bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unlocking the Genetic Diversity and Principal Component Analysis of Selected Rice (Oryza Sativa L.) Genotypes Using Morphometric and Biochemical Characteristics
Authors: Paramanik Sourav , Rao M. Subba , Panda Koustava Kumar , Jaishi Ajay , Pati Aditya Kumar 
Published: June 2024

Abstract

The genetic diversity of rice grains plays a vital role in rice variety selection decisions as it influences consumer preference, market demand and yield potential. The present work was an attempt to study the genetic diversity and multivariate analysis of the 50 selected rice genotypes grown during Kharif, 2022 at the P.G. Research Farm, Ranadevi, CUTM, Paralakhemundi lying in the north-eastern ghat zone of Odisha. The study identified significant variation in grain types among different genotypes with 72% having extra-long slender grains, 18% having long bold grains, 8% having long slender grains and 2% having medium slender grains. The findings of the study reveal significant variability in biochemical traits among the genotypes. The results indicate a positive and significant correlation of iron with both copper and manganese. Similarly, manganese showed positive and highly significant correlation with zinc. The genotypes were clustered into five distinct groups based on genetic divergence and Cluster II had maximum number of genotypes (13). The highest distance among the clusters was recorded between Cluster V with Cluster I and II suggesting that the genetic divergence of genotypes within these clusters is distinct compared to those in other clusters. Copper was found to play a crucial role contributing majorly to genetic divergence of the studied genotypes. The principal component analysis identified three majors out of six components which exhibited eigenvalues surpassing 1.0 contributing to a total cumulative variation of 68.95% across the examined variables. The findings of this study suggest the use of some potential genotypes from specific clusters in hybridization programs with the prime objective of enhancing the nutritional quality as well as yield in rice.
Dynamical Semantic Enhancement Network for Continuous Sign Language Recognition
Authors: WANG SUYANG , GUO LEMING , XUE WANLI 
Published: June 2024

Abstract

In the field of sign language recognition, effective interpretation of semantic information, which is primarily conveyed through facial and hand gestures, poses significant challenges.
Previous methods often struggle to simultaneously capture semantic areas and accurately assess the varying importance of different motion frames, which hampers recognition accuracy.
We propose the Dynamical Semantic Enhancement (DSE) Network which integrates the Long-Short Dependence Attention (LSDA) and Global Interaction Conv2d (GIConv2d) to address these challenges.
LSDA is designed to form long-short-range spatial dependencies by advanced large-kernel convolutions coupled with small-kernel convolutions, which effectively capture synchronous facial and hand semantic contents.
Meanwhile, GIConv2d adaptively learns the motion semantic contents by dynamically generating calibrated weights, focusing on the reasoning of frame-level contributions of rapid movement frames and static frames.
Our DSE achieves competitive performances on three widely used datasets: PHOENIX14, PHOENIX14-T, and CSL-Daily.
Additionally, visualization experiments confirm the DSE's superior capability in reinforcing semantic extraction both spatially and temporally.
Beyond the Norm: Exploring Multimorbidity Risks with Random Survival Forest in India's Aging Demographic
Authors: Kumar Ajay , Singh Bharti 
Published: June 2024

Abstract

India is passing through a parallel phase of demographic and epidemiological transition with the double burden of Non-communicable Diseases (NCDs) and Communicable Diseases (CDs). Unhealthy ageing has been identified as the primary driver of the multimorbidity and morbidity burden among older adults in India. This study aims to assess the relative proportional share of morbidities and multimorbidity counts, estimate multimorbidity risk for socioeconomic and demographic factors and further evaluate multimorbidity counts risk conditioned on leading factors.
The study used the data from Longitudinal Ageing Study in India (LASI), Wave – 1, 2017–18, aged 45 years and above. First, we assessed the relative proportional share of morbidities and multimorbidity over age using the stacked graph. Further, we applied the Random Survival Forest (RSF) model to estimate the risk of multimorbidity associated with socioeconomic and demographic factors over age. Further, conditional plots were utilised to assess the distribution of leading factors on multimorbidity counts.
The prevalence of multimorbidity was 43.20%. Eye disorders, followed by CVDs, had the highest proportional share over age. Endocrine diseases, Gastrointestinal Conditions and Infectious diseases showed a concordant decreasing proportional share in the ageing population. The relative proportion of five or more multimorbidity increased significantly with age. The median expected risk of multimorbidity in females (66 years) was significantly higher than in males (71 years). The study also provides empirical evidence that individuals with higher levels of education, obesity, who are currently working, and who had poor childhood health were more prone to higher risk of multimorbidity at an early age. Further, obesity was significantly associated with early multimorbidity onset and led to a pronounced escalation of complex multimorbidity, particularly in females. Targeted public health interventions are crucial to address diseases early onset and burden disparities to promote healthier aging among older adults in India
Accommodation Crafting as a Partial Mediator of the Effects of Personal Resources and Job Characteristics on Task Crafting by Employees with Hearing Limitations
Authors: Geyer Paul , Baldridge David 
Published: June 2024

