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Copyright of Revue Française de Géotechnique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revue Française de Géotechnique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revue Française de Géotechnique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Reflets de la Physique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The importance of each person's healthcare should be viewed as fundamental in the modern world due to the rise in various health issues. A decrease in the proportion of doctors is caused by an increase in the number of cases. The diagnosis is consequently delayed, or some patients are overlooked. As a result, people become more reliant on doctors for checkups. In order to retain each patient's digital identification, in light of all these worries, health and medical care systems have begun to connect and interact with the internet of things (IoT). Many health disorders in the healthcare system go undiagnosed resulting from a shortage of doctors and other medical experts, as well as a lack of access to healthcare services. These IoT-based healthcare options, on the other hand, have made it possible for patients and medical professionals to continuously track and analyze patient data. In this study, IoT for healthcare systems is discussed. These included applications, structures, and potential design snags and issues. It has been demonstrated that these systems could be very beneficial to people, especially during the (Covid-19) pandemic's global isolation and the growing challenges in treating patients intelligently. This paper also presents a survey study on the use of IoT in smart healthcare, its applicability, the future directions for its development, and a review of past researchers' applications.

The paper addresses the assessment of changed agroclimatic indicators in the conditions of the Krasnodar Territory and the analysis of their effect on the yield of grapes in the region. The effects of climate change can be both negative (increased frequency and intensity of dangerous hydrometeorological phenomena; increased range of insect pests, etc.) and positive (long-term growing season and heat supply for agricultural crops, etc.). Thus, reliable and timely data on the climate state and change are needed for a comprehensive understanding of the problem. The results of data analysis and conclusions about the climate state and change in the Krasnodar Territory, in particular the city of Novorossiysk, form the data basis for development of adaptation measures and play an important role in studies into climate variability and anthropogenic climate change.

In engineering, the use of mobile robots to teach automatic control is becoming more common because of the interesting experiments that can be conducted with them. In this paper, a mobile robot that applies reinforcement learning in different scenarios is shown, to get rewards, the agent learns by acting in the environment. creating a balance between new information and our current understanding of the environment. In this way, the algorithm can be divided into two stages: the learning stage and the operational stage. In the first phase, the robot learns how to go from where it is to a known destination, it builds a learning matrix that is subsequently utilized during the operational stage using the rewards and environment data. In this paper, the algorithm was studied in terms of rapid learning for the mobile robot and reducing the process of repetition in learning by specifying the values of alpha (α) and gamma (γ) in a way that is appropriate for preserving the variance and differentiation between them. To evaluate the robot’s adaptability to various dynamic situations, several simulated test scenarios were executed. In the testing situations, several target motion kinds and numbers of obstacles with various dynamicity patterns were used. The test scenarios illustrated the robot’s adaptability to various settings.

Foot-and-Mouth Disease (FMD) poses a significant threat to livestock worldwide, necessitating innovative approaches to combat its causative agent, the FMD virus (FMDV). On the other hand, Moringa oleifera is a feed alternative for cattles with numerous bioactive compounds. This paper delves into the captivating realm of Moringa oleifera (MO) bioactives and their potential in thwarting FMDV replication by targeting the essential enzyme, 3C Protease (3CP). To elucidate the inhibitory potential of these bioactives, a rigorous investigation involving molecular docking and molecular dynamics simulations was conducted. Specifically, the 3CP was modeled based on the amino acid sequence of FMDV Indonesian Serotype. Results showed that most of the compounds from MO outperformed Ribavirin as the standard therapy for FMD. Among them, Baicalin, Chlorogenic Acid, and Rutin have binding affinity -9.1, -8.1, and -8.1 kcal/mol, respectively. Those compounds also formed more hydrogen bonds than Ribavirin through their binding sites. Molecular dynamics simulation also revealed that interaction of 3CP with those compounds had minor influence on its structural stability. The conformation of those compounds is also more stable than Ribavirin, supported by more hydrogen bonds. In summary, this research highlighted the potential mechanism of MO bioactives in preventing severe FMDV infection through inhibition of viral replication.

Automated diagnosis of eye diseases using machine and deep learning models has become increasingly popular. Glaucoma, cataracts, diabetic retinopathy, Myopia, and age-related macular degeneration are common eye diseases that can cause severe damage. It is crucial to detect eye diseases early to prevent any potentially serious consequences. Early detection of eye disease is vital for effective treatment. Doing in-depth reading to identify any potential signs of eye disease is highly recommended. This paper will review all machine learning models built to detect and classify eye diseases in addition to helping grasp all limitations and challenges in this field. Recognizing eye diseases is a difficult task that typically requires several years of medical experience. This research is to be conducted to serve as a starting point for finding the most versatile solution. This research aims to review eye disease classification using deep learning models, including VGG16, ResNet, and Inception. The general classification model consists of these steps: The first step is to collect the globally obtainable datasets for the eye disease and pre-process them to ensure the generalization of experiments. The goal is to train the model to recognize disease symptoms instead of tweaking the outcomes for a specific dataset section. With the successful deployment of deep learning techniques for image classification and object recognition, research is now directed towards deep learning techniques instead of traditional handcrafted methods. One possible solution for the eye diseases classification challenge is to use a pre-trained deep CNN model for representation and feature extraction. This solution can be followed by classifier methods, such as support vector machines (SVM), multilayer perceptron (MLP), etc. It has been detected that CNN-based methods learned on large-scale marked datasets can be used for eye disease classification tasks with limited training datasets.

