Search Results (4,303)

This paper addresses the optimal design of a multi-layer security system for protecting borders or sensitive areas against intruders who may deploy decoys. The system comprises successive layers of imperfect sensors and a limited number of mobile response units. Intruders that evade detection or neutralization in one layer proceed to the next. Our objective is to minimize the overall probability of a threat escaping the entire system. We formulate a nonlinear integer programming model within a queuing-theoretic framework to jointly determine the optimal number of security layers and the allocation of sensors and response units across them. A simulated annealing heuristic is proposed to solve this complex optimization problem. Furthermore, we extend the model to analyze the impact of decoys—objects that trigger intentional false alarms—which strategically drain system resources and increase the evasion risk for genuine threats. Numerical experiments demonstrate that the optimized multi-layer configuration significantly reduces the final escape probability compared to a single-layer baseline, validating the efficacy of the proposed framework for enhancing security in resource-constrained environments. Full article

Selenium nanoparticles (SeNPs) show great potential for sustainable agriculture, but their green synthesis and practical application still need further optimization. This study established a green synthesis method for SeNPs using lyophilized rose (Rosa rugosa Thunb.) powder as both a reducing and stabilizing agent to reduce sodium selenite (Na₂SeO₃), key parameters, including template concentration, Na₂SeO₃/VC ratio, and reaction temperature were systematically optimized. This process yielded stable, spherical SeNPs with optimal properties, exhibiting a diameter of 90 nm and a zeta potential of −35 mV. Structural characterization confirmed that selenium forms chelation complexes through carboxyl and hydroxyl oxygen-binding sites. The SeNPs exhibited exceptional stability (retained 426 days at 25 °C) and pH tolerance (pH 4–10), though divalent cations (Ca²⁺) triggered aggregation. In agricultural application tests, 5 mg/L SeNPs increased tomato plant biomass by 84% and antioxidant capacity by 152% compared to controls, and the biosynthesis pathways of salicylic acid and jasmonic acid were upregulated. Moreover, the SeNPs exhibited strong concentration-dependent antifungal activity against several major pathogens. Among these pathogens, tomato gray mold (Botrytis cinerea) was the most sensitive, as evidenced by its low EC₅₀ (4.86 mg/L) and sustained high inhibition rates, which remained substantial even at 1 mg/L and reached 94% at 10 mg/L. These findings highlight SeNPs as a friendly alternative for minimizing agrochemical use in sustainable agriculture. Full article

In this study, we propose a novel hybrid framework for assessing the invasiveness of an in-house dataset of 114 pathologically proven lung adenocarcinomas presenting as subsolid nodules on Computed Tomography (CT). Nodules were classified into group 1 (G1), which included atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinomas, and group 2 (G2), which included invasive adenocarcinomas. Our approach includes a three-way Integration of Visual, Spatial, and Temporal features with Attention, referred to as I-VISTA, obtained from three processing algorithms designed based on Deep Learning (DL) and radiomic models, leading to a more comprehensive analysis of nodule variations. The aforementioned processing algorithms are arranged in the following three parallel paths: (i) The Shifted Window (SWin) Transformer path, which is a hierarchical vision Transformer that extracts nodules’ related spatial features; (ii) The Convolutional Auto-Encoder (CAE) Transformer path, which captures informative features related to inter-slice relations via a modified Transformer encoder architecture; and (iii) a 3D Radiomic-based path that collects quantitative features based on texture analysis of each nodule. Extracted feature sets are then passed through the Criss-Cross attention fusion module to discover the most informative feature patterns and classify nodules type. The experiments were evaluated based on a ten-fold cross-validation scheme. I-VISTA framework achieved the best performance of overall accuracy, sensitivity, and specificity (mean ± std) of 93.93 ± 6.80%, 92.66 ± 9.04%, and 94.99 ± 7.63% with an Area under the ROC Curve (AUC) of 0.93 ± 0.08 for lung nodule classification among ten folds. The hybrid framework integrating DL and hand-crafted 3D Radiomic model outperformed the standalone DL and hand-crafted 3D Radiomic model in differentiating G1 from G2 subsolid nodules identified on CT. Full article

