bluuebunny/crossref_metadata_2025_split huggingface dataset:
Grain Size Evaluation of Selectively Heated Sintered PM Preforms under Cold Compression
Authors: Ramakrishnan Tharmaraj , Maharajan Joseph Davidson 
Published: October 2023

Abstract
The mechanical qualities of the materials determine the final product’s performance in engineering applications, and the analysis of material grain size could change these attributes. Controlling grain size is critical in the metal forming process for obtaining good mechanical characteristics, which improves the final component’s performance. The purpose of this research is to determine the impact of selective heating (SH) on the microstructure of sintered powder metallurgy aluminum- and titanium-based preforms for various titanium weight fractions (2–6 %) and starting relative densities (80–90 %). Cold upsetting tests of sintered aluminum-titanium workpieces were carried out in a hydraulic press for several ranges of SH (100°C–250°C). The microstructure of aluminum-titanium specimens was examined using an optical microscope, and the intercept line approach was employed to calculate the average grain size (AGS). Because of dynamic recrystallization and grain development, the AGS of aluminum-titanium preforms is found to increase as the SH temperature rises.
Quality management for design – Bid-build versus design-and-build contracts for infrastructure projects
Authors: Gashi Esat , Ivezaj Marjan 
Published: October 2023

AbstractSelecting the construction delivery method during the contracting period is one of the most important decisions determining the quality of large-scale infrastructure projects. Infrastructure projects have the most complex production processes in civil engineering. Infrastructure projects are among the most complex and resource-intensive endeavours in civil engineering due to their size, scope, multidisciplinary nature, regulatory requirements, financing challenges, environmental considerations, and the need for long-term planning and maintenance. Effective project management, collaboration, and a deep understanding of these challenges are crucial for the successful execution of infrastructure projects. Implementing such projects inevitably demands proper quality management throughout the project lifecycle. Two primary types of construction contracts are under implementation worldwide: Design-Bid-Build (DBB) and Design–and–Build (DB) contracts. In the Western Balkans region, both types of contracts are utilized for infrastructure projects, A noticeable trend is emerging toward transitioning from DBB to DB contracts. This paper provides a comprehensive analysis of quality management within the context of construction contracts with a focus on the roles and responsibilities of key stakeholders and how these factors affect the achievement of quality objectives while managing constraints related to cost and time. This research aims to improve construction practices by selecting an adequate type of contract for construction practices and ensuring successful project outcomes.
Formation and development of Orthodox rehabilitation pedagogy in relation to social orphans in Russia
Authors: Semyannikov S. N. , Savinykh L. N. 
Published: October 2023

Historically, the Joseph Volokolamsk Monastery in all periods of its existence played a significant role not only in the spiritual, political, ideological, and economic life of Russia, but was also an important educational center. The monastery continues development of these traditions, founded by venerable Joseph Volotsky, to this day. The article examines a new phenomenon for Russia, social orphanhood, as a problem of modern society. Legislative acts defining the status of a social orphan and his relationship with society are given. Based on archival documents, a retrospective of the activities of the Joseph Volokolamsk Monastery in working with youth and helping orphans and the suffering is given. The historical aspects of Orthodox rehabilitation pedagogy in relation to orphans are indicated. Statistics of these activities and a historical retrospective of the relationship between the church and the state in the pre-revolutionary years and the years of Soviet power are given. The works of a number of Orthodox authors — teachers, ascetics, philosophers and educators, providing the basis for the development of Orthodox rehabilitation pedagogy — are examined in retrospective. The patristic foundations of the spiritual and moral education of modern teenagers — social orphans in Russia are indicated. The historical foundations and principles of modern educational, spiritual and moral rehabilitation mission of the brethren of the monastery in collaboration with the teams of social rehabilitation centers for minors are given. Individual programs for spiritual and moral rehabilitation of social orphans are offered.
Sleep quality and associated risk factors among youth attending health communication centre of a medical college in a rural area of Ujjain
Authors: Deshpande Kirti , Khardenavis Archit , Bhatnagar Dinesh 
Published: October 2023

Background: Sound sleep is extremely important to maintain good health, especially for youth, as it is the most socio-economically productive age group of any community. Evidence suggests that poor sleep is associated with various physical and mental health problems. The present study aims to find out the quality of sleep, sleep disturbances, and associated risk factors among youth in a rural area of Ujjain.
Methods: Pittsburgh’s sleep quality index (PSQI) questionnaire was used to assess the sleep quality of young adults in the age group of 18-30 years attending health communication centre of a medical college.
Results: Mean PSQI Score of the study participants was 8.37±5.644. PSQI score for females was higher (7.076±5.49 vs 9.258±5.59) than PSQI Score of males. Majority (68.3%) had poor sleep quality but only few (11. 8%) self-rated their sleep quality as poor. Female gender and mobile phone addiction were the risk factors found significantly associated with poor sleep quality.
Conclusions: Poor sleep quality among youth is an important concern as it has health consequences. Lack of self-awareness regarding sleep quality and mobile phone addiction among youth needs educational intervention.
Algının Mekan Değerlendirmesi Üzerine Etkisi: Bursa Hanlar Bölgesi Örneği
Authors: ENDER ALTAY Elvan , ŞENGÜL Sena 
Published: October 2023

