bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification and management of a yellow -grains actinomycetoma caused by Micromonospora olivasterospora in Sudan. The First Report
Authors: Mhmoud Najwa A 
Published: May 2024

Abstract

We describe a case of yellow grains actinomycetoma in the second toe of the left foot of Sudanese female caused by Micromonospora olivasterospora that does not match any known agent of this infection. The strain was identified as a Micromonospora olivasterospora based on the cultural and molecular techniques in combination with an updated taxonomy were successfully used to identify the yellow grain- producing bacterial Micromonospora olivasterospora as a new causative agent of human actinomycetoma. The clinical strain showed low MIC to doxycycline (0.5 µg/ml) followed by amikacin (4µg/ml), ceftriaxone (4µg/ml), trimethoprim/sulfamethoxazole (TMP-SXT) (≤ 2/38µg/ml), linezolid (8µg/ml), imipenem (≤ 13 µg/ml), and cefotaxime (≥ 14 µg/ml). In contrast, was not susceptible to minocycline (≥ 4µg/ml), amoxicillin-clavulanic acid (> 32µg/ml) and ciprofloxacin (≥ 21µg/ml). Therefore our patient received amikacin sulfate (15 mg/kg twice daily for 5 weeks), folic acid 5 mg once per day and trimethoprim/sulfamethoxazole (TMP-SXT) (co-trimoxazole 960 mg twice daily for 6 weeks); the cycles are repeated until the patient is cured. In conclusion: Updated taxonomy is essential to identify the new causative agents to the species level and is fundamental to elucidate the actinomycetoma associated with this unusual bacterial and to determine the appropriate therapy.
Exploring Facebook Engagement Among Postgraduate Diploma in Teaching (PGDT) Students at Jimma University: A Phenomenological Inquiry
Authors: Tadesse Yared Mulat , Bekele Adula , Birhanu Zewdei 
Published: May 2024

Abstract

This study sought to deeply explore the Facebook usage patterns and experiences of Postgraduate Diploma in Teaching (PGDT) students within the context of Jimma University. Employing a methodological approach that combined in-depth interviews and reflective essay reports, the research adopted a hermeneutic phenomenological design to uncover the nuanced layers of participants' lived experiences with the platform. The findings illuminated that participants, on average, devoted approximately two hours per day and fourteen hours per week to engaging with Facebook, primarily for purposes of socialization and entertainment. However, alongside its recreational allure, Facebook also surfaced a spectrum of negative emotions and concerns, notably stemming from the prevalence of hate speech within the platform's discourse. Furthermore, the study revealed a notable gap in participants' utilization of Facebook for academic learning, indicating a need for enhanced knowledge and skills in leveraging the platform for educational purposes. This insight underscores a critical area for improvement within the educational landscape. In light of these findings, the study advocates for proactive measures to address the identified challenges, emphasizing the importance of fostering a more balanced and constructive engagement with Facebook among PGDT students. Additionally, the research highlights avenues for future exploration, signaling the need for continued inquiry into the evolving dynamics of social media usage within educational contexts.
Vibration Analysis of a Soft Hydrodynamic Manipulator
Authors: Maliha Sabi Jahan , Nehal Tasnemul Hasan , Ahmed Shugata , Hasan Muhammad Hasibul 
Published: May 2024

Abstract

Soft robotic manipulators, driven by fluidic actuation, surpass rigid counterparts with increased degrees of freedom, enabling exploration of intricate workspaces. Despite potential vibrations from power fluctuations, these can be mitigated using damping fluids. This paper focuses on experimentally analyzing the vibrations of a soft hydrodynamic manipulator. To this end, a soft manipulator was fabricated, and a corresponding test setup was devised. Employing three distinct test fluids water, engine oil, and mustard oil with large viscosity variations, the research characterized vibrations through the ripple factor. This metric was computed based on temporal shifts in linear and angular displacements across various planes. Notably, the findings indicate that, owing to its lower viscosity, water exhibits maximum deflection at a higher Reynolds number (Re ≥ 100) compared to engine oil (Re= 9.3) and mustard oil (Re= 8.31). Additionally, the ripple factors suggest that, for water, vibration is higher those observed in the other two test fluids.
Multi-featured short-term electricity load forecasting based on error-optimal weighting method and improved combination forecasting model
Authors: Yu Huiqun , Sun Haoyi , Li Yueze , Xu Chunmei , Du Chenkun 
Published: May 2024

