bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Middleware to Analyze and Confirm Data Centric Parameters for Data Aggregation in Federated Cloud Environment
Authors: Vikas K Kolekar
Published: November 2024

In the last decade rapid data evolution has contributed to tremendous data movement. The efficient data movement from origin to cloud storage and vice versa leads to the significant performance of application. A software layer situated between user and cloud storage called middleware plays major role in handling data to full fill user goals and requirements. In this paper, proposed a development of middleware frameworks from generic to context aware framework. Explicit importance is given to the adaptation feature of the middleware. A preliminary background is presented to list out importance and details of adaptability of the middleware and curious differentiation in static and self-adaptive adaptation features of the middleware. The literature started with the description of many generic middleware which can be used for building applications in variety of domains and further the limitations imposed are exposed. The issues like multidisciplinary data, vendor lock-in, disparity of service and interoperability related to federated cloud services serving to the fast-developing technology are highlighted. Along with that the use of context aware data management techniques for efficient handling of federated cloud system are defined. An extensive and comprehensive analysis of context-aware middleware is presented. For skilful data management processes need of context aware application with self-adaptation capability is presented especially in response to dynamic situations in federated cloud environment. At the end three techniques to design and develop context-aware middleware are presented. Alongside, four different perspectives of realizing adaptive middleware are also detailed. The presented work concludes with the highlighted need of context aware middleware framework to enhance the performance of federated cloud system.
Enhancing Breast Cancer Detection through Progressive Convolutional Neural Networks with Adaptive Kernel Pooling
Authors: Ms. Jyoti Pandurang Kshirsagar
Published: November 2024

Breast cancer is one of the leading causes of mortality among women worldwide, making early detection a crucial component in reducing fatality rates. Traditional diagnostic methods often rely on manual interpretation of mammograms or histopathological images, which can be time-consuming and prone to human error. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated significant potential in automating and enhancing breast cancer detection. Breast cancer detection remains a critical challenge in medical diagnostics, where early identification can significantly improve patient outcomes. Leveraging advanced deep learning techniques, this research proposes a novel approach by integrating Progressive Convolutional Neural Networks (PCNNs) with Adaptive Kernel Pooling (AKP) for enhanced image classification. The model architecture dynamically adjusts convolutional filter sizes across layers to capture both coarse and fine-grained features, while AKP adapts pooling operations based on spatial dimensions, optimizing feature retention. Through rigorous experimentation on a large breast cancer histopathological dataset, the proposed method achieved an impressive accuracy of 98%, demonstrating its potential in real-world clinical applications. The high accuracy indicates the model's robustness in differentiating between malignant and benign tissues. This approach not only enhances detection precision but also addresses the challenge of feature loss during pooling, a common issue in traditional Convolutional Neural Networks (CNNs). Additionally, this study contributes to the growing field of medical imaging by offering an adaptable and scalable architecture that could be employed across various medical diagnostics beyond breast cancer detection. The proposed PCNN-AKP framework sets a new benchmark for automated cancer detection models, providing a pathway for future innovations in the use of deep learning for healthcare diagnostics.
Anomaly Detection in Video Surveillance: A Comparative Analysis of Deep Learning Models
Authors: Sangita Mahendra Rajput
Published: November 2024

Anomaly detection in video surveillance is critical for enhancing security and public safety across various applications, including traffic monitoring, public spaces, and industrial settings. Traditional methods often struggle with the complexity and variability of real-world data, prompting a shift towards advanced machine learning models. This paper presents a comprehensive analysis of deep learning algorithms, including YOLOv5, 3D CNNs, LSTM, Deep SVDD, Vision Transformers, Temporal Transformers, and Autoencoders, applied to three benchmark datasets: CIFAR-10, MVTec AD, and UCSD Anomaly Detection. We compare these algorithms based on accuracy, precision, recall, and F1-score, providing insight into their strengths and weaknesses. The results suggest that Vision Transformers and CNN-LSTM hybrids offer superior performance across spatial and temporal anomaly detection tasks.
Assessment of Bitumen Paver and HMP Efficiency for a Road Project
Authors: Ghanasham C. Sarode
Published: November 2024

One of the important and dependable sectors of the national economy is the equipment used in highway construction. In the process of building constructions, efficient utilization of labour, supplies, and machinery must be fervently pursued through competent project management. The adoption of innovative procedures and new equipment has resulted in a dramatic advance in construction technology in recent decades. The selection of the right type and size of construction equipment typically affects how productive a project is on the job site. Thus, it is essential that site managers and construction planners understand the primary equipment categories that are most frequently used in construction. Technical aspects and working conditions are taken into consideration in the article's method of calculating the operational productivity and cost of asphalt pavers. This increases the accuracy of decisions made about how to organize and manage highway maintenance activities on Chikhali-Tarsod NH-6.
BES-Optimized SMOTE Variant to Improve Dataset Scaling for Enhanced Privacy-Preserving Classification
Authors: Vijayendra S.Gaikwad
Published: November 2024

The research paper undertakes a comprehensive comparative assessment of various Synthetic Minority Over-sampling Technique (SMOTE) variants in the context of medical datasets. The study is dedicated to mitigating the challenges posed by imbalanced data in medical applications. The performance of SMOTE, SMOTE-ENN, Borderline SMOTE, SMOTE-Tomek Links, Safe Level SMOTE, Density-based SMOTE, SMOTE-Rough Set Theory, and a developed BES-Optimized SMOTE are evaluated across five distinct medical datasets such as heart disease, arrhythmia, sick euthyroid, hypothyroid and breast cancer. Additionally, the K-Nearest Neighbors (KNN), Decision Forest (DF), Random forest (RF), and Support Vector Machine (SVM) algorithms are utilized to analyze the performance of SMOTE variants on each dataset, where the developed Bald Eagle search (BES) optimized SMOTE shows superior performance than other SMOTE variants, indicating its effectiveness in generating balanced scaled datasets. Subsequently, the BES-optimized SMOTE is chosen as the most effective SMOTE variant to scale and balance the respective datasets. Following a thorough examination of results, datasets that are appropriately scaled with the selected SMOTE variant are employed for ensuing research endeavors. These refined and balanced datasets will serve as the foundation for performance analysis of our future work on efficient privacy preserving KNN classification. To facilitate this, the scaled datasets in this work will be encrypted using the Paillier Encryption Scheme.
Modelling and Analysis of Advanced Dynamic Learning Techniques and Anisotropic Diffusion Filtering for Automated Crack Detection in Civil Structures
Authors: Rahul B. Diwate
Published: November 2024

