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The paper deals with the problems of ensuring the food security of correctional facilities through the development of fish farms in the correctional system of the Russian Federation. The rational use of fish stocks in inland waters and increasing their productivity is one of the most important and urgent fisheries problems, the complexity and versatility of which require a systematic approach and the use of program methods to solve it. The paper proposes a statement of the problem of fish catching control in the form of an optimal control problem, for the solution of which a numerical optimization method implemented in the software is used. The results of the software tool operation for various values of input parameters are given. The developed software tool can be used to provide decision support in the production of fish products.

In recent years, solar energy, wind energy and other new energy sources have been highly praised, especially solar energy, which is considered as one of the cleanest renewable new energy sources, and solar thermal power stations have been built in many countries. Tower solar photothermal power generation is a heat absorber that reflects sunlight to the top of the tower through heliostat field. Molten salt absorbs heat through the heat absorber, heats water supply and promotes thermal power generation. However, solar energy is intermittent and unstable, so the tower solar thermal power station is equipped with heat storage molten salt tank. Solid heat storage technology has the advantages of cheap heat storage medium, no harm to the environment, strong reliability and operability of the system, and can provide cost-effective heat storage solutions for industrial process heat energy and power generation. In this paper, the molten salt tank is replaced by a solid heat accumulator. After the molten salt absorbs heat during the day, part of it heats the heat accumulator through the unit tube in the heat accumulator to store the heat in the heat accumulator, and the other part heats the water to promote the steam power cycle. At night, the heat accumulator heats the molten salt to release heat, which continues to promote the steam power cycle. In this paper, CFD software will be used to numerically simulate the change of temperature characteristics inside the solid regenerator with the time of heat storage and release, and draw the temperature nephogram and change curve.

The buried pipe is an important part of the geothermal heat pumps system. One of the main reasons for the high initial installation cost of the ground source heat pump system is that its heavy drilling workload, long construction period and high cost account for the system cost. Especially in some complex geological conditions, the high proportion of drilling costs directly increase the overall cost of the geothermal heat pumps system, which to a certain extent restricts the popularization and application of geothermal heat pumps system. Based on the comparative analysis of different types of geological conditions and different ground source heat pump drilling technologies, this paper starts with the research and analysis of basic geological structure. On the premise of meeting the requirements of the system, this paper aims to improve the drilling efficiency and reduce the cost by rationally designing pipe Wells and optimizing the drilling technology scheme.

Against the backdrop of clean rural heating and dual carbon targets, this paper investigates and develops a household combined heat and power (CHP) unit. The performance of the unit under natural gas operating conditions was tested and evaluated in the laboratory. The test results show that at rated operating conditions the unit has an electrical efficiency of 29% and an overall efficiency of 96%. The micro-CHP was then tested in a civil building in a demonstration site in Xuzhou, Jiangsu province, for trial operation under biogas working conditions. The test results show that when the unit in biogas working condition is heating the room, the unit has a rated working condition power generation of 3.2 KW and still has an average electrical efficiency of about 26.8% and a thermal efficiency of 65.6%, which remains at a high level. And the room temperature data shows that the heat supply meets the heating demand. Finally, this paper compares the performance with micro-CHP units available in the European and American markets. The results show that the performance of this unit is superior to many micro-CHP units available overseas. This paper demonstrates through test results that rural micro-CHP system can meet the daily energy needs of farmers, improve energy use efficiency and reduce carbon emissions.

At present, Chinese renewable energy generation is growing rapidly, distributed power trading as a market-oriented means to promote the consumption of renewable energy power has attracted wide attention. It is urgent to optimize the mechanism design and build a credible and efficient trading environment to meet the needs of credibility and timeliness of large-scale multi-agent transactions. This paper first constructs a distributed power trading mechanism considering multi-subject transactions, and then elaborates the design idea of the power distributed trading platform based on blockchain technology from three aspects: total volume architecture, platform core technology and platform deployment. Finally, based on the operation of the actual power trading center in a certain region, field application verification is carried out, which proves the effectiveness and superiority of the distributed trading platform designed in this paper based on blockchain technology. The next research will continue to expand the typical model application pilot in the applicable areas to further improve the platform functions.

As China's economic development has entered a new normal, it is in China’s self-interest to achieve carbon peaking before 2030. and the decomposition and analysis of the influencing factors of carbon emissions are not only conducive to predicting the peaking time of carbon emissions, but also crucial to develop differentiated emission reduction policies in Jinhua. This paper firstly decomposes and analyzes the influencing factors of carbon emissions in Jinhua based on LMDI decomposition technology and hidden Markov chain, and then the dynamic relationship among the influencing factors of carbon emissions in Jinhua is explored by the panel vector autoregressive model. In addition, this paper also uses STIRPAT model to forecast and analyze the peaking time of carbon emissions in Jinhua and puts forward targeted suggestions.

