Search Results (53,039)

Gastroesophageal reflux disease (GERD) is a prevalent chronic condition affecting the well-being of both adults and children in general medical practice. Research on the effects of fermented soybean (SB) supplementation in managing GERD is relatively new, with limited studies available. The existing research often lacks sufficient dosing regimens and study durations to differentiate between transient placebo effects and sustained benefits. In this study, the beneficial effects of FSB supplementation were investigated in 110 voluntary participants (NCT06524271). The participants were required to take 1 g of FSB supplement once daily for 12 weeks. GERD symptoms were evaluated using the Reflux Disease Questionnaire (RDQ), while inflammatory markers, including interleukin-4 (IL-4), interleukin-6 (IL-6), and interleukin-8 (IL-8), were measured to assess inflammation. The Quality of Life in Reflux and Dyspepsia (QOLRAD) questionnaire was used to evaluate participants’ quality of life. The results indicated that FSB supplementation significantly (p < 0.05) alleviated heartburn and regurgitation symptoms and reduced levels of IL-4, IL-6, and IL-8, indicating a notable anti-inflammatory effect. Additionally, significant (p < 0.05) improvements were observed in QOLRAD scores, particularly in vitality, emotional distress, and physical/social functioning. Collectively, our findings support the use of FSB as an adjuvant approach in managing GERD, with notable improvements in patients’ quality of life. Full article

Demand fluctuates in actual production. When manufacturers face demand under their maximum capacity, suspension shifts are crucial for cost reduction and on-time delivery. In this case, suspension shifts are needed to minimize idle time and prevent inventory buildup. Thus, it is essential to integrate suspension shifts with scheduling under an uncertain production environment. This paper addresses the two-stage hybrid flow shop scheduling problem (THFSP) with suspension shifts under uncertain processing times, aiming to minimize the weighted sum of earliness and tardiness. We develop a stochastic integer programming model and validate it using the Gurobi solver. Additionally, we propose a dual-space co-evolutionary biased random key genetic algorithm (DCE-BRKGA) with parallel evolution of solutions and scenarios. Considering decision-makers’ risk preferences, we use both average and pessimistic criteria for fitness evaluation, generating two types of solutions and scenario populations. Testing with 28 datasets, we use the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) to quantify benefits. Compared to the average scenario, the VSS shows that the proposed algorithm achieves additional value gains of 0.9% to 69.9%. Furthermore, the EVPI indicates that after eliminating uncertainty, the algorithm yields potential improvements of 2.4% to 20.3%. These findings indicate that DCE-BRKGA effectively supports varying decision-making risk preferences, providing robust solutions even without known processing time distributions. Full article

Vanadis 8 steel is a tool steel manufactured by powder metallurgic processing. Its main alloy elements are V, Cr and Mo. By implementing an experimental design with five factors—all of them are related to the thermal processing of this steel and with ionic nitriding—the effects of said factors on adhesive wear resistance and corrosion resistance were studied. For this purpose, Pin-on-Disc wear tests and lineal polarization resistance tests were carried out using an aqueous solution with 3.5% NaCl by weight. The main aim was to increase this steel use in more aggressive environmental conditions, such as in coastal environments. By means of XRD, the percentage of retained austenite was determined, and by SEM-EDX, the microstructure was revealed. The conclusion is that adhesive wear resistance is improved if thermal processing parameters are at such levels that increase austenite destabilization and reduce retained austenite content. This means to destabilize austenite at 1180 °C during 1 h, with oil quenching, tempering at 520 °C during 2 h and ionic nitriding at 520 °C during 2 h. Corrosion resistance is highly improved with ionic nitriding. At the same time, to compensate for the negative effect on corrosion resistance of a high density of primary and secondary carbides, it is essential to carry out the ionic nitriding treatment. The harmful effect of electrochemical microcells that appear in the carbide/matrix interface is compensated by the passivating effect generated by the nitrided surface. Full article

