Search Results (9,130)

This article develops an analytic hierarchy process (AHP)-assisted model-reduction approach for a higher-order airport-centric microgrid (ACM). The full-order ACM model is approximated by a computationally simpler lower-order transfer function while retaining its essential steady-state and transient characteristics. The model approximation helps in analysis and controller design. For the same, a multi-objective fitness function is formulated from selected time moments and Markov parameters of the original and reduced models. AHP is used to determine the relative importance of the individual matching objectives through pairwise comparisons and normalized priority weights. The resulting weighted optimization problem is solved using the brown-bear optimization algorithm (BBOA) to estimate coefficients of the reduced model. During optimization, exact matching of steady-state gain is imposed to eliminate steady-state mismatch, while Hurwitz stability conditions ensure that the reduced model remains stable. Effectiveness of the proposed approach is evaluated using step and impulse responses, frequency-domain characteristics, time-domain specifications, and integral error indices. The results show that the AHP–BBOA-based reduced model closely reproduces the dominant dynamics of the higher-order ACM while offering a compact representation suitable for controller design, simulation, and real-time frequency-response studies. Full article

Surface electromyography (sEMG) signals acquired with low-cost sensors tend to exhibit variable degradation that can compromise the reliability of myoelectric control systems outside controlled conditions. This work presents an adaptive processing pipeline for sEMG signals composed of three chained stages: a motor-intent classifier based on dilated temporal convolutional networks, whose function is to determine whether a signal window contains muscle activity associated with a voluntary gesture; a dual-output quality assessor that estimates a continuous score and a binary acceptability label, aimed at deciding whether the signal can be used directly or requires intervention; and a convolutional autoencoder that recovers the morphology of degraded windows before they are used in prosthetic control. The decision policy for reconstruction operates on two independent thresholds and classifies each window into one of three states: signal discard, direct acceptance, or active restoration. The models within the proposed architecture are trained on the public NinaPro DB1, DB3, and DB10 datasets and validated without recalibration on signals recorded from two participants with transradial amputation over three to four weekly sessions. The results show that the pipeline correctly handles signal profiles with opposing characteristics. Inference latency remained below 5 ms at the 50th percentile across all scenarios, consistent with real-time operation. All inference was executed on a host computer; a prototype using an Arduino UNO R4 WiFi as a peripheral interface was built to display the pipeline’s decisions on physical hardware and to confirm that the serial-communication link does not introduce additional latency, not to perform on-board inference. A downstream evaluation further showed that, while restoration improved signal-level fidelity metrics, it did not translate into improved motor-intent classification accuracy relative to using the degraded signal directly, a limitation discussed explicitly in this work. Full article

Traditional weight measurement methods need cutting or weighing of the fruit, and this is not practical for preharvest evaluation and market transactions. The suggested approach gives a practical solution for farmers and sellers by giving accurate weight predictions using only external characteristics. Modern technologies, especially artificial intelligence, data analytics and machine learning, are making big changes in the agricultural area. One of the machine learning models used in agriculture is Artificial Neural Networks (ANN). ANN became an important tool in analyzing agricultural data, predicting plant growth processes, finding diseases, determining the effects of environmental factors, reaching productivity goals and detecting weeds and harmful plants. A dataset is created by comprehensively examining 52 watermelons. The dataset includes acoustic properties, geometric measurements, and visual characteristics. The dataset is divided into 40 training samples and 12 test samples. Balanced representation is ensured by using stratified sampling when selecting test samples. The Min-Max normalization method is used for data preprocessing. Nine different training algorithms are comprehensively evaluated within the scope of the study. Eight critical parameters of the ANN models are optimized using four different optimization algorithms to increase the accuracy rate and avoid overfitting. Artificial Bee Colony (ABC), Artificial Fish Swarm Algorithm (AFSA), Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are used as optimization methods. Assessed by five-fold cross-validation, the best configuration (One Step Secant with WOA) achieved a mean absolute error of 0.92 ± 0.28 kg and an RMSE of 1.23 ± 0.37 kg, corresponding to about 10% of the mean fruit weight. Developing a real-time mobile application using the optimized best model will provide practicality in large-scale agricultural enterprises, controlled environments such as greenhouses, and agricultural markets. Full article

