bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing Deadlock Detection Strategies for Nested Transactions in Complex Systems
Authors: Meenu meenu , Shanker Udai 
Published: October 2024

Abstract

Background
Deadlock detection in nested transactions is a critical aspect of maintaining system stability and ensuring efficient transaction processing. In nested transaction (NT) systems, deadlocks can occur not only between top-level transactions but also among subtransactions, leading to intricate cycles that complicate deadlock resolution. Traditional approaches, which often rely on examining the entire Wait-For Graph (WFG) for cycles, can be time-consuming and resource-intensive. As the complexity of transactions increases, efficient deadlock detection methods become increasingly essential for optimizing resource utilization and overall system performance.
Objective
The primary objective of this research is to enhance deadlock detection techniques in nested transactions by developing an efficient algorithm based on Depth-First Search (DFS). The focus is on identifying hierarchical deadlocks that arise from subtransactions, aiming to improve the speed and accuracy of deadlock detection in complex transaction scenarios.
Method
We propose a novel approach that models transaction relationships as a directed graph, where nodes represent transactions and edges denote dependencies. The DFS algorithm is iteratively applied to the transaction graph to identify cycles indicative of potential deadlocks. Upon detection, the algorithm provides insights into the involved transactions, the deadlock cycle, and indirect dependencies. Additionally, we compute key performance metrics such as execution time, memory usage, system throughput, response time, and success ratio during the deadlock detection process. This approach was implemented and validated in a MATLAB simulation environment.
Result
The implementation of the DFS-based algorithm demonstrated significant improvements in deadlock detection efficiency within nested transactional systems. The results indicated that the algorithm effectively identifies deadlocks arising from hierarchical dependencies, providing detailed insights into transaction relationships. Key metrics showed favourable performance in terms of execution time, memory usage, and overall system throughput, highlighting the algorithm's capability to manage varying transaction complexities.
Conclusion
This research contributes to the advancement of deadlock detection methods in nested transactions by presenting a robust DFS-based algorithm that effectively addresses the challenges of hierarchical deadlocks. The findings underscore the importance of efficient deadlock detection techniques for optimizing resource utilization and enhancing the performance of concurrent systems. Our approach lays the groundwork for future research and development of more resilient and dependable transaction management systems.
Improved super-twisting fuzzy sliding mode control based on fuel regulator
Authors: Qi Jun-zheng , Liu Wei-Wei , Wang Yang , Zhang Hai-Xing , Song Jian-Rong , Guo Zai-Lin 
Published: October 2024

Abstract

In this study, we address the flow fluctuations caused by the continuous jittering of the metering valve spool under system perturbations during the operation of the booster fuel regulator. By analyzing the mathematical model of the core metering valve and incorporating a speed-up factor into the super-twisting sliding mode control (STC), the system's tracking speed is significantly enhanced. Fuzzy logic is then introduced to achieve parameter self-adaptation in the improved super-twisting sliding mode controller (FSTC), effectively suppressing the jitter. This results in a stable metering device system, improved tracking speed, and reduced spool position error under perturbations. When compared to conventional sliding mode control (SMC) and unimproved super-twisting sliding mode control, using quasi-impulse, step, and ramp signals as inputs, the results show that FSTC reduces tracking error by an average of 120% under quasi-pulse signals compared to the unimproved STC, and enhances tracking speed by 327.2% under step signals, and the overall result is much better than SMC.
Knowledge and Attitudes of Pediatric Dentists Regarding Temporomandibular Disorders
Authors: de Santillana Irene Aurora Espinosa , Quintana Gabriel Muñoz , Quintana Gisela Nataly Rubín de Celis , Soto Olga Patricia López , Hernández Adriana María Martínez , Mendoza Guillermina Coba 
Published: October 2024

