bluuebunny/crossref_metadata_2025 huggingface dataset:
Other Effective Area-Based Conservation Measures, Biodiversity Stewardship and Statutory Intervention – A South African Perspective
Authors: Paterson Alexander
Published: June 2023

Area-based approaches are a central component of global efforts to conserve biodiversity. While the focus of many countries has been mainly on protected areas, other effective area-based conservation measures (OECMS) have been accorded global recognition in the past decade as a vital complementary approach to protected areas. This recognition has been reemphasised in the Kunming-Montreal Global Biodiversity Framework adopted by parties to the Convention on Biological Diversity in December 2022, with its Target 3 ratchetting up area-based coverage targets to 30 per cent by 2030. A growing focus and reliance on OECMs to contribute towards achieving this target is anticipated. The international community has in the past few years introduced some guidance to identify, secure, manage, monitor and verify the anticipated long-term biodiversity conservation outcomes of OECMs. Some commentators have argued for domestic legal intervention to complement this general international guidance. The South African Government has recognised the potential contribution of OECMs towards the achievement of domestic and global area-based biodiversity targets in its National Protected Areas Expansion Strategy (2018) but has alluded to the need for legal intervention to ensure that they achieve positive and sustained long-term outcomes for the in situ conservation of biodiversity. Some domestic commentators have highlighted the strong link between biodiversity stewardship (particularly conservation areas) and OECMs, advocating that these conservation areas should form the priority focus of domestic efforts to identify OECMs. This article scopes this potential link and specifically considers whether the current domestic legal and policy framework applicable to these conservation areas is sufficiently robust to ensure that only appropriate areas are identified as OECMs and that once recognised, they are governed and effectively managed in the long term. It highlights several frailties of the existing framework and drawing from anticipated legal reform in the Western Cape relating to biodiversity stewardship, it proposes a possible model for future national legislation regulating OECMs.
The Influence of Reasonableness in Determining Delictual or Tort Liability for Emotional Distress or Mental Harm in American and French Law
Authors: Ahmed Raheel
Published: June 2023

American and French law, like South African law recognises claims for emotional or mental harm. Emotional, mental, or psychological harm was only recognised by the courts in the 1800's and even though the mind and body in a sense is considered as a unit, these types of claims are not on par with claims for physical bodily injury. In fact, these types of claims can be regarded as sui generis but within the broader ambit of delictual or tort liability. Finding delictual or tort liability for emotional, mental or psychological harm has been problematic not only in South Africa but also in the United States of America and France. Even though there are fundamental differences in the law between these jurisdictions, the broader questions the courts face is whether a claimant is entitled to claim, the amount of damages that should be awarded and how to limit liability with these types of claims. Limiting liability for emotional or mental harm is generally the main policy concern but the courts have found ways of using the elements or concepts such as reasonable foreseeability of harm to limit the claims. American, French, and South African law recognise claims for emotional, mental or psychological harm by primary and secondary victims. Thus emotional, mental, or psychological harm caused directly or indirectly is compensable. In American and French law, the concept of reasonableness plays an important role, whether it be implicit or explicit, in determining delictual or tort liability for emotional or mental harm. In a sense also, reasonableness plays an overarching role in determing the liability. In a previous contribution, the influence of reasonableness in determining delictual or tort liability for psychiatric or psychological harm in English and South African law was discussed. In this contribution, the focus is on the influence of reasonableness in determining delictual or tort liability for emotional or mental harm in American and French law.
Dates of Holocene environmental changes in Lake Bafa: A hierarchical Bayesian analysis of change points
Authors: ÖN Zeki Bora
Published: June 2023

The timing of the separation of Lake Bafa from the Aegean Sea and its subsequent transition to a
lake remains a topic of debate, with considerable uncertainties associated with the proposed dates
for these transitions. This study introduces a novel approach, hierarchical Bayesian change point
analysis (HBCPA), to identify tipping points in paleoenvironmental time series. The objective is to
offer more precise and objectively selected results concerning the previously uncertain transition
dates of Lake Bafa’s geological evolution in the Holocene. The method presented in this study is
applied to analyze stable oxygen and carbon isotope data from foraminifer and ostracod shells,
as well as total organic carbon (TOC) data, obtained from the BAFA09P02 sediment core. Results
indicate that the transition from a marine to an isolated lagoon environment occurred around
2060 years BP, with an 89% uncertainty interval ranging from 2250 to 1870 years BP. Additionally,
the transition from an isolated lagoon to a brackish lake environment is estimated to have occurred
around 595 years BP, with an 89% uncertainty interval ranging from 780 to 425 years BP. The
results of this study illustrate that the suggested HBCPA approach holds the capability to identify
tipping points in environmental data while quantifying their intrinsic uncertainties.
Aircraft Bleed Air System Fault Prediction based on Encoder-Decoder with Attention Mechanism
Authors: Su Siyu , Sun Youchao , Peng Chong , Wang Yifan
Published: June 2023

