Search Results (364)

Finding an optimal solution of the min-max programming problem with addition-overlap function constraints has been studied in the literature. Since the definition of overlap functions is very general and has no explicit formulations, a bisection method was proposed to yield a uniform-optimal solution for such optimization problems with a general overlap function involved in its constraint part. The uniform-optimal solution could be improved if the system manager wants extra properties such as also yielding lower cost performance. The minimal-optimal solution is proposed other than the uniform-optimal solution to answer manager’s call. In this paper, we propose an iterative method to yield at least one minimal-optimal solution. Our method starts from the uniform-optimal solution and systematically reduces the values of some non-binding variables while preserving the feasibility and optimality. To rigorously establish our method, we specify explicitly a nontrivial “min-shifted” overlap function in place of the general overlap function of the min-max programming problem. The proof of finding a minimal-optimal solution of our algorithm is given. By shifting the searching sequence of decision variables, our algorithm may find other minimal-optimal solutions which provide more flexibility for the system manager to choose. Numerical examples are provided to illustrate the procedures of the algorithm. Full article

Background and Objectives: Diabetic foot gangrene remains a major cause of lower limb amputation, driven by vascular, neuropathic, and infectious mechanisms. Identifying predictors for amputation type is essential to optimizing outcomes and reducing disability. We aimed to analyze the burden of diabetic foot gangrene and the patients’ characteristics according to the type of surgery, minor or major amputations. Methods: We conducted a retrospective observational study including 295 diabetic patients who underwent surgery for foot lesions at a Romanian tertiary center (January 2023–December 2024). Patients were classified according to surgical outcome as minor (toe/foot-level) or major (below/above-knee) amputations. Clinical, demographic, and pathological variables were compared between groups. Statistical analysis was performed with IBM SPSS Statistics 20.0. Categorical variables were expressed as frequencies and percentages, and continuous variables as mean ± SD or median (min–max). Group comparisons used Student’s t-test, Mann–Whitney U, Chi-square, or Fisher’s exact test, and binary logistic regression was applied to calculate odds ratios (OR) with 95% confidence intervals (CI). Results: Among the patients included (mean age 64.8 ± 10.8 years; 69.2% male), 191 (64.7%) underwent minor amputations/debridement and 104 (35.3%) required major amputations. Patients with major amputations were older (66.8 ± 11.3 vs. 63.7 ± 10.4 years, p = 0.012) and less frequently male (56.7% vs. 75.9%, p = 0.001). Lesion extension to the foot or beyond strongly predicted major amputation (p < 0.001). Peripheral arterial disease was more prevalent in the major group (85.6% vs. 65.4%, OR = 3.13, 95% CI = 1.68–5.84), while neuropathy was associated with minor procedures (12.6% vs. 3.8%, p = 0.015). Anemia (70.2% vs. 56.5%, p = 0.021) and leukocytosis (68.3% vs. 49.2%, p = 0.002) were also independent predictors of major amputation. Conclusions: The study highlights the need for early detection, coordinated multidisciplinary care, and personalized assessment of diabetes burden and its complications to minimize the risk of major limb amputation. Full article