Abstract

Introduction: Temporomandibular disorders (TMD) are musculoskeletal and neuromuscular conditions involving the temporomandibular joint, masticatory muscles, and other related structures. There is a paucity of studies that have evaluated knowledge and attitudes of pediatric dentists regarding TMD. Objective: To evaluate knowledge and attitudes of pediatric dentists toward diagnosing and treating TMD in pediatric patients. Materials and Methods: Observational, analytical, cross-sectional study; 266 pediatric dentists responded to a 41-item questionnaire, of which 35 items related to knowledge (divided into four domains: Pathophysiological, Psychophysiological, Psychiatric Disorders, and Chronic Pain), and six items related to attitudes. The data were analyzed using descriptive statistics, and a chi-square test was applied for comparisons; (p < 0.05). Results: The mean correct response rate across the four domains was 49.5%, with the pathophysiological domain exhibiting the lowest rate at 33%, the psychophysiological domain demonstrating a 50% response rate, the psychiatric disorders domain showing a 64.1% response rate, the chronic pain domain exhibiting a 50.4% response rate. The rate was 83.1%, while 16.87% of respondents indicated a preference for referring patients with TMD. Furthermore, 31.2% of respondents indicated that they did not consider it necessary to provide preventive education on TMD. Statistically significant differences were found in four out of 13 items in the Pathophysiological domain and in two out of nine items in the Psychophysiological domain. Conclusion: The pediatric dentists demonstrated limited knowledge of TMD, yet they expressed positive attitudes toward providing proper diagnosis and treatment. However, they did not recognize their role in preventive education on the subject.
Juçara Pulp Drum Drying: an Alternative Technology for Value Chain Expansion
Authors: CASTELAN FLORENCE POLEGATO , JACOMINO ANGELO PEDRO , SOARES JACKELINE CINTRA , DE OLIVEIRA THALISSON JOHANN MICHELON , PURGATTO EDUARDO , GERMER SILVIA PIMENTEL MARCONI 
Published: October 2024

Abstract

Juçara (Euterpe edulis Martius) is a native Brazilian Atlantic Rainforest palm tree that is commonly found in small farming communities located in well-preserved forest remnants. Although juçara is a seasonal and highly perishable fruit, requiring freezing temperatures for storage, drying presents a viable alternative to avoid the cold chain. This study evaluated the effectiveness of drum drying for juçara pulp under three conditions: (1) without carrier agents, (2) with 5% (dry basis) corn starch, and (3) with 10% (dry basis) organic rice flour. The dried juçara pulp was analyzed for its chemical composition and bioactive properties. Drum drying of juçara pulp is viable with or without the use of carrier agents., resulting in juçara flakes with a moisture content of 2.3–3.5% (wet basis). While juçara flakes maintained the same total phenolic content as the fresh pulp (p > 0.05), anthocyanin levels and antioxidant capacity were slightly reduced (p > 0.05). Despite the need for further stability analysis, the product prepared without carrier agents emerged as a promising option for producing 100% juçara pulp flakes.
A cross-sectional study of the clinical manifestations of Sickles Cell Disease in Ghana, Nigeria and Tanzania and its association with foetal haemoglobin parameters
Authors: Amuzu Evans Xorse , Urio Florence , Dogbe Elliot Eli , Ponsian Peter , Abubakar Suraj Yawnumah , Okeke Chinedu , Olumuyiwa Balogun Olasinbo , Ozumba Petronilla , Osei-Akoto Alex , Paintsil Vivian , Nnodu Obiageli , Balandya Emmanuel , Makani Julie , Anazoeze Madu , Ansong Daniel , Nkya Siana 
Published: October 2024

