bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Investigation of Implosion Water Treatment as Compared to Conventional Techniques
Authors: Singh Basudev , Mahato Jayanta Kumar , Sharma Jyoti
Published: October 2024

Quality of water is the major concern among all humans. Most of the available water can’t be used directly. There are some techniques which are commonly used for treat water for its best use. These techniques involve filtration, ultra filtration, reverse osmosis, UV treatment, chemical treatments etc. All these techniques for water treatment are used singly or in combined form. All these techniques have some advantages and some disadvantages. We have to make effort by treatment through various for making the water potable. Reverse Osmosis is the major technique used for water treatment along with filtration and UV treatment. One more technique which in not used in general practice but can be very useful is Implosion technology. Filtration is the technique which only removes suspended particles; it does not work on dissolved solids.For bacterial contamination UV treatment is effective. Reverse Osmosis effectively removes the dissolved solids and minerals;this may cause harmful impact for the human body on regular intake. The study shows that reverse osmosis decreases dissolved solids from 60 to 95 %, a sharp decline in pH of water is also observed after reverse osmosis which is not suitable for human body on regular basis. Implosion technique is only technique which does not impact negatively on human body and also sustainable technique. It makes positive on the chemistry of water as it removes turbidity from water (20 to 80%), make appearance and taste better, increases the level of dissolved oxygen (5 to 20%), removes hardness to little extent (10 to 30%), and also helps to removes bacterial contamination in some cases. Implosion technique maintains the mineral content in water. Hence it is concluded that for the water which have acceptable dissolved solids (150mg/l to 600 mg/l) implosion technique is best for use, Reverse osmosis is best to use when dissolved solids in water are much higher (>600mg/l). These all techniques may be used in combination of two or more for obtaining desired water quality.
Frequeny of CMV Testing During Pregnancy- A Retrospective Study
Authors: Hadjiiona Antigoni , Michaelides Ioannis , Kummer Peter , Kappelmeyer Maurice , Koeninger Angela , Reuschel Edith
Published: October 2024

Purpose
The cytomegalovirus (CMV) belongs to the family of human Herpesviridae and is distributed worldwide. It is the most common cause of viral congenital infections and can have serious consequences for the health of the fetus in the event of a vertical infection. The aim of this study is to evaluate the frequency of CMV diagnosis during pregnancy of pregnant women.
Methods
Retrospectively, 1000 pregnant patients aged 17 to 45 years who were treated in the University Clinic St. Hedwig, Germany, were included in the study. It was investigated whether a CMV test was carried out during pregnancy and which results were obtained.
Results
597 patients (59.7%) had not received a CMV test during pregnancy. Among the 403 (40.3%) patients who had undergone CMV testing, immunity was detected in 143 (35.5%). 257 patients (63.8%) showed no immunity, while 3 (0.74%) had an active infection.
Conclusion
Although CMV is the most common pathogen of viral congenital infections, which can severely impair the health of affected newborns, and both hyperimmune globulin administration as well as antiviral therapy now show good preventive and therapeutic success, CMV diagnostics during pregnancy is still not an integral part of the maternity guidelines in Germany, but rather an individualized healthcare service (IGeL). Unfortunately, there is currently no vaccination available to prevent vertical transmission, which is why early diagnosis and hygiene measures are the most important means of preventing seroconversion of the mother and possible infection of the fetus.
Pan-Immune-Inflammation Value is a novel prognostic biomarker in advanced Gastric Cancer Patients
Authors: Wang Mingyu , Wang Xinyi , Hu Yingjia , Li Jian , Niu Xingjian , Wang Fujing
Published: October 2024

Background: Advanced gastric cancer (GC) is a common malignancy with a poor prognosis, which remains the leading cause of cancer death worldwide. Identifying novel biomarkers is needed to predict survival for this highly progressive cancer. Many studies have confirmed that pan-immune-inflammation value (PIV) is related to the prognosis of various tumors in recent years. However, the prognostic value of PIV remains unclear in gastric cancer. The purpose of this study was to discuss the prognostic role of PIV in stage III and IV gastric cancer.
Methods: The clinical data of 646 patients with gastric cancer after gastrectomy were retrospectively analyzed. The calculation method of PIV is PIV=neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). Patients were divided into high and low PIV group by cut-off value based on receiver operating characteristic (ROC) curve. Effects of PIV and other IIB on survival were analyzed based on the ROC curves. Kaplan-Meier method was plotted to indicate the value of immune-inflammatory biomarkers (IIBs) in predicting the overall survival of gastric cancer. The overall survival (OS) in advanced gastric cancer patients were analyzed and univariate and multivariate statistics were used to evaluate the prognostic value.
Results: PIV had the most significantly predictive value in advanced GC patients compared with other peripheral blood parameters and IIBs. Cases in the high PIV group were more likely to have low serum albumin (Alb) level, larger tumor size compared with those in the low PIV group. PIV was identified an independent prognostic indicator for survival outcome in advanced GC patients in univariate and multivariate models.
Conclusions: This study confirmed that PIV can reflect the prognosis of advanced GC patients who have undergone gastrectomy, suggesting a potential application of PIV in GC treatment outcomes. Compared to other IIb indicators, PIV is more sensitive as a prognostic indicator. PIV will also provide some insight into the underlying mechanisms of immune and inflammatory effects in GC development and progression.
Trial registration: This study was a retrospective analysis, which has no intervention on human participants.
Effects of Weight Loss on Non-Alcoholic Fatty Liver Disease (NAFLD) in a United Arab Emirates Based Population: A Comparative Analysis of Bariatric Surgery and Non-Surgical Interventions
Authors: Barajas-Gamboa Juan S. , Khan Mohammed Sakid Ihsan , Gobbo Gabriel Diaz Del , Rivas Homero , Pantoja Juan Pablo , Abril Carlos , Moreno Cristobal , Raza Javed , Khan Fawad , Ghazanfar Rahat , Chiha Maguy , Saadi Hussein , Corcelles Ricard , Rodriguez John , Kroh Matthew
Published: October 2024

