bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of shear wave elastography and attenuation imaging for predicting life-threatening event in patients with metabolic dysfunction-associated steatotic liver disease
Authors: Fujiwara Yudai , Kuroda Hidekatsu , Abe Tamami , Nagasawa Tomoaki , Nakaya Ippeki , Ito Asami , Watanabe Takuya , Yusa Kenji , Sato Hiroki , Suzuki Akiko , Endo Kei , Yoshida Yuichi , Oikawa Takayoshi , Kakisaka Keisuke , Sawara Kei , Tada Toshifumi , Miyasaka Akio , Oguri Takuma , Kamiyama Naohisa , Matsumoto Takayuki
Published: October 2024

We aimed to elucidate the value of ultrasound-based biomarkers for predicting the major life-threatening events in metabolic dysfunction-associated steatotic liver disease (MASLD). We established a prospective cohort of 279 patients who underwent two-dimensional shear wave elastography (2D-SWE), ultrasound-guided attenuation parameter (UGAP). An area under the curve analysis was performed to determine the cutoff values of liver stiffness measurements (LSM) by 2D-SWE and attenuation coefficient (AC) by UGAP for a moderate fibrosis and a moderate steatosis. We then classified the cohort into Groups A (low LSM and low AC), B (low LSM and high AC), C (high LSM and high AC), and D (high LSM and low AC). We compared the incidence of events between the groups, and estimated the hazard ratios (HRs) with 95% confidence intervals (CIs). The LSM and AC cut off values were 8.37 kPa and 0.62 dB/cm/MHz, respectively. The cumulative incidence rate in Groups A, B, C, and D were 11.2%, 12.2%, 29.5%, and 31.0%/5years, respectively (p < 0.05). LSM (HRs = 1.20, 95%CIs: 1.09–1.32, p < 0.01), and AC (HRs = 1.62, 95%CIs: 1.04–2.51, p = 0.03) were associated with life-threatening events. A combination of 2D-SWE and UGAP may help identify patients with MASLD at high risk for subsequent life-threatening events.
Liver Enzymes in End Stage Renal Disease Patients Diabetic and Non- Diabetic. Does it Differ than General Population?
Authors: Abdel-Atty-Gadallah Abdel-Nasser , Bahnasy Abdallah Abdel-Aziz , Milad Sahar , Toulan Safwa Othman
Published: October 2024

Background: End-stage renal disease (ESRD) often necessitates hemodialysis and is frequently caused by diabetes mellitus (DM). Liver enzyme levels are vital for assessing liver function but are complex to interpret in ESRD patients due to uremia, dialysis, and comorbidities. The effect of DM on liver enzymes in ESRD patients remains unclear. This study aims to evaluate liver enzyme levels in ESRD patients on hemodialysis and determine the impact of ESRD per se on liver enzymes and of ESRD with diabetes mellitus on liver enzymes levels.
Methods: This cross-sectional study was conducted on 140 participants, divided into ESRD (70 patients) and control (70 healthy individuals) groups. The ESRD group was further classified into diabetic (n=14) and non-diabetic (n=56) subgroups. Laboratory parameters including liver enzymes, serum albumin, bilirubin, urea, creatinine, sodium, potassium, coagulation parameters, hematological indices, and glycemic control were collected.
Results: ESRD group exhibited significantly higher AST (P=0.012) and ALP (P<0.001) levels, and lower serum albumin (P<0.001) compared to controls. No significant differences in ALT and total bilirubin were observed. Diabetic ESRD patients had significantly higher AST (P=0.017), ALT (P=0.003), serum albumin (P=0.034), and ALP (P=0.013) levels compared to non-diabetic ESRD patients. The urea reduction ratio and kt/v were significantly lower in diabetic ESRD patients (P=0.005 and P=0.039, respectively).
Conclusion: ESRD patients, particularly those with diabetes, have significantly altered liver enzyme profiles than general population. These results may change our view to liver enzymes levels in ESRD patients particulary ESRD patients with diabetes in routine monthly follow up and in other occasions.
Effect of genetically proxied use of PDE5 inhibitors on risk of CHD: A drug target Mendelian Randomization study
Authors: Zhang Hongchen , Dong Zhenhua , Hao Wei , Cao Guohua , Dong Qi , Chen Yiyang , Li Shihui , Chen Lanlan , Fan Zhongqi , Lv Guoyue
Published: October 2024

Background and aim
There have been previous and recent studies generating conflicting findings on effect of phosphodiesterase 5 (PDE5) inhibitors with CHD, Diabetes, breast cancer and prostatic cancer. Since we intended to use naturally occurring variation in genes encoding PDE5 to investigate association between genetically proxied PDE5 inhibitors and them.
Method
We used drug target mendelian randomization as main analytical method. 60,801 cases and 123,504 controls from 48 GWAS studies, mainly European ancestry (77%) for CHD were included. Inverse-variance weighted (IVW) random-effects model was employed as the primary analysis method to estimate the causal effect. And several robust methods were also used to detect the causal association as sensitive analysis. What’s more, colocalization analysis was also applied to verify the association.
Result
We observed strong evidence for the causal effect of genetically proxied PDE5 inhibitors on decreased CHD risk (IVW method, OR 1.22, 95% confidence interval (95%CI) 1.14-1.29, p-value=7.16E-07). Colocalization analysis also proved the association (H4=99.99%). Besides, we found weak association between genetically proxied inhibition of PDE5 and lower risk of breast cancer as well as prostatic cancer. At last, we found no evidence for effect of PDE5 inhibitors on risk of Diabetes.
Conclusion
We performed a drug target mendelian randomization supporting that use of PDE5 inhibitors is associated with lower risk of CHD. Though in 1990s studies found frustrating effect of PDE5 inhibitors on curing CHD, future RCT studies can focus on protective effect of PDE5 inhibitors on CHD. Our result may enlighten clinical use of PDE5 inhibitors.
Effect of Balanced Energy Protein Supplementation, Azithromycin and Micronutrients During Pregnancy on Birth Outcomes- A Four arm Assessor Blinded Open-label Randomized Controlled Trial
Authors: Jehan Fyezah , Pasha Aneela , Muhammad Ameer , Balouch Benazir , Shafiq Yasir , Nisar Muhammad Imran , Rizvi Arjumand , Shahid Shahira
Published: October 2024

