bluuebunny/crossref_metadata_2025 huggingface dataset:
On Accentuations of Character in the Structure of a Comprehensive Study of the Criminal Personality
Authors: Nezhkina Larisa
Published: December 2023

The article provides theoretical materials and psychological analysis of practical experience of key criteria in the study of the criminal personality. The personality of a human, who has committed a crime, is the subject for close examination of many sciences, and psychology plays an important role in studying the problem. Depending on the socio-historical conditions, the requirements of social practice and the level of scientific achievements, scientists raised and solved important questions in different ways. These questions were supposed to find out: what a criminal's personality is, what his/her role in committing a crime is, what his/her range of rights and obligations is, and how to influence his/her in order to prevent criminal actions from his/her side in the future. In the modern era, the problem of the criminal's identity has become particularly relevant. Reflecting the general situation of the study, the relevance of this particular problem is predetermined, first of all, by the fact that it is impossible to effectively combat crime in general without scientific knowledge of those who commit crimes. The purpose of the research is to study theoretically the formation of character accentuations, the development of illegal behavior in the structure of a comprehensive consideration of the criminal's personality. The research methodology is based on a theoretical analysis of the main scientific approaches to the study of the criminal's personality, character accentuations. The information base of the research was a generalization of the research results, in accordance with the facts of life and specific conditions described in the works of art. The scientific novelty is presented in the analysis of the trajectory of the formation of accentuated personalities, their potential propensities to certain forms of crimes, taking into account external social factors. The conducted research made it possible to show the theoretical aspects of studying the personality of a criminal as a complex system that can be known only if all the sides of the issue are considered. At the same time, it is important to focus on the character of a person, in particular his accentuations as a driving force of the personality, that triggers the process of social adaptation or maladaptation, including criminal behavior.
Technical Measures for Prevention of Theft Committed with the Use of Information and Telecommunication Technologies
Authors: Starostenko Oleg
Published: December 2023

Theft committed with the use of information and telecommunication technologies has taken a lead in cybercrimes and is a major concern of developing countries. Provision of effective prevention activity in the sphere under study poses a serious problem. Its solution depends on a systematic approach to resolving organizational and legal aspects of social prevention of this type of theft. The specificity of IT-theft (stealing and fraud) calls for correction of established methods of special prevention measures and building new approaches to such activity (technologisation of the process). The article allocates a set of major perspective technical measures for prevention of IT-theft aimed to develop and implement a single monitoring system (data mining and extraction of useful data from total data volumes); the fight against anonymity (the possibility of de-anonymization of users is considered). The study also highlights countering fraudulent spam (the possibility of using the technology of «gray lists», the introduction of a specialized tariff plan when using e-mail as well as the development of domestic software based on individual Bayesian filters capable of analyzing the statistics of the occurrence of words in the texts of fraudulent messages). The main directions of practical implementation of the proposed approaches are determined. In conclusion, the author emphasizes the fact that the application of these technical preventive measures will allow the entire cybercrime prevention system to be holistic and logically complete.
DNA extraction from rectal mucosa biopsies and matched faecal samples taken from surgery for microbiome analysis v1
Authors: Murphy Lee , Maclean Alan , Hafezi Katarzyna
Published: December 2023

This protocol outlines the procedure for manual DNA extraction of microbiome DNA from rectal mucosa biopsy tissue and matched faecal samples taken during surgery and frozen at -80oC. The protocol covers1. DNA extraction from faecal samples using MP Biomedicals FastDNA Spin Kit and FastPrep-24 for bead-beating 2. DNA extraction from rectal mucosa biopsy tissue using a modification of MP Biomedicals FastDNA Spin Kit and FastPrep-24 for bead-beatingOther steps not covered in this protocol are 3. Microbial DNA enrichment of extracted samples using NEB NEBNext Microbiome DNA Enrichment kit 4. DNA Quantitation of extracted DNA using the Qubit Flourometer 5. Library Preparation for microbiome WGS using NEB NEBNext Ultra II FS DNA Library Prep Kit for Illumina 6. DNA sequencing on the Illumina NextSeq2000
Ambulatory Testing - UroMOCA and ColoMOCA v1
Authors: Hanzlicek Brett , Damsaser Margot , Bourbeau Dennis
Published: December 2023

Ambulatory Recordings of Wireless Bladder (UroMOCA) and Colon (ColoMOCA) Devices in Pigs.
SPANISH AND UKRAINIAN AS TWO LANGUAGES COMING FROM THE ONLY PROTO-INDO-EUROPEAN LINGUISTIC SOURCE: TYPOLOGICAL AND SOCIOLINGUISTIC APPROACHES
Authors:   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Korbozerova Nina
Published: December 2023