The engine bleed air system (BAS) is one of the important systems for civil aircraft, and fault prediction of BAS is necessary to improve aircraft safety and the operator's profit. A dual-stage two-phase attention-based encoder-decoder (DSTP-ED) prediction model is proposed for BAS normal state estimation. Unlike traditional ED networks, the DSTP-ED combines spatial and temporal attention to better capture the spatiotemporal relationships to achieve higher prediction accuracy. Five data-driven algorithms, autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), ED, and DSTP-ED, are applied to build prediction models for BAS. The comparison experiments show that the DSTP-ED model outperforms the other four data-driven models. An exponentially weighted moving average (EWMA) control chart is used as the evaluation criterion for the BAS failure warning. An empirical study based on Quick Access Recorder (QAR) data from Airbus A320 series aircraft demonstrates that the proposed method can effectively predict failures.
Design and Implementation of a Mechatronic System for Unmanned Aerial Vehicles (UAVS)
Authors: Singh Amit Kumar
Published: June 2023

The design and implementation of mechatronic systems for unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Mechatronics combines mechanical, electrical, and computer engineering principles to create integrated systems with enhanced functionality and performance. This paper presents a comprehensive study on the design and implementation of a mechatronic system specifically tailored for UAVs, with a focus on improving their flight capabilities, autonomy, and overall operational efficiency.The development of UAVs has revolutionized various industries, including aerial surveillance, disaster management, agriculture, and delivery services. However, there are several challenges associated with their design and operation. This research aims to address these challenges by proposing a mechatronic system that integrates advanced sensing, actuation, and control technologies. This research presents a comprehensive approach to the design and implementation of a mechatronic system for UAVs. The integration of mechanical, electrical, and computer engineering principles enables the development of enhanced flight capabilities, autonomy, and operational efficiency. The proposed system addresses the challenges associated with UAV design and operation, making significant contributions to the field of unmanned aerial vehicles. Future work can focus on further optimizing the mechatronic system and exploring additional applications in different domains.
Advanced Control Strategies for Multi-Robot Cooperation in Mechatronic Systems
Authors: Bangari Raghubeer Singh
Published: June 2023

In recent years, the field of robotics has witnessed a rapid development and deployment of multi-robot systems in various applications. Multi-robot cooperation in mechatronic systems has become an area of great interest, as it offers numerous advantages such as increased efficiency, robustness, and versatility. However, achieving effective coordination and collaboration among multiple robots remains a challenging task due to the complexity and dynamism of the environment.This research focuses on the development and analysis of advanced control strategies for multi-robot cooperation in mechatronic systems. The objective is to enhance the overall performance of the system by enabling efficient task allocation, coordination, and communication among the robots. This research contributes to the field of multi-robot cooperation in mechatronic systems by proposing and analysing advanced control strategies. The developed strategies address the challenges of task allocation, coordination, and communication, and leverage advanced control techniques to enhance system performance. The findings of this research have implications for various applications, including manufacturing, logistics, search and rescue, and environmental monitoring, where multi-robot cooperation is crucial for achieving complex tasks efficiently and effectively
Design and Development of Mechatronic Systems for Micro/Nano Manipulation
Authors: Negi Pankaj
Published: June 2023

The design and development of mechatronic systems for micro/nano manipulation have become a crucial field of study in recent years. With the increasing demand for precise and controlled manipulation at the micro/nano scale, researchers and engineers have focused on developing advanced mechatronic systems capable of achieving high accuracy and dexterity. This abstract presents a comprehensive overview of the key aspects involved in the design and development of mechatronic systems for micro/nano manipulation. The design and development of mechatronic systems for micro/nano manipulation represent a rapidly evolving field with significant potential. This abstract provides a comprehensive overview of the fundamental challenges, key components and technologies, design considerations and methodologies, applications, and future trends in this area. The advancements in mechatronic systems for micro/nano manipulation have the potential to revolutionize various industries and pave the way for new technological breakthroughs
Innovations in Soft Robotics: Design and Control of Flexible Mechatronic Systems
Authors: Joshi Gaurav
Published: June 2023

