bluuebunny/crossref_metadata_2025_split huggingface dataset:
The driver of global agricultural value chains: Evidence from 6 ASEAN Countries
Authors: Irhamna Ariyo Dharma Pahla , Firdaus Muhammad , Arifin Bustanul , Ratnawati Anny 
Published: February 2025

<p dir="ltr"><span>This study investigates the drivers of global agricultural value chain (GAVC) participation in six ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Employing panel geographically weighted regression, the paper analyzes the spatial heterogeneity of factors influencing global agricultural value chain participation. Our findings reveal significant variations in the impact of tariffs, FDI, agricultural land area, inland waters, and trade balance across countries. Tariffs emerge as a crucial factor in enhancing competitiveness in Indonesia, Singapore, and Thailand, aligning with trade theory. FDI positively influences Malaysia's global agricultural value chain participation, emphasizing attracting foreign investment. Agricultural land area plays a pivotal role in Indonesia and Thailand, highlighting the significance of resource endowments. Inland waters contribute significantly to agriculture in the Philippines, while their impact in Indonesia suggests potential inefficiencies in water management. Trade balance in food products positively affects global agricultural value chain involvement in Thailand and Vietnam. These findings underscore the need for tailored policies to address the unique characteristics of each ASEAN country. Future research should explore the long-term implications of these factors and consider broader socio-economic and environmental contexts. </span></p>
Machine Learning-Based Crop Yield Prediction for Sustainable Agriculture in Eastern Ethiopia
Authors: Abate Jemal , Aman Araba , Adem Dima 
Published: February 2025

Abstract

This study focuses on developing a machine learning-based crop yield prediction model suitable for agricultural conditions in Eastern Ethiopia, presents research that has taken cognizance of the fact that agriculture occupies the leading role in the economy through providing a source of livelihood for most Ethiopians and being one of the main contributors to the nation's GDP. Thus, addressing challenges such as low productivity and food insecurity induced by climate change and environmental factors becomes paramount. Agriculture in Ethiopia is very sensitive to changes in weather conditions, soil degradation, and other ecological factors; hence, exact yield forecasting is quite indispensable for proper planning and resource management of the sector. It integrates various data sets that include but are not limited to local agri-data, historical yield records, and satellite-derived environmental information. Advanced machine learning algorithms have been considered in the model, such as Random Forest, Gradient Boosting, K Neighbors, and Decision Tree regressors for the prediction of yields precisely. The methodology involved rigorous data preprocessing, feature selection, and model training to give robustness and reliability to predictions. Results clearly indicated that the Random Forest Regressor model outperformed all other alternative models and showed its prospect in enhancing agricultural productivity by informing decisions to be taken by the farmers and other stakeholders. This research underlines technology utilization as a means of optimizing crop management practices, one of the crucial pre-conditions for improved food security and sustainable agricultural development in the region. The results of this study do not end at the immediate farming outcomes, but also remind that technology-driven interventions may potentially address broader socio-economic problems in Eastern Ethiopia.
Risks of Campylobacter jejuni and Campylobacter coli contamination in broiler's meat, table eggs, and the food environment in correlation to human enteritis.
Authors: Tawfik Tawfik Esmat Abdel-Hafeez , Yousif Amany Abbass 
Published: February 2025

Abstract

Campylobacter in broiler meat presents a significant challenge to food safety. Human campylobacteriosis predominantly arises from multiple sources, with poultry serving as the most substantial contributor. The objective of this study was to ascertain the presence of Campylobacter contamination of retail broiler meat at various critical junctures, including slaughter, processing, food preparation, and potential cross-contamination events occurring in both slaughterhouses and restaurants, processed chicken products, table eggs, and stool samples from humans with enteritis. To fulfill this, samples were systemically collected to detect Campylobacter contamination during various stages of slaughter, processing, food preparation, and potential cross-contamination scenarios at slaughterhouses and restaurants. A total of 460 samples were procured, encompassing 120 samples of chicken meat, 100 table eggs, 120 samples of human stool, and 120 environmental food samples, all obtained from Sohag, Egypt. Each sample underwent bacteriological, biochemical analysis and multiplex PCR enabled the detection of the 23S rRNA, hip O, and gly A genes for the precise identification and differentiation of Campylobacter at the species level. The observed prevalence rates of Campylobacter in broiler meat, table eggs, environment, and human stool samples determined by multiplex PCR were 9.17, 2, 7.5, and 6%, respectively, with overall positive samples of 6.3% (28/460). 75% (21/28) of the isolates were Campylobacter jejuni, 25% (7/28) were Campylobacter coli, and 1 isolate had mixed contamination. Poultry fecal matter, broiler meat, and table eggs could be a high risk of C. jejuni and C. coli to humans, highlighting the need for targeted interventions in the poultry, and egg industry to mitigate the risk of Campylobacter infections. Improved food handling practices at restaurant and house kitchens are essential to reduce contamination.
Hybrid Image Denoising using Adaptive Conductance Function and Bi-dimensional Empirical Mode Decomposition
Authors: Benziane Sarah 
Published: February 2025