Introduction: Non-Alcoholic Fatty Liver Disease (NAFLD) is highly prevalent in the United Arab Emirates, but the comparative effects of bariatric surgery and medical weight loss on NAFLD in this population remain understudied. This study aimed to evaluate the impact of these interventions on NAFLD parameters in severely obese patients.
Methods: This prospective study compared 51 patients undergoing bariatric surgery (surgical cohort) with 35 patients undergoing supervised medical weight loss (medical cohort). Anthropometric measurements, comprehensive laboratory tests, and transient elastography (Fibroscan®) were performed at baseline and 12-month follow-up. Intraoperative liver biopsies were conducted for the surgical cohort.
Results: At 12-month follow-up, the surgical cohort showed significantly greater improvements in BMI (29.65 ± 5.39 vs 38.42 ± 6.39 kg/m², p<0.001), total weight loss (29.25% vs 1.34%, p<0.001), and liver function markers including ALT (16.55 vs 30.29 U/L, p<0.001). HbA1c levels were lower in the surgical group (5.07% vs 5.57%, p=0.014). Lipid profiles improved more in the surgical cohort. Fibroscan results demonstrated greater reductions in controlled attenuation parameter (321.26 to 234.08 vs 316.88 to 321.00 dB/m, p<0.001) and liver stiffness (5.74 to 4.56 vs 5.84 to 5.36 kPa, p=0.062) in the surgical group, indicating improved liver fat content and fibrosis.
Conclusion: Bariatric surgery resulted in superior outcomes for weight loss, metabolic parameters, and improvement in NAFLD markers compared to medical weight loss in this UAE-based population. This suggests its efficacy as an intervention for NAFLD in severely obese patients, though long-term studies are needed to confirm the sustainability of these improvements.
Enhancing sustainability in LNG carriers through integrated alternative propulsion systems with Flettner rotor assistance
Authors:   Department of Marine Engineering, Faculty of Maritime Studies, King Abdulaziz University, 21589 Jeddah, Saudi Arabia., R. Ammar Nader ,   Department of Naval Architecture and Marine Engineering, Faculty of Engineering, Alexandria University, 21544 Alexandria, Egypt, S. Seddiek Ibrahim ,   Department of Marine Engineering Technology, Faculty of Maritime Transport & Technology, Arab Academy for Science, Technology & Maritime Transport, 1029 Alexandria, Egypt
Published: October 2024

The maritime industry is actively seeking sustainable solutions to reduce ship emissions and enhance energy efficiency. This study explores the use of alternative fuels and renewable energy sources, focusing on the potential environmental and economic benefits of combining natural gas (NG) fuel with Flettner rotor (FR) technology. The research employs technical, environmental, and economic models to evaluate various scenarios integrating NG fuel with FR in alternative propulsion systems. It investigates three propulsion configurations for LNG vessels: diesel engine (DE-FR), dual fuel diesel engine (DFE-FR), and combined gas and steam turbine engines (COGAS-FR). Results indicate that utilizing six Flettner rotors produces 1.254 MW, resulting in fuel savings of 3.49% to 4.49%, along with notable emission reductions. The COGAS-FR system emerges as the most environmentally friendly option, enhancing energy efficiency by 4.68% based on current ship standards. From an economic perspective, transitioning to the DFE-FR system is identified as an optimal eco-friendly choice, leading to a 9.85% reduction in the levelized energy cost compared to DE-FR. However, it is noteworthy that the COGAS-FR system has the most significant environmental impact, with a cost-effectiveness of $10,954.6 per ton.
Guest Editorial. Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind
Authors: Riascos-Salas Jaime A , Rosa Cota Vinícius , Villota Hernán , Betancur Vasquez Daniel
Published: October 2024

The quest to decipher the intricate workings of the human brain continues to captivate scientists and researchers worldwide. In this endeavor, the Latin American Workshop on Computational Neuroscience (LAWCN) plays a pivotal role by fostering a vibrant community committed to understanding the intersection of neuroscience, computational approaches, and cutting-edge technological innovations. LAWCN was born in 2012 at the Federal University of Rio Grande do Sul (UFRGS) where it later formally became a biannual event in 2017. Since its beginning, its objective has been to support collaborative learning and knowledge sharing through events hosted in unique Brazilian cities such as Porto Alegre (2017), São João del-Rei (2019), and São Luís de Maranhão (2021).
A Reliable and Secure Crowdfunding Platform Using Decentralized Blockchain
Authors: Tanise Hironori , Nguyen Quoc Duy Nam , Ahmed Istiaque , Tran Thi Hong
Published: October 2024