This research employs advanced data analysis techniques to predict crop health outcomes during harvest seasons, with a focus on insect count, pesticide use, and soil type. The study encompasses two main components: feature correlation and predictive modeling. Feature engineering techniques are applied to capture variations in pesticide use and insect infestation, enhancing predictive capabilities. Ensemble methods, including Random Forest, XGBoost, and Decision Trees, are employed to forecast patterns of crop damage based on identified trends. Decision Trees exhibit robust capabilities, achieving an impressive accuracy rate of 90.03%. Random Forest excels with a robust accuracy of 90.35%, highlighting its classification abilities. XGBoost stands out with an accuracy rate of 86.51%. In contrast, Logistic Regression, Naive Bayes, and Convolutional Neural Networks face challenges, displaying lower accuracy. The evaluation further emphasizes the strength of ensemble methods and Decision Trees through precision, recall, and F1-Score metrics, providing a comprehensive understanding of relationships within pesticide damage. The framework of the study introduced in this paper can be seen as a major step forward with regard to agricultural decision-making. We present actionable strategies to enhance crop health while reducing damage through the integration of feature correlation, predictive modeling and precise evaluation metrics. The innovativeness is in the use of ensemble methods and Decision Trees that are implemented to promote informed decision-making among stakeholders through a sustainable approach to agriculture.

The paper provides data on the water consumption of cotton and its coefficient of water consumption in arid zones, meadow-alluvial soils of the Bukhara region. Conducted studies have shown that the most economical water consumption in the fields of Bukhara-8 cotton can be achieved with an irrigation regime of 65–70–65% of the FPV with a cotton yield of 42.7 centners per hectare. In this case, the water consumption coefficient is 88 m3/c.

Industry 4.0 aims to create more efficient and flexible manufacturing processes that can respond quickly to changes in demand and customer requirements. The integration of digital technologies and physical systems allows data to be collected, analysed, monitored and controlled in real time. This can increase productivity, reduce downtime and improve product quality. In this paper, remote control, monitoring and online data storage of industrial machines based on human-machine interface (HMI) module are facilitated. HMI integrates the control system with the Industrial Internet of Things (IIOT). On the other hand, it can be controlled and monitored anywhere in the world via the Internet using a Virtual Private Network (VPN) to open different navigation private channel and HMI interface. It was clear from the collection of recorded data and the results of practical testing that the designed system facilitated the process of accessing the industrial machine

The paper deals with the determination and study of the drying rate. The authors believe that it is necessary to present the curves for changing the mass of the product and time, to take into account the possible ranges of changes in the parameters of the coolant. In addition, the paper discusses the actual speeds of airflow around the product, the required drying modes will become known owing to the knowledge of the features of the drying process. In this regard, the authors of the paper consider the volumetric and hydrodynamic characteristics of the product layer. The purpose of the study was to determine the area of the free section in the layer of products, the speed of air movement, the pressure loss in the layer. The results of computational and experimental studies have shown the possibility of determining the true speed of drying of various products.

Individual Home Address is considered as an important sub set of attributes, and never find personnel record, whether a person is student, patient, or employee, etc. without having a home address. It is required to be recorded, registered, and documented in all personnel information systems, whether the system is manual or computerized. This research concern on the Coding, and Digitization of home address of Iraqi personnel in order to enhancing storage efficiency and reduce processing time. The conventional method of maintaining paper-based records for personnel home addresses has proven to be costly, and timeconsuming, and in most cases inefficient. The proposed solution involves the conversion of physical addresses into digital formats, allowing for streamlined data storage and faster retrieval, and update processes. The work starting by indicate the current challenges related to the paper-based address systems, including the demands on physical storage space and the delays incurred during information retrieval. Subsequently, it explores the potential benefits of digitizing home addresses, such as reduced storage requirements, improved data accessibility, and enhanced overall organizational efficiency. To implement this digitization process, the research investigates various technological solutions, including geographic information systems (GIS), databases, and data management protocols. The study also addresses potential concerns related to data security and privacy, proposing measures to safeguard sensitive information. The proposed solution for Digitization, Coding, with Optimizing the Personnel Home Address provide at least 85% of Optimizing Factor for each individual personnel record required storage space and processing time, this solution subsequently leed to better daily decision-making process in most business organizations.

