bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovative Control Paradigms for DC Motors Control
Authors:   Universidad Politécnica Salesiana, Pavón Vallejos Wilson
Published: February 2024

This research proposes innovative control paradigms for DC motors control, integrating variable coefficient fractional-order proportional-integral-derivative (PID), model predictive control (MPC), and State Feedback with Ackerman and Bessel Polynomial Strategies for Optimal Performance. The fractional order is utilized to enhance the control performance of the system. The optimal tuning of the proposed controller is achieved through a hybrid optimization algorithm that combines the particle swarm optimization (PSO) and the genetic algorithm (GA). The PSO algorithm searches for the optimal set of proportional, integral, and derivative gains, while the GA is employed to optimize the fractional order and the controller coefficients. The hybrid algorithm can efficiently obtain the optimal controller parameters and fractional order with a small number of iterations.
Performance of Public Transport Appraisal using Machine Learning
Authors: R. Thiagarajan Et al.
Published: February 2024

Public passenger transport holds immense significance in the overall transportation system. Forecasting the movement of public transport has emerged as a crucial problem in transport planning due to its practical implications. Recently, there has been a lot of significant attention in Intelligent Transportation Systems (ITS), introducing various advancements and innovative applications to develop conditions for public transit that are safer, more effective, and fun. To fully leverage the potential of ITS applications and deal with road situations proactively, it becomes crucial to have a reliable method for predicting traffic flow. This opens up opportunities for ITS applications to anticipate and address potential challenges in advance. Enhancing the efficient functioning of Public Transport (PT) networks is a primary objective for urban area authorities, and the proliferation of location and communication devices has led to an abundance of operational data. Applying appropriate Machine Learning (ML) methods can help identify patterns in the data to improve the Schedule Plan. This research focuses on heterogeneous information that influences the prediction value, aiming to predict the required transport demand for specific routes and the arrival time of public transport. Utilizing DBSCAN clustering with SARIMA Algorithm, real-time passenger demand forecasting is extensively promoted to enhance dynamic bus scheduling and management. Furthermore, this paper compares the accuracy of the proposed Prophet Model with traditional time series models like ARIMA and SARIMA. The aim is to provide precise and robust passenger demand predictions, enabling more effective planning and management of PT services.
Optimal PV Distributed Generators Allocation using Metaheuristic Algorithm to Enhance Voltage Profile
Authors: Shaik Aarif Et al.
Published: February 2024

In this paper, the authors propose a methodology to identify the key locations to install integration of Solar & windbased distributed generators((DG) and the optimal amount of CapacityDGs required to maintain a steady voltage profile and voltage stability. The optimal amount of DG required is obtained via Gorilla troops optimizer(GTO)Metaheuristic algorithm and ,the locations where Integration of DG installation results in maximum benefit are obtained through the voltage stability index. The methodology tested in the Indian28 bus system shows that the proposed technique effectively identifies the critical locations and optimizes the required Integration DG capacity.
Revolutionizing Breast Cancer Detection: Enhanced Mammogram Analysis with Modified Xception and Self-Attention
Authors: Sreevidya Nelli Et al.
Published: February 2024

In the realm of precision medicine, the intersection of state-of-the-art technology and disease identification has ushered in a new era of accuracy in healthcare diagnostics. This paper reviews a groundbreaking research endeavor aiming to enhance breast cancer detection in mammograms through the synergistic application of transfer learning, architectural modifications, and advanced optimization techniques.

At the core of this research is the concept of transfer learning, leveraging insights gained from one domain to illuminate another. The study extends this paradigm to the Xception architecture, renowned for its proficiency in discerning intricate patterns within images. However, the novelty lies in the intelligent modification of Xception to be specifically attuned to breast cancer detection. By fine-tuning the network's final layers, the model's innate ability to understand features is harmonized with the complexities of mammographic images, ensuring sensitivity to nuanced markers of potential malignancy.

A distinctive aspect of this research is the incorporation of a self-attention mechanism, mirroring human visual processing. This mechanism dynamically highlights crucial regions within mammograms, transforming the model into an active interpreter capable of identifying subtle textures, shapes, and edges indicative of breast cancer. This adaptive approach enhances the model's finesse in navigating diverse breast cancer cases effectively.

Throughout the training phase, optimization is pivotal for steering the research towards success. The utilization of the Adam optimizer, known for its adaptability in learning rates and moment estimations, guides the process, ensuring precise gradient descent through complex patterns. The integration of the rectified linear unit (ReLU) activation function further empowers the model to capture intricate relationships within data, enhancing its ability to identify subtle cancer markers. This review comprehensively explores the innovative strides made in breast cancer detection, shedding light on the nuanced interplay of transfer learning, architectural adjustments, self-attention mechanisms, and advanced optimization techniques. The modified Xception model emerges as a promising tool in the pursuit of accurate and sensitive breast cancer diagnostics.
A-Novel in this Technique Analysis Tuberculosis Patient Using Hybrid CNN and SVM Model
Authors: Nisha Et al.
Published: February 2024

