Efficient System Research Articles

Underground Cable Fault Detection

Underground cables play a crucial role in delivering electricity, ensuring a dependable power supply in both urban and industrial regions. However, detecting and resolving faults in these cables can be difficult due to their hidden placement. This project aims to develop an effective underground cable fault detection system that accurately identifies fault locations, reducing downtime. The system will combine electrical principles with modern sensing technologies to detect faults such as short circuits, open circuits, and insulation issues. By injecting a small current into the cable and analyzing the resulting voltage drop, the system will calculate the distance to the fault from the measurement point. Using a microcontroller along with sensors and signal processing units, it will determine and display the fault location in kilometers. Real-time fault detection will significantly decrease maintenance time and costs by quickly locating problem areas, enabling faster repairs and reducing power outages. This project aims to improve the efficiency and reliability of power transmission systems. Additionally, the system can be integrated with communication technologies for remote monitoring, allowing utility companies to detect and manage faults from centralized control centers.

Pipe Size Calculation for Steam Supply

Pipe designers and engineers often encounter complex mathematical equations to determine the diameter required for adequate steam discharge capacity at specific flow velocities. This article aims to simplify the calculation of steam pipe sizes. The formulas and factors derived herein can be utilized for determining pipe diameters in steam applications. Ultimately, the findings provide engineers and designers with practical tools to enhance the reliability and efficiency of steam system.

A Comparative Study on the Behaviour of Various Methods of Curing Concrete

Abstract: The properties of cement-based products, especially their durability, are predominantly influenced by curing, since it has a remarkable effect on the hydration of the cement. Proper curing of cement-based products is crucial to obtaining design strength and maximum durability. The curing period depends on the type of cement works, the purpose for which it is to be used and the surrounding atmosphere, namely temperature and relative humidity. Curing is designed primarily to maintain the moisture, by avoiding the loss of moisture from the period in which it is gaining strength. Curing may be applied in a number of different processes and the mass appropriate means of curing may be directed by the site or the construction method. In this critique, a paper struggle has been made to understand the working and efficiency of curing system which are generally adopted in the construction industry and compared with the standard water curing method.

Assessment of the efficiency of functioning of the environmental management system of enterprises

Purpose. A multifactor model is developed to assess the efficiency level of an enterprise’s environmental management system, depending on the effectiveness of organizational environmental measures. Methodology. The authors’ method for determining a comprehensive criterion for monitoring the effectiveness of the environmental management system, which characterizes the efficiency of operation and determines the level of environmental safety of enterprises, is proposed and used. To solve the tasks, a complex research method was also used, which included the analysis and generalization of literary and patent sources, and analytical, experimental research using computer and mathematical modeling methods. Findings. According to the studies and calculations of the generalized environmental quality indicator – 0.64, 0.66 and 0.66, the largest negative impact on the environment is caused by the activities of enterprises in the Pecherskyi, Podilskyi, and Solomianskyi districts of Kyiv, respectively. The obtained data testify to the relationship between the effectiveness of the implemented environmental measures (saving and rational use of resources, application of environmental technologies, advanced training, and environmental competence of employees) and the level of efficiency of the environmental management system of enterprises. Originality. As a result of the studies conducted using actual data, a system of indicators of the generalized environmental quality indicator has been proposed for the first time, which allows determining the environmental efficiency and effectiveness of the implemented environmental measures to assess the effectiveness of the functioning of the implemented environmental management system of the enterprise. Practical value. Based on the research results, a technique is proposed to assess the effectiveness of the environmental management system of enterprises by determining a generalized environmental quality indicator in terms of reducing the negative impact of the enterprise’s activities on the environment. Such an assessment system will help the management of the enterprise to promptly introduce corrective actions to improve the efficiency of the environmental management system and increase the level of environmental safety.