Aims: The present study evaluated the acute morning effect of melatonin supplementation (5 mg) on cardiometabolic responses. Methods: For this purpose, 12 physically active men (22.1 ± 1.3 years; 1.7 ± 01 m; 74.7 ± 12.1 kg; 24.3 ± 2.7 m/kg²; VO₂max: 46.9 ± 2.3 mL/kg/min; 17.3 ± 5.2%F) were measured in a double-blind crossover protocol, where participants were measured before, during, and after a high-intensity interval exercise (HIIE) protocol [4 × 4 min at 95% of maximum heart rate (HRmax) with a 3 min interval at 60–70% of HRmax] followed by 30 min of recovery. At rest, the following variables were measured: HR, systolic blood pressure (SBP), diastolic blood pressure (DBP), lactate, and maximum oxygen consumption (VO₂max). At the end of each stage and interval, VO₂, respiratory exchange ratio (RER), and HR were measured. During recovery, VO₂, VCO₂, RER, SBP, DBP, and HR were measured. Results: Melatonin significantly enhanced recovery metabolism, as evidenced by increased VO₂ at Interval 3 (+2.2 mL/kg/min, p = 0.03, d = 0.69) and 5 min postexercise (+2.4 mL/kg/min, p = 0.02, d = 0.81). The RER was higher during Sprint 4 (+0.08, p = 0.01, d = 0.84), indicating greater carbohydrate reliance. Cardiovascular recovery was also improved, with a reduced HR at 30 min (−5 bpm, p = 0.04, d = 0.66) and lower SBP at 15 min (−8 mmHg, p = 0.02, d = 0.75). Lactate concentration at 30 min was lower with melatonin (−0.7 mmol/L, p = 0.03, d = 0.72). No significant effects were observed at rest or during early exercise. Conclusions: Acute morning melatonin intake may amplify metabolic responses to HIIE while facilitating cardiometabolic recovery. This dual-phase action may benefit athletes aiming to optimize energy expenditure, fat metabolism, and recovery during early-day training. Full article

Background: Rowing is a highly demanding endurance sport, requiring simultaneous work of approximately 70% of the body’s muscle mass and the combined contribution of aerobic and anaerobic energy systems. Objective: This study aimed to analyze the cardiorespiratory responses and performance characteristics of elite junior male and female rowers during maximal effort over 2000 m on a rowing ergometer. Methods: Fifteen junior rowers (six males aged 15–17 and nine females aged 15–18) participated in the study. Anthropometric data (body height, weight, and body surface area) were recorded. All participants performed a maximal 2000 m test on a Concept2 D-model ergometer. Throughout the test, oxygen uptake (VO₂), carbon dioxide production (VCO₂), heart rate, and ventilation parameters were continuously measured. Performance and physiological data were analyzed in three intensity zones, defined by ventilatory thresholds (VT1–VT3), as well as at peak exercise. Results: Significant anthropometric differences were observed between genders. In terms of performance, males completed the 2000 m test significantly faster than females (208.83 ± 87.66 s vs. 333.78 ± 97.51 s, p = 0.0253). Relative VO₂ at peak exercise was higher in males (58.73 ± 5.25 mL·kg⁻¹·min⁻¹) than females (48.32 ± 6.09 mL·kg⁻¹·min⁻¹, p = 0.0046). In most cardiorespiratory parameters, males outperformed females significantly, except for heart rate and ventilatory equivalents. Ranking analysis revealed that higher VO₂max values were generally associated with a better placement in both genders, though this relationship was not perfectly linear. Performance time was negatively correlated with VO₂Peak (r = −0.8286; p < 0.001), rVO₂Peak (r = −0.6781; p < 0.01), and O₂P_Peak (r = −0.7729; p < 0.01). Conclusions: The findings confirm significant gender differences in anthropometric and cardiorespiratory characteristics of elite junior rowers and reinforce VO₂max as a key determinant of performance. Yet, deviations from a direct VO₂max–rank correlation highlight the influence of tactical, psychological, and biomechanical factors. Future research should provide practical recommendations for monitoring performance and tailoring training to optimize adaptation and long-term athlete development. Full article

Known attacks on the tropical implementation of Stickel protocol involve finding minimal covers for a certain covering problem, and this leads to an exponential growth in the worst case time required to recover the secret key as the used polynomial degree increases. The computational inefficiency of this attack is also observed in practice, unless the number of explored covers is limited, on the expense of the success rate of the attack. Consequently, it can be argued that Alice and Bob can still repel these attacks on tropical Stickel protocol by utilizing very high polynomial degrees, a feasible approach due to the efficiency of tropical operations. The same is true for the implementation of Stickel protocol over some other semirings with idempotent addition (such as the max–min or digital semiring). In this paper, we propose alternative methods to attack the Stickel protocols that avoid solving the covering problem. These methods involve framing the attacks as a mixed integer linear programming (MILP) problem or applying certain heuristic global optimization techniques. We also include a number of numerical experiments to analyze the success rate and the time required to execute the suggested attacks in practice. Full article

