Operational Tool Research Articles

IDENTIFYING THE DRIVERS OF HOUSEHOLD WATER USAGE EFFICIENCY IN TERENGGANU: THEORY OF PLANNED BEHAVIOR (TPB) APPROACH

In the contemporary era, the issue of water scarcity has emerged as a critical concern on a global scale. Factors such as rapid population expansion, urbanization, and the uncertainties associated with climate change have exerted significant influence on water availability, particularly in the context of domestic consumption. Regions including Klang Valley, Selangor, Johor, Perlis, Melaka, and Negeri Sembilan are currently grappling with water shortages. Despite Malaysia's reputation for substantial rainfall, the nation is increasingly confronted with freshwater scarcity concerns. Consequently, the primary objectives encompass an examination of water consumption patterns and the identification of determinants driving water conservation within households. The study sample encompasses 271 households. Results indicate that both attitude and subjective norms are significantly correlated with water use efficiency, with corresponding p-values of 0.076 and 0.86, respectively. The insights gleaned from water usage patterns can serve as valuable tools for water operators in the management and educational efforts targeted at households, including campaigns promoting sustainable water usage and conservation practices.

A Collection of Machine Learning Models for Improved Airport Operations Amidst Adverse Weather Conditions

In the face of escalating climate change, airports worldwide are finding themselves at the mercy of extreme weather events. This research paper presents a comprehensive system that models key indicators, aiding airport management during such challenging weather conditions. The system adopts an integrated approach, combining various machine learning models to provide a detailed projection of an airport's future state, drawing from past occurrences. The heart of the system is a model that focuses on the airport's peak service rate. This model meticulously correlates weather conditions and runway configurations with the 99th percentile of observed throughput from the training dataset. As such, the peak service rate model provides an estimate of the airport's capacity, which is essential for effective planning and resource allocation. Moreover, the system includes a predictive model that assesses the likelihood of air traffic flow management regulations based on weather data and calendar information. The robustness of this model against noise and uncertainty in the training dataset is fortified by the application of confident learning techniques and the inclusion of monotonic constraints. The system further enhances its capabilities by forecasting the potential entry rate of regulations, expressed in hourly arrivals, providing valuable insights that can guide proactive decision-making. By seamlessly integrating these three models, the system serves as an effective tool for airport operators and airlines. It enables operational optimisation and the development of strategic plans to mitigate the effects of increasing weather-related disruptions.

Nutrition Care Model for Nutritional Assessment and Dietary Counseling of Adults Living with HIV in India

Abstract Background: A high prevalence of undernutrition (40%) exists among adults living with HIV (ALHIV) in India due to their increased metabolic demands. Diminished nutritional status in turn has been associated with increased risk of morbidity and mortality among ALHIV. Nutritional assessment and its management are an integral part of HIV care; however, no strategy exists for screening of undernutrition among ALHIV. Materials and Methods: The nutrition care model has been developed for undertaking nutritional assessment by classifying the severity of undernutrition based on body mass index (BMI) and identification of early signs of undernutrition including anemia and hypoalbuminemia among ALHIV. Results and Conclusion: The model suggests energy and protein intake based on the BMI and indicates desirable body weight corresponding to a BMI of 21 kg/m2. Based on these requirements, the portion size of each major food group is provided for individualized dietary counseling by frontline workers. ALHIV found with signs and symptoms of anemia, low hemoglobin, and serum albumin are referred to the nearest health facility. Large-scale operational research and field validation studies are planned to better understand the effectiveness of the model in improving the nutritional and clinical outcomes and the strengths and limitations as an operational tool in programmatic settings.

Research on Evolutionary Path of Land Development System Towards Carbon Neutrality

Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving toward a low-carbon path. Firstly, through nonlinear dynamics and bifurcation analysis, this study identifies three typical transformation paths that the system may experience: gradual, transitional, and hybrid, emphasizing the nonlinear, phased, and highly context-dependent characteristics of the transformation process. On this basis, early warning indicators and robustness analysis methods are introduced, which provide operational tools for identifying critical turning points in the system and improving the effectiveness and resilience of regulatory strategies. Furthermore, this paper proposes a multi-level regulation mechanism design framework, which combines the immediate feedback with the historical cumulative effect to achieve the refined guidance of land development patterns and carbon emission paths. The results provide a scientific basis and practical enlightenment for land use optimization, green infrastructure construction, and industrial structure adjustment under the background of realizing the “3060” dual carbon goal and the reform of territorial spatial planning in China. In the future, it is necessary to strengthen the empirical calibration of parameters, data-driven optimization, and collaborative research of multiple policy tools to further improve the applicability and decision-making reference value of the model.

