bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing Pronated Foot Correction: A Comparative Analysis of Structured Exercise Programs and Conventional Barefoot Orthotic Support
Authors: Mandar Malawade Et al. 
Published: January 2024

Introduction: The ankle joint, which connects the lower leg and foot and permits dorsiflexion and plantarflexion, is an important synovial hinge joint. The 26-foot bones that make up the ankle joint complex include 33 joints: talocalcaneal, tibiotalar, and transverse tarsal. Significant forces can be applied on the foot's ability to adapt to various surfaces during walking and running. This study looks at the effects of a structured exercise programme and a modified barefoot orthotic support on lower limb injuries, foot deformities, and biomechanics in individuals with pronated foot issues. The relationship between low back pain, knee adduction moments, pelvic motion, and foot mechanics is investigated in this study. Plantar pressure distribution and the effectiveness of bespoke foot orthoses for pronated feet symptoms are impacted by extrinsic rearfoot posts. To enhance pronation-related therapies, the study looks into foot type responses and dose-response effects.
Material and Methods: The study conducted at Krishna College of Physiotherapy in KIMSDU, Karad, "The Effectiveness of Structured Exercise Programme and Modified Barefoot Orthotic Support on Pronated Foot in Collegiates," uses a strict experimental design. A prevalence study of flexible flat foot was used to estimate the sample size of 100. SPSS-25 is used for statistical analysis, including paired and unpaired t-tests, in this study. Strict inclusion criteria centre on marked pronation, and measuring tools and materials are carefully selected for biomechanical evaluation. A thorough assessment is provided by outcome measures such as the Rearfoot Angle, Foot Posture Index, and Navicular Drop Test. A comprehensive investigation of long-term consequences can be conducted within the one-year timeframe, and the academic context lends legitimacy. The methodology makes a substantial contribution to the field of musculoskeletal health research by guaranteeing accuracy, validity, and relevance when examining therapies for pronated foot.
Result: The Structured Exercise Programme and Modified Barefoot Orthotic Support were implemented, and the results showed a considerable improvement in navicular drop, foot posture index, and rarefoot angle scores. These results provide strong proof of the usefulness of these therapies in reducing the negative consequences of pronated feet. The study supports the alternate theory while also validating the main hypothesis and offering insightful information to researchers and healthcare professionals.
Conclusion: In summary, this study advances our knowledge of successful treatments for disorders involving the pronated foot by highlighting the advantages of a structured exercise regimen and modified barefoot orthotic support. Improved management of pronated foot issues in a variety of populations is made possible by the thorough statistical analyses and thorough interpretation of the results, which highlight the effectiveness and dependability of these therapies.
Enhancing Temporomandibular Joint Function in Cerebral Palsy: A Comprehensive Investigation of the Impact and Efficacy of a Structured Exercise Program
Authors: Mandar Malawade Et al. 
Published: January 2024

Introduction: A structured exercise routine for cerebral palsy children with TMD is explored in this study. TMD impacts jaw joint function, while CP causes non-progressive mental and physical issues. Current treatments lack demographic-specific exercise. The yearlong study examines the program's TMJ pain and range of motion benefits. An experimental technique shows personalised therapy for CP and TMD youngsters, filling a knowledge gap.
Background: Cerebral Palsy children typically have Temporomandibular Disorders (TMD), which cause jaw pain and restriction. This demographic has little information on structured exercise programmes for TMD despite their increased susceptibility. This study tackles this gap with a one-year experiment at Krishna College of Physiotherapy, KIMSDU, Karad. The study uses the Helkimo Index and Three Finger Index to evaluate a structured exercise programme on Cerebral Palsy children's Temporomandibular joint pain and motions, providing vital insights into evidence-based therapies for this particular pediatric group.
Objectives: A year-long planned exercise routine for children with cerebral palsy and temporomandibular problems will be examined in the study. The programme aims to reduce temporomandibular joint pain and motions. The intervention at Krishna College of Physiotherapy, a pre- and post-test experimental investigation, and statistical analysis utilising paired t tests for within-group comparisons are further goals. This study will provide this pediatric group with evidence-based TMD rehabilitation strategies.
Material & Method: Goniometers, heated moist packs, and soft tissue mobilisation tools are used in the study. Organised exercise for cerebral palsy children with temporomandibular issues includes TMJ mobilisation and posture education. Krishna College of Physiotherapy, KIMSDU, Karad, conducted the one-year experimental study including pre- and post-tests. Robust statistical analysis using SPSS 25 paired t-tests. The three-day-a-week, six-week fitness course gradually increases repetitions.
Result: The study found that structured exercise improved Cerebral Palsy patients' Temporomandibular Disorders. The Helkimo Index decreased while the Three Finger Index increased, indicating significant improvements in temporomandibular joint pain and motions. These results support the alternative hypothesis that the intervention worked.
Conclusion: A study found that organised exercise aids cerebral palsy TMD patients. It worked because the Helkimo Index and Three Finger Index statistically improved temporomandibular joint pain and motions. The topic population is diverse and geographic coverage is limited, yet successful. This suggests more Cerebral Palsy Temporomandibular Disorder rehabilitation research.
Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification
Authors: Sreekala K K Et al. 
Published: January 2024