Post-COVID-19 in 2022, reported an increase in chicken egg consumption by 2.7 percent in Indonesia consumer’s. In 2021, the amount of eggs consumption was 18.92 Kg/Capita/year up to 20.02 Kg/capita/year in 2022. Estimated output consumption in 2023-2026 is estimated to grow by 1.16% per year. This condition is an excellent opportunity for laying hen farmers to maximize productivity and profits. However, generally the farmers still carry out the open house traditional farming system, its need to implement smart farming systems in the production process, and rely on one marketing channel. This paper is a review article and is collaborated with existing conditions in current laying hen farms. The first objective of this study is to overview the potential of laying hen farms using a smart farming system approach with the aim of farmers being able to diversify products with segmented distribution channels. The second is to build a segmented marketing network according to the product needs of each consumer. The expected result of this study is that farmers can maximize productivity and increase profits through segmented distribution channels. The innovation of this marketing system will be called ^Eggs sell points^ an integrated chicken egg marketing system through a sales point connected to the Internet of Things.

Currently, one of the main directions for the development of the agro-industrial complex is the development of vegetable production, in particular onions, which occupies a significant place in the total production of vegetables, but the production structure currently in Russia is not able to fully provide the population with the domestic product year-round, which leads to dependence on imports. Thus, the industrial production of commercial onions, and therefore the study aimed at improving the quality of onion planting with the development and use of onion seed planters that increase their productivity seem quite relevant. The paper presents the designed three-dimensional model of the pulse reducer, describes its operation, and provides a justification of the design parameters of the parts of the proposed pulse gear reducer of the onion planter, which makes it possible to ensure the downward planting of the onion bulb stem in the speed range from 3.6 m/s to 5.4 m/s.

Human face recognition and tracking (FRT) plays a vital role in various fields, including security, authentication, and human-computer interaction. The main modules of the FRT system are detection, feature extraction, and FRT. Using a database, these units recognize faces as well as their location, movement, and visible features. The framework aims to process large visual data in real-time, enabling accurate and fast FRT. The paper develops a real-time FRT framework using a lightweight CNN convolutional neural network to accurately match images of faces and environments with different illumination and expression differences to improve performance. This paper focused on the development of real-time facial recognition and tracking systems. The model used to achieve this is based on deep learning (DL) using a lightweight convolutional neural network (CNN) and post-feature extraction using linear discriminant analysis (LDA). Histogram of Oriented Gradients (HOG) experiments demonstrate that DL with lightweight CNN models provides a good solution for FRT tasks, even in challenging situations including changes in position, expression, illumination, and occlusion. The results of CNN-based DL were compared with several experiments. The model was also compared with many modern methods and achieved better results. The lightweight CNN model for DL outperformed it 100% of the time. When the split rate is 70:30 and the learning rate is 0.001, the epoch is 100. This demonstrates the dominance of DL over other techniques and shows how well it handles FRT tasks using lightweight and even real-time CNN methods.

Since the 1990s, electric vehicles have been extensively utilized, with a focus on extending the life of storage systems, lowering costs, and providing flexible grid connectivity—a feature that is currently being studied. The substitution of alternative energy sources for conventional fuels has resulted in significant advancements in energy conservation and greenhouse gas emission reduction. Electric vehicle power sources are discovered in power plants. Traditionally, the power grid supplies the power plant with its electricity. A power plant that uses a hybrid solar-wind system can save more energy and reduce greenhouse gas emissions more significantly. An overview of the latest wireless charging methods and optimization techniques is given in this article. Firstly, the essential characteristics of an electric car (EV), wireless charging techniques, and the type of charging system that is dependent on the location of the vehicle charging have been provided in detailed descriptions. After that, a few of the most common optimization techniques to determine the location and size of EV charging stations are presented. With a focus on its explicit application in electric vehicles, the paper provided researchers and scholars with an in-depth knowledge of the fundamentals of WPT and its mechanism of operation.

This paper presents a three-input high-gain DC-DC converter with bipolar symmetric outputs. The advantages of a bipolar DC grid are higher reliability, flexibility, and efficiency as compared with the unipolar DC grid. Therefore, the proposed converter is designed to connect directly with the bipolar DC grid due to its bipolar symmetric outputs. The three input voltage sources to the converter are operated in a sequential manner from the first voltage source to the last one, with each source providing power at a level proportional to its duty cycle. Similarly, when the sources are equal, they operate in a sequential manner with their specific power. In addition, the proposed converter is designed to provide the required power to the grid under both simultaneous or individual from three input voltage sources. A double-loop PI controller is applied to regulate the converter output voltages and the control loop is analyzed and the transfer function relating reference voltage to output voltage is derived. Voltage balance control is provided to ensure that the outputs remain symmetrical despite changes in load conditions or input voltage. A converter with a power rating of 1000W and output voltage levels of 200V, -200V, and 400V is designed. The three supply voltage of 150V,100V, and 75V are used. The converter is tested under a variation of supply voltages and load using MATLAB/SIMULINK. The results show the feasibility and effectiveness of the proposed converter.

