bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Hybrid CNN-RNN Model Changes the Game for Side-Channel Attack Prediction
Authors: Alemerien Khalid , Al-Suhemat Sadeq , Altarawneh Enshirah , Alksasbeh Malek , Alrawashdeh Tawfiq , Alsuhimat Fadi , Alhalhouli Zaid T. 
Published: October 2024

Abstract

A common type of cyberattacks is the Side Channel Attack (SCA), which affects many devices and equipment connected to a network. These attacks have different types, such as power attacks like Differential Power Analysis (DPA), electromagnetic attacks, storage attacks, and others. Researchers and cyber security experts are concerned about SCA targeting devices, which can lead to losing and stealing important information. Many researchers have tried to reduce the impact of this attack. Deep Learning (DL) based solutions have shown significant promise in detecting SCAs due to their ability to identify complex patterns and anomalies in large datasets. Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), including Long Short-Term Memory (LSTM) networks, are effective for analyzing time-series data and identifying temporal dependencies in side channel data. Despite the DL models having been successfully applied to detect power analysis, electromagnetic, and timing attacks, they still face data collection, model generalization, adversarial, and robustness challenges. This study aims to enhance side-channel attack prediction using deep-learning algorithms by investigating the use of the DL models in predicted SCA that focus on DPA, the DPA contest v4.2 dataset was used to predict power attacks, it contains 3253 features and 80,000 traces. Several data preprocessing techniques, including Hamming and Hanning windows, and feature selection using SelectKBest, were used to enhance model performance. The efficacy of CNN, RNN, and a hybrid CNN-RNN model was explored. The study results indicated that the hybrid CNN-RNN model achieved accuracy (99.6\%), loss (0.0270), and executed time (15,240.42 seconds). This study underscores the importance of optimal hyperparameter tuning, selecting appropriate activation functions (SeLU) and activation (Adam optimizer), in improving DL model performance.
Prevalence of Brucella Infections among Students of Amran University, Yemen
Authors: Al-Hadheq Ali Ahmed , Al-Mahbashi Anas , Al-Wajeeh Talal , Al-Haj Asia , Edrees Wadhah Hassan 
Published: October 2024

Abstract

Background and Aim: Brucella infection in animals is considered a significant problem in most countries around the world and is regarded as one of the important pathogenic zoonotic infections affecting humans. This study aimed to determine the prevalence of Brucella infections among students at Amran University, Yemen.
Methods: This is a cross-sectional study carried out among 247 students between January 2023 and November 2023. Blood samples were collected and analyzed using immunochromatographic technology.
Results: Out of 247 human samples, 2 (1.75%) were positive among males, and 15 (12.5%) were positive among females. Additionally, 49 (19.8%) of the positive cases were from Amran city, while 198 (80.2%) were from rural areas, with significant differences (P < 0.05). The risk factors for Brucella infection included ranching, consuming unpasteurized milk, and direct contact with livestock.
Conclusion: The prevalence of brucellosis was low among the study participants; however, it could become a serious problem that threatens the healthcare system in Yemen.
An Innovative Approach to Predict Drinking Water Risks in Michigan Using System, Community, and Regulatory Characteristics
Authors: Ye Liangfei , Dong Qianqian , McCright Aaron , Gasteyer Stephen 
Published: October 2024

Abstract

Background: Robust predictive models are essential for preventing and mitigating risks associated with public drinking water systems (PWS), which pose significant public health threats and incur substantial medical costs.
Methods: This study introduces a novel approach by comparing the performance of Logit, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models in predicting risks based on PWS characteristics, community attributes, and regulatory developments, rather than relying on water quality and hydrological parameters.
Results: The study yields three key findings: (1) XGBoost outperforms Logit and SVM, though all models perform less effectively for predicting health-based risks; (2) community and regulatory characteristics exert a greater influence on risk predictions than PWS characteristics; and (3) XGBoost performs comparably to the water parameter-based prediction approach, with the added benefits of lower cost and suitability for long-term forecasting.
Conclusions: This innovative approach offers substantial potential for residents, environmental advocates, and policymakers to better anticipate and address PWS risks by focusing on fundamental social determinants.
WITHDRAWN: Translation of Code-Mixed and Code-Switched Tweets Using LLMs for Enhanced Sentiment Analysis Outcomes
Authors: Sharma Durga , Johari Rahul , Gupta Kalpana 
Published: October 2024