A key challenge in port–road logistics is the need to align operational priorities with efficient and sustainable freight transportation decisions. This study develops an Analytic Hierarchy Process (AHP)-based decision-support system for the Multi-Objective Electric Vehicle Routing Problem (MOEVRP) in port–road logistics. Initially, the AHP method is used to determine the relative importance of cost-efficient route planning, vehicle and port management, environmental impact management, energy efficiency, and operational efficiency and well-being based on expert judgment. Next, the resulting priority weights are then used to inform the decision-making framework for the MOEVRP, which determines routing decisions by minimizing total cost, carbon emissions, and maximum vehicle working time while accounting for electric vehicle constraints and charging behavior. This issue is critical in rapidly developing industrial corridors such as Thailand’s Eastern Economic Corridor, where growing freight demand, energy constraints, and environmental pressures must be managed simultaneously. By linking port–road operational priorities with the routing model, the proposed framework provides a structured approach for evaluating trade-offs between economic, environmental, and operational considerations during the transition toward low-carbon freight transportation. A case study in Chonburi–Rayong provinces demonstrates the applicability of the integrated system in a real-world maritime–land logistics corridor. The findings contribute to the design of more sustainable supply chain systems that support renewable and decarbonized logistics. Full article

Background/Objectives: Reliable molecular testing is essential for selecting targeted therapies for thyroid tumors; however, assay performance can be affected by pre-analytical tissue conditions. This retrospective, single-center study evaluated real-world formalin-fixed, paraffin-embedded (FFPE) specimens to identify pathology practices associated with successful companion diagnostic testing using the next-generation sequencing (NGS)-based Oncomine Dx assay and the polymerase chain reaction (PCR)-based BRAF3 assay widely used in routine clinical care in Japan. Methods: We retrospectively analyzed 177 cases with thyroid tumors tested at a high-volume thyroid center in Japan between 2022 and 2026. Results: All 79 specimens analyzed using the BRAF3 assay yielded successful test results, including specimens fixed in unbuffered formalin and archival blocks stored for up to 27 years. In contrast, the Oncomine Dx assay was successful in 93 of 98 specimens (94.9%), with all five failures occurring in specimens fixed in unbuffered formalin. DNA failure was more common than RNA failure. All five Oncomine Dx failures occurred in archived tissue blocks stored for 3–9 years, all of which had been fixed in unbuffered formalin. The BRAF3 test had a shorter turnaround time and was more frequently used in rapidly progressive tumors, such as anaplastic thyroid carcinoma. Conclusions: The NGS-based Oncomine Dx assay showed reduced success in samples stored long-term or in unbuffered specimens, whereas RNA preservation was high. In contrast, the PCR-based BRAF3 assay remained highly reliable even in long-stored archival blocks. Because fixative type and storage duration were confounded in this cohort, the independent effects of these variables could not be determined. The study’s findings provide practical real-world insights into how specimen handling, fixation, and storage conditions may influence the reliability of genomic diagnostics in routine practice. Full article

The efficient use of existing road infrastructure has become increasingly important in densely developed urban areas where large-scale roadway expansion is constrained. Variable directional lanes (VDLs) and traffic signal control can reallocate roadway capacity and improve network performance. However, most existing studies optimize lane configurations and signal timing under a fixed route-flow distribution and therefore do not capture the feedback between control decisions and travelers’ route choices. To address this limitation, this study proposes a bi-level framework for coordinating VDLs and traffic signals at multiple intersections. The upper-level model determines the VDL functions and signal-control parameters to minimize total system travel time, while the lower-level static Logit-based stochastic user equilibrium model endogenously redistributes fixed origin–destination (OD) demand among candidate paths. Thus, OD demand remains fixed within each analysis period, whereas the route-flow distribution responds endogenously to the interaction between traffic control and aggregate route-choice responses. A hybrid solution procedure combining the Non-dominated Sorting Genetic Algorithm II and the Method of Successive Averages is used to solve the coupled control–assignment problem. Numerical experiments on a hypothetical network showed that incorporating route-choice feedback improved coordinated VDL–signal control under the tested conditions. In a supplementary comparison with the pre-optimization BPR-based reference scenario, the average route travel time decreased by 7.67–12.84% across the five representative demand periods, including reductions of 12.52% and 12.84% during the morning and evening peak periods, respectively. Microscopic simulation provided an additional numerical consistency check, with average discrepancies of 4.66% before optimization and 4.38% after optimization between the analytical and simulation results. These findings indicate that incorporating aggregate route-choice feedback can support sustainable urban traffic management by reducing travel time and congestion and improving the utilization of existing transportation infrastructure. However, further validation using real-world data and larger-scale networks is required, and environmental benefits should be evaluated explicitly using energy-consumption and emission indicators. Full article