Tuberculosis (TB) remains a global health challenge with a significant impact on public health worldwide. This study addresses the prevalence and detection of T.B., focusing on India, a country experiencing rapid economic growth and notable transitions in various sectors. Despite advancements in healthcare delivery, communicable and non-communicable diseases, including TB, continue to pose substantial threats to health security. The proposed method for T.B. analysis integrates key modules, encompassing data collection, preprocessing, feature extraction, image splitting, classification, and performance estimation. Utilizing machine learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN), the study aims to enhance accuracy and efficiency in T.B. patient detection. The dataset includes chest X-rays from diverse sources, aiming to provide a comprehensive understanding of T.B. patterns. The study emphasizes the interconnected challenges of malnutrition and T.B., highlighting the importance of nutritional status assessment in public health. The linkage between undernutrition and infectious diseases, including TB, underscores the need for early detection and intervention. The proposed method offers a systematic approach to evaluating and addressing nutritional deficiencies in patients with chronic illnesses, contributing to improved clinical outcomes.
Review of Sustainable Irrigation Technological Practices in Agriculture
Authors: Baskar M Et al.
Published: February 2024

The paper focuses on the increasing demand for water and its impact on irrigated agriculture, emphasizing the importance of effective water management. It reviews the use of soil moisture sensors, IoT, big data analytics, and machine learning in agriculture, particularly in the context of Indian agriculture. The study explores the potential of IoT technologies, such as sensors, drones, and machine learning algorithms, to optimize water usage, minimize waste, and enhance crop yields. The role of big data analytics in sustainable water irrigation management and decision support systems is highlighted. The integration of IoT and sensory systems in smart agriculture is discussed, addressing both the challenges and benefits of implementing sensory-based irrigation systems. Additionally, the paper describes an automated irrigation system developed to optimize water use for crops, utilizing a distributed wireless network of sensors and a web application. The system, powered by photovoltaic panels, demonstrated significant water savings of up to 90% compared to traditional irrigation methods in a sage crop field. The system's energy autonomy and cost-effectiveness suggest its potential utility in water-limited and geographically isolated areas.
Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images
Authors: Sharda Rani Et al.
Published: February 2024

To maximize the accuracy of classification for medical images especially in chest- X ray, we need to improve quality of CXR images or high resolute images will be needed. Pneumonia is a lung infection caused by organism like bacteria or virus. Mostly Chest X-Ray (CXR) is used to detect the infection, but due to limitation of existing equipment, bandwidth, storage space we obtain low quality images. Spatial resolution of medical images is reduced due to image acquisition time, low radiation dose. Quality in medical images plays a major role for clinical diagnosis of disease in deep learning. There is no doubt that noise, low resolution and annotations in chest images are major constraint to the performance of deep learning. Researchers used famous image enhancement algorithms: Histogram equalization (HE), Contrast-limited Adaptive Histogram Equalization (CLAHE), De-noising, Discrete Wavelet Transform (DWT), Gamma Correction (GC), but it is still a challenging task to improve features in images. Computer vision and Super resolution are growing fields of deep learning. Super resolution is also feasible for mono chromatic medical images, which improve the region of interest. Multiple low-resolution images mix with high resolution and then reconstruct a target input image to high quality image by using Super Convolution Neural Network (SRCNN). The objective evaluation based on pixel difference-based PSNR and Human visual system SSIM metric are used for quality measurement. In this study we achieve effective value of PSNR (40 to 43 dB) by considering 30 images of different category (normal, viral or bacterial pneumonia) and SSIM value varies from 97% to 98%. The experiment shows that image quality of CXR is increased by SRCNN, and then high qualitative images will be used for further classification, so that significant parameter of accuracy will be finding in diagnosis of disease in deep learning.
Emerging Therapies in Retinal Diseases: From Gene Therapy to Stem Cell Interventions
Authors: Renuks Sarwate, Rohit Kundap Girish Arun Gadre, Prajakta Patil, D. B. Shirke,
Published: February 2024

Retinal disorders pose a serious threat to eye health as they frequently result in blindness and reduced vision. There is hope that the treatment of many illnesses will be revolutionised by emerging medicines, especially gene therapy and stem cell approaches. This study explores the current state of these innovative therapies and how they could affect retinal disorders. By replacing or repairing damaged genes, gene therapy, which uses precise genetic modification, shows promise in treating hereditary retinal problems. Clinical trials have yielded promising results, including improvements in visual function and optimism for patients with illnesses such as choroideremia and Leber congenital amaurosis. Regenerative approaches are provided by stem cell therapies, which restore damaged retinal tissues. Numerous stem cell varieties, including as embryonic and induced pluripotent stem cells, show promise in preclinical research and early-stage clinical trials, suggesting that cell replacement techniques may be a viable means of recovering vision. On the other hand, effective delivery, long-term safety, and ethical issues provide obstacles on the path to clinical application. To fully realise the transformational potential of these medicines, it is imperative to address these obstacles. There is potential for improved visual outcomes, targeted therapies, and personalised care as gene therapy and stem cell interventions advance. These developments highlight the promising future of treating retinal illnesses.
Recent Advances in Reproductive Endocrinology: Implications for Fertility Treatment in Gynecology
Authors: Manisha laddad Et al.
Published: February 2024

Within gynaecology, reproductive endocrinology is a crucial field that orchestrates complex hormonal systems that impact fertility outcomes. The purpose of this study is to examine the latest developments in reproductive endocrinology and how they may improve fertility therapies. Significant advancements have been achieved in reproductive medicine over the last ten years in a number of areas, including ovarian stimulation, assisted reproductive technologies (ART), hormone regulation, endocrine problem management, and future possibilities for clinical practice.

