bluuebunny/crossref_metadata_2025_split huggingface dataset:
Violations of Sovereignty in “Cyberspace” Under the United Nations Charter
Authors: Ассаф Алаа 
Published: January 2024

Affirming that violating State sovereignty through and against “cyber” infrastructure could be covered by the scope of Art. 2(4) and (7) of the United Nations Charter is one of the most pressing challenges that faces international law today. This article aims to address this issue by expanding on a general taxonomy outlined in the Tallinn Manual 2.0 on violations of sovereignty in “cyberspace”. These violations are categorised as conducts leading to either “infringement upon the target State’s territorial integrity” or “interference or usurpation of inherently governmental functions”. In order to map the taxonomy of the Tallinn Manual 2.0 onto Art. 2(4) and (7), it is necessary to highlight the convergence between territorial sovereignty and “cyberspace” that allows for extending the scope of application of Art. 2. Through recognising data as “assets” that can be subject to a functional sovereignty, that in turn could be subject to unlawful use of force in violation of the general ban codified in Art. 2(4) as an “infringement upon the target State’s territorial integrity”. Extending the scope of Art. 2(7) is contingent upon defining the concept of intervention as a conduct aiming to unlawfully assume an exclusive competence of a State by another State. Under this concept, intervention in “cyberspace” could be envisaged as attempts to gain control over the functionality of certain “cyberspace” infrastructure that is instrumental for the manifestation of State exclusive competences. A process that demands taking control of that entity to an extent impinging the regular functioning of the targeted entity beyond the mere manipulation of data. Under the proposed definition of intervention such conduct of “interference or usurpation of inherently governmental functions” can constitute a violation to the principle of non-intervention as codified by Art. 2(7).
Энергетический союз Европейского союза как механизм правового регулирования развития внутреннего электроэнергетического рынка
Authors: Белова Ольга Сергеевна 
Published: January 2024

В международных отношениях союз традиционно понимают как объединение, сотрудничество субъектов международного права. В данной работе рассматривается Энергетический союз Европейского союза — не как новый союз государств, но как механизм правового регулирования развития внутреннего электроэнергетического рынка Европейского союза. В соответствии с документами Европейского союза Энергетический союз представляет собой стратегию, определяющую механизмы правового регулирования и цели в области климатической нейтральности. При этом данные механизмы не подразумевают унификации национального законодательства государств — членов Европейского союза. В статье рассмотрены особенности реализации Энергетического союза как стратегии по двум направлениям: нормативное регулирование правоотношений в рамках формирования и функционирования внутреннего электроэнергетического рынка Европейского союза и создание системы планирования, мониторинга и управления реформированием внутреннего электроэнергетического рынка Европейского союза. В работе проведен анализ предпосылок и целей реформы внутреннего электроэнергетического рынка Европейского союза, включая рамочную программу в отношении политики в области энергетики и климата Европейского союза на период до 2030 года и Парижское соглашение об изменении климата. Рассмотрено состояние внутреннего электроэнергетического рынка и энергетической инфраструктуры Европейского союза после внедрения документов «третьего энергопакета» и перспективы реформы внутреннего электроэнергетического рынка Европейского союза, приведена оценка эффективности «четвертого энергопакета» Европейского союза.
Rough Set Techniques in Wireless Sensor Networks using Membership Function and Rough Labelling Graphs for Energy Aware Routing
Authors: R. Nithya Et al. 
Published: January 2024