Lithium–sulfur (Li-S) batteries are promising energy storage solutions due to their high density and cost-effectiveness. However, the risk of thermal failure limits their widespread use. Understanding thermal failure characteristics and developing accurate prediction methods are crucial for ensuring battery safety and reliability. This study aims to analyze the thermal failure characteristics of Li-S batteries and offer machine learning-based prediction methods for the early detection of potential thermal failures. The research begins with collecting temperature data from sensors deployed over numerous planes of a Li-S battery module under varied operating conditions. The data are created using proven numerical models that simulate various failure conditions. To improve model stability and learning efficiency, temperature data are preprocessed using min–max normalization to scale them to a consistent range. We suggest using a machine learning algorithm, such as the Energy Valley Optimizer Muted Multilayer Perceptrons with Mutual Information (EneVO-MPMI) algorithm. These models are trained on temperature data which are combined with Multilayer Perceptrons (MPs) to capture complicated, nonlinear correlations in thermal failure predictions, whereas the Energy Valley Optimizer (EneVO) optimizes the model’s structure and hyperparameters to avoid overfitting. Mutual Information (MI) assists in the selection of relevant features, resulting in accurate prediction from sensor data. To assess the models’ generalizability, five-fold cross-validation is used and achieves an average F1-score of 97.2%, a recall of 97.6%, an accuracy of 97.3%, and a precision of 96.9%. The EneVO-MPMI method emerges as the most effective, delivering a higher accuracy in forecasting thermal failure while requiring less training and prediction time. It shows that the EneVO-MPMI method is the most accurate and efficient at forecasting thermal breakdown in Li-S batteries. The technique can be used to identify Li-S battery defects early on, reducing the possibility of thermal instability and improving battery safety in a variety of applications. Full article

Connectivity is one of the essential needs in today’s standards in many aspects of life, starting with personal relationships, education, and remote work and ending with the security and economy of countries. However, connectivity is susceptible to intentional and unintentional disruptions, leading to great impact on critical infrastructures. Hence, maintaining connectivity is a crucial task to sustain the continuous flow of life. The challenge is to find an optimal recovery plan to reconnect all demands as soon as possible after the disruptive event, ensuring fairness in the process of reallocating the remaining resources. In this paper, we present a post-disruption recovery framework for networked systems to optimize the recovery plan to reconnect the network demands as soon as possible. More specifically, we introduce an algorithmic approach using a mathematical programming model that optimally recovers the disrupted arcs of the network while ensuring the highest connectivity. The proposed approach considers both fairness and efficiency through finding the MMF (max-min fairness) resource allocation throughout the recovery process. The proposed approach is tested on a variety of benchmark networks under a set of disruption levels; then, the results are compared with the maximum-flow model. Full article

This study investigated the influence of aerobic capacity on lactate clearance rate and heart rate recovery during ice hockey matches. Considering the growing intensity and anaerobic demands of modern ice hockey, the ability to recover quickly between high-intensity shifts is essential for optimal performance. Thirty-eight amateur ice hockey players (age: 35 ± 5 years; VO₂max: 48.93 ± 3.88 mL·min⁻¹·kg⁻¹) from the Silesian Amateur Hockey League underwent laboratory ramp tests to determine VO₂max, followed by on-ice repeated sprint tests and heart rate monitoring during matches. The results demonstrated significant positive correlations between VO₂max and lactate clearance (ΔLa4–8min [mmol/L]= 2.55 ± 0.58 mmol·L⁻¹; rho = 0.545; p < 0.001), as well as heart rate recovery (Δ%HRmax = 25.88 ± 2.09%; rho = 0.682; p < 0.001). Players with higher VO₂max exhibited a faster reduction in heart rate during recovery periods between shifts and maintained better sprint performance (rho = –0.877; p < 0.001). These findings confirm that higher aerobic capacity enhances both metabolic and autonomic recovery processes, enabling players to sustain high-intensity efforts more effectively during the game. The study highlights the importance of developing aerobic fitness in hockey training to improve recovery efficiency and match performance. Full article