Technological developments in ovarian stimulation regimens and pharmaceuticals have transformed assisted reproduction by providing customised methods for maximising ovarian response and reducing side effects. enhanced oocyte yields and embryo quality are showing encouraging results from new stimulation regimens and enhanced gonadotropin formulations. In addition, advances in artificial reproductive technologies (ART) like time-lapse imaging and longer culture systems have improved embryo selection strategies, resulting in fewer multiple pregnancies and greater implantation rates.

Strategies for hormonal regulation that are tailored to each individual's profile have been shown to significantly improve the results of conception. Accurate ovarian stimulation and higher response rates are made possible by biomarker-guided methods that use gonadotropin-releasing hormone (GnRH) and anti-Müllerian hormone (AMH) analogues. Furthermore, current practices emphasise specialised treatments to lessen the influence of endocrine diseases on fertility, such as endometriosis, thyroid dysregulations, and polycystic ovarian syndrome (PCOS).

Future directions are also considered in the study, including the integration of precision medicine, advancements in fertility preservation, ethical considerations, and the critical need for interdisciplinary cooperation. The use of AI technology, patient-centered care, and genetic profiling signals a revolutionary change towards customised reproductive therapies. To maintain fair access and moral practice in reproductive endocrinology, it is imperative that the field's ethical, legal, and social ramifications be recognised and addressed as it develops.
Survey of Applications of Ml in Stress Detection
Authors: Prachi S Ramteke Et al.
Published: February 2024

Stress is a common and pervasive issue that affects millions of people worldwide. It can lead to a variety of negative health outcomes, including anxiety, depression, and physical health problems. Early detection of stress is crucial for effective management and prevention of these negative outcomes. Stress detection technologies using machine learning algorithms can provide individuals with valuable information about their stress levels and help them manage their stress in more effective ways. This can lead to improved mental and physical health outcomes, as well as increased productivity and overall well-being. Therefore, stress detection is an important area of research that has the potential to positively impact the lives of many people.  

             This paper presents a survey of techniques applicable to the field of stress detection using machine learning (ML) algorithms. We categorize these techniques based on the approach they take and discuss various challenges, open questions, and future work in this area. We present a taxonomy of existing research and finally discuss gaps and future directions of research to advance the study of stress management using most recent ML techniques. These technologies

provide individuals with valuable information about their stress levels and can help them manage their stress in more effective ways.
Comparison of Regression Algorithms for Predicting Students' Academic Performance
Authors: Mamta Saxena Et al.
Published: February 2024

In recent years, higher education has reached the peak level in growth. Many Universities, educational institutes and colleges are being set up at private level and public level for the growth of the education sector and for the well being of students [1]. Each of the institutions aims at providing higher knowledge to the students by grooming their faculties and implementing best teaching and educational practices. But still the problem persists as most of the students are at risk of unemployment due to poor academic performance. DM techniques can be used to analyse vast quantity of data that is readily available in bulk, extract usable knowledge, and help decision-making in order to forecast the academic success of students. [2].

The amount of data present in the education sector is examined, and by extracting the data from it, beneficial patterns are found. This is known as educational data mining. This paper discusses numerous regression techniques that can be used in educational institutions to forecast students' academic achievement. To forecast student performance, regression techniques such as Bayesian networks and decision trees can be used. Applications of the CART, J48, Random Forest, and ID3 decision tree algorithms are made to undergraduate student data obtained from private colleges in order to forecast how well they will perform on the final exam of the semester and determine whether or not they will be promoted for the following year. [3].
Case Study on Early-Stage Risk Prediction by Machine Learning Algorithms
Authors: T. Deepthi Et al.
Published: February 2024

In the healthcare sector, we generally require continuous monitoring of the patient, The model is designed to assist patients in understanding the potential hazards associated with the infectious illness by using Machine Learning algorithms. It provides recommendations as per the infection severity, enabling patients to effectively monitor their condition independently. All the data records are initialized in the dataset, these are stored in the database which helps in more accuracy of illness prediction. This software interface is simple, based on symptoms of the patient the algorithms will process and gives appropriate infection details, suggestions on severity of infection, recommends to consult a doctor or not. It also ensures the seasonal diseases ongoing in particular areas, identifies them, and gives regular alerts to the users. Non-Communicable Diseases (NCDs) are currently causing more infections are highlighted, and alerts the user by giving information and precautions to be taken for improvement of health. Predictive models and classification algorithms, examine the symptoms specified by the patient as input. Then the most possible disease name will be displayed as an output. Decision Tree, Naive Bayes Classifier, and Random Forest Algorithm are used to forecast the disease. Disease prediction is accomplished by employing ML Algorithms.
Non-Metaheuristic Clustering Algorithms for Energy-Efficient Cooperative Communication in Wireless Sensor Networks: A Comparative Study
Authors: Amuthavalli L Et al.
Published: February 2024