Maternal undernutrition predicts poor birth outcomes. We performed a community-based, open-label, assessor-blinded, randomized controlled trial in Pakistan. Pregnant women (PW) with ultrasound-confirmed gestational age (GA) 8–18·9 weeks, were assigned (1:1:1:1) to one of four groups: control, BEP alone (10·5 g protein, 400 kcal per sachet, two sachets daily), BEP plus AZ (two doses of 2 g orally at weeks 20 and 28), or BEP plus choline (450 mg orally daily) and nicotinamide (100 mg orally daily). All arms received standard-of-care. Interventions began at enrollment and continued until birth. The primary outcome for the modified intention-to-treat analysis included infants born alive with anthropometric measurements taken within 72 hours of birth (n = 1678). The unadjusted difference in birth weight between BEP plus AZ arm compared to the controls was 104g; 95% CI: 20 to 188 (p = 0.008) and BEP plus choline/nicotinamide was 58g; 95% CI: -26, 142 (p = 0.07). Early neonatal mortality (NND) was 14.0 (95% CI: 5.2, 30.3; p = 0.04) in the BEP plus AZ arm, and 21.3 (95% CI:9.8, 40.1; p = 0.17) in BEP plus choline/ nicotinamide arm, as compared to 31.7 (95% CI: 16.9, 53.6) per 1000 live births in controls. The synergistic use of BEP with AZ, or with choline/nicotinamide in PW could improve birth outcomes. This trial is registered with ClinicalTrials.gov Identifier: NCT04012177.
Investigating the behavioral and antioxidative effects of ethanolic extract from Schumanniophyton magnificum (K. Schum) in ketamine-induced mice.
Authors: Asowata-Ayodele Abiola , Olusola Ayomide , Olatuyi lekan , afolabi felix , Olatunji Babawale , salemcity aanuoluwapo
Published: October 2024

This work presents an investigation on the behavioral and antioxidative effects of ethanolic extract from Schumanniophyton magnificum (K. Schum) in mice. Psychosis has several detrimental effects on health, as a result, a therapeutic strategy for managing psychosis is crucial to maintaining the global community’s health because when mental illness comes up in the human brain it’s reflected through different display of behavior like slow reasoning ability, depression, memory loss and isolating of oneself from others. However, it is not known whether Schumanniophyton magnificum leaves could cure or prevent psychosis because no study has ever cover the aspect of this plant nor any ethnobotanical study done on its activity in managing psychosis, thisfurther emphasizes the urgency for innovative treatment approaches. The plant was administered through oral route to group of ketamine induced mice at a dosages of 100, 200 and 400 mg/kg; compared with controls group of mice for 14 days. The behavioral assessment was demonstrated using the Open Field Test for assessing the ketamine-induced hyperlocomotion, Y-Maze test was used for assessing the behavior, learning and memory (cognition) and novel object recognition test to evaluate the willingness of the mice to explore new environment or object in animal models of the central nervous disorders. Thereafter, some biochemical assay were conducted. This research shows that after 14 days of administration, the animals were sacrificed and antioxidant bioassay was carried out on the brain. Schumanniophyton magnificum treatment significantly (P < 0.05) reduced hyper-locomotion induced by ketamine, which is a predictor of positive symptoms. Schumanniophyton magnificum treatment (100 and 400 mg/kg) significantly enhanced spatial memory formation preventing cognitive deficits by ketamine. Schumanniophyton magnificum treatments significantly increased the SOD & CAT activities, as well as decreased MDA levels, this is suggesting that the antipsychotic-like action of Schumanniophyton magnificummaybe through inhibition of oxidative crises induced by ketamine. This study could potentially pave new way for cost-effective interventions that address both the health and socioeconomic burdens associated with psychosis.
The Nexus Between Changes in CEO Successor’s Attributes and Firm Performance: Evidence from Emerging Countries
Authors: Hazem Sama Mohamed , Ali Assoc. Prof. Dr. Heba Ahmed Abass , Abou-Elkheir Prof. Dr. Ehab Kamel
Published: October 2024

