ITEGAM-JETIA

Mechanical Properties Characterization of Welded 3CR12 Stainless Steel

2024-12-12T12:35:09-03:00

The use of metal inert gas (MIG) welding for joining metals frequently results in the change of the mechanical properties and the microstructure of the metal at the welded areas. This is due to the welding heat input and heat transfer. 3CR12 is a low-cost special stainless steel containing 12% chromium. It is considered to have good mechanical properties and corrosion resistance as a base metal, and understanding how these properties change as a result of MIG welding enables the drawing of proper conclusions regarding the properties of MIG welded 3CR12 stainless steel. However, little is known about its properties and behavior after welding with metal inert gas (MIG) welding, which is the most common welding process for welding stainless steel, and little is known about its weldability. Due to inadequate information about MIG welded 3CR12 stainless steel, it is hard to make a reliable statement about its properties and behavior. Thus, necessitating the need to gain knowledge regarding MIG welded 3CR12 stainless steel. This paper aims to characterize the mechanical properties of welded 3CR12 stainless steel. The focus is on investigating how the mechanical properties and the microstructure of 3CR12 stainless steel evolve due to MIG welding.

A New PID Type Controller Based on Modified Crisp Logic

2024-12-12T12:35:09-03:00

A PID type fuzzy logic controller (FLC) is a control scheme that utilizes fuzzy inference systems to replicate the behavior of a classical PID controller. This approach provides an alternative for systems where traditional PID control encounters difficulties, particularly in scenarios involving human expertise or non-linear behavior. In this study, we propose a novel PID-like controller that employs a modified crisp logic method—a rule-based approach designed to implement the reasoning process. This method aims to reduce processing time in fuzzy inference systems by using crisp sets instead of fuzzy sets and simple calculations to generate the output. Simulation results demonstrate the effectiveness of the proposed method, achieving comparable performance with the added benefit of reduced processing time.

Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models

2024-12-12T12:35:09-03:00

This study analyzes the detection of substation fouling failures in District Heating and Cooling (DHC) systems using synthetic data. In the study, high, medium and low levels of contamination are considered and both machine learning and deep learning techniques are applied for the detection of these failure types. Within the scope of the analysis, machine learning algorithms such as K-Nearest Neighbors, XGBoost and AdaBoost are compared with the proposed Convolutional Neural Network (CNN) model. The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. The results obtained show that the proposed Convolutional Neural Network model provides higher accuracy and overall success compared to machine learning methods. High performance measures such as Matthews correlation coefficient 0.944 and accuracy rate 0.972 were achieved with the CNN model. These findings reveal that contamination detection in substations can be done effectively with CNN-based approaches, especially for situations that require high accuracy. This study on fault detection in DHC systems provides a new and reliable solution for industrial applications.

Techno-Economic Evaluation of Rice Husk Co-Firing as a Sustainable Biomass Fuel Alternative

2024-12-12T12:35:09-03:00

Co-firing is a technology that blends coal with biomass at a specific ratio in steam power plants. In the Lontar area, which is surrounded by vast rice fields, there is significant potential to utilize rice husk waste as a biomass feedstock. The aim of this design is to reduce Indonesia's dependence on coal, which is considered a non-renewable energy source, while promoting the transition to renewable energy. As coal consumption continues to rise each year, this research explores the potential to reduce coal usage by co-firing it with rice husk biomass. The co-firing design implements a biomass blending ratio of 2-5% with coal. The study's results indicate that, on average, only 0.608 kg of fuel is needed to generate 1 kWh of electricity. Additionally, the Net Plant Heat Rate achieved is 2,557.5 kcal/kWh, which, when compared to non-co-firing values, demonstrates an improvement in the power plant’s efficiency. From an economic perspective, the power plant in the Lontar area could save fuel costs amounting to Rp 9.24 billion over a four-month period, with the average production cost of co-firing being only Rp 346.77/kWh. Based on these technical and economic results, the co-firing design is deemed feasible and promising for further implementation in the coming years.