Enzymatic recycling offers a promising route for recovering fibres from textile waste; however, processes developed predominantly on single-fabric model substrates have rarely been validated using authentic post-consumer garments. In this study, 110 post-consumer children’s garments (15.6 kg) were collected and characterised, revealing a fibre composition of 84.0% cotton, 12.8% polyester, and 3.2% other fibres. Cellulases produced by Aspergillus niger and Trichoderma reesei were first evaluated using a bead-assisted hydrolysis approach. Although efficient textile disintegration was achieved for an in-house woven polycotton fabric (84% cotton, 16% polyester), resulting in separation yields of up to 92%, the same process performed poorly on post-consumer cotton garments, with more than 78% of the textile structure remaining intact after treatment. These results identified substrate accessibility as a major limitation to the enzymatic processing of real textile waste. To address this challenge, an optimised mechano-enzymatic process incorporating intermittent grinding during hydrolysis was developed. A grinding duration of 4 min combined with 4 h of hydrolysis produced the highest separation yield, and the integrated treatment consistently outperformed grinding alone by 12–14% across all hydrolysis times investigated. The optimised process was subsequently validated using twenty post-consumer garments comprising ten cotton-rich textiles (98–100% cotton) and ten cotton–polyester blends containing 35–85% cotton. Separation yields of 91.7–99.7% (mean 97.1%) and recoverable fibre yields of 85.6–97.4% (mean 90.7%) were achieved across all garments. ATR-FTIR spectroscopy, optical microscopy, and thermogravimetric analysis of model cotton and polycotton fabrics before and after treatment demonstrated selective hydrolysis of cellulose while preserving polyester fibres. Cellulose-associated FTIR bands decreased by 10–98%, whereas characteristic polyester ester and aromatic bands remained unchanged. Microscopy further confirmed the physical separation of intact polyester filaments from liberated cotton fibres. Overall, the results demonstrate that substrate accessibility is a critical barrier to enzymatic textile recycling and show that intermittent grinding substantially enhances fibre liberation from post-consumer textiles. The recovered fibre fractions represent a promising feedstock for textile recycling; however, detailed assessment of compositional purity and fibre quality is required to determine their suitability for closed-loop applications. Full article

The increasing adoption of sustainable production practices in the textile industry has highlighted the need to evaluate recycled raw materials from economic, environmental, and operational perspectives. This study develops an optimization-based decision-support framework to determine the preferred recycled cotton ratio in sock manufacturing by separately minimizing production cost and carbon emissions under operational constraints. The framework was developed using real industrial data from a single sock manufacturer, with the recycled cotton ratio (r) defined as the decision variable and wastage rate, defect rate, and production time incorporated as production-performance constraints. The model was implemented in Python 3.13.15 and solved using the Sequential Least Squares Programming (SLSQP) algorithm, with the solutions independently assessed through grid-based global verification and Pareto dominance analysis. Sensitivity, production constraint, raw-material price, electricity price, and break-even analyses were conducted to evaluate the stability of the preferred solution under different production and economic conditions. Under the baseline conditions, r = 1 was the preferred solution for both objectives, reducing production cost by 18.7% and carbon emissions by 34.9% compared with r = 0. The Pareto assessment identified r = 1 as the only non-dominated solution under the baseline parameterization. Although the preferred solution remained stable across the examined sensitivity and price scenarios, the break-even analysis showed that the economic preference shifted from r = 1 to r = 0 when the recycled-to-virgin cotton price ratio approached approximately 0.98. Overall, the proposed framework provides a structured decision-support approach for evaluating recycled cotton utilization by integrating economic, environmental, and operational considerations while identifying the conditions under which the preferred solution remains stable or changes. Full article