Accurate thermal modeling of Terminal Test Masses (TTMs) is crucial for optimizing the sensitivity of gravitational wave interferometers like Virgo. In fact, in such gravitational wave detectors even minimal laser power absorption can induce performance-limiting thermal effects. This paper presents a detailed investigation into the steady-state thermal behavior of TTMs. In particular, future scenarios of increased intracavity laser beam power and optical coating absorption are considered. We develop and compare two numerical models: a comprehensive model incorporating volumetric heat absorption in both the multilayer coating and the bulk substrate, and a simplified reduced model where the coating’s thermal impact is represented as an effective surface boundary condition on the substrate. Our simulations were focused on a ternary coating design, which is a candidate for use in next-generation detectors. Results reveal that higher coating absorption localizes peak temperatures near the coating–vacuum interface. Importantly, the comparative analysis demonstrates that temperature predictions from the reduced model differ from the detailed model by only milli-Kelvins, a discrepancy often within the experimental uncertainties of the system’s thermo-physical parameters. This finding suggests that computationally efficient reduced models can provide sufficiently accurate results for thermal management and first-order distortion analyses. Moreover, the critical role of accurately characterizing the total power absorbed by the coating is emphasized. Full article

Foodborne pathogens remain a significant public health concern, necessitating the development of rapid, sensitive, and reliable detection methods for various food matrices. Traditional biosensors, while effective in many contexts, often face limitations related to complex sample environments, signal interpretation, and on-site usability. The integration of artificial intelligence (AI) into biosensing platforms offers a transformative approach to address these challenges. This review critically examines recent advancements in AI-assisted biosensors for detecting foodborne pathogens in various food samples, including meat, dairy products, fresh produce, and ready-to-eat foods. Emphasis is placed on the application of machine learning and deep learning to improve biosensor accuracy, reduce detection time, and automate data interpretation. AI models have demonstrated capabilities in enhancing sensitivity, minimizing false results, and enabling real-time, on-site analysis through innovative interfaces. Additionally, the review highlights the types of biosensing mechanisms employed, such as electrochemical, optical, and piezoelectric, and how AI optimizes their performance. While these developments show promising outcomes, challenges remain in terms of data quality, algorithm transparency, and regulatory acceptance. The future integration of standardized datasets, explainable AI models, and robust validation protocols will be essential to fully harness the potential of AI-enhanced biosensors for next-generation food safety monitoring. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

Newborn screening (NBS) has evolved significantly since its inception, yet many treatable rare diseases remain unscreened due to technical limitations. The BabyDetect study used gene panel sequencing to expand NBS to treatable conditions not covered by conventional biochemical screening. We present here the analytical validation of this workflow, assessing sensitivity, precision, and reproducibility using dried blood spots from newborns. We implemented strict quality control thresholds for sequencing, coverage, and contamination, ensuring high reliability. Longitudinal monitoring confirmed consistent performance across more than 5900 samples. Automation of DNA extraction improved scalability, and a panel redesign enhanced the coverage and selection of targeted regions. By focusing on known pathogenic/likely pathogenic variants, we minimized false positives and maintained clinical actionability. Our findings demonstrate that gene panel sequencing-based NBS is feasible, accurate, and scalable, addressing critical gaps in current screening programs. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) poses a significant challenge, costing annually approximately USD 1.2 billion to the U.S. swine industry due to production losses associated with, but not limited to, reproductive failure, abortion, and high pre-weaning mortality among piglets. PRRSV is endemic, with thirty percent of the U.S. breeding herd experiencing outbreaks annually. The shedding status of animals on a farm is typically assessed using serum or processing fluids from piglets, but tongue tips from deceased animals are emerging as a potential alternative specimen to support farm stability assessment. This study explored the impact of various processing and testing strategies on tongue tips to enhance the sensitivity and specificity of PRRSV detection in sow herds. We collected tongue tips from 20 dead piglets across seven sow farms, testing different pooling strategies (individual testing, and pools of n = 5 or n = 20) and laboratory processing methods (tongue tip fluid—TTF, versus tongue tissue homogenate—TTH). Additionally, we simulated storage and shipping conditions, comparing frozen samples to refrigerated ones tested at intervals of 1, 4, and 7 days post collection. RT-PCR testing revealed higher sensitivity and lower cycle threshold (Ct) values for TTF compared to TTH, suggesting that tongue tips are better tested as TTF rather than TTH for PRRSV detection. Pooling samples reduced diagnostic accuracy. Frozen samples had lower absolute Ct values, and Ct values increased by 0.2 Ct values each day post collection when the sample was kept refrigerated, emphasizing the importance of minimizing shipping delays. Tongue tips are a practical, easy-to-collect specimen that target potentially infected animals (dead piglets), offering valuable insights into swine herd health, but sample processing approaches significantly influence diagnostic outcomes. If tongue tips are used by veterinarians to assess viral presence on a farm, testing the TTF instead of TTH should be prioritized. Storage and shipment conditions should be considered to optimize laboratory results. Full article