Wireless Sensor Networks (WSNs) are now considered a vital technology that enables the gathering and distribution of data in various applications, such as environmental monitoring and industrial automation. Nevertheless, the finite energy resources of sensor nodes pose significant obstacles to the long-term viability and effectiveness of these networks. Researchers have developed and studied various non-meta algorithms to improve energy efficiency, data transfer, and network lifespan. These efforts contribute to enhancing cooperative communication modules. This analysis conducts a detailed examination and comparative evaluation of different well-known clustering methods in the field of Wireless Sensor Networks (WSNs), providing significant insights for improving cooperative communication. Our purpose is to provide a comprehensive perspective on the contributions of these algorithms to improving energy efficiency in WSNs. This will be achieved by examining their practical implementations, underlying mathematical principles, strengths, shortcomings, real-world applications, and potential for further improvement.
An Enhanced Deep Learning Approach for Detection and Classification of Retinal Diseases using OCT Images
Authors: K. Sathish Et al.
Published: February 2024

Ophthalmologists can make diagnoses based on the layers of the retina using optical coherence tomography (OCT), a non-invasive procedure produces cross section images of retina layer of the eye. As a result, it is a crucial modality for the identification and measurement of retinal disorders and illnesses. Ophthalmologists must spend a lot of time analysing the pictures since OCT produces many images for each patient. The OCT pictures of patients are divided into four groups in this work using deep learning models: Normal, Drusen, Diabetic Macular Edoema (DME), and Choroidal neo-vascularization (CNV). There are two distinct models suggested. Normal, Drusen, Diabetic macular edoema (DME), and Choroidal neo-vascularization are the four groups that may be classified utilising segmentation using curvature-based ROI and classification using the Folded RESNET101 algorithm (CNV). The proposed approach has the highest accuracy, sensitivity, and specificity of 0.99. The accuracy of binary classifier for the Normal is 0.99. These outcomes demonstrate their capability to function as a primary approach for ophthalmologists.
Mapping Oman's Artificial Intelligence Logistic Fuzzy Social Media Research Landscape Through Bibliometrics
Authors: Madhan N Et al.
Published: February 2024

Artificial intelligence and related technologies are becoming increasingly important internationally. This bibliometric study looks at Oman's research environment in AI, social media, fuzzy logic, and logistic regression from 2014 to 2024. Data for 251 English papers were obtained from Web of Science. The publication and citation patterns were examined using bibliometric approaches such as Bradford's law, Lotka's law, and network mapping. The data show a substantial increase in production and impact, particularly at younger colleges. However, national collaboration remains limited, while international participation has increased. In conclusion, Oman's emergence as an AI research hub is promising, but more concerted work and wider collaborations are required to reach its full potential. This report provides a baseline for monitoring Oman's progress toward strategic research targets. Further effort should focus on building national networks and boosting international collaboration.
Bridging the Gap: Exploring new ways to deliver online Grocery shopping using Smart Software
Authors: Manish Rana Et al.
Published: February 2024

Its great convenience and the availability of delivery and pickup options, online grocery shopping has transformed the procurement of food and household goods. These platforms frequently outperform brick-and-mortar businesses in terms of product selection. Technology, such as mobile apps and websites, has played a critical role in this process by delivering user-friendly interfaces, personalized recommendations, and smooth transactions. Artificial intelligence and data analytics improve the shopping experience even further by personalizing options and streamlining inventory management for shops.However, there are significant hurdles to online grocery buying, including delivery problems, the need for strong cybersecurity, and worries about the environmental impact of packing and transportation. Despite these obstacles, the online grocery business continues to be a dynamic and exciting component of modern retail as technology evolves and customer behaviors change.
Security Framework for IoT Implementing Random Forest Classifier
Authors: Raju Ch Et al.
Published: February 2024

The Internet of Things will become commonplace by making global connections possible at any moment. The necessity of well-thought-out, carefully implemented, and strictly enforced security standards over the entire lifecycle of IoT devices cannot be overstated.The Internet of Things (IoT) is a relatively new phenomenon that connects disparate computing infrastructures and infrastructure components. Given that the vast majority of the data collected will be shared with an unknowable audience, security is of paramount importance when connecting multiple independent IoT units across the Internet.  This article provides a comprehensive review of the state of security in the Internet of Things.The essay emphasizes the necessity to provide security in the device itself alongside conventional security solutions to offer a method employing machine learning exible for preventing, detecting, diagnosing, isolating, and counteracting successful breaches.The bulk of IoT end hosts are low-end devices, This means that many common security practices cannot be used to protect IoTdevices., leaving IoT services and the wider Internet vulnerable to attacks and exploits.To solve this problem, this article presents a unified IoT framework that employs machine learning to implementthe proposed GNRS&NC architecture. This framework's primary goal is to ensure the safety of IoT devices.The framework makes use of random forest classifier. The suggested architecture allows for the seamless incorporation of regional IoT infrastructures into global frameworks without compromising on usability, interoperability, or security.
The Relationship between Green SCM Technological Innovation and Sustainability in Chinese SME Manufacturing in the 4IR Era: The Mediating Effect of Green Dynamic Capabilities
Authors:   Korean Academy Of International Commerce, Inc., Kim Chang-Bong , Liu Xin-Tong
Published: February 2024