Abstract

Background: Prevalence of Sickle Cell Disease (SCD) across African countries ranges between 1–3% and contributes up to 7-16% of under-five mortality. Nigeria, Ghana and Tanzania are among the top ten countries globally, with over 11,000 babies born with SCD annually. In order to bridge the gap in management and cognate research, the SickleInAfrica consortium was established in 2017 to facilitate collaboration among African nations in order to establish regionally relevant healthcare standards for SCD patients. This work utilised the SickleInAfrica platform to study haematological, clinical and genetic profiles of participants in the consortium.
Methods: This was a cross-sectional omni-directional study involving three sites in Ghana, Nigeria and Tanzania.This study enrolled 290 individuals with SCD aged five years and above who were confirmed to have SCD at steady state and were hydroxyurea naïve. Clinical history was obtained using an interviewer administered questionnaire. Haematological parameters were determined by automated haematology analyzer while quantification of HbF and F cells was implemented by high performance liquid chromatography and flow cytometry, respectively. Age adjusted logistic regression was employed to assess the association of HbF with the clinical manifestations.
Results: Participants were predominantly less than 18 years (63%) across the three countries. Most of the participants (across sites) had received blood transfusion in their lifetime. The most reported complication of SCD, requiring management in a hospital setting (in-patient or outpatient) was pain crises, ranging from 66-96% with the highest in Tanzania and lowest in Ghana. Overall, participants from Ghana had higher HbF levels(median = 8.20 with IQR= 4.80, 14.68, n = 68) compared with the level of HbF among participants from Tanzania (median = 4.45 with IQR= 2.33, 7.15, n = 92) and Nigeria (median = 4.30 with IQR= 2.60, 7.20, n = 7.20) and such difference was statistically significant, p < 0.001.
Conclusion: This work highlights important differences and similarities across SCD populations in the three countries. This is important especially in development of interventions for patients with SCD in the light of personalised medicine. A larger dataset is required for further analysis and validation of the findings.
Hybrid estimation using L-moments for the r-largest extreme value model with hydrological applications
Authors: Busababodhin Piyapatr , Shin Yire , Park Jeong-Soo 
Published: October 2024

Abstract

Incorporating more data using the r-largest order statistics (rLOS), rather than relying solely on a single set of maxima from each block, can enhance the precision of extreme value distribution estimation. The maximum likelihood estimation (MLE) method is commonly employed for parameter estimation of the rLOS model. However, the MLE tends to overestimate high quantiles, particularly for small sample and for distributions with heavy right tails. To mitigate this issue, we explored an L-moment-based estimation method tailored to rLOS, which retains some of the advantages of the L-moment estimator (LME). We proposed a weighted LME (WelMET), which transforms certain order statistics to resemble block maxima, and we introduced a hybrid estimator combining WelMET with MLE. Our simulation study demonstrated that while WelMET tends to underestimate high quantiles, the hybrid estimator is nearly unbiased due to compensating for the biases in both directions. Furthermore, the hybrid estimator exhibited a lower mean squared error than other methods, including MLE and WelMET. The optimal weight for the hybrid estimator was determined through Monte Carlo simulation. We applied this approach to annual 5-largest daily rainfall data from Maha Sarakham, Thailand.
The Relationship between Symptom Management, Depression Status, and Spiritual Care Requirement in Hemodialysis Patients in Turkey: A Correlational Study
Authors: Cantekin Işın , BURUCU RUKİYE 
Published: October 2024

Abstract

This study aimed to investigate the relationship between depression status, spiritual care needs, and symptom management in patients on hemodialysis. METHOD:. A previous study reported that spiritual health was one of the negative predictors of depression in hemodialysis patients (Upper: 0.90, Lower: 0.75, R2: 0.58) (24). based on the results of above study, the sample size was calculated using the G*Power software Version 3.1.9.7. Accordingly, it was found that the sample size should be at least 115 with a power of 0.80 and a margin of error of 0.05. The study sample included 119 individuals. The post hoc power of the study was 0.84. RESULTS: Upon correlation analysis, there was a moderate positive correlation between dry weight and BDI and a weak negative correlation between DSI and SCNI. The difference was statistically significant (p < 0.005) (Table 5). There was no significant relationship between other scale scores CONCLUSION: All HD patients had depressive traits. The spiritual care needs of these patients are much higher compared to average. The most important results of this study were that all HD patients had depressive symptoms, symptom burden was higher, and the need for spiritual care was higher.
Vehicle detection for Indian scenario: An
enhanced YOLO-NAS model
Authors: Bondili Balvindersingh 
Published: October 2024

