Search Results (4,181)

This paper studies a risk-averse stochastic unit commitment framework for an energy aggregator, operating a portfolio of conventional generators, renewable units, and battery energy storage in a network-constrained environment. Renewable generation and demand uncertainty are represented through a scenario-based extensive-form mixed-integer linear program. To avoid exposure to rare but high cost events, the model incorporates conditional value-at-risk as part of the objective function. The approach captures key market interactions, including day-ahead commitments, imbalance penalties, and power exchange with a neighboring network, while respecting generator constraints, storage dynamics, line flow limits, and bus voltage security. A comprehensive parametric study is conducted to quantify the influence of two risk parameters: the conditional value-at-risk confidence level $\alpha$ and the risk-aversion weight $\lambda$. Using a 300-scenario test set on a modified IEEE 9-bus system, the results show that risk-neutral scheduling exposes the aggregator to larger operational costs in extreme scenarios. Minor levels of risk aversion (0.1–0.5) reduce CVaR and tighten the distribution of costs. Increasing $\lambda$ further yields diminishing returns, while higher $\alpha$ values focus risk mitigation on the most severe outcomes. The results demonstrate how CVaR-based stochastic scheduling can support aggregator decision-making by quantifying downside risk under renewable uncertainty. Full article

The present study aimed to optimize the fabrication conditions of walnut oil–fructooligosaccharide emulsion and evaluate its efficacy in improving the quality of cooked rice. Three food-grade emulsifiers, namely sucrose fatty acid ester (SE), polyglycerol fatty acid esters (PGEs), and diacetyl tartaric acid ester of mono- and diglycerides (DATEM), were adopted to investigate the influences of water–oil ratio, emulsifier dosage, and high-pressure microfluidization (HPM) pressure on emulsion indicators including the emulsion stability index (ESI), particle size, polydispersity index (PDI), zeta potential, micromorphology, and storage stability. The results reveal that SE exhibited the superior emulsifying capacity, with the optimal water–oil ratio and SE dosage determined to be 6:4 and 4% (w/w, based on oil mass), respectively. HPM treatment further reduced droplet size and elevated the ESI, and 150 MPa was identified as the optimal homogenization pressure. Subsequently, the optimized emulsion was applied during rice cooking, with water, pure walnut oil, pure fructooligosaccharide (FOS), simple physical mixture of the two raw materials and crude emulsion set as parallel control groups. Compared with all control treatments, rice cooked with 150 MPa homogenized emulsion possessed the minimum hardness value of 3297.22 and the maximum springiness of 0.74, accompanied by the optimal water retention capacity, luminosity, and the most abundant volatile organic compounds (VOCs). In conclusion, walnut oil–fructooligosaccharide emulsion fabricated via HPM exerted synergistic improvements on the texture and flavor of cooked rice, which could provide a technical reference for the functional modification of staple foods. Full article

Fuzzy set theory, proposed by Lotfi Zadeh, is applied with great success in embedded, control-oriented systems based on microcontrollers or programmable logic devices. Algorithms used in these systems operate on fuzzy sets represented by membership functions. In practically implemented control systems, these functions typically possess simple linear shapes, such as trapezoidal or triangular. To compute the function value, the classical method relies on multiplication and division. These operations are complex to implement in software or hardware, consume significant resources, and are time-consuming. This paper proposes a fixed-point computation method to obtain the value of a membership function without relying on these operations. For this purpose, the bisection algorithm was adopted, replacing its complex arithmetic with addition and binary shift operations. This approach allows for a reduction in computation time and the hardware or software overhead required by resource-constrained embedded systems, especially architectures without dedicated hardware accelerators for multiplication and division. Sample software and hardware implementations demonstrate its suitability for real-world systems. Full article