The article examines the Spanish and Ukrainian languages in a comparative aspect. An attempt is made to consider these languages in terms of their origin and the possibility of multiple comparison. Is proposed a comprehensive research method based on the symbiosis of historical, typological, taxonomic, semantic and pragmatic approaches. In contemporary linguistics, there are a number of general linguistic techniques and methods for comparing linguistic structures. Today, linguists are expanding the scope of comparative research. Lately they resort to the discursive aspect, which makes it possible to carry out comparative linguistic analysis at the communicative, pragmatic, cognitive, linguo-cultural and linguo-ethnic levels. The comparative method verifies the value and continuous development of modern research of Ukrainian scientists in the field of linguistics, which, without a doubt, will contribute to the intensification of the process of learning foreign languages and the improvement of the training of translators highly rated. The use of the comparative taxonomic research method allows us to carry out an analysis of multiple aspects at any language level and find isomorphic nuances of the functional-semantic and structural level when comparing linguistic phenomena in two or more languages at both levels, whether synchronic or diachronic. Currently, genealogical, typological and territorial areas are considered the current areas of language systematization. Each of the directions is characterized by its own approaches, methods and criteria of discursive research. Thus, the genealogical approach involves taking into account historical factors, thanks to which it is possible to identify family links between the analyzed languages. The typological approach allows, in turn, to classify languages, according to linguistic type, according to the taxonomic criteria of structural kinship or non-kinship. Furthermore, linguistic typology deals with the study of type, universals, isomorphism and allomorphism in language. And finally, the territorial approach to the classification takes into account the principle of regional research, the aim of which is to study languages that have been in close contact for a long time and even under the influence of linguistic convergence.
Development of A Learning Model for Blended Collaborative Knowledge Construction in Vocational Education
Authors: Biquan Mo Et al.
Published: December 2023

Vocational Education readies students for specific careers and industries. A robust learning approach is crucial to meet modern workforce demands and foster knowledge exchange. This article introduces the origination of a Learning Model for Blended Collaborative Knowledge Construction in Vocational Education. This model merges methods from blended learning with collaborative strategies for constructing knowledge, fuelling student engagement, skills acquisition, and shared knowledge creation. The research utilizes the Delphi method to accumulate convincing proof for learning models prioritizing blended collaboration in knowledge generation. The sample pool comprises 60 students spanning two Zigong Vocational Training Institute classes, gathered via selective sampling. Data collection involved pre and post-testing of the student’s performance in the experimental and control groups, reinforced by the satisfaction survey from the experimental group. Data analysis employed statistical tools like percentages, means, standard deviations, and the t-test for related samples. The study found that pretest scores and SPSS data analysis showed that the f-test significant value is .702, which exceeds .05. This implies that, under the assumption of equal variance, the mean equivalence t-test findings for the first row of data, as shown in the table above, show that the experimental class averages 81.02 and the control class 81.05. The experimental and control groups' pretest scores are not significantly different, as the computed p-value of  .984 is larger than .05. Thus, teaching practice is reasonable. The experimental group's student satisfaction measure averaged 4.27, ranging from 4.10 to 4.43, indicating that students are content with their academics. The control group's student satisfaction measure averaged 2.87, ranging from 2.50 to 3.10, indicating low satisfaction with learning. Students in a vocational education setting were compared using a hybrid collaborative knowledge creation learning model, demonstrating its potential to enhance student satisfaction and outcomes.
Measuring Efficiency of Public Higher Education Using DEA Model for Sichuan in China
Authors: Zhipei Xu Et al.
Published: December 2023

This study uses Data Envelopment Analysis (DEA) and the Malmquist method to investigate efficiency in Sichuan Province’s public undergraduate universities by employing a dynamic unbalanced panel data approach and Refining input-output indicators through the application of the Factorial Component Analysis (FCA) method. We find average comprehensive efficiency (0.6601), pure technical efficiency (0.8562), scale efficiency (0.7723), and total factor productivity progress (0.932) for 27 institutions from 2018 to 2022. Despite the increased investment, efficiency gains are modest. Hierarchical correlation with input-output efficiency is noted, and total factor productivity shows an upward trend influenced by financial resources and economies of scale. These findings provide insights for university administrators and policymakers to address inefficiencies and optimize education resources for sustainable development.
Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk
Authors: Kokane Chandrakant Deelip , Pathak Kishor R , Mohadikar Gopal , Pagar Rakhi Subhash , Chavan Suhas , Kshirsagar Sopan Bapu
Published: December 2023

In today's daily life we are getting so many anonymous calls. Some calls are related to loan marketing and finance. As per the survey, one person is getting 26% spam calls in a day. The proposed methodology accepts user calls and based on the conversation the spam numbers are identified and the same information is provided to the other callers. This is possible because of machine learning-based sentiment analysis. Sentiment analysis is the subdomain of machine learning. The goal of this research is to propose an adaptive methodology for incoming calls. The sentiment-based incoming calls help desk works with freely available lexical resources WordNet, SemCor, and OMSTI. The discussed methodology accepts user conversations in audio format the speech-to-text conversion of the audio will be done. After pre-processing the keyword is detected from the statement. The word2Vec word embedding technique is used for representing words from document space to vector space. The 150-200 dimensional word vector is generated. The WordNet is used for sense mapping and keyword identification. Based on the sentiment analysis of input calls the decision is taken whether to accept or reject calls. This methodology is generating superior results for supervised machine learning models.
Predicting Outcomes of Horse Racing using Machine Learning
Authors: Gupta Meenakshi , Singh Latika
Published: December 2023

