Search Results (758)

The Jiaoshiba area, as an important production capacity contribution block for the Fuling shale gas field, is of great significance for its long-term stable production. This study is based on continuous coring, and uses methods such as whole-rock mineral analysis, porosity and permeability analysis, gas content analysis, and organic geochemistry to systematically analyze the influencing factors of reservoir properties and gas content in the studied interval. Combined with the variation law of TOC and other parameters with depth, the target reservoir is comprehensively evaluated, and the evaluation results are verified based on actual production data. The results show that the influence of minerals on permeability is very weak, and cracks can greatly improve permeability, but their contribution to porosity is not significant. Porosity has a certain impact on gas content, but it is not the main controlling factor. The pores related to quartz (organic silicon) are mostly organic pores, which host a large amount of shale gas, while clay minerals are not conducive to the occurrence of shale gas. Organic matter (OM) maturity contributes more to porosity than OM abundance, but OM abundance has a stronger impact on gas content than its maturity. The research intervals can be divided into four categories: Class I (①–③) is the best, followed by Class II (⑦–⑨); Class III (④–⑥) is poor, and Class IV (top, non-gas-bearing layer) is the worst. Full article

With the increasing requirements for speed and travel distance in tracked vehicles on various terrains and the increasing mass ratio of powertrains, the vibration problem of high-power powertrains becomes a critical challenge. In this paper, in order to reflect on the vibration transmission relationship between the powertrain and the complex carrier, the magnetorheological damping system of a powertrain is studied in a whole vehicle model. The transfer matrix and equations of each component, including the magnetorheological mount, are derived by the Rui Method. Then, the electromechanical coupling multibody dynamic model of the vehicle–powertrain magnetorheological damping system is established. Consequently, the fast solution of vehicle–powertrain vibration characteristics under various road excitations is realized. The dynamic and static coupling characteristics of the powertrain system and the factors affecting its performance are analyzed in a moving vehicle. The simulation results indicate that the vibration reduction performance is the worst in the X-direction, whereas the vibration reduction performance is the best in the Y-direction. Under the E-class road condition at 10 m/s, the RMS acceleration reduction in the powertrain is 41.63% in the Y-direction relative to the chassis. Both the resonant frequency of the powertrain and chassis are 86.93 Hz in the Y-direction. Finally, the accuracy of the results is verified by simulation and driving experiments. The research results can provide theoretical guidance for the design and optimization of the powertrain mount of a tracked vehicle. Moreover, it provides a new technical means of studying the vibration characteristics of a complex multibody system. The simulation results demonstrate notable directional variations in the vibration attenuation performance of the powertrain damping system. Specifically, the X-direction shows the poorest vibration attenuation, whereas the Y-direction exhibits the best damping characteristics. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