As global focus sharpens on carbon emissions and environmental protection; the pursuit of sustainable development permeates every sector. Against the backdrop of increasing fossil fuel prices and relentless energy demand, the exploration of clean energy has become paramount. This paper presents a comprehensive review of bioenergy. It introduces the concept and underscores its importance, tracing the historical stages and accomplishments in its development. The paper explicates different types of bioenergy and their chemical operating principles. The integral system of bioenergy is also evaluated, focusing on crucial components: bioenergy feedstocks, processing technologies, transport process, storage, and grid integration. The paper concludes with an assessment of bioenergy's economic and environmental impacts, considering market dynamics and future prospects, and suggests potential mitigation measures against its environmental repercussions.

Indonesia planned to stop building new Coal-Fired Power Plants after 2023 which are in line with its NDC for Global Climate Change Regimes. But, Joko Widodo, Indonesia President, issued regulations that the government will allow to build new coal-fired power plants until 2030. This paper will explain Indonesia policy in allowing the construction of new coal-fired power plants. By using international regimes in renewable energy approach, Indonesia's role in fulfilling the needs in energy and also commitment to transform to clean energy will be explained. The Results of this paper show that Indonesia used rhetoric to show their commitment to get international support in funding for renewable energy developments. Yet, Indonesia allowing the construction of new coal-fired power plants has been used for bargaining positions for most of its interest in international forums. Indonesia got $20 billion deals from G7 Countries for transitions in The Just Energy Transition Partnership (JETP) scheme but is still getting international attention to get more investments. Indonesia also handled most of Non Governmental Organizations efforts to stop building new coal-fired power plants by making public statements for its reliable plant of transitions. In conclusion, Indonesia is using coal-fired power plants rhetoric to get funding for its transitions coping with the climate changes agenda in energy sectors.

Science-Based Target (SBT) has become popular since 2020 as a promising platform for reducing greenhouse gas emissions. Recent studies found that SBT diffusion in low- and medium-income countries remains unresponsive even in emission-intensive sectors. Setting realistic targets for aligning actions across each chain, planning and implementing targeting methods, and developing SBT governance to respond to stakeholder interests can influence the climate change response. This paper describes how the international supply chains influence Indonesian companies to apply SBT through the demand side. This paper will depict early initiatives and evidence with descriptives data that announce the Indonesian company's plan to achieve established SBT. The result shows that Indonesian company still rare to implement SBT and the initiatives begin when triggered by international demand on supply chain.

This paper gives an essential contribution to the energy engineering topic, especially the energy harvesting field. Wireless power transmission (WPT) is one of the most widely used methods in this field recently to generate electricity in a clean way for the environment like the rectenna systems. The main component of the rectenna systems is the microstrip patch antenna (MPA). This is the main reason why this article proposes a novel conception of 1×4 circular polarized MPA array to operate in the industrial, scientific, and medical (ISM) band at the resonant frequency of 2.45 GHz for radio frequency energy harvesting (RFEH) systems. The basic MPA element is a square antenna using a center slot and V-slits at the four corners combined with a defected ground structure (DGS) method. This proposed conception is developed in order to improve the antenna’s performance to integrate with a rectifier circuit for the Rectenna systems which are the most used systems in RFEH. The simulation results obtained by CST MWS software and HFSS solver show that this novel design in this paper gives good performances in terms of the reflection coefficient, voltage standing wave ratio, axial ratio, directivity, and gain at 2.45 [GHz]. This developed MPA is suitable for various RFEH applications.

The rapid growth of e-commerce has placed considerable strain on traditional logistics systems, prompting a need for innovative solutions to optimize delivery processes and enhance customer satisfaction. This research paper presents a decentralized crowdsourced delivery application that leverages blockchain technology and smart contracts to address the challenges faced by centralized logistics models. The proposed system allows e-commerce companies to outsource product deliveries to carriers from a diverse pool, offering greater flexibility and cost-effectiveness while also enhancing transparency and trust among all parties involved. Built on the Ethereum blockchain, the application manages both the delivery and return processes, generating verifiable proofs of delivery (PoD) and proofs of return (PoR) for each transaction. The paper provides a comprehensive analysis of the system architecture and the implementation of the application using smart contracts. Furthermore, it explores the potential impact of the proposed system on e-commerce companies, carriers, and customers, and identifies challenges and future directions for research and development in this field. The findings of this study contribute to the ongoing discourse on the transformative potential of blockchain technology and crowdsourcing in the e-commerce logistics industry, offering valuable insights into the design and real-world application of a decentralized delivery system.

In addressing climate change and meeting countries National Determined Contribution (NDC) and national cooling action plan (NCAP) on energy ambitious targets in the year 2030, Kenya made several strides in drafting the appliances energy performance and labelling regulations of 2016. Energy efficiency enables distributed off-grid renewable energy systems, from solar home systems to renewable micro-grids, to deliver energy services that otherwise might be economically or technically infeasible in resource-constrained settings. This paper reviews specific multivariate econometric models over the period of 2016 to 2022 and summarizes the impact of minimum energy performance (MEPs) and the energy labels policies and regulations enacted. This paper assesses the energy efficiency label for cold chain appliances and identifies issues, potential improvements, and challenges for successfully nudging consumers towards highly energy efficiency cold chain appliances.

