bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis, Docking Analysis, and Assessment of Chalcones for Antibacterial and Anthelmintic Activities
Authors: Agarwal Uma , Yadav Vivek , Roper Ruchi , Bhutani Rubina , Tonk Rajiv Kumar
Published: October 2024

Background:

Chalcones have been demonstrated to possess numerous therapeutic
qualities in recent years, such as antibacterial, antiviral, anti-ulcerative, antioxidant, antiinflammatory,
antihyperglycemic, antimalarial, antitubercular, analgesic, antiplatelet, and
anticancer activities.

Objective:

To explore the synthesis, docking, and therapeutic characteristics of chalcones as
antibacterial and anthelmintic compounds.

Methods:

The chalcone derivatives (3a-3k) and (6l-6v) were synthesized via two selective different
reactions, based on the Claisen-Schmidt reaction. All synthesized compounds were evaluated for
their antibacterial activity using an in vitro cup-plate method, and their anthelmintic activity was
assessed using an in vitro earthworm paralysis and death assay. To validate these findings,
conducted molecular docking experiments between the dihydrofolate reductase receptor (PDB ID:
4LAE) and the synthesised compounds (3a-3k) and (6l-6v) to determine catalytic interactions

Results:

Compound 6(n) exhibited the greatest efficacy in biological in vitro activity against S.
aureus compared to all other compounds examined. Compound 6(o) exhibited substantial efficacy
against P. posthuma and E. coli. Emphasizing these findings, the compounds 3(a), 3(g), 3(i), 6(n),
and 6(o) demonstrated hydrogen bond interactions with certain amino acid residues of the receptor,
including THR 122, ASN 18, ASN19, GLN 96, SER 50, and ALA 8, during molecular docking.

Conclusion:

The study results showed that the synthesised derivative (E)-1-(napthalen-2-yl)-3-(4-
(trifluoromethoxy)phenyl)prop-2-en-1-one 6(n) had beneficial antibacterial properties against S.
aureus, while derivative (E)-1-(Napthalen-2-yl)-3-(4-trifluoromethyl)phenyl)prop-2-en-1-one 6(o)
exhibited antibacterial activity against E. coli and anthlelmintic activity against P. posthuma.
Cutting Edge Nanoplatforms with Smart Bio-sensing Applications: Paving the Way for Sustainable Green Approaches
Authors: Debnath Rabin , Singh Arshdeep , Saini Aniket , Seni Kushal , Sharma Anjali , Bisht Deepak Singh , Sharma Kalicharan , Goel Honey , Chawla Viney , Chawla Pooja A
Published: October 2024

In the era of automation, sustainable technologies employing eco-friendly materials and
manufacturing techniques such as ‘Green nanobiosensors’ have taken centre stage, owing to their
opulent portfolio encompassing renewable fabrication and design from biomaterials, biocompatibility,
and ease of functionalization. Generally, sensors utilize nanomaterials sourced from renewable
resources or with minimal environmental impact, such as cellulose nanocrystals, chitosan,
and biopolymers, owing to their exceptional properties such as high surface area.
With the advent of environmentally conscious attributes in the cutting-edge nano biosensing technology,
green nano-biosensors offer innovative avenues for sensitive and selective detection and
monitoring of myriad analytes with minimal environmental repercussions. Further, such sensors
operate at low energy levels, contributing to reduced energy consumption, and can be mass-produced
with minimal environmental influence.
The present outlay of literature aims to decipher the utilization of eco-friendly materials and sustainable
manufacturing techniques in creating nano-biosensors and subsequently promulgating
their advantages in terms of energy efficiency, low environmental impact, and use of renewable resources.
Furthermore, this study embellishes a comprehensive framework that delineates the diverse
applications of these green nanobiosensors as eco-friendly technological solutions across diverse
sectors primarily agriculture, environmental monitoring, and biomedicine, showcasing their
potential to revolutionize these domains while minimizing environmental impact.
Qualitative Study of Utilization of Local Food for the Nutritional Needs of Stunted Children in Indonesia
Authors: Harahap Juliandi , Utami Tri Niswati , Eliska Eliska , Lubis Zulhaida , Sari Dina Keumala , Aprianti Lubis Nenni Dwi , Amelia Rina , Rohmawati Lili , Andayani Lita Sri , Aulia Destanul , Astuty Delfriana Ayu
Published: October 2024

Introduction:

Stunting is a national problem in Indonesia due to an imbalance in children's nutritional intake. Diversity in balanced food consumption is one of the efforts to prevent stunting.

Objectives:

Exploring the potential of various types of food ingredients, utilization, equipment, capabilities, and expectations of the community in utilizing local food to fulfill stunting children's nutrition.

Methods:

Qualitative studies use a phenomenological approach. Research informants 15 people. Data were collected through Focus Group Discussion activities. ATLAS.Ti22 software is used to analyze data from focus group discussions.