Entity-aware quality assessment may reduce false interpretations of electronic health record (EHR) data, but evidence from small, rule-aligned benchmarks cannot establish operational effectiveness. We revised H-StreamQ as a proof-of-concept framework and evaluated its laboratory component using the complete MIMIC-IV v3.1 labevents file (158,374,764 events; 313,442 patients). Ten thousand patients were sampled across laboratory-activity quintiles and split at patient level into training (6000), threshold-calibration (2000), and test (2000) groups. The independent test set contained 918,651 numeric laboratory events. Without excluding naturally alerted records, 54,788 mutually exclusive defects were introduced using subtle value shifts, unit/scale errors, mapping errors, delayed records, and patient-clustered correlated defects. Rules, a context-aware Isolation Forest, their union (Hybrid), a context-free Isolation Forest, Local Outlier Factor (LOF), and linear and radial-basis-function (RBF) One-Class support vector machines (OCSVMs) were compared at a threshold fixed by a 2.5% calibration alert budget. Patient-cluster bootstrap intervals and event-micro and patient-macro results were reported. Rules alone achieved the highest event-micro F1-score (0.637; 95% confidence interval [CI] 0.547–0.722), followed by Hybrid (0.576; 0.484–0.668) and RBF One-Class SVM (0.559; 0.433–0.670). Hybrid increased recall over rules by only 0.004 (95% CI 0.003–0.006) while reducing F1 by 0.061 and increasing the background-alert rate by 0.015. Context conditioning did not improve aggregate Isolation Forest performance. In six batch-level drift simulations, an exponentially weighted moving average (EWMA) and a fixed-window monitor detected 97–100% and 98–100% of changes, respectively, whereas a custom Hoeffding adaptive-window detector was more conservative and often missed smaller or recurrent changes. These results support H-StreamQ as an explainable research framework, not as a validated clinical or production system. Patient-macro F1, which weights every patient equally, was substantially lower than event-micro F1 for every method (rules 0.395 versus 0.637; Hybrid 0.320 versus 0.576), indicating that event-level performance is weighted towards high-activity patients. Precision and F1 are computed relative to injected synthetic labels and are not clinically adjudicated estimates. The entity-aware architecture spans patients, admissions, diagnoses, transfers, and dictionaries, but the quantitative detection benchmark evaluates the numeric laboratory component only; other entities are used for linkage and contextual attachment and are audited descriptively rather than evaluated against labels. Full article

Background/Objectives: Epoxide hydrolase 2 (EPHX2), which encodes soluble epoxide hydrolase (sEH), is involved in arachidonic acid metabolism and has been associated with inflammation, lipid metabolism, and tumor biology. However, its prognostic significance and biological associations in lung adenocarcinoma (LUAD) remain unclear. This study aimed to investigate the expression profile, prognostic value, and molecular pathways of EPHX2 in LUAD using bioinformatics analyses. Methods: EPHX2 expression was evaluated using TNMplot, GEPIA2, and GEO datasets, while protein expression was assessed using the Human Protein Atlas and CPTAC/UALCAN platforms. Prognostic analyses were performed using Kaplan–Meier Plotter, GEPIA2, and Human Protein Atlas datasets. Co-expression and gene set enrichment analyses were conducted using LinkedOmics, and functional enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome databases. Correlation and protein–protein interaction (PPI) analyses were evaluated using GEPIA2 and STRING. Results: EPHX2 expression was significantly reduced in LUAD tissues compared with normal lung tissues across datasets, and these findings were supported at the protein level. High EPHX2 expression was associated with better overall survival and retained independent prognostic significance in multivariate Cox analysis. Functional enrichment analyses demonstrated associations with lipid metabolism, arachidonic acid metabolism, cytochrome P450-related pathways, and oxidative processes. Correlation analyses suggested potential associations between EPHX2 and angiogenesis, extracellular matrix remodeling, and hypoxia-related pathways. Conclusions: Bioinformatics analyses suggest that EPHX2 may participate in metabolic and tumor progression-related regulatory networks and may serve as a prognostic biomarker in LUAD. Full article