Rough set theory and rough graphs are employed for data analysis. Rough graphs utilize approximations, and this paper presents a method for implementing rough graphs using rough membership functions and graph labeling for data structures and reduction. This paper proposes a rough graph labeling method, termed rough -labeling similarity graph, that utilizes a similarity measure for vertex and edge labeling. This method aims to minimize boundary regions in rough graphs and is applicable to wireless sensor networks (WSNs). In WSN, the proposed algorithms such as PSO-LSTM, COA-LSTM, LOA-LSTM integrates with rough set theory based rough -labeling for boundary region identification. The proposed method incorporates the membership functions encapsulates the rough labeling graphs for cluster boundary region identification for WSN. The cluster boundary for WSN is based on rough set membership and rough labelling is termed as rough set membership boundary region (RMB). RMB in WSN and implementation of proposed routing algorithm such as LOA-LSTM provides high throughput and energy saving when compared to existing cluster boundary structure methods such as Voronoi, Spectrum and Chain.
A Review on Prediction of Heart Disease based on Machine Learning and Datamining Techniques
Authors: Durga Bhavani Adla Et al. 
Published: January 2024

Heart is the important organ in human body which supplies blood to all organs of the body. The abnormal situation of heart is considered as heart disease. According to WHO data cardiovascular, respiratory and neonatal conditions are the top three causes of Deaths in the World. In the year 2019 Heart diseases occupies 16%(9 million) of overall deaths happened in World. From two decades there is 4 times increase in the deaths with heart diseases this is because of change in life style, lack of physical activity, food habits, obesity ,stress, cholesterol ,high blood pressure and diabetes.so there is a need to work on prediction of heart diseases to save many lives because prediction is the only way to prevent the disease. In this paper we will discuss about existing algorithms and existing work done in different machine learning and datamining techniques, which are concentrated more on the classification and prediction. main objective is to evaluate the performance of these algorithms and identify the most accurate and efficient approach for diagnosing heart diseases.
Some of the machine learning and data mining techniques are Artificial Neural Network(ANN),Decision Tree, Naive Bayes, SVM(Support Vector Machine),k-Nearest Neighbours (KNN),J48,SMO,Random forest and classification Tree.
Lung Cancer Classification Using Modified Squeezenet
Authors: Nishiya Vijayan Et al. 
Published: January 2024

Lung cancer is the primary cause of mortality in individuals diagnosed with cancer. Detecting and diagnosing lung cancer early significantly reduces the mortality rate. The early diagnosis of lung cancer is greatly facilitated by medical imaging. The recommendation is to undergo a CT scan, as it has a higher probability of detecting lung cancer during its initial phases. The detection of lung cancer greatly depends on the utilization of advanced deep learning technology, specifically convolutional neural networks, which assist in accurately classifying the CT image. This paper proposed a Modified light weight SqueezeNet architecture that mixes bottleneck residual network and fully connected layer along with global average pooling in the original network. This modification enhances the classification performance with a slight rise in computational complexity. CT images of 330 patients are used as a data set for testing the proposed technique, which is executed in MATLAB 2022a platform. The proposed method can identify lung cancer and categorize it as either malignant or normal with test Accuracy of 95.76%, Recall-92.94%, Precision of 98.75%, Specificity-98.75%, and AUC-0.9977. The Modified SqueezeNet gives better classification performance against the base SqueezeNet model. The proposed method outperforms traditional deep learning networks like AlexNet, ShuffleNet, ResNet-50, and GoogleNet.
Investigations of Naive Bayes, Random Forest and Decision Tree Algorithm for building aeroponics system
Authors: Arunraja A Et al. 
Published: January 2024

Urban farming has becoming popular and most adopted recently as society has become more conscious about the food quality they consume and conservation of resources. The Internet of Things (IoT) and machine learning algorithms are employed to build and implement an automated aeroponics system. This system contains Arduino as a main processing unit and uses Node MCU for cloud storage. It gets inputs from sensors like ultrasonic sensor, light sensor, humidity sensor, temperature sensor and exhibit the output in a LCD unit. In response to the inputs, water pump, cooler fan and light acts automatically. The data are stored in cloud as a record for future use. Using machine learning algorithms Naive Bayes, Random Forest and Decision Tree the datasets from cloud are trained and tested to know the crop suitable for the given condition. According to the results, the system has the potential to save more labour and reducing water consumption than conventional methods.
Exploring the Evolving Dynamics of Data Privacy, Ethical Considerations, and Data Protection in the Digital Era
Authors: Deepak Hajoary Et al. 
Published: January 2024