Abstract

India’s rich linguistic diversity often leads to the frequentuse of code-mixed languages, especially on social media platforms like X(formerly Twitter). This study explores the use of Large Language Models (LLMs), specifically Anthropic and OpenAI, for the translation ofHindi-English (Hi-En) code-mixed and code-switched tweets to identify instances of cyberbullying directed at Indian athletes through the analysis of over 29,000 tweets. Focusing on athletes from Cricket, Boxing, andWrestling, this research analyses the prevalence of cyberbullying over a decade long period from July 2013 to June 2023. The aim is to quantify cyberbullying by classifying tweets into positive, neutral, and negativecategories, with sentiment scores ranging from -1 to +1. The study identified shifts from neutral to positive and neutral to negative sentiments,where previously misclassified neutral tweets were more accurately recognized. The findings highlight the fluctuating public perception of athletes, driven by their performance and media narratives. This researchunderscores the challenges of processing code-mixed content and emphasises the need for improved NLP tools to more effectively detect harmfulsentiment and cyberbullying.
HDAMMF: Hierarchical Data Aggregation Method using Mobile sink and Fuzzy logic in Wireless Sensor networks
Authors: Naghibi Maryam , Barati Hamid , Barati Ali 
Published: October 2024

Abstract

Nodes in wireless sensor networks (WSNs) have limited energy reserves. A primary goal is to collect data efficiently while minimizing energy use. Clustering is an approach that can help reduce energy consumption in these networks, but data transmission to a stationary sink can cause energy holes. Using a mobile sink helps mitigate this issue, enhancing network performance. This paper presents a hierarchical cluster data aggregation method based on fuzzy logic and a mobile sink. This method consists of two phases: the clustering phase based on fuzzy logic and the data aggregation phase. The clustering phase includes two steps: selecting cluster heads and forming clusters. First, the fuzzy inference system calculates the probability of each node becoming a cluster head. Nodes with the highest scores, based on residual energy, node degree, and centrality, are chosen as cluster heads, while those with the second-highest scores are selected as backup cluster heads. In the second step, clusters are formed around the selected cluster heads. In the data aggregation phase, the cluster heads collect data from their cluster members and transfer it to either a mobile sink or the base station. Cluster heads within a direct region (with a maximum distance of rrr from the BS) send their data directly to the BS. Data from other cluster heads is routed to the BS through a mobile sink. This method ensures efficient data transmission and energy usage, enhancing overall network performance. The HDAMMF method significantly outperformed previous methods in various aspects, including the mean amount of energy used, latency, packet delivery rate, and network longevity, according to a comparison of their respective performances.
Evaluation of the effectiveness of Vernonia cinerea for the treatment of smoking cessation: a systematic review and meta-analysis
Authors: De Santi Omar , Niro Cecilia A. Di , Rungruanghiranya Suthat , Greco Vanina 
Published: October 2024