This paper aims to comprehensively analyze the impact of Chief Executive Officer (CEO) succession on the firm’s post-succession performance, specifically through investigating the direction of changes in CEO successor attributes compared to the predecessor CEO. The paper uses a sample of 1,729 firms in twenty-six emerging countries from 2000 to 2019. The sample yields 9,498 firm-year observations embracing 913 CEO successions. Using fixed effect (FE) regression, the findings conclude that hiring an insider CEO successor significantly decreases the negative impact of CEO succession on the firm’s asset utilization. However, hiring a CEO successor of the same gender, younger, less experienced, or with smaller social networks significantly amplifies the negative impact of CEO succession on the firm’s asset utilization. Moreover, the direction of change in CEO education and busyness has revealed mixed results on the firm’s asset utilization. In addition, hiring an insider, of the same gender, more experienced, with a larger social network and less busy CEO successor significantly decreases the negative impact of CEO succession on the firm’s liquidity. Finally, hiring an outsider, a different gender, younger, less educated, less experienced, with a larger social network or busier CEO successor significantly amplifies the negative impact of CEO succession on the firm’s liquidity.
JEL: C12, C23, G41, O16
Saguna Rice Technology Adoption in Maharashtra: Benefits, Challenges, and Pathways to Sustainable Agriculture
Authors: Wadghane Rahul , Sonawaney Vandana , Gotise Piyush , Bhandarkar Pankaj
Published: October 2024

This study investigates the adoption of Saguna Rice Technology (SRT) by 27 farmers across Maharashtra, India. This research highlights the challenges and potential benefits of SRT, with a focus on the technical, economic, and institutional factors influencing adoption. Utilizing purposive and snowball sampling, data were collected through five-minute telephone interviews and face-to-face discussions. Thematic analysis using NVivo software revealed that while SRT offers economic benefits such as a 30–40% reduction in production costs and improved soil fertility, challenges such as weed management and the need for specialized equipment hinder widespread adoption. Farmers suggest solutions, including the timely use of herbicides, enhanced availability of SRT-specific tools, and targeted training for effective implementation. This study provides valuable insights for policymakers and stakeholders aiming to promote sustainable agricultural practices in Maharashtra.
Poliface: A Multi-pose Synchronous Imaging System
Authors: Nguyen Viet-Bac , Vu Van-Lieu , Truong Duc-Toan , Dao Viet-Anh , Nguyen Dang-Ha , Tran Thom , Hoang Van-Xiem , Nguyen Hoang-Anh The
Published: October 2024

To enhance the accuracy of face recognition technology in real-world scenarios, it is necessary to train deep learning models on datasets that contain a large number of labeled human face images under multiple poses, lighting, and accessory variations. In this paper, we introduce a novel acquisition system named the Poliface. This system can capture multiple high-resolution images simultaneously around the human head. We designed this system with a well-built aluminum structure, control electronic circuits, and high-performing in-house software. The results demonstrate the precise operation and exceptional stability of this system. Using this Poliface system, we have collected over 6 million photos, which can be used to train and evaluate facial recognition models, and exploited for three-dimensional (3D) virtual face reconstruction.
DETERMINAÇÃO POR ESPECTROMETRIA DE MASSAS DO POTENCIAL DE BIOACUMULAÇÃO DE CONTAMINANTES EMERGENTES EM BIOFILME
Authors: Takahasi Marques Luana Mayumi , Reichert Gabriela , de Mello Cesar Rebeca , Nastally Cano Thais Cristine , Rodrigues de Azevedo Júlio César
Published: October 2024

DETERMINATION BY MASS SPECTROMETRY OF THE BIOACCUMULATION POTENTIAL OF EMERGING CONTAMINANTS IN BIOFILM. Personal hygiene products, such as UV filters, are consumed daily and released at sewage treatment plants. The presence of these compounds in aquatic environments may cause concern, as their impacts are not yet fully known. Biofilms can be an innovative and simple tool for monitoring the bioaccumulation of contaminants such as UV filters in aquatic environments. The present work aimed to determine and evaluate the potential of bioaccumulation of these compounds in biofilm and in the surface waters of the Barigui River. The determination of contaminants in environmental matrices was performed by liquid chromatography coupled to triple-quadrupole mass spectrometry (LC-MS/MS). The validation of the methodology used was based on the acceptance criteria of the National Institute of Metrology, Quality and Technology (Inmetro). The results obtained in the samples ranged from the maximums of 5.85 μg L–1 of benzophenone-1 for water samples to 1907.40 μg kg–1 of benzophenone-3 for biofilm. The study showed that biofilm is a very useful new tool in environmental monitoring, mainly due to the bioaccumulation capacity of UV filters.
Assessing Cheese Texture with Deformeter: New Instrumental Options
Authors: Mayorov Alexander , Musina Olga , Loginov Vitaly
Published: October 2024

A deformeter is a deflection device that measures such structural and mechanical properties of cheese as hardness and elasticity. This technology facilitates cheese quality control. The article describes its operation principles and application options in cheese production. A penetrometer is the most common device for assessing the hardness of cheese. However, it cannot measure plasticity. A deformeter, on the other hand, tests both the elastic and plastic properties of cheese. This research featured Reokon 240 deflection device developed at the Siberian Research Institute of Cheese Making. Hardness was expressed as the maximal value of the force applied to the indenter under compression while elasticity was the reduction magnitude on the indenter. The indenter moved at a constant speed, compressing the cheese sample from the original size of 10 mm to that of 6.4 mm. Fixed in this position, the force on the indenter decreased as the stress in the compressed cheese sample relaxed. The test results were compared with those obtained by traditional methods. The experimental hardness data registered with Reokon 240 correlated well with the those obtained by the classical penetration method. The minimal relaxation coefficient belonged to the sample of Parmesan cheese, which had the greatest hardness and was less prone to stress relaxation. The highest relaxation coefficient belonged to the Feta cheese sample, which appeared to be more plastic. These data correlated well with the mass fraction of moisture in the cheese samples. Reokon 240 proved to be an objective instrument of cheese texture assessment. In addition, it could also measure cheese plasticity during pressing. The scope of application could be extended to the rheological properties of fruits and berries.
The ecological operation of ChiTan hydropower station based on hydrological alteration using the PCA method
Authors: Uddin Md Mohi , Fang Guohua , Huang Xianfeng , Intriago Gordillo Jhandre Ronald , Tapu Md Al Imran , Abdulla-Al-Mamun
Published: October 2024