Abstract

In digital imaging, effective noise reduction is essential for maintaining image clarity, especially in fields such as medical imaging, remote sensing, and computer vision. Traditional denoising techniques often involve a trade-off between noise suppression and detail preservation, which can result in loss of essential image features. This paper presents a novel hybrid denoising approach that combines an Adaptive Conductance Function (ACF) with Bi-dimensional Empirical Mode Decomposition (BEMD). The proposed method leverages the adaptive properties of ACF to dynamically reduce noise based on local image gradients, preserving edges and fine details. The BEMD component decomposes the image across multiple frequency scales, allowing selective noise reduction on high-frequency components without affecting the underlying structural information. Experimental evaluations on synthetic and real-world datasets, including images with Gaussian and salt-and-pepper noise, demonstrate significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to traditional methods. This hybrid method shows strong potential for applications requiring high-fidelity image denoising and detail preservation.
Whole-Brain Single-Neuron Atlas Reveals Microglial Security Hole Accelerating Neuronal Vulnerability
Authors: Ueda Hiroki , Mitani Tomoki , Yamaura Kosei , Yamada Rikuhiro , Matsumoto Katsuhiko , Susaki Etsuo , Sahara Naruhiko , Yanai Rin , Ikenaka Kensuke , Jiang Shiying , Hayakawa Hideki , Mochizuki Hideki , Baba Kousuke 
Published: February 2025

Abstract

Neurodegenerative diseases are characterized by progressive neuronal loss, yet subtle degeneration remains difficult to detect, hindering advancements in early diagnosis and intervention strategies. Here, we present a 3D, whole-brain, single-neuron atlas that aligns and compares neurons at single-cell resolution, surpassing 2D spatial limitations to precisely detect heterogeneous neurodegenerative lesions. Using the AppNL-G-F model of Alzheimer’s disease, we found that neuronal loss begins concurrently with amyloid-β deposition, indicating a more acute timeline than proposed by the amyloid hypothesis. We also identified age-dependent microglial redistribution from gray-matter–rich to white-matter–rich regions in wild-type mice, accelerated in AppNL-G-F, potentially reducing neuroprotective function. Spatial single-cell-resolution risk analysis revealed microglial depletion, rather than proliferation, as an early risk indicator for neuronal loss, and microgliosis often coexists with microglial loss, suggesting heightened microglial vulnerability. Integration with spatial transcriptomics showed neurons near microglia with reduced homeostatic gene expression are especially vulnerable. Together, these findings demonstrated that amyloid pathology skews microglial distribution and promotes their exhaustion, leading to microglial spatial disorganization and neuronal vulnerability, resulting in “microglial security holes”. Our study underscores the transformative potential of 3D analysis in neurodegenerative research, offering a versatile platform for organ-level spatial multi-omics integration to advance detection and therapeutic strategies.
A broadly neutralizing antibody recognizing a unique epitope with signature motif common across coronaviral families
Authors: Yang Guang , Yan Lei 
Published: February 2025

Abstract

Cross-reactive antibodies that bind to diverse epitopes have been identified in Sarbecoviruses, but the precise molecular mechanisms remain poorly understood. This study describes the isolation of a broadly neutralizing monoclonal antibody, 3D1, derived from a fully human combinatorial antibody library that targets the highly conserved HR1 domain of Sarbecoviruses. 3D1 uniquely recognizes a type 1 β-turn fold consisting of a 6-mer epitopic peptide, pepDVVNQN/Q, which forms during a pre-hairpin transition state—a conformation that occurs exclusively before membrane fusion during viral infection. It effectively neutralizes a wide range of live SARS-CoV-2 wild-type strains and variants of concern (VOCs), with the exception of Omicron, which evades neutralization due to a detrimental point mutation (Q954H). Notably, this cryptic epitope reveals a signature binding motif that appears to extend throughout the core region of the glycoprotein of coronaviruses and is also present in various RNA virus orthologs, including those of HIV and Marburgvirus. 3D1 functions as a natural or background antibody capable of binding to a diverse array of non-self antigens. Its cross-reactivity toward a wide variety of antigens underscores the effectiveness of the combinatorial antibody library approach, which encompasses the entire antibody repertoire.
Significant enhancement of carrier localization in InGaN MQWs by underlying high-In-content superlattice
Authors: Mickevičius Juras , Širvinskytė A. , Nomeika K. , Malinauskas T. , Kadys A. , Aleksiejūnas R. 
Published: February 2025