Soft robotics, an emerging field at the intersection of robotics and materials science, has gained significant attention in recent years due to its potential for creating highly adaptable and versatile robotic systems. Unlike traditional rigid robots, soft robotics focuses on designing and controlling flexible mechatronic systems that can mimic the natural movements and interactions of living organisms. This paper presents an overview of the recent innovations in soft robotics, specifically focusing on the design and control aspects of flexible mechatronic systems.The design of soft robots involves the integration of advanced materials and mechanisms that enable compliance and flexibility in the robot's body structure. Various materials, such as elastomers, hydrogels, and shape-memory polymers, have been explored for constructing soft robotic components that can deform and recover their shape. These materials exhibit unique properties, such as stretchability, elasticity, and self-healing capabilities, allowing soft robots to adapt to complex and dynamic environments. Additionally, the design of soft robotic systems often incorporates pneumatic or hydraulic actuation mechanisms to achieve locomotion and manipulation.In conclusion, this paper provides an overview of the recent innovations in soft robotics, focusing on the design and control of flexible mechatronic systems. Soft robots have the potential to revolutionize various fields by providing adaptive and versatile robotic systems. The integration of advanced materials, novel actuation mechanisms, and innovative control strategies has paved the way for the development of soft robots with remarkable capabilities. However, further research is needed to address the existing challenges and unlock the full potential of soft robotics in practical applications.
Intelligent Control Systems for Fault Detection and Diagnostics in Mechatronic Systems
Authors: Dhiman Tarun Kumar
Published: June 2023

Intelligent control systems have emerged as a promising solution for fault detection and diagnostics in mechatronic systems. With the increasing complexity of modern mechatronic systems, the ability to identify and diagnose faults in real-time has become critical for ensuring efficient and reliable operation. This abstract presents an overview of intelligent control systems for fault detection and diagnostics in mechatronic systems, highlighting their key features, benefits, and applications. The main objective of intelligent control systems is to enhance the performance and robustness of mechatronic systems by continuously monitoring their behaviour and identifying potential faults. These systems leverage advanced techniques such as machine learning, artificial intelligence, and data-driven approaches to analyse the system's operational data and detect anomalies that may indicate the presence of faults. By employing intelligent algorithms, these systems can not only identify faults but also provide diagnostic information to localize and classify the detected faults.

In the outcome, intelligent control systems offer significant advantages in fault detection and diagnostics for mechatronic systems. Their ability to adapt and learn from the system's behaviour, combined with advanced machine learning and data-driven techniques, enables accurate and timely detection of faults. These systems have broad applications in robotics, manufacturing, automotive, and aerospace systems, where they play a crucial role in maintaining system performance, safety, and reliability. Future research in this field will focus on improving the efficiency, scalability, and interpretability of intelligent control systems for fault detection and diagnostics in mechatronic systems.
Nonlinear Dynamics and Vibration Analysis of Rotating Machinery
Authors: Dogra Vaishally
Published: June 2023

In a variety of engineering disciplines, including the mechanical, aerospace, and energy industries, the study of nonlinear dynamics and vibration analysis of rotating machinery has become increasingly important. In many applications, including turbines, engines, compressors, and pumps, rotating machinery is essential for both the performance and safety of the entire system. Nonlinearities, on the other hand, add intricate dynamic behaviours and vibration phenomena into these systems, which can result in subpar performance, excessive wear, and even catastrophic failures.The goal of this research is to improve rotating machinery's design, operation, and maintenance by examining the nonlinear dynamics and vibration properties.In order to comprehend the complex interplay between the dynamics of the rotor, the dynamics of the bearing, and the nonlinear forces, the analysis makes use of sophisticated mathematical models and computer tools. To appropriately depict the system's nonlinear behaviour, important factors including rotor imbalance, shaft misalignment, bearing clearance, and non-circular journal shapes are taken into account.The objective of the project is to create reliable techniques for locating and forecasting important vibration modes, resonances, and instabilities in rotating machinery. Techniques like nonlinear time series analysis, bifurcation analysis, and chaos theory are used in this. The results of this study can help in the creation of efficient vibration mitigation plans and condition monitoring tools as well as useful insights into the underlying physics of rotating machinery dynamics. This paper findings may help to increase the performance, reliability, and lifetime of rotating machinery, which would increase operational effectiveness and lower maintenance costs across a range of industrial sectors.
Design and Control of Advanced Robotic Systems for Manufacturing and Assembly
Authors: Verma Deepak
Published: June 2023