Crowdfunding is a significant avenue for raising funds over the Internet to bring ideas into reality without relying on traditional funding sources. However, conventional centralized crowdfunding systems suffer from issues such as trustworthiness and transparency. In other words, it is necessary to ensure the reliability of information regarding project details, progress, and money exchanges, and to store this information in a form that cannot be altered. To ensure that the quality of projects is not degraded due to these causes and resolve the existing limitations, we propose a decentralized crowdfunding system using blockchain technology with two major contributions: "Decentralized Voting", and the "Decentralized Evaluating" methods. The Decentralized Voting Method aims to solve a particular platform's biased review by voting on the project's prospects and credibility. The Decentralized Evaluating Method aims to ensure project quality by exploiting the transparency of the invested projects. In this proposed blockchain-based solution, we used React and TypeScript for the front end and Rust-empowered Substrate at the backend. By using these methods, we verify through simulation that the implemented system works as proposed. This study identifies problems in crowdfunding and their causes, and then proposes a system that uses the two methods described above. The proposed system is expected to be a reliable distributed crowdfunding system.
Miradas locales, acciones internacionales - Caminos de la internacionalización territorial
Authors: Ávila Gómez Viviana , Denisse Denisse , Ojeda Tahina , Fagaburu Debora , Juan Carlos Juan Carlos
Published: October 2024

La Universidad de Bogotá Jorge Tadeo Lozano, en asocio con académicos de la Universidad de Guadalajara, la Universidad EAN y la Red de Expertos en Paradiplomacia e Internacionalización Territorial (REPIT) coordinan la presente obra que tiene como objetivo debatir y reflexionar sobre la paradiplomacia y la internacionalización que llevan a cabo los territorios como estrategia para el desarrollo sostenible y como mecanismo para la innovación social. En sus ocho ensayos se leen aproximaciones teóricas o prácticas que reflexionan sobre temas de gran interés para estudiantes y académicos que deseen profundizar sobre el marco general de la paradiplomacia, pero también es de interés para los tomadores de decisiones, pues permite situar su ejercicio en un contexto de diálogo y responsabilidad local-global. En esta obra, que cuenta con trabajos de investigadores de Colombia, México y Argentina, se aprecia un rico abordaje multidisciplinario que abarca la administración, la ciencia política, la cooperación internacional, el desarrollo económico, la economía, las finanzas, gobierno, gobiernos y actores locales, la historia, la integración, las políticas públicas y las relaciones internacionales.
AN MULTI THRESHOLD MODEL FOR COVID PATIENTS WITH INITIAL IDENTIFICATION OF DISEASE
Authors:   Knowledge Institute of Technology, India, M Ramkumar , Preeya V. Amirtha ,   Presidency University, India, V Sathiyapriya ,   Knowledge Institute of Technology, India, M Marimuthu ,   Sona College of Technology, India
Published: October 2024

Many strains of corona virus such as alpha, beta, gamma, delta, and omicron are still prevalent in various parts of the world. The new type of corona virus is called a variant when it is caused by more than one genetic mutation from the previous type of corona virus. Various strains around the world have come so far. The cough may persist for more than an hour or three or four times in 24 hours and body heat is high. You may not be able to feel the smell or taste. Researchers say that some people may have symptoms similar to those of a severe cold. In this paper, a multi threshold model was proposed to find the initial infection detection of COVID disease. Based on the initial health symptoms these methods observe the inputs of the patients. Then the observed symptoms are compared with the existing database and identify the spreading of the disease. This report was directly monitored by the patient and doctor. This model was helpful to provide the periodical monitoring and perfect treatments to the infected patients.
MAN-MADE OBJECT EXTRACTION FROM REMOTE SENSING IMAGES USING GABOR ENERGY FEATURES AND PROBABILISTIC NEURAL NETWORKS
Authors:   Aliah University, India, Sheikh Md. Abdul Alim , Maity Tanmoy ,   Indian Institute of Technology, Dhanbad, India, Kole Alok ,   RCC Institute of Information Technology, India
Published: October 2024

This paper presents a novel approach for man-made object extraction in remote sensing images. This paper focuses on the design and implementation of a system that allows a user to extract multiple objects such as buildings or roads from an input image without much user intervention. The framework includes five main stages: 1) Pre-processing Stage. 2) Extraction of Local energy features using edge information and Gabor filter followed by down sampling to reduce the redundant information. 3) Further reduction of the size of feature vectors using Wavelet decomposition. 4) Classification and recognition of man-made structures using Probabilistic Neural Network (PNN) 5) NDVI based post-classification refinement. Experiments are conducted on a dataset of 200 RS images. The proposed framework yields overall accuracy of 93%. Experimental results validate the effective performance of the suggested technique for extracting man-made objects from RS images. Compared with other methods; the proposed framework exhibits significantly improved accuracy results and computationally much more efficient. Most notably, it has a much smaller input size, which makes it more feasible in practical applications.
ROBUSTCAPS: A TRANSFORMATION-ROBUST CAPSULE NETWORK FOR IMAGE CLASSIFICATION
Authors:   Sri Sathya Sai Institute of Higher Learning, India, Venkataraman Sai Raam , S Balasubramanian ,   Sri Sathya Sai Institute of Higher Learning, India, Sarma R. Raghunatha ,   Sri Sathya Sai Institute of Higher Learning, India
Published: October 2024

