bluuebunny/crossref_metadata_2025 huggingface dataset:
Dialektisk atferdsterapi for barn: En pilotstudie av ferdighetstreningsgrupper for foreldre
Authors: Coldevin Marit , Skyberg Maia Daasvand , Kirchhofer Solveig M. , Løken Per Martin , Jenssen Synne , Stubberud Jan
Published: January 2024

Background and purpose: The overall aim of the present pilot study was to examine how parents report treatment outcomes after participating in skill training groups in dialectical behaviour therapy for children (DBT-C). DBT-C was developed to meet the therapeutical needs of children with severe emotional dysregulation. To our knowledge, this is the first study in the world to explore treatment outcomes of skill training groups for parents in DBT-C.

Methods and results: The sample in this within-subjects study consisted of parents of 23 children from an outpatient sample aged between eight and 13 years (Mdn = 11, 74 % boys). The outcome measures were the Eyberg Child Behaviour Inventory (ECBI), comparing scores pre- and post-intervention, and an evaluation of the perceived feasibility and acceptance of the intervention. Findings indicated a reduction in the frequency of disruptive behaviours (ECBI Intensity) (t(21) = 5.127, p < .001) and in the extent to which the parent finds the child’s behaviour troublesome (ECBI Problem) (t(19) = 2.714, p = .014) following intervention.

Conclusion: With reservations for the small sample size and the lack of a control group, the present pilot study shows preliminary evidence that DBT-C skill training groups for parents reduce behavioural problems in children with emotional dysregulation. The study contributes to the scant research conducted to date on the promising effects of DBT-C and paves the way for further research of the treatment method.

Keywords: DBT-C, DMDD, skill training groups, parent intervention, disruptive behaviour
A Systematic Load Frequency Regulation in Two Area Renewable Micro-grid Cluster Utilizing Bald Eagle Search Algorithm
Authors: Roy S.P. Et al.
Published: January 2024

This paper works on the systematic load frequency and tie-lie power transfer regulation within the prescribed limit. The proposed two area renewable energy based micro-grid cluster comprises of dish stirling solar generator, micro hydro turbine, biogas generator as energy generators in area 1. Also, wind turbine, tidal generator, biogas generator as energy generators in area 2. The Flywheel and battery act as mechanical and electrical energy storage devices in their respective areas. Moreover, to make the frequency oscillation die out quickly super magnetic storage devices are connected in the system. A comparison of performance of system with respect to objective function error is made to determine superior controller with minimum error and most stability. The controller parameters is adjusted with Bald eagle search optimizer, Black widow optimizer algorithm, Giza pyramid construction, Teaching learning based optimizer and Genetic algorithm. Lastly, best controller-algorithm i.e. (tilt integral tilt derivative with filter - Bald eagle search optimizer) combination with minimized objective function integral square error is examined under different scenarios.
Synchronous Generator Abnormality and Fault Analysis
Authors: Xaxa L.B. Et al.
Published: January 2024

The trouble-free operation of alternators is unavoidable to meet the rising demand for electrical energy. Any alternator failure causes overloading of the remainder of the units in a grid, posing a serious threat to network stability. In a stand-alone generation, production is seriously affected. The repairs of large capacity alternators require a trained workforce, which is not always locally available. When an alternator operates under typical fault conditions, it reduces its efficiency and shortens its lifespan. The synchronous generator’s prolonged outage has an adverse impact on network reliability and impacts the economy. As a result, early detection of alternator anomalies significantly reduces fault extension, loss of supply, and maintenance costs, while also improving the alternator’s life. In many cases, the early fault detection scheme for higher capacity alternators cannot be readily applied to a lower capacity one. The article discusses some practical root cause analyses of the commonly occurring faults in turboalternators. The identification, characteristics, and analysis of multiple fault occurrences have been thoroughly addressed. This article would provide utility and protection engineers impetus in determining the urgency, selecting, and implementing the appropriate protective system for various abnormality events.
Fault Analysis of Microgrid With Grid-Connected and Islanded mode Using IoT-Wavelet Approach
Authors: Dhanalakshmi K.V. Et al.
Published: January 2024

Now a days microgrid is one of the most widely used method in power network to reduce system losses as well as improve the reliability in the field of electrical systems. Integration of power projects typically involves adding new distributed energy sources with and without compensating devices to an existing power system network. It is essential to design new protection scheme due to changes in the topology and dynamic behavior of the system. Now fast fault detection algorithmic approaches are necessary to integrate different types of generating sources and loads under smart environment. The protection scheme must provide physical monitoring as well as parametrical with the help of new technologies. Internet-of-things(IoT) is one of the source to monitor electrical systems under various environmental conditions of the system. Wavelet (WT) basically investigates the fault transient signals of different frequency and divides the waveform into different approximate and detailed coefficient values, which provides the important knowledge about the classification and location of fault. The detection of faulty-line and the location of fault by implementation wavelet detailed coefficients of Bior1.5 mother wavelet. This proposed method provides fault analysis of IoT based protection of microgrid with Grid-connected and Islanded Mode Using Wavelet Approach under various types of faults.
Evaluation Algorithm of TV Program Host Performance Based on Emotion Recognition
Authors: Lu Bai Lu Bai
Published: January 2024