Purpose: This paper analyzes the impact of Green SCM technological innovations on corporate sustainability in the 4IR era. Furthermore, it examines the mediating effect of green dynamic capabilities (GDCs) and the moderated mediating effect of government technology support in this process.
Research design, data, and methodology: F or t his p aper, a s urvey o f s mall a nd medium-sized manufacturing enterprises was conducted in Shandong Province, China, collecting 282 valid data sets. The data were analyzed using SPSS v. 28.0 and the Process Macro v. 3.5 extension program.
Results: First, green SCM technological innovation significantly enhances GDCs. Second, green SCM technological innovation has a significant positive impact on sustainability. Third, in the relationship between green SCM technological innovation and sustainability performance, GDCs have a significant mediating effect. Lastly, depending on the GDCs, government technology support has a statistically significant moderated mediating effect on the relationship pathway impacting sustainability.
Conclusions: Implementing innovations in green SCM contributes to the enhancement of an enterprise's GDCs and improves sustainable outcomes. Therefore, small and medium-sized manufacturing enterprises should actively incorporate advanced technologies into green SCM to enhance supply chain efficiency while mitigating environmental impacts.
A comparison of the efficacy of a probiotic toothpaste, a fluoridated toothpaste in management of Streptococci mutans in plaque around orthodontic brackets- An in vivo study
Authors: Bolya Piyush , Mutha Animesh , Nagora Archana , Sharma Rituraj , Choudhary Sushil
Published: February 2024

The objective of study was to evaluate and compare the efficacy of Probiotic toothpaste, fluoridated toothpaste and non-fluoridated toothpaste (control) on the levels in the plaque surrounding orthodontic brackets.

The randomly selected patients are divided into 3 groups (20 each) on the basis of type of toothpaste given to them. The patients in groups were asked to brush twice daily with given toothpastes and to discontinue using their normal toothpaste. Plaque specimens were collected from the labial surfaces immediately surrounding the orthodontic brackets of the maxillary lateral incisors by four passes, each along the tooth at the bracket inter-face at the gingival, mesial, distal, and occlusal aspects. The isolated plaque is sent to Microbiology laboratory soon after collecting in a sterile container. The presence of was evaluated in colony forming unit through culture formation and statistical analysis was performed, and comparisons were made.

After one month application of a probiotic containing toothpaste (group 3), a fluoridated containing toothpaste (group 2) and a non-fluoridated toothpaste(group 1) the level of significantly reduced in group 2 (non-fluoridated toothpaste) and group3 (Probiotic toothpaste) & minimal reduction is shown in gruop 1. Conclusion: The use of probiotic toothpaste causes a significant decrease in the levels in the plaque around orthodontic brackets in orthodontic patients.
To identify anatomic relationship between central incisor roots and alveolar bone thickness in individuals with variable sagittal malocclusions
Authors: Anupama  , Goyal Merry , Mittal Sanjay , Aggarwal Isha , Vishavkarma Pallavi
Published: February 2024

The predisposition of central-incisors and its association with alveolar bone in surrounding area required assessment before any decision for treatment which involves anteroposterior (AP) movement of incisor inside the jaws.

To identify anatomic relationship between central incisor roots and alveolar bone thickness in individuals with variable sagittal malocclusions and to also correlate if any sexual dimorphism among samples.

The present study was conducted on 100 pre-treatment lateral cephalograms of young adult patients aged 15 to 30 years. They were divided into 2 groups on the basis of Angle’s system of classification as Class I and Class II with 50 subjects each group. patients were selected from the pool of patients who reported to All the lateral cephalograms were hand traced by same operator and tracings included mandibular symphysis, 1 molar central incisors, the OP, incisal edges, root apices, incisor long axes, upper incisor CEJ and upper incisor root midpoint. Various parameters were then measured for both the groups. All data were analysed using the Statistical Package for the Social Sciences software (SPSS version 24.0 Inc, Chicago, IL, USA. Independent sample t-test was used to determine possible statistically significant differences between the Class I and Class II for various parameters.

U1-lab was statistically significant increased value in Class I sample, Where as L1-ling was statistically significant increased value in Class II sample. A very small but significant difference was found for the  mandibular alveolar bone thickness in males and females in both Class I and Class II samples.

A significant variance between alveolar (AV) process of class II and Class I occlusion. There was also a significant variation in males and females of the sample group.
Time-dependent force depreciation of intraoral orthodontic elastics of variable force and lumen sizes – An in vitro study
Authors: Raza Monis , Sethi Mridu , Kumar Piush , Sharma Payal , Shahi Ankit Kumar
Published: February 2024

: To evaluate force decay of elastics of different dimensions and different force values over 48-hours.

Steps included extension and immersion of elastics in artificial saliva and measurement of force levels at a specified point of time. A model with vertical pins placed at in the oral cavity. The initial force measurement was done using Universal testing machine before the elastics were engaged onto the pins 37⁰C in artificial saliva. The reading was taken after this in the Universal testing machine for the samples. The elastics were then placed back into the static simulation for the next 23 hours and again into the dynamic simulation for the next 1 hour.

Statistically significant difference (P<0.05) was observed between force depreciation between rest and maximum stretch of the elastic. Statistically significant difference was observed in force depreciation with varying time intervals of a particular elastic sample. Lumen size and pre-determined force values affect the overall force decrease pattern of elastics.