Results:

It was found that the theme of ability and expectation has the highest co-occurrence value of 0.76, meaning that hope is very strongly related to the ability of the community. The theme of ability and availability of food ingredients has a co-occurrence value of 0.58, meaning that the ability to process local food ingredients is strongly related to the availability of food ingredients. Utilization and ability co-occurrence value of 0.16 means that the use of local food ingredients is weakly related to the ability to process food.

Conclusion:

Diversity in food consumption is related to food availability, utilization, and ability to process food. Suggestion: local food processing assistance is needed for the stunting target group to increase the community's ability to process local food. Inter-agency collaboration is needed in specific nutrition intervention programs by utilizing local food ingredients.
Nurses' cultural competence and its impact on work engagement and teamwork in community medical centers healthcare network in Central California, USA
Authors:   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines, Imperial Gemma Stela L. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines, Feliciano Evelyn E. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines, Feliciano Alfredo Z. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines,   Encompass Health Rehabilitation Hospital, Mesa, Arizona, USA, Bagaoisan Mary Angelica P. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines, Agunod Cyrelle D. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines,   College of Nursing, Northern Private College of Nursing, Arar, Saudi Arabia, Magtubo Delma Joie D. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines,   Graduate School, Holy Angel University, Angeles, Philippines, Paano Anna Lyn M. ,   College of Nursing and Graduate Studies, Angeles University Foundation, Angeles, Philippines
Published: October 2024

This study explores the relationship between nurses' cultural competence and their work engagement and teamwork in hospitals at a tertiary private medical center in Central California. Using a correlational method, data were collected from 357 nurses, primarily female, aged 37-38, and of Asian descent. The Self-Assessment Cultural Competence Checklist, Utrecht Work Engagement Scale (WES-9), and Nursing Teamwork Scale (NTS) were used to measure these variables. Pearson’s correlation analysis revealed a positive association between cultural competence, work engagement, and teamwork, indicating that differences in beliefs can enhance collaboration and team cohesion among healthcare workers from diverse backgrounds. These findings highlight the importance of cultural competence in fostering effective teamwork and relationships in a multicultural healthcare environment.
Effects of Biofeedback Training on Shooters' Performance, Stress Levels, and HRV
Authors: Donghai Huang , Abdul Wahab Muhammad Nubli , Xiuling Zhang , Dongya Ji
Published: October 2024

This study aimed to explore the impact of a 12-week biofeedback training on shooters' performance, stress levels, and heart rate variability (HRV). Thirty-six shooters aged 18-24 (18 subjects in each group) participated. The experimental group underwent 12 weeks of biofeedback and shooting training, while the control group only received shooting training. Results showed significant improvements in performance, stress levels, and HRV parameters in the experimental group, reflecting enhanced parasympathetic activity. It was further revealed that TP value was negatively correlated with stress level and positively correlated with performance, and stress level was negatively correlated with performance.
Exploring the Concept of Islamization of Knowledge from Scholarly Perspectives
Authors: Samsuri Maimanah , Tengku Paris Tengku Nazatul Shima , Mazlan Siti Aisyah , Mohd Ismath Nurul Hanilah , Kembaren Mardiah Mawar , Br Kembaren Farida Repelita Waty
Published: October 2024

The study investigates the process of Islamization of Knowledge by conducting a bibliometric analysis of documents published in the Scopus database between 2010 and 2024. The findings demonstrate a favourable yearly pattern of publications and emphasise Malaysia's prominent position in this domain, with notable contributions from two well-known universities. The study also shows Muslim thinkers' diverse vocabulary and conceptual methodologies. This analysis serves as a helpful point of reference for future studies. It contributes to a deeper comprehension of the process of Islamization of Knowledge by identifying areas where further research is needed.
IMPROVING MEDICAL IMAGE PROCESSING USING AN ENHANCED DEEP LEARNING ALGORITHM
Authors:   Codecraft Technologies, Bangalore, Karnataka, India, Kumar C. Kiran , R. Gayathri ,   Rajalakshmi Engineering College, India, S Thirukumaran ,   Jain University, India, P.T. Kalaivaani ,   Vivekanandha College of Engineering for Women, India
Published: October 2024

The use of ML methods with the objective of selecting wheat varieties that have a higher level of rust resistance encoded in their genomes is referred to as rust selection. In addition to that, the categorization of wheat illnesses by means of machine learning It has been attempted to classify wheat diseases by making use of a wide variety of machine learning techniques. In this paper, we develop an enhanced deep learning model to classify the disease present in the wheat plant. The study uses an improved convolutional neural network to classify the plant disease using a series of layers. The simulation is conducted in terms of the accuracy, precision, recall and f-measure. The results show that the proposed method achieves higher rate of accuracy than its predecessor.
GAUSSIAN DIFFUSIVE HARTIGAN MULTIDIMENSIONAL DEEP BELIEF DIVERGENCE FEATURE LEARNING USING PARTIAL DIFFERENTIAL EQUATION FOR FACE RECOGNITION
Authors:   Government Arts College, Salem, India, S Moorthi , S Karthikeyan ,   Government Arts College, Salem, India
Published: October 2024