In this paper, we investigate the propagation of electromagnetic waves in periodic comblike waveguides structure made of dangling side branches (resonators) and finite waveguides. We introduce two defective resonators inserted in two different sites inside the perfect periodic system. This kind of system has been studied theoretically using Green’s function method (GFM), the main objective of this work is to make a comparative study between the results found in the literature and the results found by the Transfer Matrix Method (TMM) for the first time in such photonic defective comb-like system. Noted that the calculations made by the transfer matrix method (based on Maxwell’s equation and the continuity conditions) are simpler compared to the calculations when applying the Green function method. The results founding in this paper either at the level of the transmission and reflection rates are in agreement with the results found by the Green function method, which gives an idea of the effectiveness of this simple method and their agreement with the experience.

Construction is an industry that carries the risk of causing human death, and the destruction of assets and the environment. Several cases such as girder collapse, crane overturning, formwork collapse, and material landslides have occurred recently in large-scale infrastructure projects in Indonesia. Currently, construction accident theory describes active and latent failures in management, and human as well as technical issues. The purpose of this paper is to propose the theory of no failure no accident in any man-made disaster. A further review of literature studies is used to outline current research findings on management, technical, and human failures leading to catastrophic events. Evidence-based reasoning from 5 cases of catastrophic events in construction is described to reveal failure modes that lead to incidents and accidents. In this paper, firstly the failure modes and their effects are described based on the literature review, and secondly, empirical evidence of failure modes is presented using selected disaster cases recorded by the Construction Safety Committee, Ministry of Public Works, and Public Housing. Finally, this paper concludes that failure modes represent the quality of systems, services, procedures, goods, and personnel that is defective to contribute to a catastrophic event that causes damage to people, the public, property, and the environment.

Location-Routing-Inventory (LRI) model is used in logistic chain to select the location of facilities (distribution center, warehouse, or store), the transportation routes between facilities, and the allocation of inventory stored in each facility. This paper proposes developing the LRI model by considering carbon emissions through carbon cap & trade policy, carbon cap policy, and carbon tax policy. Cold chain logistics is a type of logistics chains that keeps perishable products in cold temperatures and produces higher carbon emissions than conventional logistics chain. The development of the LRI model in this paper aims not only to optimize the cost of selecting facility locations, transportation routes, and inventory amount, but also includes optimizing the cost of purchasing raw materials from suppliers under the implementation of carbon emission policies in cold chain logistic. The optimal solution is carried out using LINGO 18.0 and shows that this model can optimize the costs in cold chain logistics by considering carbon emission policies. The results show that the carbon cap policy is the most profitable policy for the company because it produces the lowest total cost compared to other carbon emission policies.

The Industry 4.0 concept is a new manufacturing approach that integrates smart factories, smart machines, smart systems, smart production, and smart processes into a unified network. Through applying CPS (Cyber-Physical Systems) technology, Industry 4.0 combines the physical and virtual worlds to increase company productivity and efficiency. This paper aims to explore research trends related to smart (connected) products in the Industry 4.0 era and find studies that can be developed in the future using a systematic literature review (SLR). The results of the SLR show that from 57 papers, it was found that product engineering and CPS technology were the aspects of the study that were mainly carried out. New research potentials that can be developed in the future have been identified in product engineering, smart factory, and standardization studies. Future research on SCP can be applied to the case of electric motorcycles swappable battery (EMSB).

A BLDC motor is commonly used as the driver of an electric vehicle. So that this part becomes a critical component in the electric vehicle system. Any faults in the motor can cause the vehicle to not operate. Early detection of motor faults can avoid sudden motor failure. This paper aims to diagnose the possible faults in a BLDC motor using the least squares support vector (LSSVM) model. In this paper, the motor in normal condition and the motor with bearing, unbalance, and stator faults are examined. The vibration signals are measured from the BLDC motor operating at 430 rpm. The signals are captured at a 20 kHz sampling rate. The signals are smoothed using a moving average filter. The feature selection is based on the ability to segregate the different fault conditions through visual observation. The kurtosis and frequency centre value features are selected as fault predictors. The diagnosis process is performed by the classification of motor conditions using the LSSVM model. The model is built from the training data. The result shows that the LSSVM model performs very well in diagnosing BLDC motor faults. The diagnosis accuracy is 100%, both for training and testing data.

This paper discusses how a manufacturer and two competing retailers interact to develop an inventory model considering a carbon tax, green investment, and product selling price under stochastic demand to maximize the profitability of the supply chain. It is assumed that the manufacturer replenishes all the retailers at the same time and period. Since there are two competing retailers, the product selling price at each retailer impacts not only its demand but also the other retailer’s demand. This paper also considers the implementation of green technology since there is a carbon tax for activities that produce carbon emissions such as production, transportation, and holding activities in the supply chain. The validity of the inventory model will be tested through sensitivity analysis and numerical examples.