Computer technology has employed Machine Learning models in a variety of applications to improve performance. The hyperparameter of a machine learning model must be adapted to overcome learning limitations and increase its performance. In this research, the hyperparameters of machine learning classifiers are tuned to identify cases of benign or malignant breast abnormalities. An experimental investigation was conducted using the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. A fusion model, Bayesian Optimization Hyper Band-Naïve Bayes (BOHB-NB) is employed, which is combined with conventional classification approaches like Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). The proposed methods are compared to cutting-edge models like SVM, NB, LR, K-Nearest Neighbour (KNN), Random Forest, and Decision Tree using a wide range of parametric measures, such as Precision, Recall, Specificity, F-measure, Accuracy, True Positivity Rate (TPR), and False Positivity Rate (FPR). The results show that the proposed methods outperform the leading models.
Cross-Domain Aspect Extraction using Adversarial Domain Adaptation
Authors: Sampathirao Suneetha Et. al 
Published: January 2024

Aspect extraction, the task of identifying and categorizing aspects or features in text, plays a crucial role in sentiment analysis. However, aspect extraction models often struggle to generalize well across different domains due to domain-specific language patterns and variations. In order to tackle this challenge, we propose an approach called "Cross-Domain Aspect Extraction using Adversarial-Based Domain Adaptation". Our model combines the power of pre-trained language models, such as BERT, with adversarial training techniques to enable effective aspect extraction in diverse domains. The model learns to extract domain-invariant aspects by incorporating a domain discriminator, making it adaptable to different domains. We evaluate our model on datasets from multiple domains and demonstrate its effectiveness in achieving cross-domain aspect extraction. The results of our experiments reveal that our model outperforms baseline techniques, resulting in significant gains in aspect extraction across various domains. Our approach opens new possibilities for domain adaptation in aspect extraction tasks, providing valuable insights for sentiment analysis in diverse domains.
Immunotherapy Combinations in Cancer Treatment: Synergistic Approaches and Challenges
Authors: Rashmi Gudur Et al. 
Published: January 2024

Combinations of immunotherapies are at the forefront of cancer treatment innovation, providing a variety of approaches to strengthen the immune system against cancer. The synergistic potential, clinical results, and difficulties associated with combining different immunotherapies to fight cancer are examined in this study. Examined are the mechanisms that underlie synergy, including dual immune checkpoint blockage and the interaction between immune modulation and adoptive cell therapy. Challenges including toxicity control, patient selection, and cost concerns are addressed alongside important therapeutic benefits across a range of malignancies, such as extended survival and increased response rates.
Prospects for the future include personalised treatment utilising cutting-edge technology such as artificial intelligence, novel immunomodulatory drugs, and investigation of the gut-immune axis. Key tactics include integrating immunotherapy with conventional medicines and enabling medication delivery using nanotechnology. To ensure the practical translation and accessibility of these treatments, it is imperative to solve problems through collaborative efforts and strong clinical studies to validate them.
Liquid Biopsies in Oncology: Revolutionizing Cancer Diagnosis and Monitoring
Authors: Anand Gudur Et al. 
Published: January 2024

