Coverage Ratio Research Articles

The experimental and numerical analysis of semitransparent double-skin ventilated window for heating, shading and air purification based on a flexible thermal catalysis film

A novel semitransparent thermal-catalytic glass (TC-glass) for heating, daylighting and air purification was proposed, which was applied to double-skin window (TC-glass window). Firstly, a novel flexible TC film was designed, the purification performance and kinetic model were investigated. Secondly, the experimental system of TC-glass window was built and the thermal performance and purification performance were investigated by all-day experiment. Thirdly, the heat and mass transfer model of TC-glass window was established and verified. Main results were: (1) The adding of polyvinyl alcohol in TC materials increased 10.07 kJ/mol activation energy. (2) The daylighting and formaldehyde removal analysis on TC-glass showed that the catalyst coverage ratio of 60% was recommended as the optimal ratio. (3) The experimental average thermal, formaldehyde once-through conversion and daily total clean air volume of TC-glass window glass were 40.8%, 31.1% and 238.18 m³, respectively. (4) Throughout the year, with 40%, 60% and 80% catalyst coverage ratios, the heat gain from channel air over heating season were 213.47, 261.62, and 284.22 MJ/m2, respectively, the total clean air were 11952, 16672, and 19851 m3/m2, respectively, the heat carried away by the channel air over summer were 274.53,287.2 and 297.44 MJ/m2, respectively.

Study on Path Planning in Cotton Fields Based on Prior Navigation Information

Aiming at the operation scenario of existing crop coverage and the need for precise row alignment, the sowing prior navigation information of cotton fields in Xinjiang was used as the basis for the study of path planning for subsequent operations to improve the planning quality and operation accuracy. Firstly, the characteristics of typical turnaround methods were analyzed, the turnaround strategy for dividing planning units was proposed, and the horizontal and vertical operation connection methods were put forward. Secondly, the obstacle avoidance strategies were determined according to the traits of obstacles. The circular arc–linear and cubic spline curve obstacle avoidance path generation methods were proposed. Considering the dual attributes of walking and the operation of agricultural machinery, four kinds of operation semantic points were embedded into the path. Finally, path generation software was designed. The simulation and field test results indicated that the operation coverage ratio CR ≥ 98.21% positively correlated with the plot area and the operation distance ratio DR ≥ 86.89% when non-essential reversing and obstacles were ignored. CR and DR were negatively correlated with the number of obstacles when considering obstacles. When considering non-essential reversing, the full coverage of operating rows could be achieved, but DR would be reduced correspondingly.

FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis

Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis.

Liquidity coverage ratio and profitability: an inverted U-shaped pattern

Liquidity coverage ratio and profitability: an inverted U-shaped pattern

Changes in visitor behaviour across COVID-19 pandemic: Unveiling urban visitation dynamics and non-linear relationships with the built environment using mobile big data

COVID-19 has significantly affected the behavioural patterns of urban visitors. However, the non-linear relationships between visitor behaviour and built environments, particularly how these relationships have evolved during the pandemic, have not yet been extensively studied. Using over 10 million mobile big data records collected over three years in Fukuoka, Japan, incorporating the XGBoost machine learning model and SHAP-PDP interpretation method, we identified non-linear relationships between visitor behaviours and built environments. Our findings uncovered significant non-linear impacts on visitor behaviour of several urban characteristics, such as floor area ratio, building coverage ratio, road density, and POI richness. Notably, the floor area ratio exhibits a negative correlation with visit frequency below 1 but a positive correlation above 2. Building coverage ratio positively impacts visit frequency up to 1000 m2 per 10,000 m2, after which it turns negative; this correlation shifted towards a consistent negative trend during the pandemic. Road density, which usually correlates negatively with visit duration, became positively correlated above 700 m2 per 10,000 m2 during the pandemic. Similarly, the influence of floor area ratio on visit duration reversed from negative to positive under pandemic conditions. Revealing the dynamic and non-monotonic nature of how urban visitors respond to the built environment under the influence of the pandemic, our results provide valuable insights for urban planning strategies in response to crisis resiliently.