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

Aspects of Developing an Innovative, Energy-Efficient, Low-Emission Co-Generator

Purpose. To develop a model and construct an experimental, innovative co-generator with high energy efficiency and low emissions. This involves developing a system that can efficiently generate both electricity and heat simultaneously while minimizing emissions, contributing to sustainability efforts, and addressing energy demands in a more environmentally friendly manner. Methodology. To achieve the goal, a system approach is utilized, enabling the selection of modelling types for the development of an experimental, cutting-edge co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. For this purpose, the following steps were completed: processing and summarizing available literature and patent sources, analysing scientific and technical papers on the selection and application of modelling types in co-generation systems, and considering principles and individual approaches to input data formation for mathematical modelling. This process enables the selection of a mechanism and the creation of simulation models for effective energy production at various enterprises. Findings. Assessment is performed of the energy efficiency of the co-generator system under various operating conditions, comparing it with existing conventional methods of electricity and heat generation. Results are presented of performance testing to determine the system’s capability to simultaneously generate electricity and heat efficiently, considering factors such as output stability, load responsiveness, and overall reliability. Identification and evaluation are performed of innovative technologies and methodologies integrated into the co-generator design, highlighting their effectiveness in enhancing energy efficiency and reducing emissions. Insights into any operational challenges encountered during the construction, testing, and optimization phases, along with proposed solutions or improvements to solve these problems. The analysis of the overall environmental impact of deploying the co-generator showed potential benefits in terms of reduced greenhouse gas emissions and local air quality improvement. Originality. Using a combination of scientific approaches encompassing physics and heat transfer engineering, in accordance with the first law of thermodynamics, such as the conservation laws of energy and entropy, and principles of heat exchange employed to transfer heat between different mediums, gas kinetics have yielded values for the energy transformation coefficient, indicating qualitative characteristics of fuel thermolysis and power generation as the final product. Practical value. The results provide for developing a comprehensive model of an advanced co-generation system capable of efficiently producing both electricity and heat with superior energy efficiency and minimal emissions. It also entails determining the types of models for mathematical modelling at all management levels and establishing a new method for input data formation for both technologies and their subsystems, incorporating additional technological implementations.

Optimizing solar panel tilt angles across diverse Algerian terrain

Purpose. To optimize the efficiency and performance of a solar system by maximizing the capture of solar radiation through determining the most optimal solar panel tilt angle. Methodology. Stochastic techniques are presently utilized for estimating, optimizing, and predicting various solar energy systems. The authors have developed an algorithm that simulates the echolocation behavior of bats. Findings. To attain this objective, we evaluated different angles of inclination for the incident energy surface that will maximize the sunlight. Next, we compared the intensity of incident solar energy from a horizontal surface and the same surface tilted at the optimum angle. As a result, we determined the optimal tilt angles for other Algerian cities not covered by this study, based on two factors: geometry and climate, using multiple linear regression analysis. The results obtained reflect average monthly and annual values for solar panel tilt angles. These results depend on the latitude and sunshine levels of the locations studied. Originality. The present study introduces a computational algorithm that utilizes the echolocation behavior of bats to determine the most advantageous tilt angle for a photovoltaic (PV) panel. Practical value. Optimizing solar panel angles across various terrains in Algeria is crucial for maximizing the energy efficiency of photovoltaic installations. The optimization algorithm based on the echolocation behavior of bats allows for the determination of optimal angles by taking into account regional, climatic, and seasonal variations, thereby increasing energy production. This innovative approach offers an effective solution for improving the profitability of solar systems while contributing to sustainable development.

Optimization of TiO2-based UV-LED Photocatalytic System for Mixed Dyes and Pharmaceutical Contaminants

To optimize the working efficiency of the novel UV-LED system based on TiO2 photocatalyst, the influence mechanism of LED lamp arrangement, light source wavelength and working voltage on photocatalytic efficiency was investigated. Acid red 26 (AR 26), acetaminophen (ACT) and diclofenac (DCF) were used as contaminant targets of the photocatalytic system. LED lamp arrangement had almost no effect on the degradation of AR26. However, the degradation efficiency of ACT and DCF was improved under a higher light uniformity. The ACT concentration and DCF concentration at 360 min decreased by 14% and 15%, respectively, with increasing light distribution from 45% to 66.5%. The main reason for this discrepancy in effect was whether the rate-determining step of the degradation mechanism was affected by the light uniformity. The short wavelength and high working voltage of LEDs were conducive to the photocatalytic degradation of contaminants to a different degree. When the wavelength was reduced from 405nm to 365nm, the conversion of AR26, ACT, and DCF increased by 77%, 227%, and 106%, respectively. The conversion rates of AR26, ACT, and DCF increased by 28%, 54%, and 32%, respectively, with voltage increasing from 3 V to 4 V. The data of this work will provide support for optimizing the working efficiency of UV-LED systems based on TiO2 photocatalysts. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Utilization of big data analytics to identify population health trends and optimize healthcare delivery system efficiency