Evaluating Productivity and Costs of Concrete Casting for Structural Elements

2024-12-12T12:35:09-03:00

Indonesia has many construction companies due to its growing building construction sector. The productivity of heavy equipment plays a crucial role in project completion time and construction costs. A study comparing casting volume using Revit software is essential for the construction sector. Researchers are using Revit software to determine casting materials and assess casting equipment's productivity, such as portable pumps used for casting beams and floor slabs, concrete buckets used for casting columns, and casting costs. This quantitative research is a testament to our commitment to precision, as it utilizes actual project data, such as daily casting mapping and concrete supply. The data is then meticulously processed to produce output, such as differences in concrete volume between Revit software and on-site realization, the productivity of casting equipment, and casting costs. The analysis shows significant differences in material usage for casting on the 10th and 11th floors, directly impacting future construction projects' efficiency and cost-effectiveness.

DTC of a 5-Level MMC Fed 3-Φ Induction Motor with PI and FLC Using CBAPOD PWM Technique

2024-12-12T12:35:10-03:00

Industrial motor drives, particularly three phase induction motor drives, have been adopting Modular Multilevel Converters (MMC). MMC is utilized in the design of power and control circuits to give the appropriate switching sequences that yield the corresponding output voltage levels. This paper describes the Alternate Phase opposition Disposition Pulse Width Modulation (APOD-PWM) method and fuzzy logic controller used to regulate the 5-level MMC topology of an induction motor. The MMC's switching mechanism is essential for enhancing the induction motor drive's power quality. The converter can be used as a source of controlled voltage because it has numerous distinct voltage levels accessible. Researchers have improved the application of fuzzy logic for Direct Torque Control (DTC) in variable speed drives that rely on multilevel inverters in the past few years. The MATLAB/SIMULINK simulator is the foundation of our suggested method, which measures the effectiveness of direct torque control with respect to ripple in current, speed, torque, and transient response. Both the fuzzy logic controllers (FLC) as well as the PID controller were compared in this study. THD content will significantly decrease as a result of this.

Assessment of the Adequacy of Electrical Energy Demand Forecast Model for the Nigeria Power Distribution System via Stationarity Test

2024-12-12T12:35:10-03:00

Electric energy demand forecasting model is an essential tool in the course of planning in electricity industry. Though, there has been increasing concern to fix models for various domains. The adequacy and accuracy of these models for forecasting reasonable energy generation capacity, scheduling and system management planning are paramount. Inaccurate model will give forecasting that are either underrated that will incapacitate socio-economic growth by not supply enough electrical energy for development, or overestimated leading to excess electrical energy generation without commensurate returns on investment, another form of economic jeopardy. In this paper, Assessment of the adequacy of Electrical Energy Demand Forecasting Model for the Nigeria Power Distribution System via Stationarity test was performed as crucial stage in development of time series technique of energy demand forecast model. In the stationarity investigation of data set under the null hypothesis as a test tool for the confirmation of stationarity and non-stationarity of energy demand data for processing and further analyses of energy demand in power distribution system in Nigeria. Data were collected from five 33kV feeders each with sixty-point of monthly peak Load demand for five years (2015-2019) from Ibadan Electricity distribution Company (IBEDC). R- Software was used as optimization tool for the analyses. The end result was interpreted by Critical values for Augmented Dickey-Fuller method. Findings shows that data from three of the feeders were non-stationary they will go through data differencing to make the data suitable for further investigations as a mixture of an autoregressive integrated moving average ARIMA while two are stationary and can be authenticated for further analyses. The application of this test to further difference the datapoints that are non-stationary will lead to stationary dataset, hence, give viable model for accurate energy demand forecasting model development.

The Impact Study of Flexible Alternating Current Transmission System on Transient Stability of Power Systems Using MATLAB CODE and Power World Simulator

2024-12-12T12:35:10-03:00

The purpose of this work is the study of the influence of FACTS on transient stability of power systems using a Power World Simulator software, and the computer code transient stability code 'TRANS_STAB_CODE' 'which was created in MATLAB. We tried in the introduction of this work to provide a description on the transient stability: definition, transient stability criteria, and equations. Then we gave an overview on the modelling of FACTS in power systems, the definition and types of Flexible Alternating Current Transmission System, their schemes, and their equations, then presented the model of UPFC (Unified Power Flow Controller) as an example. Secondly, we discussed the code, 'TRANS_STAB_CODE' that was created in MATLAB by giving a description of the code and their structure with graphics windows, and then I described privately the Power World

Simulator simulation software. Then we presented the test electrical network, and the results of transient stability of this network systems with the code 'TRANS_STAB_CODE' 'and with Power World Simulator where no and where there Flexible Alternating Current Transmission System. At the end, we analyzed the results of both programs in both cases: with and without Flexible Alternating Current Transmission.