Modern industrial processes must now be designed and controlled with advanced robotic systems for manufacture and assembly to increase productivity, accuracy, and flexibility. The main factors involved in creating and overseeing these robotic systems are presented in detail in this work.In the design stage, manipulators, sensors, and end-effectors that are customised for the particular manufacturing or assembly task are chosen and integrated. To ensure accurate and efficient task execution, additional control algorithms and programming techniques are also developed. This covers adaptive control techniques, path optimisation, trajectory planning, and collision avoidance.The real-time monitoring, coordinating, and synchronisation of several robots and their interactions with the environment are the main goals of the control component. The integration of human operators with robotic systems is made possible by centralised or decentralised control frameworks, which promote secure and effective production and assembly procedures.The study also discusses how to improve robotic systems' abilities to handle complicated jobs and adapt to shifting production settings by integrating cutting-edge technologies like machine learning, computer vision, and augmented reality.The difficulties and possibilities posed by the application of sophisticated robotic systems for manufacturing and assembly are also covered. These include the dependability of the system, the ethical effects of automation in the workplace, the detection and recovery of faults, and human-robot collaboration.The ultimate goal of this work is to give readers a thorough grasp of the design and control principles required for creating advanced robotic systems for manufacturing and assembly, hence promoting higher levels of productivity, quality, and adaptability in industrial settings.
Investigation of Random Matrix Theory for Statistical Mechanics and Quantum Physics Applications
Authors: Kumar Akshay
Published: June 2023

Due to its capability to explain large systems with chaotic behaviour, Random Matrix Theory (RMT) has drawn a lot of interest in the domains of Statistical Mechanics and Quantum Physics. Investigating RMT's uses in these fields is the main goal of this study.RMT offers a potent framework for studying the behaviour of sizable ensembles of interacting particles in statistical mechanics. RMT enables the examination of different physical features including energy spectra and correlation functions by using random matrices as models for Hamiltonians. Advancements in disciplines like condensed matter physics and nuclear physics have been made possible by the universality aspects of RMT, which allow for the comprehension of universal features seen in many-body systems.RMT is an essential tool for understanding the statistical behaviour of quantum systems in quantum physics, especially when there are strong interactions present. It gives scientists a useful tool for describing the energy states and wave functions of complicated quantum systems, including those with chaotic dynamics. Understanding of phenomena including quantum transport, quantum entanglement, and quantum chaos has benefited by the knowledge obtained from RMT.With this study, we hope to add to the body of knowledge on RMT's uses in quantum physics and statistical mechanics. It offers important insights into the behaviour of complex systems and paves the road for improvements in these domains by investigating the characteristics of random matrices and their relationships to physical systems.
Design and Development of Mathematical Models for Computational Neuroscience
Authors: Bisht Raj Kishor
Published: June 2023

Through the use of mathematical and computer models, computational neuroscience is an interdisciplinary study that seeks to comprehend the principles and mechanisms underlying the functioning of the brain. These models are essential for understanding intricate neurological processes, giving information about the dynamics of the brain, and directing experimental research. The design and creation of mathematical models for computational neuroscience are presented in-depth in this study.The sections that follow explore several mathematical modelling strategies used in computational neuroscience. These include statistical models that analyse and infer associations from experimental data, biophysical models that explain the electrical properties of individual neurons and their connections, and network models that capture the connectivity and dynamics of brain circuits. Each modelling strategy is examined in terms of its mathematical foundation, underlying presuppositions, and potential limits, as well as instances of how it has been applied to certain areas of neuroscience study.The research also examines the model parameterization, validation, and refinement stages of the model creation process. It emphasises how the integration of experimental evidence, theoretical understanding, and computational simulations leads to iterative model refining. The difficulties and unanswered concerns associated with modelling complex neurological systems are also covered, emphasising the necessity of multi-scale and multi-modal methods to fully capture the complex dynamics of the brain.The paper ends with a prognosis for mathematical modeling's future in computational neuroscience. The development of virtual brain simulations for comprehending brain illnesses and planning therapeutic approaches are some of the rising trends that are highlighted in this article, along with the incorporation of machine learning techniques, anatomical and physiological restrictions, and the assimilation of these trends into models.
Innovative Techniques for Soil Stabilization in Civil Engineering
Authors: Danu Ritiksha
Published: June 2023

Soil stabilization is a critical aspect of civil engineering that aims to enhance the mechanical properties and stability of soil, thus enabling it to support the construction of various infrastructure projects. Over the years, researchers and engineers have continually sought innovative techniques to improve soil stabilization methods, leading to the development of several effective and sustainable solutions. This abstract provides an overview of some of the innovative techniques for soil stabilization in civil engineering, highlighting their principles, advantages, and potential applications.One innovative technique gaining attention in soil stabilization is the use of geosynthetics. Geosynthetics are synthetic materials specifically designed for geotechnical applications. They include geotextiles, geogrids, geocells, and geomembranes. Geosynthetics offer various benefits, such as reinforcing the soil, reducing soil erosion, improving drainage, and increasing the bearing capacity of weak soils. They can be used in different configurations based on project requirements, making them versatile and adaptable to a wide range of soil stabilization applications.
Risk Assessment and Management in Construction Projects
Authors: Negi Ankit
Published: June 2023