Machine learning with its vast framework is making its way into every aspect of modern society. The segment of betting sports particularly horse racing calls for the attention from a large spectrum of research community owing to its value to the stakeholders and the amount of money involved. Horse racing prediction is a complex problem as there are a large number of influencing variables. The present study aims to contribute in this domain by training machine learning algorithms for predicting horse racing results or outcomes. For this, data for a whole racing season from 2017 to 2019 of races conducted by Turf Club of India was considered which amounts to over 14,700 races.  Six algorithms namely Logistic Regression, Random Forest, Naive Bayes, and k-Nearest Neighbors) k-NN were used to predict the winning horse for each race. Synthetic Minority Oversampling Technique (SMOTE) technique was applied to the imbalanced horse racing data set and the attributes of the horse race repository were analyzed. The results were compared with other sampling methods to evaluate the relative effectiveness of this method. The proposed framework is able to give an accuracy of 97.6% which is substantially higher when compared to other similar studies. The research can be beneficial to the stakeholders as well as researchers in the same area to do further analysis and experiments.
Health Block: A Blockchain Based Secure Healthcare Data Storage and Retrieval System for Cloud Computing
Authors: Anil K. , Kamble Megha
Published: December 2023

Data in healthcare domain is highly sensitive in nature. Besides, there is need for maintaining integrity of such data. Blockchain technology has emerged to solve the problem of data integrity and non-repudiation with immutable storage in distributed repository. Thus secure data storage and retrieval in cloud environments is made possible using blockchain implementation. There are many existing healthcare systems with blockchain integration found in the literature. However, there is need for a system that supports complete set of operations that are governed by smart contracts. Another important consideration is that end users should be able to operate healthcare system without the need for knowledge of blockchain technology. Towards this end, in this paper, we proposed a Blockchain based secure healthcare data storage and retrieval system known as HealthBlock for cloud computing environments. We defined smart contract with underlying structures and functions using Solidity language for Ethereum blockchain platform. We also proposed and implemented an algorithm known as Healthcare Transactions over Blockchain (HToB). This algorithm supports secure blockchain based data storage and retrieval governed by smart contracts. Our system is evaluated using user-friendly web based client application. The experimental results showed that our system is able to ensure data integrity and non-repudiation besides reaping all benefits of blockchain technology.
Laser based Optical Wireless Communication using Log Normal Distribution and SIM BPSK Modulation
Authors: Belgaonkar Vijayashri V , Sundaraguru R.
Published: December 2023

Laser based Optical Wireless Communication(OWC) is emerging as one of the key advancements for the 5G and beyond technologies. This technology provides high bandwidth, extremely high data rate and high speed communication due to the use of optical waves for data transmission in the field of wireless communication. The system performance is limited due to the presence of turbulence in the atmosphere.We have developed system model using log normal channel distribution in the weak to moderate atmospheric turbulence. Closed form of expression is derived which provides the channel capacity in the presence of weak turbulence using log normal distribution. Subcarrier Internsity Modulation (SIM) BPSK modulation  technique is used in OWC system which  provides bettter performance in terms of received BER as compared to NRZ – OOK modulation.
Health Information Seeking Behaviour among Teenagers Sri Lanka
Authors: Manjula Gunarathna Et al.
Published: December 2023

In the contemporary era characterized by technological advancements, teenagers are displaying a growing inclination towards acquiring access to health-related information, which is crucial for teenagers to make informed decisions on their overall health and well-being. The findings indicate that the Internet is the primary source of health information, followed by kin and acquaintances, mass media, and medical practitioners among teenagers in Sri Lanka. Gender is a significant factor, as males prefer utilizing mass media and the internet, whilst females tend to rely more on medical practitioners, kinship networks, and friends. Education has a significant impact on teenagers’ health information-seeking behaviour; low-education teenagers depend more on kin and acquaintances, while higher-education teenagers are more inclined to utilize the Internet. Moreover, highly physically active teenagers rely on kin and acquaintances, while less active tend to rely on the Internet. The findings of the multinomial regression analysis emphasize that teenagers mostly rely on the Internet and medical experts in Sri Lanka. Education, perceived severity of health conditions, and health efficacy have an impact on these behaviours. This study adds to the body of knowledge regarding the information-seeking behaviours of teenagers, offering significant insights that can inform the progress of customized health promotion strategies and culturally appropriate digital health communication.
A Comprehensive Survey on Deepfake Methods: Generation, Detection, and Applications
Authors: Battula Thirumaleshwari Devi Et al.
Published: December 2023