Elastics with higher force and smaller lumen size show comparatively higher loss of force than the lighter elastics with larger internal diameter. The maximum force loss of a particular elastic happens within the first 24 hours of elastic stretch.

The study would help provide information about how an elastic's lumen size and initial force are interdependent, giving clinicians a better understanding of how to prescribe the right elastics for the force they need to apply for treatment.
Far-right Extremism in the USA
Authors: Rutskoy Danila
Published: February 2024

The author of the article examines the current state of far-right extremism in the USA. The article points out several features typical of such movements in the political realia of the Unites States at the beginning of the 2020s. Among those are: decentralization, the metamorphosis of the idea of ‘white supremacy’, contacts with like-minded individuals and movements abroad, the unifying role of D. Trump, as well as the growing popularity of far-right views among American voters. The author concludes that far-right extremism is still a force with which one has to reckon and which may play an essential role in the upcoming presidential election of 2024.
Democratic Party Crisis as Result of the Middle East Conflict-2023
Authors: Shumilina Inna V.
Published: February 2024

The unprecedented October 7, 2023, attack by the paramilitary wing of Hamas against Israeli civilians and peaceful targets has thoroughly shaken up social and political forces in the United States, Israel's main traditional ally. The ideological outbreak of pro-Palestinian Americans, many of whom constituted the core constituency of the Democratic Party candidate in the last presidential election, briefly weakened the cohesion of Biden's supporters in the 2024 election. Whether the Democratic Party will be able to hold on to its supporters under the pressure of ideological differences over the Middle East conflict is an open question.
BARRIERS ASSOCIATED WITH INACCESSIBLE MENTAL HEALTH SERVICES FOR YOUTH REFUGEES AND ASYLUM SEEKERS: AN INTEGRATIVE REVIEW
Authors: Barbo Geneveave , Petrucka Pammla
Published: February 2024

Youth refugees and asylum seekers are vulnerable to mental health conditions. Although their mental health needs are well documented, evidence reveals that they are underutilizing mental health services. This integrative literature review aims to examine the evidence on barriers to mental health access experienced by youth refugees and asylum seekers, determine the literature gaps, and identify the future direction of research in the field. Academic databases, such as CINAHL, OVID MEDLINE (R), PsycINFO, EMBASE, and Web of Science, as well as grey literature, were used to identify eligible articles. A total of 29 articles were included in this review. Our findings revealed 5 major themes: (a) approachability and ability to perceive; (b) acceptability and ability to seek; (c) availability, accommodation, and ability to reach; (d) affordability and ability to pay; and (e) appropriateness and ability to engage. These findings can assist multiple stakeholders in improving mental health access, quality, and provision.
Home-Garden Production and Women Dietary Diversity: An Experience from Success of Micro-Intervention in Boru Meda Kebele, Ethiopia
Authors: Mohammed Jemal Abdulkerime , Mulat Asegie Asrat
Published: February 2024

Background: In order to combat malnutrition and micronutrient deficiencies, Nutrition Sensitive Agriculture (NSA) gives nutrient-dense foods, dietary diversity, and food fortification the highest priority. This micro-intervention project's goal is to increase home-gardening system's contribution to raising women's dietary diversity score (WDDS) by diversifying family farming practices. Methods: In Ethiopia's Boru-Meda Kebele, The authors applied the intervention to 40 specifically chosen female beneficiaries between the ages of 15 and 49. FAO's standardized questionnaire was used as a tool to conduct an initial and end-line dietary diversity survey by giving careful consideration to cultural and religious elements. The qualitative information was also gathered using case studies, key informant interviews, and focus group discussions.  Results: The local population's diet was dominated by staple cereal crops, and women were less knowledgeable about home gardening and diverse diets. Before intervention, the average number of food groups produced and consumed was 2.63±1.00 and 3.68±1.16 respectively. The intervention enhanced the WDDS to 6.13±0.76, and the production diversity to 5.63±0.90. The intervention changed the community's consumption and production patterns, especially among women. Conclusion: Home-garden agriculture helps to increase WDDS. As a result, development partners must concentrate on farming system diversification by increasing the accessibility of vegetable seeds and educating local farmers about the contribution of diversified vegetable production and consumption. Strengthening the cooperation among stakeholders and maintaining monitoring of activities should also be given due emphasis.
Clustering of Leading Food Products in the south Sumatra Region
Authors: Putra Pratama Muhamad Rezky , Pasmawati Yanti , Desi Kusmindari Christofora , Gunarto Muji
Published: February 2024

This research aims to identify and cluster regional superior food products in the South Sumatra region based on the agricultural, plantation and fisheries sectors using the Location Quotient (LQ) method and a descriptive quantitative approach. South Sumatra agricultural, plantation and fisheries production data starting from 2020-2022 is compared to total production in Indonesia to assess the LQ of each commodity. Policy recommendations include: developing location-specific agricultural infrastructure and technology, increasing added value and standardizing quality, as well as promoting cluster-based products to increase the competitiveness of South Sumatra's superior food products. The research results show 3 main clusters, namely rice (agriculture) producing a total production of 2.7 million tons, production based in Banyuasin Regency, oil palm (plantation) producing a total production of 2.4 million tons, production based in Musi Banyuasin Regency, fisheries which resulted in total production reaching 236 thousand tons of production based in Musi Rawas Regency.
Rentowność i płynność przedsiębiorstw w kontekście wyzwań Polskiego Ładu. Raport sektorowy
Authors: Kochalski Cezary , Szutowski Dawid
Published: February 2024