. Risk assessment and management play a pivotal role in ensuring the successful execution of construction projects. As the complexity and scale of construction projects continue to grow, project stakeholders face an increasing number of uncertainties and potential risks that can adversely impact project outcomes. Therefore, it becomes crucial to adopt an effective risk assessment and management framework to proactively identify, evaluate, and mitigate potential risks.This paper presents a comprehensive overview of risk assessment and management in the context of construction projects. It explores various techniques, tools, and strategies that can be employed to assess, analyse, and control risks throughout the project lifecycle. The objective is to provide project managers, contractors, and stakeholders with insights into best practices for minimizing project uncertainties and improving overall project performance.
Green Buildings: Strategies for Energy-Efficient and Eco-Friendly Designs
Authors: Negi Ankit
Published: June 2023

Green buildings have emerged as a critical solution to address the environmental challenges caused by conventional building practices. This abstract explores various strategies for designing energy-efficient and eco-friendly green buildings. The purpose of this study is to highlight the importance of incorporating sustainable design principles and technologies in building construction, with a focus on reducing energy consumption and promoting environmental stewardship. The abstract discusses key strategies such as passive design, energy-efficient systems, renewable energy integration, materials selection, and water conservation.Passive design techniques play a vital role in achieving energy efficiency and comfort in green buildings. These strategies involve optimizing the building's orientation, maximizing natural lighting, and utilizing natural ventilation for cooling. By utilizing these passive strategies, green buildings can significantly reduce energy demands for heating, cooling, and lighting, thereby minimizing their carbon footprint.Energy-efficient systems further enhance the performance of green buildings. Advanced heating, ventilation, and air conditioning (HVAC) systems with high-efficiency components and intelligent controls help minimize energy consumption while maintaining indoor air quality and thermal comfort. Additionally, incorporating energy-efficient lighting systems, such as LED technology, can significantly reduce electricity usage.
Analysis of Keypoint-based Copy-Move Forgery Detection in Digital Images
Authors: Agnihotri Aditya
Published: June 2023

The images are used as the element that is primary of in multitudinous operations and the dupe- move phony is considerably used to induce the forged images by manipulating the content of digital images. With the advancement of various image editing software Photoshop that is including Draw, GIMP, etc make it's easier to manipulate the digital images. Thus, Copy- Move Forgery Detection is a task that is challenging. Some of the dupe- move phony discovery styles analogous as Symmetry predicated features that are original SIFT- Symmetry, Clustering with J- relation Algorithm, SIFT- KAZE crossbred Features, point Matching predicated on frippery- bin-First Algorithm, Binary Discriminant Featuresetc. are compared and analysed in this paper.
Robotic Process Automation for Stock Selection Process and Price Prediction Model using Machine Learning Techniques
Authors: Chattopadhyay Saumitra
Published: June 2023

The usage of AI in the financial industry has increased the reliance on stochastic models for forecasting market behaviour. Quantitative analysts are constantly working to increase the precision with which machine learning models predict stock returns. Regression models using Support Vector Machine (SVM) and Random Forest are well renowned for their ability to predict closing prices with high accuracy. This study suggests a method for analysing and forecasting stock prices using an ensemble of these algorithms.Using basic market price data from India's National Stock Exchange (NSE), datasets are used that have been preprocessed to add common technical indicators as features. The size of the training dataset is decreased by using feature selection techniques to rank the features according to their impact on the final closing price. Sentiment analysis is also used in the study to examine the effect of investor sentiment on stock prices. Twitter postings with a specific corporate hashtag are rated as good or bad using a trained Word2Vec model. The ensemble model is then trained on a fresh dataset made up of counts of both positive and negative tweets across time as well as technical indicator data.This paper makes a contribution to the area by offering an ensemble model for stock price prediction that blends SVM and Random Forest regression models. The study illustrates the significance of feature selection in lowering dataset size as well as the limited influence of aggregated sentiment analysis from Twitter data on the performance of the model as a whole. For researchers and quantitative analysts looking to improve the precision of stock price prediction models in the financial industry, these findings offer invaluable insights.
A Survey on Improved Mobile Crowdsensing with Integration of Emerging Technologies
Authors: Pandey Chetan
Published: June 2023