Due to recent advancements in AI and deep learning, several methods and tools for multimedia transformation, known as deepfake, have emerged. A deepfake is a synthetic media where a person's resemblance is used to substitute their presence in an already-existing image or video. Deepfakes have both positive and negative implications. They can be used in politics to simulate events or speeches, in translation to provide natural-sounding translations, in education for virtual experiences, and in entertainment for realistic special effects. The emergence of deepfake face forgery on the internet has raised significant societal concerns. As a result, detecting these forgeries has become an emerging field of research, and many deepfake detection methods have been proposed. This paper has introduced deepfakes and explained the different types of deepfakes that exist. It also explains a summary of various deep fake generation techniques, both traditional and AI detection techniques. Datasets used for deepfake-generating that are freely accessible are emphasized. To further advance the deepfake research field, we aim to provide relevant research findings, identify existing gaps, and propose emerging trends for future study.
Exploring the Potential of Metaverse Platforms in Real Estate Transactions
Authors: Oh Yeon-Jae
Published: December 2023

COVID-19 has posed significant challenges to face-to-face sales of apartments and requires innovative solutions. In order to solve this problem, this paper proposes a model of a metaverse apartment sales promotion system. The proposed system utilizes the Zep metaverse platform to provide customers with virtual model houses and comprehensive real estate information. It provides various views, including satellite maps and 360° video, allowing customers to remotely explore the location of their apartments. Customers can explore the interior of the apartment via avatar, access 2D drawings and immersive 360° 3D videos. In addition, detailed sales conditions can be conveniently checked through a YouTube link, and customer convenience has been enhanced through online consultation with a sales agent. The proposed system aims to enhance the apartment buying and selling process by providing an immersive and convenient metaverse experience.
Optimizing University Mobility : An Internal Navigation and Crowd Management System
Authors: D. V. S. Prabhash, T. K. Weerasinghe, R. T. K. Weerasiri D. I. De Sliva, H. P. A. H. Piyumantha,
Published: December 2023

In the evolving landscape of educational technology, the article explores the critical frontier of indoor navigation systems, focusing on universities. Traditional approaches in higher education often fall short of meeting dynamic user expectations, necessitating revolutionary solutions. This research introduces an innovative internal navigation and crowd management system that seamlessly integrates augmented reality, natural language processing, machine learning, and image processing technologies. The Android platform serves as the foundation, harnessing augmented reality's transformative capabilities to provide real-time visual cues and personalized wayfinding experiences. The voice interaction module, backed by NLP and ML, creates an intelligent, context-aware assistant. The crowd management module, employing advanced image processing, delivers real-time crowd density insights. Personalized recommendations, powered by NLP and ML, offer tailored canteen suggestions based on user preferences. The agmented reality navigation module, using Mapbox, Unity Hub, AR Core, and Vuforia, enriches the user experience with dynamic visual cues. Results reveal the success of each module: the voice interaction module showcases continuous learning, user-centric feedback, contextual guidance excellence, robust security, and multimodal interaction flexibility. The crowd management module excels in video feed processing, image processing with OpenCV, and real-time availability information retrieval. The personalized recommendations module demonstrates high accuracy, equilibrium, and robust performance. The AR navigation module impresses with precision, enriched navigation, and tailored routes through machine learning. This cohesive system sets new benchmarks for user-centric technology in universities. Future work includes multi-university integration, intelligent spatial design, and real-time decision support, paving the way for more efficient, user-centered university experiences and contributing to the advancement of smart university environments. The research serves as a pivotal force in reshaping interactions within university spaces, envisioning a future where technology seamlessly enhances the essence of human interaction in educational environments.
DeepSegNet: An Innovative Framework for Accurate Blood Cell Image Segmentation
Authors: B. Arunadevi, M. Pompapathi, Amar Choudhary, Ashok Bekkanti D. Vetrithangam, Subba Reddy, Naresh Kumar Pegada,
Published: December 2023

Image segmentation plays a crucial and indispensable role in computer vision, as it allows the partitioning of an image into meaningful regions or objects. Among its numerous applications, image segmentation holds particular significance in the domains of medical diagnosis and healthcare. Its vital role in this field stems from its ability to extract and delineate specific anatomical structures, tumors, lesions, and other critical regions from medical images. In medical diagnosis, accurate and precise segmentation of organs and abnormalities is paramount for effective treatment planning, disease monitoring, and surgical interventions. Blood cell image segmentation is highly valuable for medical diagnosis and research, particularly in the domains of hematology and pathology. Precisely segmenting blood cells from microscopic images is essential, as it offers critical insights into various blood-related disorders and diseases. Although deep learning segmentation models have exhibited promising results in blood cell image segmentation, they suffer from several limitations. These drawbacks encompass scarce data availability, inefficient feature extraction, extended computation time, limited generalization to unseen data, challenges with variations, and artifacts. Consequently, these limitations can adversely impact the overall performance of the models. Blood cell image segmentation encounters persistent challenges due to factors like irregular cell shapes, which pose difficulties in boundary delineation, imperfect cell separation in smears, and low cell contrast, leading to visibility issues during segmentation. This research article introduces the innovative DeepSegNet framework, a powerful solution for precise blood cell image segmentation. The performance of widely-used segmentation models like PSPNet, FPN, and DeepLabv3+ is enhanced through the use of sophisticated preprocessing techniques, improving generalization capability, data diversity, and training stability. Additionally, the incorporation of diverse dilated convolutions and feature fusion further contributes to the improvement of these models. The Improved PSPNet, Improved FPN, Deep Lab V3, and Improved Deep Lab V3+ achieved 98.25%, 99.04%, 98.23%, and 99.31% accuracy, respectively, and the Improved Deep Lab V3+ model outperformed well and produced a Dice Coefficient of 99.32% and Precision of 99.38%. The proposed DeepSegNet framework improves overall performance with an increased accuracy of 8.91%, 3.72%, 17.73%, 22.83%, 7.96%, 9.61%, 17.36%, 6.22%, 13.32%, and 14.32% compared to the existing models. This framework, which can be applied to accurately identify and quantify different cell types from blood cell images, is instrumental in diagnosing a variety of hematological disorders and diseases.
An Integrated Framework for Delivering Healthcare Services through Information Technology
Authors: Ibrahim Mahmoud El-Henawy, Engy Yehia Romany Zareef Shafik,
Published: December 2023