Pengaruh Leverage dan Sales Growth Terhadap Financial Distress dengan Profitabilitas Sebagai Moderasi

The transportation sector in Indonesia is one of the important sectors supporting the national economy. This sector consists of a variety of companies and has great potential for development. This research is based on the problem of decreasing demand due to restrictions on mobility and economic activity due to the pandemic. Causing a decrease in revenue and profits for transportation companies which can increase the risk of financial distress. This research uses financial leverage ratio calculations with DER (Debt Equity Ratio) and Sales growth to determine the effect on financial distress calculated using ICR and the role of profitability with ROA (Return On Assets) as moderation. The method used in this research is descriptive methods with a quantitative approach. Research on the transportation sector listed on the BEI in 2022. The research population was 49 companies with a sample of 41 companies. The financial distress method used is to calculate the ICR ratio (Interest Coverage Ratio) which is symbolized by a dummy. Data analysis uses multiple linear analysis and Moderating Regression Analysis (MRA) with the SPSS 26 application. Based on the research and discussion that has been carried out, the research results can be concluded that leverage has an effect but is not significant on financial distress. Sales growth has an effect but is not significant on financial distress. Profitability is able to moderate the relationship between leverage and financial distress and sales growth with financial distress.

Evaluating the accessibility of collective fixed-route paratransit service in developing cities: A case study of Djibouti

This study provides a comprehensive evaluation of accessibility for collective fixed-route paratransit services in Djibouti, examining current conditions through diverse data sources, including geographic information systems and household surveys. The proposed accessibility model considers walking time, waiting time, and coverage ratio. It correlates the decrease in accessibility based on walking time with the reduction in paratransit ridership. To assess the impact of waiting time, the model compares an acceptable waiting time (5 min) with the average waiting time of the service. A threshold walking time of 8 min defines the service coverage area. Results indicate that the collective fixed-route service covers only 39.49% of the city’s urban area. Although accessibility is higher in the city center, the outskirts face substantial accessibility gaps. This analysis not only reveals the spatial distribution of accessibility within the city but also highlights areas with urgent improvement needs. The study includes paratransit accessibility maps with color-coded levels of accessibility across the city. These findings contribute to the growing research on paratransit accessibility, emphasizing the integration of spatial and temporal factors for enhanced urban mobility planning. The insights from this research can support evidence-based policy-making and promote sustainable urban development in the region.

Exploring the effect of clothing moisture content on heat and moisture transfer from the human body using a sweating thermal manikin

Evaporative heat loss is an important component of the human body's energy balance and several developed thermal comfort models are available to assess heat and mass transfer during human sweating. However, the impact of sweat-soaked clothing due to human perspiration on heat and moisture transfer from the body remains understudied. In this study, the thermal and moisture properties of typical summer clothing were evaluated and moisture adsorption and desorption curves were obtained. The heat losses of the human body in different sweating states were compared using a sweating thermal manikin. It was found that the clothing moisture content and the clothing coverage ratio had a significant effect on the human body heat loss. As the sweating rate increases, additional resistances of the sweat moisture transfer will be added in the sweat-soaked clothing, which would adversely affect the skin temperature. The results show that the total thermal resistance of fully wetted clothing decreased by an average of almost 30% compared to dry clothing ensembles. To account for such differences, the clothing thermal and evaporative resistance, along with the temperature and vapor pressure at varying moisture contents were evaluated and predicted with empirical equations. This enables integrating a dynamic heat and moisture transfer model for the sweat-soaked clothing into a prevailing thermo-physiological model. Accounting for real clothing physical parameters is vital when evaluating thermal comfort in hot and humid weather conditions.