Estuarine turbidity fronts are narrow transition zones where suspended particulate matter concentrations change sharply. Their detection from remote sensing imagery remains challenging because conventional methods rely on empirical thresholds, are sensitive to mixed pixels, and often lack transferability and physical interpretability. Here, we evaluate the potential of foundation-model representations by integrating AlphaEarth 64-dimensional embeddings with Sentinel-2 spectral and multi-scale spatial features. A 229-dimensional feature set is constructed and fed into a two-step framework combining random forest classification with an empirically calibrated optical mask based on low red-band reflectance. The fused feature set achieves an overall accuracy of 91.2%, an F1 score of 87.5%, and a Kappa coefficient of 0.807, outperforming both spectral–spatial features alone and AlphaEarth embeddings alone. To elucidate the contribution mechanism of AlphaEarth embeddings, we conduct two complementary SHAP analyses: one evaluating each dimension’s direct contribution to front classification, and the other assessing its capacity to predict Sentinel-2 band reflectance. Only nine dimensions overlap between the respective top 20 lists, revealing a clear functional division within the embedding space—some dimensions primarily encode spectral reflectance information, while others encode spatial context, edge patterns, or topological structures that are not directly accessible from local spectral features. This division represents the added value of AlphaEarth beyond conventional optical data. The empirically calibrated optical mask reduces candidate frontal area by 59.09% in turbid estuaries and restores linear front morphology. However, leave-one-estuary validation yields F1 scores ranging from 0.33 to 0.84, substantially below the within-estuary score of 0.93, demonstrating limited cross-region transferability and challenging the assumption of domain invariance in foundation-model embeddings. These findings highlight both the value of fusing foundation-model representations with local spectral–spatial features and the critical need for domain-adaptation strategies to improve generalization across contrasting estuarine hydrodynamic regimes. Full article

Objectives: Heart failure (HF) arises from multiple interrelated pathological processes. Among these, lysosomal impairment and loss of autophagic homeostasis are increasingly recognized as important contributors to myocardial damage and ventricular remodeling. This study sought to identify lysophagy-associated signature genes in HF and to define their biological roles, cellular origins, and potential diagnostic relevance. Methods: Bulk myocardial transcriptome datasets, including GSE16499, GSE57338, and GSE76701, were integrated with the human cardiac single-cell dataset GSE145154. Differential expression analysis was first performed to identify lysophagy-related differentially expressed genes (DEGs). Candidate hub genes were then screened using support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression. Functional enrichment analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration assessment, single-cell transcriptomic mapping, and regulatory network analysis were subsequently conducted. The expression profiles of the selected genes were validated in a murine HF model, and VAMP8 overexpression assays were performed in H9c2 cells. Results: Five hub genes, namely VAMP8, STX2, MCOLN1, DERL1, and PTP4A2, were consistently and markedly decreased in failing myocardial tissue. These genes were mainly linked to SNARE-dependent vesicle trafficking and lysophagy regulation. A diagnostic model incorporating these hub genes demonstrated good discriminatory performance in both the training dataset and a small independent validation cohort, supporting further evaluation of their potential diagnostic value. Single-cell analysis further indicated that these genes were primarily enriched in cardiac FOLR2⁺ tissue-resident macrophages (TRMs). Pseudotime and cell–cell communication analyses associated this module with FOLR2⁺ TRM cell states and predicted interactions with cardiac stromal cells. In the HF mouse model, the mRNA levels of all five hub genes were decreased, with concurrent reductions in VAMP8, MCOLN1 and DERL1 protein expression. In Ang II/LLOMe-induced H9c2 cells, VAMP8 overexpression was associated with reduced cardiomyocyte injury, attenuation of changes in the abundance of lysosome- and autophagy-related proteins, and fewer ultrastructural abnormalities, suggesting a potential cardioprotective effect. Conclusions: VAMP8, STX2, MCOLN1, DERL1, and PTP4A2 were identified as candidate molecular markers of HF that reflect alterations in a lysophagy- and vesicular-transport-related program associated with FOLR2⁺ tissue-resident macrophages. These findings provide new insights into immune-microenvironment remodeling in HF and suggest potential directions for mechanistic and therapeutic investigations. Full article