Water infrastructure affects the quantity, quality, and environment of water. During reservoir construction, only the social-economic benefits were considered and the health of the downstream river was neglected. Due to the severe downstream river system, the operators and managers faced the challenge of the downstream ecosystem. Many scholars are concerned with the reservoir's optimum functioning, which considers social and economic advantages. Numerous significant opinions and research findings are being presented on ecological scheduling at the moment, both in China and overseas, which surely deepen the connotations of ecological scheduling and serve as the research basis and critical reference of this work. The influence of hydropower development on hydrological conditions, the categorization and calculation of environmental runoff, and the building and solution of ecological scheduling models are all discussed and researched in this work. The study examined how the ChiTan hydropower project influenced the Jinxi River's flow. The total hydrologic alteration calculated using RVA is 50.53%. The component analysis is also utilized to eliminate the statistical redundancy in the hydrologic indicators. The indicators’ monthly flow for July and August's mean minimum flow for 7 days are relevant indicators for the Jinxi River basin's hydrologic modification regime.
Functionalized biochar derived from novel Neolamarchia cadamba leaf extracts for the adsorption of Congo Red dye: kinetics, optimization, and reusability studies
Authors: Paul Abhay Sahil , Khan Saima Habib , Siddiqui Shaziya Haseeb
Published: October 2024

The present research emphasized on the removal of Congo Red (CR) dye from aqueous solutions using an adsorbent synthesized by utilizing the leaf extract of Neolamarchia cadamba as a bio-template. This facilitates the formation of zinc oxide nanoparticles which are then carbonized to enhance adsorption capabilities. This synthesized material is referred to as NC@ZnC, for coherent adsorption of CR dye. Various operating parameters were used for the adsorption of CR onto NC@ZnC. The maximum monolayer decontamination of CR dye was 303.03 mg/g when it was incubated for 90 min at a pH of 5. The specific surface area of amalgamated NC@ZnC was reported to be 6.509 m2/g using Bruaneur–Emmett–Teller analysis. Field-emission scanning electron microscopy was used to show the rough surface area, X-ray diffraction analysis was used to determine the crystalline structure of the adsorbent with a grain size of 20.062 nm. Elemental dispersive X-ray analysis was used to determine the elemental composition of NC@ZnC. Raman spectroscopy demonstrates a lysine group that, upon adsorption, interacts with oxygen to form a bond. NC@ZnC regresses pseudo-second-order kinetics and follows the Langmuir isotherm for the adsorption process. The sorption activity with respect to temperature appears to be displaying +ΔH° and +ΔS°, which suggests an endothermic and impulsive nature.
Characterisation of honey using high-frequency ohmic heating based on image segmentation
Authors: Dwi Hartono Elvianto , Lastriyanto Anang , Zubaidah Elok , Hendrawan Yusuf
Published: October 2024

In the field of computer vision, image segmentation using a clustering approach was employed. This non-destructive method was applied to process ohmic heating in honey, aiming to achieve an efficient and time-saving mass production process. The K-means clustering algorithm converted RGB color data to Lab color space for effective segmentation. The validation of outcomes was conducted through the evolution of RMSE values and regression analysis for each frequency. Notably, at a precision frequency of 1 kHz, the results were as follows: RMSE Red 1.4902, RMSE Green 0.7017, RMSE Blue 0.3328, Regression Red 0.0792, Regression Green 0.5782, Regression Blue 0.202, and heat penetration regression 0.658. This proposed method was benchmarked against the conventional heat penetration analysis in ohmic heating.
ITERATIVE COLLABORATIVE ROUTING AMONG EQUIVARIANT CAPSULES FOR TRANSFORMATION-ROBUST CAPSULE NETWORKS
Authors:   Sri Sathya Sai Institute of Higher Learning, India, Venkataraman Sai Raam , S Balasubramanian ,   Sri Sathya Sai Institute of Higher Learning, India, Sarma Raghunatha ,   Sri Sathya Sai Institute of Higher Learning, India
Published: October 2024

Transformation-robustness is an important feature for machine learning models that perform image classification. Many methods aim to bestow this property to models by the use of data augmentation strategies, while more formal guarantees are obtained via the use of equivariant models. We recognise that compositional, or part-whole structure is also an important aspect of images that has to be considered for building transformation-robust models. Thus, we propose a capsule network model that is, at once, equivariant and compositionality aware. Equivariance of our capsule network model comes from the use of equivariant convolutions in a carefully-chosen novel architecture. The awareness of compositionality comes from the use of our proposed novel, iterative, graph-based routing algorithm, termed Iterative collaborative routing (ICR). ICR, the core of our contribution, weights the predictions made for capsules based on an iteratively averaged score of the degree-centralities of its nearest neighbours. Experiments on transformed image classification on FashionMNIST, CIFAR-10, and CIFAR-100 show that our model that uses ICR outperforms convolutional and capsule baselines to achieve state-of-the-art performance.
XAI USING FORMAL CONCEPT LATTICE FOR IMAGE DATA
Authors:   Sri Sathya Sai Institute of Higher Learning, India, Venkatsubramaniam Bhaskaran , Baruah Pallav Kumar ,   Sri Sathya Sai Institute of Higher Learning, India
Published: October 2024