Causes, Effects, and Practical Methods of Harmonic Reduction in Iranian Cement Factories with a Focus on Plant Development

2024-12-12T12:35:10-03:00

Cement factories in Iran are considered among the oldest industries. Due to favorable domestic and international markets, these plants have pursued development, whether willingly or unwillingly. Development involves improving the existing structure to enhance efficiency and adding production lines parallel to the old ones. In this situation, the presence of very large nonlinear loads in these industries, which are mostly formed by high-power variable-speed electric drives, has always caused serious problems due to harmonic distortions imposed on the factory and distribution lines. These effects should be considered in various sections when developing and designing new electrical systems. Despite various studies on this subject, none has presented a comprehensive approach specifically for this industry. This article delves into a thoroughly practical and empirical examination of the causes and consequences stemming from harmonics, alongside the constraints posed by standards. It also scrutinizes implementable techniques for solving harmonic-related problems and mitigating their effects with a focus on the development outlook of cement factories.

Using Business Analysis to Enhance Sustainability and Environmental Compliance in Oil and Gas: A Strategic Framework for Reducing Carbon Footprint

2024-12-12T12:35:10-03:00

The oil and gas industry has been identified also as a major source of greenhouse gases and contributes approximately 42% of the total global CO₂ emissions. As the world continues to strive towards sustainable development goals including those of the Paris accord, this industry is under pressure to decrease its emission of carbon. This review discusses the application of business analysis tools on a strategy that supports sustainability and aligns oil and gas companies with environmental standards. The framework is centred on the deployment of next-generation technologies as carbon capture and storage, on establishing global standards of corporate climate policies, on engaging stakeholders, on optimizing business processes, on facing climate risks and on experimenting with new forms of biofuels. Some of the problems include high costs, complications due to state regulations, and negative attitudes from the public and stakeholders.These issues can be overcome through effective public-private partnerships, sharing of information, and diversification of research spending. In addition to mitigating emissions the envisaged framework is designed for companies to derive strategies that will keep them competitive and socially responsive. The present review also underlines the necessity to use a complex approach to increasing the sustainability of the oil and gas industry.

Multi Criteria Model of Supply Chain Sustainability Evaluation & Development Strategy for Agritech Start-up

2024-12-12T12:35:10-03:00

The presence of agritech startups has successfully become a visionary solution at the agriculture supply chain to make it more efficient. Unfortunately, the supply chain in agritech start-ups is not sustainable and has various disadvantages. Therefore, this study aims to 1) analyze the current situation of supply chain at agritech start-ups, 2) analyze the sustainability index at agritech start-ups, and 3) analyze the strategy formulation needed by agritech start-ups through a soft system methodology approach. The research methods used were mixed methods. The stages of the research consisted of identifying current supply chain conditions, determining supply chain performance indicators by involving experts, assessing the supply chain sustainability index, and designing a conceptual model. The results showed the quality of the commodities produced did not meet standards and became a waste. The results of sustainability analysis in multidimensional results show a value of 48.84, economic 58.51, social 46.93, ecological 32.61, technology 42.36 and institutional 63.80. The results of the soft system methodology show that the strategies include contract farming, periodic coordination between stakeholders, GAP and OHS assistance, preparation of SOP for cultivation, application of borrowed tools under supervision, application of socialization of environmental literacy, and implementation of reserve supply chain.