With the rapid development of information technology and system norms and the incongruity of personnel training speed, the development of news media reports has caused a large number of negative effects. From the current situation of the development of news media reports, due to the lack of perfect management, the difference in media literacy between users and enterprises, entertainment to death, rampant consumer culture, and many other factors, the network media reports are chaotic. In the proposed model Fuzzy Fredholm Integral Market Efficiency (FFI-ME) for the media coverage. With the FFI-ME model, the information technology computes the news media data for the estimation of the coverage to achieve market efficiency. The FFI-ME model uses the Fredholm Integral model to compute the market efficiency for the media data in China. The FFI-ME model computes the news data in China and clusters the data for the classification and detection of the instances in the equation. Through the Integral Fredholm model, the features of the news are estimated to compute the media coverage and efficiency of the news media data. The model uses the Deep learning model for the classification of the data instance in the media data. The simulation analysis expressed that the proposed FFI-ME model achieves a higher classification data accuracy of the 98%.
A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency
Authors: E.V.N.Jyothi Et al.
Published: January 2024

This research culminates in a robust Traffic Flow Prediction System poised to redefine the landscape of Intelligent Transportation Systems (ITS). Our findings highlight the substantial promise of this system through a meticulously structured methodology spanning data generation, dynamic network construction, multi-modal data integration, and the employment of state-of-the-art Graph Neural Networks (GNNs). Notably, the "Current Framework" stands out, demonstrating superior performance over alternative regression models, substantiated by a remarkable 35% reduction in Mean Squared Error (MSE) and a commendable 7% increase in R-squared (R²). Nevertheless, this system is not without its caveats. Ongoing model refinement, adaptability to the ever-evolving traffic landscape, and scalability considerations are essential for future exploration. These achievements usher in a new era for traffic management, with the potential to curtail congestion by up to 20%, bolster safety measures, and usher in an era of enhanced urban transportation efficiency.
Enhancing Cloud Data Privacy with a Scalable Hybrid Approach: HE-DP-SMC
Authors: Jaibir Singh Et al.
Published: January 2024

Cloud computing has become a popular way to store and access data. However, there are concerns about the privacy of data stored in the cloud. This paper proposes a novel privacy-preserving mechanism that uses a combination of homomorphic encryption, differential privacy, and secure multi-party computation. The mechanism allows users to store their data in the cloud while still ensuring the privacy of their data. The mechanism combines three cryptographic techniques: homomorphic encryption(HE), differential privacy(DP), and secure multi-party computation(SMC). Homomorphic encryption enables computations on encrypted data, ensuring that confidentiality is maintained throughout the storage process. Differential privacy(DP) techniques add an additional layer of protection by injecting controlled noise into query responses, preserving the privacy of individual data items. Secure multi-party computation protocols ensure that computations involving multiple cloud servers are performed securely without compromising the confidentiality of data. The mechanism is efficient and scalable. It can be used to protect the privacy of data in a wide range of applications, including healthcare, finance, and government. The quantitative results obtained from the performance evaluation of the proposed privacy-preserving mechanism reveal valuable insights into its efficiency and effectiveness. The mechanism shows promising results for small to medium-sized datasets. As the dataset size increases, the execution time, communication overhead, and storage requirements also increase proportionally. However, the mechanism maintains its scalability and efficiency. In comparison with baseline systems, the proposed mechanism outperforms systems without privacy-preserving mechanisms and only exhibits slightly slower performance than systems with traditional encryption. The proposed mechanism is a promising new approach to privacy-preserving cloud computing. It provides a strong level of protection against a variety of attacks, while still being efficient and scalable. It has the potential to revolutionize the way we store and share sensitive data..
Blockchain-Based Medical Record Sharing in Healthcare IoT: Building Trust and Transparency through Secure Provenance Tracking
Authors: Satish V. Kakade Et al.
Published: January 2024