Research on Pole-climbing Robots that Can Perform Multi-degree-offreedom Motion and Working at the Top of Distribution Network Poles

Background: In order to complete power maintenance, equipment renewal and other loss reduction work and reduce customer outage time at the same time, live electrical work has become a necessary means. Pole-climbing robots, as an important auxiliary tool for power-carrying operations, frequently appear in papers and patents. Objective: The pole climbing robot is designed to work at any position in the working space with electric distribution pole, effectively and safely replacing manual work. Methods: Based on the physical parameters of the cement utility pole and the range of space for live electrical work, the functional relationships are established between the structural parameters of the robot and the kinematic and dynamical models, and the optimal range of parameters is solved. Results: The robot can rotate and climb around the pole with five steering angles: 0°, 22.5°, 45°, 67.5° and 90°. The control insulated operating rod can transport special tools to any station within 1m radius of the distribution pole for live work. It has a maximum load of 11kg and a maximum climbing speed of 33.6mm/s. Conclusion: The pole-climbing robot can climb cement utility pole, manipulate the insulated operating lever it carries, and transport special tools to the workstation on the cable for carrying out live electrical work.

Operational Risk Assessment of Commercial Banks’ Supply Chain Finance

Supply chain finance (SCF) operations require extensive activities and a high level of information transparency, making them vulnerable to operational issues that pose significant risks of financial loss for commercial banks. Accurately assessing operational risks is crucial for ensuring market stability. This research aims to provide a reliable operational risk assessment tool for commercial banks’ SCF businesses and to deeply examine the features of operational risk events. To achieve these goals, the study explores the dependency structure of risk cells and proposes a quantitative measurement framework for operational risk in SCF. The loss distribution analysis (LDA) is improved to align with the marginal loss distribution of segmented operational risks at both high and low frequencies. A tailored copula function is developed to capture the dependency structure between various risk cells, and the Monte Carlo algorithm is utilized to compute operational risk values. An empirical investigation is conducted using SCF loss data from commercial banks, creating a comprehensive database documenting over 400 entries of SCF loss events from 2012 to 2022. This database is analyzed to identify behaviors, trends, frequencies, and the severity of loss events. The results indicate that fraud risk and compliance risk are the primary sources of operational risks in SCF. The proposed approach is validated through backtesting, revealing a value at risk of CNY 179.3 million and an expected shortfall of CNY 204.9 million at the 99.9% significance level. This study pioneers the measurement of SCF operational risk, offering a comprehensive view of operational risks in SCF and providing an effective risk management tool for financial institutions and policymakers.

The Effect of Product Innovation on the Performance of Food SMEs: The Moderator Effect of Quadruple Helix

Small and Medium Enterprises (SMEs) play a vital role in driving Indonesia's economy, contributing significantly to GDP and employment. However, SMEs, particularly in the food sector, face challenges such as changing market demands, limited resources, and restricted access to innovation support systems. This study investigates the influence of product innovation on the performance of food SMEs in Malang Regency and examines the moderating effect of the Quadruple Helix framework, which involves collaboration among academia, business, government, and mass media. Using a quantitative research design, data were collected from 335 food SMEs registered with the SME Business Communication Forum (FKPU) and analyzed using Warp-PLS SEM to evaluate structural and measurement models. The findings reveal that product innovation significantly enhances SME performance, as reflected in improved customer satisfaction, expanded market share, increased revenue, and profitability. The Quadruple Helix actors were found to play critical roles in amplifying this relationship. Academia supports SMEs by bridging research and practice, offering training, and providing operational tools. Government interventions such as subsidies, tax incentives, and promotional activities foster a conducive environment for innovation. Businesses contribute by mentoring, sharing market knowledge, and opening new opportunities. Mass media amplifies these efforts by promoting innovative products, disseminating market information, and connecting SMEs with broader consumer bases. This study highlights the importance of integrating collaborative frameworks into innovation ecosystems to ensure sustainable SME growth. While the research provides valuable insights for policymakers and practitioners, it is limited to food SMEs in Malang Regency. Future studies should explore other sectors and regions to validate these findings and uncover additional dimensions of the Quadruple Helix framework's impact.

INterpolated FLOod Surface (INFLOS), a Rapid and Operational Tool to Estimate Flood Depths from Earth Observation Data for Emergency Management

The INterpolated FLOod Surface (INFLOS) tool was developed to meet the operational needs of the Copernicus Emergency Management Service (CEMS) Rapid Mapping (RM) component, which delivers critical crisis information within hours during and after disasters. With increasing demand for accurate and real-time flood depth estimates, INFLOS provides a rapid, adaptable solution for estimating floodwater depth across diverse flood scenarios, using remotely sensed data and high-resolution Digital Terrain Models (DTMs). INFLOS calculates flood depth by interpolating water surface elevation from sample points along flooded area boundaries, derived from satellite imagery. This tool is capable of delivering flood depth estimates in a rapid mapping context, leveraging a multistep interpolation and filtering process for improved accuracy. Tested across fourteen regions in Europe and South America, INFLOS has been successfully integrated into CEMS RM operations. The tool’s computational optimisations further enhance efficiency, improving computation times by up to 15-fold, compared to similar techniques. Indeed, it is able to process areas of up to 6000 ha in a median time of 5.2 min, and up to 30 min at most. In conclusion, INFLOS is currently operational and consistently generates flood depth products quickly, supporting real-time emergency management and reinforcing the CEMS RM portfolio.