Procurement and Logistic Processes

2024-12-12T12:35:10-03:00

Nowadays, managing procurement processes in companies and supply chains facilitates competitiveness among actors. In this study, the objective was to evaluate the performance of procurement processes in Ecuadorian companies. To achieve this, a modified verification instrument was used, composed of the dimensions: the importance of procurement and its relation to supply chains, procurement, suppliers, cycle, cost and human talent training. The instrument evaluated the performance of procurement areas in 162 Ecuadorian companies. The instrument was validated and the data was processed using descriptive and inferential statistics. The results of this study show a low performance level in the procurement areas in the observed companies. The weakest variables were: procurement, demand and providers. The study contributres with relevant data that can be used to create strategies to improve the procurement performance in Ecuadorian companies .

Machining Parameters Optimization in Wet Turning of EN31 Material Using Box-Behnken Approach of RSM

2024-12-12T12:35:10-03:00

Manufacturing sectors are always challenged to enhance surface quality and tool life while reducing machining costs and setup times in hard-turning operations. In line with this, the current study focused on surface roughness optimization employing machining parameters such as cutting speed: ν (160-260 m/min), feed: f (0.1-0.2 mm/rev), depth of cut: d (0.05-0.15 mm), and tool nose radius: re (0.4-1.2 mm) as functions. The experiment was designed by using Box-Behnken approach of RSM and carried out on a commercial CNC machine using EN31 material at 47 HRC. The research found that machining parameters have a considerable effect on surface roughness, as do stresses, vibrations, heat generation, and increased material per pass. The experimental surface roughness observed in between 1.34-2.81 μm whereas estimated surface roughness have R2=0.9976. The Anova design model showed face value of 356.21 which indicates the developed model is noteworthy. The significant and marginal effect of machining parameters are evaluated by considering p-value and overcall error observed within the range of 1.613-1.974%.

A Radio Frequency System for Smart Attendance in Schools

2024-12-12T12:35:10-03:00

Attendance is necessary in schools for curbing staff and students abseitism and lateness to classes. Traditional method of taking attendance where students write and sign on a sheet of paper is time consuming and labour intensive. This work presents smart attendance system that eases the way attendance is taken in lecture halls. The system integrates database with hardware components such as Radio Frequency Identification (RFID) card, RFID reader, I2C driver, display unit and Node Microcontroller Unit (NodeMCU) which has lower power consumption and better processing speed than Arduino microctontroller commonly used in the literature. Performance test conducted shows that the system performs its functions satisfactorily. The system reads and transfers students’ data to the database in 3 seconds. In addition, students are asked to evaluate the performance of the sytem in terms of ease of use, functionality, efficiency, friendly user interface and speed of data transfer to the database. It is found that a good number of the students rate the ease of use, functionality, friendliness in the user interface and speed of data transfer excellent while more than average of the students rate the efficiency of the system good. The system is recomended for use in schools

Microstructural investigations on the Fractural behavior of SS304 butt joints developed through vibratory welding

2024-12-12T12:35:10-03:00

Determination of weld strength through the fractural behaviour at different loads plays dominant role for selection of weld process in real-time applications. Assistance of vibratory treatment to the conventional welding process can bring major changes in the microstructure of weldment at greater extent. The intervention of increment and decrement in the mechanical properties at different vibratory welding parameters dependent on the grain refinement. The study focuses on analyzing microstructural transformations' influence in the weldments on different mechanical properties at various amplitudes and voltages of 2, 4, 6V. The tests such as tensile, Rockwell hardness and Charpy impact are conducted and fracture modes are identified. SEM analysis is performed to characterize the microstructure of different fractures between the developed butt joint on SS304 and susceptibility to crack is investigated. The fracture is exhibited as more ductile for samples developed with the assistance of vibratory treatment to the gas metal arc welding process. The decrease of columnar dendrites at weld zone and paradigmatic shift to equiaxed shapes under vibratory treatment prompted to achieve maximum enhancement in the mechanical properties and reduction of delta ferrite is responsible for decrement for further ranges.