Background and Objectives: The C-reactive protein-to-albumin ratio (CAR) is associated with mortality and poor outcome after acute ischemic stroke, but the association is not the same as added clinical usefulness. We evaluated whether admission CAR and follow-up CAR provide prognostic information beyond a prespecified parsimonious clinical reference model comprising age, neurological severity, and admission glucose in critically ill patients with acute ischemic stroke. Materials and Methods: In this single-center retrospective cohort of 146 adults with acute ischemic stroke managed in intensive care, CAR was calculated from C-reactive protein and serum albumin at emergency department admission and at the first intensive care laboratory assessment. The primary outcome was 90-day all-cause mortality. A clinical reference model (age, admission National Institutes of Health Stroke Scale [NIHSS] score, admission glucose) was compared with the same model augmented by log-transformed CAR using the area under the receiver operating characteristic curve (AUC), the DeLong test, likelihood-ratio (LR) testing, and bootstrap optimism-corrected performance. Results: Ninety-day mortality occurred in 32 of 146 patients (21.9%) (full cohort; primary complete-case analysis: 141 patients with 31 events). In the primary complete-case analysis (n = 141; 31 events), admission CAR was not independently associated with mortality (odds ratio per 1-SD log CAR 1.46, 95% confidence interval 0.91–2.33; p = 0.115). Adding admission CAR changed the AUC from 0.768 (0.676–0.860) to 0.783 (0.693–0.874) (ΔAUC +0.016, 95% confidence interval −0.024 to +0.055; DeLong p = 0.444; LR p = 0.113), with optimism-corrected point estimates of 0.750 and 0.754. Follow-up CAR did not add value (LR p = 0.159), and change in CAR did not improve discrimination (ΔAUC +0.001; LR p = 0.945); because intensive care sampling times were not standardized, these analyses assess incremental prognostic information rather than CAR kinetics. Admission CAR was not associated with poor 90-day functional outcome (odds ratio 1.09, 95% confidence interval 0.83–1.44; p = 0.530). Conclusions: Admission CAR did not demonstrate measurable incremental prognostic value beyond the prespecified clinical reference model, and follow-up CAR and change-based analyses did not improve prediction, although non-standardized sampling times mean that serial CAR kinetics were not fully evaluated. The confidence intervals remain compatible with both no effect and a positive effect of uncertain clinical relevance, but the observed improvement was insufficient to support CAR as a stand-alone or routinely additive prognostic marker in this setting. Full article

We prove the existence and uniqueness of weak solutions to an obstacle problem for a nonlinear elliptic operator in divergence form. The variational inequality under consideration involves an integral over the domain of the Frobenius inner product of the operator $\mathcal{S}(z,\nabla u-\mathcal{O}(u\left)\right)$ with the gradient difference $\nabla (v-u)$, plus the Euclidean inner product of u and $v-u$, which is required to be nonnegative for all admissible functions v. The admissible set consists of functions in the Sobolev space $W^{1,2}(\mathrm{\Omega };{\mathbb{R}}^m)$ with prescribed Dirichlet boundary trace and lying above a given obstacle $\psi$ almost everywhere. The obstacle condition $v\ge \psi$ a.e. models a lower bound constraint (e.g., a membrane or a displacement limit) that the admissible functions must respect, while the boundary value $\delta$ prescribes the Dirichlet data. The principal part contains a solution-dependent shift $\mathcal{O}\left(u\right)$, which is Lipschitz continuous, while $\mathcal{S}$ is assumed to be globally Lipschitz and strongly monotone with respect to equal shifts, with quadratic growth and coercivity. This structural framework can be interpreted in terms of symmetry: the strong monotonicity condition expresses a quantitative symmetry property of $\mathcal{S}$ with respect to equal shifts, and the shift $\mathcal{O}\left(u\right)$ introduces a symmetry-breaking coupling. The smallness condition ensures that this asymmetry remains under control. However, we do not pursue a full group-invariance or Lie-symmetry analysis; the symmetry perspective is used here as a heuristic and interpretative tool. The main difficulty lies in the mismatch of shifts when comparing two admissible functions. This is resolved by a three-term decomposition of the monotonicity estimate, combined with Young’s inequality and Poincaré’s inequality, under the smallness condition that the product of the Lipschitz constant of $\mathcal{S}$, the Lipschitz constant of $\mathcal{O}$, and the Poincaré constant is bounded above by one quarter of the strong monotonicity modulus. Existence follows from the Kinderlehrer–Stampacchia theorem; uniqueness is obtained from the same decomposition. The result unifies and extends previous contributions that treated either the lower-order term or the shift coupling separately, and it does so within a unified quadratic framework that avoids the technical overhead of variable exponents and Young measures. Full article