Older people’s social work safe staffing supply and workforce demands analysis: A case study from Northern Ireland

Abstract In the UK, a range of operational tools and policy guidelines regulate staffing in various Health and Social Care sectors. Nonetheless, frameworks to ensure safe staffing in social work remain less advanced. This study focuses on older people’s social work community teams in Northern Ireland due to the high volume of cases and vacancies within these teams. Our findings provide evidence based on actual caseloads (direct work with service users) and workloads at individual, team, and regional levels within this programme of care. The analysis revealed systemic issues that require systemic solutions in relation to staffing supply and service demands. Frontline social workers and managers frequently faced overwhelming worker-to-caseload ratios, routine use of waiting lists, and team vacancies. These findings lay the groundwork for evidence-based strategic planning, guiding the development and enactment of safer and more effective social work policies and legislation in the years ahead. The project was commissioned by the Department of Health, Northern Ireland in preparation for policy and legislative developments expected between 2025 and 2028.

Nonlinear valley selection rules and all-optical probe of broken time-reversal symmetry in monolayer WSe2

AbstractIn monolayer transition metal dichalcogenides, time-reversal symmetry—combined with broken space-inversion symmetry—defines the spin–valley degree of freedom. As such, the engineering and control of time-reversal symmetry by optical or magnetic fields constitutes the foundation of valleytronics. Here we propose a new approach for the detection of broken time-reversal symmetry and valley imbalance in monolayer WSe2 based on second-harmonic generation. At room temperature, our method can selectively probe a net valley imbalance generated by ultrafast, coherent and valley-exclusive optical Stark and Bloch–Siegert effects. This work demonstrates the potential and unique capabilities of nonlinear optics as a probe of broken time-reversal symmetry as well as a tool for ultrafast and non-destructive valleytronic operations.

Flood Evacuation in Informal Settlements: Application of an Agent-Based Model to Kibera Using Open Data

Flood incident management involves taking actions to save lives and reduce damages during a flood. Agent-based modelling tools have recently been developed to simulate the dynamic interactions between people and floodwater as a flood event unfolds. To date, these have only been applied in locations with a wealth of data, relying upon bespoke local or national datasets. Although informal settlements have a concentration of vulnerable people and are often more exposed to natural hazards, data availability is often limited, posing challenges for planning and implementing flood incident management actions. In this study, a model that was first applied in the UK is adapted and applied to simulate flood evacuations in Kibera, a densely populated informal settlement in Nairobi. Although data quality limits some of the model’s potential, the results reproduce patterns of observed behaviour. Evacuation shelters in the Northwest, North, and Northeast are shown to perform best. A major exit route to the South, a bridge crossing, and a river path are shown to be especially prone to congestion during evacuations. This paper reports on the first application of an agent-based model to an informal settlement, Kibera. The demonstration is an important step towards an operational tool for flood incident management planning in informal settlements around the world.

Deep BiLSTM Attention Model for Spatial and Temporal Anomaly Detection in Video Surveillance

Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed. This change increases the demand for automated systems that detect abnormal events or anomalies, such as road accidents, fighting, snatching, car fires, and explosions in real-time. These systems improve detection accuracy, minimize human error, and make security operations more efficient. In this study, we proposed the Composite Recurrent Bi-Attention (CRBA) model for detecting anomalies in surveillance videos. The CRBA model combines DenseNet201 for robust spatial feature extraction with BiLSTM networks that capture temporal dependencies across video frames. A multi-attention mechanism was also incorporated to direct the model’s focus to critical spatiotemporal regions. This improves the system’s ability to distinguish between normal and abnormal behaviors. By integrating these methodologies, the CRBA model improves the detection and classification of anomalies in surveillance videos, effectively addressing both spatial and temporal challenges. Experimental assessments demonstrate that the CRBA model achieves high accuracy on both the University of Central Florida (UCF) and the newly developed Road Anomaly Dataset (RAD). This model enhances detection accuracy while also improving resource efficiency and minimizing response times in critical situations. These advantages make it an invaluable tool for public safety and security operations, where rapid and accurate responses are needed for maintaining safety.