The paper presents scientific and technical developments to solve the environmental problems of recycling fibrous waste from various industries. The efficiency of creating and using devices for precompression of fibrous materials before extruding them in a plane-shaped granulator was proven. Mixing, precompression and microgranulation are used to form technogenic fibrous wastes. These processes are implemented in specially designed patent-protected devices.

The production of alternative fuels is a promising direction in the development of energy. Nowadays, in science the technology of heat treatment of various materials, including household solid waste is used. In order to obtain an alternative type of fuel from waste, the technology of heat treatment of raw materials without access to oxygen – thermolysis is used. Under the influence of low temperatures (400–500ºС) and without access to oxygen, the material decomposes into several components: water, gas, fuel oil and dark heating oil. To ensure the correct operation of such a line, it is required to supply finely divided raw materials to form the required plug in the loading zone from raw materials. Moreover, raw material should not be homogeneous. It is known that a mixture of rubber and paper gives cleaner results during heat treatment. To ensure proper operation and obtain the desired mixed raw materials, a rotary centrifugal unit with a complex dynamic effect on the material was developed. Calculations and studies showed that such a grinder design is capable of qualitatively grinding raw materials and mixing it in the right proportions. Small dimensions and a design solution in the form of a single rotor with several mechanisms of action on the material allow reducing energy consumption for grinding. The practical value of the paper is in the creation of an improved design of a rotary centrifugal unit on the basis of theoretical and experimental studies. It increases the productivity and quality of the resulting mixture. The results of the study can be used in the industry of waste processing plants.

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.

Benoit Mandelbrot coined the word “fractal” in the late 1970s, but an object is now defined as fractals in form known to artists and mathematicians for centuries. A fractal object is self-similar in that the subsections of the object are somewhat similar to the whole object. No matter how small the subdivision is, the subsection contains no less detail than the whole. Atypical example of a fractal body is the “snowflake curve” (invented by Helga von Koch (1870-1924) in 1904. There are as many relationships between architecture, the arts, and mathematics as symmetry. The golden ratio, the Fibonacci sequence in this paper explain the method of counting box and measuring the roughness ratio. And small scale analysis after calculating the box to understand fractal concepts, we must know two dimensions. Through analyzing the samples in the research, it has been proven that fractal geometry is present everywhere in our lives in nature, in buildings, and even in plants and its role in architecture is to find fractal systems that appeal to our inclinations for dynamic vitality. Therefore, finding such fractals enables us to create high-performance structures that achieve psychological, aesthetic and environmental aspects in an integrated design. Therefore, Self- Similarity Dimension (Ds) Box-counting Dimension (Db.) All of these dimensions are directly related to the fractional dimension of Mandelbrot (D). In all similar constructions there is a relationship between the scale factor and the number of the smaller pieces the original construction is divided into.

The soil water characteristics curve (SWCC) demonstrates a link between soil moisture content and suction. The SWCC is almost treated as an index parameter in unsaturated soil. The soil permeability and shear strength can be linked to SWCC. SWCC is established using filter paper for the wetting path. This paper compares SWCC for both cases stress-dependent and no stress (reference) in the wetting path. The samples of soil are brought from Al-Najaf City with a gypsum content of 29%, Iraq. The stress-SWCC is studied using a modified Oedometer cell with controlled water and air entrance to apply a specific matric suction. While the reference SWCC is estimated using the filter paper method. Matric suction is conducted at a range of pressures from 90 kPa to 0 kPa. The tests use three net normal stresses of 100, 200, and 400 kPa. The results show that there are decreasing values of the SWCC with increasing normal stresses. An interesting result is that this decrease is high and has zero matric suction. The water entry value (WEV) with the most significant value of the water entry change is represented by the matric suction of 50 kPa.

The food industry and agro-industrial branch produce large amount of wastewater containing organic substances in high concentrations, suspended solids and cleaning agents. For small pollutant concentrations, plants dilute wastewater to standard values. However, expensive treatment is required for most of the water production process. Adoption of the most suitable wastewater treatment scheme for a fruit and vegetable enterprise is relevant because it assumes economic benefits primarily for the enterprise. The article provides an overview of existing purification systems that are suitable for cleaning specific juices of fruit and vegetable production. Using the hierarchy analysis method in this paper, an optimal wastewater treatment system of a fruit and vegetable enterprise has been selected, the main advantage of which is the possibility of returning wastewater to production.