Climate change and aging infrastructure are undermining the resilience of urban flood management systems, reducing their reliability and increasing the likelihood of systemic failure that may culminate in regime shifts. This study develops a conceptual and practitioner-oriented screening framework based on a quantitative mathematical model to examine facility-level resilience and identify threshold conditions that may trigger regime transitions under external disturbances and varying pre-disturbance facility conditions. The framework adopts the composite sigmoid function (CSF) to capture nonlinear performance trajectories of infrastructure systems. Building on this model, this study extends its application by developing a parameterization scheme directly linked to four resilience dimensions: robustness, redundancy, rapidity, and resourcefulness (4Rs), which can be derived from field investigations or expert surveys. The normalized 4R scores are mapped to the CSF parameters, thereby converting static resilience assessment results into degradation and recovery curves. To search for threshold conditions, a parametric analysis was conducted by systematically varying the 4R values across their defined ranges. Rather than indicating a single universal threshold value, the results revealed critical threshold regions formed by specific combinations of the 4R dimensions. Lower robustness reduced the initial performance buffer, and low redundancy accelerated and extended performance degradation, while insufficient rapidity and resourcefulness delayed or limited recovery, increasing the likelihood of transition into an alternative degraded regime. For example, even when R1 and R2 were set to relatively high normalized values of 0.90, and R3 was set to its maximum value of 1.00, full recovery could not be achieved when R4 decreased below approximately 0.20. An illustrative application was conducted using preliminary 4R assessment results for flood-control facilities in three districts of Seoul, Korea. The model-derived trajectories were qualitatively compared with reported historical vulnerability patterns. While this comparison was intended as a contextual assessment rather than an event-specific empirical validation, our framework supports comparative, scenario-based screening of potentially vulnerable facilities for preliminary maintenance and investment prioritization. Full article

Background/Objective: Cinnamaldehyde (CA) is a naturally occurring bioactive compound derived from the leaves, bark, roots, and flowers of the Chinese medicinal plant Cinnamomum cassia. It exhibits a broad spectrum of pharmacological properties, encompassing antioxidant, antibacterial, anti-diabetic, antifungal, and anticancer activities. Notably, it has shown potential therapeutic benefits in the management of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD). Ochratoxin A (OTA), a common contaminant found in foods such as cereals, coffee, and raisins, is also present in traditional Chinese medicinal materials, including Astragalus and liquorice. T2DM and NAFLD share intertwined pathophysiological pathways, including insulin resistance, dyslipidaemia, chronic low-grade inflammation and oxidative stress, with insulin resistance serving as the common pathological hub for both conditions. Consequently, they frequently co-occur and exacerbate each other. OTA exerts dual-targeted toxicity to the pancreas and liver, which may synergistically drive the development of the comorbidity of T2DM and NAFLD. These two processes are mutually causal and together constitute the pathological basis of metabolic comorbidity. Methods: Network toxicology employs toxicological data, gene expression, and protein–protein interaction (PPI) networks to predict the targets of toxins, while network pharmacology, based on systems biology principles, reveals how drugs exert regulatory effects through multiple targets and pathways. In this study, we employed an integrated network toxicology and network pharmacology approach to jointly decipher the potential mechanisms by which CA intervenes in OTA-induced comorbid T2DM-NAFLD. First, a network toxicology approach was employed to preliminarily screen for core toxicological targets responsible for OTA’s pathogenicity. Subsequently, network pharmacology was used to identify potential targets of CA-mediated intervention in the disease. Finally, the common overlap among the CA intervention targets, OTA toxicity targets, and disease targets was defined as the final set of potential targets for CA-mediated intervention in OTA-induced T2DM-NAFLD comorbidity. A PPI network was constructed using the STRING database, and topological analysis was performed with Cytoscape. Core targets were selected using the median values of six parameters—betweenness centrality, closeness centrality, degree centrality, eigenvector centrality, LAC (local average connectivity) score, and network centrality—as cut-off thresholds, and the top 10 key genes were further identified using the cytoHubba plugin. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database, and the results were visualized on the CNSknowall platform. Lastly, molecular docking of the core targets was performed using the CB-DOCK2 platform to validate binding affinity. Results: Based on an integrated analysis of network toxicology, network pharmacology, and molecular docking, 10 key targets were systematically identified. These may serve as potential mediators of cinnamaldehyde in the treatment of OTA-induced T2DM-NAFLD comorbidity. Among these, six targets—albumin (ALB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), interleukin-6 (IL-6), tumor necrosis factor (TNF), actin beta (ACTB), and estrogen receptor 1 (ESR1)—possess crystal structures amenable to molecular docking. KEGG enrichment analysis revealed that CA and OTA jointly participate in key pathological processes such as the cancer pathway, the lipid and atherosclerosis pathway, the advanced glycation end-products–receptor for advanced glycation end-products (AGE-RAGE) signaling pathway, the phosphatidylinositol 3-kinase–protein kinase B (PI3K-Akt) signaling pathway, the TNF signaling pathway, and the interleukin-17 (IL-17) signaling pathway. OTA exacerbates inflammatory responses, impairs insulin signaling, promotes hepatic steatosis, and disrupts systemic metabolic homeostasis, ultimately contributing to T2DM-NAFLD comorbidity. Conversely, cinnamaldehyde counteracts these pathological processes through multiple mechanisms, including antioxidant and anti-inflammatory effects as well as regulation of glucose and lipid metabolism, thereby restoring metabolic homeostasis. Conclusions: This study has preliminarily identified the toxicological targets of OTA and the potential intervention targets of CA, offering new avenues for preventing and intervening in OTA-induced metabolic toxicity. Furthermore, it provides a theoretical basis for CA as a potential multi-target therapeutic agent and presents novel insights worthy of further investigation into the prevention of T2DM-NAFLD comorbidity. Full article