Raport (edycja druga, pierwsza była wydana w 2023 roku) składa się z trzech części. W części pierwszej wyodrębniono filary Polskiego Ładu, zidentyfikowano dla każdego z nich zmienne makroekonomiczne oraz przedstawiono kształtowanie się wielkości zmiennych makroekonomicznych w latach 2011–2021. Część drugą poświęcono rentowności polskich przedsiębiorstw w badanym okresie, koncentrując się na kształtowaniu się jej podstawowych wskaźników. W części trzeciej przedstawiono zdolność rozpatrywanych przedsiębiorstw do regulowania zobowiązań krótkoterminowych na podstawie podstawowych wskaźników statycznych płynności finansowej i zadłużenia. Wyniki przeprowadzonej analizy zamieszczono w podsumowaniu. W raporcie zamieszczono również komentarz dotyczący zmian płynności i rentowności przedsiębiorstw w okresie postpandemicznym. Metodycznie obie edycje raportu bazują na identycznych rozwiązaniach, co umożliwia porównywalność zaprezentowanych w nich wyników. Raport drugi uwzględnia szerszy zakres czasowy oraz komentarz dotyczący zmian płynności i rentowności przedsiębiorstw w okresie postpandemicznym. Publikacja dofinansowana ze środków budżetu państwa w ramach programu Ministra Edukacji i Nauki pod nazwą „Nauka dla Społeczeństwa”, nr projektu NdS/543640/2021/2022, kwota dofinansowania 12 000 zł, całkowita wartość projektu 699 200 zł.
Obras tridimensionais do acervo do MAM São Paulo feitas por artistas mulheres premiadas nos Panoramas da Arte Brasileira
Authors: Ferraz Tatiana Sampaio
Published: February 2024

O artigo busca contribuir para os estudos sobre o campo ampliado da escultura no Brasil, pouco historicizado entre nós, tomando como objeto o conjunto de obras tridimensionais feitas por artistas mulheres premiadas nos Panoramas da Arte Brasileira do Museu de Arte Moderna de São Paulo e que foram incorporadas ao acervo do museu. O recorte sobre a produção tridimensional das artistas Mary Viera, Valquíria Chiarion, Ada Yamaguishi e Lídia Sano, Eliane Prolik, Nazareth Pacheco, Vera Chaves Barcellos, Rosana Paulino e Marilá Dardot fortalece o crescente interesse pelos estudos feministas que buscam visibilizar a atuação das mulheres. A pesquisa sobre as aquisições de obras expostas nos Panoramas sinaliza a importância do programa institucional para o fomento e a difusão da produção nacional, e foi viabilizada por meio do Edital Laboratório de Pesquisa do museu em 2022.
circuito particular de exposições de arte no Rio de Janeiro na década de 1880
Authors: Cardoso Franciane Canêz , Ribeiro Niura Legramante
Published: February 2024

O artigo constitui parte de uma pesquisa de doutorado que pretende analisar a trajetória de cinco salas particulares de exposições atuantes na década de 1880 no Rio de Janeiro: Salão De Wilde, Glace Élégante, Casa Vieitas, Galeria Moncada e o estúdio de Insley Pacheco. Com base nos dados coletados até então, em textos publicados entre 1880 e 1889, disponíveis na Hemeroteca Digital da Biblioteca Nacional e que citam os espaços do circuito privado, objetiva-se analisar as características que compunham a imagem do artista, a experiência estética, a concepção de arte e a estrutura institucional vigentes durante o recorte temporal e geográfico da investigação. Amparado nas pesquisas de Larry Shiner, em que o filósofo defende uma possível organização sistêmica da arte em diferentes períodos históricos e na contribuição da autora Bruna Fetter, que discute a importância da organização institucional de cada época, este estudo apura as atividades artísticas e as relações entre os agentes do circuito privado do período e busca demonstrar que a década em questão constituiu uma fase de transição do sistema acadêmico para o moderno.
Universidade Federal do Sul da Bahia
Authors: Borba Renata Ribeiro , Ferraz Marcelo Inácio  Ferreira , Pinheiro Lessí Inês Farias
Published: February 2024

A Universidade Nova trata-se de uma proposta de reformulação acadêmica do nível superior inspirada nas ideias “anisianas” e reformas de amplitude mundial. Integra, atualmente, estruturas acadêmicas de algumas universidades públicas brasileiras, através da oferta de cursos interdisciplinares de formação geral, por exemplo.  Assim, este artigo traz um levantamento teórico quanto ao panorama mundial no que se diz respeito às reformulações que marcaram esta instituição – a universidade – e também a abordagem em âmbito nacional. A partir da explanação temporal compreende-se o surgimento da Universidade Nova e certifica-se que a estrutura acadêmica da Universidade Federal do Sul da Bahia, desde a sua origem, molda-se nesta filosofia, pois implementa como um de seus mecanismos a formação em ciclos e a interdisciplinaridade.
Patrimônio cultural da USP São Carlos
Authors: Faraco André Frota Contreras , Vizioli Simone Helena Tanoue
Published: February 2024