The Slime Mould Algorithm (SMA) is a widely used swarm intelligence algorithm. Encouraged by the theory of no free lunch and the inherent shortcomings of the SMA, this work proposes a new variant of the SMA, called the BWSMA, in which three improvement mechanisms are integrated. The adaptive greedy mechanism is used to accelerate the convergence of the algorithm and avoid ineffective updates. The best–worst management strategy improves the quality of the population and increases its search capability. The stagnant replacement mechanism prevents the algorithm from falling into a local optimum by replacing stalled individuals. In order to verify the effectiveness of the proposed method, this paper conducts a full range of experiments on the CEC2018 test suite and the CEC2022 test suite and compares BWSMA with three derived algorithms, eight SMA variants, and eight other improved algorithms. The experimental results are analyzed using the Wilcoxon rank-sum test, the Friedman test, and the Nemenyi test. The results indicate that the BWSMA significantly outperforms these compared algorithms. In the comparison with the SMA variants, the BWSMA obtained average rankings of 1.414, 1.138, 1.069, and 1.414. In comparison with other improved algorithms, the BWSMA obtained average rankings of 2.583 and 1.833. Finally, the applicability of the BWSMA is further validated through two structural optimization problems. In conclusion, the proposed BWSMA is a promising algorithm with excellent search accuracy and robustness. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Objectives: Most antiviral vaccines are created by inactivating the virus using chemical methods. The inactivation and production of viral vaccine preparations after the irradiation of viruses with accelerated electrons has a number of significant advantages. Determining the integrity of the genome of the resulting viral particles is necessary to assess the quality and degree of inactivation after irradiation. Methods: This work was performed on the Sabin 2 model polio virus. To determine the most sensitive and most radiation-resistant part, the polio virus genome was divided into 20 segments. After irradiation at temperatures of 25 °C, 2–8 °C, −20 °C, or −70 °C, the amplification intensity of these segments was measured in real time. Results: The best correlation between the amplification cycle and the irradiation dose at all temperatures was observed for segment 3D, left. Consequently, this section of the poliovirus genome is the least resistant to the action of accelerated electrons and is the most representative for determining genome integrity. The worst dependence was observed for the VP1 right section, which, therefore, cannot be used to determine genome integrity during inactivation. The electrochemical approach was also employed for a comparative assessment of viral RNA integrity before and after irradiation. An increase in the irradiation dose was accompanied by an increase in signals indicating the electrooxidation of RNA heterocyclic bases. The increase in peak current intensity of viral RNA electrochemical signals confirmed the breaking of viral RNA strands during irradiation. The shorter the RNA fragments, the greater the peak current intensities. In turn, this made the heterocyclic bases more accessible to electrooxidation on the electrode. Conclusions: These results are necessary for characterizing the integrity of the viral genome for the purpose of creating of antiviral vaccines. Full article

Background/Objectives: Quality of life (QOL) is a multifaceted concept involving a variety of factors which define the overall well-being of individuals. Food security, which implies a resilient food system, is one factor that is central to the calculus of the QOL status of a community considering that food is a staple of life. Advancing food security as a strategy for attaining sustained improvement in community QOL hinges on recognizing that food security is embedded in a matrix of other factors that work with it to generate the QOL the community experiences. The lived experience of the community defines the community’s QOL value matrix and the relative position of food security in that value matrix. Our thesis is that the role of food security in the lived experience of low-income communities depends on the position food security is accorded relative to other factors in the QOL value matrix of the community. Methods: This study employed a multimethod approach to define the QOL value matrix of low-income Guilford County residents, identifying the relative position of the value components and demographic segments based on priority ranking. First, an in-depth interview was conducted and then a telephone survey (280 sample) was used for collecting data. The ISAC Analysis Procedure and Best–Worst Scaling methods were used to identify and rank components of the QOL value matrix in terms of their relative impact on QOL. Results: The analysis revealed that spiritual well-being is the most important contributor to QOL, with a weight of 0.23, followed by access to health services (0.21) and economic opportunities (0.16), while food security has a moderate impact with 0.07. Conclusions: These findings emphasize the need for targeted policy interventions that consider the specific needs of different demographic segments to effectively improve QOL and inform the design of resilient food systems that reflect the lived experiences of low-income communities. Food security policies must be integrated with broader quality of life interventions, particularly for unemployed, low-educated, and single individuals, to ensure that a resilient food system effectively reduces inequities and address community-specific vulnerabilities. Full article

Currently, most studies evaluating storm surges are conducted at the provincial level, and there is a lack of detailed research focusing on cities. This paper focuses on the urban scale, using some fine-scale data of coastal areas obtained through remote sensing images. This research is based on the Hazard–Exposure–Vulnerability (H-E-V) framework and PPRR (Prevention, Preparedness, Response, and Recovery) crisis management theory. It focuses on 52 Chinese coastal cities as the research subject. The evaluation system for the disaster response capabilities of Chinese coastal cities was constructed based on three aspects: the stability of the disaster-incubating environment (S), the risk of disaster-causing factors (R), and the vulnerability of disaster-bearing bodies (V). The significance of this study is that the storm surge capability of China’s coastal cities can be analyzed based on the results of the evaluation, and the evaluation model can be used to identify its deficiencies. In this paper, these storm surge disaster response capabilities of coastal cities were scored using the entropy weighted TOPSIS method and the weight rank sum ratio (WRSR), and the results were also analyzed. The results indicate that Wenzhou has the best comprehensive disaster response capability, while Yancheng has the worst. Moreover, Tianjin, Ningde, and Shenzhen performed well in the three aspects of vulnerability of disaster-bearing bodies, risk of disaster-causing factors, and stability of disaster-incubating environment separately. On the contrary, Dandong (tied with Qinzhou), Jiaxing, and Chaozhou performed poorly in the above three areas. Full article