This study introduces a cutting-edge automated system that leverages dynamic learning methods for detecting cracks in civil structures. By combining pioneering preprocessing techniques with leading-edge machine learning algorithms, the proposed system aims to surpass the constraints of conventional manual inspection approaches. The methodology incorporates Anisotropic Diffusion Non-Local Mean Filtering (ADNLMF) for noise reduction and YOLO v3 for precise object detection. The system's effectiveness is substantiated using an extensive dataset, displaying substantial enhancements in both accuracy and efficiency.
Neural Architecture Search: Automating the Design of Convolutional Models for Scalability
Authors: Neha J. Zade
Published: November 2024

Neural Architecture Search (NAS) has changed the way convolutional neural networks (CNNs) are designed and optimized by providing an automatic way to make models that are scalable and perform well. Traditional ways of making CNN designs depend on a lot of expert knowledge and a lot of hand tuning, which can make them less efficient and scalable. NAS simplifies this process by using powerful search algorithms to look through huge architecture areas and find the best models for each job. This method not only makes CNNs more scalable, but it also speeds up the planning process, which means that highly efficient systems can be made with little help from humans. Most of the time, NAS uses gradient-based methods, genetic algorithms, or reinforcement learning to find and analyze possible designs. NAS can quickly look through big search areas using these methods, which make the best use of measures like accuracy, delay, and processing cost. So, NAS has shown that it can perform better than models that were made by hand in a number of situations, such as picture classification, object recognition, and segmentation. When it comes to scaling, NAS adds freedom to model design, so designs can be changed to fit different hardware and application settings, such as cloud-based systems and edge devices. NAS speeds up testing and feedback by automating the design search. This cuts down on the time and work needed for model development. This flexibility is very important in current deep learning apps, which need models that can work on a lot of different systems. NAS has created new design patterns, like brain cell structures and efficient building blocks, that have raised the bar for how well and efficiently models work. Because of these improvements, NAS has become an important part of neural network design, pushing the limits of what is possible in terms of implementation and growth. This essay looks at the ideas behind NAS, how it affects the scaling of CNN, and how future progress could be made in automatically designing neural networks for a wide range of uses.
Mechanical Characters of Geo-Polymer Concrete for Major Alteration of Cement by Fly Ash
Authors: Vishal Gajghate
Published: November 2024

Fly Ash (FA) production exceeded 250 million tons in India by the year 2020 and it would exceeds 450 million tons by the year 2025. This creates huge problems for storage of waste. And it requires hectors of lands to store. It is classified in Class – C and Class – F. Class – C has Pozzolanic properties and self-Cementitious rather to Class – F which attains the same properties by addition of activator. Alkali and Sulfate content in Class – C are higher. Use of Major volume fly ash in addition to lime and admixtures may be permitted to act as Pozzolanic Binder in ordinary concrete to form Geo-Polymer Concrete (GPC). Basically GPC, a Green Concrete (Eco-Friendly Concrete) also made in another way by adding FA, Rice Husk Ash (RHA), Steel Furnace Slag (SFS) and Alkali Activators (AA) following the same process as that of OPC. As the previous studies focuses on strength evaluation of Class – C. The prime objective of this study to examine the comparative characteristics of major alteration of Class – F with Low Calcium GPC subjected to the Tropical Environment with OPC. The findings are observed in the term of Consistencies and it varies from 31 % to 45 % for 0 % alteration of cement to 80 % and lowering and undefined value for 90 % alteration and 100 %. The consistency findings are also observed in progressive range of 32 % to 39 % for addition of Dry Lime from 0 % to 4.5 % (Table - 8) and undefined for 5 % addition. Later on it is observed for 10 % and 15 % addition which shows progressive results. Compression Character (Table - 10) and Flexural Character (Table - 11) is determined for various percentage alteration of PFA and it shows progressive results up to 80 % alteration and percentage difference of 69 % to 85 % for 90 % alteration.
Implementing Swarm Robotics for Coordinated Multi-Agent Systems in Search and Rescue Operations to Improve Efficiency and Success Rates in Disaster Response
Authors: Harsha Avinash Bhute
Published: November 2024

When there is a crisis, search and rescue efforts that are quick and successful can mean the difference between life and death. But standard search and rescue methods often have a hard time in emergency areas because they are so complicated and hard to plan for. This study suggests using swarm robots for organized multi-agent systems in search and rescue operations to deal with these problems. The objective is to form these operations more effective and effective. Swarm robots employments the thoughts of self-organization and decentralized control that came from studying social bug colonies to form frameworks with numerous free specialists that can act as a entirety. These bots, which are as a rule exceptionally basic and as it were have fundamental sense and talking capacities, work together without any issues to reach their objectives. These mechanical frameworks are solid, adaptable, and adaptable since they utilize the control of swarm insights. These are all imperative qualities for dealing with changing and perilous fiasco settings. One of the finest things almost utilizing swarm robots for look and protect is that it lets you rapidly cover huge zones. Conventional strategies that depend on a little bunch of organized robots or human labourers may not be able to look expansive calamity scenes rapidly. Swarm robots, on the other hand, lets you send out numerous units that can investigate the area at the same time, making it simpler to reach and quicker to reply. Swarm mechanical technology is additionally more strong when things go off-base or the world changes since it isn't controlled . The system can change and reform without centralized control if individual workers run into problems or run into problems. This keeps the search and recovery efforts going.
Data Pipeline Integrating Apache Kafka and Rabbit MQ
Authors: Greeshma Arya
Published: November 2024