This paper presents a comprehensive exploration of the evolving dynamics of data privacy, ethical considerations, and data protection in the rapidly advancing digital era. Recognizing the profound impact of technologies such as big data, IoT, and AI, this study delves into the intricate interplay between technological innovation and the pressing need for robust data-governance frameworks. It addresses the challenges and opportunities presented by the massive surge in data generation, focusing on the implications for personal privacy, the ethical use of data, and the development of legal and regulatory measures. Through a bibliometric analysis spanning from 2015 to 2023, this research systematically examines the literature in this domain, identifying key trends, influential authors, and pivotal publications. This study also investigates collaboration networks among leading data privacy scholars, revealing significant insights into the intellectual landscape of this field. The findings highlight a marked increase in academic interest in data privacy and ethics, particularly post-2020, underscoring the growing societal and scholarly attention to these issues.
This study emphasizes the criticality of balancing the benefits of data-driven innovation with ethical and privacy concerns. This underscores the role of education and awareness in fostering a responsible, data-literate society capable of navigating the complexities of a digitalized economy. In conclusion, the study calls for ongoing reforms and a proactive approach to data governance, advocating a future in which technological advancements and ethical data use coexist harmoniously.
Advancements in Joint Replacement Surgery: Improving Long-Term Outcomes and Patient Satisfaction
Authors: K.L. Gaonkar Et al. 
Published: January 2024

Orthopaedic procedures have changed dramatically in the last several years due to the spectacular developments in joint replacement surgery. The purpose of this review article is to clarify the various developments in joint replacement surgery with an emphasis on improving patient satisfaction and long-term results. The study looks at five important topics: patient selection, prosthetic materials, surgical techniques, technological advancements, and long-term outcomes.
Precision and implant longevity have been transformed by technological advancements such as 3D printing, robotic-assisted surgery, and computer-assisted navigation. Modern surgical methods have a strong emphasis on tissue preservation, less invasive procedures, and improved recovery protocols, which greatly lower postoperative problems and speed up recovery. Prosthetic material advancements including highly cross-linked polyethylene and customised designs have enhanced patient-specific results and implant longevity. In order to maximise surgical success and satisfaction, patient selection criteria, customised care regimens, and psychological support have become essential. When long-term results are assessed, functional abilities, implant durability, and patient satisfaction all show notable improvements; nonetheless, problems with complications and revision operations still exist.
Impact of Mild Level Exercise Program on Incidence of Falls, Gait and Balance Among Elderly People at Selected Rural Areas of Belagavi District.
Authors: Shakeelahmed Mujawar Et al. 
Published: January 2024

Context: Exercises that are low-intensity can aid with balance and gait. Regular low-intensity exercise is a beneficial substitute for treating balance and gait issues brought on by ageing. Objective: To assess incidence of falls, gait and balance of elderly people before and after low intensity exercise program and to assess effectiveness of low intensity exercise program on incidence of falls, gait and balance of elderly people.
Setting and design: The study was conducted in selected rural areas of Belgaum district and pre experimental one group pretest post test study design was used.
Materials and Methods: Population was comprised of elderly people aged 60 years and above. The sample size was 30 and non probability purposive sampling technique was used to select the samples. Berg balance scale and Performance oriented mobility assessment scale for gait was used to assess gait and balance..
Statistical Analysis used:Descriptiveand inferential statistics were used with the helpof SPSSversion 20.
Results: In pretest12 (40%) of elders had incidence of fall during the one month prior to pre test and is reduced in post test as 7(23.3%) were had incidence of fall after one month to interventional program. The obtained value of ‘ t’ (29) = 6.97 for gait and ‘ t’ (29) = 2.65 for balance are found significant at 0.05 level of significance, indicating that the low intensity exercise program has helped elderly to improve their gait and balance.
Conclusions: Low intensity exercise program helped elderly people to improve their gait and balance. So, such programs need to be implemented in community area to prevent incidence of fall and improve gait and balance among the elderly population.
“Impact of Mild Level Exercises on Gait, Balance, Functional Status to Prevent Fall Among Elderly In Selected Areas of Belagavi District, Karnataka – A Study Protocol”
Authors: Shakeelahmed Mujawar Et al. 
Published: January 2024