EFR-Net: Enhanced Fracture Prediction in Osteoporosis with U-Net-Based Analysis

2024-12-20T20:02:59-03:00

Osteoporosis, a prevalent bone disease, is characterized by the equation , where is bone density, is maximum bone density, and is osteoporosis rate. Conventional imaging techniques, governed by the formula where accuracy is, is image thresholding, and is scan resolution), often yield a detection accuracy of merely 75%. In this work, we introduce the EFR-Net: a U-Net-based deep learning model. Its efficacy is represented by the equation , where is the new accuracy, is the fraction of fracture-prone regions detected, is the Dice coefficient, and is the noise reduction factor. Leveraging a comprehensive dataset of 10,000 bone scans, our model, adhering to the above equation, achieved a commendable accuracy rate of 89%. This translates to a mathematical improvement represented by , yielding a 14% enhancement over traditional methods. Moreover, the reduction in false negatives, a critical metric in medical diagnoses, can be quantified by , where and are the old and new false negatives respectively. EFR-Net's innovative approach and promising results underline its potential in revolutionizing osteoporosis-related fracture prediction, offering a robust bridge between computational advancements and clinical necessities.

Smart Intersection and IoT: Priority Driven Approach to Ubran Mobility

2024-12-20T20:02:58-03:00

The recent growth in car use and population have been identified as potential drivers of municipal traffic congestion, particularly in emerging nations with inadequate road networks. In Nigeria, for example, traffic wardens and traffic lights are prominent traffic control measures used to ease traffic congestion at major road intersections. However, stress, public anger, and rash traffic signal judgements restrict the effectiveness of these tactics, resulting in delayed mobility, decreased transit times, and a climate disaster. Recent solutions have emphasized emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and artificial neural networks (ANW). Consequently, the efficient use of these technologies can provide a sustainable future for city traffic management in Sub-Saharan Africa. This model seeks to develop a low cost internet-of-things traffic surveillance system to improve vehicle mobility on a Nigerian closed campus. The goal is to alleviate the academic community's problem of peak-hour traffic congestion by delivering real-time traffic updates.

A Review of Poloxamer 407-Induced Hyperlipidemia in In Vivo Studies

2024-12-20T20:02:58-03:00

Poloxamer-407, a surfactant and emulsifier commonly used in pharmaceutical formulations, has attracted attention as a potential contributor to increased lipid levels in the body based on in vivo research. A systematic review was conducted in January 2023 to examine the mechanisms by which poloxamer 407 contributes to the development of hyperlipidemia in in vivo studies published between 2010 and 2022, yielding 1240 results. Study selection was done using the PRISMA method. Manual screening, quality assessment, and data extraction from the search results were rigorously conducted in accordance with inclusion and exclusion criteria. Seventeen identified studies showed a correlation between the use of poloxamer 407 and a significant increase in blood lipid levels, creating conditions of hyperlipidemia. The significance of these findings lies in a deeper understanding of the potential side effects of poloxamer 407, especially in the context of human health. Its implications can guide further developments in the use of this compound or similar chemicals in pharmaceutical formulations. Therefore, this research provides a foundation for further studies that can detail the long-term impacts, underlying mechanisms, and possible mitigation strategies to manage side effects associated with the use of poloxamer 407.

Impact of Nitrogen Incorporation on Band Gap Bowing in Zinc-Blende GaAs₁₋ₓNₓ:A First-Principles Study

2024-12-20T20:02:57-03:00

This study, utilizing full-potential linear muffin-tin orbital (FPLMTO) calculations within density functional theory (DFT), delved into the structural properties of zinc-blende GaAs_1-xN_x alloys. By varying the nitrogen concentration (x= (0.125, 0.083, and 0.063), we observed deviations from Vegard's law for lattice parameters and nonlinear behavior of the bulk modulus. The band gap bowing was primarily attributed to volume deformation effects, as elucidated by the Ferhat approach. Our findings demonstrate that the electronic and structural properties of GaAs_1-xN_x are strongly influenced by the nitrogen concentration. These variations present exciting opportunities for bandgap engineering and the design of wide-bandgap optoelectronic devices.