The serious issue of antibiotic contamination has become a pressing concern. Antibioticcontaminated wastewater that is not effectively treated can have detrimental effects on both aquatic environments and human health. Therefore, it is imperative to intensify research efforts towards developing efficient and effective processes for removing antibiotics from aquatic environments. This paper provides a comprehensive review of common antibiotic removal processes used in sewage treatment plants, including physical adsorption and membrane filtration. It also examines various chemical treatments such as ozone oxidation, hydrolysis, photolysis, Fenton oxidation, persulfate oxidation, and electrochemical oxidation. In addition, the paper discusses biological treatments including anaerobic biological treatment, aerobic biological treatment, and constructed wetland treatment, as well as the combination of related processes. Through this review of the relevant literature, it is evident that advanced oxidation technology demonstrates superior performance in single removal processes for antibiotics. Furthermore, the combination of different removal processes yields even more effective antibiotic removal outcomes, indicating great potential for future development. Finally, this paper highlights the importance of future research in antibiotic removal to further advance the field and promote the growth of antibiotic removal technology.

Energy consumption in the building industry occupies a significant portion of the world’s total energy consumption, and heating, ventilation and air conditioning (HVAC) systems are one of the largest energy consumers in buildings. Therefore, research on energy-saving technologies and measures for HVAC systems is essential. This paper first analyzes the workflow of HVAC systems and the causes of energy consumption, followed by combing and summarizing the existing research on energy-saving technologies for HVAC systems, namely passive energy-saving technologies, active energy-saving technologies and intelligent energy-saving technologies. In order to better understand and apply aforementioned energy-saving technologies, this paper takes a science and technology building built 30 years ago in southwest China as an example and applies the relevant energy-saving technologies to upgrade the HVAC system in a targeted manner.

Due to the serious development of cracks in precast box girders, there is no reliable method to detect the loss of prestress and carry out effective reinforcement of box girders. This paper proposes a reinforcement method of steel plate - concrete with unbonded prestressing, and applies this method to a 30m reinforced concrete prestressed box girder bridge. This paper uses FEA software to carry out a comparative analysis of the load-deflection curve of the reinforced mid-span section by comparing with the conventional bonded steel reinforcement method and the load test data, and through the analysis results, the calculation formula of the flexural load capacity of the steel plate-concrete with unbonded prestressing reinforcement method is derived. The results show that the flexural load carrying capacity of the strengthened mid-span section is significantly improved, and the proposed calculation formulae deviate from the finite element simulation results within 5%, which can be applied to actual projects.

Introduction – Supply chain strategy is a cluster of decisions related to goals, actions to take, and management resources to achieve the corporate goals. In the current situation, the business environment has dramatically changed. Under the VUCA (volatility, uncertainty, complexity, ambiguity), a suitable supply chain strategy will support the company to meet its goals. Supply chain resilience is a crucial area that many academics suggest helps the company better performance and competitive advantage in the industry. Purpose –This paper reviews supply chain strategies that affect supply chain resilience and sustainability. It focuses on the tea producers' supply chains in Thailand. Methodology/Approach – This paper reviews the empirical knowledge and research for supply chain strategy and resilience related to the VUCA situation. Originality/ Value/ Implication – The study results found that planning, collaboration, and innovation are related to supply chain resilience. They support supply chain performance and sustainability through supply chain resilience.

In this research paper, we will be exploring the need to have environmentally friendly transportation logistics operations and how the government can play a role in promoting the clean and green transportation logistics initiative. The purpose of this research study will be to give awareness and understanding of the problem of pollution caused by vehicles used in transportation logistics operations. Then after it, we will discuss the possible solutions to either minimize the reason for pollution entirely or make the effect of it as low as possible; To make solutions possible, the government’s role will be the key in order to bind people through policies and laws and also providing the ease in acquiring the resources required by the private sector for the implementation of the environmentally friendly solution. To be mention that this paper will be limited to providing different ways for countries’ governments to go towards sustainable development, from which each country’s government can take away any practical solution for them and can successfully implement it in their country.

This paper presents an initial exploration of enhancing student well-being through the use of AI Chat GPT in the smart education university learning environment. With the increasing integration of artificial intelligence (AI) technologies in educational settings, AI Chat GPT has emerged as a promising tool to support student well-being. The study begins with a comprehensive literature review to examine the existing research and relevant sources on the topic. Various empirical studies, journal articles, and books related to the use of AI Chat GPT in higher education are analyzed to gain insights into its potential impact on student well-being. The findings from the literature review suggest this paper provides an initial exploration of how AI Chat GPT can enhance student well-being in the smart education university learning environment through a comprehensive literature review. The findings underscore the potential benefits and considerations of integrating AI Chat GPT into educational settings. Further research and empirical studies are needed to validate and expand upon these initial findings.

Nowadays, corruption is a serious issue in Malaysia and international levels. Digital technology that can detect and prevent fraud and corruption is particularly important to manage processes in public sector activities more transparently. The availability of information and the application process to support the delivery system in certain public sectors is more transparent to the public if through digital technology. Therefore, this paper aims to understand the use of digital technology in an effort to eliminate the symptoms of corruption, overcome fraud, and increase transparency in the government service delivery system for the well-being of the community. The method used is literature review analysis. The paper's results can provide information to the government about the importance of improving digital technology for more efficient control mechanisms in Malaysia. Therefore, the government should focus on efforts to streamline better regulatory affairs and standards by using additional anti-corruption technology in service delivery processes with more transparent access to the public through digital technology.