A hybrid solar and RF energy harvester for applications of self-Powered wireless sensor nodes

A hybrid solar and RF energy harvester is proposed for applications in self-powered wireless sensor nodes. A planar slot antenna array backed by substrate integrated waveguide (SIW) cavity is produced for RF energy harvesting. A designed rectifier connected to the antenna array converts the received RF energy into DC energy. Solar cells are placed on the rest of the antenna array except slots, fully utilizing the array's surface. The solar cell coverage ratio reaches 87%. The measurement results indicate that under solar simulator illumination, the solar energy harvester has a short-circuit current of 15.7 A, an open-circuit voltage of 0.542 V, and a maximum output power of 5.81 W. Under the RF power density of 85.5 μW/cm2, the RF energy harvester reaches its peak power conversion efficiency (PCE) of 69%. All simulated and measured results are in good agreement, verifying the capabilities of the designed hybrid harvester.

News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, particularly in anticipating upcoming positions of bank liquidity risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the liquidity coverage ratio (LCR) under Basel III reforms, focusing on its direction (up, down, stable) rather than on exact values, thus distinguishing itself from previous studies. The research objectively explores the influence of external signals, particularly news sentiment, on liquidity prediction, through novel data augmentation, supported by empirical research, as qualitative factors to build a model predicting LCR positions using AI techniques such as deep and convolutional neural networks. Focused on a semi-private Islamic bank in Iran incorporating 4,288,829 Persian economic news articles from 2004 to 2020, this study compared various AI algorithms. It revealed that real-time news content offers valuable insights into impending changes in LCR, particularly in Islamic banks with elevated liquidity risks, achieving a predictive accuracy of 88.6%. This discovery underscores the importance of complementing traditional qualitative metrics with contemporary news sentiments as a signal, particularly when traditional measures require time-consuming data preparation, offering a promising avenue for risk managers seeking more robust liquidity risk forecasts.

Effects of droplet dynamical behavior in flow channel of proton exchange membrane fuel cell under vibration conditions on cell performance

Proton exchange membrane fuel cells are subjected to severe vibration in aerospace applications. Understanding the droplet transport characteristics in the flow channelunder vibration conditions is essential for improving cell performance. However, the underlying patterns and mechanisms of the vibration effects on cell performance and its internal water transport remain unclear, and there is a lack of improvement methods for drainage under vibration conditions. This study experimentally investigates the effect of vibration on cell performance. Experimental results indicate that vibration predominantly affects cell performance through its modulation of water transport. Therefore, the droplet dynamic behavior in the flow channel is further studied using the volume of fluid method. The results indicate that vibrations can enhance the average performance of the fuel cells, with the largest increase of 8.30 %. This is attributed to vibrations promoting the detachment of droplets from the gas diffusion layer surface and reducing the water coverage ratio on said surface. Under conditions of low gas velocity, high surface hydrophobicity, and large droplet size, droplets exhibited oscillatory and jumping behavior in the flow channel due to vibration, resulting in the largest 53.5 % increase in peak water coverage ratio on the gas diffusion layer surface compared to no-vibration conditions. Furthermore, enhancing the hydrophobicity of the gas diffusion layer and the hydrophilicity of the channel walls promotes droplet detachment and discharge compared to no-vibration, as well as contributing to the improvement of vibration effects on the drainage process.

Achieving Panoramic View Coverage in Visual Mobile Crowd-Sensing Networks for Emergency Monitoring Applications