Abstract

In the developing countries like India, traffic maintenance on roads is some what crucial task. Vehicle detection plays an important role and is the basic step for automatic monitoring of traffic. Vehicle detection is the procedure that recognizes vehicles from image or video acquired by cameras. It has an important role in a variety of applications, including traffic monitoring, surveillance, self-driving cars, and intelligent transportation systems. The emergence of deep learning (DL) models with convolution neural networks (CNN) have shown a path to the vehi- cle detection problem. Several DL models with CNNs have been presented in the literature to tackle the problem of vehicle detection. All these models were not up to the mark to produce results especially for Indian scenario due to hectic traf- fic conditions. To address this, a new vehicle detection method using enhanced neural architecture search for Indian scenario is presented in this paper. The experimental findings show that, on the FGVD and IRUVD datasets, the proposed model outperforms YOLO NAS by 10.94% and 1.29%, respectively, with precision rates of 84.51% and 96.61%.
Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration
Authors: Zhao Ruichao , Zhiwen Zheng 
Published: October 2024

ABSTRACT

Global climate change and human activities have profoundly impacted the geological and hydrological processes in watersheds, increasing the challenges in streamflow prediction. In this study, we propose a streamflow prediction model based on deep learning and dual-mode decomposition specifically tailored for the Wujiang River basin, a significant tributary on the southern bank of the upper Yangtze River. This model effectively addresses the prediction error issue caused by high-frequency components through dual-mode decomposition of time-series data. The results demonstrate that employing the coupled complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational mode decomposition (VMD)–temporal convolutional network (TCN)–long short-term memory network (LSTM) model reduces prediction errors by at least 35% compared to single decomposition models. Furthermore, compared to individual TCN or LSTM models, the TCN–LSTM coupled model exhibits greater stability and higher prediction accuracy during training, with reductions in mean absolute error and root mean square error by 43.13 and 24.57%, respectively. This model holds promising prospects for application and can provide crucial insights for water resource management.
Exploring the Regulatory Interaction of Differentially Expressed Proteins in Cleft Palate Induced by Retinoic Acid
Authors: Chen Liyun , Ma Aiwei , Jiang Lewen , Fan Jufeng , Jiang Wenshi , Xu Mengjing , Bai Xujue , Zhou Jianda , Zhang Wancong , Tang Shijie 
Published: October 2024

Objective:
This study aimed to identify novel proteins involved in retinoic acid (RA)-induced
embryonic cleft palate development.

Method:
The palate tissues of the control and RA-treated E14.5 were dissected and subjected to
iTRAQ-based proteomic analysis.

Results:
Differential expression analysis identified 196 significantly upregulated and 149 downregulated
considerably proteins in RA-induced palate tissues. Comprehensive Gene Ontology and
Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed the significant involvement
of cytoplasmic translation, ribosome biogenesis, glycolysis/gluconeogenesis, and glutathione
metabolism pathways in cleft palate pathogenesis triggered by RA. In particular, ribosome-related
pathways were highly enriched, while glycolysis was disrupted. Protein-protein interaction analysis,
facilitated by the STRING database, revealed a tightly interconnected network of differentially
expressed proteins. Further analysis using the cytoHubba plugin in Cytoscape identified ten hub
proteins, including Eif4a1, Gapdh, Eno1, Imp3, Rps20, Rps27a, Eef2, Hsp90ab1, Rpl19, and Rps16,
indicating their potential roles in RA-induced cleft palate development, and thus positioning
them as potential biomarkers for cleft palate.

Conclusion:
These findings provide valuable insights into the proteomic changes associated with
RA-induced cleft palate and shed light on key pathways and proteins that can contribute significantly
to the pathogenesis of this congenital condition.
Comparison of the Characteristics of Circulating Small
Extracellular Vesicles Isolated by Ultracentrifugation and a
Commercial Kit
Authors: Afrisham Reza , Farrokhi Vida , Moradi Roya , Alizadeh Shaban 
Published: October 2024

Introduction:
The market offers a wide range of extracellular vesicles (EVs)
isolation products, but their lack of standardization is a concern. Therefore, it is important
to carefully assess the quality of the EVs obtained using these products to patent
the ideal method. In this study, we compared the EXOCIB kit with the ultracentrifuge
method, which is considered the gold standard for small EV isolation.

Methods:
After overnight fasting, small plasma EVs were extracted from four individuals
using both the ultracentrifuge and the EXOCIB kit methods. The pooled EVs were
then compared for the presence of the cluster of differentiation 63 (CD63) protein using
the western blot analysis, and their size and zeta potential were performed by Dynamic
Light Scattering (DLS). In addition, the size and morphology of small EVs were determined
by using the Transmission Electron Microscopy (TEM) technique.