This paper shows path of controlling hybrid system with wind and diesel generators via a programmable logic controller, these systems produce the required electrical power from different sources. This system can be used in different areas that have wind speeds between (10-180) km/h. When the winds are within this range, then the wind turbine generator is starting and suppling the electrical power to the load, if there is any fault is occurred or the wind is not within the working range then the wind turbine generator ceases and the diesel generator will start and supply the electrical power to the load.

Agro-industrial waste at all stages of agricultural production, processing and consumption increases annually and reaches an enormous level. Agricultural waste is often rich in valuable biologically active substances, therefore, it is important to consider methods of its efficientprocessing. Waste recycling is required to reduce the negative impact on the environment, minimize waste and increase the efficiency of the use of raw materials. The paper presents the results of chemical analysis of phytogenic waste: potato and carrot tops. Potato and carrot top wastes are considered the agricultural waste. The sown areas of these crops are huge. All this was the reason for choosing the aboveground part of potatoes and carrots as objects of study in order to obtain a sufficient amount of biologically active substances. Lipids were extracted from potato and carrottops by hexane extraction, washed, and the unsaponifiable substancesweresplit by column chromatography on silica gel. The fraction composition was determined by physicochemical methods. The results of the experimental study showed that potato and carrot tops contain a significant amount of biologically active components, among which triterpene compounds present a particular interest. Triterpene compounds have properties that can help to fight against oncology and viral diseases: prevent inflammation, inhibit the active division of cells and the growth of pathogenic neoplasms, activate programmed cell death, which makes it possible to recommend the aboveground part of potatoes and carrots as a cheap source of biologically active substances for the pharmaceutical and perfumery industry.

Encryption is vital for data security, converting information into an unreadable format to ensure privacy in online communication and sensitive sectors. Advanced encryption balances innovation and security in user-friendly applications. Image encryption employs techniques to protect image data from unauthorized access during transmission or storage, particularly crucial for safeguarding sensitive images in various applications. The goal is to prevent unauthorized access and ensure the safety of associated information. In this paper, I present a study on previous research related to my investigation, which focuses on encryption in general and image encryption in particular. The paper also discusses the methods used, particularly those closely related to my work, involving either SHA-256, MD5, or a combination of both. The review will look at the many strategies and techniques employed, as well as how precisely the task was completed by applying a set of parameters in comparison to earlier studies.

For sustainable management of natural resources and effective integrated use of the raw materials, it is necessary to introduce into production innovative developments for the effective extraction of valuable substances from production waste. Waste recycling is crucial to reduce the negative impact on the environment, minimize waste and increase the efficiency of using raw materials. The paper addresses the problem of environmental pollution by grape processing waste. The results of chemical analysis of CO2 and alcohol extracts from grape processing waste, including grape pomace, are presented. For the study, samples of carbon dioxide extract from white and red grape pomace were isolated. In carbon dioxide extracts, indicators of oxidative spoilage, mass fraction of unsaponifiable substances and fatty acid composition were determined. It is shown that for the most efficient extraction of biologically active substances from grape pomace, a two-stage extraction should be carried out: CO2 extraction and extraction with an alcohol solution. The experimental results indicate that the secondary extraction from grape pomace and its individual components with liquefied carbon dioxide results in the formation of easily decomposable waste and extraction of a lipid fraction containing valuable substances that can be used in the food and cosmetic industries. The results obtained show that the use of a 70% alcohol solution is technologically substantiated for obtaining extracts from grape pomace with a highest content of phenols, flavonoids, anthocyanins and tannins.

This paper poses the problem of fuel energy and ecology in Russia. The paper discusses in detail the operation of biogas production plants. Advantages and disadvantages of such production, material costs, and raw material costs are presented. The statistics of biofuel production in the countries of the world is visualised. The paper compares different types of propulsion systems using different fuels, identifies the advantages and disadvantages, and examines the operation of one of the largest biogas plants in the world.

The paper studies the environmental situation on the territory of CJSC “KTK-R” and in the zone of its direct influence. Direct sources of environmental pollution on the territory of the enterprise were identified. The results of biotesting substrate toxicity by sprouts of various indicator plants were analysed, the concentrations of heavy metals in soil, nitrates, sulphates and acid-alkaline balance were determined. Measures to reduce the negative environmental impact of wastewater treatment facilities were proposed.

Multi-layer perceptron (MLP) algorithms play a critical role in improving the accuracy and effectiveness of heart disease diagnosis in the context of the machine learning research. This paper presents an approach of heart disease prediction involves RReliefF-based feature importance assessment then MLP-based classification of features into three groups based on importance scores is proposed. The study employs three feedforward neural networks to classify effectively the clustered groups. Furthermore, an integrated approach utilizes XGBoost ensemble classification, leveraging boosted ensemble learning to enhance overall classification of the outputs of FNN models. By partitioning Cleveland dataset into 70% training and 30% testing sets creates independent datasets, the incorporation of MLP outputs into the XGBoost model yields satisfied testing performance. The confusion matrix showcases accurate classifications, with 96.67% accuracy, 95.92% sensitivity, and 97.92% precision. The F1-Score, at 96.91%, validates the model's balanced performance in precision and recall. This study exemplifies the efficacy of integrating data processing, feature engineering, and ensemble learning techniques for robust cardiovascular disease prediction, providing a reliable and efficient methodology for healthcare applications.