Human face recognition has the most noteworthy role in detecting a person in many real-world scenarios in computer vision like identification, authentication, security, and so on. Face recognition typically acquires the features and compares them to a dataset to discover the best match. The existing methods failed to accurately extract the robust features for face recognition. To solve these issues, Gaussian Diffusive Hartigan Multidimensional Deep Belief Divergence Feature Learning (GDH-MDBDFL) method is proposed based on the Fractional Partial Differential Equation (FPDE) for face recognition. The proposed GDH-MDBDFL method is designed to improve the accuracy of face recognition using FPDE. The proposed GDH-MDBDFL method comprises three different layers such as input, output, and four hidden layers. Both qualitative and quantitative result analysis is presented to verify the effectiveness of the proposed GDH-MDBDFL method. The simulation results, the proposed GDH-MDBDFL method gives the higher recognition accuracy, and precision with lesser recognition time compared to the conventional methods.
ENHANCED AI BASED FEATURE EXTRACTION TECHNIQUE IN MULTIMEDIA IMAGE RETRIEVAL
Authors:   Rajalakshmi Engineering College, India, R Gayathri , T Gobinath ,   Chettinad College of Engineering and Technology, India, A Muthumari ,   University College of Engineering, India, Swathi R.S.V. Rama ,   Koneru Lakshmaiah Education Foundation, India
Published: October 2024

In the era of rapid technological advancements, the demand for efficient and accurate identification and retrieval of information from multimedia images has seen a substantial increase. To meet this growing demand, artificial intelligence (AI)-based technologies, particularly feature extraction techniques, have gained significant popularity. Feature extraction involves the extraction of salient features from multimedia images, such as edges, lines, curves, textures, and colors, with the aim of representing the data in a more suitable format for analysis. This paper presents an enhanced AI-based feature extraction technique for multimedia image retrieval. The proposed method introduces a novel approach that combines the power of deep learning and evolutionary algorithms in a neuro-symbolic computation framework. Specifically, the renowned VGG16 deep learning algorithm is employed as the initial feature extractor. VGG16 is a state-of-the-art deep convolutional neural network that has demonstrated exceptional performance in various computer vision tasks, including image classification and feature extraction. The primary idea behind this approach is to leverage the capabilities of AI to extract the most discriminative features from the source images using VGG16. These features are then further refined using evolutionary algorithms, which employ a search and optimization process inspired by natural evolution. By iteratively improving the extracted features through the evolutionary algorithms, the method aims to enhance the discriminative power and representational quality of the extracted features. To evaluate the performance of the proposed approach, extensive experiments were conducted. The results demonstrate that the method achieves superior performance in terms of precision, recall, and F-measure when compared to conventional feature extraction techniques. Furthermore, a comprehensive comparison with state-of-the-art AI-based feature extraction techniques further highlights the potential and effectiveness of the proposed approach in multimedia image retrieval applications.
REAL-TIME OBJECT DETECTION IN VIDEOS USING DEEP LEARNING MODELS
Authors:   Cambridge Institute of Technology, India, M Monika , Rajender Udutha ,   Vaageswari College of Engineering, India, A Tamizhselvi ,   St. Joseph’s College of Engineering, India, S Rumale Aniruddha ,   Sandip Institute of Technology and Research Centre, India
Published: October 2024

Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications.
ENHANCING IMAGE SUPER-RESOLUTION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS
Authors:   Mandsaur University, India, Tomar Archana , Patidar Harish ,   Mandsaur University, India
Published: October 2024

In computer vision, image super-resolution plays a pivotal role in improving the visual quality of low-resolution images, thereby enhancing various applications such as medical imaging, surveillance, and digital entertainment. The problem at hand involves the inherent limitations of conventional methods in restoring high-frequency information lost during image downscaling. This research aims to bridge this gap by leveraging DCNNs, exploiting their ability to learn complex mappings between low and high-resolution image spaces. This study addresses the challenge of image super-resolution through the application of Deep Convolutional Neural Networks (DCNNs). The research involves the design and training of a novel DCNN architecture tailored specifically for image super-resolution. We employ a large dataset of low and high-resolution image pairs to facilitate supervised learning. The network is trained to intelligently infer high-frequency details from low-resolution inputs, enabling the generation of visually compelling super-resolved images. Results from extensive experiments showcase the superior performance of the proposed DCNN-based approach compared to traditional methods. Quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI), demonstrate significant improvements in image quality. Additionally, qualitative assessments highlight the network’s ability to reconstruct fine details, edges, and textures, resulting in visually pleasing super-resolved images.
DEEP NEURAL MULTI WAVELET SEGMENTATION AND RESNET-50 IMAGE CLASSIFICATION (DNMWS-RESNET-50IC) BASED EARLY GLAUCOMA DETECTION
Authors:   Coimbatore Institute of Engineering and Technology, India, K Pushpalatha
Published: October 2024