Results:
An average hydrodynamic size of 135.7 nm and a zeta potential of -6.33 Mv at
25°C was found for small EVs isolated by the ultracentrifuge, whereas the kit method resulted
in small EVs with a hydrodynamic size of 102.8 nm and a zeta potential of -0.907.
Notably, the size of the particles in the kit samples was smaller compared to those obtained
through the ultracentrifuge (P < 0.001). The western blot method confirmed the
expression of CD63 in both methods, so the ultracentrifuge yielded small EVs with a
higher level of purity compared to the kit-based approach (P = 0.036).

Conclusion:
The DLS findings revealed the existence of vesicles within the appropriate
size range for small EVs like exosomes in both isolation techniques. The results of the
western blot analysis, in conjunction with DLS, displayed that the ultracentrifuge method
extracted small EVs with a greater degree of purity than the kit-based approach.
Decoding Multidrug Resistance: Genetic Architecture and Codon Usage Patterns in ESKAPE Pathogens
Authors: Dahal Ujwal , Bansal Anu , Chitara Dheeraj 
Published: October 2024

Introduction:
The escalating challenge of multidrug resistance among
ESKAPE pathogens has become a prominent concern for global healthcare providers,
leading to amplified morbidity and mortality rates.

Methods:
We conducted this study to elucidate the genetic architecture of ESKAPE constituents
with the intent of ameliorating pathogenicity and facilitating drug development
efforts. A comprehensive array of computational tools and statistical methodologies were
employed to scrutinize the genomes of ESKAPE pathogens.

Results:
Translational selection profoundly influences the codon usage bias within this
pathogenic cohort. Notably, leucine emerged as the predominant amino acid, except in the
case of Acinetobacter baumannii, where arginine exhibited preeminence. There was
a universal preference for at least one histidine codon across all ESKAPE pathogens. GpC
emerged as the most prominently overrepresented dinucleotide at the codon pair junction
in all ESKAPE pathogens. Furthermore, a comparison of gyrB gene sequences and phylogenic
tree construction showed a distinct evolutionary relationship between AT-rich and
GC-rich ESKAPE pathogens. Additionally, identification, characterization, and phylogenetic
analysis of multiple antibiotic resistance genes revealed distinct evolutionary relationships.

Conclusion:
It was discerned that despite substantial variability amongst antibiotic resistance
genes of pathogens, leucine emerged as the predominant amino acid.
Naloxone Coprescribing and the Prevention of Opioid Overdoses: Quasi-Experimental Metacognitive Assessment of a Novel Education Initiative
Authors: Enich Michael , Morton Cory , Jermyn Richard 
Published: October 2024

Abstract

Background
Critical evaluation of naloxone coprescription academic detailing programs has been positive, but little research has focused on how participant thinking changes during academic detailing.

Objective
The dual purposes of this study were to (1) present a metacognitive evaluation of a naloxone coprescription academic detailing intervention and (2) describe the application of a metacognitive evaluation for future medical education interventions.

Methods
Data were obtained from a pre-post knowledge assessment of a web-based, self-paced intervention designed to increase knowledge of clinical and organizational best practices for the coprescription of naloxone. To assess metacognition, items were designed with confidence-weighted true-false scoring. Multiple metacognitive scores were calculated: 3 content knowledge scores and 5 confidence-weighted true-false scores. Statistical analysis examined whether there were significant differences in scores before and after intervention. Analysis of overall content knowledge showed significant improvement at posttest.

Results
There was a significant positive increase in absolute accuracy of participant confidence judgments, confidence in correct probability, and confidence in incorrect probability (all P values were <.05). Overall, results suggest an improvement in content knowledge scores after intervention and, metacognitively, suggest that individuals were more confident in their answer choices, regardless of correctness.

Conclusions
Implications include the potential application of metacognitive evaluations to assess nuances in learner performance during academic detailing interventions and as a feedback mechanism to reinforce learning and guide curricular design.