Mobile sight is rapidly expanding and changing as mobile technology and associated disciplines of technology continue to progress. This seminar, titled "Mobile crowd-seeing (hosts)," seeks to investigate and offer answers for the effect, inflow, and processing of data via commercial mobile devices used by enormous numbers of people. This opens the door for research into novel monitoring techniques in a variety of industries, including social networks, daily living, healthcare, and smart transportation. The hardware and software of mobile devices are used by hosts, including but not limited to GPS and other related technologies, smart wearables, and smartphones with the Android and iOS operating systems. These gather data from their surroundings, effectively storing it, and either transferring it to a database to be processed there or doing the same actions on the raw data directly. This topic has a wide range of applications for both individuals and society as a whole. This article lists the essential structural elements that sustain the host's skeleton. However, significant technological advancements lessen the chance of problems. The accessibility of hosts makes it much easier for nefarious groups and individuals to gain access to, breach, and seize control of the data stores of the different components (including those of humans). The most current problems with the hosts' findings are investigated, and the advantages and disadvantages of many viewpoints are listed. Once some time has elapsed, the issues with unrestricted exploration and the upcoming challenges for hosts become increasingly important.
Tampering Detection and Content Authentication Using Encrypted Perceptual Hash for Image Database
Authors: Jain Ayushi
Published: June 2023

To detect and locate unapproved alterations or modifications made to digital pictures, tampering detection in images is a crucial study topic in the field of image forensics. As sophisticated picture altering tools become more widely available, it has become more difficult to guarantee the integrity and authenticity of digital visual output. This overview of the literature explores methodology, findings, and contributions in key tampering detection research studies. The breakthroughs in deep learning techniques and steganalysis are highlighted together with more conventional approaches like statistical analysis and error level analysis. The difficulties tampering detection faces are discussed, including generalisation, dataset accessibility, resilience against adversarial assaults, computing effectiveness, and moral and legal issues. With the goal of advancing the creation of precise, reliable, and effective algorithms for maintaining the integrity and authenticity of digital pictures, the paper offers insights into the current state of the art, limits, and future research paths in tampering detection.
Staff Management for Android
Authors: Bhatt Chandradeep
Published: June 2023

The employees or the staffs are believed to be as the backbone of any organization and so their management becomes the deciding aspect for any firm to be successful. The proposed thesis seeks to design a personnel management model that may be accessed by the admin as well as the user. However, the primary purpose of the author is to target the healthcare sector and offer the thesis in managing the nursing staff. The model is intended to be put on two platforms that may be accessed by the user as well as the admin. In this example, the user is the one who is seeking for a job in the healthcare field. For this goal, an Android platform is constructed using the principles and programming language of JAVA; whilst on the other hand an application interface of web app is being offered to the admin as the back end. C# and Asp.net are the programming languages that are used on the back end of the system. The implemented software allows the user the ability to look for a job in nearby place and apply for a vacant position. The user is also provided the ability to schedule a space as per his feasibility. On the other side, the admin is granted a complete access to staff’s mobility and he may monitor it through the web app. The admin is given the opportunity to manage vacancies and allocate respective jobs to the workforce. Hence, it can be claimed that this proposed system model delivers an efficient manner to the admin to monitor and control the workforce. Further, the system is also planned to be implemented in the cloud using MS Azure.
Data Security in Cloud Computing
Authors: Vimal Vrince
Published: June 2023

Due to the growing use of cloud services and the possible hazards involved with storing and processing sensitive data in remote locations, data security is a crucial concern in cloud computing. This literature review analyses the state of the art in data security research for cloud computing and emphasises the major discoveries, difficulties, and recommendations for the future. Encryption, access control, network security, physical security, security monitoring, data segregation, backup and disaster recovery, compliance, and safe development practises are just a few of the data security-related topics included in the assessment. The study notes that a problem is the size and quantity of the body of literature as well as the requirement to stay up with the continuously changing area. The need for thorough coverage, evaluation of source quality and reliability, and overcoming language and accessibility challenges are also mentioned as drawbacks. The review goes into further detail on issues including time limits, subjectivity in selection and interpretation, information overload, and integration and synthesis of findings. It is recommended that future research in data security for cloud computing put a particular emphasis on advanced encryption methods, secure data sharing and collaboration, privacy-preserving methods, cloud forensics, threat intelligence, machine learning, security assurance and auditing, blockchain and distributed ledger technologies, as well as user awareness and education. Data security in cloud computing may be further reinforced to secure sensitive data and guarantee the reliability of cloud services by solving these issues and making progress in the sector.
A Review on the Detection of Diabetic Retinopathy through the Use of Deep Learning
Authors: Sharma Abhishek
Published: June 2023

One of the most common and complicated forms of diabetes, diabetic retinopathy (DR) is now recognised as the major cause of blindness on a global scale. It is also one of the most commonly observed forms of the disease. The development of DR is associated with a degeneration of the blood vessels in the retina. Over the course of the past few years, numerous approaches to DR detection have been put forward as a result of the growing recognition that an early diagnosis is critical to effective treatment. However, research done in recent times has revealed a fact that deep learning-based CNN structures and Transfer Learning-based Medent’s are widely employed in DR detection because of their superior performance in the medical sector. This is a reality that has been brought to light as a result of recent developments. This article presents a study on automated ways that are used to diagnose diabetic retinopathy utilising image processing and disease classification techniques as a result of such improvements in Deep Learning methodology. These advancements have led to the writing of this article.
Prediction of Employee Turnover based on Machine Learning Models
Authors: Chauhan Rahul
Published: June 2023