Big data analytics has emerged as an incredibly valuable tool in understanding population health trends and improving the effectiveness of healthcare delivery systems. By leveraging big data sources from various domains, including the patients’ electronic health records, claims data, wearables, and social media accounts, healthcare organizations can obtain novel and rich insights regarding population health characteristics, predisposing factors, and disease progression. The arrival of big data has transformed healthcare organizations by providing a scientific population health management and health system enhancement tool. Using superior analytical tools, healthcare entrepreneurs and policymakers can discover relations, rates, and patterns that are concealed in large data sets. Such knowledge can be used for early detection of possible interventions, management of resources, and prevention measures, thus leading to better health and less spending on health issues. Moreover, big data analytics helps in early diagnostics and the development of management strategies for high-risk groups, which in turn improves the functioning and effectiveness of healthcare systems. It is also important to note that big data solutions in healthcare are not limited to population health management, but also include functional aspects of healthcare organizations. Additionally, using data about patient movements, resource consumption, and clinical activity, it is possible to determine inefficiencies and opportunities to improve processes in healthcare organizations. This approach of collecting and analyzing data helps in decision making thus reducing time and improving patient flow and experience. However, incorporating real-time data into the clinical decision support systems can improve diagnostic capabilities, treatments offered, and patient tracking resulting in improved quality of services delivered.

Exploring the Efficiency of Hybrid Recommender Systems Implemented with TensorFlow Framework

In recent years, the field of recommendation systems has seen significant advancement with the introduction of hybrid approaches. These systems combine the strengths of multiple recommendation techniques to provide more accurate and diverse recommendations to users. In this research, we propose and evaluate the effectiveness of a hybrid recommender system that utilizes TensorFlow, an open-source machine learning framework, to implement the system. The proposed system combines both collaborative and content-based methods to remove the cold start problem and make personalized recommendations that can recommend similar movies to the users based on features extracted by the model. The results of our experiments demonstrate that the proposed hybrid system outperforms traditional singular methods and can be effectively implemented using TensorFlow. This research provides insights into the potential of TensorFlow for building efficient hybrid recommendation systems and the benefits of combining multiple recommendation techniques.

Optimization of Hybrid Energy Systems Based on MPC-LSTM-KAN: A Case Study of a High-Altitude Wind Energy Work Umbrella Control System

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments.

Strengths and weaknesses of computerized clinical decision support systems: insights from a digital control center (C3 COVID-19) for early and personalized treatment for COVID-19.

Clinical Decision Support Systems (CDSS) are computer-based tools that leverage the analysis of large volumes of health data to assist healthcare professionals in making clinical decisions, whether preventive, diagnostic, or therapeutic. This review examines the impact of CDSS on clinical practice, highlighting both their potential benefits and their limitations and challenges. We detail the experience of clinical medical professionals in the development of a virtual control center for COVID-19 patients (C3 COVID-19) in Spain during the SARS-CoV-2 pandemic. This tool enabled real-time monitoring of clinical data for hospitalized COVID-19 patients, optimizing personalized and informed medical decision-making. CDSS can offer significant advantages, such as improving the quality of inpatient care, promoting evidence-based clinical and therapeutic decision-making, facilitating treatment personalization, and enhancing healthcare system efficiency and productivity. However, the implementation of CDSS presents challenges, including the need for physicians to become familiar with the systems and software, and the necessity for ongoing updates and technical support of the systems.