Blockchain technology has been incorporated into the Healthcare Internet of Things (IoT) landscape as a revolutionary solution to tackle issues related to the sharing of medical records. This paper presents an innovative method that utilizes Temporal Blockchain for the purpose of Provenance Tracking. The introductory section provides context by delineating the significance of trust and transparency in medical data sharing within the healthcare IoT ecosystem. The study examines current blockchain solutions, delving into frameworks such as Hyperledger Fabric, Ethereum, Corda, and specialized approaches like temporal blockchain. The paper examines the difficulties associated with tracking the origin of data, concerns regarding privacy, problems related to scalability, and the need to comply with regulations. These challenges provide the context for the proposed methodology. The main emphasis is on Temporal Blockchain, integrating temporal elements to improve the tracking of origin and history. The evaluation parameters, such as security, provenance tracking, scalability, interoperability, privacy compliance, and performance, undergo a thorough assessment. The attained values demonstrate a strong emphasis on security at a high level, thorough tracking of origin and history, and strict adherence to privacy regulations. Nevertheless, the need for scalability and interoperability necessitates meticulous consideration. The study showcases the capacity of Temporal Blockchain to establish trust and enhance transparency in the sharing of medical records. The future scope focuses on tackling scalability challenges, improving interoperability, and making continuous optimization efforts. The proposed approach highlights notable accomplishments and emphasizes the continuous development and collaborative aspect of Blockchain-Based Medical Record Sharing in Healthcare IoT.
GAN-Enhanced Medical Image Synthesis: Augmenting CXR Data for Disease Diagnosis and Improving Deep Learning Performance
Authors: Prakash Patil Et al.
Published: January 2024

Deep learning is increasing the need for accurate and reliable medical image analysis tools, especially for CXR disease diagnosis. This study proposes the Attention Mechanisms based Cycle-Consistent GAN (AM-CGAN) to address the lack of annotated medical data. To produce realistic and clinically relevant CXR images, our model uses Generative Adversarial Networks (GAN) and attention mechanisms. Downstream deep learning models for disease classification improve with this enhancement. The Attention Mechanisms based Cycle-Consistent GAN (AM-CGAN) improves the accuracy and reliability of deep learning models used for medical image analysis, specifically for Chest X-ray (CXR) data. CXR data is enhanced to improve disease diagnosis. Generative Adversarial Networks (GAN) create realistic medical images in the proposed model. It also uses attention mechanisms to highlight key areas in generated images. This research aims to address the lack of annotated medical data, particularly for CXR images. Training deep learning models is difficult due to the lack of diverse and well-annotated datasets. Our proposed AM-CGAN uses attention mechanisms to generate synthetic CXR images that closely resemble medical images and highlight disease-specific characteristics. AM-CGAN uses Cycle-Consistent GAN to ensure that generated images match the input distribution and prevent mode collapse. While synthesizing images, the model can selectively focus on important anatomical structures and pathological indicators using attention mechanisms. This attention-driven approach improves the clinical significance of generated images, making them better for training accurate and reliable disease classification models. Many experiments were done to test the AM-CGAN on CXR images of COVID-19, pneumonia, and normal cases. The quantitative results show high precision (98.15% accuracy). This shows the model's ability to create medical-data-like synthetic images. Downstream deep learning models trained on the augmented dataset perform better at capturing disease-specific characteristics. This study advances GAN-enhanced medical image synthesis research and addresses the data shortage in medical imaging research. The AM-CGAN attention-driven focus on disease-related regions in CXR data suggests a promising way to improve diagnostic models, especially in situations with few labeled datasets. The AM-CGAN bridges the gap between diverse data and sophisticated deep learning models for disease diagnosis, making it a major advancement in medical image analysis.
Deep Learning for Automated Diagnosis of Plant Diseases: A Technological Approach
Authors: Ashok Kumar Et al.
Published: January 2024

- With the world's population growing, it is vital that food crops and medicinal plants are required to be developed in large quantity. A model that can help us understand the process more comprehensively will be explored. Based on a thorough understanding, we are exploring a model that will enable us to extract the precise information and in-depth knowledge we require. There are many plants that contract lethal diseases every year, including food plants. This reduces their output rates. We must employ automated methods to locate the issue inside the facility if we do not want manufacturing prices to rise. The detection of plant diseases can be automated through robotics and image processing. As a result of recent technological advancements, we now have the ability to simplify our artwork. A deep learning and image processing approach can increase the efficiency of detection processes. A great deal of progress has been made in diagnosing plant diseases. An artificial intelligence model was trained to assist in diagnosing flora disorders. Based on the photo information (leaf), the model can be controlled. Identifying the problem and discovering the condition in this study, we propose the use of computer vision technology combined with fuzzy logic to detect and grade leaf diseases. GLCM is performed to extract texture features, and fuzzy logic is applied to grade the disease. K-means clustering is applied for determining defected areas; GLCM is used to determine defected areas; and fuzzy logic is used for diagnosing diseases. About 70% of classifications are accurate according to the model. By using Speed Up Robust Features (SURF), DENSE, and Bag of Visual Words (BOVW) in addition to the global features, the accuracy of the system can be enhanced. A treatment program will help people better understand the illness if one is offered.
A Decentralized Approach to Threat Intelligence using Federated Learning in Privacy-Preserving Cyber Security
Authors: Nitin N. Sakhare Et al.
Published: January 2024