Egyptian integrated healthcare services delivery is undergoing a transformative shift driven by the rapid advancements in information technology . This paper presents an integrated framework designed to leverage IT solutions for the efficient delivery of healthcare services for patients. The proposed framework encompasses various components, including electronic health records , telemedicine, and interoperability standards.The integration of electronic health records serves as the foundation for seamless information exchange among healthcare providers, ensuring a comprehensive and up-to-date view of patient health. Telemedicine, a key aspect of the framework, facilitates remote consultations, enabling healthcare professionals to extend their reach and provide timely interventions. Additionally, the framework incorporates advanced data analytics techniques to derive meaningful insights from the vast amount of healthcare data generated.
Execution of Serverless Functions Lambda in AWS Serverless Environment
Authors: Shashank Srivastava Et al.
Published: December 2023

Serverless computing, epitomized by AWS Lambda, has revolutionized application development and deployment by abstracting away server management and offering auto-scalability. AWS Lambda's 15-minute maximum timeout for function execution, however, presents a unique challenge for users aiming to process longer tasks within the serverless framework. In this paper, we dive deep into the execution dynamics of AWS Lambda within the AWS serverless environment, with a particular focus on extended execution times. As serverless computing gains traction, understanding the nuances and limitations of AWS Lambda's default settings becomes paramount.

This paper investigates different request handling mechanisms within AWS Lambda and presents empirical data to demystify the underlying processes. By addressing the challenges posed by long-running serverless functions, we aim to provide practical insights and potential solutions for developers and architects seeking to optimize their serverless applications. In the quest for efficient triggering mechanisms for extended serverless functions, this paper offers valuable guidance, empowering users to leverage AWS Lambda effectively while circumventing the default timeout constraints.
Patient Health Care Opinion Systems using Ensemble Learning
Authors: C. Arulananthan Et al.
Published: December 2023

The patients’ experience is considered a dominant reputation in the hospital administration and medical fields. Online patient reviews are recognized as an important criterion for evaluating hospital service quality and performance. The classical approach of evaluating service excellence is often found to be tedious. But with machine learning classifiers and opinion mining techniques the data assessing, and evaluation is made casual and its saves time. Currently, patient satisfaction and quality of service for patients in hospitals plays a major role in health care sector. In this paper a novel Ensemble Model is proposed to Analyze Patient Health Care Opinion Systems. The Classification models are used to classify patients’ feelings as positive, negative, or neutral using a machine learning approach to predict superlative models in data analysis. Ensemble techniques are used to analyze the opinions classified by the model, and the recommendation for health care is analyzed based on sentiment polarity.
Securing Cloud Data: An Enhanced Approach through Attribute-Based Access Control Mechanism
Authors: K. Raja Et al.
Published: December 2023

Cloud computing is considered one of the most dominant paradigms in the Information Technology (IT) industry these days. It offers new cost-effective services on-demand such as Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). However, with all of these services promising facilities and benefits, there are still a number of challenges associated with utilizing cloud computing such as data security, abuse of cloud services, malicious insider and cyber-attacks. Among all security requirements of cloud computing, access control is one of the fundamental requirements in order to avoid unauthorized access to systems and protect organizations assets. Although, various access control models and policies have been developed such as Attribute based Access Control (ABAC) and Role Based Access Control (RBAC) for different environments, these models may not fulfil cloud’s access control requirements. In this paper, an enhanced attribute-based access control based on strategy mechanism is proposed by introducing a strategy for providing access control to the users in the cloud environment.
The Evolution of Access Control in Cloud Security: A Survey of Key Literature
Authors: K. Raja Et al.
Published: December 2023

Cloud Computing is a distributed model that enables users to access, meet and exchange resources and resource requirements hosted by various service providers. Authentication of requesting users and the permitting of their rights of access are extremely necessary in order to avoid unauthorised or unlawful access to cloud services. In the cloud scenario, access control of distributed resources is most critical. Access control is a basic aspect of the security of information that is directly linked to the fundamental features of confidentiality, honesty and availability. This article provides a literature analysis of the Cloud Computing Access Control Mechanism.
Smart Agri Disease Detection of Black Gram Plant Leaf Using the Cloud-Based Yolov8 Intelligent System
Authors: P. M. Vijaya Raju Et al.
Published: December 2023