This paper focuses on determining routes for logistics service providers from distribution centers to customers with the objective of minimizing the total costs. Despite being a particular example of the vehicle routing problem (VRP), this issue is more complicated than the basic VRP, especially since each vehicle contains heterogeneous capacity. This paper presents the novel strategy of Differential Evolution (NSDE) to solve multi-fleet size and Vehicle Routing Problems in logistics service providers (MFSVRP-LSP). Our work aims to minimize distance. The validation of NSDE (i.e., DE, NSDE1, NSDE2, NSDE3, NSDE4) was conducted by the comparison of the current practice. The relative improvement (RI) between the standard DE and the NSDE1, NSDE2, NSDE3, and NSDE4 in the MFSVRP-LSP. The NSDE4 outperformed the standard DE follow by my report. Furthermore, our suggested technique can be used for similar logistics in Thailand.

A decentralized and automated online voting system refers to an electronic voting method that leverages Blockchain technology to guarantee both transparency and security. It removes the need for intermediaries, making it more efficient and cost-effective than traditional voting methods. Aim of the paper is to develop an online voting system using Blockchain technology for secure and transparent voting. The proposed approach involves developing a decentralized voting system using Ethereum Blockchain technology. Smart contracts are used to manage the voting process and ensure its integrity. The datasets used in this paper will consist of mock voting data to simulate a real-world scenario. The results and discussions include an analysis of the system’s performance in terms of security, scalability, and usability. Future enhancements include integrating biometric authentication for enhanced security and accessibility.

A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes the evolution of handwriting more complex. It is difficult to train an Optical Character Recognition (OCR) system using these requirements. In order to convert handwritten material into digital form, this work aims to categorize each unique handwritten word. Because Convolutional Neural Networks (CNNs) are so good at this task, they are the best method for handwriting recognition system. The method will be used to identify writings in various formats.

Speech Emotion Recognition (SER) poses a significant challenge with promising applications in psychology, speech therapy, and customer service. This research paper proposes the development of an SER system utilizing machine learning techniques, particularly deep learning and recurrent neural networks. The model will be trained on a carefully labeled dataset of diverse speech samples representing various emotions. By analyzing crucial audio features such as pitch, rhythm, and prosody, the system aims to achieve accurate emotion recognition for novel speech samples. The primary objective of this paper is to contribute to the advancement of SER by improving accuracy, reliability, and gaining deeper insights into establishing a sustainable complex relationship between emotions and speech. This innovative system has the potential to facilitate the practical implementation of emotion recognition technologies across multiple domains.

Criminal identification using image recognition is a crucial task for law enforcement agencies. It involves identifying and tracking down criminals by analysing their facial features from images and videos. The system aims to improve the efficiency and accuracy of criminal identification while reducing human error. The system will be trained on large datasets of criminal images to ensure high accuracy and reliability. The paper will use publicly available datasets such as the Face Recognition Technology (FERET) and the Labelled Faces in the Wild (LFW) dataset to train and test the model. Metrics including accuracy, precision, recall, and F1-score will be used to assess the system’s efficacy. The results and discussions will focus on the strengths and weaknesses of the model, and potential avenues for improvement. In conclusion, the proposed face recognition system shows promising results in identifying criminals and reducing the burden on law enforcement agencies. The manuscript discusses the objective of the paper, provides an overview of facial recognition technology, and summarizes existing approaches in criminal identification using image recognition.

In the field of medical science, classifying brain tumours is vital. In order to get an effective and proper treatment for the disease, accurate and finding type of the brain tumour is much essential in the case of brain tumour treatment. In addition to providing treatment for tumours as early as possible, it also helps in saving a life by allowing medication to be administered in due course. DL has developed into a fantastic tool for medical professionals and researchers to act quickly and decisively with tumour patients. In this paper, we suggest Sustainable development of flexible approach aimed at multi-model organization of brain tumours using the popular deep learning architecture ResNet-50. By leveraging the flexibility of ResNet-50, we aim to achieve improved accuracy and robustness in classifying brain tumours across a diverse range of datasets. Our approach integrates multiple ResNet-50 models, each specialized in identifying specific tumour types, enabling a comprehensive classification framework. Experimental findings show that our strategy is successful and more accurate than other approaches. In this paper we provide an interface that can be used to classify and label the tumours. We used Keras and Tensorflow to create a cutting-edge Convolutional Neural Network (CNN) architecture to categorise 3 kinds of growth or tumours namely - Meningioma, Gliomaand Pituitary using ResNet50 algorithm. It is estimated that this model has a maximum mean accuracy score of 98.88%.

Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explores various machine learning algorithms employed for intrusion detection and evaluates their performance. Findings indicate that machine learning algorithms exhibit a significant improvement in intrusion detection accuracy compared to traditional methods, achieving an accuracy rate of approximately 90 percent. It is worth noting that the previous work experienced computational challenges due to the time-consuming nature of the utilized algorithm when processing datasets. In this paper, we propose the exertion of more efficient algorithms to compute datasets, resulting in reduced processing time and increased precision compared to other algorithms to provide sustainability. This approach proves particularly when computational resources are limited or when the relationship between features and target variables is relatively straightforward.