Failure to take appropriate measures for waste utilisation and neutralisation has led to the fact that many poultry farms have now become sources of environmental pollution, thereby causing serious economic, environmental and social damage. In this paper we have assessed the impact of poultry farms on some components of the environment by the example of the CJSC Poultry Farm “Novorossiysk”. The main sources of impact of the poultry farm “Novorossiysk” on the environment are identified. The activity of the enterprise as a source of waste generation is studied. The level of atmospheric pollution created by emissions of the poultry farm was analysed. Easily and moderately soluble forms of elements and acid-alkaline conditions in soil were determined. The results of biotesting substrate toxicity by sprouts of indicator plants were analysed. Measures to improve the environmental situation on the territory of the CJSC Poultry Farm “Novorossiysk” and in the zone of its direct influence were recommended.

Wireless Sensor Network (WSN) is one of the trend technologies. It was aggregation, processing, and transferring a huge amount of data in different applications that deal with the surrounding environment. Using a huge number of sensors deployed or organized in a linear configuration between two parallel lines or carvings, a specific kind of WSN is constructed to monitor a particular type of infrastructure. This type of WSN is Known as Linear Wireless Sensor Network (LWSN). LWSNs are used in monitoring applications that are arranged in linear form like underground Pipelines, Bridges, Tunnels, Railway lines, Highways, and Borderlines. A transmission process must be reliable and efficient to ensure end-to-end packet delivery. The backbone or the shortest path approach is used to improve the network performance and prolong the network lifetime. In such networks, more packets are ultimately forwarded or relayed by the sensors closest to the sink than by other types of sensors. In this paper, a backbone approach has been implemented where data is sent from the backbone and the node outside the backbone. Any node outside the backbone selects the closest backbone and sends the sensing data to it.

Recognizing sign language is one of the most challenging tasks of our time. Researchers in this field have focused on different types of signaling applications to get to know typically, the goal of sign language recognition is to classify sign language recognition into specific classes of expression labels. This paper surveys sign language recognition classification based on machine learning (ML), deep learning (DL), and optimization algorithms. A technique called sign language recognition uses a computer as an assistant with specific algorithms to evaluate basic sign language recognition. The letters of the alphabet were represented through sign language, relying on hand movement to communicate between deaf people and normal people. This paper presents a literature survey of the most important techniques used in sign language recognition models

Skin cancer is regarded as one of the most perilous forms of cancer and is recognized as a leading contributor to mortality worldwide. The likelihood of fatalities can be diminished significantly if skin cancer is identified at an early stage. Among the various types of skin cancer, melanoma stands out due to its remarkably high fatality rates. This is primarily attributed to its propensity to metastasize to other bodily regions if not promptly detected and treated. The process of diagnosing melanoma is notably intricate, even for seasoned dermatologists, primarily due to the extensive morphological diversity observed in patients’ moles. Consequently, the automated diagnosis of melanoma presents a formidable challenge that necessitates the development of proficient computational techniques capable of facilitating diagnosis, thereby assisting dermatologists in their decision-making process. In this study, we meticulously examined the most recent scientific papers on melanoma diagnosis, specifically focusing on applying deep learning and machine learning techniques in conjunction with optimization techniques.

Alzheimer’s disease (AD) stands as a formidable global health challenge, impacting millions of lives. Timely detection and localization of affected brain regions are pivotal for understanding its progression and developing effective treatments. This research introduces a cutting-edge approach to address these critical concerns. Traditionally, exploring the influence of AD on the human brain has been a complex task. Existing methods often face limitations in accurately localizing the most affected brain regions, impeding our understanding of the disease's focal impact. Additionally, the need for efficient and precise cortical thickness analysis techniques has driven the quest for innovative solutions. In this paper, we proposed the DL+DiReCT method, a high-precision strategy that integrates deep learning-based neuroanatomy segmentations with Diffeomorphic Registration-based Cortical Thickness (DiReCT). This approach streamlines the measurement of cortical thickness, enabling rapid and precise localization of AD-affected regions within the brain. Our method significantly contributes to enhancing our understanding of the localized effects of Alzheimer’s disease. Our extensive study, involving 434 subjects from the ADNI dataset and rigorous data augmentation and optimization, has yielded remarkable outcomes. This approach has far-reaching implications for discerning the specific regions of the brain affected by AD, shedding light on their consequences for essential physiological factors.