A Formal concept lattice can be used to generate explanations from a black box model. This novel approach has been applied and proven for tabular data. It has also been compared to popular techniques in XAI. In this work, we apply the approach to image data. Image data, in general, comprises large dimensions and hence poses a challenge to build a formal concept lattice from such large data. We break the image into parts and build multiple sub-lattices. Using the combination of sub-lattice explanations, we generate the complete explanation for the entire image. We present our work beginning with a simple synthetic dataset for providing an intuitive idea of explanations and its credibility. This is followed by explanations of a model built on the popular MNIST dataset proving consistency of explanations on a real dataset. Text explanations from the lattice are converted to images for ease of visual understanding. We compare our work with DeepLIFT by viewing image masks obtained through contrastive explanation for specific digits from the MNIST dataset. This work proves the feasibility of using formal concept lattices for image data.
COV-CT-NET - A DEEP LEARNING MODEL FOR COVID-19, COMMUNITY-ACQUIRED PNEUMONIA DETECTION USING CT IMAGES
Authors:   Government College of Engineering and Textile Technology, Berhampore, India, Hasnat Abul , Das Mangalmay ,   Murshidabad College of Engineering and Technology, India, Halder Santanu ,   Government College of Engineering and Leather Technology, Kolkata, India, Bhattacharjee Debotosh ,   Jadavpur University, India 4
Published: October 2024

The world has witnessed the deadly impact of the Novel Corona Virus (COVID-19), claiming millions of lives since its outbreak in early December 2019. Early virus detection plays a crucial role in controlling this highly contagious disease. Though Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the current standard for confirmation of COVID-19, it is time-consuming. Computed Tomography (CT) imaging of the lungs can preferably be used for fast diagnosis of the disease as it is more sensitive and can detect complications. Due to the unavailability of adequate expertise, a deep learning-based model on CT images is a potential solution for fast detecting SARS Cov2 virus. In this study, we developed a simple but robust Convolution Neural Network model with multiclass detection ability between normal lungs, COVID-19 infected lungs and any other Community-Acquired Pneumonia (CAP) infection using Chest CT images. It is tested on a publicly available dataset, COVID-CT-MD and it achieved slice level accuracy of 99% on test dataset. We also attempted slice-level prediction of the unlabelled slices available in the dataset of COVID-19 and CAP cases.
IMAGE AND VIDEO ANOMALY DETECTION USING AI BASED DEEPANOMALY DETECTORS
Authors:   Vels Institute of Science, Technology and Advanced Studies, India, M Elavarasi , R Pramodhini ,   Nitte Meenakshi Institute of Technology, India, Deepak M. Deshmukh ,   Pravara Rural Engineering College, India, R Mekala ,   M. Kumarasamy College of Engineering, India, Kaur Chamandeep ,   Jazan University, Saudi Arabia
Published: October 2024

In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method.
IMAGE PROCESSING-DRIVEN DEEP LEARNING MODEL FOR PLANT DISEASE DETECTION TO ENHANCE IRRIGATION EFFICIENCY IN SMART AGRICULTURE
Authors:   P.A. College of Engineering and Technology, India, M Yuvaraja , G Aravindh ,   P.A. College of Engineering and Technology, India, R Priya ,   Pollachi Institute of Engineering and Technology, India, Babu V. Suresh ,   Sri Shanmugha College of Engineering and Technology, India
Published: October 2024

In the era of smart agriculture, efficient irrigation management is crucial for optimizing crop yield and resource use. Traditional methods of plant disease detection often rely on manual inspection, which is time-consuming and prone to errors. Smart agriculture leverages technology to improve agricultural practices. Accurate and timely plant disease detection is vital for effective irrigation management and overall crop health. Current methods are limited by their manual nature and inability to process large volumes of data quickly. Manual plant disease detection is labor-intensive and may not provide timely information, leading to inefficient irrigation practices. This inefficiency can result in reduced crop yield and wasted resources. LeNet integrates advanced image processing techniques with a deep learning architecture tailored for plant disease detection. The model utilizes convolutional neural networks (CNNs) to analyze plant leaf images, identifying disease patterns with high precision. LeNet incorporates preprocessing steps such as image normalization and augmentation to enhance model robustness. The network is trained on a comprehensive dataset of plant disease images, employing transfer learning to leverage pre-trained weights for improved accuracy. Evaluation of LeNet on a test dataset comprising 10,000 images demonstrated an impressive accuracy of 92.5%, with a precision of 90.3% and recall of 94.1%. The model significantly outperforms traditional methods, reducing disease detection time by 60% and enhancing irrigation efficiency by 30%. The reduction in water usage and increased crop yield were observed in practical trials.
DEEPMARKNET FOR ROBUST IMAGE AND VIDEO WATERMARKING EMBEDDING AND DETECTION
Authors:   Dr. A. P. J. Abdul Kalam Technical University, India, Bajpai Chhavi , Gaur Manish ,   Dr. A. P. J. Abdul Kalam Technical University, India, N. Mori Gajendrasinh ,   The Mandvi Education Society Technical Campus, India, Keshwani Palak ,   ICFAI University, Raipur, India
Published: October 2024

