Search Results (236)

Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation. Full article

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between deaf individuals and the hearing population. However, achieving subject-independent SLR remains a significant challenge due to variations in signing styles, hand shapes, and movement patterns among users. Traditional Markov Chain-based models struggle with generalizing across different signers, often leading to reduced recognition accuracy and increased uncertainty. These limitations arise from the inability of conventional models to effectively capture diverse gesture dynamics while maintaining robustness to inter-user variability. To address these challenges, this study proposes an adaptive SLR framework that integrates Markov Chains with a Niching Genetic Algorithm (NGA). The NGA optimizes the transition probabilities and structural parameters of the Markov Chain model, enabling it to learn diverse signing patterns while avoiding premature convergence to suboptimal solutions. In the proposed SLR framework, GA is employed to determine the optimal transition probabilities for the Markov Chain components operating across multiple signing contexts. To enhance the diversity of the initial population and improve the model’s adaptability to signer variations, a niche model is integrated using a Context-Based Clearing (CBC) technique. This approach mitigates premature convergence by promoting genetic diversity, ensuring that the population maintains a wide range of potential solutions. By minimizing gene association within chromosomes, the CBC technique enhances the model’s ability to learn diverse gesture transitions and movement dynamics across different users. This optimization process enables the Markov Chain to better generalize subject-independent sign language recognition, leading to improved classification accuracy, robustness against signer variability, and reduced misclassification rates. Experimental evaluations demonstrate a significant improvement in recognition performance, reduced error rates, and enhanced generalization across unseen signers, validating the effectiveness of the proposed approach. Full article

South Africa’s transition to a renewable-powered grid faces critical challenges due to the inherent variability of wind and solar generation as well as the need for economically viable and reliable dispatch strategies. This study proposes a scenario-based stochastic optimization framework that integrates machine learning forecasting and uncertainty modeling to enhance operational decision making. A hybrid Long Short-Term Memory–XGBoost model is employed to forecast wind, photovoltaic (PV) power, concentrated solar power (CSP), and electricity demand, with Monte Carlo dropout and quantile regression used for uncertainty quantification. Scenarios are generated using appropriate probability distributions and are reduced via Temporal-Aware K-Means Scenario Reduction for tractability. A two-stage stochastic program then optimizes power dispatch under uncertainty, benchmarked against Deterministic, Rule-Based, and Perfect Information models. Simulation results over 7 days using five years of real-world South African energy data show that the stochastic model strikes a favorable balance between cost and reliability. It incurs a total system cost of ZAR 1.748 billion, with 1625 MWh of load shedding and 1283 MWh of curtailment, significantly outperforming the deterministic model (ZAR 1.763 billion; 3538 MWh load shedding; 59 MWh curtailment) and the rule-based model (ZAR 1.760 billion, 1.809 MWh load shedding; 1475 MWh curtailment). The proposed stochastic framework demonstrates strong potential for improving renewable integration, reducing system penalties, and enhancing grid resilience in the face of forecast uncertainty. Full article

The structure of the odd-A silver isotopes ${}^{103–115}\mathrm{Ag}$ is discussed within the frame of the interacting boson–fermion model (IBFM). An overview of their key properties is presented, with a particular attention paid to the “J-1 anomaly”, represented by an abnormal ordering of the lowest ${\mathrm{7/2}}^{+}$ and ${\mathrm{9/2}}^{+}$ states. By examining previously published data and newly performed calculations, it is demonstrated that the experimentally known level schemes and electromagnetic properties of ${}^{103–115}\mathrm{Ag}$ can be reproduced well within IBFM-1 by using a consistent set of model parameters. The contribution of different single-particle orbitals to the structure of the lowest-lying excited nuclear states in ${}^{103–115}\mathrm{Ag}$ is discussed. Given that the J-1 anomaly brings down the ${\mathrm{7/2}}^{+}$ level from the $j^{-3}$ multiplet to energies, which can be thermally populated in hot stellar environments, the importance of low-lying excited states in odd-A silver isotopes for astrophysical processes is outlined. Full article