Background: Extracellular DNA (extracellular DNA) is a promising biomarker for tumor burden and systemic inflammation. However, its clinical significance in urothelial carcinoma remains unclear. This study evaluated plasma extracellular DNA (nuclear and mitochondrial fractions) and DNase activity in patients with bladder cancer (BC) in comparison to healthy controls, and explored their associations with tumor stage, survival, and heart rate variability (HRV) as a non-invasive marker of autonomic regulation. Methods: We prospectively analyzed 84 subjects: 63 patients with urothelial carcinoma (52 non-muscle-invasive [NMIBC]; 11 muscle-invasive [MIBC]) and 21 healthy controls. Plasma extracellular DNA was quantified fluorometrically, while ncDNA and mtDNA fractions were assessed via quantitative real-time PCR. DNase activity was determined using the single radial enzyme diffusion assay. Clinical associations were evaluated through group comparisons, multivariable logistic regression, survival analysis, and correlation with HRV parameters. Results: Patients with BC exhibited slightly but significantly higher extracellular DNA compared to healthy controls (by 9%); ncDNA showed a similar trend. Regarding disease progression, ncDNA demonstrated a stronger association with tumor stage than total extracellular DNA, with the highest concentrations observed in patients with MIBC. In multivariable logistic regression models adjusted for age, BMI, sex, smoking, and alcohol consumption, extracellular DNA remained independently associated with BC status (OR 5.99, 95% CI 1.43–25.12, p = 0.015). Log-transformed ncDNA was also associated with BC status, although the model was constrained by missing data. No significant differences in overall survival were observed when stratified by cutoff values of extracellular DNA, ncDNA, mtDNA, or DNase activity. Notably, while extracellular DNA correlated significantly with several HRV parameters in healthy individuals (after FDR correction), this physiological coupling was absent in patients with BC. Conclusions: Circulating extracellular DNA and ncDNA are independently associated with BC, with ncDNA showing a stronger correlation with tumor stage. While prognostic value regarding survival was not confirmed in this cohort, the absence of extracellular DNA–HRV correlations in patients, despite significant associations in healthy controls, suggests the loss of physiological coupling between circulating DNA and autonomic regulation. Further longitudinal studies are needed to validate the clinical utility and biological interpretation of these markers. Full article

Regenerative chatter severely limits productivity in machining operations, and tuned mass dampers (TMDs) are widely used for passive chatter suppression. However, most existing TMD designs neglect workpiece dynamics and rely on two-degree-of-freedom assumptions, leading to suboptimal performance when tool and workpiece dynamics are comparable. This paper presents a three-degree-of-freedom analytical stability model incorporating the coupled dynamics of the cutting tool, workpiece, and TMD. Stability lobes are derived in the frequency domain, and a max–min optimization strategy is proposed to determine optimal TMD parameters across varying workpiece dynamic conditions. The results indicate that variation in workpiece dynamics significantly hinders the improvement in machining stability achieved by the TMD, and its effectiveness is drastically affected when the cutting tool and workpiece have similar dynamic characteristics. To enhance TMD effectiveness in changing workpiece dynamic conditions, tuning parameters should be optimized to reflect these variations. The proposed analytical and optimization framework may provide a basis for future adaptive chatter-control systems that identify changes in tool–workpiece dynamics online and use them to determine appropriate absorber tuning parameters. However, the present study is limited to offline optimization of a passive TMD and does not implement real-time parameter adaptation. Experimental validation using an additively manufactured TMD demonstrates a clear modification of the fundamental dynamic behaviour, wherein the original single resonance is split into two distinct natural frequencies. Though the extent of this frequency separation is marginal, a significant reduction in peak amplitude is observed: 91% in the low-stiffness workpiece and 69.8% in the high-stiffness workpiece, indicating stronger interaction between the absorber and the machining system under compliant conditions. Full article