Amidst the dynamic realm of big data, the convergence of Apache Kafka and RabbitMQ into a cohesive symphony of data orchestration represents a seminal undertaking. This ambitious research project endeavors to harmonize these potent technologies, employing sophisticated methodologies such as data batching and compression to maximize their collaborative potential. The resulting amalgamation, a vanguard in big data, pledges to redefine the frontiers of data management and messaging systems. This integration affords unparalleled flexibility in selecting the most apt tool for a given task without compromising on performance or reliability. Furthermore, a unified management and monitoring interface empowers administrators and developers with comprehensive insights, simplifying the intricate orchestration of these two distinct but complementary platforms [1]. Ultimately, this research seeks not merely to unify Apache Kafka with RabbitMQ but to usher forth a paradigm where their union transcends the individual, revolutionizing the landscape of big data processing and transmission.
Automated Lymph Node Classification and Gastric Cancer Segmentation in CT Images Using Advanced Segmentation Techniques
Authors: Sugat Ramrao Pawar
Published: November 2024

The growing number of cases of stomach cancer makes it even more important to have accurate and quick diagnosis tools. This study uses cutting edge image processing techniques to try to solve this problem. The proposed method study uses a wide range of CT pictures that show different types of patients and imaging conditions. The method used to make sure the reliability of later studies includes pre-processing steps like noise reduction and intensity adjustment. For lymph node classification, the method created a complex program with advanced features that made it easier for lymph nodes to be automatically found and put into groups in the pictures. Similarly, the method used cutting-edge segmentation tools to correctly separate cancerous areas in the stomach cancer segmentation. The suggested method has been thoroughly tested using standard criteria, showing that it can accurately classify lymph nodes and divide stomach cancer into segments. Comparing the deep learning method to existing ones showed that it was better, showing that it has the ability to make a big difference in how medical picture analysis is done now. As part of the conversation, the proposed method strengths and weaknesses were emphasized, along with how the results were interpreted. The results show that the deep learning model could have a positive effect on patients by making diagnoses more accurately, making examination easier for the doctors, and eventually better patient outcomes. The proposed method for automatic lymph node labelling and the stomach cancer segmentation system are both exciting steps forward in the field of medical image analysis. The paper study's strong points and high levels of accuracy using deep learning network show that this approach has the ability to completely change how stomach cancer is diagnosed, leading to better patient care and results.
Advanced Techniques in Post-Quantum Cryptography for Ensuring Data Security in the Quantum Era
Authors: Latika Rahul Desai
Published: November 2024

The arrival of quantum computers is a major threat to current security methods, which depend on problems like discrete logarithms and integer factorization being hard. Post-quantum cryptography (PQC) is getting to be an critical field for making secure strategies that can't be broken by quantum assaults. This exposition talks almost progressed PQC strategies, centered on the most up to date thoughts and what they cruel for securing information within the quantum age. To begin with, we see at lattice-based cryptography, which appears like a great choice since it has solid security roots and can be utilized in numerous circumstances. This incorporates strategies such as Learning With Mistakes (LWE) and Ring-LWE, which are exceptionally great at ensuring against quantum dangers and are simple to utilize. Another critical zone is code-based cryptography, which employments the trouble of breaking irregular straight codes as an case. The McEliece cryptosystem is one such framework. It is checked to see how valuable and successful these strategies are in real-life circumstances. We see into multivariate polynomial encryption, which builds security on the truth that it's difficult to illuminate sets of multivariate quadratic equations. People are particularly curious about this strategy since it may be utilized to form proficient marking plans. Another imperative strategy is hash-based cryptography, which employments hash capacities to create secure advanced marks. The consider moreover talks around blended cryptography frameworks that utilize both conventional and post-quantum strategies. These frameworks make beyond any doubt that the switch to quantum computing goes easily and give way better security. We see at the issues that come up with implementation, like additional work that has to be done on the computer and joining it with current frameworks, and come up with ways to urge around these issues. At long last, moving to post-quantum security is necessary, but it comes with a part of problems that ought to be illuminated in numerous ways.
A Comparative approach of Depression Detection: A state of Art
Authors: Ganesh D. Jadhav
Published: November 2024

Depression is an unconscious state of mind; it directly affects the mental health condition of patients, so it is required to accurately detect with a certain time to prevent severe depression. Depression detection with traditional approaches includes the patient's interviews and self-reporting to doctors or psychotherapists, but this approach faces challenges like time consumption and subjectivity. Another approach is to detect depression by using some computational techniques, including ‘machine learning (ML)’, ‘deep learning’, ‘natural language processing’, and also some wearable technologies or devices. The study compares the hybrid ensemble model and the late fusion method with other classifiers that are used for depression detection based on various performance parameters, like ‘accuracy’, classification report, ‘mean squared error’, and R2 score  , etc. This study also discusses some ethical terms, such as the privacy of patient data and the risk of misdiagnosis. The study highlights the multimodal approach of various modality to build an effective and personalized system for depression detection.
Tổng quan vai trò tiêm corticosteroid xuyên màng nhĩ với cải thiện triệu chứng chóng mặt và thính lực trong bệnh ménière
Authors: Thuỷ Nguyễn Thị , Hiếu Phạm Trung , Thái Nguyễn Quang , Hưng Đỗ Bá
Published: November 2024