Geometric transformations of the training data as well as the test data present challenges to the use of deep neural networks to vision-based learning tasks. To address this issue, we present a deep neural network model that exhibits the desirable property of transformation-robustness. Our model, termed RobustCaps, uses group-equivariant convolutions in an improved capsule network model. RobustCaps uses a global context-normalised procedure in its routing algorithm to learn transformation-invariant part-whole relationships within image data. This learning of such relationships allows our model to outperform both capsule and convolutional neural network baselines on transformation-robust classification tasks. Specifically, RobustCaps achieves state-of-the-art accuracies on CIFAR-10, FashionMNIST, and CIFAR-100 when the images in these datasets are subjected to train and test-time rotations and translations.
RESTORATION OF LOW-LIGHT IMAGE BASED ON DEEP RESIDUAL NETWORKS
Authors:   Kim Il Sung University, DPR of Korea, Ri Song Jun , Choe Hyon Su ,   Kim Il Sung University, DPR of Korea, Hyok O Chung ,   Kim Il Sung University, DPR of Korea, Kim Jang Su ,   Kim Il Sung University, DPR of Korea
Published: October 2024

Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of computer vision tasks in a great extent. Existing low-light image restoration methods still have limitation in image naturalness and noise. In this paper, we propose an efficient deep residual network that learns difference map between low-light image and original image and restores the low-light image. Additionally, we propose a new low-light image generator, which is used to train the deep residual network. Especially the proposed generator can simulate low-light images containing luminance sources and completely darkness parts. Our experiments demonstrate that the proposed method achieves good results for both synthetic and natural low-light images.
ASSESSMENT TECHNIQUE USING WAVELET TRANSFORM FOR IMPROVISING THE SCREEN CONTENT IMAGE QUALITY
Authors:   Francis Xavier Engineering College, India, Chandran J. Jasper Gnana , Narayanan K. Lakshmi ,   Francis Xavier Engineering College, India, Kumar G. Sai Chaitanya ,   DVR and Dr. HS MIC College of Technology, India, Lawrence T. Samraj ,   Dambi Dollo University, Ethiopia
Published: October 2024

Within the confines of this article, we advocate for the utilisation of wavelet transforms in teleconferencing environments as a means of improving the overall video quality. This can be accomplished by increasing the number of participants in the conference. The fundamental concept behind this is to use a collection of high-quality, professionally captured facial images as examples for the purposes of training, and the collection should include as many unique faces as is practically feasible. Images are often changed to make the skin tones and contrast of the facial regions more appealing to the viewer gaze. Adjustments are made to the colouring of a new picture so that the colour distribution in the face region will be comparable to that of the training pictures. This method is very effective when it comes to computation, and it also makes it much simpler to automate the process of making enhancements. The results of user research experiments are presented here, which demonstrate how the suggested method can improve the viewer perception of the video overall quality. The experiments were carried out to determine how the suggested method can accomplish this.
QUALITY OF VIDEO RENDERING TECHNIQUES USING ARTIFICIAL INTELLIGENCE
Authors:   Government B.Ed. Training College Kalinga, India, D.K. Mohanty , G.R. Thippeswamy ,   Don Bosco Institute of Technology, India, G Erappa ,   RR Institute of Technology, India, Puranik Vishal Gangadhar ,   JSPM’s Bhivarabai Sawant Institute of Technology and Research, India
Published: October 2024

In this paper, we propose a novel method that makes use of artificial intelligence to determine in a quick and accurate manner which bitrate ladder is best suited to each specific video scenario. This method is included as part of our overall contribution to this body of research. To accomplish fast entropy-based scene recognition using the artificial intelligence technique, a CNN model is utilised as part of the overall strategy. We were able to significantly reduce the amount of processing time necessary to recognise the scenes because we were dealing with versions of the video sequences that had both a lower quality and a lower bitrate. This allowed us to work more quickly. We first generated a training dataset that was large enough to train a convolutional neural network utilising the x264 video codec, and then we used that dataset to generate multiple encodings with varying bitrates, presets, and resolutions. The training dataset was created using the x264 video codec. As a result of the research that we carried out, we concluded that a particular collection of input features for the CNN model can be used to acquire a more accurate prediction of the level of video quality that will be produced. By predicting the PSNR quality measure for the segments, the suggested CNN model brings down the MAE and MSE to 0.2 and 0.05, respectively. This is accomplished by reducing the number of segments. This serves to reduce the amount of error overall.
PREDICTION OF MICRO PLASMA IMPACTS IN ORGANIC VEGETABLES USING DEEP LEARNING
Authors:   St. Mother Theresa Engineering College, India, Chandran J. Jasper Gnana , Senthamil L. Sahaya ,   PSNA College of Engineering and Technology, India, C Thamilarasi ,   P.S.V College of Engineering and Technology, India, J Jaganpradeep ,   SSM College of Engineering, India
Published: October 2024