Abstract

Addressing the problems of high randomness and low prediction accuracy in short-term power load forecasting, this paper proposes a multi-featured short-term power load prediction model based on the error optimal weighting method and the improved combination prediction model. Firstly, the combined algorithm of grey correlation analysis and radial kernel principal component analysis is used to deal with the multi-factor coupled input data. Secondly, the prediction results of the improved sparrow search algorithm optimized convolutional neural network and deep bidirectional gated recurrent unit combination model, convolutional neural network and long short-term memory network as well as selective attention mechanism combination model, and improved time convolutional network and channel enhanced attention mechanism combination model are processed by the error optimal weighting method to obtain the final prediction results. Then, the Bagging and Extreme Gradient Boosting combination model optimized by Bayesian theory is used to optimize the prediction error. Finally, a validation model is constructed, and by comparing with many mainstream algorithmic models and combining with various error data to verify that the strategy proposed has better performance and can improve the accuracy of short-term power load forecasting.
An Evaluation of the Mine Water Inrush based on the Data expansion and Deep learning
Authors: Tang Shoufeng , Zhang Ye 
Published: May 2024

Abstract

The accuracy of coal mine water inrush prediction models is affected mainly by the small number of samples and difficulty in feature extraction. In this paper, a new data augmentation water inrush prediction method is proposed. This method uses a natural neighbors theory and mutual information dropout sparse autoencoder -improved SMOTE to augment and predict the risk of water inrush in coal mines. By learning water intrusion features through the autoencoder, we can achieve better separation between classes and weaken the influence of data overlap between classes in the original sample. Then, the natural neighbors search algorithm is used to determine the intrinsic neighbor relationships between samples, remove outliers and noise samples, and use different oversampling methods for borderline samples and center samples in the minority class. Synthetic samples are generated in the feature space, mapped back to the original space and merged with the original samples to form an expanded water inrush dataset. Finally, the effectiveness of the proposed method is confirmed by comparing the measured water inrush data and prediction model results in typical mining areas in North China. The results from this study can be used to more accurately analyze the characteristics of water inrush accidents, improve the accuracy of water inrush accident prediction, and promote the application of machine learning in water inrush prediction.
Identification of severity level of concrete surface crack through image processing
Authors: Verma Akash , Kumar Virendra , Pandey Shashi Ranjan 
Published: May 2024

Abstract

The study employs Python-based image processing and laboratory experiments to detect and monitor cracks in structures. It tracks crack growth, severity, and angle over time, offering a comprehensive analysis of each crack that forms on the surface of the concrete by forming segments. The program's capabilities include precise crack detection by four directional matrices operation and minimal error rates of crack development. The algorithm can be worked in any condition due to supervised automation. The mathematical formulation for threshold performs better than OTSU in the case of concrete surfaces. The actual crack length is calculated with the help of the tortuosity index, formulated with respect to the mean width of the crack. Crack width and crack length are both taken as parameters for severity based on which its level is decided. The total five grades of severity level are defined. This approach provides a systematic and organized method for monitoring and analyzing changes in cracks, which is crucial for maintaining structural safety.
Elevating Security Analysis: The MTLPT Framework for Enhanced Vulnerability Prediction
Authors: HUI ZHANFA , LIU LAN , CHEN GUIMING , CAI TINGFENG , ZHOU CHIYU 
Published: May 2024