This research paper introduces a new design being Minkowski-like fractal filtering-balun MLFFB based on the dual-mode ring resonator. The structure has three ports, converting the signal from one unbalanced port to two balanced ports (i.e., 180° phase shift). The proposed design can act as a filter and balun simultaneously using one dual-mode ring resonator. The fractal curves are applied to have compact designs. The 0th, 1st, and 2nd iterations of the Minkowski-like fractal curves are applied to demonstrate the miniaturization ratio obtained in the proposed work. The miniaturization ratios are 47.8% and 73% of the 1st iteration and 2nd iteration of the Minkowski balun-filtering components, respectively, compared to the 0th iteration. Also, the filtering balun has a distinctive feature, which is the control of the phase error and magnitude imbalance of the two balanced ports, depending on the size of the perturbation part. Several structures have been designed, modeled, and analyzed utilizing the Advanced Design System (ADS) software. Each design iteration is evaluated and optimized to attain the best performance. The operating frequency is 2.4 GHz, and the realized transmission coefficients are S21 = -7 dB and S31 = -6.5 dB, which are less than expected ideal values because the substrate is a lossy type, being FR4. The S11 is less than -10 dB. The proposed design is a good candidate for narrow-band wireless applications.

A modulation technique has recently developed known as “OFDM with Subcarrier Power Modulation (OFDM-SPM)”. This technique utilizes two types of modulation. The first one called power modulation, where the power level of each subcarrier changes depending on the bit stream while transmit uses another type of conventional modulation such as a QPSK. The results shows that the adoption of OFDM with SPM improve the throughput, save the power, reduce the complexity, and so on. On the negative side, it shows degradation in the “Bit Error Rate” (BER) performance. In this paper, the convolutional code is the proposed to improve the BER while paired with the vitribi hard decision decoder which is considered easy to implement of the hardware and costs. the suggested methods will be discussed in the terms of throughput and BER with the three power reallocation policies ) PRPs).

The relevance of the study is due to the problems of agricultural development and man-made challenges. In this regard, this article aims to identify the need to build an ideal model of urban-type settlements. The leading approach to the study of this problem is the system method, allowing comprehensively considering the study of green economy in the modern city as a whole, identifying a variety of cause-and-effect links and relations taking place within the system under study and in its interaction with the external environment. This paper considers the ecological rating of regions, provides statistics on man-made and natural emergencies. The authors have concluded that an effective adaptation to technogenic challenges is the creation of a new institutional model for the development of modern urban-type settlements. The materials of the article are of practical value for the authorities at the regional level when making managerial decisions on sustainable development of the region.

The paper presents the results of experimental studies on the combined pre-sowing treatment of the tomato seed material of a tomato variety “Malva”, which consisted of exposure to visible radiation in the range from 460 to 625 nm and an alternating magnetic field with induction from 6.5 to 65 mTl. Experimental studies were conducted at all stages of plant development: from pre-sowing seed treatment to the final products (tomatoes). As a result of the experimental studies, we revealed that the best results obtained about the dry matter content in tomato seedlings and about the yield on the root of the final products (tomatoes) are achieved at presowing seed treatment with two influencing factors. The first is optical radiation with a wavelength of 460 nm and treatment time of 60 s. The second is magnetic field with an induction of 39 mTl and treatment time of 30 s. After such combined treatment, the seeds were cured for 120 hours before sowing. As a result, we experimentally recorded that the dry matter content in tomato seedlings increased by 25.41%, and the tomato yield increased by 38.00%, compared to the control variant. This allows us to speak about such seed treatment before sowing as an effective agro-technological method, which can be recommended for use in vegetable growing.

As an environmentally friendly method of distributing energy production, the integration of photovoltaic systems into micro grids has drawn in significant focus on. Our goal in doing is to examine features regarding micro grid that is linked to the power grid, with a focus on photovoltaic energy management in particular. Finding the optimal micro grid capacity for the solar system seeks to increase energy efficiency, decrease dependence on main grid, and promote an utilization of green power. The outlined optimization approach evaluates the micro grid’s dynamic interactions using state-of-the-art modelling and simulation tools. These components include photovoltaic panels, energy storage systems, alongside the main grid. The refinement method takes into account crucial factors including patterns of load demand, costs of the grid electricity, and variations in solar irradiation. Finding a happy medium between increasing the amount of power generated by renewable sources and decreasing overall energy costs is the objective. That study takes a multi-scenario approach to determining how various micro grid sizes affect overall system efficiency. Using scenario-based simulations and techno-economic criteria, the appropriate size of the photovoltaic system was determined. Factors like payback time, ROI, and system reliability are taken into account here. The study’s findings provide light on grid-connected micro grids, particularly in regards to photovoltaic energy management, which is crucial for their planning and implementation. In order to make educated decisions towards more robust and ecologically friendly power systems, stakeholders, lawmakers, and decision-makers can use the optimal micro grid size as a benchmark for future renewable power projects. This paper reviews the relevant literature and proposes a division and performance strategy based on its findings. By classifying energy management into three groups according to grid connection, configuration, and control method, this article provides a description of the performance, application, advantages, and disadvantages of algorithms that may be used as a reference for selecting an appropriate algorithm. Also included is a comparison table for the control strategies that were used to regulate a micro grid system that is connected to the grid.