The need to protect data privacy and improve threat intelligence in the constantly changing field of cybersecurity has prompted the investigation of novel approaches. This research presents, within the scope of privacy-preserving cyber security, a decentralized method of threat intelligence using Federated Learning (FL). Sensitive threat intelligence data is maintained locally thanks to the decentralized structure of the suggested solution, which reduces the dangers associated with centralized repositories. The cornerstone is federated learning, which permits cooperative model training between dispersed entities without disclosing raw data. Differential privacy and homomorphic encryption are two privacy-preserving strategies that are combined to protect personal information while learning collaboratively. Updates to the model are safely combined and added to a global threat intelligence model without jeopardizing the privacy of the entities involved. The article delves into the nuances of this decentralized strategy, with a focus on building strong security and governance frameworks, being flexible enough to respond to new threats, and continuously improving through feedback loops. This decentralized method offers a viable model for threat intelligence in the future of cyber security by encouraging cooperation, protecting privacy, and strengthening the group's protection against cyberattacks.
Improving WSN Performance through Fuzzy-Based Traffic Data Analysis
Authors: Anuradha P. Gharge Et al.
Published: January 2024

In the present time in Wireless Sensor Network plays an essential role in the monitoring of different physical phenomena. Monitoring of city traffic data analysis is very important in different metro cities due to rapid increase in population.  This research work proposes a model for traffic data analysis using wireless sensor network  incorporated with fuzzy technique. The proposed model is tested for performance parameters such as node dead rate , data packed received. The proposed model improved the efficiency compared to existing techniques of the WSN network for traffic data collection and analysis..
Application of Relying on 3D Modeling Technology in the Design of Urban Sculpture
Authors: Lei Liu Et al.
Published: January 2024

Urban sculptures have long been a reflection of the cultural and historical identity of a city, serving as both artistic expressions and landmarks.  The application of deep learning in the context of sculptures within urban areas presents an intriguing intersection of art and technology. Deep learning algorithms, particularly those related to computer vision, have the capacity to analyze and understand the intricate details of sculptures. This paper presented a efficient 3D-AWE (3D-Weighted Architecture Estimation) in the analysis of urban sculptures. In an era where the preservation and interpretation of cultural heritage are of paramount importance, this study investigates the potential of advanced technology to enhance understanding of artistic and historical artifacts within urban environments. The proposed 3D-AWE model uses the weighted estimation with computation of the pixels in the scupturs. Additionally, the proposed 3D-AWE model uses the min-max estimation model for the computation of the features in the images. With the estimated features the deep learning through weighted model for the analysis of the sculptures. The research focuses on the accurate classification of urban sculptures into specific styles and periods, such as Baroque, Renaissance, Modernist, Abstract, Ancient, and Contemporary. Utilizing precision, recall, F1 Scores, and overall accuracy, the study highlights the model's ability to minimize errors and provide reliable categorization. Furthermore, the application of 3D-AWE for feature extraction reveals quantifiable representations of sculpture attributes, offering valuable insights for sculpture categorization, similarity analysis, and the automated management of museum collections. The implications of these findings extend to art history, cultural heritage preservation, and urban planning, underscoring the significance of advanced technology in efforts to safeguard and interpret cultural legacy.
English Sentence Semantic Feature Extraction Method Based on Fuzzy Logic Algorithm
Authors: Zhenzhen Qi Zhenzhen Qi
Published: January 2024

Semantic features play a pivotal role in natural language processing, providing a deeper understanding of the meaning and context within textual data. In the realm of machine learning and artificial intelligence, semantic feature extraction involves translating linguistic elements into numerical representations, often utilizing advanced techniques like word embeddings and deep learning models. The integration of semantic features enhances the precision and context-awareness of language models, enabling applications such as sentiment analysis, document categorization, and information retrieval to operate with greater accuracy and relevance. The paper introduces a novel approach, Hierarchical Mandhami Optimized Semantic Feature Extraction (HMOSFE), designed to enhance semantic feature extraction from English sentences. The proposed HMOSFE model comprises fusion of hierarchical clustering and fuzzy-based feature extraction, HMOSFE aims to capture intricate semantic relationships within sentences, providing nuanced insights into the underlying meaning of textual content. The model employs pre-trained word embeddings for term representation, calculates a similarity matrix using cosine similarity, and utilizes hierarchical clustering for document grouping. Fuzzy logic contributes to assigning weights to features, enabling a more refined understanding of semantic significance. The paper presents comprehensive results, including semantic similarity estimations, clustering distances, and fuzzy memberships, demonstrating the effectiveness of HMOSFE across diverse documents.
Problems and Development Strategies of Music Education in Primary and Secondary Schools Based on Network Information Technology
Authors: Li Liu Li Liu
Published: January 2024