Context: Falls, a frequent and preventable cause of morbidity, raise the likelihood of functional independence loss and the requirement for hospitalization and institutionalization, placing a strain on the healthcare system. The annual expenses for acute care linked to fractures from falls are estimated at $10 billion, despite the fact that the entire expenditures for acute care connected to falls remain unclear.
Aims: To administer the mild level exercise programme of elderly among experimental group and to determine the effect of mild level exercise program on improvement of gait, balance and functional status of elderly among experimental group.
Settings and Design: The samples from the selected rural areas of Belagavi district were selected using convenient sampling technique. A quantitative approach with post test only control group design was adopted for the study.
Subjects and Methods: Elderly above the age of 60 years of selected rural areas of gokak taluka belagavi district and total 250 samples selected by using convenient sampling technique and my research approach is Quantitative Research Approach, Research deign is Post test only control group design and Sampling technique is Non-Probability; Convenient Sampling Technique respectively.
Conclusions: The mild level exercise program was very much effective and the elders had great interest to learn and to do the exercises to improve their gait, balance, functional and to reduce the incidence of fall. Continuing nursing education programs can be conducted in hospitals and community regarding the importance of low intensity exercise program in improving gait, balance, functional status and in reducing the incidence of falls in the elderly people.
Effectiveness of Learning Package on Impact of COVID-19 Pandemic on Physical, Mental, Social and Economic Status among Adults at Selected Villages of Gokak Taluk, Karnataka.
Authors: Prakash Hiremath Et al. 
Published: January 2024

Context: As of march 11, 2021 more than 11 million people in India had been infected with COVID-19, and about 158,000 had died. The spread of COVID-19 in India is of great concern due to the country’s large and densely populated areas with widespread poverty and high migration rates, coupled with a high prevalence of chronic conditions that are generally poorly controlled.
Objective: To develop and evaluate learning package to overcome from the impact of COVID-19 among adults.
Setting and design: The study was conducted in selected villages of Gokak Taluk and pre-experimental research design was used.
Materials and Methods: Population was comprised of adults aged between 18-60 years old. The sample size was 06 and simple random sampling technique was used to select the samples. Self constructed assessment scales were used. Learning package was developed and evaluated for its effectiveness.
Statistical Analysis used: Descriptive and inferential statistics were used.
Results: The pre-test mean score for functional status was 11.50 and SD was 2.429, anxiety status was 13.67 and SD was 2.685, stress status was 30.00 and SD was 2.898, social status was 26.50 and SD was 4.370 economic status was 5.67and SD was 0.816. In post-test mean score for functional status was 7.50 and SD was 2.258 with mean difference score of 4.00, anxiety status was 7.33 and SD was 1.366 with mean difference score of 6.33, stress status was 7.33 and SD was 0.81565 with mean difference score of 22.66, social status was 17.00 and SD was 2.098 with mean difference score of 9.50, economic status was 3.33 and SD was 0.516 with mean difference score of 2.33. The statistical paired ‘t’ implies that the difference in the pre test and post test score was found to be statistically significant at 5% level. There exists a statistical in the enhancement of score indicating the positive impact of learning package.
conclusions: The impact of COVID-19 was more before distributing the learning package. Hence the learning package was effective.
Design, Simulation and Analysis of a Replacement-free Implanted Thermoelectric Generator based Pacemaker
Authors: Shwetha.M Et al. 
Published: January 2024