In the digital age, securing multimedia content against unauthorized use is critical. Traditional watermarking techniques often struggle with robustness against various attacks. This study introduces a novel DeepMarkNet approach for robust image and video watermarking. DeepMarkNet leverages deep learning to embed and detect watermarks with high resilience to common distortions. The method employs a Convolutional Neural Network (CNN) for embedding and a dual- stream architecture for detection. Experimental results demonstrate DeepMarkNet effectiveness, achieving a 98.5% detection accuracy and maintaining watermark integrity under compression and noise attacks. This outperforms conventional techniques by 15% in robustness.
AI-ENHANCED TRACKSEGNET AN ADVANCED MACHINE LEARNING TECHNIQUE FOR VIDEO SEGMENTATION AND OBJECT TRACKING
Authors:   Institute of Technology and Management, India, Kushwah Jitendra Singh , Dave Maitriben Harshadbhai ,   Government Polytechnic, Gandhinagar, India, Sharma Ankita ,   Jodhpur Institute of Engineering and Technology, India, Shrivastava Keerti ,   ITM University, India, Sharma Rajeev ,   Jiwaji University, India, Ahmed Mohammad Nadeem ,   King Khalid University, Saudi Arabia
Published: October 2024

Video segmentation and object tracking are critical tasks in computer vision with applications spanning surveillance, autonomous driving, and interactive media. Traditional methods often struggle with the dynamic nature of video data, where object occlusions, variations in illumination, and complex motion patterns present significant challenges. Existing segmentation and tracking systems frequently suffer from inaccuracies in handling real-time video sequences, particularly in distinguishing and tracking multiple overlapping objects. The limitations of current models in addressing these issues necessitate the development of more advanced techniques that can effectively manage dynamic scenes and improve tracking accuracy. To address these challenges, we propose an advanced machine learning technique, AI-Enhanced TrackSegNet, which integrates deep learning with novel attention mechanisms for improved video segmentation and object tracking. Our method utilizes a combination of Convolutional Neural Networks (CNNs) for feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. We introduce an attention-based mechanism to dynamically focus on relevant features, enhancing the model's ability to handle occlusions and varying object appearances. The model was trained on a diverse dataset of video sequences, incorporating both synthetic and real-world footage. The AI-Enhanced TrackSegNet demonstrated significant improvements in performance compared to existing techniques. Our method achieved an average Intersection over Union (IoU) score of 86.7% for segmentation and a tracking precision rate of 91.3% on the MOT17 benchmark dataset. These results represent a 10.2% improvement in IoU and a 7.5% increase in tracking precision compared to state-of-the-art methods. The model also exhibited enhanced robustness in complex scenes, handling occlusions and motion variations with greater accuracy.
Moral Injury: A Theme in Search of Definition
Authors: Mohiyeddini Changiz , Carlson Elizabeth , Sukhon Deena , Almarsi Wesam , Saad Marwa , Eshun Daniel , Baker Stephanie
Published: October 2024

Moral injury refers to a complex psychological condition that is assumed to emerge following exposure to distressing events that challenge the moral values or ethical beliefs of individuals by direct commission, omission, observation, or betrayal by a trusted leader. Moral injury can be found across various occupational domains, such as military, healthcare, and law enforcement. Symptoms of moral injury can include guilt, a sense of moral disorientation, struggle with spiritual or existential issues, and more. This paper aims to summarize current debates on the definition, symptomatology, and assessment of moral injury. Furthermore, it discusses the conceptual and psychological overlap and differences between moral injury and Post-Traumatic Stress Disorder (PTSD) and addresses current therapeutic approaches for treating patients with moral injury.
Metabolic flexibility to predict lifestyle interventions outcomes (MEPHISTO): a protocol for a predictive validation study and randomized control trial (Preprint)
Authors: Ludlova Monika , Koudelkova Katerina , Pallova Jitka , Koudelkova Barbora , Siklova Michaela , Cahova Monika , Vetrovsky Tomas , Šteffl Michal , Gojda Jan
Published: October 2024

BACKGROUND

Weight loss is a cornerstone of diabetes mellitus type 2 (T2D) management, but its implementation in clinical practice is limited by its perceived burden and variability in response. Personalising interventions to increase their success rate is an unmet clinical need.

OBJECTIVE

Identification of predictive factors associated with successful weight loss after sequential exercise in women with obesity.

METHODS

The study will consist of a two-stage analytical approach including a predictive validation study and a 2:1 randomised cross-over controlled trial.
Participants Women aged 25-45 years with obesity (BMI>30) will be included in the study.
Intervention The intervention will consist of a progressive protocol of aerobic exercise on a treadmill and a bicycle ergometer.
Outcomes FM/FFM weight loss, metabolic flexibility via ΔRQ (change in respiratory quotient (VCO2/VO2) between basal and insulin-stimulated state during glucose clamp), insulin sensitivity, glucose tolerance, HbA1c, microbiome composition, metabolomic signatures will be measured.

RESULTS

Recruitment for the trial began in January 2024. 12 participants were enrolled and randomised. Six participants have completed the first phase of the A-arm and 6 participants have completed the control period of the B-arm and their intervention is ongoing. Recruitment is ongoing. We expect the preliminary data from this study to be completed in 2026.

CONCLUSIONS

This intervention will investigate whether whole body and gut MetFlex can be further explored and used as ex-ante predictors of successful weight loss following exercise and dietary interventions, providing proof of concept and paving the way for personalised lifestyle interventions.