Music education has traditionally followed a one-size-fits-all approach, often failing to address the diverse needs and preferences of individual students. A recommender system for music education is a specialized software or algorithm that helps students, educators, or music enthusiasts discover and access relevant music content, learning materials, or educational resources. This paper introduces the Graph Theory-based Information Technology(GTIT) music learning system, a revolutionary platform designed to personalize music education. The GTIT system leverages advanced technology, data-driven recommendations, and social learning networks to enhance the musical journey for students. The proposed GTIT model uses the graph theory-based music education model for the estimation of the data features with information technology. The proposed GTIT model comprises the stacked recommender model for the classification of the appropriate features in the music education data. The data for the analysis of the proposed GTIT model is collected for the existing sources. With the extracted features the recommender system is designed for the classification and recommendation of the features related to the music education. The proposed model explores the key components of the GTIT system, including the assessment of students' musical preferences, skill levels, and learning patterns. These factors are analyzed to provide tailored music recommendations that keep students engaged and motivated. Additionally, the GTIT system employs graph theory to create connections among students, fostering collaboration and social learning. Simulation results reveals that students who engage with the learning system experience significant improvements in their musical skills and knowledge. The personalized recommendations and collaborative learning environment contribute to enhanced skill development and musical progress. Moreover, the sense of community established by the system encourages peer interaction and mentorship. This paper provides a comprehensive overview of the GTIT music education system's impact on students' learning experiences, skill development, and collaborative efforts. The analysis expressed the transformative potential of personalized recommendations and networked learning environments in music education. The GTIT system serves as a model for the future of music instruction, offering a promising approach for educators and institutions seeking to provide engaging and personalized music education.
An Efficient Multi-User Groupwise Integrity CP-ABE(GI-CPABE) for Homogeneous and Heterogeneous Cloud Blockchain Transactions
Authors: Keesara Sravanthi Et al.
Published: January 2024

As the size of medical data grows exponentially, safeguarding Electronic Health Records (EHR) via cloud blockchain transactions becomes increasingly critical. This paper presents the Multi-User Groupwise Integrity Ciphertext-Policy Attribute-Based Encryption (GI-CPABE), an efficient framework designed to ensure robust data security. By implementing an Improved Ciphertext-Policy Attribute-Based Encryption (ICP-ABE), the GI-CPABE framework is designed to be compatible with both homogeneous and heterogeneous cloud environments, providing secure access that is role-specific and reliable. A distinctive feature of this framework is its role-based access control mechanism, which efficiently assigns user access rights and roles based on multi-user group attributes, considered as integrity measures. An experimental setup was established on a real-time cloud server, simulating a cloud based EHR system. Test data, mimicking real-world EHRs, was generated and subjected to encryption and decryption processes, evaluating the time efficiency of these operations. The proposed GI-CPABE framework demonstrated optimized storage utilization by calculating the overhead of encrypted data, ensuring data integrity through defined attribute-based policies, and enabling secure, controlled access to medical data. Furthermore, a comparative analysis was conducted to ascertain the effectiveness of the proposed framework against existing models. The implementation of access control mechanisms exhibited robust enforcement of access controls tailored to different user roles such as healthcare providers, patients, and administrators.
Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks
Authors: Balasaheb Balkhande Et al.
Published: January 2024

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning.   Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.
A Network-Centred Optimization Technique for Operative Target Selection
Authors: Vijay Rathod Et al.
Published: January 2024

The process of accomplishing strategic objectives by concentrating on effects as opposed to attrition-based destruction is known as effects-based operations, or EBO. Finding important nodes in an adversary network is a critical step in the EBO process for a successful implementation. In this paper, propose a network-based method to identify the most influential nodes by combining network centrality and optimization. To determine the node influence, the adversary's network structure is analyzed using degree and between centralities. Given the dynamic nature of the adversary network struct[1]ure and the centrality results, an optimization model that takes resource constraints into account chooses the key nodes. Our findings demonstrate that various network properties, such as between and degree centralities, influence the priorities of nodes as targets, and that using an optimization model yields better priorities with decreasing marginal properties. There is a discussion of the implications for theory and sensible decision-making.
National Education Policy 2020 and Gender Inclusiveness
Authors: Dr. Seema Ghanghas, Dr. Waseem Ahmad Khan Prabha Singh Parihar,
Published: January 2024