This paper deals with a novel design approach to pacemakers that will enable the implanted pacemakers to be replacement free. The major drawback of the current design of pacemakers is their expiry date of around twelve years. This timeframe is intensified by the lowered age range when heart disease turns critical. Pacemaker failure is one of the most common forms of heart disease, and implanting an artificial pacemaker is the most acceptable and viable solution.
Prediction of Osteoarthritis in the Knee by KOA Diagnosis Using Artificial Intelligence Based Techniques
Authors: Ramya Praba Et al. 
Published: January 2024

Knee Osteoarthritis is the most common form of knee arthritis. A healthy knee's articular cartilage, a smooth, slick tissue, allows for simple bending and straightening. The ends of the leg bones that make up your knee are covered, safeguarded, and cushioned by this material. Two c-shaped segments of meniscal cartilage between your bones serve as "shock absorbers" to cushion your knee joint. Osteoarthritis develops gradually. The cartilage becomes rough and ragged as it ages. It hurts to move the bones along this exposed area. The most prevalent signs and symptoms of knee osteoarthritis are pain and stiffness. After being inactive for a while or in the morning, symptoms typically worsen. The earlier you begin treatment, the better, as osteoarthritis gets worse with time. Your doctor may prescribe additional tests after reviewing your symptoms and medical history, such as X-rays, which can reveal bone and cartilage damage as well as the existence of bone spurs, and magnetic resonance imaging (MRI) scans. So, the KOA diagnosis will be based on two modalities: X-rays and MRIs. Depending upon the patient’s opinion about their knee problems, the doctor will decide whether to take X-rays or MRIs for KOA diagnosis.
To avoid such severe knee issues, researchers used many various techniques for KOA diagnosis in the existing system in order to treat it at an early stage of knee problems. In order to do that, we conducted a survey of research articles based on the KOA diagnosis published in the years 2021 and 2022 for this work. From the conducted survey, the methodologies which are AI-based techniques used for KOA diagnosis are discussed and their performance metrics are compared. The current review paper's goal is to do research on the various KOA diagnosis approaches employed in the years 2021 and 2022. As a result of this study, the methodologies which are AI-based techniques used for KOA diagnosis achieves good efficiency in predicting the osteoarthritis in the knee.
Novel Cross Optimization based Trusted and Opportunistic Routing Scheme in Wireless Sensor Network
Authors: Pritesh A. Patil Et al. 
Published: January 2024

Opportunistic routing in wireless networks under mobile computing domain is one of the point of attraction for the research. Consistency along with routing security are the challenges faced by many old schemes which results performance to be compromised. However the existing techniques developed for effective routing are cost effective without the concern for the exact positioning of nodes in the network but while designing the scheme, security still requires attention. A novel cross optimization based trusted and opportunistic routing scheme for WSN is proposed in this paper which is referred as Unified Trusted and Optimized Routing (UTOR) scheme. The prime intention of UTOR is to realize optimized data movement along with routing and data security. UTOR functions in two stages, the first stage recognizes and unites the unified nodes depending on defined liberal constant(LC) which has definite trust, link stability, and quality components. Definite trust as well as link stability can be measured directly but time to live and the associated delay are considered for measuring the quality. Whereas in second stage optimized nodes selection takes place with the help of proposed UTOR over defined proclivity function(PF) based on definite_trust(DT), link_stability (LS), quality_of_node(QN) and distance. Proposed UTOR’s performance is evaluated based on performance measures for Ad-Hoc sensor network of varying range of dynamic nodes in the presence of black_hole and DoS attacks. UTOR exposes relatively superior throughput and detection rate at the same time showcases minimal distance and delay, which are comparatively better than competing schemes. Significantly higher throughput and detection_rate as 44.1 and 55.7 respectively alongside low distance and delay as 168.2 and 13 are shown by UTOR which are comparatively better performance parameters than competing schemes. UTOR’s effective use in WSN may be under real-time scenarios such as environmental monitoring, smart farming, automation industries etc.