Este artigo apresenta uma ação de Educação Patrimonial realizada com um grupo de universitários do campus São Carlos da USP no 2º semestre de 2021. O objetivo da ação foi realizar um processo educativo que oportunizasse aos alunos a desnaturalização do território da universidade sob a perspectiva do Patrimônio Cultural, de forma a identificá-lo, interpretá-lo e representá-lo. A ação ocorreu em 3 etapas: 1) mobilização das experiências e vivências dos alunos; 2) entrecruzamento dessas experiências com os conhecimentos do campo do Patrimônio Cultural e da representação e linguagem; 3) sistematização do conhecimento em um Inventário Participativo. Espera-se, com este trabalho, lançar luz sobre o Patrimônio Cultural dos universitários, além de consolidar o referencial teórico-conceitual utilizado como referência, as estratégias de ação e as ferramentas utilizadas.
OSMANLI’DA HASTA ROLÜ, HASTA TALEPLERİ VE BÜROKRASİ (19. YÜZYIL)
Authors: KILIÇ Rüya
Published: February 2024

Bu çalışmada, 19. yüzyıl Osmanlı toplumunda hasta ve yakınlarının verdiği dilekçelerde dile getirilen talepler ile bürokratik makamların bu taleplere verdiği cevaplar ele alınmaktadır. İnsan, sağlık sebebiyle en temel ihtiyaçlar bakımından başkasına geçici veya kalıcı olarak bağımlı hâle gelebildiği gibi bazı sorumluluklardan da kurtulabilir. Sorumluluktan kurtulma talebi, özellikle devlet kurumlarında çalışan kimseler için resmî olarak dile getirilmesi gereken bir süreçtir. Bu süreç, birtakım yazışmalar sebebiyle kayda geçtiğinden yazışmalar veya talepler günümüze veri olarak intikal eder. Devlet kurumlarında çalışan kimselerin sağlık sebebiyle sunduğu yazılı taleplere yönetici makamdan da cevap gelir. Bu da bize, bürokratik mekanizmanın hasta ve hastalık durumu karşısındaki tutumunu değerlendirme fırsatı verir.

Osmanlı arşivlerinde, hasta talepleri ve bunlara verilen cevapları içeren çok sayıda belge bulunmaktadır. Bu çalışmanın dayandığı belge grubu, hastaların hastalıkla başa çıkma çabaları ile bürokratik makamların hasta taleplerine verdikleri idari tepkileri birlikte görme imkânı sağlamaktadır. Bu sürece doğrudan veya dolaylı olarak hasta yakınları da dâhil olmaktadır. Böylece hasta, hasta yakını ve yetkili merciler arasında yürütülen yazışmalar üzerinden hastaların hastalıkla baş etme çabaları tespit edildiği gibi, hastalık vesilesiyle yapılan idari işlemler de takip edilebilmektedir. Süreç; merhamet, hak ediş, hastalık bahanesi, sorumluluk ve meşruiyet açılarından işlemektedir. Bu sebeple, bireysel hasta tecrübeleri ve buna dayalı bütün süreçleri yansıtması bakımından, çalışmanın kurgusunu tek tek hikâyeler üzerinden oluşturmayı yöntem olarak tercih ettik. Birbirinden farklı her bir özel hikâyenin bizi, genel bir tutum, anlayış ve yaklaşıma götürmesini; bu genel tabloya dayalı olarak da hasta rolü, talepleri ve bürokratik uygulamalar üzerine bir değerlendirme yapmaya sevk etmesini umduk.
8. YÜZYILDA AFGANİSTAN'DA HALAÇ PRENSESLERİ, GÜNÜMÜZDE ORTA İRAN'DA HALAÇLAR VE DİLLERİ
Authors: CENGİZ Mikail , ERDAL Marcel
Published: February 2024

Bu yazı, 12 Mart 2009 tarihinde Hacettepe Üniversitesi Türkiyat Araştırmaları Enstitüsü'nde Prof.Dr. Semih Tezcan tarafından verilen "8. yüzyılda Afganistan'da Halaç Prensesleri, Günümüzde Orta İran'da Halaçlar ve Dilleri" adlı konferansın deşifre edilmiş hâlidir. Konferansta atıfta bulunulan kaynaklar dipnotlarda gösterilmiştir. Tezcan konferansında, Halaççanın keşfine ve açıklanmasına yol açan Orta İran saha çalışmalarını anlatmış ve bu dil üzerine yaptığı araştırmalar üzerine açıklamalar yapmış ve Halaççanın Türkoloji açısından önemini vurgulamıştır. O ayrıca Nicholas Sims-Williams tarafından yayımlanan, Halaç prenseslerinden söz edilen Orta-İran Bahter dili adlı çalışmadaki belgelerde görülen Türkçe kelimeler ile ilgili konuşmuş ve bu dokümantasyonun Türkoloji açısından önemini vurgulamıştır: Bu, en erken Arap kaynaklarından önce Halaçlar hakkında verilen ilk bilgidir.