Abstract

Background: Smoking should be interpreted as a multidimensional and multicausal problem, for which different prevention and treatment strategies must be explored.
Aims: To review the efficacy and safety of the plant, Vernonia cinerea (VC), for smoking cessation.
Design: This systematic review complied with “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) Protocols. A comprehensive search was carried out, identifying randomized controlled trials (RCTs).
Settings: health centers of any level.
Participants: persons of any age or gender, smokers.
Interventions: VC versus placebo with behavioral therapy and any other standard pharmacotherapy approved for smoking cessation.
Findings: We identified 13 RCTs, all from Thailand.
Ten RCTs compared VC to placebo covering 1878 patients, 943 of whom received VC, delivering an RR of 1.51 (95% CI 1.16 to 1.97; I2=10%;
low-quality evidence). However, there is no certainty of this association when considering clinical trials with longer follow-ups and rated as having a low risk of bias (CAR for at least 24 weeks: RR of 0.98, 95% CI = 0.68 to 1.41; participants = 1489; I2= 0%;
low-quality evidence). Thus, its efficacy in smoking cessation was only consistent in those trials where participants smoked less than 15 cigarettes per day (RR of 2.06, 95% CI= 1.25 to 3.41; participants= 246;
moderate-quality evidence). There was not a clear benefit for VC in the CAR compared with NRT or nortriptyline. Non-serious AEs were tongue numbness, nausea, vomiting, drowsiness, and dizziness, found to be mild and transient
Conclusions: Vernonia cinerea is a promising smoking cessation aid for those smoking fewer than 15 cigarettes daily, demonstrating efficacy only in trials with follow-ups of less than 12 weeks. It has a benign safety profile even in patients with non-communicable diseases. It underscores the need for further research, particularly outside Thailand, to fully explore its therapeutic potential
Preliminary trial of the cultivation of Pleurotus djamor var. fuscopruinosus from southern Thailand using sawdust substrate and applications
Authors: Phonemany Monthien , Sysouphanthong Phonguen , Rujanapun Narawadee , Thongklang Naritsada , Charoensup Rawiwan 
Published: October 2024

Abstract

Pleurotus is an edible mushroom, that has been consumed and cultivated worldwide. Pleurotus djamor var. fuscopruinosus (MFLU24-0016) is a new record in Thailand that provided morphology descriptions with phylogeny analysis and provided valuable information for the possibility of cultivation. The mycelium of P. djamor var. fuscopruinosus strain MFLUCC24-0056 was grown on PDA medium, and sorghum medium showed the best spawn production. Sawdust is suitable for the growing mycelium. The wild strain of P. djamor var. fuscopruinosus produced fruiting bodies at 25–28°C and 70–80% humidity. Primordia formed four weeks after the mycelia fully colonised the substrate and illuminated the conditions for the development of a mature fruiting bodies in 4–5 days. Nutritional analysis of 100 g of dried P. djamor var. fuscopruinosus showed 15.99 ± 0.2% of carbohydrates, 25.00 ± 1.5 (g/100 g) of protein, 30.25 ± 0.5% of moisture, 19.10 ± 0.9% of fibre, 7.59 ± 1.0% of ash, and 2.07 ± 0.5 (g/100 g) of fat content. Antioxidant activities of P. djamor var. fuscopruinosus 50% (v/v), ethyl acetate extract showed the strongest 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity at 694.47 ± 3.92 µg/ml compared to 2,2'-azino-bis (3-ethylbenzthiazoline-6-sulphonic acid) radical cation scavenging activity at 652.92 ± 2.53 µg/ml. In vitro anticancer effects were evaluated using the (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The results showed the extract of P. djamor var. fuscopruinosus exhibited higher cytotoxicity against lung cancer A549 than colorectal cancer SW480 with IC50 values of 382.03 ± 4.55, and 245.73 ± 7.60 µg/ml, respectively, compared to the positive control doxobrucirin with IC50 values of 14.96 ± 1.58 µg/ml. The exhibition cytotoxicity against RAW 264.7 cells had IC50 values of 213.08 ± 4.08 µg/ml. Antidiabetic properties were investigated. Inhibition of the α-glucosidase enzyme in vitro showed that the hot water extract was higher at 582.91 ± 3.0 µg/ml, followed by ethyl acetate at 473.87 ± 1.4 µg/ml, and less was methanol at 357.63 ± 3.3 µg/ml based on comparison with acarbose (635.70 ± 4.9 µg/ml). Furthermore, 3T3-L1 cells were evaluated, and the glucose consumption assay of this mushroom was shown at 582.91 ± 3.0 µg/ml from hot water extract compared to metformin (IC50 at 99.58 ± 0.59 µg/ml).
Efficacy and safety of Ashwagandha Root Extract on Sexual Health in Healthy Women: Findings of a Prospective, Randomized, Double-Blind, Placebo-Controlled Study
Authors: Mutha Amit Shrenikraj , Mutha Sonali Amit , Tejuja Anupama Hem , Langade Deepak K. 
Published: October 2024