The potential of micro plasma impacts in the growth of organic vegetables has been very difficult to predict. With the advent of deep learning methods, scientists are now able to develop predictive models that can accurately assess the effects of these impacts. Deep learning algorithms can be used to analyze the various environmental factors that influence the growth of organic vegetables, including temperature, humidity, sunlight, and soil type. With these inputs, the deep learning algorithms can learn complex relationships between these elements and the output of the growth of organic vegetables. By considering the interactions between environment and micro plasma impacts, the deep learning algorithms can make accurate predictions regarding the effects of these impacts on organic vegetable yields. The algorithm can be optimized to accurately predict the effect of these impacts in the future, allowing farmers to better plan for their crops. In addition, the deep learning algorithms can be used to analyze the effects of various factors on micro plasma impacts in organic vegetables. For example, the algorithm can analyze the effects of different combinations of fertilizer, water, and chemical inputs on the micro plasma impacts, allowing farmers to find the optimal crop growth conditions more quickly. The use of deep learning to predict the effects of micro plasma impacts on organic vegetable growth has the potential to improve crop yields, leading to more efficient agriculture practices.
DEEP LEARNING ALGORITHMS FOR DETECTION AND CLASSIFICATION OF CONGENITAL BRAIN ANOMALY
Authors:   Smt. Kashibai Navale College of Engineering, India, Navale Manisha Pandurang , P. Gupta Brijendra ,   Smt. Kashibai Navale College of Engineering, India
Published: October 2024

Congenital brain anomalies are structural abnormalities that occur during fetal development and can have a significant impact on an individual neurological function. Detecting and classifying these anomalies accurately and efficiently is crucial for early diagnosis, intervention, and treatment planning. In recent years, recurrent neural networks (RNNs) have emerged as powerful tools for analyzing sequential and time-series data in various domains, including medical imaging. This research presents an overview of RNN-based algorithms for the detection and classification of congenital brain anomalies. Specifically, Long Short-Term Memory (LSTM) networks and Convolutional LSTM networks have demonstrated great potential in this domain. LSTMs excel at capturing long-range dependencies in sequential data and mitigating the vanishing gradient problem, making them well-suited for analyzing brain scans or other medical imaging sequences. Convolutional LSTM networks combine the strengths of convolutional neural networks (CNNs) and LSTMs, enabling them to extract spatial features from brain images while preserving temporal dependencies. The application of RNN algorithms in the detection and classification of congenital brain anomalies shows promising results, enabling accurate and timely identification of these abnormalities. However, further research is needed to validate and refine these algorithms, improve their interpretability, and enhance their clinical utility in real-world scenarios.
SPECTRAL-SPATIAL DEEP DENSENET LEARNING FOR MULTISPECTRAL IMAGE CLASSIFICATION AND ANALYSIS
Authors:   Gnanamani College of Technology, India, Karuppannan Anand , Reddy K. Subba ,   Prakasam Engineering College, India, Patil Nilesh Madhukar ,   SVKM Dwarkadas J Sanghvi College of Engineering, India, Akana Chandra Mouli Venkata Srinivas ,   Bonam Venkata Chalamayya Engineering College, India
Published: October 2024

In this research, a novel model for multispectral image classification and analysis, leveraging Spectral-Spatial Deep DenseNet Learning is presented. This proposed framework combines spectral and spatial information to enhance the discriminative power of deep neural networks, enabling accurate classification of multispectral images. We conduct extensive experiments on benchmark datasets, demonstrating the superior performance of our method compared to existing approaches. Furthermore, we provide a comprehensive analysis of the learned features, shedding light on the interpretability and effectiveness of our model for multispectral image analysis tasks.
GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE SYNTHESIS AND STYLE TRANSFER IN VIDEOS
Authors:   Vivekanandha College of Arts and Sciences for Women, India, K Ramesh , Lavanya B. Muni ,   JNTUA College of Engineering, India, Kumar B. Rajesh ,   Dhanalakshmi Srinivasan College of Engineering, India, Vyas Narayan Krishan ,   Government Engineering College, Jhalawar, India, Al Ansari Mohammed Saleh ,   University of Bahrain, Bahrain
Published: October 2024

In computer vision and artistic expression, the synthesis of visually compelling images and the transfer of artistic styles onto videos have gained significant attention. This research addresses the challenges in achieving realistic image synthesis and style transfer in the dynamic context of videos. Existing methods often struggle to maintain temporal coherence and fail to capture intricate details, prompting the need for innovative approaches. The conventional methods for image synthesis and style transfer in videos encounter difficulties in preserving the natural flow of motion and consistency across frames. This research aims to bridge this gap by leveraging the power of Generative Adversarial Networks (GANs) to enhance the quality and temporal coherence of synthesized images in video sequences. While GANs have demonstrated success in image generation, their application to video synthesis and style transfer remains an underexplored domain. The research seeks to address this gap by proposing a novel methodology that optimizes GANs for video-challenges, aiming for realistic, high-quality, and temporally consistent results. Our approach involves the development of a specialized GAN architecture tailored for video synthesis, incorporating temporal-aware modules to ensure smooth transitions between frames. Additionally, a style transfer mechanism is integrated, enabling the transfer of artistic styles onto videos seamlessly. The model is trained on diverse datasets to enhance its generalization capabilities. Experimental results showcase the efficacy of the proposed methodology in generating lifelike images and seamlessly transferring styles across video frames. Comparative analyses demonstrate the superiority of our approach over existing methods, highlighting its ability to address the temporal challenges inherent in video synthesis and style transfer.
DEEP LEARNING-BASED IMAGE DEHAZING AND VISIBILITY ENHANCEMENT FOR IMPROVED VISUAL PERCEPTION
Authors:   Sona College of Technology, India, Dakshinamurthi Vidyabharathi , G.P. Suja ,   Muslim Arts College, India, P Murugan ,   Vels Institute of Science Technology and Advanced Studies, India, Hussain Sk. Riyaz ,   Rajiv Gandhi University of Knowledge Technologies, India
Published: October 2024