Oncology has been transformed by liquid biopsies, which offer non-invasive techniques for cancer detection and tracking. This article examines how circulating tumour cells (CTCs), extracellular vesicles (EVs), cell-free DNA (cfDNA), circulating microRNAs (miRNAs), and their therapeutic uses might revolutionise cancer therapy. CTCs provide information on tumour heterogeneity and metastatic potential since they are excreted from primary or metastatic tumours. Released by necrotic or apoptotic tumour cells, cfDNA is a genetically altered material that helps track the effectiveness of therapy. EVs, which are made up of microvesicles and exosomes, are capable of carrying cancer biomarkers and transferring biomolecules. Stable in circulation, miRNAs show dysregulation in cancer and are therefore useful indicators for both diagnosis and prognosis.
Clinical uses include tracking illness development, evaluating treatment response, and early identification. Early therapies are made possible by the diagnosis of minimal residual illness by liquid biopsies. Therapeutic decisions are guided by real-time monitoring of treatment response, and dynamic evaluations facilitate the development of individualised treatment plans.
Technical difficulties, problems with standardisation, concerns with cost-effectiveness, and difficulties interpreting data are among the challenges. The development of technology, its incorporation into clinical practice, personalised medicine, the identification of biomarkers, and cooperative efforts to overcome obstacles are the main focuses of future directions.
Which Factors Can Contribute to the Success of Environmental and Animal Protection Projects in Donation-based Crowdfunding? A Neural Network Model
Authors: Chang Jiang Et al. 
Published: January 2024

The crowdfunding industry has developed rapidly in recent years, the existing research shows that crowdfunding can help in many fields such as entrepreneurship, creative products, or donations. Due to global meteorological issues, more and more people are paying attention to the environment and animal protection. However, fundraising in these areas has been the biggest problem, the emergence of donation crowdfunding (DCF) can alleviate this dilemma. Currently, in academia, there is still less research focused on crowdfunding for environmental and animal protection. This paper aims to study the factors influencing the successful financing of environmental and animal protection projects in the DCF.
This paper analyses 700 DCF environmental and animal protection projects in China as samples, and creatively introduces financial transparency scoring indicators. Through binary logistic regression, financial transparency was found to be the most critical positive factor affecting project success. At the same time, donors receive NPO-initiated projects well, and the number of donors can also positively impact the results. However, the excessive description of the projects can have the opposite effect. This study also introduced a neural network model, and found that the neural network model can optimize the discriminant accuracy of the traditional binary logistic regression model.
Risk Prioritization using A FUZZY BASED Approach in Software Development Design Phase
Authors: Tabrez Khan Et al. 
Published: January 2024

The success of a software project's objective is directly proportional to the degree to which it satisfies all of the stakeholders' concerns regarding the project's requirements, including the budget, schedule, and overall performance. Risks can occur throughout the software development lifecycle (SDLC) phases and affect every phase. The design phase of the SDLC yields an overview of the software and can be defined as the software's blueprint. Different types of software have their own unique design phases and have different types of risks. With the high number of interacting components, complex systems have a greater propensity to be more volatile, which increases the risk. It is necessary to prioritize the risks in order of their severity levels. The issue at hand is the lack of effective methods to prioritize and mitigate the risk. Recent studies have suggested several methods for prioritizing risks, but it is clear that few of these have been implemented. These methods are overly complicated, time-consuming, prone to inconsistency, and challenging to put into practice. This paper proposes a novel Fuzzy-based approach to risk prioritization in the software design phase using MATLAB software. Fuzzy-based models have been shown to be more accurate than other techniques when using standard datasets to prioritize risks. Fuzzy-based methods that have been proposed take into account the characteristics of risks by modelling those characteristics as fuzzy
Design and Development of a High Performance Hybrid Patch Antenna with Enhanced Bandwidth for Wireless Communication
Authors: Neera Agarwal Et al. 
Published: January 2024

In the current scenario of modern antenna design, patch antennas have emerged as a fundamental component in modern wireless technology due to their compact size, low profile, and versatility in diverse applications. However, to meet the ever-increasing demands for higher data rates, extended bandwidth, and improved radiation characteristics, innovative design approaches have become essential. In conclusion, this research paper showcases the current state of modern patch antenna design, emphasizing the significant improvements in performance and versatility. The research work introspects into the design of a hybrid patch antenna with a modified cutting-edge design employing an optimization techniques resulting in a high performance Patch antenna with better bandwidth and high gain in comparison to its counterparts. The results obtained insights provided in this study will be valuable for researchers, engineers, and designers in the field of antenna technology, paving the way for the development of more efficient designs in hybrid antenna technology.
IoT Safeguarding in Saudi Tourism Sector: Crafting a Preliminary Security Model for Enhancing Cyber Resilience
Authors: Sarah Alghamdi, Et al. 
Published: January 2024