Abstract

Are proactive behaviors like task crafting an answer to the long-sought social goal of leveling the playing field for employees with disabilities? This question is important because, after three decades, legal remedies such as the Americans with Disabilities Act, theoretical and empirical advancements, and skill development interventions have yet to close enough of the well-documented gap in employment opportunities being faced by employees with disabilities. The current study seeks to shed light on this topic by addressing the research question “Does accommodation crafting partially mediate the effects of personal resources (self-efficacy and political skill) and job characteristics (job autonomy and perceived organizational support) on employees with hearing limitations’ decisions to engage in task crafting?” Methods. Employees with hearing limitations (n=225) completed an on-line questionnaire. Participants responded under conditions of anonymity unless they chose to participate in a cash drawing by providing their email address. All participants worked full-time (30 or more hours per week) in the United States. Results. As hypothesized, a significant positive relationship between accommodation crafting and task crafting was confirmed. Accommodation crafting was also found to partially mediate the significant direct effects of political skill, self-efficacy, and job autonomy on task crafting. Perceived organizational support was not found to be significantly related to accommodation crafting and was therefore excluded from partial mediation analyses. Conclusion. This study suggests that an enhanced understanding of task crafting by employees with hearing limitations is conceivable when considering the antecedent effects of personal resources and organizational characteristics. Although personal resources and organization characteristics seem to be direct antecedents of task crafting for employees with hearing limitations, the availability of crafted accommodations seems to be important as a partial mediator of these relationships.
Role of cow dung and sawdust during the bioconversion of swine waste through the rotary drum composting process
Authors: Chhakchhuak Lalthlansanga , Pottipati Suryateja , Mohanty Bijayananda , Kalamdhad Ajay S. 
Published: June 2024

Abstract

This research aimed to assess the outcome of adding cow dung as a microbiological inoculum and sawdust as a bulking agent to swine waste in the invessel-composting process. The biodegradation was conducted using a rotary drum composter (RDC), which raised the feedstock's temperature to > 40°C in just 24 hours, promoting thermophilic decomposition. Surprisingly, the cow dung-amended trial (RDC1) maintained the thermophilic phase for 16 days, whereas the sawdust-amended trial (RDC2) only lasted 7 days. After 20 days, the RDC1 product exhibited superior nutritional characteristics, boosting a total nitrogen content of 2.52%. Moreover, the coliform drastically decreased in RDC1, and the overall weight loss was 25%. These findings highlight that incorporating cow dung (10% w/w) into swine waste and bulking agents through RDC produces high-quality compost in just 20 days. Thus, the livestock industry benefits significantly from this proven method of improving waste management by producing valuable bioproducts employing RDC.
Android Malware Detection using HexCode Features
Authors: Anand Abhishek , Singh Jyoti Prakash , Dhoundiyal Vatsal 
Published: June 2024

Abstract

With the widespread adoption of smartphones, Android has emerged as a preferred and highly targeted platform by malware. The proliferation of malware for Android devices has been exponential and to counter this Android malware detection together with familial classification has to be automated. This paper introduces a dual-pronged approach for Android malware detection and familial classification. The proposed approach employs a static analysis approach to extract Java ARchive (JAR) files from Android application packages (APKs). Our methodology involves utilizing extensive hex strings derived from JAR files and applying n-gram sliding window technique to extract features. To validate the robustness of our model and assess its versatility, we employed both standard and obfuscated malware datasets. A range of machine learning models, including Naive Bayes(NB), Random Forest(RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) and a Convolutional Neural Network (CNN) for familial classification, were employed. The experiments encompassed non-obfuscated malware samples (5560), obfuscated malware samples (15479), and benign samples (6200). Additionally, we conducted a comparative analysis of our model's performance against existing methods, including those based on deep learning.
Longitudinal cough frequency monitoring in persistent coughers:Daily Variability and Predictability
Authors: Chung Kian Fan , Chaccour Carlos , Jover Lola , Galvosas Mindaugas , Song Woo-jung , Rudd Matthew , Small Peter 
Published: June 2024