In recent years, image dehazing has gained significant attention in the field of computer vision and image processing due to its crucial role in enhancing visibility and improving visual perception. The presence of haze in images captured under adverse weather conditions or polluted environments poses a challenge to various computer vision applications, such as autonomous driving, surveillance, and satellite imagery. Traditional image dehazing methods often struggle to achieve optimal results, particularly in complex scenes with varying degrees of haze and intricate details. The need for a robust and efficient dehazing approach has become imperative for addressing real-world challenges in computer vision applications. Despite the advancements in traditional methods, a research gap exists in developing a comprehensive solution that can handle diverse atmospheric conditions and complex scenes effectively. The integration of deep learning techniques presents an opportunity to bridge this gap, leveraging the power of neural networks to learn and adapt to intricate patterns in hazy images. This research proposes a novel deep learning-based approach for image dehazing and visibility enhancement. A Convolutional Neural Network (CNN) architecture is designed to learn complex relationships between hazy and clear images, allowing the model to effectively remove haze and enhance visibility. The network is trained on a diverse dataset encompassing various atmospheric conditions and scene complexities to ensure generalization. Experimental results demonstrate the superior performance of the proposed deep learning approach compared to traditional methods. The model exhibits robustness in handling challenging scenarios, achieving significant improvements in image clarity, contrast, and overall visibility. The findings highlight the potential of deep learning in addressing the limitations of existing dehazing techniques.
ENHANCED BRAIN CANCER DETECTION IN MRI SCANS THROUGH TEMPLATE REGRESSION SIAMESE REGIONAL PROPOSED NETWORK FOR SEGMENTATION AND FUZZY LOGIC FUSION OF SEGMENTED REGIONS
Authors:   Manonmaniam Sundaranar University, India, Sharmili S.A. Anlet , M Sankari ,   Lekshmipuram College of Arts and Science, India
Published: October 2024

Accurate detection and segmentation of brain cancer in MRI scans are critical for effective diagnosis and treatment planning. Traditional methods often struggle with the complexities of tumor morphology and variations in scan quality. Existing detection systems can be slow and may not effectively handle the variability in tumor appearances, leading to potential delays in diagnosis and treatment. To address these challenges, we propose an enhanced detection framework using a Siamese Regional Proposed Network (SRPN). The SRPN integrates template branch and bounding box regression to expedite detection processes. The system utilizes an extended Siamese network to learn the distance between tracklet pairs, capturing the local and global features of tumors. These features are transferred to bidirectional gated recurrent units (GRUs), which generate tracklets and segment them into shorter sub-tracklets based on local distances. The segmented sub- tracklets are then reconnected into longer trajectories using similarities derived from temporal pooling global features. Additionally, fuzzy logic fusion is employed to combine segmented regions for improved accuracy. The SRPN-based framework demonstrated a significant improvement in detection speed and accuracy. Experimental results show an accuracy increase of 12% over traditional methods, achieving 94% accuracy with a detection time reduction of 30%. The system also improved segmentation precision, with a mean Intersection over Union (IoU) score of 85%, compared to 75% in conventional approaches.
Associations of Wearable Activity Tracker Use With Physical Activity and Health Outcomes in Patients With Cancer: Findings from a Population-Based Survey Study
Authors: Zhou Weijiao , Shang Shaomei , Cho Youmin
Published: October 2024

Background

Physical inactivity is a global issue for cancer survivors. Wearable activity trackers are promising to address physical inactivity by providing real-time feedback on physical activity and offering opportunities for self-monitoring and goal setting. Meta-analysis has reported the effects of interventions that incorporate wearable activity trackers on improved physical inactivity and related health outcomes (eg, BMI, anxiety and depression, and self-rated health status). However, wearable activity trackers were often used as an adjunct to physical activity interventions, and the effectiveness of wearable activity trackers alone is unknown.

Objective

This study aims to determine the association of wearable activity trackers with physical activity and health outcomes in patients with cancer.

Methods

Data from 957 cancer survivors from the Health Information National Trends Survey–Surveillance, Epidemiology, and End Results (HINTS-SEER) were analyzed. The outcome variables examined were time spent in moderate to vigorous physical activity, weekly frequency of strength training, BMI, anxiety and depression levels, and self-assessed health status. The primary independent variable was whether cancer survivors had used wearable activity trackers within the past 12 months. Design-based linear regression for continuous outcome variables and ordinal logistic regression for ordinal outcome variables were conducted to determine the associations after controlling for sociodemographic, cancer-related, and health-related factors. All data analyses accounted for the complex survey design and sample weights.

Results

Only 29% of cancer survivors reported wearable activity tracker use. Bivariate analyses showed that younger age (P<.001), higher education (P=.04), higher income (P<.001), and an employed status (P<.001) were significantly associated with wearable activity tracker use. Wearable activity tracker use was significantly associated with higher time spent in moderate to vigorous physical activity (adjusted =37.94, 95% CI 8.38-67.5; P=.01), more frequent strength training per week (adjusted odds ratio [OR] 1.50, 95% CI 1.09-2.06; P=.01), and better self-rated health status (adjusted OR 1.58, 95% CI 1.09-2.29; P=.01), but not with BMI or anxiety and depression.