Abstract

In the current field of vulnerability prediction, accurate forecasting and identification of potential vulnerabilities in software are crucial, especially when dealing with real-world vulnerability data. The challenges posed by data imbalance and complex dependency relationships often make prediction tasks exceptionally difficult. Traditional single-task learning methods and ensemble learning methods typically perform poorly when handling highly imbalanced datasets. These methods often overlook minority categories, which frequently contain the most critical vulnerabilities, and fail to fully learn vulnerability features. To address these issues, we propose a novel multi-task learning model called MTLPT, aimed at enhancing the accuracy and efficiency of vulnerability prediction through a multi-task learning framework. The MTLPT model combines custom lightweight Transformer blocks and position encoding layers to effectively capture long-range dependencies and complex contextual information from source code. With this structural design, MTLPT can simultaneously handle various vulnerability prediction tasks, thereby learning the latent relationships between different vulnerability types and improving the model’s sensitivity to rare but severe vulnerabilities. Additionally, the MTLPT model introduces a loss function based on dynamic weights, which dynamically adjusts loss weights based on the prediction difficulty of different tasks, effectively mitigating the challenges posed by imbalanced data. We conducted comparative experiments on a subset of highly imbalanced real-world vulnerability dataset. The experimental results demonstrate that, compared to existing single-task learning and ensemble learning methods, MTLPT exhibits significant advantages across multiple key performance metrics, particularly in identifying minority class vulnerabilities with higher sensitivity and accuracy. This performance enhancement validates the effectiveness of our proposed multi-task learning framework in handling complex and imbalanced vulnerability data, highlighting the importance of the MTLPT framework, custom lightweight Transformer blocks, position encoding layers, and the dynamic weight loss function in practical applications. Furthermore, we conducted a series of ablation experiments to thoroughly evaluate the contributions of individual components within the MTLPT model, confirming the role of custom lightweight Transformer blocks and position encoding layers in enhancing the model’s ability to learn complex code structures and behavioral patterns, while also demonstrating the critical role of the dynamic weight loss function in optimizing the training process of the multi-task learning model.
Real time Violence and Weapon detection and
Alert System
Authors: More Pradnya , Patil Sakshi , Pattanshetti Tanuja 
Published: May 2024

Abstract

With the proliferation of surveillance systems and the increasing need for public safety, real-time violence detection has emerged as a crucial research area. This project presents a novel approach to real-time violence detection leveraging the power of deep learning through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (LSTM) and weapon detection using You Only Look Once algorithm. The proposed system not only accurately identifies violent activities and weapons in video streams but also promptly issues alerts for timely intervention. The proposed system aims to address the challenges associated with accurately and swiftly identifying violent activities and weapons in video streams, making it applicable to diverse settings such as public spaces. The CNN component of the model is designed to automatically extract relevant spatial features from video frames, enabling the network to discern patterns associated with violent actions. Simultaneously, the Bi-LSTM network is employed to capture temporal dependencies and dynamic sequences within the video stream, allowing the model to understand the evolution of events over time. The fusion of spatial and temporal information enhances the robustness and accuracy of violence detection, especially in complex scenarios where contextual information is crucial. To further augment the system's capabilities, we use the You Only Look Once (YOLO) for weapon detection. YOLO is a state-of-the-art object detection algorithm that operates in real-time with high accuracy. By incorporating YOLO into our system, we enhance its ability to not only detect violent activities but also identify the presence of weapons in the scene. This additional functionality adds a crucial layer of security and enables authorities to respond more effectively to potential threats. Overall, the combined approach of CNN, Bi-LSTM, and YOLO offers a comprehensive solution for real-time violence detection in video streams, with the added capability of weapon detection. This integrated system contributes to enhancing public safety and security in various settings, including public spaces, by enabling timely intervention and response to potential threats.
Weight Management Therapies in Metabolic Dysfunction-Associated Steatotic Liver Disease are not affected by Comorbid Psychiatric Disease
Authors: Bollinger Bryan , Cotter Robert , Mehal Wajahat , Ilagan-Ying Ysabel , Gupta Vikas 
Published: May 2024