Infectious diseases affecting plants can have a major negative impact on economic output. Phenotyping plants is an essential part of plant characterization for tracking plant development over time. The urdbean leaf crinkle virus (ULCV) causes crinkled leaf disease, a plant disease that mostly affects black gram (Vigna mungo), a significant pulse crop in India. Crinkled disease is devastating to crops and the agricultural economy as a whole, causing farmers to lose out on a lot of money and harvests. Plant diseases cost USD 220 billion annually, according to the FAO (Food & Agriculture Organization). The early and accurate diagnosis of a disease (ULCV) is crucial for any economy that relies on agriculture. computer vision and Image processing are among the many cutting-edge methods for detecting plant diseases early. Preprocessing images, segmenting those images, extracting features, and applying machine learning algorithms to diagnose diseases are all viable options. Grey Level Co-occurrence Matrix (GLCM) is used for feature extraction. One of the machine learning methods used for detection is YOLO, which is based on a Convolutional Neural Network (CNN). In other words, YOLO led to more reliable disease diagnosis.
Units of Account Between Countries at Different Times
Authors: Tilyakhodzhaeva Fazilya Muhamedjanovna
Published: December 2023

Finance has been one of the most valuable source of life for many decades. This article compares monetary units of account between countries at different times. Moreover, the article examines the emergence and role of coins in commodity-money relations. The question of the possibility of using barter relations is also demonstrated in the following article.
Assessment of Phytochemicals, Antioxidant Activity and Enzyme Production of Endophytic Fungi Isolated from Medicinal Plant Sources
Authors: Sriya Choudhury  , Sibashish Baksi  , Bidyut Bandyopadhyay  , Debjani Roy
Published: December 2023

Endophytic fungi are mitosporic and meiosporic ascomycetes that asymptomatically reside in the internal tissues of plants beneath the epidermal cell layer, where fungi colonise healthy and living tissue via quiescent infections. Endophytes are important components of microbial diversity. Endophytic fungi isolated from medicinal plants more likely exhibit pharmaceutical potentials. These plentiful natural products isolated from endophytes represent a huge reservoir which offers an enormous potential for exploitation for medicinal, agricultural and industrial uses. There has been a great interest in endophytic fungi as potential producers of novel, biologically active products. Endophytes are believed to carry out a resistance mechanism to overcome pathogenic invasion by producing secondary metabolites. Globally, there are at least one million species of endophytic fungi in all plants which can potentially provide a variety of structurally unique natural products such as alkaloid, benzopyranones, chinones, flavanoids, phenols, steroids, xanthones and others. Therefore, there is an ample opportunity to unearth novel and interesting endophytic microorganisms with significant therapeutic efficacy. The objectives of our current study are to isolate endophytic fungi from specific medicinal plants found locally and carry out their characterization and isolation, followed by qualitative and quantitative assessment of secondary metabolites produced by them and study their antioxidant and enzyme activities. The present study, therefore would highlight the growing concept that the bioactive compounds produced by the endophytes not only establishes host endophyte relationship but also have an immense chance of application in the field of medicine, agriculture and industry.
Toxic Components of Plastic Pose Carcinogenic Threat to Public Health
Authors: Priti Nandi  , Rupesh Dutta Banik
Published: December 2023

The toxicity of plastics and its components is increasing day by day for human needs. In modern days, plastics are widely used in every product in our daily life. In 2021, it was predicted that the world would produce 390.7 million metric tons of plastic. A year's worth of plastic waste is produced in India at 3.4 million tons. Many products are manufactured with plastic due to its impressive characteristics such as inert, flexibility, durability, versatility, lightweight, and less costly. They encourage dangers to the planet and human health. Numerous chemical and toxic contaminants, including Bisphenol A (BPA), phthalates, antimonitroxide, brominated flame retardants, poly-fluorinated chemicals, nonylphenol, polychlorinated biphenylethers, dichlorodiphenyldichloroethylene, phenanthrene, among others, are found in plastics and pose a major risk to human health and the environment. These chemical and hazardous compounds are used in the manufacturing of plastic bottles, containers, pharmaceuticals, food packaging, baby toys, etc. The impact of plastics on the environment gets more dangerous day by day. Not only the environment but also exposure to plastics leads to various human health risks. Toxic components of plastics lead to several serious cancers even;they are also possessing their genotoxic effects on human health. This review contains the toxicity of plastic components on human health and the environment by low-level exposure and gives a proper awareness about the health problems and focuses on degrading ways to reduce its effects.
Ultrasound Diagnosis of Urolithiasis
Authors: Akhmedov Farkhod Khakimovich
Published: December 2023