Incorporating the Internet of Things (IoT) has transformed technological landscapes, facilitating seamless communication across diverse devices and systems. However, this increased connectivity exposes critical sectors, including government and tourism, to elevated cybersecurity risks. There is a lack of knowledge regarding how organizations within the Saudi tourism sector address the cybersecurity risks associated with IoT systems. While much of the existing IoT literature concentrates on adopting IoT systems, a better understanding can be attained by proposing a preliminary research model encompassing the most significant factors influencing IoT security and related cybersecurity attacks. Despite limited empirical research on IoT security adoption in the Saudi tourism sector, this study seeks to address this gap. Motivated by this concern, this research investigates IoT security among Saudi organizations in the government tourism sector by developing a research model. inspired by the Technology Acceptance Model (TAM) literature. The model incorporates a total of eight factors (privacy, confidentiality, data integrity, access control, availability, trust, IoT standards and policies, and IoT Awareness) and seven cybersecurity attacks (denial of service (DoS & DDoS), replay attack, eavesdropping attack, man-in-the-middle (MiTM) attack, spoofing attack, Sybil attack, and physical attack) identified from various literature sources. The proposed research model is a valuable tool for understanding IoT security in Saudi tourism, offering guidelines for organizations considering introducing IoT security measures. These guidelines highlight specific factors that tourism organizations should consider, enhancing the likelihood of successful IoT security adoption in the tourism context. Additionally, this study encourages IoT researchers to replicate the research in another industry sector within Saudi Arabia or other countries, particularly within the Arabian Gulf region.
Segmentation and Classification of Arecanut Bunches before harvesting
Authors: Dhanesha R. Et al. 
Published: January 2024

In the agriculture sector, arecanuts are an extremely valuable crop. The price of an arecanut depends on its stage of ripeness. As a result of a lack of expertise in judging the maturity level of arecanut bunches before harvest, farmers often lose profit. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Therefore, accuracy in segmenting arecanut bunches is vital for automated maturity level identification. In proposed work S-channel, Cr-channel and Pr-channel of HSV, YCbCr and YPbPr respectively color models are used to segment arecanut bunches. Three color features (i.e., mean of an arecanut bunch image on red, green, and blue bands), and two texture features (i.e, correlation, and entropy) were used in classification procedure. A random forest classifier was employed to classify maturity levels of arecanut bunch. This experiment uses a dataset of 1017 images of arecanut bunches to assess the segmentation performance of each color model. As a result of the experiment, it has been concluded that the S-channel of the HSV color model was effective in segmenting arecanut bunches from input images. The proposed methodology effectively classifies arecanut bunch maturity levels with an accuracy of 87.80%.
Exploring Software Development Change Analysis with an Emphasis on Requirements
Authors: Syed Adnan Afaq Et al. 
Published: January 2024

Most software requirements are not definitive of the development process. Rapid changes in user expectations, market conditions, and company practises all need regular updates to software. Requirement change management is a crucial and difficult component of every software development project. Project failure or cancellation often occurs because of requirements changes. Requirements Change refers to requirements that are added, removed, or amended during the system development life cycle. Requirements Change requires additional work in the design phase, which boosts the cost of developing the system, lengthens the time required, and reduces system quality. The paper investigates research efforts in the topic of requirement change and helps in determining the study's purpose. Various Requirement Change Management concepts and approaches are provided, and numerous activities are conducted to mitigate the effects of requirement changes. The study emphasizes on causes, attributes, prioritization of changed requirement, framework for RCM and Change Impact Analysis. This study briefly describes all the possible fact and figure about requirement change. The study includes various phases of requirement change management such as Requirement Elicitation, Requirement Change Identification in Requirement Document using Two Phase Requirement Document Comparison Algorithm, Prioritization of Changed Requirement using Fuzzy approach, Interdependency analysis and change impact analysis on various software project parameters such as time, cost and human resources.
Using Tiktok as a Live Marketing Strategy to Attract Female Consumers' Interest in Purchasing Cosmetic Products in Indonesia
Authors: Nur Fita Sari Et al. 
Published: January 2024

This research examines TikTok's role as a direct marketing strategy to engage female consumers in purchasing cosmetic products in Indonesia. It employs a quantitative approach, surveying 250 active female TikTok users who interacted with cosmetic content. The study investigates various factors, including TikTok user recommendations, shopping experiences, content interactivity, shopping guidance, product visibility, influencer impact, customer engagement, and repurchase intention. Respondents rate these factors on a Likert Scale. The analysis, carried out utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), intends to uncover how these variables affect individuals' intentions to make repurchases

This study aims to offer an understanding of how TikTok serves as an effective marketing platform for cosmetics targeted at female consumers in Indonesia.. The PLS-SEM approach is chosen for its ability to handle complex relationships in the data, including non-linear ones. By examining the relationships and conducting statistical analysis, this study aims to contribute to our understanding of how TikTok influences consumer behavior in the cosmetic industry.