integration is rule-based, enabling real-time adjustments based on session duration, head movement (degrees/s), and average heart rate (bpm). Fifty-five participants (aged 18–35) with self-reported moderate to high fear of spiders completed seven sessions using the VET system. Participants were not clinically diagnosed, which limits the generalizability of findings to clinical populations. Ethical approval was obtained, and informed consent was secured. Behavioral responses were analyzed using AR(p)–GARCH (1,1) models to account for intra-session volatility in anxiety-related indicators. The presence of ARCH effects was confirmed through the Lagrange Multiplier test, validating the model choice. Results: Results demonstrated a 21.4% reduction in completion time and a 16.7% decrease in average heart rate across sessions. Head movement variability declined, indicating increased user composure. These changes suggest a trend toward reduced phobic response over repeated exposures. Conclusions: While findings support the potential of AI-assisted VR exposure therapy, they remain preliminary due to the non-clinical sample and absence of a control group. Findings indicate expected symptom improvement across sessions; additionally, within-session volatility metrics (persistence/half-life) provided incremental predictive information about later change beyond session means, with results replicated using simple volatility proxies. These process measures are offered as complements to standard analyses, not replacements. Full article

Background: The clinical characteristics of patients who have a first episode of congestive heart failure (CHF) may have changed in recent years. Methods: A retrospective cohort study was performed on 19,796 patients discharged from medical departments with a diagnosis of CHF between 1 January 2000 and 31 December 2022. Data were drawn from two data sets of the Minimum Basic Data Set-Hospital Data Set (MBDS) of the Lucus Augusti University Hospital (Spain): hospitalizations and patients. Patient characteristics (including the period of their first admission) and the association rules between diseases determined using the Apriori algorithm were studied in five consecutive time periods. Results: The general characteristics of patients on first admission for CHF changed over time. There were increases in mean age (75.9 ± SD 11.2 vs. 81.6 ± SD 11.5 years; p < 0.0001), the proportion of women (48.3% vs. 51.4; p = 0.0001), the number of acute diseases (1.1 ± SD 1.4 to 2.7 ± SD 2.5; p < 0.0001), and the number of chronic diseases (3.6 ± SD 1.9 to 6.5 ± SD 2.6); p < 0.001). Accordingly, the median number of diagnoses (from 3 to 7) and itemsets per patient increased (mean number of items 1.75 vs. 3.4; p < 0.0001), and the associations of diseases leading to CHF became more complex. Conclusions: This single-center study shows that in the last two decades, the characteristics of patients with a first hospital admission for CHF have changed. Patients are older, there is a predominance of women, and they have a greater number of acute and chronic concomitant diseases, making their clinical management more difficult. Full article

Background/Objectives: Conventional cancer screening relies heavily on imaging and invasive procedures, leading to high false-positive rates and limited uptake, while leaving several high-mortality cancers without routine screening options. This study evaluated a protein-based multi-cancer early detection (MCED) test designed to detect five high-burden cancers with high sensitivity, specificity, and tissue-of-origin (TOO) accuracy. Methods: Serum from 141 patients with confirmed breast, lung, colorectal, ovarian, or pancreatic cancer and 119 healthy controls was analyzed using a 16-parameter protein biomarker panel. The assay measured extracellular protein kinase A (xPKA) activity, additional kinase activities, and cancer-associated antibodies (IgG, IgM). A supervised, rule-based classification framework was developed for cancer detection and TOO assignment. Results: The MCED test achieved 100% sensitivity across all five cancer types and 97% overall specificity, with 98% TOO accuracy. Importantly, 100% of Stage I cancers were detected. Cancer specificities ranged from 96.6% (breast) to 100% (ovarian, pancreatic, and colorectal). Conclusions: This protein-based MCED approach demonstrates exceptional performance for multi-cancer detection and TOO identification, including robust early-stage detection, and may reduce the downstream diagnostic burden relative to the existing system. Full article

This study investigated the interaction between cannabidiol (CBD) derivatives and the GPR55 receptor using a bioinformatics-driven molecular docking approach. GPR55, implicated in central nervous system (CNS) pathologies, represents a promising target for novel therapeutics. Drug-likeness evaluation via SwissADME confirmed that all selected derivatives complied with Lipinski′s Rule of Five, exhibiting favorable physicochemical properties with molecular weights below 500 Da and acceptable logP values. Molecular docking simulations, performed using AutoDock Vina through PyRx, revealed strong binding affinities, with docking scores ranging from −9.2 to −7.2 kcal/mol, indicating thermodynamically feasible interactions. Visualization and interaction analysis identified a conserved binding pocket involving key residues, including TYR101, PHE102, TYR106, ILE156, PHE169, MET172, TRP177, PRO184, LEU185, LEU270 and MET274. Ligand clustering in this region further supports the presence of a structurally defined binding site. Molecular dynamics simulations of GPR55 in complex with the three top-scoring ligands (3″-HOCBD, THC, and CBL) revealed that all ligands remained stably bound within the cavity over 100 ns, with ligand-specific rearrangements. Predicted oral bioavailability was moderate (0.55), consistent with the need for optimized formulations to enhance systemic absorption. These findings suggest that CBD derivatives may act as potential modulators of GPR55, offering a basis for the development of novel CNS-targeted therapeutics. Full article

Maritime Autonomous Surface Ships (MASSs) raise safety and regulatory challenges that extend beyond technical reliability. This study builds on a published system-theoretic process analysis (STPA) of degraded operations that identified 92 loss scenarios. These scenarios were reformulated into a two-round Delphi survey with 20 experts from academic, industry, seafaring, and regulatory backgrounds. Panelists rated each scenario on severity, likelihood, and detectability. To avoid rank reversal, common in the Risk Priority Number, an adjusted index was applied. Initial concordance was low (Kendall’s W = 0.07), reflecting diverse perspectives. After feedback, Round 2 reached substantial agreement (W = 0.693, χ² = 3265.42, df = 91, p < 0.001) and produced a stable Top 10. High-priority items involved propulsion and machinery, communication links, sensing, integrated control, and human–machine interaction. These risks are further exacerbated by oceanographic conditions, such as strong currents, wave-induced motions, and biofouling, which can impair propulsion efficiency and sensor accuracy. This highlights the importance of environmental resilience in MASS safety. These clusters were translated into five action bundles that addressed fallback procedures, link assurance, sensor fusion, control chain verification, and alarm governance. The findings show that Remote Operator competence and oversight are central to MASS safety. At the same time, MASSs rely on artificial intelligence systems that can fail in degraded states, for example, through reduced explainability in decision making, vulnerabilities in sensor fusion, or adversarial conditions such as fog-obscured cameras. Recognizing these AI-specific challenges highlights the need for both human oversight and resilient algorithmic design. They support explicit inclusion of Remote Operators in the STCW convention, along with watchkeeping and fatigue rules for Remote Operation Centers. This study provides a consensus-based baseline for regulatory debate, while future work should extend these insights through quantitative system modeling. Full article

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach. Full article

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article