Abstract

Background and Aims:
Metabolic dysfunction-associated steatotic liver disease (MASLD) and resultant steatohepatitis (MASH) have been linked to psychiatric comorbidities. The treatment of MASLD/MASH primarily relies upon weight loss. We aimed to determine whether metabolic and hepatic outcomes in MASLD/MASH patients were significantly different with the presence of comorbid psychiatric disease in an interdisciplinary clinic that integrates weight management and hepatology care.
Methods:
We conducted a retrospective cohort study comparing outcomes of MASLD/MASH patients aged ≥ 18 years with mood and/or anxiety disorders to those without followed for 1 year. The primary outcome was reaching 7% total body weight loss (TBWL) at 12 months with logistic regression models used to identify treatments which predicted 7% TBWL. Secondary outcomes were noninvasive assessment of steatohepatitis improvement, including change in ALT and FIB-4 scoring.
Results:
Of 567 patients with MASLD/MASH, 366 (64.6%) had a comorbid mood and/or anxiety disorder. Presence of comorbid psychiatric disease was not a significant predictor of weight loss or any of the secondary outcome measures at 12 months. Significant predictors of achieving 7% TBWL at 12 months among all patients with MASLD/MASH included semaglutide, phentermine-topiramate, and bariatric surgery. Significant predictors of achieving 7% TBWL at 12 months in patients with MASLD/MASH and a psychiatric comorbidity included semaglutide, topiramate, phentermine-topiramate, and bariatric surgery. Both groups experienced similar improvements in hepatic outcomes.
Conclusions:
Our findings suggest that obesity management in patients with MASLD performs similarly in the presence of comorbid psychiatric disorders. Topiramate and phentermine may be particularly effective in this patient population yet are underutilized in hepatology practices.
B-FADE: Bayesian-FAtigue moDel Estimator in Python and its Application to the Probabilistic Estimation of El Haddad Curves
Authors: Tognan Alessandro , Salvati Enrico 
Published: May 2024

Abstract

Accurate calibration of semi-empirical fatigue models against experimental evidence is a critical step for achieving reliable predictions. Amongst many semi-empirical fatigue models, El Haddad’s (EH) curve is widely exploited to characterise the fatigue endurance limit of defect-laden and cracked metallic alloys. A few deterministic computational models exist in this respect, however, they lack a robust probabilistic perspective and their implementation code is not publicly accessible. The authors of the present work have recently exploited Maximum a Posteriori (MAP) to robustly and probabilistically estimate EH's curves, even in case of data scarcity or incomplete datasets, combining experimental evidence and prior knowledge taken from literature. Whilst the implementation scheme was published, the associated code was not made available. Hereby, the authors present B-FADE, a flexible open-source Python package, aimed at releasing the implementation of the MAP approach with improvements, as well as several pre- and post-processing utilities to facilitate its deployment. The package is conferred with a sufficient level of abstraction, thus turning out to be easily extensible to future implementation of other relevant fatigue models.
Unveiling the Superior Provenances of Khejri (Prosopis cineraria) trees in Arid Lands of Western Rajasthan
Authors: Choudhary Khushwant B. , Moharana P. C. , Sanyal Archana 
Published: May 2024

Abstract

The study aimed to assess and identify the superior khejri (Prosopis cineraria) provenance in the western arid drylands through habitat site characterization using GIS mapping, in-situ variability studies, germination and progeny-cum-provenance trial. Eight provenances among the nine identified were selected for in-situ study based on their distinct growth characteristics. The analysis of various morphometric traits revealed significant differences among provenances, emphasizing their genetic variability. Correlation analysis demonstrated a strong positive relationship between plant height and both stem girth (r = 0.70*) and crown diameter (r = 0.93**), indicating the importance of considering tree height as a reliable indicator of early growth assessment during provenance selection. Germination (ranging from 75% in Karola (Jalore) to 82% in Lamba (Jodhpur)) also indicated the variable germination potential of these provenances. Subsequently, in progeny-cum-provenance trial, at 5 months after transplanting (MAT), no significant differences in plant height were observed among the provenances. However, at 15 MAT, significant variations in plant height emerged, with Absar (Churu) exhibiting the highest height of 87.22 cm. By 25 MAT, all provenances displayed further increases in plant height, with Bhadriya (Jaisalmer) attaining the greatest height of 181.11 cm. Based on the ranking of growth across the three-time intervals, the study concluded that Absar consistently demonstrated superior growth performance. Thus, Absar can be identified as the most favourable provenance considered for khejri cultivation in the western drylands.
Prognostic significance of contrast enhancement in newly diagnosed glioblastoma, IDH-wildtype in adults
Authors: Roux Alexandre , Elia Angela , Hudelist Benoit , Benzakoun Joseph , Dezamis Edouard , Parraga Eduardo , Moiraghi Alessandro , Simboli Giorgia Antonia , Chretien Fabrice , Oppenheim Catherine , Zanello Marc , Pallud Johan 
Published: May 2024