Antipredator behaviors in animals often vary with developmental stage, microhabitat, and social context, yet few studies examine how these factors interact in species that undergo ontogenetic shifts in chemical defense. The spotted lanternfly (Lycorma delicatula) is an invasive planthopper whose nymphs transition from cryptically colored early instars to aposematically colored fourth instars that feed primarily on chemically defended host plants. We conducted 1460 simulated predator attacks on nymphs across four developmental stages to examine how antipredator behavior varies with instar, plant location (leaf vs. stem), host plant species, and local conspecific density. Nymphs exhibited three primary responses: hiding, sidestepping, or jumping. We found that location on the plant had the strongest effect, with nymphs on stems more likely to hide than those on leaves. Older instars were significantly less likely to hide and more likely to sidestep, particularly on stems, suggesting reduced reliance on energetically costly escape behaviors as chemical defenses accumulate. First instars were less likely to jump from their preferred host plant (tree of heaven) compared to other plant species. Higher local conspecific density reduced hiding probability, likely due to the dilution effect. These results demonstrate that antipredator strategies in L. delicatula are flexibly deployed based on developmental stage, microhabitat structure, and social context, with implications for understanding evolution of antipredator behavior in chemically protected species. Full article

This paper investigates point-to-point multimodal digital semantic communications in a task-oriented setup, where messages are classified at the receiver. We employ a pre-trained transformer model to extract semantic information and propose three methods for generating semantic codewords. First, we propose semantic quantization that uses quantized embeddings of source realizations as a codebook. We investigate the fixed-length coding, considering the source semantic structure and end-to-end semantic distortion. We propose a neural network-based codeword assignment mechanism incorporating codeword transition probabilities to minimize the expected semantic distortion. Second, we present semantic compression that clusters embeddings, exploiting the inherent semantic redundancies to reduce the codebook size, i.e., further compression. Third, we introduce a semantic vector-quantized autoencoder (VQ-AE) that learns a codebook through training. In all cases, we follow this semantic source code with a standard channel code to transmit over the wireless channel. In addition to classification accuracy, we assess pre-communication overhead via a novel metric we term system time efficiency. Extensive experiments demonstrate that our proposed semantic source-coding approaches provide comparable accuracy and better system time efficiency compared to their learning-based counterparts. Full article

Consider an evolving epidemic in which each person is either (S) susceptible and healthy; (E) exposed, contagious but asymptomatic; (I) infected, symptomatic, and quarantined; or (R) recovered, healthy, and susceptible. The inference problem, given (i) who is showing symptoms (I) and who is not (S, E, R) and (ii) the distribution of meetings among people each day, is to predict the number of infected people (state I) in future days (e.g., 1 through 20 days out into the future) for the purpose of planning resources (e.g., needles, medicine, staffing) and policy responses (e.g., masking). Each prediction horizon has different uses. For example, staffing may require forecasts of only a few days, while logistics (i.e., which supplies to order) may require a two- or three-week horizon. Our algorithm and system EpiInfer is a non-Markovian approach to forecasting infection rates. It is non-Markovian because it looks at infection rates over the past several days in order to make predictions about the future. In addition, it makes use of the following information: (i) the distribution of the number of meetings per person and (ii) the transition probabilities between states and uses those estimates to forecast future infection rates. In both simulated and real data, EpiInfer performs better than the standard (in epidemiology) differential equation approaches as well as general-purpose neural network approaches. Compared to ARIMA, EpiInfer is better starting with 6-day forecasts, while ARIMA is better for shorter forecast horizons. In fact, our operational recommendation would be to use ARIMA (1,1,1) for short predictions (5 days or less) and then EpiInfer thereafter. Doing so would reduce relative Root Mean Squared Error (RMSE) over any state of the art method by up to a factor of 4. Predictions of this accuracy could be useful for people, supply, and policy planning. Full article

Perovskite solar cells (PSCs) leverage the exceptional photoelectric properties of perovskite materials, yet interfacial energy level mismatches limit carrier extraction efficiency. In this work, energy level alignment was exploited to reduce the charge transport barrier, which can be conducive to the transmission of photo-generated carriers and reduce the probability of electron–hole recombination. We designed a dual-transition perovskite solar cell (PSC) with the structure of FTO/TiO₂/Nb₂O₅/CH₃NH₃PbI₃/MoO₃/Spiro-OMeTAD/Au by finite element analysis methods. Compared with the pristine device (FTO/TiO₂/CH₃NH₃PbI₃/Spiro-OMeTAD/Au), the open-circuit voltage of the optimized cell increases from 0.98 V to 1.06 V. Furthermore, the design of a circular platform light-trapping structure makes up for the light loss caused by the transition at the interface. The short-circuit current density of the optimized device increases from 19.81 mA/cm² to 20.36 mA/cm², and the champion device’s power conversion efficiency (PCE) reaches 17.83%, which is an 18.47% improvement over the planar device. This model provides new insight for the optimization of perovskite devices. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