This paper proposes a calculation method for the optimal solution of the inverse kinematics of redundant robots. Specifically, eight sets of vector solutions of redundant robots are solved by the D-H parameter method. Then, an objective function is designed to measure the accuracy of the robot’s inverse kinematics solution and the smoothness of the robot’s joint motion. By adjusting the weights of each item, the optimal solution that meets different requirements can be selected. In addition, this paper also introduces an improved artificial potential field method to solve the problem of discontinuous changes in gravitational potential in path planning and the problem of excessive joint torque caused by excessive gravitational potential. Finally, the application of the rapidly exploring random tree (RRT) algorithm in robot path planning and obstacle avoidance is introduced. The above-mentioned calculation method and path planning algorithm were verified in the joint simulation environment of MATLAB Robot Toolbox and CoppeliaSim. The proposed inverse solution method is compared with the inverse solution generated in the CoppeliaSim simulation environment, and the angle error of each joint is less than 0.01 rad. Full article

Hospitality services play a crucial role in shaping tourist satisfaction and revisiting intention toward destinations. Traditional feedback methods like surveys often fail to capture the nuanced and real-time experiences of tourists. Digital platforms such as TripAdvisor, Yelp, and Google Reviews provide a rich source of user-generated content, but the sheer volume of reviews makes manual analysis impractical. This study proposes integrating aspect-based sentiment analysis with zero-shot learning to analyze online tourist reviews effectively without requiring extensive annotated datasets. Using pretrained models like RoBERTa, the research framework involves keyword extraction, sentence segment detection, aspect construction, and sentiment polarity measurement. The dataset, sourced from TripAdvisor reviews of attractions, hotels, and restaurants in Central Java, Indonesia, underwent preprocessing to ensure suitability for analysis. The results highlight the importance of aspects such as food, accommodation, and cultural experiences in tourist satisfaction. The findings indicate a need for continuous service improvement to meet evolving tourist expectations, demonstrating the potential of advanced natural language processing techniques in enhancing hospitality services and customer satisfaction. Full article

This paper investigates the secondary control problem of shipboard microgrids (SMGs) with a high percentage of new energy sources under general noise. Firstly, a polymorphic SMG model is constructed, which enables the software-defined functionality of the control strategy and allows heterogeneous distributed generators (DGs) in AC SMGs to exchange packets of different types. Secondly, due to the presence of highly dynamic and high-power loads in the SMGs, a containment-based distributed secondary control strategy is proposed to improve the flexibility of the DG voltage regulation. Then, considering the complexity and diversity of disturbances during ship navigation, general noise is introduced instead of white noise to describe various disturbances. Furthermore, based on the random differential equations (RDEs), the NOS stability of the proposed strategy is proved using Lyapunov theory, which proves the effectiveness of the containment-based distributed secondary control strategy under general noise. And, the containment error is obtained to prove that the voltage and frequency of the system converge to the convex hull spanned by the virtual leaders, ensuring the high quality of the power supply. Finally, the validity of the proposed containment-based strategy is verified by an AC SMG model with four DGs in three cases. Full article

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases. Full article

The increasing volume of information for cancer care, and the evolution of molecularly guided therapies, have increased the need for molecular tumor boards (MTBs), which can integrate such data into personalized treatment plans to improve patient outcomes. However, recommendations for improving the sustainability of MTBs are lacking. A diverse committee of MTB experts was assembled (February–March 2023), with extensive experience in sustainability in healthcare ecosystems. The aim was to identify MTB-related hurdles throughout the patient journey and develop a general framework for MTBs to operate on larger scales locally, nationally, and internationally. The committee identified ten key pillars for sustainable and scalable MTBs, including technical solutions for data integration and visualization, interoperability, learning loops, clinical trial access, legal considerations, criteria for patient testing, decision standardization, making MTBs official bodies for treatment decisions, local leaders, and international networks. The need for scalable frameworks at academic and community levels was recognized, along with integrating MTBs into national health systems to enhance sustainability and ensure optimal treatment decisions. Irrespective of the health ecosystem, the sustainability and scalability of MTBs are essential. Our framework provides guidelines to address this and to help MTBs evolve towards integrated, essential components of the oncology healthcare system. Full article

Compared with the standard finite-set model-based predictive current control (FS-MPCC), the finite-set ultra-local model-based predictive current control (FS-ULMPCC) removes the use of actual system parameters and thus has some advantages like good robustness and easy implementation. However, the steady-state performance of FS-ULMPCC is relatively weak. In this paper, a novel FS-ULMPCC method is proposed and applied to the AC/DC converter of a direct-driven wind power generation system. The proposed method is designed based on a linear-extended state observer (LESO). In particular, a new control set reconstruction strategy is proposed to improve the steady-state performance. Only three options are included in the reconstructed control set, and each one is associated with two independent, active voltage vectors and their durations. The proposed FS-ULMPCC method is compared with the traditional one through experiments. The proposed method includes enhanced steady-state performance and reduced computational burden. Full article

Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model’s performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model’s prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting. Full article

In recent years, the advancement of photovoltaic power generation technology has led to a surge in the construction of photovoltaic power stations in desert gravel areas. However, traditional equal cross-section photovoltaic bracket pile foundations require improvements to adapt to the unique challenges of these environments. This paper introduces a new type of photovoltaic bracket pile foundation named the “serpentine pile foundation” based on the principle of biomimicry. Utilizing experimental data, numerical simulation technology was employed to comprehensively investigate the pullout resistance, compressive resistance, and horizontal bearing performance of the serpentine pile foundation. Comparative analysis with traditional square and circular pile foundations revealed the serpentine pile foundation’s significant advantages in all performance indexes. The serpentine pile exhibits a significantly higher ultimate uplift bearing capacity of 70.25 kN, which is 8.56 times that of the square pile and 10.94 times that of the circular pile. This study not only offers valuable technical support for the construction of photovoltaic power plants in desert gravel areas but also holds great significance in advancing the sustainable development of the global photovoltaic industry. Full article

The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of $2.50\times {10}^{-15}$ for GD and $5.15\times {10}^{-06}$ for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization. Full article

This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic algorithms for bin-packing, an extreme point priority sorting method is proposed to sort the generated extreme points according to their waste spaces to improve space utilization. In addition, a 3D grid representation of the space status of the container is used, and some partial support constraints are proposed to increase the possibilities for stacking objects and enhance overall space utilization. In the PPO algorithm, some heuristic algorithms are integrated, and the reward function and the action space of the policy network are designed so that the proposed method can effectively complete the online 3D bin-packing task. Some experimental results illustrate that the proposed method has good results in achieving online 3D bin-packing tasks in some simulation environments. In addition, an environment with image vision is constructed to show that the proposed method indeed enables an actual robot manipulator to successfully and effectively complete the bin-packing task in a real environment. Full article

To deal with low recognition accuracy and large time-consumption for dim, small targets in a smart micro-light sight, we propose a lightweight model DS_YOLO (dim and small target detection). We introduce the adaptive channel convolution module (ACConv) to reduce computational redundancy while maximizing the utilization of channel features. To address the misalignment problem in multi-task learning, we also design a lightweight dynamic task alignment detection head (LTD_Head), which utilizes GroupNorm to improve the performance of detection head localization and classification, and shares convolutions to make the model lightweight. Additionally, to improve the network’s capacity to detect small-scale targets while maintaining its generalization to multi-scale target detection, we extract high-resolution feature map information to establish a new detection head. Ultimately, the incorporation of the attention pyramid pooling layer (SPPFLska) enhances the model’s regression accuracy. We conduct an evaluation of the proposed algorithm DS_YOLO on four distinct datasets: CityPersons, WiderPerson, DOTA, and TinyPerson, achieving a 66.6% mAP on the CityPersons dataset, a 4.3% improvement over the original model. Meanwhile, our model reduces the parameter count by 33.3% compared to the baseline model. Full article

This study estimates the relative contribution of different factors to the wide variation in agricultural economic water productivity (EWP) across Colorado River Basin counties. It updates EWP measures for Basin counties using more detailed, localized data for the Colorado River mainstem. Using the Schwarz Bayesian Information Criterion for variable selection, regression analysis and productivity accounting methods identified factors contributing to EWP differences. The EWP was USD 1033 (USD 2023)/acre foot (af) for Lower Basin Counties on the U.S.–Mexico Border, USD 729 (USD 2023)/af for other Lower Basin Counties, and USD 168 (USD 2023)/af for Upper Basin Counties. Adoption rates for improved irrigation technologies showed little inter-county variation and so did not have a statistically significant impact on EWP. Counties with the lowest EWP consumed 25% of the Basin’s agricultural water (>2.3 million af) to generate 3% of the Basin’s crop revenue. Low populations/remoteness and more irrigated acreage per farm were negatively associated with EWP. Warmer winter temperatures and greater July humidity were positively associated with EWP. When controlling for other factors, being on the Border increased a county’s EWP by USD 570 (2023 USD)/af. Border Counties have greater access to labor from Mexico, enabling greater production of high-value, labor-intensive specialty crops. Full article