Bu araştırma kamusal bir mekan ile kullanıcı etkileşimindeki mekansal algıların anlaşılması için peyzaj mimarlığı disiplininde daha fonksiyonel ve bütüncül tasarımların ortaya konmasına katkı sağlamak amacıyla yapılmıştır. Araştırmada tarihi bir dokuya sahip olan araştırma alanı, kent kimliği kapsamında mekanı algılama kavramları ile değerlendirilerek incelenmiştir. Kentsel mekanlarda mekanın kent kimliği yönüyle değerlendirilmesinin yanında insan zihninde hangi ölçütlerle olumlu duygular oluşturulmasının araştırılması yönüyle çalışma önem kazanmaktadır. Araştırma alanı, Bursa'da Osmangazi İlçesi’nde bulunan Hanlar Bölgesi’dir. Araştırma; ölçütlerin belirlenmesi için literatür taraması, alan analizi, anket çalışması, verilerin analizi, sonuç ve önerilerin geliştirilmesi olmak üzere beş aşamada gerçekleştirilmiştir. Alan analizi 49 ölçütün
değerlendirilmesiyle gerçekleştirilmiştir. 1 ile 5 puan aralığında seçeneklerin bulunduğu bir puanlama sistemi içeren likert ölçeği ile değerlendirilen ölçütler sonucunda 25 ölçütün ortalamanın altında kaldığı, hiçbir ölçütün en yüksek puan olan 5’e yakın olmadığı bulunmuştur. Bu durum Hanlar Bölgesi’nin mekansal algı açısından kullanıcıya olumlu duygular yüklemediğini göstermektedir. Araştırma sonucunda belirlenen ölçütlerin ve elde edilen bulguların gelecek çalışmalar için bir altlık niteliği taşıması hedeflenmiştir.
Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks
Authors: Escheikh Mohamed , Yakoub wiem Taktak 
Published: October 2023

Abstract
The proliferation of the ever-increasing number of highly heterogeneous smart devices and the emerging of a wide range of diverse applications in 5G mobile network ecosystems impose to tackle new set of raising challenges related to agile and automated service orchestration and management. Fully leveraging key enablers technologies such as Software Defined Network (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) capabilities in such environment is of paramount importance to address Service Function Chaining (SFC) orchestration issues according to user requirements and network constraints. To meet these challenges, we propose in this paper a Deep Reinforcement Learning (DRL) approach to investigate online Quality of Experience (QoE)/Quality of Service (QoS) aware SFC orchestration problem. The objective of this work is to fulfill intelligent, elastic and automated Virtual Network Functions (VNF)s/Container Network Function (CNF)s deployment optimizing end-to-end user experience while respecting QoS constraints. We implement the DRL approach through using a variant of Deep-Q-Network (DQN) algorithm referred to as Double DQN. We show how DRL agent behaves along the learning process for different PSN scales. We highlight also the impact of a set of hyper-parameters such as batch size and learning rate on solving the sequential decision problem related to SFC orchestration. The evaluation of the learning process is achieved based on the quality of learning with respect to the number of runs. In this regard, we use QoE metric to define a score quantifying the quality of learning.
Modeling the diagnosis and control infertility couples with different factors
Authors: Ghasemabadi Atena , Hafezi Somayeh Ghiasi , Effati Sohrab , Namakin Kosar , Zand Mohammadreza Ahmadi , Ghamsary Mark 
Published: October 2023