This paper proposes a resilience assessment model for irregular intersections that combines conflict analysis, channelization conditions, and traffic operational data to identify turning movements with high cascading-failure potential. Turning movements are the basic analytical unit. A channelization-weighted conflict matrix captures coupling among traffic flows by incorporating signal phase separation and lane function allocation. A CLI (Conflict Load Index) integrates the weighted conflict degree, a saturation correction factor, and expert risk scores to rank turning movements by cascading-failure risk. Three failure scenarios are compared: random failure, descending conflict-degree failure, and descending CLI failure. A failure propagation probability function based on weighted conflict-degree load distribution is paired with a resilience loss index that quantifies cumulative intersection capacity loss during failure propagation. The model is applied to the irregular intersection near Donggang Passenger Station. Among 16 turning movements, R2-T2, R4-T2, R5-T2, and R1-T1 have the four highest CLI values and form the high-risk set. The high-saturation movement R5-T2 (saturation 0.738) rises to rank 3 in the revised model, three positions higher than in the topology-only model, reflecting its risk level under actual traffic conditions. Sensitivity analysis shows that the relative ranking of the three failure modes is consistent across all parameter combinations, confirming the robustness of the conclusions. The model provides a quantitative basis for resilience diagnosis, risk early warning, and improvement planning at irregular intersections. Full article

Electrospinning is a widely used nanofabrication technique capable of producing fibers with a range of diameters, morphologies, and porosities through the adjustment of experimental parameters. However, achieving reliable fiber diameter tuning remains challenging because of the complex, nonlinear interdependence among multiple electrospinning variables. In this study, a data-driven methodology is proposed that integrates predictive machine learning (ML) modeling with evolutionary algorithm-based optimization, specifically employing a genetic algorithm (GA), to predict fiber diameter and guide electrospinning parameter selection across nano- and microscale ranges. A curated dataset comprising 388 data points from 30 scientific publications was developed, focusing exclusively on polyacrylonitrile (PAN) dissolved in dimethylformamide (DMF). Multiple ML models were trained and tested to predict fiber diameter as a function of key electrospinning parameters. Among the evaluated ML models, the eXtreme gradient boosting (XGB) model achieved the highest predictive performance, yielding a coefficient of determination (R²) value of 0.93 with low prediction errors (root mean square error [RMSE]: 127.76 nm, mean absolute error [MAE]: 56.27 nm) on the test set. Experimental validation was performed by fabricating electrospun PAN nanofibers under one independent set of conditions, with scanning electron microscopy (SEM) showing close agreement between predicted and actual fiber diameters. The trained XGB model was subsequently integrated with a GA to identify electrospinning parameter sets for user-defined target fiber diameters ranging from 100 to 2000 nm. The evolutionary optimization process exhibited rapid convergence with low fitness error when evaluated using the trained predictive model. Overall, this study demonstrates the potential of a data-driven methodology to generate model-guided candidate conditions for target-driven PAN-DMF electrospinning, subject to broader experimental validation. Full article