Objectives: Antimicrobial resistance is a major global health threat. This study aimed to assess the impact of antimicrobial-resistant bacterial pneumonia on in-hospital mortality and length of hospital stay in Spain using a large, nationally representative cohort. Methods: A retrospective cohort study that used data from Spain’s Registry of Specialized Health Care Activity (RAE-CMBD) between 2017 and 2022. Hospitalized adults with bacterial pneumonia were included. Hospitalization episodes with bacterial antimicrobial resistance, defined according to ICD-10-CM codes for antimicrobial resistance (Z16.1, Z16.2), were analyzed versus hospitalization episodes without these codes. Multivariate logistic regression models, adjusted for potential confounders (e.g., age, comorbidity, intensive care unit admission) and sensitivity analyses (Poisson regression and propensity score matching test), were performed. Results: Of the 116,901 eligible hospitalizations, 6017 (5.15%) involved antimicrobial-resistant bacteria. Patients with antimicrobial-resistant bacterial pneumonia were older (median 75 vs. 72 years), had greater comorbidity (Elixhauser–van Walraven index: 8 vs. 5), and were more frequently admitted to the intensive care unit (22% vs. 14%). Crude in-hospital mortality was higher in the antimicrobial resistance group (18.46% vs. 10.05%, p < 0.0001), with an adjusted odds ratio of 1.47 (95% confidence interval, 1.36–1.58), p < 0.0001. Length of hospital stay was prolonged in antimicrobial resistance patients (median 14 vs. 8 days; adjusted incident rate ratio of 1.46; 95% confidence interval of 1.41 to 1.50). The most prevalent antimicrobial resistant pathogens were Staphylococcus aureus and Gram-negative bacilli (Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli). Conclusions: Antimicrobial resistance is associated with longer hospital stays and an up to 50% higher risk of mortality. Despite the implementation of control policies in place over the past decade, policymakers must strengthen AMR surveillance and ensure adequate resource allocation. Clinicians, in turn, must reinforce antimicrobial stewardship and incorporate rapid diagnostic tools to minimize the impact of antimicrobial resistance on patient outcomes. Full article

Rare earth elements (REEs) make up integral components in personal electronics, healthcare instrumentation, and modern energy technologies. REE leaching with organic acids is an environmentally friendly alternative to traditional extraction methods. Our previous study demonstrated that batch ultrasound-assisted organic acid leaching of REEs can significantly decrease environmental impacts compared to traditional bioleaching. The batch method is limited to small volumes and is unsuitable for industrial implementation. This study proposes a novel approach to increase reaction volume using a continuous ultrasound-assisted organic acid leaching method. Laboratory experiments showed that continuous ultrasound-assisted leaching increased the leaching rate (µg/h) 11.3–24.5 times compared to our previously reported batch method. Techno-economic analysis estimates the cost of the continuous approach using commercially purchased organic acids is $9465/kg of extracted REEs and $4325/kg of extracted REEs, using gluconic acid and citric acid, respectively. The sensitivity analysis reveals that substituting commercially purchased organic acids with microbially produced biolixiviant can reduce the process cost by approximately 99% while minimally increasing energy consumption. Environmental assessment shows that most of the emissions stemmed from the energy required to power the ultrasound reactor. We concluded that increased leaching capacity using a continuous ultrasound-assisted approach is feasible, but process modifications are needed to reduce the environmental impact. Full article

Background: The accuracy and consistency of complete-arch digital impressions are fundamental for long-term success of implant-supported rehabilitations. Recently, artificial intelligence (AI)-assisted tools, such as SmartX (Medit Link v3.4.2, MEDIT Corp., Seoul, South of Korea), have been introduced to enhance scan body recognition and data alignment during intraoral scanning. Objective: This in vitro study aimed to evaluate the impact of SmartX on impression accuracy, consistency, operator confidence, and technique sensitivity in complete-arch implant workflows. Methods: Seventy-two digital impressions were recorded on edentulous mandibular models with four dummy implants, using six experimental subgroups based on scan body design (double- or single-wing), scanning technique (occlusal or combined straight/zigzag), and presence/absence of SmartX tool. Each group was scanned by both an expert and a novice operator (n = 6 scans per subgroup). Root mean square (RMS) deviation and scanning time were assessed. Data were tested for normality (Shapiro–Wilk). Parametric tests (t-test, repeated measures ANOVA with Greenhouse–Geisser correction) or non-parametric equivalents (Mann–Whitney U, Friedman) were applied as appropriate. Post hoc comparisons used Tukey HSD or Dunn–Bonferroni tests (α = 0.05). Results: SmartX significantly improved consistency and operator confidence, especially among novices, although it did not yield statistically significant differences in scan accuracy (p > 0.05). The tool mitigated early scanning errors and reduced dependence on operator technique. SmartX also enabled successful library alignment with minimal data; however, scanning time was generally longer with its use, particularly for beginners. Conclusions: While SmartX did not directly enhance trueness, it substantially improved scan reliability and user experience in complete-arch workflows. Its ability to minimize technique sensitivity and improve reproducibility makes it a valuable aid in both training and clinical settings. Further clinical validation is warranted to support its integration into routine practice. Full article