Visual Mobile Crowd-Sensing(VMCS) collects photos by leveraging camera embedded in mobile users’ phones. There are two important issues in VMCS: determining whether photos collected by mobile users meet the requirements or not and designing an appropriate mechanism to attract mobile users to provide photos that meet the requirements. In this paper, we address those two issues when VMCS is applied to emergency monitoring applications. We first model an emergency scene as a disk region and define a coverage angle metric that quantifies the coverage ratio provided by each photo, and then formulate Maximize Coverage Angle under a Limited Budget(MCALB) problem. The goal of this paper is to recruit mobile users to provide panoramic view coverage for a disk while the total reward paid to participants does not exceed the budget. In our solution, we first propose a Coverage Angle Computation (CAC) algorithm to calculate the coverage angle of each uploaded photo. Then two incentive mechanisms: Guidance based Incentive Mechanism (GIM) and Coverage Prediction Incentive Mechanism (CPIM) are designed to encourage mobile users to upload photos with coverage angle that are not provided by other mobile users. Finally, we design a mobile app called I-share in android system to implement system. Meanwhile, we have recruited students to install I-share and simulated the information interaction between mobile users and server. We have conducted experiments by using I-share without/with embedded CAC algorithm to validate the efficiency of two incentive mechanisms. The experimental results demonstrate that our proposed incentive mechanisms effectively attract mobile users to provide panoramic view coverage of emergency scenes when the budget allows. Additionally, the CPIM outperforms the GIM, offering a higher coverage ratio with lower rewards.

Multi-Buoy Deployment Method Based on an Improved Tuna Swarm Optimizer Enhanced with Fractional-Order Calculus Method for Marine Observation

Ocean buoys play a critical role in marine hydrological, water quality, and meteorological monitoring, with applications in navigation, environmental observation, and communication. However, accurately modeling and deploying a multi-buoy system in the complex marine environment presents significant challenges. To address these challenges, this study proposes an enhanced deployment strategy using the tuna swarm optimizer enhanced with the fractional-order calculus method for marine observation. The proposed method first introduces a detailed observation model that precisely captures the performance of buoys in terms of coverage and communication efficiency. By integrating the observation coverage ratio and communication energy consumption, we establish an optimal multi-buoy deployment model. The proposed method leverages tent chaotic mapping to improve the diversity of initial solution generation and incorporates fractional-order calculus to strengthen its search capabilities. Simulation experiments and statistical analysis verify the effectiveness of the proposed deployment model, with the proposed method achieving the best performance in deploying the multi-buoy system, reaching a final fitness value of 0.190052 at iteration 449, outperforming TSA, PSO, GWO, and WOA. These results highlight the potential of the proposed method in optimizing multi-buoy system deployment in marine observation.

Developing theoretical models for effective debt service management in high-volatility industries: a focus on risk mitigation and financial stability in the oil and gas sector

Effective debt service management is crucial for maintaining financial stability and mitigating risks in high-volatility industries, particularly in the oil and gas sector. This abstract explores the development of theoretical models aimed at enhancing debt service management practices in such industries. The oil and gas sector is inherently exposed to price volatility, geopolitical uncertainties, and regulatory challenges, necessitating robust strategies for managing debt obligations. This review emphasizes the importance of integrating risk mitigation techniques into debt service management frameworks to safeguard against financial distress and optimize capital structure efficiency. Key theoretical models examined include proactive debt restructuring, dynamic financial modeling, and scenario analysis tailored to the cyclical nature of the industry. These models emphasize adaptive strategies that enable companies to adjust debt repayment schedules based on market conditions and cash flow projections. Additionally, financial stability metrics such as debt-to-equity ratios, liquidity ratios, and debt coverage ratios are utilized to gauge the sector's resilience to external shocks. Incorporating risk management principles into debt service management frameworks enhances decision-making processes, facilitates early detection of financial vulnerabilities, and ensures alignment with long-term strategic goals. Case studies and empirical data illustrate the practical application of theoretical models in optimizing capital allocation and maintaining investor confidence amid industry fluctuations. The theoretical insights presented contribute to a comprehensive understanding of effective debt service management in high-volatility industries, highlighting the role of financial prudence and strategic foresight in sustaining operational resilience. Recommendations are provided for industry practitioners and policymakers to enhance regulatory frameworks, promote transparency, and foster sustainable growth in the oil and gas sector.. Keywords: Debt Service Management, Risk Mitigation, Financial Stability, Oil and Gas Sector, High-Volatility Industries, Theoretical Models, Debt Restructuring, Financial Modeling, Scenario Analysis, Capital Structure

Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization.

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.

Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district

In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.