Đánh giá hiệu quả của phương pháp tiêm corticosteroid xuyên màng nhĩ với thính lực và triệu chứng chóng mặt trong điều trị bệnh Ménière (MD). Nghiên cứu tổng quan luận điểm bao gồm các nghiên cứu từ nguồn cơ sở dữ liệu trang thông tin điện tử Pubmed, Cochrane Library. Tiêu chí lựa chọn là những nghiên cứu sử dụng phương pháp tiêm Corticosteroid xuyên màng nhĩ trong điều trị bệnh Ménière. Chúng tôi đã tìm được 16 bài báo phù hợp với các tiêu chuẩn lựa chọn liên quan đến 1474 bệnh nhân với 2 loại thuốc tiêm vào hòm nhĩ khác nhau bao gồm: Dexamethasone, Methylprednisolone. Chúng tôi đã tiến hành phân tích về các đặc điểm và hiệu quả trong điều trị của các loại thuốc trên. Mặc dù, hiệu quả còn nhiều tranh luận nhưng tiêm Corticosteroid xuyên màng nhĩ tương đối an toàn và có hiệu quả kiểm soát tình trạng chóng mặt mặc dù giảm dần theo thời gian. Không thấy sự cải thiện đáng kể về khả năng thính giác.
Sản xuất mẫu ngoại kiểm tra chất lượng xét nghiệm đếm tế bào máu ngoại vi
Authors: Hùng Nguyễn Hữu , Dung Đặng Thị Ngọc , Dũng Nguyễn Ngọc , Hường Đỗ Thị , Hảo Nguyễn Thị , Thảo Lê Thanh
Published: November 2024

Mẫu ngoại kiểm đóng vai trò quan trọng trong ngoại kiểm tra chất lượng xét nghiệm. Nghiên cứu nhằm sản xuất mẫu ngoại kiểm cho xét nghiệm đếm tế bào máu ngoại vi. Ba lô mẫu (24 mẫu/lô, V = 2mL) với mức số lượng tế bào khác nhau đã được sản xuất dưới dạng máu toàn phần mô phỏng, chứa các thành phần: tế bào hồng cầu người cố định bởi Glutaraldehyde 0,015%, tế bào hồng cầu gà cố định bởi Glutaraldehyde 0,18% (để mô phỏng bạch cầu người), tế bào tiểu cầu người cố định bởi Glutaraldehyde 0,007% và huyết thanh người bổ sung kháng sinh. Mẫu sản phẩm được đánh giá chất lượng theo tiêu chuẩn ISO 13528:2015trên hai tiêu chí: độ đồng nhất và độ ổn định. Kết quả thu được cho thấy sản phẩm đạt độ đồng nhất trên 11 thông số: RBC, HGB, HCT, MCV, MCH, MCHC, RDW%, WBC, PLT, MPV, PCT. Trong điều kiện vận chuyển (khoảng cách vận chuyển > 600km, thời gian vận chuyển < 72h), sản phẩm đạt độ ổn định trên 10 thông số: RBC, HGB, HCT, MCV, MCH, MCHC, WBC, PLT, MPV, PCT. Trong điều kiện bảo quản 2 - 8oC sản phẩm đạt độ ổn định đến 4 tuần trên 6 thông số: RBC, WBC, PLT và HGB, MCH, PCT.
Kết quả hóa chất dẫn đầu Gemcitabine - Cisplatin theo sau bởi hóa xạ trị ung thư vòm mũi họng giai đoạn III-IVA tại Bệnh viện K
Authors: Đăng Nguyễn Văn , Chi Đỗ Huyền
Published: November 2024

Ung thư vòm mũi họng là một trong những bệnh ung thư vùng đầu cổ phổ biến nhất ở Việt Nam. Ở giai đoạn tiến triển, hóa xạ trị đồng thời là điều trị nền tảng với tỷ lệ kiểm soát tại chỗ tại vùng cao. Tuy nhiên, thất bại điều trị chủ yếu do di căn xa, đặt ra vấn đề bổ sung liệu pháp toàn thân. Trong khi vai trò của hóa chất bổ trợ chưa rõ ràng, hóa chất dẫn đầu là hướng điều trị mang lại kết quả khả quan. Đây là nghiên cứu trên 57 bệnh nhân ung thư vòm mũi họng giai đoạn III-IVA điều trị hóa chất dẫn đầu Gemcitabine-Cisplatin theo sau bởi hóa xạ trị tại Bệnh viện K từ 2019 đến 2021. Nghiên cứu ghi nhận tỷ lệ đáp ứng sau hóa chất dẫn đầu đạt 94,8%, đáp ứng hoàn toàn sau điều trị đạt 86%. Tỷ lệ sống thêm 1 năm và 2 năm là 95,8% và 79,8%. Ít gặp tác dụng phụ cấp và mãn tính độ ≥ 3. Nghiên cứu cho thấy phác đồ hóa chất dẫn đầu gemcitabine-cisplatin theo sau bởi hóa xạ trị cho tỷ lệ đáp ứng cao và khả năng dung nạp tốt.
Liên quan giữa kết quả mật độ xương và nồng độ canxi máu ở phụ nữ mãn kinh
Authors: Nghĩa Trần Trung , Tùng Lê Đình , Tuệ Nguyễn Trọng
Published: November 2024