Monitoring traffic flow across large-scale transportation networks is essential for effective traffic management, yet comprehensive sensor deployment is often infeasible due to financial and practical constraints. The traffic sensor location problem (TSLP) aims to determine the minimal set of sensor placements needed to achieve full link flow observability. Existing solutions primarily rely on algebraic or optimization-based approaches, but often neglect the impact of sensor measurement errors and struggle with scalability in large, complex networks. This study proposes a new scalable and robust methodology for solving the TSLP under uncertainty, incorporating a formulation that explicitly models the propagation of measurement errors in sensor data. Two nonlinear integer optimization models, Min-Max and Min-Sum, are developed to minimize the inference error across the network. To solve these models efficiently, we introduce the BBA Algorithm (BBA) as an adaptive metaheuristic optimizer, not as a subject of comparative study, but as an enabler of scalability within the proposed framework. The methodology integrates LU decomposition for efficient matrix inversion and employs a node-based flow inference technique that ensures observability without requiring full path enumeration. Tested on benchmark and real-world networks (e.g., fishbone, Sioux Falls, Barcelona), the proposed framework demonstrates strong performance in minimizing error and maintaining scalability, highlighting its practical applicability for resilient traffic monitoring system design. Full article

Against the backdrop of ever-increasing energy demand and growing awareness of environmental protection, the research and optimization of hydrogen-related multi-energy systems have become a key and hot issue due to their zero-carbon and clean characteristics. In the scheduling of such multi-energy systems, a typical problem is how to describe and deal with the uncertainties of multiple types of energy. Scenario-based methods and robust optimization methods are the two most widely used methods. The first one combines probability to describe uncertainties with typical scenarios, and the second one essentially selects the worst scenario in the uncertainty set to characterize uncertainties. The selection of these scenarios is essentially a trade-off between the economy and robustness of the solution. In this paper, to achieve a better balance between economy and robustness while avoiding the complex min-max structure in robust optimization, we leverage artificial intelligence (AI) technology to generate enough scenarios, from which economic scenarios and feasible scenarios are screened out. While applying a simple single-layer framework of scenario-based methods, it also achieves both economy and robustness. Specifically, first, a Transformer architecture is used to predict uncertainty realizations. Then, a Generative Adversarial Network (GAN) is employed to generate enough uncertainty scenarios satisfying the actual operation. Finally, based on the forecast data, the economic scenarios and feasible scenarios are sequentially screened out from the large number of generated scenarios, and a balance between economy and robustness is maintained. On this basis, a multi-energy collaborative optimization method is proposed for a hydrogen-based multi-energy microgrid with consideration of the coupling relationships between energy sources. The effectiveness of this method has been fully verified through numerical experiments. Data show that on the premise of ensuring scheduling feasibility, the economic cost of the proposed method is 0.67% higher than that of the method considering only economic scenarios. It not only has a certain degree of robustness but also possesses good economic performance. Full article