Using Machine Learning to evaluate Industry 4.0 Maturity: A comprehensive analysis highlighting Lean's impact on Digital Transformation

2024-12-20T20:02:57-03:00

The rise of digital technologies in manufacturing and industries, known as the fourth industrial revolution, has created both opportunities and challenges for businesses. To succeed in this era of "Industry 4.0," companies need to assess their digital maturity. Through this study, we analyze the global state of Industry 4.0 maturity, identifying industry-specific trends, challenges, and potential growth. Leveraging advanced machine learning techniques, including data analysis, prediction, and recommendations. The study explores the complexities and evolution of Industry 4.0. Additionally, we show how machine learning plays a pivotal role in this analysis, contributing to enhanced insights and decision-making capabilities. Our research aims to not only assess the current state but also forecast future roadmaps while providing tailored recommendations for enhancing maturity levels. We aim to evaluate various machine learning based approaches for addressing these inquiries, focusing on Decision Tree, Support Vector Machine, and Random Forest models. We will choose the best performing model for our scenario. Initially, we use unsupervised learning through Hierarchical Clustering for grouping data, followed by data expansion. Subsequently, we employ supervised learning techniques, particularly Decision Tree, for descriptive, predictive, and perspective analysis. Among our recommendations for enhancing Industry 4.0 maturity levels, we advocate for extensive interventions, but exclusively for companies meeting predetermined criteria delineated within the decision tree node. Furthermore, we examine the influence of Lean on digital transformation. Through this interdisciplinary approach, our findings contribute to a deeper understanding of Industry 4.0 evolution and offer practical insights for strategic decision-making in the era of digitalization.

Enhanced Torque Estimation Based on a Cognitive Training Model for Robust PMSM in EV Applications

2024-12-20T20:02:56-03:00

Permanent Magnet Synchronous Motors (PMSM) which are used in commercial applications, requires precise torque calculation, which is necessary for the intended control. Conventional Model Predictive Control (MPC) performance is hampered by model parameter mismatches and high computational demands, precise torque control often necessitates the knowledge of rotor speed and position, which are traditionally obtained using mechanical sensors. The paper proposes Feedforward Neural Network model to estimate the parameter for desired switching of inverter for accurate position of rotor in optimized time. However, this model uses the d-q axis currents, voltages, rotor angle as inputs, and electromagnetic torque as the output. The model is developed with the help of Python programming based on Hyperband algorithm for hyperparameter tuning. Hyperband algorithm, efficiently optimizes hyperparameters by adaptive resource allocation, early stopping, reducing training time and improving accuracy. This integration allows the neural network(NN) to dynamically optimize its architecture, ensuring precise torque estimation. This approach addresses computational challenges and enhances the system's efficiency and responsiveness to real-time parameter variations and disturbances, leading to more robust and high-performing motor control applications.

Hybrid UM-LT-AHE Technique for Contrast Enhancement of Medical Images

2024-12-20T20:02:56-03:00

This paper is concerned with combinations of un-sharp masking, logarithmic transformation, and adaptive histogram equalization techniques to arrive at a hybrid method for enhancement of different types of medical image's contrast. Motivation behind the hybridization is the need to have a contrast enhancement method that is not application-specific and that can be deployed to several medical image enhancements. Four different types of medical images: X-ray, ultrasound, magnetic resonance, and computer tomographic images are utilized in the evaluations of the proposed hybrid contrast enhancement method. As performance metrics, absolute mean brightness error, mean square error, peak signal to noise ratio, and entropy are used. Comparative results, both qualitative and quantitative, were conducted at the end of the research, and the proposed method outperformed the other three (CLAHE, fuzzy-based, and wavelet transform-based) related selected methods in the field that used the same dataset in terms of testing accuracy. The enhancement quality of the proposed method was found to be satisfactory and can be used for any time of medical image; thus, the proposed hybrid technique produces better enhanced medical images from different medical image inputs.