Urolithiasis is a metabolic disease caused by various exogenous or endogenous factors, often has a hereditary nature and is determined by the presence of a stone in the urinary system. The causes and mechanism of stone formation remain open in urology. There is still no definitive theory for the development of urolithiasis. The main symptoms of urolithiasis are pain, hematuria, dysuria and stone passage. Urolithiasis (UCD) is one of the most common urological diseases and ranks second in the world after inflammatory nonspecific diseases of the kidneys and urinary tract.
FABRICATION OF COPPER OXIDE NANOPARTICLES DOPED PCL/PVP NANOFIBROUS MATS BY ELECTROSPINNING AND EVALUATION OF THEIR ANTIBACTERIAL ACTIVITIES
Authors: ERCİ Fatih , BAYRAM SARIİPEK Fatma
Published: December 2023

In this study, PCL/PVP/CuO nanofibrous mats for antibacterial applications were fabricated by electrospinning technique using PCL/PVP as a biopolymer matrix and copper oxide nanoparticles (CuONPs) as antimicrobial agents. PCL/PVP/CuO nanofibrous mats were successfully produced by doping different ratios of CuONPs (0.5%, 1%, and 2% wt) into the PCL/PVP solution. The chemical, morphological, and wetting properties of the prepared composite nanofibrous mats were evaluated by FT-IR, FE-SEM analysis, and water contact angle measurements. The morphological investigation indicated that the fiber diameters of the resulting nanofibers decreased as the CuONP content added to the PCL/PVP matrix increased, and the mean fiber diameter value measured for the PCL/PVP/2%CuONPs nanofibrous sample was 186.73. Moreover, wetting behavior of nanofiber surfaces displayed that the incorporation of PVP significantly enhanced the surface wettability of PCL with hydrophobic properties, but the addition of CuONPs to the obtained PCL/PVP matrix decreased it. An in vitro bactericidal assay was performed to investigate the efficacy of PCL/PVP/CuO nanofibrous samples against Staphylococcus aureus. The addition of CuONPs to the fibers led to antibacterial activity, which was found to increase with higher doping ratios. The results showed the potential of PCL/PVP/CuO nanofibrous mats to serve as an effective biomaterial for antibacterial applications.
PAMUK VE VİSKON LİFLERİ KULLANARAK TEK BOYA BANYOSUNDA DEGRADE EFEKTLİ ÖRME KUMAŞ GELİŞTİRİLMESİ
Authors: DEMİRCİ Fatma , ERCOŞMAN Selver Zilan
Published: December 2023

Dünya ve Türkiye pazarında büyük paya sahip olan tekstil sektöründe rekabetin gün geçtikçe artması, yaşam standardının yükselmesiyle birlikte müşterilerin her geçen gün farklı taleplerinin doğmasına neden olmuştur. Tekstil sektöründe en fazla kullanılan pamuk ve viskon lifler ile karışımları kullanılarak yeni ürün tasarımlarının yapılmasına ve üretim yöntemlerini bulmaya yöneltmiştir. Yeni ürün tasarımlarına bakıldığında yeni hammadde kaynakları ya da mevcut ham maddelerin farklı renklendirilmeleriyle mümkün olmaktadır. Sürdürülebilir tasarımda hem estetik ve işlevsellik açısından hem de çevre açısından en uygun malzemelerin seçilmesi gerekmektedir. Bu bağlamda farklı ürünler elde etmek için çeşitli boyama yöntemleri geliştirmiştir. Bu yöntemlerden birisi de degrade boyama yöntemidir. Degrade boyama yöntemi tek bir tekstil ürünü üzerine belirli aralıklar ile farklı renklerin belirli bir desene göre elde edilmesi olarak tanımlanmaktadır. Piyasada enine çizgili desenlerin renk geçişlerinin yumuşak olduğu yüzey efekti degrade efekti olarak adlandırılmaktadır. Bu çalışma kapsamında pamuk ve viskon lifleri farklı karışım oranlarında kullanılarak iplikler üretilmiştir ve ardından iplik karışımlarının sıralı olarak örülmesiyle örme kumaşlar geliştirilmiştir. Bu kumaşların içeriğinde pamuk ve viskon liflerinin farklı amorf/kristalin yapılarına sahip olması su/boya alma kapasitesi farklılığından faydalanılarak tek banyoda tek adımda boyama prosesi ile degrade efekti elde edilmiştir. Elde edilen kumaşlara ait renk ve performans özellikleri incelenmiştir.
YÜKSEK ÇÖZÜNÜRLÜKLÜ SPEKTROGRAM GÖRÜNTÜLERİNDEN AKAZE YÖNTEMİ İLE SES SAHTECİLİĞİ TESPİTİ
Authors: ÜSTÜBİOĞLU Beste , TAHAOGLU Gul
Published: December 2023