Information Technology (IT) Sector is known for its fast paced and dynamic work environment, which often leads to role conflict among IT professionals. This paper presents a bibliometric analysis and systematic literature review of the phenomenon of role conflict in the IT sector. The study analyses a total 77 academic papers published till 2022, indexed in Scopus and Web of Science database. The bibliometric analysis identifies the most influential authors, journals and keywords related to the topic of role conflict in the IT sector. The systematic literature review identifies and synthesizes the major themes, theoretical perspectives and research method used in the literature on role conflict in the IT sector. The findings suggests that role conflict in the IT sector is a complex phenomenon that can result from a variety of factors including conflicting expectations, unclear job responsibilities, and organisational, and environmental factors that contribute to role conflict, as well as the strategies that can be used to manage and reduce it. The study concludes with recommendations for future research and practical implications for organisations seeking to mitigate the negative effects of role conflict on their employees and performance.

The objective of paper is to detect phishing URLs using machine learning algorithms. Phishing is a fraudulent activity that involves tricking users into giving away sensitive information, such as passwords and credit card numbers, by impersonating legitimate websites. The main objective of this work is to build a model that can accurately detect viable phishing URLs and classify them as either legitimate or fraudulent. This will help to prevent users from falling victim to phishing attacks and protect their personal information. The model will be trained on a large dataset of annotated URLs and will be optimised for high accuracy and low false positive rates. The paper consists of two datasets in which one of the dataset consists of phishing URLs and other datasets consist of features of URLs. The performance of the phishing detection model will be evaluated using various metrics, such as precision, recall, and F1 score. We will also conduct an in-depth analysis of the results and discuss the effectiveness of the approach. This work aims to build a robust model for phishing URL detection using machine learning algorithms. Future enhancements to this work could include incorporating more advanced feature extraction techniques, exploring the use of deep learning models, and expanding the dataset to include more diverse types of URLs.

The “Sustainable Smart Safety Helmet for Coal Miners” paper aims to enhance the safety and well-being of coal miners by implementing an innovative monitoring system. The paper utilizes the ESP32 microcontroller, integrated with a suite of sensors, including DHT11 for temperature and humidity measurement, MQ135 for gas detection, IR Sensor for helmet status monitoring, and a Heartbeat Sensor for real-time pulse rate tracking. A Buzzer is also integrated to provide immediate audible alerts when potential hazards are detected. The system continuously collects sensor data in real-time, which is then processed and analysed within the ESP32 microcontroller. The data is transmitted to a user-friendly mobile application developed using Blynk. Through the Blynk app, miners and relevant personnel receive critical information regarding environmental conditions and the miners' vital signs. In case of any hazardous conditions, the system generates prompt alerts, triggering the Buzzer and sending notifications to the app to ensure timely responses to potential threats. By combining advanced sensor technology, real-time data monitoring, and swift alert mechanisms, this paper endeavours to safeguard the lives of coal miners in hazardous working environments, providing them with a smart safety solution and empowering them to work with increased confidence and security.

The energy collected from various renewable resources, such as sunlight, geothermal warmth, wind, and water, is referred to as renewable power. The sun is the ultimate source of vitality. Solar radiation vitality can be utilized in many functions, such as heating buildings and dusty foliage crops, dying chicken, wood taste, and treating modern elements. All the energy patterns in nature asking them are the origin of the sun. The solar air heater (SAH) is very common along with commonly applied sun-oriented heating apparatus. Perhaps, SAH is utilized for a wide range of applications, from residential to commercial. Improving the SAH efficiency is done by applying rib elements on the bottom side of the roughness element ribs, grooves, fins, baffles, twisted tapes, deflectors, etc., and improved Thermo-hydraulic performance (THP). Various investigators have prepared several practices in their research work to obtain a greater transfer of thermal energy between these solar collector heaters by applying various rib elements to the absorbent surface. The objective of that study is obliged to describe and conclude tests on the impact of modest rib element height and face projection upon absorber plate along duct surface as artificial rib elements of different shapes on heat transfer along friction factor. The research on artificially roughened SAH ducting is thoroughly analyzed in this paper. The purpose of this paper is to summarize several studies on the thermal and hydraulic performance of synthetically roughened SAH ducting. For the analyzed range of parameters, it was discovered that the usage of arc-shaped-shaped rib geometry and metal grit ribs has the maximum THP parameters in comparison to often roughness geometry. For the tested range of parameters, the use of broken arc ribs has the highest Nu compared to the plain arc-shaped rib roughness. Arc configuration of roughness element exhibits fewer ranges pressure diminishes than the V form design because the produced secondary flow has a curved structure and is rougher.

Manageability has gotten a significant measure of interest and has been assigned to the board. As a contextual investigation, this paper presents a survey of articles focusing on the meaning of creation and the activities of executives in the manageability of the assembling business, with a specific focus on fossil fuel by-products involving the food and drink industry. This study reviewed current articles that cut across production quality, operation management, and tools for the sustainable development of food and beverages in manufacturing industries. The papers are arranged and examined by the three fundamental areas of activities of the board (issue, execution methodology, and improvement). The concentrate additionally dissects the papers as indicated by whether they address the importance of the activity the board manages towards Nutrition, Health, and Wellness Company, which will be the case study. From this research, it was observed that the activity of the executives shows up as a pertinent variable in all center areas of supportable assembly research; however, the bearing and strength of its effect remain dubious. The research looks at how administrators can effectively manage the problems associated with fossil fuels through products and quality nutrition, health, and wellness of the company for both consumers and employees.