The Application of Near Field Communication (NFC) in Public Transportation: Current Practices, Challenges, and Future Prospects

In the field of public transportation, the introduction of NFC technology can realize contactless payment and automatic ticket checking functions, significantly improving system efficiency and passenger experience. The article first reviews the development history of the public transportation system and the basic principles of NFC technology, and then analyzes the technical architecture and working principle of the NFC payment system. Through the cases of public transportation systems in many cities around the world, the practical application and effect of NFC technology are demonstrated. This article also verifies the transaction speed, stability and user satisfaction of NFC technology in the public transportation system through a series of experimental evaluations. The results show that NFC technology has excellent performance in terms of processing speed, system stability and user satisfaction, especially during peak hours, its advantages are more obvious. At the same time, the research also pointed out the limitations of current NFC technology applications and the challenges faced in practical applications. In the future, NFC technology will become more complete and is expected to provide passengers with a more personalized and intelligent service experience.

MoS2/porous carbon nanofiber heterostructures for efficient evaporation-driven generators.

Evaporation power generators (EPGs) based on natural water evaporation can directly convert heat energy from the surrounding environment into electrical energy. However, EPGs are difficult to commercialize due to the low charge generation and transport efficiency of single material systems, resulting in unsatisfactory open-circuit voltages and short-circuit currents. Here, we step by step prepared MoS2/porous carbon nanofiber (PCNF) heterogeneous systems by electrospinning and hydrothermal methods. Electron microscope measurements proven the uniform coating of high-crystalline quality MoS2 nanosheets on PCNF fabrics, and the uneven concave-convex surface increased the specific surface area. MoS2 covered PCNF fabrics retained good hydrophilicity, which was suitable for absorbing water and keeping the surface wet during long-term evaporation. Moreover, layered MoS2 with rich surface charge improved the charge transfer of the MoS2/PCNF fabrics. As a result, the open-circuit voltage and short-circuit current of the EPGs prepared with MoS2/PCNF fabrics were improved to 0.25 V and 75 μA, respectively, compared with those based on PCNF fabrics, which demonstrated that the MoS2 coatings improved the interaction area with water and the charge transfer effect of the EPGs. This heterogeneous combination strategy provides ideas for the preparation of high-performance EPG materials.&#xD.

Development of health care expenditures in Finland up to 2040

Abstract Background Increasing health care expenditures are threatening public health by complicating the supply of adequate health services. Exploration of possible future developments of the expenditures is needed to understand how to prepare for future or to change an unwanted course of development. We provided projections by health care sectors taking into account demographic and other developments. Methods We used national data on the realized expenditures per capita by age group, sex and health care sectors from 2017 to 2020, and official forecasts of the population in Finland up to 2040. Future per capita expenditures were projected with Bayesian linear regression models incorporating a priori dependency structure for the slope parameters of the age groups such that the time trends of adjacent groups would be more similar than other groups. These projections were then combined with the population forecasts to estimate the total expenditures. Results The greatest increase (164%) was estimated for the total costs of the primary outpatient healthcare: from 2153 to 5677 million euros in 2020-2040, such that 7% of the growth was attributed to demographic developments. The estimated growths were 68% for the primary inpatient healthcare, 14% for the specialised somatic healthcare, and 52% for the urgent and emergency health services, of which 91%, 98% and 21% were attributed to demographic developments, respectively. Conclusions Notable increases in expenditures are expected in all considered health care sectors, but there are great differences in the proportions of the growth attributed to demographic developments. Due to the limited role of demographic changes, it is worth focusing on controlling the impacts of other developments by, e.g., improving the population health and the efficiency of the health care system. Key messages • Notable increases are expected in health care expenditures up to 2040. • Impacts of demographic changes varies markedly between health care sectors.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Analysis Of The Impact Of Tax Reform On Income Tax (PPH) 21 Revenue In Indonesia

This research aims to analyze the impact of tax reform on Income Tax (PPH) 21 revenues in Indonesia. Tax reform was carried out by the government to increase efficiency and fairness in the tax system. The main focus of this research is identifying tax policy changes that have been implemented and analyzing how these changes affect PPH Article 21 revenue. This research uses a qualitative approach with descriptive methods, while the type of research uses literature study research to analyze tax policy, as well as the impact of tax reform on revenue. PPH Article 21. It is hoped that the results of this research will provide a better understanding of the effectiveness of tax reform in increasing income tax revenues from high-income employees.