Loãng xương là một hội chứng với đặc điểm sức bền của xương bị suy giảm dẫn đến gia tăng nguy cơ gãy xương. Nhằm cung cấp thêm thông tin cho các bác sĩ lâm sàng trong chẩn đoán, điều trị và dự phòng sớm loãng xương, nghiên cứu tiến hành với mục tiêu: Xác định mối liên quan giữa mật độ xương và nồng độ canxi máu ở phụ nữ mãn kinh. Nghiên cứu mô tả cắt ngang trên 306 phụ nữ mãn kinh đến khám sức khỏe định kì tại Bệnh viện Đại học Y Hà Nội từ tháng 12/2023 đến tháng 5/2024. Tất cả các đối tượng nghiên cứu được làm xét nghiệm nồng độ canxi máu và đo mật độ xương bằng phương pháp DXA tại vị trí cổ xương đùi và đốt sống thắt lưng. Kết quả nghiên cứu của chúng tôi cho thấy tỉ lệ giảm mật độ xương, loãng xương ở phụ nữ mãn kinh chiếm tỉ lệ cao trong khi tỉ lệ bất thường về nồng độ canxi ion cũng như canxi toàn phần lại thấp (p < 0,05). Do vậy, trong thực hành lâm sàng, để dự phòng và điều trị loãng xương ở phụ nữ mãn kinh cần theo dõi kết quả mật độ xương ngay cả khi có nồng độ canxi ion hoặc canxi toàn phần trong máu trong giới hạn bình thường.
So sánh kết quả tạo phôi trong thụ tinh ống nghiệm giữa phác đồ kích thích buồng trứng có mồi Progestin và GNRH-Antagonist tại Bệnh viện Đa khoa Tâm Anh
Authors: Trang Đỗ Thị Thu , Đạt Đỗ Tuấn , Hoàng Lê , Phương Giáp Thị Mai , Anh Phí Thị Tú , Thắng Lê Đức , Hiếu Nguyễn Phúc , Anh Phạm Thị
Published: November 2024

Nghiên cứu hồi cứu 730 phụ nữ vô sinh thực hiện IVF, gồm hai nhóm: nhóm sử dụng PPOS với dydrogesterone (n = 365) và nhóm sử dụng GnRH-ant (n = 365), nhằm so sánh hiệu quả của hai phác đồ kích thích buồng trứng. Kết quả không có sự khác biệt đáng kể về các đặc điểm cơ bản (tuổi, BMI, AMH, AFC) giữa hai nhóm nghiên cứu. Số lượng phôi ngày 3 chất lượng tốt (4,85 ± 3,8 vs 4,89 ± 3,52), số lượng noãn, noãn trưởng thành (MII), phôi ngày 3 và phôi nang thu được là tương đương giữa hai nhóm PPOS và GnRH-ant. Tổng liều FSH không có sự khác biệt. Tuy nhiên, nồng độ estradiol vào ngày tiêm hCG ở nhóm PPOS cao hơn có ý nghĩa thống kê so với nhóm GnRH-ant. Không ghi nhận trường hợp OHSS từ mức độ trung bình đến nặng ở cả hai nhóm. Kết luận phác đồ PPOS sử dụng dydrogesterone mang lại kết quả tương đương với phác đồ GnRH-ant về số lượng và chất lượng noãn, phôi. PPOS là một phác đồ kích thích buồng trứng an toàn, thuận tiện và có thể là một lựa chọn thay thế hiệu quả cho GnRH-ant trong IVF.
U nhầy ruột thừa: Thông báo lâm sàng và tổng quan y văn
Authors: Sơn Trần Quế , Thành Nguyễn Tuấn
Published: November 2024

U nhầy ruột thừa là bệnh ít gặp, thường lành tính đặc trưng bởi tăng sinh chất nhầy tích tụ trong lòng ruột thừa, bệnh có nguy cơ ác tính. Chẩn đoán, kỹ thuật mổ và theo dõi sau mổ còn nhiều tranh luận. Chúng tôi thông báo ca bệnh nữ 72 tuổi, vào viện vì đau bụng vùng hố chậu phải 4 ngày, không sốt, không nôn. BMI 18,2 kg/m2, bạch cầu 12,8 G/L (trung tính 85,3%). Nội soi đại tràng thấy khối 4x4cm đẩy lồi vào trong, bề mặt nhẵn nằm ở gốc ruột thừa. Chụp cắt lớp vi tính bụng: đường kính ruột thừa là 28mm, thành dày có vôi hóa, trong lòng chứa dịch có tỷ trọng 17HU. Người bệnh được mổ nội soi cắt đáy manh tràng kèm ruột thừa bằng máy cắt nối tự động (băng ghim 60mm và 45mm). Thời gian mổ và nằm viện là 55 phút và 4 ngày, không biến chứng. Giải phẫu bệnh sau mổ là u nhầy ruột thừa độ thấp, không thấy tế bào ác tính. Kết luận: cắt bỏ ruột thừa và một phần manh tràng bằng phẫu thuật nội soi có sử dụng máy cắt tự động là phương pháp ít xâm lấn, an toàn và hiệu quả đối với phần lớn các ca bệnh u nhầy ruột thừa.
Tác dụng chống viêm của bài thuốc bại độc tán gia vị trên động vật thực nghiệm
Authors: Anh Đặng Hồng , Quyên Đỗ Linh , Lợi Vũ Đức
Published: November 2024

Bại độc tán gia vị là bài thuốc được hình thành trên bài thuốc cổ phương Bại độc tán, gia thêm bốn dược liệu gồm Dây đau xương, Cốt khí củ, Lá khôi, Ô tặc cốt. Nghiên cứu được thực hiện trên động vật thực nghiệm với mục tiêu đánh giá tác dụng chống viêm cấp và chống viêm mạn của bài thuốc Bại độc tán gia vị. Tác dụng chống viêm cấp được đánh giá trên hai mô hình: gây phù chân chuột và gây viêm màng bụng trên chuột cống trắng chủng Wistar với thuốc đối chứng là diclofenac đường uống liều 10 mg/kg/ngày. Tác dụng chống viêm mạn được đánh giá bằng mô hình gây viêm mạn bằng u hạt amiant trên chuột nhắt trắng chủng Swiss với thuốc đối chứng là prednisolon đường uống liều 5 mg/kg/ngày. Kết quả nghiên cứu cho thấy, Bại độc tán gia vị ở cả hai liều 21 g/kg/ngày và 42 g/kg/ngày đều thể hiện xu hướng tác dụng chống viêm cấp trên các mô hình. Bại độc tán gia vị liều 72 g/kg/ngày thể hiện tác dụng chống viêm mạn rõ rệt thông qua khả năng làm giảm trọng lượng khối u hạt.
Application of Hierarchical Bayesian Models for Modeling Economic Costs in the Implementation of New Diagnostic Tests
Authors: Karel Tomáš , Plašil Miroslav
Published: November 2024