Abstract

To improve the emission efficiency of InGaN/GaN multiple quantum wells (MQWs), we used the high indium content short-period superlattice (SPSL) underlayer and validated the approach by studying the structural and optical properties. The SPSL underlayers were deposited at temperatures equal to or below the growth temperature of active region MQWs. The impact of such underlayers was studied using XRD, photoluminescence (PL), and light-induced transient grating (LITG) techniques. The large redshift of the PL band and higher PL efficiency at low excitations were achieved in the MQWs grown on SPSLs, albeit at the expense of shorter carrier lifetime, compared to the structures without SPSL underlayer. We attribute the observed changes to the interplay between the transformation of localizing potential and the generation of additional point defects, while the coincidence of modifications results in the formation of regions having high density of both localized states and nonradiative recombination centers.
Intelligent Spam Filtering and Analysis Using Web Automation, ReportLab, and Ensemble Machine Learning Techniques
Authors: Eppakayala Sathvik , Ankula Shiva Kumar Goud 
Published: February 2025

Abstract

The greatest cybersecurity problem of spam emails results in security risks and unnecessary mails in inboxes. To resolve this issue, we developed a real-time automated spam filtering system that efficiently detects and filters spam emails. The goal was to build a model that not only achieves high accuracy but also continuously works without the intervention of a user. We have trained and tested five machine learning classifiers: logistic regression, decision tree, K-nearest neighbors (KNN), Gaussian naive Bayes, and AdaBoost. We have compared their performances with precision, recall, and F1-score. Among them, AdaBoost has performed the best, showing the highest accuracy in classifying spam and legitimate emails. In order to improve the reliability, we combined and balanced two different spam email datasets so that our model adapts well to the various types of emails. This meant that we implemented a fully automatic system by constructing a web application using Python Flask. We deployed Selenium to get emails from a user's inbox automatically and to classify them real-time. Subsequently, it generates an automatically created PDF report using ReportLab, which highlights the detected patterns of spam mail and the success rate of spam filtering. A hands-free mechanism ensures that this process does not require users to check and go through their spam emails manually and thus increases both efficiency and security. Our results indicated that real-time machine learning-based spam filtering along with automation boosts accuracy and reliability. Our system is scalable and adaptable, so it can be useful for many email platforms. We will try to improve the model further, adapting it in order to follow the evolving techniques of spammers, improving speed of processing, and integrating some additional security features. Our contribution is toward making advanced, real-time spam-filtering solutions able to protect the users from unwanted and harmful e-mails.
Complex congenital cardiac anomalies in an infant with Multi-system disorders presenting in Heart failure; A case report
Authors: Umar Ahmad Bashir , Rabiu Abbas , ALIOKE Ikechukwuka Ifeanyichukwu , ADEBIYI Niyi Mustapha , YAKUBU Abdulmumin Ohida 
Published: February 2025

Abstract

Background:
Congenital anomalies are rare but can be extremely complex, presenting significant challenges for both diagnosis and treatment, especially in developing countries like Nigeria. These conditions often require precise imaging and a personalized approach to care.
Case Presentation:
We present the case of a 10-month-old infant with a rare combination of left atrial isomerism with a single atrium, complete atrioventricular septal defect (AVSD), and severe pulmonary stenosis, who presented with features of cardiac failure. Imaging played a crucial role in identifying the anomalies, guiding diagnosis, and informing management strategies. Despite financial constraints limiting surgical intervention, the patient continues to be managed with an anti-failure regimen.
Conclusions:
Advanced imaging and a multidisciplinary approach are critical in managing congenital heart defects. This case highlights the importance of early detection and thorough evaluation, even in resource-limited settings, to optimize patient outcomes.
Effect of a modified scanning technique on the accuracy of complete-arch implant scanning: An in vitro study
Authors: Wei Luming , Ji Jianhua , Zha Xuzhe , Yuan Changyong , Wang Penglai 
Published: February 2025