Disparities between men and women have persisted in society for a long time. Even though various measures have been taken the relevance of school textbooks and the curriculum that students are subjected in terms of encouraging gender-exclusivity cannot be overstated in order to promote gender equality and the removal of current gender norms. The formation of gender stereotypes in youngsters is aided by every aspect of our education, including textbooks, stories, images, and examples. An overview of the New Policy 2020 was used to conduct a comparative analysis of Indian educational institutions in order to identify the flaws in our system, suggesting that the our nation still has a long way to go in achieving SDGs 4 and 5. The researchers have also proposed various approaches and prospective solutions aimed at revolutionising India's educational landscape and promoting academic equity and inclusivity. In addition, this study examines the Education 2030 Framework’s recommendations for 'Refurbishing Public Education in India with an intent to make It Gender-Inclusive,' and it proposes a multi-sectoral strategy to empower women as a prerequisite.
Enduring, Intersectional Cancer Mortality Disparities in Persistent Poverty Counties
Authors: Moss Jennifer
Published: January 2024

Counties with higher levels of poverty since 1980 (“persistent poverty”) tend to be rural and racially diverse, but it is not clear how poverty, rurality, and race work together to influence trends in cancer mortality. We analyzed cancer deaths from 1990-1992 and 2014-2018. During each time period, compared to other counties, persistent poverty counties had about 12 excess cancer deaths per 100,000 people per year (1990-1992: 12.7, 95% confidence interval[CI]=11.4-14.1; 2014-2018: 11.0, 95% CI=10.2-11.8). Over time, disparities by persistent poverty widened for colorectal and breast cancers, but they were stable or mixed for the remaining cancer types. The highest mortality rates were observed among African American or Black residents of rural, persistent poverty counties for colorectal, oropharyngeal, breast, cervical, and prostate cancers. Since several factors influence cancer mortality, a multi-faceted approach is needed to improve outcomes in persistent poverty counties.
Experiences of Racially/Ethnically Diverse Young Breast Cancer Survivors: Preliminary Survey Observations
Authors: Jones Tarsha
Published: January 2024

This presentation will discuss preliminary observations from a survey of diverse young breast cancer survivors.
Distinct MUNC lncRNA structural domains regulate transcription of different promyogenic factors
Authors: Przanowska Roza
Published: January 2024

Long non-coding RNAs (lncRNA) are a type of RNA that is not translated into protein. Many lncRNAs have been discovered using high-throughput sequencing data; however, it is unclear what fraction of them is functional and what structural properties affect their phenotype. One of the lncRNAs important for skeletal muscle differentiation is MUNC acting in cis as an enhancer RNA for the Myod1 gene and in trans by recruiting the cohesin complex. Here, experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by prediction algorithms in the absence of experimental information. We show that these specific and structurally distinct domains are required for induction of muscle-related genes, for binding genomic sites and gene expression regulation, and for binding the cohesin complex. Our study reveals unexpectedly complex, structure-driven functions for the MUNC and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.
Are beliefs about the importance of genetics for cancer prevention and early detection associated with high risk cancer genetic testing in the U.S. Population?
Authors: Makhnoon Sukh
Published: January 2024

Public attitudes towards germline genetic testing for inherited cancers have been found to be generally positive. We know that diverse causal beliefs about cancer and sociodemographic factors are associated with uptake of cancer genetic testing. However, it is unclear how beliefs about genetically informed cancer prevention and genetically informed early detection of cancer shape testing behaviors. Using data from the National Health Information National Trends Survey, we studied this relationship between belief about cancer genetics and participation in cancer genetic testing. Interestingly, we find that belief in the importance of genetics for early detection of cancer was associated with testing however, belief in the importance of genetics for cancer prevention was not. The pattern of association is a surprising finding that warrants further investigation as better understanding cancer prevention beliefs and their impact on genetic test uptake may inform population genetic testing efforts.
Anorectal Melanoma: Not All Dark, Bleeding, and Painful Masses are Hemorrhoids
Authors: Elvis Vargas Castillo , Mariangela Pérez , Ingrid Melo Amaral , Dimas Garcilazo , Manuel Prados
Published: January 2024