Safety and security are major concerns in the modern day. People and organizations can employ security mechanisms to safeguard their property for their homes or commercial enterprises. Present security systems involve the utilization of Assorted Sensors in cameras for video surveillance. This paper aims at providing one such idea to ensure the protection and security of one’s property. This technique performs Face Recognition as an authentication procedure when a new face is detected by a snapshot. We propose to present a sensible, smart and sustainable Closed - Circuit Television (CCTV) camera with intrusion detection using the LBPH-Local binary pattern histogram algorithm, SIM-Structural Similarity Index Measure, Haar Cascade Classifier, and TKinter. By utilizing intrusion detection, CCTV cameras record real-time videos and process the video at the time of recording to search out the unwanted people arriving within the surveillance area. Our GUI has different buttons supported with features. Adding DL support would create broad scope in this paper such as with DL we would be able to add up much more functionality. We can have future enhancements on this paper such as creating Portable CCTV, Deadly weapon detection, Accident Detection, Fire Detection.

Cricket is a popular sport that involves a high degree of variability in terms of game conditions and player performance. The ability to accurately predict cricket scores could provide valuable insights for coaches, analysts, and fans, as well as offer opportunities for sports betting and fantasy games. This paper explores the use of machine learning techniques to predict cricket scores based on a variety of contextual and historical factors. The publicly available cricket dataset is used to build and evaluate several regression models that predict the total runs scored by a team in a limited-overs cricket match. This analysis includes feature engineering to extract and transform relevant input variables, model selection to compare and choose among different regression algorithms, and performance evaluation to assess the accuracy and robustness of the models. This paper also conducts sensitivity analysis to identify the most influential predictors and explore the potential biases and limitations of the models. The results indicate that machine learning techniques can effectively predict cricket scores and provide valuable insights into the factors that contribute to team performance. Automated cricket prediction is useful for cricket teams, coaches, and analysts who seek to improve their game strategies and player selection, as well as for sports betting and fantasy game platforms that seek to provide more accurate experiences for users.

Credit Card Customer Churn Analysis (C4A) is a phenomenon where customers stop using a specific business credit card service. Predicting customer churn is crucial for Credit Card (CC) companies because it enables them to spot at-risk customers and take precautions to retain them. The aim of the paper named C4A is to create a model that accurately predicts customers who are most likely to stop using CC. The paper involves gathering and analyzing customer information from Kaggle, including transaction history, demographics and credit card usage patterns for prediction. Machine learning algorithms namely, Logistic Regression, KNN, XGBoost Classifier, Decision Tree and Hybrid Models integrating Logistic Regression and KNN, Logistic Regression and Decision Tree are used to train to find patterns and correlations that point to customer churn. The accuracy of the proposed method is 0.846 with LR, 0.849 with KNN, 0.90 with a hybrid model integrating LR and KNN, 0.928 by integrating LR and DT, 0.91 with DT, and 0.93 with XGBoost.

Image deblurring is a challenging task that aims to restore a sharp and clear image from a blurred one. This problem is usually caused by camera motion or defocus blur. The objective of this paper is to develop a model that can effectively remove Gaussian blur from an image and improve its quality using deep learning techniques. Automated image deblurring is achieved using deep learning, this approach involves implementing a combination of convolutional neural networks (CNN) and simple auto encoders to train the model on a dataset of blurred and corresponding sharp images. The model is then used to deblur the test images and improve their quality. The paper uses a dataset of blurred and corresponding sharp images to train the model, and the performance of the model is evaluated based on metrics such as PSNR and SSIM. The results and discussions focus on the effectiveness of the model in removing Gaussian blur and improving the quality of the images. In conclusion, the paper demonstrates the effectiveness of using deep learning techniques for image deblurring and provides scope for future enhancements such as incorporating more complex models and exploring other types of blur removal techniques.

The main objective of this paper is to develop a sustainable Wireless power transmission (WPT) model. WPT is the technique of transferring electric energy from a power source to a load without any contact between them. This process can be done through different methods using different topologies, which include Series Series (SS), Series Parallel (SP), Parallel Series (PS), and Parallel Parallel (PP). One of the most widely used methods in WPT is inductive power transfer. The main advantages of WPT – it allows charging for multiple devices, has high charging speeds, low cost, less maintenance, and higher efficiency than wired charging. Despite significant advancements in charging solutions, the system still has various issues, such as coil position, the number of receiving coils, the distance between transmitter and receiver coils, high initial cost, and impacts on its sustainability. This paper discusses the simulation model of the WPT model using SS topology in the JMAG software and analyzes the effect of the position of coils and distance between transmitter and receiver coils on the output. This simulation model can further be used to build a prototype for charging EV batteries.