As a result of the fact that the procedure of decision making constitutes a vital component in the management of a company, the personnel of that company are seen as a valuable kind of asset by the latter. Therefore, the procedure of employing them in the first place by making the appropriate choices is generally recognised as a well-known obstacle by administrative authorities. Employee turnover may be a time-consuming and difficult process because recruiting new workers requires not only additional time but also a significant amount of financial expenditure. In addition to this, there are a number of additional elements that play a role in the selection and hiring of a qualified applicant, who in turn would provide economic returns for an organisation. In this research, I propose building a model to predict employee turnover rate using data from three datasets acquired from the Kaggle repository and a subset of their features. To analyse staff traits and forecast turnover and churn rate, the work summarised here employs machine learning approaches and pre-processing techniques. Logistic regression, AdaBoost, XGBoost, KNN, decision tress, and Naive Bayes are only some of the machine learning algorithms tried out on extracted datasets in the report's implementation experiments. Evaluating qualities against evaluation parameters like accuracy and precision factors follows thorough research and training of selected attributes.
Smart Ticket Machine: STM
Authors: Gupta Richa
Published: June 2023

In the transportation sector, Smart Ticket Machines (STMs) have arisen as automated ticketing options that provide ease and effectiveness for both users and operators. The research articles on smart ticket machines analysed in this review of the literature are synthesised, including topics like user acceptability, implementation case studies, technology, security, accessibility, data analytics, and interaction with mobile apps and the Internet of Things (IoT). The review emphasises how STMs improve transportation networks' operational effectiveness, passenger satisfaction, and sustainability. User acceptability and satisfaction, with an emphasis on usability, dependability, and a variety of payment choices, are critical variables determining the success of STMs. Implementation case studies show increases in ticketing effectiveness, shorter lines, better revenue collection, and satisfied customers. To ensure dependable and safe ticketing operations, technological factors, including hardware, software, and security measures, have undergone considerable investigation. Usability and adherence to accessibility standards are prioritised with an emphasis on catering to a variety of user groups. Optimised resource allocation, real-time information updates, and individualised services are made possible through data analytics and interaction with mobile applications and IoT. However, there are drawbacks, including a lack of generalizability, data privacy issues, maintenance and expense concerns, and hurdles to user adoption. Enhanced personalization and customization capabilities, advanced analytics and predictive modelling, seamless integration with smart city initiatives, user-centered design and accessibility improvements, sustainability considerations, and further integration with mobility platforms are some of the future research directions. The functionality, usability, and integration of smart ticket machines in transportation systems will develop by addressing these limits and pursuing future directions, encouraging effective, user-friendly, and sustainable ticketing solutions. For academics, practitioners, and stakeholders engaged in the design, deployment, and development of Smart Ticket Machines, the findings of this literature study offer invaluable insights.
Machine Learning-based Automated Diagnosis of Breast Cancer from Mammography Images
Authors: Painuly Sakshi
Published: June 2023

This paper presents a novel machine learning-based system for the automated diagnosis of breast cancer using mammography images. The proposed system employs a nature-inspired feature extraction algorithm to accurately identify and highlight the salient features within the mammographic scans. The features derived are highly representative and effective in distinguishing between malignant and benign cases, thus addressing the inherent complexity and variability within the breast tissue structures. To enhance the prediction accuracy, a hybrid decision tree and gradient boosting algorithm is introduced. The decision tree algorithm provides a transparent and interpretable model, facilitating easy understanding and justification of the diagnosis decisions. The gradient boosting algorithm further refines the model by iteratively correcting the errors of the decision tree model, leading to a substantial improvement in diagnostic performance. The proposed system was tested on a comprehensive dataset and compared with the existing state-of-the-art diagnostic tools. The results demonstrated significant improvements in terms of accuracy, sensitivity, and specificity, thus showing promise in aiding radiologists in making more accurate and confident diagnoses. This research paves the way for a more robust, reliable, and automated system in breast cancer detection, thereby enhancing the effectiveness of breast cancer screening and early detection strategies.
IMPACT OF TYPES OF ELECTROMAGNETIC RADIATION ON LIVING NATURE
Authors: Dolchinkov Nikolay Todorov , Karaivanova-Dolchinkova Bonka Encheva
Published: June 2023