Institutional Strengthening and Improvement of National Logistics Performance Through The Establishment of A National Supply Chain Agency

The logistics sector has a very important role in driving Indonesia's economic growth. However, the national logistics system currently faces various obstacles that prevent it from reaching its full potential. Many challenges slow down the development of this sector. To overcome these obstacles, it is necessary to strengthen institutions that are able to integrate and optimize logistics performance throughout Indonesia. This research suggests the establishment of a National Supply Chain Agency as a solution to improve the coordination, efficiency, and effectiveness of the logistics system. The purpose of this study is to identify and evaluate the role and impact of the establishment of the National Supply Chain Agency on improving national logistics performance. This research uses a qualitative method with data collection from literature studies. The collected data were then analyzed through the stages of filtering, presenting, and drawing conclusions. The results showed that the establishment of the National Supply Chain Agency is an important step to improve logistics performance in Indonesia. The agency is expected to address current challenges, such as inefficiency, high logistics costs, and distribution delays. Thus, the National Supply Chain Agency is proposed as a solution to improve the competitiveness of national logistics, both in the domestic and international markets, and contribute to sustainable economic growth.

Exploring the Impacts of Artificial Intelligence Interventions on Provider Practice practice

Abstract Background Despite the growing prevalence of AI in healthcare, there has been a lack of recent studies exploring its impact on providers’ practices and patient outcomes. This study investigates healthcare providers’ perceptions of AI interventions’ influence on their practice efficiency and patient outcomes, as well as the correlation between providers’ perceptions of AI and its effects on patient outcomes. Methods A self-administered questionnaire was mailed to 38 healthcare providers at a rural medical center in north Texas, with a 70% percent response rate (n = 27). In addition to descriptive statistics, multivariable logistic regression test was conducted to discern the relationship between their perceptions of AI and its impact on patient outcomes. Results The findings revealed that a majority of providers perceived AI to still be in its early stages, posing challenges for practice and having a limited impact on patient outcomes. Additionally, various factors such as age, gender, a user-centered design approach, AI experience, along with perceptions of workplace support and stress, significantly shape providers’ attitudes toward AI and ultimately affecting patient outcomes. Conclusions Before AI technology can fulfill its promise of transforming healthcare through integration with other technologies, the healthcare sector must address numerous hurdles. It is crucial to approach the development and testing of intricate systems like AI-integrated electronic health record (EHR) systems with caution to ensure their reliability and dependability in clinical decision-making. Additionally, navigating medico-legal responsibilities and pursuing fair distribution of benefits are equally crucial. Key messages • This study offers insights into the extent of AI interventions’ impact on healthcare. • This study specifically focuses on enhancing patient care, reducing costs, and improving the efficiency of the healthcare system.

Protein Scaffold-Mediated Multi-Enzyme Self-Assembly and Ordered Co-Immobilization of Flavin-Dependent Halogenase-Coenzyme Cycle System for Efficient Biosynthesis of 6-Cl-L-Trp.

Flavin-dependent halogenase (FDH) is highly prized in pharmaceutical and chemical industries for its exceptional capacity to produce halogenated aromatic compounds with precise regioselectivity. This study has devised a multi-enzyme self-assembly strategy to construct an effective and reliable in vitro coenzyme cycling system tailored for FDHs. Initially, tri-enzyme self-assembling nanoclusters (TESNCs) were developed, comprising glucose dehydrogenase (GDH), flavin reductase (FR) and FDH. The TESNCs exhibited enhanced thermal stability and conversion efficiency compared to free triple enzyme mixtures during the conversion of L-Trp to 6-Cl-L-Trp, resulting in a 2.1-fold increase in yield. Subsequently, an ordered co-immobilization of GDH, FR, and FDH was established, further amplifying the stability and catalytic efficiency of the FDH coenzyme cycle system. Compared to the free TESNCs, the immobilized TESNCs demonstrated a 4.2-fold increase in catalytic efficiency in a 5 mL reaction system. This research provides an effective strategy for developing a robust and efficient coenzyme recycling system for FDHs.