Abstract

Background: Ashwagandha is an herbal extract known for its potential benefits on various aspects of human health, including stress reduction and hormone regulation.
Objective: This prospective, randomized, double-blind, placebo-controlled study investigated the role of Ashwagandha Root Extract (ARE) in enhancing sexual function in healthy women with Female Sexual Dysfunction (FSD).
Materials and Methods: Sixty-two healthy women aged 18-50 years were randomly assigned to receive Ashwagandha root extract (ARE) 600mg/d (n=31), or identical placebo (n=31) for 8 weeks. Study assessments were done at baseline, week 2, week 4, and week 8. The primary outcome was Female Sexual Function Index (FSFI) scores, whereas secondary outcomes were Female Sexual Distress Scale (FSDS-R), Satisfying Sexual Encounters (SSEs), Perceived Stress Scale (PSS-10), and Quality of Life (SF-12 QoL) assessed at all visits. Laboratory assessments included serum sex hormones (Follicle-Stimulating Hormone (FSH), Luteinizing Hormone (LH), Progesterone, Estrogen- E2, Prolactin, and Testosterone), liver enzymes and renal parameters estimated at baseline and week 8.
Results: One patient was lost to follow-up and data of 61 (31 ARE, 30 placebo) patients was included for efficacy analysis. ARE significantly improved the mean (SD) FSFI total scores from 51.65 (10.23) at baseline to 71.94 (10.33) at week 8 as compared to placebo 51.77 (9.68) and 61.83 (8.38) baseline and week 8 respectively (p=0.002). This improvement was observed in ‘desire’ and ‘sexual satisfaction’ domains of FSFI scale. Similarly, assessments of sexual activity (SSE) shows greater improvements with ARE in ‘satisfying sexual events’ and ‘sexual desire’ (p<0.0001). The scores for PSS (p=0.0009), and SF-12 (p=0.044) were better with ARE compared to placebo at week 8. No significant changes or abnormalities were observed for hormones. Only one woman with placebo reported mild dizziness.
Conclusion: Ashwagandha root extract holds promise as a safe and effective option for improving sexual health in women.
Evaluation of proximate composition and mineral contents of Sorghum Blended with Peanut
Authors: Ateye Mahamed Dol , Ali Abdulkarim Mohammed , Hassen Shamsedin Mahdi , Jama Hodo Mohamed 
Published: October 2024

Abstract

Improving the nutritional status of infants requires the optimal use of available food resources. This study aimed to develop a product and evaluate its proximate composition and mineral content by blending sorghum with peanuts (SBP) to enhance the nutritional quality of sorghum-based foods. Seven formulations of composite flours were created, consisting of 100% sorghum flour with 0% peanut flour, 92.5% sorghum flour with 7.5% peanut flour, 85% sorghum flour with 15% peanut flour, 77.5% sorghum flour with 22.5% peanut flour, and 70% sorghum flour with 30% peanut flour. The technological properties and nutritional characteristics of these composite flours were determined using AOAC methods. The moisture content of the SBP sample flours ranged from 5.34–6.35%, with the mixture containing 70 grams of sorghum and 30 grams of peanut having the highest moisture content. This finding suggests that increasing the peanut proportion raises the moisture content of the flour. The crude protein content of the SBP samples ranged from 12.53–17.38%, with the highest protein concentration (17.38%) observed in the 70:30 sorghum-to-peanut mixture, indicating its potential as a valuable source of plant-based protein. The crude fat content in the sorghum and peanut flours varied between 4.40% and 15.61%. The total carbohydrate content ranged from 54.08–75.33%, with the highest carbohydrate content observed in the 100% sorghum flour and the lowest in the 70:30 sorghum-to-peanut mixture. The total energy content of the SBP sample flours varied between 388.86 kcal/100g and 423.80 kcal/100g. Based on the results, this study suggests that SBP composite flours can be used as complementary foods, meeting the daily energy requirements for infants aged 6–12 months.
Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration
Authors: Xie Wei , Ma Shuoyoucheng , Wang Zhenhua , Wang Enze , Wang Baosheng , Su Jinshu 
Published: October 2024