Surface-Adhered Microbubbles Enhance the Resistance of ANAMMOX Granular Sludge to Sulfamethoxazole Stress.

The granule-based anammox process has been reported to be more resistant to the stress of antibiotics; however, the underlying resistance mechanism is still not fully understood. In this study, we found that more microbubbles stably adhered to the surface layer of anammox granular sludge (AnGS, Gs) operating under long-term sulfamethoxazole (SMZ) stress of 2 mg/L, compared to that in the control reactor (Gc). The difference in covering content can be up to over three times (1.0 ± 0.1% vs 0.3 ± 0.0%). Batch tests showed that the coverage ratio of microbubbles on Gs reached approximately 32.5%, which significantly reduced SMZ transfer into AnGS due to the adsorption of SMZ by bubbles, thus alleviating the inhibition of anammox bacterial activity by 36.5%. The adhesion force between the microbubbles and the surface layer of Gs was found to be largely enhanced by 75.0% compared to that of Gc. The increased hydrophobicity of surface layer due to the increased extracellular polymer substance (EPS, mainly proteins) content, and the larger capillary force of surface layer, owing to the unique micronano structure, were identified as key factors responsible for the stable adhesion of microbubbles on the Gs. Consequently, this study demonstrated the vital roles of the surface-adhered microbubbles in resisting the stress of SMZ and shed light on the regulation and development of robust granule-based anammox processes.

Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model

Deep-learning-based convection schemes have garnered significant attention for their notable improvements in simulating precipitation distribution and tropical convection in Earth system models. However, these schemes struggle to capture the stochastic nature of moist physics, which can degrade the simulation of large-scale circulations, climate means, and variability. To address this issue, a stochastic parameterization scheme called DIFF-MP, based on a probabilistic diffusion model, is developed. Cloud-resolving data are coarse-grained into resolved-scale variables and subgrid contributions, which serve as conditional inputs and outputs for DIFF-MP. The performance of DIFF-MP is compared with that of generative adversarial networks and variational autoencoders. The results demonstrate that DIFF-MP consistently outperforms these models in terms of prediction error, coverage ratio, and spread–skill correlation. Furthermore, the standard deviation, skewness, and kurtosis of the subgrid contributions generated by DIFF-MP more closely match the test data than those produced by the other models. Interpretability experiments confirm that DIFF-MP’s parameterization of moist physics is physically consistent.

Analisis Kinerja Keuangan Bank BUMN dan Bank Swasta Indonesia: Studi Komparatif

Purpose: This study assesses and compares the financial performance of state-owned and private banks in Indonesia from 2019 to 2021. This study focuses on evaluating key financial indicators to monitor the financial structure and efficiency of banking institutions continuously amid increasing competition in the banking sector. Methodology: This study utilizes several financial ratios and applies independent sample t-tests to analyze the financial performance of state-owned and private banks. The key ratios analyzed include (Equity to Total Assets Ratio), ECTAR (Equity to Customer Assets Ratio), IMAEAR (interest margin to average earnings assets ratio), LLCR (Loan Loss Coverage Ratio), DTCR (Debt to Capital Ratio), LDR (Loan to Deposit Ratio), and CAR (Capital Adequacy Ratio). Results: State-owned banks are financially weaker than private banks. Furthermore, there is no significant difference in the average financial performance of state-owned and private banks when measured using EATAR and ECTAR ratios. However, clear differences are observed between these two banking groups in terms of the IMAEAR, LLCR, DTCR, LDR, and CAR ratios. Limitations: This study is limited to the analysis of financial performance from 2019 to 2021, which may not fully capture long-term trends or the impact of external economic conditions, such as global financial crises or regulatory changes. Additionally, this study focuses only on a specific set of financial ratios. Contribution: This study contributes to the body of knowledge by providing a comparative analysis of the financial performance of state-owned and private Indonesian banks. It offers valuable insights for banking institutions, regulators, and investors on how financial performance differs across banking groups, highlighting areas where state-owned banks may need to improve their financial strength.