Understanding how hospital length of stay (LoS) evolves with successive SARS-CoV-2 variants is central to the multiscale modeling and forecasting of COVID-19 and other respiratory virus dynamics. Using records from 1249 COVID-19 patients admitted to Chungbuk National University Hospital (2021–2023), we quantified LoS across three distinct variant phases (Pre-Delta, Delta, and Omicron) and three age groups (0–39, 40–64, and 65+ years). A gamma-distributed multi-state model—capturing transitions between semi-critical and critical wards—incorporated variant phase and age as log-linear covariates. Parameters were estimated via maximum likelihood with 95% confidence intervals derived from bootstrap resampling, and Monte Carlo iterations yielded detailed LoS distributions. Omicron-phase stays were 5–8 days, shorter than the 10–14 days observed in earlier phases, reflecting improved treatment protocols and reduced virulence. Younger adults typically stayed 3–5 days, whereas older cohorts required 8–12 days, with prolonged admissions (over 30 days) clustering in the oldest group. These time-dependent transition probabilities can be integrated with real-time bed-availability alert systems, highlighting the need for variant-specific ward/ICU resource planning and underscoring the importance of targeted management for elderly patients during current and future pandemics. Full article

Probiotics demonstrate the ability to maintain intestinal homeostasis and promote gut health. However, their effects on gut microbiota in adults over 60 years old with chronic metabolic disease (CMD) or cardio-cerebrovascular disease (CCD) remain poorly understood. This study analyzed 1586 stool samples from 1377 adults (CMD, CCD, and healthy controls) using 16S rRNA sequencing. Cohort 1 (n = 1168) was used for cross-sectional analysis, while cohort 2 (n = 209) underwent longitudinal assessment over approximately 13 months. The results demonstrated that probiotics promoted significant gut microbiota alterations across both cohorts. Probiotic supplementation significantly increased lactobacilli in the CMD, CCD, and H groups. In both cohorts, probiotic supplementation enhanced Butyricicoccus, Clostridium sensu stricto 1, and Coprococcus in H groups, enhanced Anaerostipes and Fusicatenibacter in CMD groups, and reduced Haemophilus and Lachnospira in CCD groups. Notably, long-term supplementation not only elevated Dorea, Eubacterium hallii group, and Blautia in all groups but also suppressed Klebsiella and Bilophila in the CMD and CCD groups. Enterotype analysis revealed that probiotics increased the proportion of enterotype 1 and transition probabilities from enterotype 2 to 1 in the CMD and CCD groups, demonstrating that CCD/CMD gut microbiota exhibited greater responsiveness to probiotic modulation. Overall, this study suggests probiotics’ role in modulating adult gut microbiota and their potential benefits in chronic metabolic and cardio-cerebrovascular diseases. Full article

Background/Objectives: Gallstone formation (cholelithiasis) is a common and important consequence following bariatric surgery, though regional data from the Northern Border Region are limited. This study aimed to investigate the incidence and risk factors of gallstones in this population, with the goal of optimizing postoperative treatment and reducing morbidity. Methods: We conducted a cross-sectional study using a non-probability convenience sampling technique to recruit 509 participants with varying degrees of obesity. Four hundred and ten study participants underwent bariatric surgery, of whom 73 were excluded for preoperative cholelithiasis and/or cholecystectomy. Data were collected through a self-administered, pre-validated questionnaire distributed via various social media platforms. These data included demographics, type/timing of surgery, pre/postoperative BMI, medical history, use of gallstone prophylaxis, and gallstone outcomes. Logistic regression analysis was used to identify independent predictors of gallstone formation. Results: Postoperative cholelithiasis developed in 60.8% of patients, most commonly within the first postoperative year, with risk peaking between 7 and 12 months after surgery. Rapid and substantial postoperative weight loss, as reflected in a lower current BMI and a transition to normal or overweight status within one year, was significantly associated with an increased incidence of gallstones. Female sex (OR: 2.62, 95% CI: 1.38–4.98, p = 0.003) and non-use of gallstone prevention medication (OR: 4.12, 95% CI: 1.34–12.64, p = 0.013) were independent predictors of gallstone formation. A longer time since surgery (OR: 0.76, 95% CI: 0.63–0.91, p = 0.004) and a lower current BMI (OR: 0.48, 95% CI: 0.28–0.83, p = 0.008) were associated with a reduced risk. Smoking status and comorbidities were not significantly related to the risk of gallstones. Conclusions: Gallstone formation after bariatric surgery in this population is influenced by female sex, rapid postoperative weight loss, and lack of prophylactic medication, while the type of surgical procedure does not significantly affect risk. Focused monitoring and preventive strategies, particularly in high-risk groups, are recommended to reduce gallstone-related complications following bariatric surgery. Full article