Background/Objectives: This study assessed the individual and combined effects of green tea extract and ginger supplementation on endurance performance, metabolic responses, perceived exertion, thermal sensation, and muscle soreness in normothermic and cold environmental conditions. Methods: In a randomized, double-blind crossover trial, sixteen recreationally active males (age: 23.4 ± 0.4 years; VO₂ max: 46.8 ± 2.8 mL/kg/min) were tested in eight conditions (placebo [maltodextrin], green tea [500 mg], ginger [1000 mg], combined), all in normothermic (21–24 °C) and cold (5–7 °C) environments. All supplements and the placebo were encapsulated in identical capsules to ensure blinding. Participants completed a submaximal time-to-exhaustion (TTE) test at 70% VO₂ max on a cycle ergometer. TTE, respiratory exchange ratio (RER), perceived exertion (RPE), thermal sensation (TSS), and muscle soreness via a visual analog scale (VAS), assessed 24 h post-exercise, were measured. Results: In normothermic condition, green tea and combined supplementation significantly increased TTE and reduced RER compared to the placebo (p < 0.05), and that combined supplementation lowered RPE relative to the placebo and ginger (all p < 0.05). In cold conditions, combined supplementation significantly enhanced TTE, reduced RER, and improved TSS compared to the placebo and ginger (p < 0.05), while all supplements decreased VAS relative to the placebo (p < 0.05). Ginger alone showed no significant effect on TTE or RER but improved TSS and VAS in cold compared to the placebo (p < 0.05). Cold placebo conditions exhibited significantly higher RPE and VAS than all normothermic conditions (p < 0.05). Conclusions: Green tea enhances endurance and fat oxidation in normothermic conditions, while its combination with ginger can optimize performance, thermal comfort, and recovery in cold environments. These findings suggest a practical nutritional strategy for mitigating environmental stress during exercise, specific to the acute supplementation in males. Trial Registration: This trial was registered at ClinicalTrials.gov (Identifier: NCT07150533). Full article

Non-orthogonal multiple access (NOMA) is a key enabler for 6G networks due to its efficient spectrum utilization, which is garnering significant attention among the Internet of Things (IoT) community. This paper investigates the benefits of the improper Gaussian signaling (IGS) technique on the max–min fairness of the downlink NOMA system under imperfect successive interference cancellation (SIC), where both of the users have the potential to adopt IGS. We first investigate fairness optimization under perfect SIC. In this case, the max–min optimization is solved by the alternate optimization algorithm, where the impropriety degree and power level are iteratively optimized. The closed-form solution for conventional proper Gaussian signaling is also obtained. Then, a deep Q network-based solution is considered for the rate fairness maximization of the downlink NOMA system under IGS and imperfect SIC. The simulations presented for the IGS-aided NOMA system support the analysis, illustrating that IGS can efficiently improve the fairness achievable rate compared to the conventional proper one. Full article

This study aimed to investigate the effect of thermal sterilization on the volatile flavor of sauced duck necks. The study revealed that thermal sterilization significantly reduced the content of unsaturated fatty acids (e.g., oleic acid C18:1n9c) in sauced duck necks. This was accompanied by elevated thiobarbituric acid reactive substances (max 0.86 mg/100 g) and peroxide values (max 1.13 g/100 g), indicating intensified lipid oxidation. Through PLS-DA, six key differential free fatty acids distinguishing the sterilization treatment groups were identified: cis-9-tetradecadecarbonate, methyl tridecarbonate, cis-10-17-cetenoic acid, antioleic acid, cis-13-docosaenoic acid methyl ester, and lauric acid. The primary volatile flavor compounds in sauced duck necks were identified as alkenes and ethers. Post-sterilization alterations in volatile flavor profiles primarily resulted from compositional changes in alkenes, esters, and ethers within the total volatile compounds. Moreover, it was demonstrated that sterilization temperature exerted a significantly greater impact on the quality of sauced duck necks than sterilization duration. Following organoleptic evaluation, samples subjected to low-temperature prolonged sterilization (90 °C for 30 min) exhibited the highest aroma scores, establishing this protocol as the optimal thermal sterilization condition. This study is of great significance for selecting thermal sterilization conditions and maintaining meat flavor. Full article