Cryptography and Steganography are two main components of information security. Utilizing encryption and Steganography to establish many layers of protection is a commendable approach. Our main objective of this paper is to build an integrated method of securely transmitting data through a combination of cryptography and Steganography. Cryptography and Steganography are two common methods of secretly transmitting information. RC4 is used in this paper to change information from plaintext to cipher, and then cipher text is integrated into the image by Least Significant Bit (LSB). The results are defined in terms of the processing time, the peak signal-to-noise ratio (PSNR), and mean square error (MSE). The experimental results showed the stego image’s acceptable quality and combining the two techniques provides additional security in the original Steganography.

The fusion of chaos theory and cryptography has yielded a dynamic landscape of innovative solutions for safe random key generation. This paper presents a comparison of several studies conducted in this field, aiming to distill key insights and discern common threads. Amidst the diversity of proposals, a consistent architectural framework emerges, while the true differentiators lie in the selection, configuration, and utilization of chaotic maps. These maps, harnessed for their inherent unpredictability, have a significant impact on how reliable and secure cryptographic systems are. Thus survey highlights the enduring relevance of chaotic maps as versatile tools in the cryptography arsenal. The interplay between mathematical complexity and computational expediency stands as a central theme, illustrating the delicate equilibrium researchers must navigate. As chaos-based cryptographic systems continue to evolve, this analysis serves as a compass for both practitioners and theoreticians, offering insights into the evolving landscape of safe key generation, and the challenges and opportunities that lie ahead.

Wireless sensor networks (WSNs) with transmit-only (TO) nodes are gaining increased popularity, in particular, in applications requiring heavy deployment of sensor nodes in harsh and inaccessible environments. The data collected by the members of the WSN is relayed to a nearby base station (BS) via a cluster-head (CH). Since the nodes are usually battery-limited, it is important to optimize the location of the CH in the sensing environment to prolong the lifetime of the entire network. In this paper, we describe this problem using a general energy model and review the main articles that discuss the energy optimization problem in WSNs and summarize their important results, in addition to the basic working principles of the different widely used energy-optimization algorithms.

The advancement of assistive communication technology for the deaf and hard-of-hearing community is an area of significant research interest. In this study, we present a Convolutional Neural Network (CNN) model tailored for the recognition of Arabic Sign Language (ArSL). Our model incorporates a meticulous preprocessing pipeline that transforms input images through grayscale conversion, Gaussian blur, histogram equalization, and resizing to standardize input data and enhance feature visibility. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are employed for feature extraction to retain critical discriminative information while reducing dimensionality. The proposed CNN architecture leverages a blend of one-dimensional convolutional layers, max pooling, Leaky ReLU activation functions, and Long Short-Term Memory (LSTM) layers to efficiently capture both spatial and temporal patterns within the data. Our experiments on two separate datasets—one consisting of images and the other of videos—demonstrate exceptional recognition rates of 99.7% and 99.9%, respectively. These results significantly surpass the performance of existing models referenced in the literature. This paper discusses the methodologies, architectural considerations, and the training approach of the proposed model, alongside a comparative analysis of its performance against previous studies. The research outcomes suggest that our model not only sets a new benchmark in sign language recognition but also offers a promising foundation for the development of real-time, assistive sign language translation tools. The potential applications of such technology could greatly enhance communication accessibility, fostering greater inclusion for individuals who rely on sign language as their primary mode of communication. Future work will aim to expand the model's capabilities to more diverse datasets and investigate its deployment in practical, everyday scenarios to bridge the communication gap for the deaf and hard of hearing community.

Sharding method is separates the network into smaller groups to reduce latency and enhance blockchain speed. To reduce storage cost, divide the network into separate segments, and allow nodes to maintain track of a portion of the blockchain's data ledger, it was initially employed in databases. This technology is an excellent choice for enhancing blockchain performance because of its practical requirements and the speed at which blockchain applications are developing. It has garnered a lot of interest. There are a number of unresolved issues regarding the review and analysis of sharding. In this paper, we examine current state-of-the-art sharding schemes by categorizing them according to blockchain type and sharding technique—more specifically, cross-sharding with low communication overhead and systematically and thoroughly analyzing the benefits and drawbacks of each. Sharding lowers communication overhead since the performance of blockchain apps that use it has significantly improved over the method that should be studied for reducing the communication cost of block consensus. We present various open addresses after doing a comprehensive review and analysis of the communication overhead.