Anorectal Melanoma represents less than 1% of all gastrointestinal tumors. They are infrequent, aggressive and with little therapeutic consensus. The prognosis is usually reserved, with a five-year survival rate of less than 20%. The anorectal canal is the most common place where melanomas of the gastrointestinal mucosa appear. Thus, although it represents 0.05 to 4.6% of anorectal lesions, this constitutes the third most common location of melanoma, after the skin and eyes. Generally, their diagnosis is confusing and late, as they require a high index of suspicion; The symptoms are non-specific but they mostly present as dark, bleeding and painful masses, sometimes being confused with benign anorectal processes such as hemorrhoids. This occurred in two clinical cases that we presented in women aged 78 and 59 years with a purplish-black anorectal mass, the first case confused with hemorrhoidal thrombosis and the other with bleeding internal hemorrhoids; therefore, being treated as such in primary medical care centers and delaying their diagnosis by an average of 3 months (Figure A, B and E). Both cases with high clinical suspicion of a malignant process were later confirmed in a specialized unit, where they underwent biopsies and imaging studies. The patients underwent surgery, with subsequent chemo-immunotherapy. The first with abdomino-perineal resection (Figure C) plus inguinal lymphadenectomy (Figure E) and the other with local excision (Figure F). Both neo or adjuvant treatment and type of surgery remain controversial today.
Explaining the property crime drop: The offender perspective
Authors: Brown Rick
Published: January 2024

For more than a decade, Australia has witnessed a sustained reduction in property crime. Yet relatively little is known about what may have caused this decline. This study aimed to explore plausible explanations for the property crime drop by ‘going to the source’ and interviewing a sample of 994 police detainees as part of the Drug Use Monitoring in Australia (DUMA) Program. The results showed that less than half of police detainees were able to offer a reason for the property crime drop, highlighting the difficulties with asking for retrospective explanations for an observed event. Among those who gave a response, nine key themes were identified as potential reasons for the property crime drop. The most frequent of these related to improved security, improved policing and ‘other’ reasons. Less frequent responses related to increased affluence, increased imprisonment, improved community responses, changes in drug use, changes in the market for stolen goods and changes in crime recording. These findings provide a basis for future testing of hypotheses that might explain the property crime drop in Australia.
Where and when: A profile of armed robbery by location
Authors: Fuller Georgina
Published: January 2024

Armed robbery is a diverse, heterogeneous crime shaped by the presence or absence of a wide array of characteristics. Therefore, effectively preventing armed robbery requires a good understanding of the nature of the offence. Previous attempts to understand armed robbery have focused on the offender, primarily by interviewing incarcerated offenders to gain insight into their motivations and planning. However, this approach overlooks the unique vulnerabilities associated with the victim and/or the location of the offence. In this paper, four armed robbery profiles have been constructed, based on information contained in qualitative police narratives supplied as part of the AIC’s National Armed Robbery Monitoring Program. These profiles highlight the way in which the location, environment and offender interact to shape individual incidents of armed robbery in Australia and may assist to inform prevention strategies.
Victims' experiences of short-and long-term safety and wellbeing: Findings from an examination of an integrated response to domestic violence
Authors: Meyer Silke
Published: January 2024

One in three Australian women experience domestic violence at some point during their adult life and it is women and their children who typically suffer the most severe short and long-term consequences of this violence. In this paper the findings are presented from an evaluation of a Queensland police-led integrated service response to domestic violence incidents that was designed to better address women and children’s needs for short and long-term safety. The findings indicated that a significant improvement in women’s self-rated safety and wellbeing was generated throughout the initial six-week support period. However, subsequent follow-up interviews with a sample of participants identified that the women had continued to experience a range of abuse, harassment and stalking after the initial support period had ended. This suggests a need to provide ongoing support to women and children escaping domestic violence, as well as a stronger focus on perpetrator accountability, if improvements to the safety and wellbeing of women and children escaping domestic violence are to be sustained.
Help-seeking strategies of victim/survivors of human trafficking involving partner migration
Authors: Richards Kelly , Lyneham Samantha
Published: January 2024

Victim/survivors of human trafficking involving partner migration employ diverse help-seeking strategies, both formal and informal, to exit their exploitative situations. Drawing on primary research conducted by Lyneham and Richards (forthcoming), the authors highlight the importance of educating the community and professionals from a wide range of sectors—including health, mental health, child protection, social welfare, social work, domestic violence, migration, legal and law enforcement services—about human trafficking and the help-seeking strategies of victims/survivors in order to support them to leave exploitative situations. Enhancing Australia’s knowledge of victim/survivors’ help-seeking strategies will better inform government and community responses to this crime, improve detection and identification of human trafficking matters and subsequent referral to appropriate victim services.
Online communities: Utilising emerging technologies to improve crime prevention knowledge, practice and dissemination
Authors: Anderson Jessica
Published: January 2024

Online communities are increasingly being recognised as a way of sharing ideas and knowledge among different practitioner communities, particularly when practitioners are not able to meet face to face. This paper explores the considerations associated with establishing online communities for crime prevention practitioners, drawing on research from across the community of practice, online community and knowledge management sectors. The paper provides an overview of the administrative considerations of online community development, as well as the key barriers and enablers to practitioner engagement in an online community, and the potential implications for a crime prevention-specific practitioner community. As such, it is a useful tool for those in the crime prevention sector wanting to maximise the influence of an existing online community or to guide those contemplating the implementation of an online community of practice in the future.
Evaluation of butter produced from whey and milk fat in terms of some quality criteria and fatty acid compositions
Authors: Aydemir Mehmet Emin , Altun Serap Kılıç
Published: January 2024