Ammonia–coal co-combustion has emerged as a promising strategy for reducing carbon emissions from coal utilization, although its underlying reaction mechanisms remain insufficiently understood. The Chemkin simulation of zero-dimensional homogeneous reaction model and entrained flow reaction model was employed here, and the ROP (rate of production) and sensitivity analysis was performed for analyzing in-depth reaction mechanisms. The nitrogen conversion pathways were revealed, and the mechanisms were simplified. Based on simplified mechanisms, molecular-level reaction pathways and thermochemical conversion networks of nitrogen-containing precursors were established. The results indicate that NO emissions peak at a 30% co-firing ratio, while N₂O formation increases steadily. The NH radical facilitates NO reduction to N₂O, with NH + NO → N₂O + H identified as the dominant pathway. Enhancing NNH formation and suppressing NCO intermediates are key to improving nitrogen conversion to N₂. This paper quantifies the correlation between NO_x precursors such as HCN and NH₃ and intermediates such as NCO and NNH during ammonia–coal co-firing and emphasizes the important role of N₂O. These insights offer a molecular-level foundation for designing advanced ammonia–coal co-combustion systems aimed at minimizing NO_x emissions. Full article

The dynamic and time-sensitive nature of online food delivery, along with real-world factors like sudden changes in order volumes and the availability of couriers, distinguishes it from traditional vehicle routing scenarios. Apart from the many studies in the literature that handle this problem from specific angles, our solution proposes a new approach that provides real-time routing with the awareness of the expectations of multiple stakeholders in the ecosystem. For this purpose, we develop a Mixed Integer Programming (MIP) model that minimizes unmet demand and workforce requirements simultaneously to meet platform and courier expectations while maintaining the timeliness of the operation to meet restaurant and customer expectations. Since the model requires more time to provide good results for even small-size problems, we develop a multi-step algorithmic approach supported by strategies that hold or dissolve a part of the solutions to create opportunities for better results. A framework for agent-based simulation was created to implement the strategies and the algorithmic steps, accurately mimicking the operations and movements of couriers. The effectiveness of this solution was evaluated through experiments based on a real-world case study. The results indicate that our solution can generate high-quality results in a short time across various configurations, which are defined by different demand and supply patterns and varying problem sizes. Full article

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations. Full article

Background: Microwave ablation (MWA) is an effective, minimally invasive therapy for benign thyroid nodules; however, the treatment response varies considerably. Identifying imaging biomarkers that can predict volumetric outcomes may optimize patient selection. Diffusion-weighted MRI (DW-MRI) offers a noninvasive assessment of tissue microstructure through apparent diffusion coefficient (ADC) measurements, which may correlate with ablation efficacy. Methods: In this prospective study, 48 patients with 50 cytologically confirmed benign thyroid nodules underwent diffusion-weighted magnetic resonance imaging (DW-MRI) before minimally invasive ablation (MWA). Baseline ADC values were measured, and nodule volumes were assessed by ultrasound at baseline and 1, 3, and 6 months postprocedure. The volume reduction ratio (VRR) was calculated, and associations with baseline variables were analyzed via Pearson correlation and multivariable linear regression. ROC curve analysis was used to evaluate the diagnostic performance of ADC in predicting significant volume reduction (VRR ≥ 50%). Results: Lower baseline ADC values were strongly correlated with greater VRR at 3 months (r = −0.525, p < 0.001) and 6 months (r = −0.564, p < 0.001). Multivariable regression revealed that the baseline ADC was the sole independent predictor of the 6-month VRR (β = −19.52, p = 0.0004). ROC analysis demonstrated excellent discriminative performance (AUC = 0.915; 95% CI: 0.847–0.971), with an ADC cutoff of 2.20 × 10⁻³ mm²/s yielding 90.9% sensitivity and 83.3% specificity for predicting a favorable volumetric response. Conclusions: Baseline ADC values derived from DW-MRI strongly predict volumetric response following microwave ablation of benign thyroid nodules. Incorporating ADC assessment into preprocedural evaluation may enhance patient selection and improve therapeutic outcomes. Full article