The COVID-19 pandemic has highlighted the need for reliable and rapid diagnostic tests to control the spread of infection. The introduction of new rapid antigen tests often goes in tandem with the limited data availability, making it challenging to assess their performance at the initial phase of the pandemic. Sensitivity and specificity, the key performance characteristics provided by manufacturers, are typically derived under laboratory conditions and may not accurately reflect the tests' performance in field settings. We use the hierarchical Bayesian model to obtain their realistic estimates in real world conditions and show how it may be used in situations in which new tests with limited history are presented on the market. Proposed methodology allows for the efficient information pooling, thereby improving on the accuracy of parameter estimates for new tests. The results suggest that the application of hierarchical model on the Czech data led to a considerabile reduction in uncertainty associated with the parameter estimates as well as with potential economic cost implied by false positive test results. The model can thus assist in better informed decision-making and financial planning of both the government and corporations.
Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market
Authors: Mec Michal , Zeman Mikulas , Cermakova Klara
Published: November 2024

Using neural networks in economics time series data is a new unexplored field. Lot of companies and economics research use mostly logistic regression or statistical approach when they try to predict the movement of stock market. Neural networks became a frequent tool for prediction in recent years and this approach has been confirmed to provide more reliable and better solutions when it comes to prediction and accuracy power. Within a wider context of current debate on neural networks employment in stock market predictions, we suggest an innovative methodology based on the combination of neural networks. In our analysis we use Wasserstein Generative Adversarial Network (WGAN) on Germany stock market as an example. We present how the trading strategy could be established on the prediction of the model and how it can be compared with other models in terms of returns. Overall, the WGAN monthly prediction outperformed Random Forest by 36%, benchmark by 32% and LSTM by 26% in the testing period. Our results also suggest that the WGAN model has on average higher returns than pure investment into index. Furthermore, WGAN is less volatile, which is always the preferred option for investors. Using neural networks for stock index prediction and confirming that WGAN investment strategy brings higher returns compared to generally used models is our main contribution to the current debate.
Lessons Learned From the Implementation of an Integrated Health and Social Care Child and Family Hub – a Case Study
Authors: Loveday Sarah , White Natalie , Constable Leanne , Gates Anthony , Sanci Lena , Goldfeld Sharon , Hiscock Harriet
Published: November 2024

Introduction: Childhood adversity is associated with poor physical and mental health outcomes across the lifespan. Integration of health and social care may provide a solution to childhood adversity through practices of better detection and response. There is growing interest in the creation of child and family hubs that integrate health and social care but little literature that describes the development process.
Description: We aimed to evaluate and describe the implementation of a co-designed health and social care child and family hub in Victoria, Australia. Rapid ethnographic methodology was used to iterate the hub components. Practitioners and researchers co-created solutions to barriers identified during implementation.
Discussion: There were five key learnings: (i) Practice change takes time and intensive coaching, (ii) Lived experience is a powerful motivator for practice change, (iii) Integration of services requires more than co-location to break down silos, (iv) Reflective practice is a key driver of practice change, and (v) Using real time data enabled rapid implementation change and directly informed the development of solutions.
Conclusions: Maintaining and developing practice change during implementation requires time and access to a broad range of data to enable iteration and the development of solutions.
Rezension von: Gmelin, Julius (Hrsg.), Hällische Geschichte
Authors: Meißner Heinrich , Gmelin Julius
Published: November 2024

Geschichte der Reichsstadt Hall und ihres Gebiets von Dr. Jul. Gmelin, Pfarrer in Grossaltdorf: Hall bei Ferdinand Staib. 1896-98. Mit einer Karte der weiteren Umgebung von Hall und zwei Radierungen (Michaeliskirche und Koinburg). 830 Seiten.
Three Decades of Green: A Comprehensive Analysis of India’s Forest Cover Dynamics from 1990 to 2021 Based on World Bank Data
Authors: Philip Amal , Elzabath K. Nirupama
Published: November 2024

This study basically focuses on an overview of the dynamics of change of forest cover in India using data from the World Bank from 1990 to 2021. This research involves the quantification and analysis of trends in forest cover across different states and classes of forest types, drivers of change, and, finally, assessing the impact of conservation policy. Using World Bank reports, we adopted statistical modeling tools to assess the changes in forest cover, density, and composition. The study integrated other databases on the determinants of socioeconomics, climatic variables, and policy implementations to get an insight into the complex interplay of human activities, environmental conditions, and forest ecosystems. The results bring out a more nuanced picture of changes in forest cover across the country: Presently, some states show significant gains due to successful state policy related to afforestation, capsule restoration programs, and strict protection being followed, while in others, there were persistent pressures of deforestation, linked to urbanization, agricultural expansion, and infrastructure development. Among the various forest types, the denser forests were relatively more resilient to change compared to open and scrub forests, which showed a higher level of vulnerability to anthropogenic and-climate-induced pressures. Policy impact analysis results suggest that while the forest management at community level and the intensity of site-specific conservation interventions yielded effective results, there is also concern regarding qualitative maintenance of forests and biodiversity amidst quantitative increase in forest covers. The value that this research brings is in adding more scope to understanding India’s forest dynamics and in offering, through evidence-based recommendations, policy tools for policymakers. Specific strategies and appropriate management must be a priority in regional needs, perspective monitoring, and integrated land-use planning that can ensure sustainability in forest management and conservation in the face of growing environmental challenges.
To determine the antioxidant mechanism of action, biochemical compounds in Tecoma stans (L. Juss. ex Kunth) were analyzed using GC-MS, HPLC, UV-VIS, and FTIR techniques
Authors: T. Durgadevi  , K. Devika  , G. Anburaj  , Valliammai C. Thillaiyadi
Published: November 2024