In our daily life we are exposed to electromagnetic radiation in a very wide range. In our century, these radiations are constantly increasing, and we do not always know how to protect ourselves from them and how they affect people and the rest of living nature. An attempt has been made to analyze the impact of emissions from a different frequency spectrum - from power supply with a frequency of 50 Hertz to high-frequency mobile communications with a frequency of up to 300 GHz. Studies in countries of Europe and the world were taken as a basis, and a study was made about the impact of these electromagnetic radiations in Central Northern Bulgaria and the Veliko Tarnovo region. It is shown how morbidity changes when they affect the peripheral and central nervous systems of people and what effect they can cause at high powers and high intensity at short distances.
STUDY OF THE STATE OF INNOVATION DEVELOPMENT AND OBSOLESCENCE IN THE REPUBLIC OF BULGARIA OF COMPANIES FROM THE MECHANICAL ENGINEERING SECTOR
Authors: Petrova Desislava , Vlahova Beata , Lengerov Angel , Zlateva-Petkova Tsanka
Published: June 2023

The goal is to make an analytical study of the state of innovation development and innovation obsolescence in 15 machine-building companies in the Republic of Bulgaria. On this basis, the regularity of innovation obsolescence should be established and whether it has priority over physical wear and tear. An alternative approach for choosing solutions to reduce the harmful impact of rapid innovation obsolescence is proposed.
IDENTIFIED RESILIENT FLAX GENOTYPES WITH IMPROVED AGRONOMIC CHARACTERISTICS FOR PRE-BREEDING
Authors: Morozova Inga , Stramkale Veneranda , Kroiča Ieva , Stramkalis Aldis
Published: June 2023

Genetic diversity evaluation in a breeding program is essential produce robust, resilient crop varieties with improved agronomic characteristics under environmental challenges in the sustainable agro-ecosystems. Knowledge and full potential on the impact of genotypes and growing conditions on flax yield traits still incomplete. The aim of this study was to evaluated the influence of hydrothermal conditions on the development of agronomic important traits of flax, analysed agronomic characteristics and identified perspective genotypes for pre-breeding. Field investigations were carried out from 2014 to 2017 for agronomically important traits of flax. In the study was evaluated flax population with Latvian origin in 24 fibre flax genotypes and ‘Vega 2’ (ST) as the standard variety of Lithuanian origin under Latvian meteorological conditions. Correlation between flax genotypes of the agronomically important yield traits and years of the hydrothermal coefficients during the growth period were analysed. According to the results obtained that most of genotypes the significant higher plants stem yield, total plant height, technical plant height, number of seed vessels per plant were measured in the growing seasons with high humidity. However, the higher seed yield and 1000 seed weight were measured in the driest year. The correlation between flax fibre content, seed number per seed-vessel, oil content and humidity level differed in dependence on genotype. The coefficient of variability between years was higher for of stem yield when compared with seed yield. The flax genotypes ‘S13/5-7/5-93’ exhibited more stable, highest stem yield (840.0 g m-2) and high seed yield (162.2 g m-2). The flax was identified 80% samples the short and 20% the medium vegetation period.
INDUSTRIAL HEMP VARIETIES PRODUCTIVITY POTENTIAL IN THE LATVIAN CLIMATIC CONDITIONS
Authors: Stramkale Veneranda , Morozova Inga , Černova Larisa , Stramkalis Aldis
Published: June 2023

Varieties grown for fibre-only will be established, managed and harvested differently than varieties grown for seeds or dual-purpose. The aim was analysed yield productivity and length of vegetation period dependency of the two types of hemp variety and identified perspective hemp variety for highest yield under different meteorological conditions in Latvia. Varieties/line from diverse European and Latvian origin (seed hemp varieties/ line 'Adzelvieši', 'Pūriņi', KA-2-2011, 'Finola', 'Henola' and fibre hemp varieties 'USO 31', 'Futura 75', 'Austa') were sown in randomized field experiments in Latvia. Biomass, stem, seed yield, fibre contents and vegetation period length were assessed across tree growing seasons from 2020 to 2022. Results show that hemp, seeds and fibre varieties/line, performed well giving high yield productivity between variety. Agrometeorological conditions for hemp growth and yield were favourable in 2022. In Latvian local climatic conditions, the seed hemp line KA-2-2011 and fibre hemp variety ‘Futura 75’ identified significant higher of biomass and stem yield as well as the seed hemp variety ‘Henola’ and fibre hemp variety ‘Austa’ significant higher of seed yield and fibre contents. The yield potential dependent from length of vegetation period length. The trial analyses identified positive and significant correlations between length of vegetation period and biomass (r = 0.97), stem yield (r = 0.98), and negative between length of vegetation period and seed yield (r = -0.89).