The rumen is a complex microbial ecosystem in which anaerobic fermentation determines the availability of energy and protein to ruminant hosts and influences feed efficiency, animal productivity, and environmental emissions. This review integrates current knowledge of host–rumen microbiome interactions with particular emphasis on microbial fermentation, nutrient utilization, feed efficiency, methane (CH₄) production, and sustainable ruminant production. We examine how dietary factors, including forage-to-concentrate ratio, carbohydrate fermentability, protein degradability, and lipid supplementation, alter microbial community structure, hydrogen metabolism, volatile fatty acid production, microbial protein synthesis, and nitrogen utilization. The review further evaluates microbiome-targeted strategies, including 3-nitrooxypropanol, red seaweeds (Asparagopsis spp.), nitrate, direct-fed microbials, and plant-derived bioactive compounds, with emphasis on their effects on fermentation pathways and methanogenesis; these strategies differ substantially in evidence base and mechanistic specificity, with 3-nitrooxypropanol supported by the most consistent mechanistic and in vivo evidence and several plant-derived compounds and direct-fed microbials showing more variable responses. Evidence from microbiome-wide and genome-wide association studies indicates that host genetics contributes to variation in rumen microbial composition and function, with potential consequences for feed efficiency and CH₄ emissions. Host-side determinants of the rumen environment, such as feed intake, digesta passage rate, saliva production, and epithelial and immune function, further shape microbial responses. However, responses to microbiome-targeted interventions remain variable because of microbial functional redundancy, dietary context, adaptation, and host-specific effects. We therefore discuss the major constraints limiting the consistent translation of microbiome research into practical feeding strategies and propose an integrated framework combining functional microbiome indicators, precision nutrition, host–microbiome-informed selection, and real-time monitoring. Understanding and manipulating rumen microbial fermentation through coordinated nutritional and host-based approaches may provide a pathway toward improving ruminant productivity while reducing the environmental footprint of livestock production. Among the strategies reviewed, 3-nitrooxypropanol currently has the strongest and most reproducible evidence base, whereas plant-derived bioactives and several direct-fed microbials remain promising but inconsistent, and validated on-farm microbiome biomarkers remain a major gap. Full article

Data on the prevalence of bacteria and antimicrobial resistance (AMR) among food-producing animals in Kazakhstan remain limited in scope. As part of this regional cross-sectional descriptive study, specific bacterial taxa and AMR were characterized in farm animals in the West Kazakhstan Region. Between September 2025 and July 2026, samples were collected from 974 individual animals at 48 farms located in 12 administrative districts. Targeted bacteriological isolation was performed for Escherichia coli, presumptive Shigella spp., Salmonella spp., Yersinia enterocolitica, Campylobacter spp., and Staphylococcus aureus, followed by specific real-time PCR. Antimicrobial susceptibility was assessed in 214 E. coli isolates using the EUCAST disk diffusion method, and five selected isolates underwent exploratory screening by quantitative PCR (qPCR) for the presence of determinants associated with antimicrobial resistance. A total of 311 bacterial isolates were identified. The predominant taxon was E. coli, detected in 22.0% of the animals from which samples were collected and accounting for 68.8% of the isolates; followed by S. aureus (3.2% of animals), presumptive Shigella spp. (2.9%), Salmonella spp. (1.5%), Campylobacter spp. (1.5%), and Y. enterocolitica (0.8%). Corresponding molecular targets were detected in 303 of 311 presumptively identified isolates (97.4%). No clear age-related gradient in the prevalence of E. coli was observed, and this taxon was detected in all three study areas. Phenotypic resistance to at least one antimicrobial agent with an applicable clinical breakpoint according to EUCAST was detected in 2 of 214 E. coli isolates (0.9%), including resistance to ceftriaxone or amoxicillin with clavulanic acid. Molecular screening of five selected isolates revealed various determinants associated with antimicrobial resistance and mobile genetic elements, with blaCMY, mphA, qepA_1_2, catA1, and intI1F165_clinical being the most frequently detected. These results establish a regional baseline for comprehensive monitoring of bacteria associated with animal husbandry and antimicrobial resistance in Western Kazakhstan. Full article