The survival status of Lumnitzera littorea is near threatened globally and critically endangered in China. Clarifying its global distribution pattern and its changing trends under different future climate models is of great significance for the protection and restoration of its endangered status. To build a model for this purpose, this study selected 73 actual distribution points of Lumnitzera littorea worldwide, combined with 12 environmental factors, and simulated its potential suitable habitats in six periods: the Last Interglacial (130,000–115,000 years ago), the Last Glacial Maximum (27,000–19,000 years ago), the Mid-Holocene (6000 years ago), the present (1970–2000), and the future 2050s (2041–2060) and 2070s (2061–2080). The results show that the optimal model parameter combination is the regularization multiplier RM = 4.0 and the feature combination FC (Feature class) = L (Linear) + Q (Quadratic) + P (Product). The MaxEnt model has a low omission rate and a more concise model structure. The AUC values in each period are between 0.981 and 0.985, indicating relatively high prediction accuracy. Min temperature of the coldest month, mean diurnal range, clay content, precipitation of the warmest quarter, and elevation are the dominant environmental factors affecting its distribution. The environmental conditions for min temperature of the coldest month at ≥19.6 °C, mean diurnal range at <7.66 °C, clay content at 34.14%, precipitation of the warmest quarter at ≥570.04 mm, and elevation at >1.39 m are conducive to Lumnitzera littorea’s survival and distribution. The global potential distribution areas are located along coasts. Starting from the paleoclimate, the plant’s distribution has gradually expanded, and its adaptability has gradually improved. In China, the range of potential highly suitable habitats is relatively narrow. Hainan Island is the core potential habitat, but there are fragmented areas in regions such as Guangdong, Guangxi, and Taiwan. The modern centroid of Lumnitzera littorea is located at (109.81° E, 2.56° N), and it will shift to (108.44° E, 3.22° N) in the later stage of the high-emission scenario (2070s (SSP585)). Under global warming trends, it has a tendency to migrate to higher latitudes. The development of the aquaculture industry and human deforestation has damaged the habitats of Lumnitzera littorea, and its population size has been sharply and continuously decreasing. The breeding and renewal system has collapsed, seed abortion and seedling establishment failure are common, and genetic variation is too scarce. This may indicate why Lumnitzera littorea is near threatened globally and critically endangered in China. Therefore, the protection and restoration strategies we propose are as follows: strengthen the legislative guarantee and law enforcement supervision of the native distribution areas of Lumnitzera littorea, expanding its population size outside the native environment, and explore measures to improve its seed germination rate, systematically collecting and introducing foreign germplasm resources to increase its genetic diversity. Full article

The polymer industry gained increasing importance due to the ability of polymers to replace traditional materials such as wood, glass, and metals in various applications, offering advantages such as high strength-to-weight ratio, corrosion resistance, and ease of fabrication. Among key performance indicators, melt flow rate (MFR) plays a crucial role in determining polymer quality and processability. However, conventional offline laboratory methods for measuring MFR are time-consuming and unsuitable for real-time quality control in industrial settings. To address this challenge, the study proposes a leveraging artificial intelligence with machine learning-based melt flow rate prediction for polymer properties analysis (LAIML-MFRPPPA) model. A dataset of 1044 polymer samples was used, incorporating six input features such as reactor temperature, pressure, hydrogen-to-propylene ratio, and catalyst feed rate, with MFR as the target variable. The input features were normalized using min–max scaling. Two ensemble models—kernel extreme learning machine (KELM) and random vector functional link (RVFL)—were developed and optimized using the pelican optimization algorithm (POA) for improved predictive accuracy. The proposed method outperformed traditional and deep learning models, achieving an R² of 0.965, MAE of 0.09, RMSE of 0.12, and MAPE of 3.4%. A SHAP-based sensitivity analysis was conducted to interpret the influence of input features, confirming the dominance of melt temperature and molecular weight. Overall, the LAIML-MFRPPPA model offers a robust, accurate, and deployable solution for real-time polymer quality monitoring in manufacturing environments. Full article