The alkaloids found in Tecoma stans (L.Juss.ex Kunth) are well-known and have been utilized extensively in traditional Chinese medicine. On the other hand, the phenolic compounds have not been sufficiently studied. Thus, the objective of this study was to qualitatively and quantitatively examine the phenolic chemicals found in the plant’s roots and leaves. Either methanol or ethanol were used in the extraction processes; however, ethyl acetate and water worked better than methanol and ethanol. Phenolic chemicals were more abundant in the leaves than in the roots. The DPPH technique yielded values for the antioxidant activity of 46.59 g/ml, 40.51 g/ml, and 34.89 g/ml, in that order. The existence of phenolic compounds was confirmed by FTIR and the UV-VIS spectra. Using negative and positive ionization, GC-MS was used to examine the phenolic profile. A total of 15 compounds were identified, and HPLC analysis revealed the existence of 8 compounds that would be useful in pharmaceutical and medical applications.
An overview of crop residue management challenges, opportunities, and the way forward for sustainable food-energy security in India
Authors: Kumar Mukesh , Patle G.T. , Lal Mohan , Kumar Abhishek , Rani Komal , Kumar Sajjan , Kumar Surender
Published: November 2024

Crop residue plays an essential role in preserving vital agro-environmental services. High-yielding cultivars employed in current agricultural agriculture have significantly increased crop residue and the challenges connected with its management. In developing countries like India, burning crop remnants was commonly encouraged, especially during critical periods between two crops, such as when wheat follows rice and sugarcane harvests, due to the unsuitability of various crop residues for animal feed. Long-term residue burning provides considerable challenges to maintaining the quality of natural resources and a sustainable crop production system, particularly considering climate change. When agricultural residue is burned, its essential components are lost and converted into dangerous air pollutants, which would otherwise be able to power and locate the soil-plant system. The current study overview critically explores the potential of agricultural residue to produce soil ameliorants using thermal decomposition and composting processes. A brief description of residue-derived energy generation (liquid and gaseous fuels) is also given via thermal and thermochemical breakdown processes. This may mitigate the detrimental consequences of fossil fuels on the nation’s ecosystem and economy. The review study also discusses modern industrial uses of crop leftovers and current challenges and areas for future development in sustainable agricultural residue management.
Adsorption of Nickel (II) ions from aqueous solution using Potassium Carbonate activated Tectona grandis leaves carbon
Authors: C. Pratheep  , K. Ramesh  , M. Kiruthiga  , M. Saravanan  , M. Sugirtha  , K. Suridha
Published: November 2024

Activated carbon has been prepared from Tectona grandis leaves and characterized. This carbon is ready to sorb metal ions from its aqueous solution. It has been noted that a increasing the carbon dose with constant Ni (II) ion solution concentration, or a rise in the Ni (II) ion solution concentration with constant carbon concentration resulted in a higher and lower nickel uptake per unit weight of carbon respectively. The Langmuir and Freundlich isotherm models of this adsorption system were studied and reported. The surface adsorption capability (Q0 ) calculated from Langmuir isotherm was 38.025 mg Ni(II) g”1 at initial hydrogen ion concentration of 20 mg/l at 305 K for the particle size 150 300ìm. Dynamics of the system was studied with linearised forms of Lagergren, Ho and Webber Morris models.
ЭКОТУРИЗМ КАК НАПРАВЛЕНИЕ ОБЕСПЕЧЕНИЯ УСТОЙЧИВОГО ХАРАКТЕРА ТУРИСТСКОЙ ДЕЯТЕЛЬНОСТИ
Authors: Е.Н. ЗАХАРОВА , М.З. АБЕСАЛАШВИЛИ , М.Е. ОРДЫНСКАЯ
Published: November 2024

В статье рассмотрены вопросы, касающиеся развития экологического туризма в современных условиях и его роли в части обеспечения устойчивости регионов, в том числе Республики Адыгея. Экотуризм основан на удовлетворении потребностей туристов, туристского сектора и принимающих туристские потоки территорий с учетом текущих и потенциальных экономических, социальных и экологических последствий. Актуальность исследования объясняется тем, что в настоящее время экотуризм развивается в три раза быстрее, чем туристский сектор в целом. Проведена оценка основных доминант экотуризма в Республике Адыгея. Выработаны рекомендации, способствующие развитию туризма как направления обеспечения регионального устойчивого развития.

The paper considers issues related to the development of ecological tourism in modern conditions and its role in ensuring the sustainability of regions, including the Republic of Adygea. Ecotourism is based on meeting the needs of tourists, the tourism sector and territories receiving tourist flows, taking into account current and potential economic, social and environmental consequences. The relevance of the study is explained by the fact that ecotourism is currently developing three times faster than the tourism sector as a whole. An assessment of the main dominants of ecotourism in the Republic of Adygea was carried out. Recommendations were made to contribute the development of tourism as a direction for ensuring regional sustainable development.
Perencanaan Wisata Trekking Gunung Rungking untuk Mengangkat Potensi Desa Cigunungsari
Authors: Abidin Jenal , Sahara Lala Siti , Hasan Rinie Octaviany
Published: November 2024