Whey is a dairy product that was formed as a result of cheese making and is considered a dairy residue or by–product. Making the best use of whey is important in terms of protecting the environment, preventing economic losses and gaining added value. The aim of this study was to obtain butter, an economical and healthy product from whey, and to compare butter obtained from milk in terms of fatty acid profile, color values and some chemical quality criteria. In addition, it is to determine the butter yield of whey. For this purpose, butter was produced from raw cow's milk and from whey in accordance with the technology. Dry matter (%), ash (%), fat (%), pH, titration acidity and refractive index to determine chemical quality, color analysis with digital colorimeter to determine color values and fatty acid analysis with GC–FID to determine fatty acid profile done. It was determined that butters produced from milk and whey were not statistically different (P>0.05) in terms of other chemical properties except the fat content they contain. In terms of color values ​​and fatty acid profiles, it was determined that butter produced from milk and whey cheese had similar values. In the butter samples, palmitic and myristic acids were the most common fatty acids. The butter yield of whey was determined as 405 g butter·100 L-1. Thus, it was concluded that whey, which is an important milk residue, can be used as an alternative raw material in the production of butter and that producing butter from whey will contribute to the economy and protection of the environment.
Rezension von: Wolf, Peter, Das Backnanger Wirtschaftenwunder
Authors: Trefz Bernhard , Wolf Peter
Published: January 2024

Peter Wolf: Das Backnanger Wirtschaftenwunder. Ein kleiner Überblick. Backnang: Selbstverlag des Autors 2019. 110 S., zahlr. Abb.
Rezension von: Katholische Gesamtkirchengemeinde Backnang (Hrsg.), Festschrift
Authors: Majev Markus P. , Katholische Gesamtkirchengemeinde Backnang
Published: January 2024

Katholische Gesamtkirchengemeinde Backnang (Hrsg.): Festschrift. 125 Jahre Sankt Johannes Baptist Backnang. Backnang: Selbstverlag der Katholischen Kirchengemeinde 2019. 168 S., zahlr. Abb.
Rezension von: Städtisches Blasorchester Backnang (Hrsg.), 100 Jahre Städtisches Blasorchester Backnang
Authors: Scholz Waltraud , Städtisches Blasorchester (Backnang)
Published: January 2024

Städtisches Blasorchester Backnang (Hrsg.): 100 Jahre Städtisches Blasorchester Backnang. Backnang: Selbstverlag 2019. 38 S., zahlr. Abb.
Rezension von: Wolf, Peter, Backnang
Authors: Trefz Bernhard , Wolf Peter
Published: January 2024

Peter Wolf: Backnang. Fotoschätze aus 100 Jahren. Erfurt: Sutton Verlag GmbH 2020. 122 S., zahlr. Abb.
Rezension von: Drechsel, Falk; Krause, Heike; Oßwald, Klaus Michael, ARWA
Authors: Trefz Bernhard , Drechsel Falk , Krause Heike , Oßwald Klaus Michael
Published: January 2024

Falk Drechsel/Heike Krause/Klaus Michael Oßwald: ARWA – Geschichten aus dem Strumpfimperium. Neustadt an der Aisch: VDS Verlagsdruckerei Schmidt 2018. 228 S., zahlr. Abb.
Rezension von: Fritz, Gerhard, Murrhardter Sozialgeschichte
Authors: Fechter Armin , Fritz Gerhard
Published: January 2024

Gerhard Fritz: Murrhardter Sozialgeschichte von der Mitte des 16. Jahrhunderts bis zum Beginn des Dreißigjährigen Krieges (ca. 1550 bis 1620). Remshalden: Verlag Manfred Hennecke 2020 (= Historegio Band 10). 298 S.
Genel Olarak Terörizm Kavramı ve Yeni Bir Terörizm Alt Dalı Olarak Siber Terörizm
Authors: Değirmenci Olgun
Published: January 2024

Terrorism is a concept that deeply affects social life day by day and has completed its internationalization. Efforts have been made to define terrorism as a concept that becomes more complex day by day. However, these efforts have not succeeded in reaching a definition that can be used in all cases by the international community. Terrorism can be classified in different ways. One of the most used classifications recently is cyber terrorism. Cyber terrorism is a type of violence that aims to achieve political goals by targeting the information system infrastructure on which a society builds its social life. Particularly international political developments are used to draw the boundaries of the concept of cyber terrorism. The Anti-Terrorism Law therefore pointed out the concept of cyber terrorism, but did not directly regulate it.