Abstract

Background: This in vitro study aimed to investigate the accuracy of a modified scanning technique that incorporated a simple scan aid (SSA) and a novel scanning pattern (segmented scanning pattern, SSP) for complete-arch implant scanning.
Methods: An edentulous maxillary stone cast with 4 parallel analogs was used as the master model. 10 scans were performed with an intraoral scanner (Trios3) using different scanning methods: a conventional scanning pattern (CSP) without SSA (Group I), CSP with SSA (Group II), SSP with SSA (Group III) and conventional splinted open-tray impression (Group IV). The reference scan was obtained by digitizing the master model using a dental laboratory scanner. The related files were imported into inspection software for trueness and precision assessment. One-way ANOVA, Kruskal-Wallis, SNK-q and Bonferroni test were used to evaluate the values. The level of significance was set at α=0.05.
Results: For the global accuracy assessment (trueness and precision), group I exhibited the largest linear and angular deviation (P<0.001) followed by group II (P<0.001). For local accuracy assessment, all intraoral scan bodies (ISB A, B, C, and D) exhibited smaller linear and angular deviations in group III (P<0.05) and group IV (P<0.05) than that in group I. No differences were found between group III and IV (P>0.05), either in the overall or local accuracy assessment.
Conclusions: SSA combined with SSP could significantly improve the accuracy of complete-arch digital implant impressions.
Prioritizing App Reviews for Developer Responses on Google Play
Authors: Jafari Mohsen , Majidi Forough , Heydarnoori Abbas 
Published: February 2025

Abstract

The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models’ performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response.
Advanced Characterization of Natural Fiber Composites Using Digital Image Correlation
Authors: Deepthi K. Sudha 
Published: February 2025

Abstract

Composites made from natural fibers have drawn much interest because of their sustainability, lightweight qualities, and capacity to replace synthetic materials in a variety of engineering applications. Nevertheless, their mechanical behavior is still complicated, and precise performance assessment calls for sophisticated characterization methods. This work presents unique approaches to full-field strain and displacement. investigation of natural fiber composites mechanically employing Digital Image Correlation (DIC), a non-contact optical measurement method. The study’s use of DIC allows for the accurate capture of deformation patterns under various loading scenarios, facilitating a thorough knowledge of the fiber-matrix interactions, failure causes, and structural integrity of the composite. The experimental results are evaluated against standard methods, revealing that DIC allows a more complete and accurate evaluation of natural fiber composites’ mechanical properties. This work contributes to the wider use of natural fiber composites in sectors such as construction, automotive, and aerospace by highlighting the potential of digital image correlation (DIC) to improve material testing precision and by suggesting new approaches for optimizing natural fiber composite design.
Laser sculptural uvulopalatoplasty is an effective method of treating patients with ronchopathy and obstructive sleep apnea
Authors: Knyazkov V. B. 
Published: February 2025

Introduction. Surgical treatment of patients with ronchopathy and obstructive sleep apnea syndrome (OSAS) is a topical issue of modern medicine. The velopharyngeal muscle injury of various intensity due to surgical interventions on the soft palate leads to inflammation, tissue necrosis or partial rejection and wound healing with a fibrous scar, muscle hypotrophy and hypotonia, palatal ptosis and, as a consequence of this, disease recurrence.Aim. To evaluate the effectiveness of laser surgery on the soft palate in patients with ronchopathy and obstructive sleep apnea syndrome.Materials and methods. The results of examination and treatment of 523 patients with ronchopathy and obstructive sleep apnea syndrome of varying severity aged from 23 to 78 years (men 299, women 224) are presented. Surgical intervention on the soft palate was performed in 352 (67.3%) patients: 309 underwent laser sculptural uvulopalatoplasty, 43 underwent surgery using the Remacl method M. et al. (comparison group).Results and discussion. A comparative analysis of subjective and objective indicators obtained in the same patients according to monitoring computer pulse oximetry, polysomnography and computer somnography before and at different times after laser sculptural uvulopalatoplasty demonstrates persistent and significant (p ≥ 0.005) positive changes. The data from the study of patients in the comparison group demonstrate a positive trend in the results obtained at different times after Remacl surgery M. et al. only if they have uncomplicated snoring and mild OSA.Conclusion. Laser sculptural uvulopalatoplasty is a highly effective method of treating patients with ronchopathy, regardless of the presence and degree of obstructive sleep apnea syndrome. With high-quality and adequate selection for surgery, accurate determination of the levels of obstruction, shape and degree of collapse of the soft tissues of the upper respiratory tract, careful consideration of the individual characteristics of the structure of the soft palate and pharynx and minimization of surgical trauma to the palatine curtain, it is possible to achieve a positive effect of surgery in the vast majority of patients.