Desa Cigunungsari berada di Kecamatan Tegalwaru, Kabupaten Karawang, Jawa Barat. Desa ini memiliki banyak potensi wisata, terutama untuk budidaya bonsai dan ekowisata. Desa Cigunungsari terletak di kaki gunung Sanggabuana yang termasuk dalam wilayah Karawang Selatan yang memiliki beberapa objek wisata alam yang cukup ramai dipadati oleh pengunjung pada setiap akhir pekan salah satunya adalah Curug Cigentis. Desa Cigunungsari menawarkan suasana alam yang alami dan indah ini memiliki potensi besar untuk berkembang menjadi destinasi wisata yang menarik dan berkelanjutan yang dapat meningkatkan ekonomi lokal melalui pengembangan wisata yang berbasis Masyarakat sebagai pelengkap kegiatan berwisata bagi pengunjung yang tidak hanya mengandalkan kepada wisata Curug Cigentis. Dalam pengembangan potensi wisatanya, permasalahan yang ditemukan diwilayah Karawang Selatan ini masih terbatasnya produk dan aktifitas wisata yang ditawarkan. Sehingga perlu adanya pengembangan daya tarik wisata untuk produk dan pasar wisata yang lebih luas. Pengabdian Kepada Masyarakat ini dilaksanakan sebagai bagian dari pengembangan potensi alam Desa Cigunungsari menjadi produk wisata. Metode pelaksanaan kegiatan ini metode analisis situasi, identifikasi masalah, dan rencana pemecahan masalah melalui pelatihan perencanaan wisata. Kegiatan pengembangan wisata trekking ini dalam pelaksanaannya melibatkan organisasi Karang Taruna sebagai pelopor dalam kegiatan kepariwisataannya. Diharapkan bahwa kegiatan pendampingan dalam pengembangan Gunung Rungking sebagai wisata trekking akan membantu para pelaku kepariwisataan di Desa Cigunungsari dalam meningkatkan produk, daya tarik wisata, dan kemampuan untuk menyelenggarakan wisata trekking. Hasil dari kegiatan pengabdian ini adalah menghasilkan perencanaan produk paket wisata trekking, pedoman pelaksanaan wisata trekking, penyusunan paket wisata trekking di Desa Cigunungsari.
Peningkatan Pengetahuan Siswa SMA Muhammadiyah Bontomarannu Kabupaten Gowa Melalui Edukasi Keamanan Pangan Jajanan Anak Sekolah (PJAS) dan Food Preparation dalam Menyiapkan Bekal ke Sekolah
Authors: Hasriani Hasriani , Sumarni Sumarni , Arwati Sitti , Asih Rahma Yuniartie , Alamsyah Muh. Arief
Published: November 2024

Pengabdian kepada masyarakat ini dilatarbelakangi oleh besarnya kecenderungan para siswa Sekolah Menengah Atas (SMA) dalam mengkonsumsi jajanan yang tidak sehat, khususnya ketika berada di sekolah. Tingginya angka penyakit yang ditimbulkan dari jajanan yang tidak sehat tersebut juga dinilai sangat memprihatinkan. Selain itu, rendahnya minat siswa SMA dalam membawa bekal ke sekolah, menjadi penyebab mereka sering jajan sembarangan. Pengabdian ini secara umum bertujuan memberikan peningkatan pengetahuan pada siswa SMA Muhammadiyah Bontomarannu Kabupaten Gowa dalam hal keamanan pangan jajanan anak sekolah (PJAS) dan bagaimana melakukan food preparation dalam menyiapkan bekal ke sekolah. Metode yang digunakan pada pengabdian kali ini yakni ceramah dan demonstrasi sederhana dalam bentuk penyuluhan dan pelatihan. Kegiatan pelatihan dimulai dengan materi pengenalan Pangan Jajanan Anak Sekolah (PJAS) dan resiko yang ditimbulkan bagi tubuh jika sering jajan sembarangan. Dilanjutkan dengan pengenalan food preparation atau persiapan bahan pangan yang dibutuhkan dalam membantu mempersiapkan bekal sehat ke sekolah. Informasi digali dari para siswa dengan sesi tanya jawab untuk mengetahui sejauh mana mereka sering berbelanja ke pasar atau menemani orang tua dalam menyiapkan makanan di rumah. Hal ini menjadi dasar dalam memberikan edukasi dalam hal menyiapkan bekal sehat ke sekolah yang dapat dimulai dari rumah. Hasil yang diperoleh pada pengabdian kepada masyarakat kali ini yakni pemahaman mitra sasaran yang meningkat dan mereka mampu untuk mengaplikasikan materi yang telah diperoleh dalam kegiatan mereka sehari-hari, khususnya dalam mempersiapkan bekal sehat dan mengurangi jajan sembarangan selama di sekolah.
Penguatan Masyarakat Adat Berbasis Kemitraan Konservasi di Taman Nasional Betung Kerihun Danau Sentarum, Kapuas Hulu
Authors: Adi Bambang Tri Sasongko , Sukmana Oman , Soedarwo Vina Salviana D.
Published: November 2024

Kegiatan pengabdian masyarakat ini mengeksplorasi penguatan masyarakat adat melalui kemitraan konservasi di Taman Nasional Betung Kerihun Danau Sentarum, Kapuas Hulu, dengan tujuan mencapai keseimbangan antara usaha ekonomi dan konservasi. Kegiatan pengabdian masyarakat dilakukan di Desa Vega dan Pulau Majang dalam kerangka proyek Forest Investment Program-1 yang dilaksanakan oleh Kementerian Lingkungan Hidup dan Kehutanan (KLHK) atas dukungan dari Asian Development Bank (ADB) selama periode 2018-2023. Melalui pelatihan partisipatif dan pengembangan kapasitas, kegiatan pengabdian masyarakat ini berhasil meningkatkan keterampilan teknis, organisasi, dan ekonomi masyarakat adat Dayak dan Melayu, serta memperkuat struktur sosial dan kemitraan konservasi. Kegiatan pengabdian masyarakat berhasil meningkatkan kesejahteraan ekonomi dan pemanfaatan sumber daya alam yang berkelanjutan. Keterlibatan perempuan dalam kegiatan pengabdian ini juga cukup signifikan (melebihi target 30%).