Abstract

Purpose
We determined the cough counts and their variability in subjects with persistent cough for 30 days.
Methods
The Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analysed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.
Results
The mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS)have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as percentage of days when cough frequencies fall within that day’s 95% confidence interval. Overall Predictability was the mean of the 30 One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).
Conclusion
There is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. The clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.
Machine-Learning-Aided Method for Optimizing Beam Selection and Update Period in 5G Networks and Beyond
Authors: Marenco Ludwing , Hupalo Luiz E. , Andrade Naylson F. , Figueiredo Felipe A. P. 
Published: June 2024

Abstract

Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in the network comprising one single gNB and one single user equipment in an mmWave scenario, improvement in SINR and throughput up to 4% are observed. Improvements are gathered because of a reduction of 87.5% in beam pair selections because of an increase of approximately 2330% in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond.
An improved fault ride-through strategy for back-to-back MMC-Based VSCs in an inverter-dominated renewable energy system
Authors: Xiong Ping , Liu Dan , Ji Xiaotong , Yang Bingjian , Liu Hongzheng 
Published: June 2024

Abstract

By incorporating back-to-back voltage-source converters between the main grid and the regional grid, asynchronous interconnection of the grids can be formed to achieve a scenario of 100% renewable energy islanding operation of the regional grid. The paper begins by detailing the electrical wiring and structural configuration of the regional grid. It then performs a thorough evaluation of traditional fault current suppression techniques during a single-phase-to-ground fault in transmission lines powered solely by renewable energy. To improve low voltage ride-through capabilities, the study introduces an advanced strategy that employs an adaptive control of positive and negative sequence components, combined with DC bus voltage stabilization. The efficacy of this approach is substantiated through hardware-in-the-loop simulations and empirical field testing. Results show a substantial reduction in the regional grid's negative sequence component and harmonic content by 18.5% and 81.7%, respectively. Moreover, the DC voltage oscillation is effectively suppressed following the initiation and clearance of a single-phase-to-ground fault.
Starting from Zero: A No-Pretraining Object Detectors
Authors: Ye X , Ma T , Xiao B 
Published: June 2024

Abstract

We introduce an innovative object detector that has been trained entirely from scratch , designed to address the shortcomings of contemporary models in terms of scalability and generalization. Unlike state-of-the-art detectors that rely on networks pre-trained on large datasets like ImageNet and COCO, our model steers clear of the potential biases and constraints such training imposes. Building on the foundation laid by DSOD, we present a model that not only improves upon precision but also offers enhanced extensibility, enabling continuous refinement of both accuracy and speed.Our model also introduces a new local attention mechanism that emphasizes salient features while suppressing the less relevant ones, thereby significantly enhancing performance. This plug-and-play module computes tensor weights that can be seamlessly integrated into any network architecture, offering an instant boost in the capability of the model.Starting from ground zero, we trained our model without the crutches of pre-trained weights, an approach that has historically been considered audacious. Yet, our experiments have borne fruit, yielding a model that outperforms DSOD in real-time detection speed while also achieving a mean Average Precision (mAP) of 69.7% on the VOC2007 and 69.8% on the VOC2012 benchmark. This exemplary performance demonstrates the viability and potential of our training paradigm, paving the way for future research into self-sustaining learning frameworks that prioritize adaptability and continuous improvement.
Evaluating salt tolerance in soybean core collection: germination response under salinity stress
Authors: Gobade Aditya , S Arathi , Gijare Shreyash , Pawar Deepak , Patil Abhinandan 
Published: June 2024