Glaucoma, a leading cause of irreversible blindness worldwide, results from progressive damage to the optic nerve, often linked to elevated Intra Ocular Pressure (IOP). Early detection is critical for preventing vision loss, yet traditional diagnostic methods can be limited in accessibility and effectiveness, particularly in the early stages of the disease. Addressing the need for early detection, we propose the Deep Neural Multi-Wavelet Segmentation and ResNet-50 Image Classification (DNMWS-ResNet-50IC) method, specifically designed to detect glaucoma at an early stage using retinal fundus images. In this work, we apply Anisotropic Gaussian Filtering for the preprocessing of retinal fundus images, effectively reducing image noise while preserving the original quality of the image pixels by creating an adaptive window size and scale space. We then utilize multi- wavelet-based image segmentation, leveraging wavelet transforms to analyze and decompose the image into its various frequency components. This technique is particularly advantageous for managing images with complex structures and textures. Subsequently, the segmented features are classified using the ResNet-50 model, which categorizes the images as normal, abnormal, or indicative of early- stage glaucoma. The effectiveness of the proposed method is assessed by measuring three key performance indicators sensitivity, specificity, and accuracy on digital retinal images from the HRF image database. Additionally, the model's performance is further evaluated on a separate test set, considering metrics such as accuracy, precision, recall, and prediction time.
MULTIMEDIA CONTENT ANALYSIS FOR ALZHEIMER’S DISEASE DIAGNOSIS USING MRI SCANS AND DEEP LEARNING
Authors:   D.Y. Patil College of Engineering, India, Hasan Roomana , Raju V. Vijaya Kumar ,   Anil Neerukonda Institute of Technology and Sciences, India, M Pratussha ,   Vardhaman College of Engineering, India, Jashva Munigeti Benjmin ,   Malla Reddy University, India, S Pavithra ,   Sri Venkateshwaraa College of Engineering and Technology, India
Published: October 2024

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with early diagnosis being crucial for effective intervention. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting structural brain changes associated with AD. However, accurate and automated analysis of MRI scans remains a challenge due to the complexity and variability in brain structures. Traditional methods for analyzing MRI scans for AD diagnosis often rely on manual interpretation or basic image processing techniques, which can be time-consuming and prone to variability. There is a need for advanced automated methods that can accurately segment brain structures and extract relevant features for reliable diagnosis. This study proposes a novel approach for AD diagnosis using MRI scans, combining Conditional Attention U-Net for segmentation and Ant Colony Optimization (ACO) for feature extraction. The Conditional Attention U-Net enhances segmentation accuracy by incorporating conditional attention mechanisms to focus on relevant features while minimizing background noise. ACO is employed to optimize feature extraction by simulating the foraging behavior of ants, which efficiently selects and refines key features related to AD. The proposed model was evaluated on a dataset of 500 MRI scans, comparing performance with traditional methods using metrics such as Dice Similarity Coefficient (DSC) and classification accuracy. The Conditional Attention U-Net achieved an average DSC of 0.89 for segmentation of key brain regions, outperforming conventional methods by 10%. The ACO-enhanced feature extraction resulted in a classification accuracy of 92% for AD diagnosis, representing a 7% improvement over baseline methods. The combination of these techniques demonstrated a significant enhancement in both segmentation precision and diagnostic accuracy, showcasing the effectiveness of the proposed approach for early AD detection.
Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach
Authors: Thanh Phuc Phan , Nguyen Phung-Anh , Nguyen Nam Nhat , Hsu Min-Huei , Le Nguyen Quoc Khanh , Tran Quoc-Viet , Huang Chih-Wei , Yang Hsuan-Chia , Chen Cheng-Yu , Le Thi Anh Hoa , Le Minh Khoi , Nguyen Hoang Bac , Lu Christine Y , Hsu Jason C
Published: October 2024

Background

The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.

Objective

This study aimed to develop a personalized predictive model, using artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with type 2 diabetes mellitus (T2DM) who are prescribed antidiabetic medications.

Methods

This retrospective multicenter study used data from the Taipei Medical University Clinical Research Database, which comprises electronic medical records from 3 hospitals in Taiwan. This study applied 8 machine learning algorithms to develop prediction models, including logistic regression, linear discriminant analysis, gradient boosting machine, light gradient boosting machine, AdaBoost, random forest, extreme gradient boosting, and artificial neural network (ANN). These models incorporated a range of variables, encompassing patient characteristics, comorbidities, medication usage, laboratory results, and examination data.