Abstract
Background: There are different etiologies of infertility. Poly Cystic Ovarian Syndrome (PCOS) is a leading cause of infertility among women, and the male factor accounts for about 50 % of the total number of infertile couples. As with various etiologies, various treatments are required. Choosing the most suitable procedure of Assisted Reproductive Technology (ART) for individuals is of great importance. Therefore, mathematical models are needed to estimate the success rate of ART for every individual and to determine which treatment suits the best for each couple.
Objective:In this study, we aim to present a new dynamic model that can predict the success rate of ART models for each base on the etiology of their infertility, lab tests, and clinical findings.
Materials and Methods For this purpose, 502 participants were recruited and evaluated based on their BMI, AMH levels, the number of transferred embryos, and the etiology of infertility (PCOS, male factor, or both). For this purpose, the community is divided into eight classes: infertile susceptible couples, couples with the cause of infertility PCOS, male factor or both, couples with embryo quality A, B, and C, coupled with the positive βHCG test, couples with the positive result of clinical pregnancy and fertile couples.
Results: Our dynamic models evaluate the success rate of ART in our subgroups considering all of the effective factors such as BMI, AMH level, number of transferred embryos, and the etiology of infertility. We have found that (BMI > 30 kg/m2) and low AMH levels (< 3.5ng/mL) indicate lower success rates. Moreover, ICSI was more promising for all the underlying etiologies of male factor, PCOS, or both.
Conclusion: In conclusion, our new mathematical model is presented to investigate the dynamics and diagnose the cause of infertility in couples. It is shown that the equilibrium point free of disease is asymptotically stable. In this system, backward bifurcation occurs. When backward bifurcation occurs, we must reduce the primary reproduction number to less than one to eradicate the disease. In our system, we see that the factors that cause the reduction of the primary reproduction number are the factors used to eliminate infertility (the essential reproduction R0 depends on the recovery rate in infertile couples with positive clinical pregnancy (ξ). Therefore, backward bifurcation depends on the possibility of recovery. As the rate of recovery increases, the primary reproduction number R0 decrease and controls couple’s infertility.
When male factors and treatment methods cause infertility are IVF and IV F&ICSI, we demonstrate that there is no Hopf bifurcation, i.e., no periodic orbit emerges or vanishes due to a change in the stability of a fixed point.
StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records
Authors: Lee Ho-Joon , Schwamm Lee H. , Sansing Lauren , Kamel Hooman , de Havenon Adam , Turner Ashby C. , Sheth Kevin N. , Krishnaswamy Smita , Brandt Cynthia , Zhao Hongyu , Krumholz Harlan , Sharma Richa 
Published: October 2023

Abstract

Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists’ review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, nine base classifiers performed well with a mean cross-validated area under the receiver operating curve (AUCROC) of 0.90. Their ensemble meta-model, StrokeClassifier, achieved a mean cross-validated accuracy of 0.74 and weighted F1 of 0.74. In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70, and 0.71, respectively. SHapley Additive exPlanation analysis revealed that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2–7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.
High-Accuracy Slope Stability Analysis Using Data-Driven and Attention-Based Deep Learning Model
Authors: Zhou Yangli , Fu Haiying , Zhou Mingzhe , Zhao Yanyan , Wei Wei 
Published: October 2023

Abstract
Because of the abrupt occurrence and severe consequences of slope disasters, the analysis of slope stability has always been a focal point in the field of slope disaster prevention. In previous studies, traditional methods were employed to analyze the stability of slopes, which not only require considerable temporal and manpower investments but also fail to effectively predict the development trends of slope stability, thereby posing challenges in slope disaster prevention. However, the emergence of data-driven approaches based on deep learning has forged a novel avenue for slope disaster prevention. Recently, a transformer model is proposed, which has an attention module to deeply learn the high dimensionality correlation between the properties of slopes and the stability of slopes. In this study, we have pioneered a novel approach to slope stability analysis, utilizing the transformer model for the first time. We have developed new computer code, curated a dataset consisting of 72,000 slope samples, and demonstrated the superior predictive capabilities of the transformer model in comparison to LSTM and Attention-LSTM models. Subsequently, we conducted further study on transformer-based multi-classification and regression models. Significantly, the regression model outperformed in predicting slope stability, reaching an impressive accuracy of 99.983%. The results indicate that the deep learning approach based on the transformer model has shown great potential and advantages as a novel method for slope stability analysis. Its high accuracy and short computation time will contribute to rapid on-site decision-making in geotechnical engineering applications.
Peroxidase-like Fe0@FeMn-NC for the efficient degradation of 4-CP: Critical role of Fe0
Authors: Lv Xinxin , Liu Huilai , Li Zhihao , Cui Minshu , Cui Kangping , Guo Zhi , Dai Zhengliang , Wang Bei , Chen Xing 
Published: October 2023