Ses sahteciliği alanında yaygın olarak kullanılan Kopyala-yapıştır sahteciliği, ses içerisindeki bir sesli kısmın kopyalanıp yine aynı ses içerisinde farklı bir konuma yapıştırılmasıyla oluşturulmaktadır. Gelişmiş ses yazılımları sayesinde bu tür bir sahteciliğin uygulanması oldukça kolay olmakla birlikte, saldırganlar tarafından sahtecilik izlerini gizlemek için sahte sese uygulanan son işlem operasyonları bu sahtecilik tespitini oldukça zor hale getirmektedir. Bu amaçla, sesten elde edilen yüksek çözünürlüklü spektrogram görüntüsü üzerinde anahtar nokta tabanlı bir yaklaşım kullanarak, ses kopyala-yapıştır sahteciliğini tespit eden son işlem operasyonlarına dayanıklı yeni bir yöntem önerilmiştir. Önerilen yöntemde öncelikle ses dosyasından yüksek çözünürlüklü spektrogram görüntüsü elde edilir. Ardından, Akaze yöntemi ile spektrogram görüntüsünden anahtar noktalar ve özellik tanımlayıcıları çıkarılmaktadır. Çıkartılan özellikler g2NN algoritması ile eşleştirilmektedir. Spektrogram üzerindeki noktaların ses üzerine iz düşürülmesiyle de ses kopyala-yapıştır sahteciliği tespit edilmektedir. Elde edilen sonuçlar önerilen yöntemin son işlem operasyonları uygulansa dahi literatürdeki çalışmalarla kıyaslandığında çok yüksek doğrulukla ses kopyala-yapıştır sahteciliği tespitini yaptığını göstermektedir.
IMAGE FUSION AND DEEP LEARNING BASED EAR RECOGNITION USING THERMAL AND VISIBLE IMAGES
Authors: CİHAN Mücahit , CEYLAN Murat
Published: December 2023

Advances in imaging and deep learning have fueled interest in ear biometrics, as the structure of the ear offers unique identification features. Thermal and visible ear images capture different aspects of these features. Thermal images are light-independent, and visible images excel at capturing texture details. Combining these images creates more feature-rich composite images. This study examines the fusion of thermal and visible ear images taken under varying lighting conditions to enhance automatic ear recognition. The image fusion process involved three distinct multiresolution analysis methods: discrete wavelet transform, ridgelet transform, and curvelet transform. Subsequently, a specially designed deep learning model was used for ear recognition. The results of this study reveal that employing the complex-valued curvelet transform and thermal images achieved an impressive recognition rate of 96.82%, surpassing all other methods. Conversely, visible images exhibited the lowest recognition rate of 75.00%, especially in low-light conditions. In conclusion, the fusion of multiple data sources significantly enhances ear recognition effectiveness, and the proposed model consistently achieves remarkable recognition rates even when working with a limited number of fused ear images.
Time Limits in the Control and Supervisory Legislation
Authors: Belyakovich E. V.
Published: December 2023

The paper is devoted to the examination of the time limits established by Federal Law No. 248‑FZ of 31.07.2020 «On State Control (Supervision) and Municipal Control in the Russian Federation» and other regulatory legal acts. The author elucidates temporal principles of the control and supervisory legislation and proposes a classification of the terms of the control and supervisory procedure. The author highlights an increase in the concentration of temporality in normative regulation of control and supervisory activities, the focus of the legislator on a more detailed time limits regulation of the relevant process. The paper examines the time units, estimated and event categories used by the control and supervisory legislation in order to establish the duration of the procedural actions. The author explains uncertainty of the legal regulation of control and supervisory activities by means of the «day» time unit. The question of time management methods in the field of control and supervisory activities, depending on the state of the term, is clarified: suspension and extension (within the term) and restoration (in cases when the term has expired). There are two types of extension of the term: automatic and declarative. The list of terms that can be extended, suspended and restored is given. The set of circumstances necessary to restore the missed deadline has been determined. The question of the timing of procedural actions in the pretrial procedure is investigated. The author outlines the criteria for leaving the application without consideration due to non-compliance with the pre-trial procedure and carries out a comparative legal analysis of the timing of preventive measures.
Bringing an Expert to Criminal Responsibility under Art. 307 of the Criminal Code of the Russian Federation
Authors: Nadonenko O. N.
Published: December 2023

In the paper, the author analyzes the reasons for the negative attitude of the courts towards such evidence as expert opinions that is applied by both the defense and the investigation. However, the effectiveness of such is currently very low because during pre-trial proceedings and in court they are often recognized as inadmissible. The most common justification for refusal to include an expert opinion is the fact that the expert was not notified of criminal responsibility for giving a willfully false statement. However, this kind of caution is not provided for in the procedural code, and the expert opinion itself is not included in the disposition of Art. 307 «Willfully false statement, expert opinion, specialist or incorrect translation» of the Criminal Code of the Russian Federation, in contrast to Art. 309 «Bribery or coercion to give testimony or avoidance of giving testimony or incorrect translation» of the Criminal Code of the Russian Federation. According to the author, this is due to the inconsistency of the norms of the Code of Criminal Procedure of the Russian Federation and the Criminal Code of the Russian Federation, as a result of which the application of Art. 307 of the Criminal Code of the Russian Federation against experts for willfully false statement or opinion is virtually impossible to implement. The author proposes to make changes to the current procedural and criminal legislation in order to improve the current situation. Thus, the need to add to Art. 58 of the Code of Criminal Procedure of the Russian Federation provisions on notifying experts about criminal responsibility under Art. 307 of the Criminal Code of the Russian Federation, and in Art. 307 of the Criminal Code of the Russian Federation — expert opinions.