Estimation of the Time of Occurrence of the Maximum Electrical Demand by Selecting the Optimal Classification Model and Making Use of Unbalanced Data

2024-12-20T20:02:55-03:00

Studies on electricity demand forecasting usually focus on the magnitude of the variable, however, the methodology used in this study also addresses the time at which the peak demand occurs, crucial for planning energy generation, smoothing the demand peaks and establishing differentiated rates. To predict the time of maximum demand, supervised machine learning algorithms were used: random forests, K nearest neighbors, support vector machine, and logistic regression. The dataset consists of hourly maximum and minimum demand data from 2021 to 2024 for a country in South America, including environmental factors such as temperature and seasonality. Since the data in the peak demand prediction variable is unbalanced, the study used oversampling techniques such as SMOTE-NC (synthetic instances of the minority classes to balance the data set). A multi-criteria decision-making approach is used to select the best classification model, considering model evaluation metrics as decision criteria. The most important conclusion drawn by the study is that the model obtained with the support vector machine algorithm turned out to be optimal, and successfully predicted the time of maximum demand on 15 of the 17 test days. The findings highlight the unbalanced nature of peak demand hours, which predominantly occur around 8 pm.

An Overview of Improving Logistics Processes in Health Facilities

2024-12-20T20:02:55-03:00

Internal logistics processes in health facilities are complex and important to ensure health services and high-quality patient care. Therefore, the review is focused on issues, solutions and challenges related to logistic processes in health facilities. This review follows the PRISMA guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2000 and 2023. Based on the search and selection process, a total of 26 articles were included in the review. A qualitative content analysis was carried out. In this period analytical research dominates over design research. In terms of research strategies, a qualitative approach is preferred. The problem contexts addressed in the articles have been divided into five thematic areas. Most articles can be classified in the area dealing with sociotechnical interaction in internal logistics. The solutions presented in found articles can be divided according to the type of artefact into (1) formal approaches focused on models and algorithms, and (2) sociotechnical approaches focused on design of implementation frameworks. Challenges include the comparison of proposed solutions or their configurations in different problem contexts and regions. Further research should focus on organisational issues in internal logistics. Although the improvement of internal logistics in health facilities is a topic that has seen an increase in researcher interest over the last decade, there is a need to build a theoretical base on the findings of this research, which has been done only to a very limited extent. In terms of the use of new technologies, high persistence in the use of older IT-based solutions and rigidity can be observed in the implementation of new solutions.

Implementation Of IoT In Improving The Efficiency Of Hostage Release Operations With The QHBM Method

2024-12-20T20:02:55-03:00

In an increasingly complex security context, hostage release operations require innovative strategies to improve efficiency and safety. This article discusses the application of Internet of Things (IoT) technology and the Queen Honey Bee Migration (QHBM) method in improving the effectiveness of these operations. Conventional methods often face drawbacks, such as a lack of direct monitoring and limited communication. This study proposes the use of QHBM algorithms for optimizing troop deployment and resource allocation based on real-time data from IoT. This study uses quantitative and simulation approaches to evaluate the effectiveness of QHBM in the management of rescue operations. The results of the analysis show that QHBM is more efficient in energy consumption and bandwidth usage, reducing energy consumption by up to 10% compared to conventional methods. QHBM also shows improved connectivity stability with stronger signals at more distant nodes. With these optimizations, QHBM successfully extends the life of battery-based devices and supports more nodes without network congestion. These findings show that the application of QHBM in IoT resource management can improve communication quality and operational efficiency, providing practical guidance for professionals in the military, law enforcement, and crisis management.

Enhancing Optical Distribution Point Placement: A Decision Support System Integrating Weighted Product Method, Content-Based Filtering, and Location-Based Services

2024-12-20T20:02:54-03:00

The Optical Distribution Point (ODP) is a crucial element in fiber-optic internet networks, playing a key role in ensuring efficient service delivery. This study presents an integrated Decision Support System (DSS) that combines the Weighted Product Method (WPM), Content-Based Filtering (CBF), and Location-Based Services (LBS) to optimize ODP placement in urban areas. By considering multiple criteria such as ODP categories, customer preferences, and business types, the DSS provides a data-driven approach to strategic decision-making. The system’s ability to recommend ODPs based on customer needs, while visualizing key data through LBS, enhances the effectiveness of network expansion strategies. This comprehensive framework improves decision-making in urban internet services and offers a scalable solution for optimizing network infrastructure. The study demonstrates the potential of combining analytical models with user-focused technology to streamline service deployment and improve customer satisfaction.