Abstract
Peroxidase-like based on double transition metals have higher catalytic activity and are considered to have great potential for application in the field of pollutant degradation. First, in this paper, a novel Fe0-doped three-dimensional porous Fe0@FeMn-NC-like peroxidase was synthesized by a simple one-step thermal reduction method. The doping of manganese was able to reduce part of the iron in Fe-Mn binary oxides to Fe0 at high temperatures. In addition, Fe0@FeMn-NC has excellent peroxidase-like mimetic activity, and thus it was used for the rapid degradation of p-chlorophenol (4-CP). During the degradation process, Fe0 was able to rapidly replenish the constantly depleted Fe2+ in the reaction system and brought in a large number of additional electrons. The ineffective decomposition of H2O2 due to the use of H2O2 as an electron donor in the reduction reactions from Fe3+ to Fe2+ and from Mn3+ to Mn2+ was avoided. Finally, based on the experimental results of LC-MS and combined with theoretical calculations, the degradation process of 4-CP was rationally analyzed, in which the intermediates were mainly p-chloro-catechol, p-chloro resorcinol and p-benzoquinone. Fe0@FeMn-NC nano-enzymes have excellent catalytic activity as well as structural stability, and perform well in the treatment of simulated wastewater containing a variety of phenolic pollutants as well as real chemical wastewater. It provides some insights and methods for the application of peroxidase-like enzymes in the degradation of organic pollutants.
Overall Health Knowledge in Philippine: Multilevel Confirmatory Factor Analysis of Philippine National Demography Heath Survey 2022 Data
Authors: Lotfy Nesma 
Published: October 2023

Abstract
Objective
To confirm the underlying structure among the knowledge of communicable and noncommunicable diseases using multilevel confirmatory factor analysis and to determine if the structure differ according to gender or residence.
Methods
A secondary data analysis of Household Questionnaire from the 2022 PNDHS was conducted. PNDHS data is a two-level data; level-1 is within household (household questionnaire), and level-2 is between households (primary sampling unit (PSU)), so multilevel confirmatory factor analysis was implemented (two-level CFA and two-level variance CFA). Also, multigroup analysis was done to assess the structure difference between male/female and urban/rural. All computations were performed using STATA version 16.0.
Results
Knowledge of cancer, heart disease, diabetes, dengue fever, TB, and COVID − 19 found 96.7%, 94.9%, 97.8%, 98.4%, 96.7%, and 92.8%, respectively. According to two-level CFA, the coefficient loading of each item toward the two levels were all statistically significant (P < 0.001). Regarding two level variance CFA, the variance of level-1 is higher than the variance of the level-2 (13 and 6.7 respectively). The multigroup analysis discover that the model has measurement non-invariant (not equal) across gender and residence (P = 0.000, P = 0.000, respectively).
Conclusion
The level-1 has great effect compared to level-2 as the variance of level-1 is greater than level-2. The factor loading toward the two levels are different according to gender and residence, so more investigation is needed to determine the causes of this difference.
An Effective Procedure to Improve Covariates Balance for Multiple Groups in Randomized Trials
Authors: Li Na , Meng Zhen 
Published: October 2023

Abstract
In clinical trials, it is important to compare the differences in treatment effects among multiple groups to determine the effectiveness of certain treatments for a disease. Statistical inferences are typically made assuming that the sample is representative, with subjects randomly assigned to each treatment group and minimal allocation differences. To address these allocation differences, various randomization methods have been proposed, including complete randomization, rerandomization, pairwise sequential randomization, and k-resolution sequential randomization (k-RSR).In this paper, we propose an extension of the k-RSR method called multiple k-resolution sequential randomization (Mk-RSR).Mk-RSR aims to minimize the integrated measurement Mahalanobis distance among multiple groups with equal group size. While the original k-RSR method was developed for two groups, Mk-RSR is designed for two or more groups. By using Mk-RSR, researchers can achieve a balanced allocation and obtain a reasonable estimate of treatment effect.Theoretical results demonstrate that Mk-RSR is more likely to achieve the optimal value compared to existing methods. Extensive simulation studies and an example analysis are conducted to illustrate the superiorities of Mk-RSR.
A Matched Case-Control Analysis of Autonomous vs Human-Driven Vehicle Accidents
Authors: ding shengxuan , Abdel-Aty Mohamed 
Published: October 2023

Abstract

Despite the recent advancements that autonomous vehicles (AV) have shown in their potential to improve safety and operation, considering differences between AV and human-driven vehicles (HDV) in accidents remain unidentified due to the scarcity of real-world AV accident data. We investigate the difference in accident occurrence between AV levels and HDV by utilizing 2,100 AV (ADS and ADAS) and 35,113 HDV accident data. A matched case-control design was conducted to investigate the impact of different variables on the likelihood of accidents involving AV versus HDV. The analysis suggests that AVs tend to be safer than HDVs in many accident situations. Yet, the likelihood of an AV accident during dawn/dusk or while making turns is greater than that of HDVs in the same situations. Moreover, rear end accidents in which an AV hits an HDV are less common than those where an HDV hits an AV. Our research sheds light on the accident risk disparities between AVs and HDVs, informing future developments in AV technology and safety enhancements.