Results

This study involved a cohort of 43,068 patients diagnosed with type 2 diabetes mellitus, which accounted for a total of 1,937,692 visits. For model development and validation, 1,300,829 visits were used, while an additional 636,863 visits were reserved for external testing. The area under the curve of the prediction models range from 0.67 for the logistic regression to 0.98 for the ANNs. Based on the external test results, the model built using the ANN algorithm had the best area under the curve (0.97 for 5-year follow-up period and 0.98 for 10-year follow-up period). Based on the best model (ANN), age, gender, triglyceride, hemoglobin A1c, antidiabetic agents, stroke history, and other long-term medications were the most important predictors.

Conclusions

We have successfully developed a novel, computer-aided, dementia risk prediction model that can facilitate the clinical diagnosis and management of patients prescribed with antidiabetic medications. However, further investigation is required to assess the model’s feasibility and external validity.
Desires and Needs for Quitting Both e-Cigarettes and Cigarettes Among Young Adults: Formative Qualitative Study Informing the Development of a Smartphone Intervention for Dual Tobacco Cessation (Preprint)
Authors: Nguyen Nhung , Koester Kimberly A , Tran Christine , Ling Pamela M
Published: October 2024

BACKGROUND

Dual use of both e-cigarettes and cigarettes is popular among young adults and may lead to greater nicotine dependence and additive adverse health effects than single-product use. However, existing cessation programs target quitting either e-cigarettes or cigarettes, highlighting a need for interventions to help young adults quit both products (ie, dual tobacco cessation).

OBJECTIVE

This formative study is part of a larger project to develop a smartphone intervention for dual tobacco cessation among young adults. This study aimed to (1) explore desires for and experiences with quitting both e-cigarettes and cigarettes and (2) identify needs and preferences for dual tobacco cessation intervention programming.

METHODS

Semistructured interviews were conducted to elicit the need for and experience with dual tobacco cessation among 14 young adults (18-29 years old) recruited through Instagram (Meta) advertisements in 2023. We conducted a thematic analysis to identify common themes related to quitting experiences and cessation needs.

RESULTS

Participants expressed a strong desire for dual tobacco cessation and had attempted to quit both tobacco products, mostly “cold turkey.” The priority product for quitting first varied by the individual’s perceived harm or level of consumption. Targets for dual tobacco cessation interventions included (1) highlighting the health effects of dual tobacco use compared with single product use, (2) providing cessation support to quit one prioritized product while cutting down the other product with the explicit goal to quit both, (3) emphasizing unique facilitators and barriers to quitting each product (eg, unpleasant smell of cigarettes facilitating smoking cessation and accessibility and flavors of e-cigarettes hindering vaping cessation), and (4) addressing co-use of tobacco with alcohol or cannabis. Participants wanted personalized interventions through smartphone apps that would tailor support to their tobacco use patterns and unique quitting goals and needs. They also suggested presenting intervention content in multimedia (eg, videos, graphic pictures, quizzes, and games) to increase engagement.

CONCLUSIONS

This study provides important insights into young adults’ experiences, needs, and preferences for dual tobacco product cessation. We highlight important targets for future smartphone apps to deliver personalized and tailored support to meet the heterogeneous needs and preferences of young people who want to quit using both e-cigarettes and cigarettes.
Research on Safety Management of University Laboratories Based on Isolation Forest Algorithm
Authors: Xue Junjie
Published: October 2024

This paper mainly discusses the application of the Isolation Forest Algorithm in the safety management of university laboratories. Through the analysis of the current situation of safety management in university laboratories, the deficiencies of traditional management methods are expounded. Then, the principle and advantages of the Isolation Forest Algorithm are introduced in detail, and the effectiveness of this algorithm in the abnormal detection of laboratory safety is verified through experiments in a simulated laboratory environment. The experimental results show that the Isolation Forest Algorithm can quickly and accurately detect abnormal situations in the laboratory, providing a new and effective means for the safety management of university laboratories.
Assessing the Introduction of Genetically Modified Organisms (GMOs) into Ghana: The Perspectives of Agriculture Professionals in Northern Region
Authors: Badmus Mutiu , Abubakar Muhammed Charmawla , Zakari Baasit Abdul
Published: October 2024

The role of agriculture officers is essential and cannot be ignored when introducing technology such as Genetically Modified Organisms (GMOs). However, there is no known study presently that examines agriculture officers’ perspectives of GMOs emergence in Ghana. Our study assesses agriculture officers’ perceptions on the introduction of GMOs into Ghana and its impact on food and seed security as well as officer’s level of involvement in GMOs related activities and willingness to promote it. We further solicited their views on GMOs as a successful solution to tackle farming challenges and assessed what influenced their views. Our findings revealed that 53% of agriculture officers agreed that GMOs will have positive impact on food/ seed security/sovereignty. Also, 56% said they will champion GMOs, however, 67% said there is limited information on GMOs and 74% of the respondents indicated that they have never attended or participated in GMOs related activity. We highlighted officers’ divergent views around the introduction of GMOs as a successful solution to tackle farming challenges and the key influencers include: GMOs is unsafe for human consumption, gene escape into wild relatives, and possibility of creating insect resistant and limited capacity to genetically engineered crops locally. The study recommended that there is the need for pro and anti-GMO groups to involve officers in GMO activities for effective dissemination of information on GMO to farmers or citizens.
Influence of Bovine Myosin Heavy Chain Isoforms and Muscle Fiber Cross-Sectional Area on the Eating Quality and Connective Tissue Characteristics of 11 Different Beef Muscles
Authors: Hene Sara R. , Looper Jordan T. , Verrill Emily , Hammond Peang A. , Chun Colin K. Y. , O'Quinn Travis G. , Chao Michael D.
Published: October 2024