Conclusions

This study suggests that the uptake of wearable activity trackers is low and highlights the digital divide among patients with cancer. This study has confirmed the associations of wearable activity tracker use with physical activity and self-rated health, supporting using wearable activity trackers as a promising tool to facilitate physical activity promotion.
Effects of Postoperative Telerehabilitation in Patients with Rotator Cuff Tear:
A Systematic Review and Meta-Analysis (Preprint)
Authors: Naonaga Fumiya , Mai Ruiwen , Hendrick Paul
Published: October 2024

BACKGROUND

Rotator cuff (RC) disorders are the most common cause of shoulder pain, with a prevalence that increases with age. RC tears significantly impact patients' quality of life and pose a substantial economic burden due to high surgical and rehabilitation costs. Traditional in-person rehabilitation can be challenging for many patients due to logistical barriers. Telerehabilitation, which delivers rehabilitation services remotely, has emerged as a potential alternative, yet its effectiveness for postoperative RC repair remains uncertain.

OBJECTIVE

The primary aim of our study is to evaluate the effectiveness of telerehabilitation for patients recovering from RC repair surgery.

METHODS

This study followed PRISMA guidelines for systematic reviews and meta-analyses. A comprehensive literature search was conducted across PubMed, EMBASE, MEDLINE, CENTRAL, CINAHL, and Web of Science for English-language publications up to July 1, 2024. Randomized controlled trials (RCTs) evaluating the effectiveness of telerehabilitation's effectiveness for postoperative RC repair were included. Data were extracted on study characteristics, interventions, outcomes, and risk of bias. The primary outcome was the Constant-Murley Shoulder Outcome Score (CM score) ; secondary outcomes included the Disabilities of the Arm, Shoulder, and Hand (DASH) score and, Visual Analogue Scale (VAS). Meta-analyses were conducted using a random-effects model.

RESULTS

The search yielded 1387 articles, reduced to 925 after excluding duplicates and ineligible records. Screening led to 33 full-text articles, of which 6 met inclusion criteria. The 6 RCTs included 388 participants post-RC repair, with various telerehabilitation interventions such as mobile applications, videotape, telephone calls, and gamification. PEDro scores ranged from 4 to 8 out of 10. Two studies were high risk and four had some concerns according to ROB2. No significant differences were found between telerehabilitation and control groups in CM score, DASH score, and VAS.

CONCLUSIONS

Telerehabilitation is a viable alternative to traditional rehabilitation post-RC repair, showing comparable improvements in functional recovery and pain management. Further research is needed to standardize its implementation and assess long-term effects.
A Protocol for Passive Autonomous Data Collection: Demonstrating Tactical Combat Casualty Care in Simulated Environments (Preprint)
Authors: Little Jeanette R. , Rivera-Nichols Triana , Pavliscsak Holly H. , Badawi Omar , Gaudaen James C. , Yeoman Chevas R. , Hall Todd S. , Quist Ethan T. , Stoor-Burning Ericka L.
Published: October 2024

BACKGROUND

The Telemedicine and Advanced Technology Research Center (TATRC) commenced a new research portfolio specifically addressing Autonomous Casualty Care (AC2) in 2023. The first project within this portfolio addresses the current and historical challenges of capturing tactical combat casualty care (TCCC) data in operational settings.

OBJECTIVE

The initial AC2 effort, the Passive Data Collection using Autonomous Documentation (AutoDoc) research project, conducts systematic, simulated patient and casualty care scenarios, leveraging suites of passive sensors inputs to populate a data repository that will automate future care.

METHODS

To obtain the required data sets, TATRC will engage consented human subject care provider participants in one of six randomized simulated TCCC scenarios. These simulations will leverage mannikins (low and high fidelity) and live simulated patients (e.g., consented human subject actors). All consented participants (e.g., both the care providers and live simulated patients) will be equipped with suites of sensors that will passively collect data on care delivery actions and patient physiology.

RESULTS

At the time of this publication, data collection using the passive sensor suites inputs is ongoing. Simulated data is being collected at Fort Detrick, Maryland; Fort Sam Houston, Texas; Fort Indiantown Gap, Pennsylvania; Fort Liberty, North Carolina; and a commercial site in Greenville, North Carolina. Over the course of the research project’s data collection period, additional data collection sites will be amended to the institution review board (IRB) protocol as research partnerships and collaborations expand.