To address the visual perception challenges in automated harvesting of the ‘Sucui No.1 Pear’, this study proposes a fast detection and 3D localization method based on an improved YOLOv11 architecture and an RGB-D camera. First, a multi-scene ‘Sucui No.1 Pear’ dataset containing 5842 RGB-D image pairs and 31,559 labeled instances was constructed. Second, a lightweight YOLOv11-pear detection model optimized for pear fruit was developed; by reconstructing the feature pyramid network and introducing a global attention mechanism, using K-means++ clustering to optimize prior anchor boxes, and designing a composite loss function (Varifocal Loss + CIoU Loss + DFL), the detection accuracy was improved while maintaining lightweight design. The model adopts a “detect first, then fuse” strategy, achieving robust 3D coordinate calculation based on the median depth of the bottom region of the detection box. Experimental results show that YOLOv11-pear achieves 93.8% mAP@0.5 and 65.5% mAP@0.5:0.95 on the independent test set, with a precision of 94.2% and a recall of 91.5%. The model has only 5.8 M parameters and achieves real-time inference at 38.7 FPS on the Jetson Orin NX edge platform, with a mean absolute error of 9.9 mm for 3D localization. In severely occluded and complex lighting scenarios, the mAP@0.5 reaches 80.9% and 87.5%, respectively. After integrating the vision system into the harvesting robot platform, end-to-end closed-loop testing in a real orchard achieved an 88% harvesting success rate and 5% fruit damage rate. The average time for the visual perception stage was 1.3 s, accounting for 9.4% of the harvesting cycle. This research provides a high-precision, lightweight, and deployable vision solution for automated harvesting of the ‘Sucui No.1 Pear’, and has important reference value for promoting the development of intelligent fruit harvesting technology. Full article

Background/Objectives: Melioidosis is an environmentally acquired infection caused by Burkholderia pseudomallei (B. pseudomallei). Although historically framed as a tropical disease, evidence indicates that recognized risk can extend beyond classical endemic regions. This narrative review synthesized Digital Object Identifier (DOI)-verified evidence on environmental persistence, climate-sensitive risk, geographic emergence, and One Health preparedness. Methods: Structured narrative searches of PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Europe PubMed Central (Europe PMC), Crossref, and publisher records were conducted for literature available up to 19 June 2026. Forty-four DOI-verified sources were retained. Evidence categories were derived inductively by inferential function during thematic synthesis and used as a qualitative interpretive framework, not as a validated quantitative risk score. Results: B. pseudomallei persists in soil and water, survives nutrient limitation, and clusters in environmental microfoci, but the interpretive value of detection depends on viability, exposure context, and diagnostic endpoint. Rainfall, humidity, flooding, and cyclones are associated with incidence, severity, or mobilization in several settings, supporting climate-sensitive risk rather than uniform geographic spread. Case-based evidence is strongest when it separates importation, local acquisition, environmental establishment, source attribution, and animal sentinel signals. Human risk depends on exposure route, host susceptibility, diagnostic recognition, and access to prolonged antimicrobial management, whereas animal evidence is best interpreted as sentinel or common-exposure evidence unless reservoir or direct-transmission data are available. Conclusions: Melioidosis beyond the tropics requires graded evidence interpretation because environmental detection, modeled suitability, animal signals, and human cases support different levels of geographic and One Health inference; this approach links early signals to surveillance while reserving higher-confidence claims for convergent evidence. Full article