The objective of this study was to determine the impact of muscle fiber type, cross-sectional area (CSA), and diameter on the eating quality of 11 different beef muscles. Eleven different beef muscles were utilized in 2 separate studies. In the 2 studies, triceps brachii, rectus abdominus, rectus femoris, supraspinatus, gluteus medias, pectoralis profundi, semitendinosus, longissimus thoracis, longissimus lumborum, tensor fascia latae, and gastrocnemius were collected from 10 USDA Choice carcasses (N = 110). To determine muscle fiber type, myofibrillar proteins were extracted and separated via gel electrophoresis and immunoblot, while muscle fiber CSA and diameter were determined using a dystrophin antibody stain via fluorescence microscopy. Pearson correlation analysis was performed to determine the relationship between muscle fiber type, CSA, diameter, and the eating quality of the 11 beef cuts from previously reported studies. Muscles from both studies showed distinct differences in the relative percentage of type I and type IIA muscle fiber types, CSA, and diameter (P < 0.05). Correlation analysis from study 1 demonstrated positive correlations between type I fibers and many positive attributes of eating quality such as tenderness, juiciness, and lipid flavor intensity, while negative correlations were found between type IIA fibers and those attributes (P < 0.01). Interestingly, results from study 2 showed that increasing type I fiber percentage may also contribute to greater connective tissue content and collagen crosslink density (P < 0.01). Finally, a negative correlation was found between muscle fiber CSA and diameter with connective tissue amount (P < 0.05), and a positive correlation was found between muscle fiber CSA and diameter with tenderness measurements (P < 0.05) in both studies. Overall, muscles with greater type I fiber % delivered a more favorable eating experience than those with more glycolytic metabolism. Notably, increased CSA and fiber diameter did not diminish eating quality and were found to have a muscle-specific relationship with tenderness.
Albizia Odoratisima: Review on Morphology, Pharmacological Activity, Physicochemical, Phytochemical Study and Traditional Uses
Authors: Bhoir Komal
Published: October 2024

A black Siri’s belonging to the Fagaceae family and the Mimosaceae subfamily, Albizia odoratissima Bent (L.F.) is extensively grown in China, India, Bangladesh, Bhutan, Nepal, Myanmar, Thailand, Sri Lanka, and Vietnam. It is considered one of the best trees for nitrogen fixation. Albizia odoratissima Bent is used to treat a variety of diseases and conditions (L.F.). It has been used to treat a wide range of diseases such as antidiabetic action. All the Albizia species, Albizia odoratissima exhibits the best anti-diabetic properties. Historically, many plant parts have been used as remedies for conditions like diabetes, asthma, leprosy, bronchitis, cough, skin diseases, and inflammatory pathologies like burns and ulcers. There have been reports of a variety of pharmacological activity, including antidiabetic, antioxidant, antimicrobial, and anti inflammatory properties, for different prepared extracts of these plants and their parts. Providing a pharmacognostical description, pharmacological activities, therapeutic value, and uses is the aim of the current review study
A Brief Review: C. Rotundus of Phytochemical and Pharmacological Study
Authors: Varatha Akshada
Published: October 2024

This article's goal is to investigate and confirm the therapeutic benefits and safety margins of traditional medicines use by the tribal community to treat a range of illnesses as detailed in our nation's ancient literature. For thousands of years, herbs have significantly improved the quality of human life. The old and most popular of medicine in the world today is herbal medicine. Flavors, cosmetics, colors, and medications. Natural-source drugs are pharmacologically effective and have little side effects, making them ideal for preventative medicine. Cyprus rotundus L. Is also known as moth or in regional languages. It is available across the country. Natural-source drugs are pharmacologically effective and have little side effects, making them ideal for preventative medicine. Cyprus rotundus L. Is also known as moth or in regional languages. It is available across the country.
MILTON BABBITT’İN “THREE COMPOSITION FOR PIANO (1947)” ESERİNE YÖNELİK BİR ANALİZ
Authors: Açıkgöz Mehmet Güneş , Akın Tolga
Published: October 2024