Optimizing Porter Assignments in Hospital: A Mathematical Modeling Approach for Workload Balancing

2024-12-20T20:02:54-03:00

This case study addresses the staffing challenges faced by a hospital's porter service, which is currently insufficient to meet patient needs effectively. Due to the lack of a systematic task assignment mechanism, the head of the porters' center has had to manage patient transport manually, leading to unequal workload distribution among porters. This research aims to rectify this operational issue by developing a mathematical model and a user-friendly program for optimizing porter assignments. The methodology includes extensive data collection on existing protocols and factors affecting operations. A mathematical model is formulated with the objective of minimizing monthly workload deviations among porters. The model is executed using Excel Solver, producing an optimal assignment solution. Additionally, Visual Basic for Applications (VBA) in Excel is utilized to create a practical program for real-world application. A quantitative comparison of the standard deviation in cumulative workload from September 2022 reveals a significant improvement: the proposed program reduced the standard deviation by 5,907 seconds, or 76.17%. This outcome highlights the effectiveness of the new solution in achieving a more balanced distribution of porter assignments, thereby enhancing operational efficiency

Neural network eddy current non-destructive evaluation of conductive coatings thickness

2024-12-20T20:02:53-03:00

The proposed study is a machine learning application using a Neural Network for the prediction and identification of the thickness of aluminum placed over a steel plate. Two thousand and five hundred datasets with the eddy current method of different aluminum plate thicknesses above a steel plate and working frequencies of EC-sensor were generated using experimentally validated analytical models in our previous research. The data has three input parameters (normalized resistance, normalized reactance, and frequency) and one output (thickness). The ANN architecture involves careful consideration of the number of hidden layers and neurons within the model. The acquired data was split into two sections: the first section was used to train and test the selected model, and the second section was used to test the model on untrained data to demonstrate its high accuracy. The results obtained, as mentioned in the article, prove the validity and sensibility of the chosen model.

VaultGuard: The Advanced Keyless Security System

2024-12-20T20:02:53-03:00

The VaultGuard is a cutting-edge password management solution designed to enhance
security, convenience, and user experience. A full-featured research work named
VaultGuard is developed to assist users in safely creating, organizing, and storing strong
passwords for a variety of online accounts and programs. This paper offers a wide range of
features, including secure storage for multiple encrypted passwords using AES256 on
cloud, Random key generation algorithm for creating an advanced password, updating user
password timely, and a user-friendly dashboard for managing and organizing login
information. The system provides a seamless registration process using Fast Identity
Online 2 algorithm (FIDO2) with Web Authentication (WebAuthn) component, allowing
users to securely login without needing any password. To further enhance security, the
research paper employs AES256 Algorithm to safeguard stored passwords, protecting
them from unauthorized access and data breaches. The system also proactively monitors
the age of passwords and sends SMS and email notifications to users when their passwords
are older than a predetermined time period, such as a month, prompting them to update
their passwords for added security. By combining the convenience of password
management with authentication, strong encryption, and proactive password monitoring,
the VaultGuard sets a new standard for secure and user-friendly password management
systems, effectively addressing the shortcomings of traditional methods with 97.66 %
accuracy and 98.63% precision.

Evaluation of Wi-Fi Mesh Networks for Reading Electricity Consumption

2024-12-20T20:02:52-03:00

In Smart electrical networks, the Advanced Metering Infrastructure allows bidirectional communication between the service company and the customers. It includes smart meters and a communication infrastructure, which among other functions, is responsible for reading electricity consumption and billing. Cable or wireless technologies are used for the exchange of information between the networks that comprise it. In Cuba there is no such Advanced Metering Infrastructure, the reading of electricity consumption is carried out manually by an operator, reader-collector. That is why we are working on a project where traditional meters are modified by adding a Wi-Fi communications module that allows wireless access. This work evaluates the possibility of interconnecting several modified meters with wireless modules to form a mesh network. Protocols, programming tools, metrics to evaluate performance, scenarios, and experiments are described. As a result, the possibility of forming a mesh network is verified, with the exchange of information, with the requirements of self-configuration and self-repair of the network. This allows the reader-collector to read consumption from any point on the network through a smart device.