Abstract

High levels of soil salinity inhibit the growth of legumes such as soybeans, significantly reducing their productivity. This research aimed to assess the salt tolerance of soybean genotypes by evaluating seed germination at varying salt concentrations (100 mM, 150 mM, and 200 mM NaCl) from two seed source locations. A total of 198 soybean genotypes were analyzed post-germination using ten quantitative traits: germination percentage, seedling fresh weight, seedling dry weight, seedling length, shoot length, root length, seedling vigor index-1, seedling vigor index-2, seedling water content, and salt tolerance. Analysis of Variance (ANOVA) results indicated significant differences among treatments across both locations. Principal Component Analysis revealed that certain quantitative traits were more prominent at different salt concentrations, confirming varied responses to salt stress. Correlation analysis demonstrated a positive relationship between germination percentages and growth parameters such as fresh weight, dry weight, and vigor index. The study observed a decline in all quantitative traits as salt concentration increased, highlighting the stress experienced by plants during germination and growth under high salinity conditions. Using K-means clustering, the 198 genotypes were categorized into tolerant, moderately tolerant, moderately susceptible, and susceptible groups. This clustering helped identify genotypes exhibiting high tolerance (≥ 80% germination at 200 mM NaCl) and high susceptibility (≤ 40% germination at 100 mM NaCl) consistently across both seed source locations. Consequently, seven salt-tolerant genotypes (MACS 708, KALITUR, MACS 1037, IC 13050, MACS 1010, PK 1029, and MACS 173) and three salt-sensitive genotypes (HIMSO 1563, EC 391181, and EC 241920) were identified, providing new insights into soybean cultivation under saline conditions.
Nicorandil repurposing in orofacial pain: preclinical findings in adult zebrafish
Authors: de Araújo José Ismael Feitosa , Leite Gerlânia de Oliveira , Vieira-Neto Antônio Eufrásio , Silva-Neto Leonardo da , Campos Adriana Rolim , de Fátima Ângelo 
Published: June 2024

Abstract

This study investigated the orofacial antinociceptive activity of nicorandil in adult zebrafish and explored the involvement of TRP channels in this effect. Nicorandil, a known antianginal drug, reduced nociceptive behaviors induced by capsaicin (TRPV1 agonist), cinnamaldehyde (TRPA1 agonist), and menthol (TRPM8 agonist) without altering the locomotor activity of the zebrafish. Pre-treatment with specific TRPA1 and TRPV1 antagonists prevented the antinociceptive effects of nicorandil, indicating its action on these channels. Molecular docking studies supported these findings, demonstrating high chemical affinity and specific binding of nicorandil to the TRPV1 and TRPA1 channels, leading to stabilization and reduced biological activity of these channels. In contrast, the antinociceptive effect of nicorandil on menthol-induced nociception was not affected by a TRPM8 antagonist, suggesting that TRPM8 modulation is not involved in nicorandil’s mechanism of action. The study highlights the potential of nicorandil as an analgesic through its interaction with TRPV1 and TRPA1 channels, providing a molecular basis for repositioning nicorandil as an effective analgesic drug.
Evaluation of Ventilation System Alternatives for Sustainable Buildings With an It2 Fuzzy-integrated Dematel-anp and Fanp Methodology
Authors: Nalbant Kemal Gokhan 
Published: June 2024

Abstract

In the early 20th century, advancements in technology facilitated greater accessibility to data, thereby revealing the extent of human-induced harm to the ecosystem. The revelation of environmental resource depletion has led to a rise in international agreements, sanctions, and regulations as a precautionary measure. The notion of sustainable development has given rise to novel concepts, including eco-friendly, green, and smart buildings. The primary objectives of green buildings are to ensure safety, health, comfort, longevity, and cost-effectiveness for both occupants and the environment. In this paper, a novel integrated model is proposed by Interval Type-2 Fuzzy DEMATEL-ANP (DANP) - FANP for the evaluation of ventilation system alternatives for sustainable buildings. The ranking of the criteria was established using the DANP approach. Then, the FANP method was integrated into the DANP technique for the ranking of the alternatives. To implement pairwise comparison matrices of alternatives within sub-criteria using the DANP methodology, it is imperative to create a square matrix with the intention of producing a normalized direct relation matrix. The problem was solved through the utilization of the FANP methodology. Sensitivity analysis was performed in the last step to ascertain the method's sensitivity.
Flexibility evaluation of a new distribution system based on cooperative game-Gaussian cloud model
Authors: Xinyin Dai 
Published: June 2024

Abstract

Scientific and reasonable evaluation of the flexibility of the new distribution system is of great significance for the construction of the new distribution system. Firstly, based on the characteristics of the new distribution system, a flexibility evaluation index system with 4 first-level indicators and 12 secondary-level indicators is established from the four levels of power supply side, grid side, load side and energy storage side. Secondly, the cooperative game idea is used to combine and assign the three objective assignment methods, namely, entropy weight method, CRITIC method and coefficient of variation method, to ensure the accuracy of the weighting results. Finally, the Gaussian cloud model is used to analyze the distribution system in a region of China, and the calculation results of the Gaussian cloud model in the example are compared with the results of the fuzzy comprehensive evaluation model to verify the reliability and accuracy of the method. The results show that the proposed evaluation method can identify the weak links of the new distribution system and provide theoretical reference for accelerating the construction and sustainable development of the new distribution system.