CLINICALTRIAL

NCT06329349
Pronunciation of 雨 (ame) and 飴 (ame) for Japanese Language Education Students Batch 2020 of Brawijaya University
Authors: Zalianti Nur Halizah , Indrowaty Sri Aju
Published: October 2024

This research discusses the pronunciation of ame/rain (雨) with high pitch (↗) to low pitch (↘) and ame/candy (飴) with low pitch (↘) to high pitch (↗) in Japanese Language Education Students Batch 2020. Based on the results of preliminary studies through Google Form, not all respondents were able to distinguish the pronunciation of ame/rain (雨) and ame/candy (飴). Therefore, this study aimed to determine the Japanese pronunciation of Japanese Language Education Students Batch 2020 of Brawijaya University by pronouncing one of the homophones namely ame/rain (雨) and ame/candy (飴). This research used a descriptive qualitative method. Based on simple random sampling technique, 10 respondents of Japanese Language Education Students Batch 2020 were selected as the data source of the research. The researcher examined students' understanding using voice recordings which were then analyzed through Praat to see the pronunciation results. The result of the research was on the word ame/rain (雨) 90% could pronounce it the correct tone. Then on the word ame/candy (飴) 50% could pronounce it the correct tone. 6 respondents made mistakes, this was due to learning strategies and second language culture. As a result of this study, the researcher suggested for analyzing the noun hoshii/want (欲しい) with hoshi/star (星) focusing on pitch, intensity, and respondents from the Department of Japanese Language Education and Japanese Literature who can be used as a comparison.
Language Kinship of Wooi, Poom, Ansus, and Ambai in Yapen, Papua
Authors: Nuraeni Neni , Hendrokumoro Hendrokumoro
Published: October 2024

This study aims to determine the language kinship in Wooi, Poom, Ansus, and Ambai languages. Data were collected from an interview with the indigenous speakers of Wooi, Poom, Ansus, and Ambai languages. This research employed a historical-comparative linguistics approach, which is the lexicostatistics and glottochronology method. Data were in the form of 200 basic vocabularies and were calculated with lexicostatistics and glottochronology formula. These research findings indicate the 64% language kinship between Wooi and Ansus. That result is the highest percentage of language kinship between the four languages. 54% is between Wooi and Poom. 53% is between Ansus and Ambai. 49% is between Ansus and Poom. 45% is between Wooi and Ambai. And 35% is between Poom and Ambai, which is the lowest percentage of language kinship. Thus, this study proves that Wooi, Poom, and Ansus are the languages classified into a family. Wooi, Ansus, and Ambai are also the languages classified into a family. Meanwhile, Poom and Ambai are classified as stock.
Propiedades físicas y mecánicas de las semillas de marañón (Anacardium occidentale) cultivadas en la Cuenca del Papaloapan, Veracruz, México
Authors: Amador-Mendoza Adolfo , Rodríguez-Miranda Jesús , Castellanos-Miravete Keila , Bravo Delgado Humberto Rafael
Published: October 2024

Al árbol Anacardium occidentale, se le conoce como marañón y al fruto como la nuez de anacardo o castaña de cajú, que es a su vez la semilla y está cubierta por una cáscara dura. Es originario principalmente de Brasil y Venezuela; sin embargo, en México crece en el sureste por el tipo de clima tropical adaptable para su cultivo. El objetivo de este trabajo fue determinar las propiedades físicas y mecánicas de las semillas al considerar: longitud (l), ancho (b), grosor (t), masa (Ms), volumen (V), diámetro geométrico (Dg), diámetro aritmético (Da), forma geométrica (Sφ), porosidad (ε), área de superficie (Sa), superficie específica (Ss), hidratación (Hc), índice de hidratación (Hi), hinchamiento (Sc), índice de hinchamiento (Hi), densidad del sólido(ps), densidad aparente (pb), ángulo de reposo (∅), coeficiente de fricción (μ) y fuerza de ruptura (Rf). Los valores obtenidos fueron: longitud (l), 25.599 mm (rango 10.515-37.084); ancho (b), 12.249 mm (rango 7.416-23.926) y grosor (t), 11.851 mm (rango 7.13-19.812). Los diámetros aritmético y geométrico y el volumen correspondieron a 1.656 cm, 1.245 cm y 0.015 cm³ respectivamente. Los resultados de este estudio proporcionan una base de información útil para el diseño de equipo industrial necesario en la manipulación, el almacenaje y el transporte de la nuez.
Facultades extraordinarias del presidente de la República y control judicial: expansión de las competencias del Poder Ejecutivo en la pandemia de COVID-19 en Colombia
Authors: Cajas Sarria Mario Alberto , Sarmiento Erazo Juan Pablo
Published: October 2024

Este artículo sostiene que, con el propósito de amparar la seguridad y la salubridad durante la pandemia por COVID-19, los tribunales constitucionales y de legalidad flexibilizaron el control judicial, permitiendo así una mayor expansión y concentración de las facultades reglamentarias del Poder Ejecutivo, bajo el entendimiento de que estas medidas serían más eficientes para controlar la propagación del virus y salvar vidas humanas. Para probar este argumento, se examinan los principales fallos de control de constitucionalidad y legalidad que fortalecieron el poder presidencial en Colombia, a costa de la facultad de control y configuración legislativa, así como de los espacios de deliberación democrática, para encontrar allí las subreglas que prueban la prevalencia de los criterios de eficiencia, sobre las libertades y el equilibrio de poderes.
Perspectiva interdisciplinar na leitura do texto literário no ensino fundamental
Authors: Marschall Ana Maria Leal de Lima , Saraiva Juracy Ignez Assmann
Published: October 2024