Manufacturing processes are inherently complex, multi-objective in nature, and highly sensitive to process parameter settings. This paper presents two simple and efficient optimization algorithms—Best–Worst–Random (BWR) and Best–Mean–Random (BMR)—developed to solve both constrained and unconstrained optimization problems of manufacturing processes involving single, multi-, and many-objectives. These algorithms are free from metaphorical inspirations and require no algorithm-specific control parameters, which often complicate other metaheuristics. Extensive testing reveals that BWR and BMR consistently deliver competitive, and often superior, performance compared to established methods. Their multi- and many-objective extensions, named MO-BWR and MO-BMR, respectively, have been successfully applied to tackle 2-, 3-, and 9-objective optimization problems in advanced manufacturing processes such as friction stir processing (FSP), ultra-precision turning (UPT), laser powder bed fusion (LPBF), and wire arc additive manufacturing (WAAM). To aid in decision-making, the proposed BHARAT can be integrated with MO-BWR and MO-BMR to identify the most suitable compromise solution from among a set of Pareto-optimal alternatives. The results demonstrate the strong potential of the proposed algorithms as practical tools for intelligent decision-making in real-world manufacturing applications. Full article

The confluence of financial constraints, climate change mitigation efforts, and evolving user expectations has significantly transformed the concept of facility management (FM). Traditional FM has now evolved to enhance sustainability in the built environment. Sustainable facility management (SFM) can add value to companies, organizations, and governments by balancing the financial, environmental, and social outcomes of the FM processes. The systematic literature review revealed a limited number of studies developing a performance measurement framework for SFM in office buildings and/or other building types in the literature. Given that the lack of this theoretical basis inhibits the effective deployment of SFM practices, this study aims to fill this gap by developing a performance measurement framework for SFM in office buildings. Accordingly, an in-depth literature review was initially conducted to synthesize sustainable performance measurement factors. Next, a series of focus group discussion (FGD) sessions were organized to refine and verify the factors and develop a novel performance measurement framework for SFM. Lastly, consistency analysis, the Bayesian best worst method (BBWM), and sensitivity analysis were implemented to determine the priorities of the factors. What the proposed framework introduces is the combined use of two performance measurement mechanisms, such as continuous performance measurement and comprehensive performance measurement. The continuous performance measurement is conducted using high-priority factors. On the other hand, the comprehensive performance measurement is conducted with all the factors proposed in this study. Also, the BBWM results showed that “Energy-efficient material usage”, “Percentage of energy generated from renewable energy resources to total energy consumption”, and “Promoting hybrid or remote work conditions” are the top three factors, with scores of 0.0741, 0.0598, and 0.0555, respectively. Moreover, experts should also pay the utmost attention to factors related to waste management, indoor air quality, thermal comfort, and H&S measures. In addition to its theoretical contributions, the paper makes practical contributions by enabling decision makers to measure the SFM performance of office buildings and test the outcomes of their managerial processes in terms of performance. Full article

In this paper, we investigate the target-approaching control problem for a discrete-time first-order vehicle system where the target area is modeled as a static circular region. In the absence of absolute bearing or position information, we propose a simple local controller that relies solely on range measurements to the target obtained at two consecutive sampling instants. Specifically, if the measured distance decreases between two successive samples, the vehicle maintains a constant velocity; otherwise, it rotates its velocity vector by an angle of $\pi /2$ in the clockwise direction. This control strategy guarantees convergence to the target region, ensuring that the vehicle’s velocity direction remains unchanged in the best-case scenario and is adjusted at most three times in the worst case. The effectiveness of the proposed method is theoretically established and further validated through outdoor experiments with a mobile vehicle. Full article