Atonalite terimi, 17. ve 19. yüzyıllar arasında avrupa klasik müziğini karakterize eden tonal hiyerarşiler sistemine uymayan müziği tanımlar. Besteciler müziğin oluşumundan bu yana farklı arayışlar içerisine girerek yeni akımların ortaya çıkmasını sağlamıştır. Bu akımların en önemlilerinden biri on iki ton sistemidir. Arnold Schöberg’in geliştirdiği, Anton Webern’ in günümüze ulaştırdığı 12 ton sistemi çalışmada genel olarak örnekler ile açıklanmıştır. Bu çalışmada ise içerik analizi yöntemi uygulanarak Milton Babbitt’in “Three Composition for Piano (1947)” eserine yönelik eserin on iki ses dizisi açığa çıkarılmış, geliştirilen matris tablosu ile eser içerisinde kullanılabilecek varyasyon diziler keşfedilmiş ve eser içerisinde bu dizilerin kullanıldığı alanlar ve esere orantısı tespit edilmiştir.
Uma análise funcionalista das construções hipotáticas iniciadas por visto que, dado que e posto que
Authors: Nascimento Juliana Barboza do , Cezario Maria Maura , Castanheira Dennis
Published: October 2024

O artigo trata de uma análise dos usos das orações hipotáticas introduzidas por visto que, dado que e posto que em textos contemporâneos escritos em português brasileiro (PB). O modelo construcional que instancia tais cláusulas adverbiais se codifica como [CONECT (S) V (C)] CLÁUSULA ADVERBIAL. O trabalho baseia-se na Linguística Funcional Centrada no Uso (LFCU), que considera, em suas análises, os pressupostos teórico-metodológicos do Funcionalismo Norte-americano, da Gramática de Construções e da Sociolinguística Variacionista. A partir do princípio da não-sinonímia (GOLDBERG, 1995), considera-se, nesta pesquisa, que tais construções, embora se insiram em contextos muito semelhantes e tenham formas similares, apresentam especificidades semântico-pragmáticas que as diferenciam. Sendo assim, esta pesquisa tem o objetivo de mapear seus usos por meio de três fatores linguísticos: arranjo linear, estrutura informacional e valor semântico das construções. A pesquisa foi baseada na coleta e na análise de 450 dados dessas construções oracionais adverbiais, sendo 150 iniciadas por visto que, 150 por dado que e 150 por posto que, todas sendo extraídas da aba Web-Dialetos do Corpus do Português. Os principais resultados demonstram que tanto as orações iniciadas por dado que quanto por posto que apresentam mais de um valor semântico-pragmático, o que não ocorre com aquelas introduzidas por visto que. Além disso, as construções introduzidas por dado que possuem um maior número de ocorrências na posição anteposta, assim como apresentam mais ocorrências de orações com informações pragmaticamente pressupostas.
Discurso e HIV/Aids: uma análise de manchetes das revistas brasileiras Veja e Galileu sob a perspectiva arqueogenealógica de Michel Foucault
Authors: Ramari Thiago Henrique , Navarro Pedro
Published: October 2024

Este artigo analisa discursos a respeito da Síndrome da Imunodeficiência Adquirida (Aids) na imprensa brasileira, a partir do método arqueogenealógico de Michel Foucault. A pandemia, causada pelo Vírus da Imunodeficiência Humana (HIV), começou no início dos anos de 1980 e segue até a atualidade. O material de análise é composto por duas manchetes publicadas em décadas diferentes, a primeira da revista Veja (edição nº 1.077, de 26 de abril de 1989) e a segunda da revista Galileu (edição nº 313, de agosto de 2017). Para compreendê-las a partir da ótica discursivo-foucaultiana, o percurso teórico-analítico enfoca as regras de formação do objeto, das modalidades enunciativas e dos conceitos, abordando os entrelaçamentos das noções de enunciado, discurso, formação discursiva, a priori histórico e arquivo. Como resultado, observa-se que as manchetes trazem discursos derivados da medicina, do direito e da religião, reforçando a ligação entre a enfermidade e o grupo de homens que fazem sexo com outros homens.
Selfie: a relação entre a técnica e a prática discursiva
Authors: Soares Maraline Aparecida , Nunes Silvia Regina
Published: October 2024

O presente artigo filia-se ao campo de conhecimentos da Análise de Discurso, numa perspectiva sustentada no tripé Linguística, Materialismo Histórico e Psicanálise; e toma, como lugar de observação e análise, o âmbito da informática. Na discussão que se apresenta, partimos da premissa de que as injunções técnicas, sob a ideologia da comunicação, atrelada à de consumo, têm promovido efeitos em estudos e práticas de linguagens, como a selfie, por exemplo, e sua postagem/circulação nas redes sociais, enquanto modo de significação do sujeito contemporâneo no espaço virtual. Além disso, verificamos que, de maneira concomitante, o contexto sócio-histórico, relacionado às condições econômicas, também interfere nos estudos da linguagem e se encontra presente na base de algumas teorias linguísticas. Desse modo, compreende-se que o discurso digital ressignificou o funcionamento da língua(gem), tanto no âmbito dos estudos da língua, considerando o contexto do século XX na linguística, quanto na produção e circulação de imagens, como o surgimento da selfie e sua relação com o autorretrato.
Random Natural Gradient
Authors: Kolotouros Ioannis , Wallden Petros
Published: October 2024