Современные подходы к учёту и контролю

The article reveals the importance of accounting and control in the digital economy, their role in making strategic decisions, using digital technologies and artificial intelligence to improve management efficiency and ensure data security. The purpose of modern accounting is emphasized – providing reliable information on the financial and economic activities of a company using modern digital tools for effective business operations, ensuring compliance with legislative and regulatory requirements, capitalization and scaling. The importance of big data and digital technologies in automating accounting and control and reducing risks through data integration and the use of machine learning algorithms is noted. It is indicated that modern approaches to accounting and control are focused on optimizing the use of resources, timely detection of deviations from planned indicators for the implementation of timely corrective measures. The author substantiates the use of blockchain technologies in accounting and control, highlighting their advantages – transparency, security, decentralization and automation of processes, and also notes the challenges associated with increasing competitiveness, legal aspects and integration.

Adaptive High-Performance Optimization Tool for Real-time Operation of Renewable-based Virtual Power Plants

Adaptive High-Performance Optimization Tool for Real-time Operation of Renewable-based Virtual Power Plants

Thermal Management and Biocompatibility in Dry Machining: An Experimental Study of ZrO2-Based Cutting Tool for Bone Machining

Thermal Management and Biocompatibility in Dry Machining: An Experimental Study of ZrO2-Based Cutting Tool for Bone Machining

Development and Validity of a Novel Transplant Operative Assessment Tool

Development and Validity of a Novel Transplant Operative Assessment Tool

Recent Research and Operational Tools for Improved Understanding and Diagnosis of Tropical Cyclone Inner Core Structure

Recent Research and Operational Tools for Improved Understanding and Diagnosis of Tropical Cyclone Inner Core Structure

RPA in Financial Software: Driving Efficiency & Reducing Costs

Organizations within the dynamic financial industry have constant pressure to execute their operations efficiently, cost-effectively, and accurately. RPA is one of the transformative technologies in financial software that automates most of the repetitive, rule-based tasks, assists in minimizing human errors, and improves the speed of the process. This paper discusses the use of RPA in finance operations based on automation of processes such as data entry, transaction processing, and compliance reporting. We shall, therefore, be carrying out an investigation into the role that RPA-driven solutions have played in driving operational costs down, improving scalability, and supporting compliance with regulatory standards, through case studies and industry analysis. We also discuss challenges in implementation, risks, and best practices while considering the deployment of RPA in financial systems. The findings emphasize that the return on investment for RPA is very attractive for any financial institution, hence positioning it as one of the critical innovation tools for finance operations.

Positive data circulation established by Kyushu Smart Fisheries (QSF) team

Subsurface observations in coastal and shelf seas remain insufficient compared to profiling measurements in open oceans. To address this gap, we have developed a system that involves coastal fishermen in conducting in-situ measurements. By assimilating this observation data in real-time, we can provide valuable feedback on ocean predictions to support their fishing activities. We adapted the traditional full-scale conductivity-temperature-depth (CTD) instrument into a more conventional model dedicated to small-scale fisheries. This simple and smart CTD device allows Bluetooth communication with Android devices. Although its accuracy and pressure resistance were reduced to make it affordable, it remains effective for our data assimilation purpose. Along with acoustic current data, the accuracy of coastal ocean prediction models, particularly for velocity components, can be significantly increased. Using the intuitive and user-friendly forecasting app, cooperating fishermen have been able to reduce fuel usage, working hours, and gear damages, thereby improving profitability and working conditions. This app has become an indispensable operational tool for fishermen in the Kyushu region, motivating them to actively participate in coastal ocean observations.

SAG’s Overload Forecasting Using a CNN Physical Informed Approach

The overload problem in semi-autogenous grinding (SAG) mills is critical in the mining industry, impacting the extraction of valuable metals and overall productivity. Overloads can lead to severe operational issues, including increased wear, reduced grinding efficiency, and unscheduled shutdowns, which result in financial losses. Various strategies have been employed to address SAG mill overload, from real-time monitoring to predictive modeling and machine learning techniques. However, existing methods often lack the integration of domain-specific knowledge, particularly in handling class imbalance within operational data, leading to limitations in predictive accuracy. This paper presents a novel approach that integrates convolutional neural networks (CNNs) with physics-informed neural networks (PINNs), embedding physical laws directly into the model’s loss function. This hybrid methodology captures the complex interactions and nonlinearities inherent in SAG mill operations and leverages domain expertise to enforce physical consistency, ensuring more robust predictions. Incorporating physics-based constraints allows the model to remain sensitive to critical overload conditions while addressing the challenge of imbalanced data. Our method demonstrates a significant enhancement in prediction accuracy through extensive experiments on real-world SAG mill operational data, achieving an F1-score of 94.5%. The results confirm the importance of integrating physics-based knowledge into machine learning models, improving predictive performance, and offering a more interpretable and reliable tool for mill operators. This work sets a new benchmark in the predictive modeling of SAG mill overloads, paving the way for more advanced, physically informed predictive maintenance strategies in the mining industry.