This paper is an IoT based Sustained Belt for Visually impaired people, which uses Raspberry Pi for image identification and image processing techniques to help a blind person to identify and rectify people and things in front of them and help them navigate everyday life and have a better lifestyle like any other common man. The key techniques that will be used are real-time images and object recognition as well as image processing. First would be an image, object recognition is to capture the image and then rectify the image, rectifying the image is done through image processing and then the rectified image is converted as audio output. The scope of this paper is to scale up a level where a blind person has a normal lifestyle, just like any other normal person, right without hustles or anything and as well as they can use this as his adjacent person, where this has inbuilt GPS and it is a mini computer as well it can be also useful for general questions, usually blind people when they can’t see things they have a lot of questions and be curious, it will be easier to know about them. What differentiates our paper from usual blind people’s normal stick is that the stick helps them not to crash anything but we are not focused on that, what we focused on is to have a very normal lifestyle like normal person

The swift development of e-commerce has significantly altered consumer behavior and the availability of products and services. Online platforms have become essential for both healthcare providers and patients within the field of medical equipment. However, the high cost of purchasing healthcare equipment hinders affordability and accessibility. This research paper introduces a sustainable lease option model for healthcare equipment on e-commerce websites to address this issue. This research paper examines the theoretical framework and benefits of introducing a lease option model to online healthcare equipment platforms. This paper analyzes a sustainable e-commerce platform, which benefits of implementing a lease option model. These benefits include increased affordability, enhanced accessibility to specialized equipment, and reduced financial burdens for patients and healthcare facilities. The proposed structure outlines the essential elements of incorporating lease options into e-commerce websites, such as pricing structures, contract terms, maintenance and support services, and user experience considerations. Risk assessment, legal considerations, and customer service are just a few of the potential challenges and strategies for risk mitigation that are discussed. It highlights the potential impact of lease options on the accessibility and affordability of healthcare equipment and emphasizes the need for additional research and implementation efforts to optimize this model in real-world settings. The introduction of a lease option model on healthcare equipment e-commerce websites presents a promising opportunity to revolutionize the industry. This work leads to the actual value of sustainable web application in health care sector.

Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.

A significant component of society’s sustainable development is producing energy from renewable sources. Various energy sources, including solar, biomass, biogas, and wind, must be utilised to their full potential to fulfil current demands. In most nations like Nigeria, there are a lot of rural areas that suffer from energy supply. This has brought up many studies on how optimisation techniques can help manage the sustainability of the supply of this generated energy to such areas. Therefore, this review paper focuses on the performance analysis of several studies on the impact of mathematical modelling and various optimisation techniques on power generation systems via renewable sources. The study review papers from a quality outlet such as Elsevier, Springer, and other quality journals indexed in Scopus databases. On biomass production, wind energy production system, steam and solar energy. The finding from the study showed that the application of real-life engineering optimisation tools and models developed significantly improved the power generation process globally. Therefore, in the study’s conclusion, the authors provided sustainable recommendations on how to apply these models to improve the sustainable power generation process.

Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution.

Cryptocurrencies have been gaining popularity in India, but the lack of reliable and safe platforms for trading has been a significant concern. This research paper proposes a decentralized exchange (DEX) platform that enables Indian users to trade cryptocurrencies for Indian Rupees (INR). The system uses the Chainlink decentralized oracle network to convert INR to the equivalent ETH, the base currency for trading on the DEX platform. The proposed platform aims to provide secure and transparent trading to users, ensuring that transactions are executed instantly and at a fair market price. The paper outlines the technical details of the proposed DEX platform, including the integration with Chainlink oracles for price feeds and the development of smart contracts for trading. The platform utilizes the Ethereum blockchain and is built using Solidity, a programming language for smart contracts. Finally, the paper concludes with an evaluation of the proposed DEX platform, highlighting its potential benefits for Indian users and its potential impact on the broader cryptocurrency ecosystem in India. This research paper’s findings can guide the development of more secure and reliable DEX platforms in India and beyond.

Internet of Things (IoT) innovation is one of the fastest growing fields in various regions or aspects which include irrigation. IoT works on the character of our lives by way of bringing and cultivating modifications in many fields of exercises to motive them to come to be convenient, savvy and enriched with the aid of adequate guy-made recognition. As a result of this innovation, smart cultivating frameworks recognize a social trade towards current agri-business that is more useful, consumes less water, and is extraordinarily less luxurious. The primary goal of this paper is to make use of IoT within the agribusiness subject to accumulate facts right away (soil Moister, temperature), with a purpose to help one with staring at a few climate situations distantly, effectively and improve massively the creation and thus the pay of ranchers. The modern version is created utilizing NodeMCU innovation, which contains express sensors, and a Wifi module that assists with amassing moment records on the internet. It is worth concentrating on the testing of this model created, profoundly precise data in light of the fact that any herbal changes were outstanding in a flash and taking into consideration to decide. This paper speculating about integrating the IoT with different other technologies.

In an increasingly interconnected world, the ability to overcome language barrier has become a crucial requirement. for effective communication. Language translator headphones have emerged as a promising technological solution to facilitate seamless multilingual conversations. This research paper aims to evaluate their accuracy, usability, and potential impact on communication dynamics. The paper begins by discussing the significance of language translation technology in various domains, such as tourism, business, and diplomacy. It highlights the limitations of traditional translation methods and emphasizes the need for innovative as well as sustainable solutions that can enhance communication efficiency and convenience with the help of raspberry pi as a microcontroller.