Abstract

Background and Objectives.
Glioblastoma, IDH-wildtype presents with a typical ring-like contrast enhanced area surrounding a central necrosis on imaging. We assessed the prognosis of contrast enhancement.
Methods.
We performed an observational, retrospective, single-centre cohort study at a tertiary neurosurgical oncology centre (January 2006 - December 2022). We screened adult patients harbouring a newly-diagnosed glioblastoma, IDH-wildtype.
Results.
We included 1149 glioblastomas, IDH-wildtype: 26 (2.3%) had a no contrast enhancement, 45 had a faint and patchy contrast enhancement (4.0%), 118 had a nodular contrast enhancement (10.5%), and 960 had a ring-like contrast enhancement surrounding central necrosis (85.5%). Progression-free and overall survivals were longer in non-contrast enhanced glioblastomas (9.5 months and 26.7 months, respectively) than in contrast enhanced glioblastomas (6.5 months and 10.9 months, respectively) (p = 0.007 and p < 0.001, respectively). Non-contrast enhanced glioblastoma had a higher rate of long-term survivor (42.3%) than in contrast enhanced glioblastoma (16.3%) (p = 0.002). In the subgroup of contrast enhanced glioblastomas, the overall survival was lower in ring-like contrast enhancement (10.0 months) than in other contrast enhancement patterns (13.0 months) (p = 0.033). Cortical involvement by the contrast enhancement and surgical resection were independent predictors of longer survivals, while preoperative KPS score < 70, ventricle involvement of the contrast enhancement, tumour volume ≥ 30cm3, and postoperative residual contrast enhancement were independent predictors of shorter survivals.
Conclusion.
The ring-like pattern of contrast enhancement is present in the majority (85.5%) of glioblastomas and is associated with shorter survivals than non-contrast enhanced (2.3%) glioblastomas. The pattern of contrast enhancement is an independent survival predictor.
Heat Integration as a Strategic Economic Basic Ingredient for Viable Clean Technologies: Perspective of Return on Incremental Investment
Authors: Wagialla Kamil M. , Abuelgasim Doaa , Elhussein Aisha 
Published: May 2024

Abstract

This paper introduces a systematic economic approach to evaluate the economic benefits of Heat Exchange Networks (HENs) within the framework of clean technologies and environmental policy. It emphasizes the integration of sustainability, presenting a novel metric, Return on Incremental Investment (ROII), to assess economic profitability. Through a case study of a green grass chemical facility, the economic analysis involves heat integration via mathematical programming, estimating additional capital expenditure, utility cost savings, and ROII calculation. The study underscores the pivotal role of robust economic analysis in driving sustainable economic development, especially in the domain of cleaner technologies. It highlights the importance for stakeholders and policymakers to prioritize economically viable and cleaner technologies for achieving sustainable economic development objectives. The findings offer valuable insights for industrial experts and plant managers to make informed decisions based on economic performance benchmarking. Overall, this study contributes to advancing the understanding of economic viability within the context of clean technologies and environmental policy, facilitating informed decision-making towards sustainable economic development initiatives. The results of the economic evaluation underscore the high sensitivity of ROII to changes in utility costs.