As the global energy structure transitions towards cleaner sources, large-scale integration of wind power has become a trend for modern power systems. However, the impact of low-inertia power electronic converters and the fault propagation effects at critical nodes pose significant challenges to power system stability. To this end, a Kuramoto-like model analysis method, considering node importance, is proposed in this paper. First, virtual node technology is utilized to optimize the power grid topology model. Then an improved PageRank algorithm embedded by a critical node identification method is proposed, which simultaneously considers transmission efficiency, coupling transmission probability, and voltage influence among nodes. On this basis, the traditional uniform coupling assumption is eliminated, thereby reallocating the coupling strength between critical nodes. In addition, the Kron method is applied to simplify the power grid model, constructing a hybrid Kuramoto-like model that integrates second-order synchronous machine oscillators and first-order wind power oscillators. Based on this model, the transient stability of the wind power integrated power system is analyzed. Finally, through estimating the attraction region range of the stable equilibrium point, a transient stability criterion is proposed for fault limit removal time assessment. The simulation results of the improved IEEE 39-bus system show that coupling strength optimization based on node importance reduces the system’s average critical coupling strength by 17%, significantly improving synchronization robustness. Time-domain simulations validate the accuracy of the method, with the relative error of fault removal time estimation controlled within 10%. This research provides a new analytical tool for transient stability analysis of wind power integration. Full article

Background/Objectives: CKD-associated pruritus (CKD-aP) is a serious systemic comorbidity occurring in patients with CKD. Despite the burden of CKD-aP, there are limited efficacious treatments available for its management; difelikefalin is the only approved treatment based on its efficacy and safety demonstrated in two clinical studies, namely KALM-1 and KALM-2. This study aimed to evaluate the cost-effectiveness of difelikefalin plus best supportive care (BSC) versus BSC alone when treating moderate-to-severe CKD-aP in patients receiving in-centre haemodialysis, from the perspective of the UK healthcare system. Methods: A de novo lifetime Markov health economic model was built to assess the cost-effectiveness of difelikefalin. The modelled efficacy of difelikefalin was based on data from KALM-1 and KALM-2 pooled at the patient level. The main efficacy driver was the total 5-D Itch scale score. Per-cycle probabilities of changing health states defined by CKD-aP severity were used to derive transition matrices; the model also estimated time-dependent annual probabilities of death and transplant for people on haemodialysis. An increased risk of mortality for modelled patients with very severe, severe, or moderate CKD-aP was applied. Health state utilities and management costs were based on published evidence. Results: Modelled patients treated with difelikefalin were estimated to have a reduced severity of CKD-aP. Consequently, difelikefalin plus BSC was associated with an increased life expectancy of 0.11 years per person and improved HRQoL compared with BSC alone. This translated to higher quality-adjusted life years, at 0.26 per person gained compared to BSC alone. Improved patient outcomes were achieved at an incremental cost of £7814 per person. Conclusions: Overall, at a price of £31.90/vial, difelikefalin was estimated to be a cost-effective treatment for moderate-to-severe CKD-aP at a willingness-to-pay threshold of £30,000/QALY, with conclusions robust to sensitivity analysis. Full article

To address the impacts of source load temporal–spatial uncertainties on distribution network planning considering the global transition towards sustainable energy systems with high-penetration photovoltaic (PV) integration, this paper proposes a source–network–storage coordinated stochastic planning method. A temporal–spatial correlation probability model for PV output and load demand is constructed based on Copula theory. Scenario generation and efficient reduction are achieved through Monte Carlo sampling and K-means clustering, extracting representative daily scenarios that preserve the temporal–spatial characteristics. A coordinated planning model targeting the minimization of comprehensive costs is established to holistically optimize PV deployment, energy storage system (ESS) configuration, and network expansion schemes. Simulations on typical distribution network systems demonstrate that the proposed method, by integrating temporal–spatial correlation modeling and multi-element collaborative decision-making, significantly improves PV accommodation capacity and reduces planning costs while improving the overall economic efficiency of distribution network planning. This study provides a robust technical pathway for developing economically viable and resilient distribution networks capable of integrating large-scale renewable energy, thereby contributing to the decarbonization of the power sector and advancing the goals of sustainable energy development. Full article