Abstract

Despite their proficiency in math tasks, the mechanisms underlying LLMs' mathematical reasoning abilities remain a subject of debate. Recent studies suggest that chain-of-thought (CoT) prompts can bolster mathematical reasoning by encouraging LLMs to employ human-like logical reasoning (System 2), enabling them to excel on the Cognitive Reflection Test (CRT). To assess whether LLMs genuinely possess System 2-like logical reasoning, we introduced targeted modifications to CRT problems. Our findings reveal that, despite the use of CoT prompts, mainstream LLMs, including the latest o1-preview model, continue to exhibit a significant error rate. Further analysis indicates that they predominantly rely on System 1-like intuitive reasoning and pattern matching derived from training data, rather than demonstrating mastery of mathematical thinking. This discovery challenges the prevailing notion that LLMs possess genuine logical reasoning abilities and that CoT can enhance them. Consequently, this work may temper overly optimistic projections regarding LLMs' advancement toward artificial general intelligence.
Scaling Down Transformers: Investigating Emergent Phenomena in Tiny Models
Authors: Sinha Abhas Kumar 
Published: October 2024

Abstract

In this paper, we investigate the emergent learning capabilities of autoregressive models, particularly Generative Pre-trained Transformers (GPTs), with a focus on how these models learn and generalize from sparse datasets. Our findings show that increasing the number of training epochs enhances emergent abilities, where different loss reductions correlate with distinct task distributions in the pretraining dataset. We demonstrate how GPT models decompose sparse data into latent patterns, allowing them to learn new distribution information and extrapolate these patterns to improve cross-task predictions. For instance, the model achieves nearly 65% accuracy with just 50 samples (out of 1680 possible samples) when encoding two synthetic languages, and reaches accuracies of 93.5%, 99.75%, and 99.25% with 100, 200, and 300 samples (out of 11880 possible samples), respectively, from a larger dataset. Additionally, we highlight the influence of prompt design, dataset characteristics, and latent variables on pattern recognition and task performance. These results advance the understanding of large language models' emergent learning abilities, offering insights into their generalization power across diverse tasks.
An In-Silico Approach to Study the Effect of Phosphorylation (Ps129) on the Conformational Dynamics of Membrane Bound Α-Synuclein
Authors: Das Dorothy , Mattaparthi Venkata Satish Kumar 
Published: October 2024

Background:
Phosphorylated α-Synuclein (α-Syn) is present in relatively small levels in
normal human brains, but nearly all of the α-Syn in the Lewy bodies (LBs) that collect in the nigrostriatal
region in the brain of Parkinson's disease patients is phosphorylated on serine 129 (pS129).
Earlier studies suggested that mimicking phosphorylation at S129 may have an inhibitory effect on
α-Syn aggregation and thus control α-Syn neuropathology. Although phosphorylation at S129 is associated
with α-Syn inclusion in synucleinopathies, the mechanisms by which this post-translational
modification (PTM) influences aggregation and contributes to LB illness in the brain are yet to be
understood.

Objective:
This research aims to study the effect of phosphorylation (pS129) on the conformational
dynamics of membrane-bound α-Syn using Molecular Dynamics (MD) simulations.

Method:
Using MD simulations, this computational study has demonstrated the effect of PTM on
the conformational dynamics of pS129 α-Syn and its lipid membrane association. To better understand
the impact of pS129 on the aggregation of the α-Syn structure monomer with recent atomic
details, we have examined the MD trajectories, conducted a salt-bridge interaction study, Principal
Component Analysis (PCA), and intra and inter-molecular hydrogen bond analysis.

Results:
The conformational structure of pS129 α-Syn was observed from the MD trajectory analysis
to be stable throughout the simulation, with higher compactness and reduced flexibility. The stability
of the structure of pS129 α-Syn was also evaluated by 2-D and 3-D principal component
analysis followed by a free energy landscape plot showing the global minima. The conformational
snapshots and Ramachandran plot showed the absence of α-strands in the α-Syn's Non-Amyloid
Component Region (NAC) (71–82), which is necessary for aggregate formation.

Conclusion:
Further, the intermolecular hydrogen bonds analysis indicates that the NAC region is
not embedded into the lipid bilayer and has limited association with the other regions of the protein.
Our findings reveal salient features of pS129 modifications that inhibit α-Syn aggregation.