This study investigates the impact of thermal barrier coatings (TBCs) on the individual contributions of cooling components in impingement-jet combined cooling under low Reynolds number conditions. Using decoupled methods, numerical simulations were conducted for cylindrical, fan-shaped, and conical hole geometries. The results show that without TBCs, the conical hole provides the best cooling performance, while the fan-shaped hole performs the worst. After applying TBCs, the cooling effectiveness of the cylindrical and conical holes remains largely unchanged, but the fan-shaped hole shows significant improvement, with performance comparable to the conical hole. The cylindrical hole keeps a uniform shape, leading to increased velocity and preventing stable film formation. In contrast, the expanding flow passages of the fan-shaped and conical holes promote a gradual decrease in flow velocity, supporting stable film formation and effective thermal protection. Impingement cooling accounts for more than 75% of the overall cooling effectiveness for across hole types. For cylindrical and conical holes, the TBCs primarily enhance in-hole cooling, while for the fan-shaped hole, it increases in-hole cooling effectiveness and shifts film cooling effectiveness from negative to positive, significantly improving its overall contribution. Full article

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly rely on microclimate numerical simulations, while comparative assessments of OTC from the human thermal perception perspective remain limited. This study employs the thermal walk method, integrating microclimatic measurements with thermal perception questionnaires, to conduct on-site OTC investigations across three urban blocks with contrasting spatial morphologies—a business district (BD), a residential area (RA), and a historical neighborhood (HN)—in Beijing, a hot summer and cold winter climate city. The results reveal substantial OTC differences among the blocks. However, these differences demonstrated great seasonal and temporal variations. In summer, BD exhibited the best OTC (mTSV = 1.21), while HN performed the worst (mTSV = 1.72). In contrast, BD showed the poorest OTC in winter (mTSV = −1.57), significantly lower than HN (−1.11) and RA (−1.05). This discrepancy was caused by the unique morphology of different blocks. The sky view factor emerged as a more influential factor affecting OTC over building coverage ratio and building height, particularly in RA (r = 0.689, p < 0.01), but its impact varied by block, season, and sunlight conditions. North–South streets generally perform better OTC than East–West streets, being 0.26 units cooler in summer and 0.20 units warmer in winter on the TSV scale. The study highlights the importance of incorporating more applicable physical parameters to optimize OTC in complex urban contexts and offering theoretical support for designing climate adaptive urban spaces. Full article

Higher-education institutions are increasingly embracing digital transformation to meet the evolving expectations of students, academics, and administrators. The smart campus paradigm offers a strategic framework for this shift, yet most existing assessment models originate from high-income contexts and remain largely untested in the Global South, where infrastructural and technological conditions differ substantially. This study addresses this gap by evaluating the contextual relevance of a comprehensive smart campus assessment framework at the Papua New Guinea University of Technology (PNGUoT). A questionnaire survey of 278 participants—students and staff—was conducted using a 5-point Likert scale to assess the perceived importance of performance indicators across four key dimensions: Smart Economy, Smart Society, Smart Environment, and Smart Governance. A hybrid methodology combining the Best–Worst Method (BWM) and Public Opinion (PO) data was used to prioritise framework components. The research hypothesises that contextual factors predominantly influence the framework’s relevance in developing countries and asks: To what extent is the smart campus assessment framework relevant and adaptable in the Global South? The study aims to measure the framework’s relevance and identify contextual influences shaping its application. The findings confirm its overall applicability while revealing significant variations in stakeholder priorities, emphasising the need for context-sensitive and adaptable assessment tools. This research contributes to the refinement of smart campus frameworks and supports more inclusive and responsive digital transformation strategies in developing country higher education institutions. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article