Hybrid quantum-classical algorithms appear to be the most promising approach for near-term quantum applications. An important bottleneck is the classical optimization loop, where the multiple local minima and the emergence of barren plateaux make these approaches less appealing. To improve the optimization the Quantum Natural Gradient (QNG) method \cite{stokes2020quantum} was introduced – a method that uses information about the local geometry of the quantum state-space. While the QNG-based optimization is promising, in each step it requires more quantum resources, since to compute the QNG one requires O(m2) quantum state preparations, where m is the number of parameters in the parameterized circuit. In this work we propose two methods that reduce the resources/state preparations required for QNG, while keeping the advantages and performance of the QNG-based optimization. Specifically, we first introduce the Random Natural Gradient (RNG) that uses random measurements and the classical Fisher information matrix (as opposed to the quantum Fisher information used in QNG). The essential quantum resources reduce to linear O(m) and thus offer a quadratic "speed-up", while in our numerical simulations it matches QNG in terms of accuracy. We give some theoretical arguments for RNG and then benchmark the method with the QNG on both classical and quantum problems. Secondly, inspired by stochastic-coordinate methods, we propose a novel approximation to the QNG which we call Stochastic-Coordinate Quantum Natural Gradient that optimizes only a small (randomly sampled) fraction of the total parameters at each iteration. This method also performs equally well in our benchmarks, while it uses fewer resources than the QNG.
Potencia en régimen no-sinusoidal
Authors: Eguíluz Morán Luis Ignacio
Published: October 2024

El objetivo general es el estudio de la problemática del flujo de potencias –tanto para redes monofásicas, como trifásicas– en circuitos distorsionados. Igualmente, se analizaron las técnicas de compensación, y los medidores de potencia y energía, no sólo los utilizados en la actualidad, sino también los que deberían emplearse para la incentivación de la eficiencia en el consumo.
The Effect of Work Motivation, Work Stress and Work Environment on Employee Performance
Authors: Cahyo Kuncoroyekti Priagung Huda , Yuniawan Ahyar
Published: October 2024

The development of well-structured and sustainable human resources is the primary need of an organization, so that a form of human resource management activities emerges where these activities are a forum for employee performance development which is a series of organizational activities that will be directed to attract, develop, and retain an effective workforce. This study aims to determine the influence of work motivation, work stress, and work environment on employee performance (study at Fancy Bakery).  The population used in this study is all production employees in CV. Fancy Bakery which has 124 employees. In this study, a saturated sampling technique is used, namely a sampling technique where all members of the population are used as samples. Then the data collection method through questionnaires and data analysis uses SPSS version 24. The data analysis techniques used in this study include validity test, reliability test, classical assumption test, multiple regression analysis, hypothesis testing using t-test and determination coefficient test (R2). The results of this study show that independent variables, namely Work Motivation (X1), Work Stress (X2) and Work Environment (X3) have a positive and significant effect on Employee Performance (Y). Based on the determination coefficient of 57.3 percent, employee performance can be explained by independent variables in this study, while the remaining 42.7 percent is explained by variables that are not observed in this study.
Material Requirement Planning of Choco Roll Product with Least Total Cost (LTC) and Least Unit Cost (LUC) Method at PT. XYZ
Authors: Marbun Chandra Ericson , Ernawati Dira
Published: October 2024

PT. XYZ  is one of corporate company engaged in the snack industry. One of  PT. XYZ fast moving product is Choco Roll. Choco Roll product have many forming raw material. There is around 20 raw material and packaging material to make 1 box of Choco Roll. Problem that aries at PT. XYZ is in the the supply of raw materials related to the scheduling of ordering raw materials. The company wants to store enough raw material inventory to be able to immediately fulfill all production processes, but this method is not effective and efficient because it will increase storage costs, expiration may occur, and there is a risk of price drops at any time. When a company wants to try to reduce costs by decrease the level of inventory of raw materials on hand, namely the inventory of raw materials that are already in the warehouse, but this condition will disrupt the production process if there are frequent shortages or stockouts of raw materials. Therefore to reduce the inventory cost, it needs to be make best planning in determining the number of ordered of raw material. The company must use a more scheduled raw requirements planning method to obtain a more optimal inventory cost. The purpose of this study is to determine the exact number quantity and order frequency to reduce inventory cost. This research uses lotting technique Least Total Cost (LTC) and Least Unit Cost (LUC) base on Material Requirement Planning (MRP) method. Based on the result of the Choco Roll demand from June 2022 to May 2023 obtained the total inventory cost of IDR 67,683.53 decrease by 25% to company real planning.