O artigo parte da compreensão de que uma nova forma de ensinar está sendo instituída na escola e que o texto literário pode atuar como promotor dessa mudança. Nesse sentido, destaca a pertinência da perspectiva interdisciplinar, que permite o compartilhamento de saberes, e apresenta uma reflexão sobre o papel da literatura, particularmente da literatura infantil, no Ensino Fundamental, vinculando-a à forma como ela representa a própria escola. Para tanto, vale-se de estudos de autores como Ariès (1981), Zilberman (2003), Iser (2002) Saraiva e Mügge (2006) que tratam, respectivamente, da concepção de infância, da literatura infantil, do texto como um jogo, cujas lacunas o leitor precisa preencher, e da importância da escola na formação de leitores. O artigo conclui que a leitura do texto literário conduz os alunos a um posicionamento crítico frente à realidade, abrindo caminhos que se estendem para além do ambiente escolar.
Hiperconto e o ensino de literatura digital: uma abordagem metodológica para o Ensino Fundamental II
Authors: Ribeiro Cláudia Souza , Cardoso Rosimeiri Darc
Published: October 2024

A literatura digital é movida pelos motores da cultura contemporânea, é feita por meio de equipamentos eletrônicos e geralmente lida em suporte digital; trata-se de uma leitura interativa que dialoga com os mais variados recursos digitais. A presente pesquisa objetiva verificar a recepção de alunos do 8º ano do Ensino Fundamental, no que se refere à fruição da leitura de literatura digital, mais especificamente sobre o hiperconto de suspense Um Estudo em Vermelho, de Marcelo Spalding. Para tanto, como aporte teórico, pautou-se nos estudos de Hayles (2009), e seu caudatário Kirchof (2016), de modo a buscar entender o funcionamento e as contribuições da literatura digital para o ensino. Dessarte, o estudo se deu por meio de uma pesquisa-ação, cuja abordagem metodológica é calcada no Método Recepcional, de Bordini e Aguiar (1988), os dados aqui analisados foram coletados por meio de questionários aplicados ao início e ao final da oficina, os quais foram estudados qualitativa e quantitativamente. Como resultado, aponta-se a importância da formação do leitor e confirma-se o fato de a literatura digital, sobretudo o hiperconto, possibilitar interação aos estudantes, tornando a leitura convidativa e prazerosa.
Entonação valorativa em palavra escrita
Authors: Menegassi Renilson José
Published: October 2024

O ensaio discute como a entonação valorativa, elemento do dialogismo que compõe os discursos humanos, apresenta-se e constitui-se em discursos escritos. Para tanto, analisa, discute e reflete sobre ela em duas fábulas, a demonstrar como a materialidade linguística do estilo de linguagem permite seu reconhecimento. Também apresenta e analisa produções escritas de aluno do 4º ano do Ensino Fundamental, a relatar como a orientação mediada e coerente sobre o elemento dialógico destacado se estabelece em atividade de produção textual escrita, a permitir o trabalho para o ensino, a aprendizagem e o desenvolvimento de discurso escrito em contexto escolar.
10 anos de Diálogo das Letras: entre números, significados, perspectivas e desafios
Authors: Bessa José Cezinaldo Rocha
Published: October 2024

Para oferecer aos nossos leitores colaboradores um registro, mesmo que panorâmico, do que constitui e representa a Diálogo das Letras na/para a comunicação científica da área, pretendemos apresentar, no presente texto, um pouco da história da revista, lançando o nosso olhar para seu passado, seu presente e seu futuro. Nesse exercício, procuraremos contemplar a identidade da revista, atos inaugurais, números, transformações, atores, conquistas e desafios, e, além disso, sinalizar perspectivas que se descortinam.
Mulheres, literatura e audiovisual: ensaios sobre gênero, sexualidade e arte
Authors: Barata Camille Gouveia Castelo Branco
Published: October 2024

Em seu texto clássico sobre a prática do ensaio, Adorno considera que essa é uma forma de escrita cuja construção evoca a necessidade de liberdade de espírito por parte de seus autores, pontuando que “felicidade e jogo” são essenciais para o exercício ensaístico e para aqueles que nele adentram. Talvez Adorno não tivesse em mente que o ensaio seria uma modalidade de reflexão em grande medida praticada por escritores e escritoras cujo espírito foi privado da amplidão da liberdade – mulheres, pessoas racializadas, pessoas vivendo no Sul Global, membros das comunidades LGBTQIA+, para citar alguns exemplos. Os ensaios contidos neste livro possuem uma inclinação: por meio deles, investiguei, dialoguei e, muitas vezes, colidi com filmes, entrevistas, teorias, outros ensaios e ficções que têm em comum a tentativa de representar a multiplicidade caudalosa das experiências de mulheres, que não mais admitem serem encaradas como bloco universal, homogêneo e transcultural, mas demandam morada naquilo que Audre Lorde chamou de “casa da diferença”. A mulheridade perscrutada neste livro também é uma forma de falar sobre sexualidade, fazer artístico, desejo, Direito, Filosofia, perda, sofrimento, violência, desigualdade. As artistas para quem me voltei escapam ao moralismo fácil e abraçam a contradição como forma de vida e existência, por meio de expressões que este livro se propõe a analisar.