Large-scale public events generate concentrated passenger demand during egress periods, often overwhelming urban transit systems. This paper proposes a multi-modal evacuation framework that coordinates in-service buses temporarily diverted from existing lines and dedicated shuttle vehicles pre-positioned at depots. The problem is formulated as a two-layer stochastic optimization under travel time uncertainty: the upper layer determines pre-event shuttle fleet sizing, while the lower layer makes real-time dispatching decisions for both modes. We propose an Uncertainty-Aware Reinforcement Learning framework with Categorical DQN (UARL-CD) that learns a robust dispatching policy through a reward function aligned with the lower-level objective, explicitly accounting for travel time uncertainty via distributional value representation and stochastic training, with an action masking mechanism enforcing operational constraints. Simulation experiments based on a realistic stadium evacuation scenario demonstrate that the proposed framework significantly outperforms deterministic optimization and rule-based strategies, achieving a 31.6% reduction in evacuation completion time and a 48.4% reduction in average passenger waiting time compared to shuttles alone, while maintaining robustness to travel time uncertainty with only 4.0% performance degradation and online decisions executed within the 2-min decision interval. Full article

Bactrocera dorsalis (Hendel) is a globally significant quarantine pest, and broflanilide represents a new insecticide with potential for its management. However, the molecular responses of B. dorsalis to broflanilide, particularly the involvement of cytochrome P450 genes, remain poorly understood. To address this gap, we compared the midgut and fat body transcriptomes of adults that survived 24 h of exposure to the median lethal concentration (LC₅₀) of broflanilide with those of corresponding controls. Candidate P450 genes were further examined by quantitative real-time PCR (qRT-PCR), RNA interference (RNAi), and molecular docking. RNA-seq identified strong upregulation of CYP6G8 (log₂FC = 11.84 and 6.32) and CYP6GX5 (log₂FC = 6.48 and 7.83) in the broflanilide-exposed surviving group. qRT-PCR showed that CYP6G8 was significantly upregulated in both tissues, whereas CYP6GX5 was significantly upregulated in the fat body but showed a nonsignificant upward trend in the midgut. Silencing CYP6G8 increased mortality from 43.21% to 64.14%, and CYP6GX5 silencing increased mortality from 31.11% to 87.07% following broflanilide exposure. Docking analyses predicted possible interactions between both proteins and desmethyl-broflanilide (DM-8007), the active metabolite of broflanilide. These results identify CYP6G8 and CYP6GX5 as candidate P450 genes associated with broflanilide tolerance under the tested conditions, although their direct biochemical roles remain to be determined. Full article

Rapid identification of radioactive cesium (Cs) aerosols is important for nuclear severe-accident monitoring. However, conventional analytical methods often require extended measurement times or complex instrumentation. Here, we present a sensitive and compact sensing approach that integrates laser-induced plasma RGB imaging with a convolutional neural network (CNN). A detection configuration was established in which laser irradiation generated plasma from Cs-containing aerosols within a flowing gas stream, and the resulting emission was directly captured using a CMOS camera. Rather than resolving individual emission lines, the CNN learned subtle Cs-dependent variations in the spatial and RGB intensity distributions of the plasma images. To simplify training under limited-data conditions, the model was designed to address a binary classification task, distinguishing Cs-negative conditions (normal) from Cs-positive conditions (abnormal). Predictions from 100 consecutive laser shots were aggregated to provide a sensing decision within 5 s. Using a criterion requiring Cs-positive classification in at least 99% of independent measurements, the operational limit of detection (LOD) was determined to be 0.03 μg/m³, approximately one order of magnitude lower than values reported for the closest comparable laser-based cesium aerosol measurements. These results demonstrate that plasma RGB imaging combined with a CNN algorithm can provide a compact, highly sensitive, and real-time platform for cesium aerosol monitoring. Full article