CONCLUSIONS

The Military Healthcare System (MHS) lacks real world data sets for TCCC care at the point of injury (POI). Developing a data repository of simulated TCCC data is required as an essential step towards automating TCCC care. If TATRCs research efforts result in the ability to automate care delivery documentation, this will alleviate the cognitive burden of TCCC care providers in austere, chaotic environments. By generating a TCCC data repository through this AutoDoc research project, TATRC will have opportunities to leverage this research data to create machine learning (ML) and artificial intelligence (AI) models to advance passive, automated medical documentation across the healthcare continuum.
Study on The Response Characteristics of Monopile Offshore Wind Turbines under Non-Gaussian loads in Typhoons
Authors: Tan Xiaoyu , Li Jiaqi , Hu Yuan , Song Tao , Chen Siyi , Li Jiaxiao
Published: October 2024

The fan structure is often seriously damaged under typhoon, so the study of the fan structure under typhoon is of great significance for its disaster prevention and mitigation. The actual measurement shows that the typhoon has obvious non-Gaussian characteristics, and the influence mechanism of this characteristic on the fan response characteristics is not clear. Therefore study the response characteristics of fans under non-Gaussian load. First, based on the autoregressive AR model, combined with the Johnson conversion model (JTM); second, the wind load is determined based on the specification; then the wind load. Finally, the response characteristics of 5MW fan are studied by characterizing the numerical cases of non-Gaussian fan with different strength of typhoon. The results show that the non-high characteristics of non-Gaussian wind field and the non-Gaussian characteristics become stronger with the strength of the non-Gaussian characteristics of wind field.
Incidencia del programa PROSPERA en la asistencia escolar: evidencia de un modelo Probit para el Estado de México
Authors: Jiménez-Ramos Frida , Valencia-Romero Ramón , Lechuga-Rodríguez Eduardo Luis
Published: October 2024

Se realizó un estudio cuyo propósito fue determinar la incidencia del programa de inclusión social PROSPERA en la asistencia escolar, en niños a nivel primaria en el Estado de México. Se utilizó como metodología econométrica un modelo probit. La variable dependiente es la asistencia escolar y las independientes son beca (1 son becarios PROSPERA y 0 no becarios), ingreso trimestral de una familia, número de residentes en la vivienda, si contaron con dinero para alimentarse, hablante de alguna lengua indígena, y una interacción entre las variables beca y el número de residentes. La estimación hace uso del factor de expansión, teniendo así resultados representativos a nivel de población del Estado de México. Los resultados conseguidos indican que el programa PROSPERA influye positivamente en el aumento en la probabilidad de asistencia escolar, también se encontró que la variable renta y hablante indígena no influyen en que un niño asista o no a la escuela. En este sentido, se concluye que PROSPERA es un determinante fundamental en la asistencia de los niños a nivel primaria en el Estado de México.
Geografía cultural y gerontología: re-vivir el lugar
Authors: Capella-Miternique Hugo
Published: October 2024

El objetivo de la investigación ha sido, en primer lugar, el reconocimiento de la importancia del medio como garante de la calidad de vida para la gente mayor. En segundo lugar, también ha evidenciado cómo el rico legado espacial vivido puede ser fuente de ansiedad y frustración en la gente mayor cuando debe adecuarse a un proceso de limitaciones físicas y psíquicas crecientes, en detrimento de la calidad de vida. Finalmente se ha verificado como la lección del manejo del espacio vivido por la gente mayor, aporta una nueva perspectiva sobre el espacio de forma más integrada.
Enterococcus, la oveja negra de las bacterias ácido lácticas
Authors: Acero-Pimentel Daniel , Quirasco Maricarmen
Published: October 2024

Los Enterococcus forman parte de la microbiota nativa del tracto gastrointestinal de los seres humanos y de otros animales; a través de distintos mecanismos moleculares participan en la regulación de la homeóstasis intestinal y la reducción de la severidad de ciertos procesos infecciosos. Al ser bacterias ácido lácticas (BAL) participan en los procesos fermentativos de alimentos como los lácteos, los cárnicos y los vegetales, y en el desarrollo de olores y sabores deseables. Sin embargo, aunque las BAL son “generalmente reconocidas como seguras” (GRAS) por la Administración de Alimentos y Medicamentos de los Estados Unidos (FDA), a Enterococcus no se le incluye en esta clasificación por su alta capacidad para adquirir y ser reservorio de elementos de importancia epidemiológica y a su prevalencia como microorganismo patógeno oportunista en entornos nosocomiales, situación que lo limita a ser utilizado como probiótico o en un cultivo iniciador. ¿Hasta qué punto y en qué condiciones un Enterococcus inocuo es importante en la salud, y cuánto de lo que se conoce sobre él es comparable con otros géneros considerados como seguros? Esta revisión expone un panorama bioquímico que explica su presencia en alimentos fermentados, así como atributos que le permiten participar en procesos infecciosos intrahospitalarios.
Agua embotellada: contemplaciones sanitarias entre el derecho humano a la salud y la industria privada
Authors: Zepeda Rodríguez Luis Fernando , Tello Ibarra Jordan Vladimir
Published: October 2024

El presente artículo tiene el objetivo de analizar el consumo del agua embotellada, entre las dinámicas del agua, como derecho humano o mercancía de consumo. Parte de una metodología cualitativa, enfocada en el análisis de contenido, tomando como insumosde investigación informes nacionales e internacionales de organizaciones no gubernamentales, el marco jurídico nacional e internacional, así como resultados de investigaciones empíricas realizadas en el contexto mexicano en las últimas décadas. Los resultados se entrelazan en una reflexión académica, desde las funciones esenciales de la salud pública. Las conclusiones apuntan a la concepción del agua como un bien de consumo, antes de un derecho humano, pues la industria privada genera millones de pesos anuales por la explotación y venta del recurso público; los conflictos de intereses e intromisión en las políticas regulatorias dan evidencia de la falta de políticas de Estado para garantizar el derecho humano al agua: una vía indispensable para fortalecer el derecho humano a la salud.