bluuebunny/crossref_metadata_2025 huggingface dataset:
METHOD OF LEGAL REGULATION AND TURNOVER OF LOW-RISE HOUSING STOCK IN RUSSIA
Authors:   Academy of Labor and Social Relations, Moscow, Russia, DMITRIEVA A.A. ,   Law Firm "Yurist", LLC, Moscow, Russia, RABETZ A.M. ,   National Research Tomsk State University, Tomsk, Russia
Published: October 2024

Purpose of the study. This article is a continuation of the author’s study of the legal regime of low-rise housing stock in Russia. The purpose of the current study is to highlight the distinctive features of such features of the legal regime as the method of legal regulation of legal relations (imperative/dispositive) and the ability of objects to be circulated. Conclusions. The analysis showed that when considering these features of the legal regime in relation to the low-rise housing stock of Russia, the application of the principle of analogy of law is characteristic, expressed, in particular, in the complementary use of both imperative and dispositive methods of legal regulation. The study of the ability of objects to turn over as a sign of the legal regime made it possible to note the absence of clear distinguishable characteristics of low-rise housing stock, with the exception of the allocation of apartment buildings within the framework of social tenancy agreements into a separate category.
REGULATION OF LAWMAKING IN THE FIGHT AGAINST CRIME: PROBLEMS AND WAYS TO SOLVE THEM
Authors:   Law Office “Asnis and Partners”, Moscow, Russia, ASNIS A.YA.
Published: October 2024

The author proposes a definition of the concept of lawmaking in the fight against crime as the activity of subjects of development, public discussion, examination, coordination and adoption of draft federal laws related to criminal, criminal procedural, operational investigative, penal, criminological (preventive) legislation. Considers the background and problems of legislative and subordinate regulation of lawmaking in Russia, the reasons and conditions that contributed to the growing attention of the legislative, judicial and executive branches of government to the regulation of criminal lawmaking. Analyzes the draft federal law «On Amendments to Article 8 of the Federal Law “On the Enactment of the Criminal Code of the Russian Federation”», prepared on the basis of the conclusions of the interdepartmental working group formed by the Ministry of Justice of Russia in March 2023 to prepare proposals for the draft concept of criminal legal policy Russian Federation. Justifies the need to abandon the idea of consolidating special rules of criminal law legislation in the Introductory Law to the Criminal Code of the Russian Federation in favor of resuming work on the draft federal law “On normative legal acts in the Russian Federation” (Law on Laws).
ORGANIZATIONAL AND METHODOLOGICAL ASSISTANCE IN THE SYSTEM OF INTERNAL AFFAIRS BODIES: PROBLEMS AND DEVELOPMENT PROSPECTS
Authors:   North Caucasus Institute for Advanced Studies (branch) of the Krasnodar University of the Ministry of Internal Affairs of Russia, Nalchik, Russia, SHKHAGAPSOEV Z.L.
Published: October 2024

Improving the activities of internal affairs bodies is necessary for the effective functioning and development of the Russian state. Of particular importance in this matter is the interaction of hierarchically subordinate structural units of internal affairs bodies, as well as the effectiveness of the implementation of control powers. This allows you to timely identify problems in the activities of specific territorial units, as well as promptly respond to eliminate them. One of the forms of such response is the provision of organizational and methodological assistance to controlled bodies. The purpose of the presented research is to analyze the current state of the institution of organizational and methodological assistance in the system of internal affairs bodies, identify existing problems, determine ways to solve them, as well as prospects for the further development of this assistance. The author comes to the conclusion that organizational and methodological assistance is of a managerial nature and is focused on assisting the controlled internal affairs bodies and their departments to resolve emerging problems and eliminate negative circumstances affecting the effectiveness of the functioning of the above-mentioned bodies. Attention is focused on the existing problems of providing organizational and methodological assistance. The prospects for the development of the above-mentioned assistance to controlled bodies are noted, as well as the need to consider organizational and methodological assistance as a unified form of assistance provided to internal affairs bodies and their units, since these areas are closely interrelated.
Analysis on stall mechanism of axial flow compressor with rotating inlet distortion by dynamic mode decomposition
Authors: Qiu Jiahui , Zhong Yunhong , Liu Yang , Zhao Yijia , Zhang Min , Du Juan
Published: October 2024

To study the impact of rotating inlet distortion on stall mechanism of an axial compressor, the full-annulus unsteady simulation results are analysed using dynamic mode decomposition (DMD). Firstly, the frequency and energy ratio of each mode at near stall condition for the compressor with uniform inflow are obtained. A dominating frequency band 0.3–0.6 BPF regarded as the characteristic frequency of unsteady tip leakage flow is observed. The distribution of eigenvalues further confirms that there are a few critical unstable modes in the flow field. With further throttling, four large-scale disturbance structures are captured by the second-order mode, whose frequency is twice as the rotor frequency. The circumferential propagation velocity of the stall cell is thus deduced to be about 50% of the rotor speed. Further, when the compressor is subjected to rotating inlet distortion, the change of stalling process is related to the relative position of distortion plate and stall cell according to the DMD mode frequency and shape. The addition of rotating distortion does not change the stall type of the compressor, but the unstable modes indicate that the number and shape of the stall cells have been varied.
Weighted fast-trimmed likelihood estimator for mixture regression models
Authors: Uraibi Hassan S. , Waheed Mohammed Qasim
Published: October 2024

The fast-trimmed likelihood estimate is a robust method to estimate the parameters of a mixture regression model. However, this method is vulnerable to the presence of bad leverage points, which are outliers in the direction of independent variables. To address this issue, we propose the weighted fast-trimmed likelihood estimate to mitigate the impact of leverage points. The proposed method applies the weights of the minimum covariance determinant to the rows suspected of containing leverage points. Notably, both real data and simulation studies were considered to determine the efficiency of the proposed method compared to the previous methods. The results reveal that the weighted fast-trimmed estimate method is more robust and reliable than the fast-trimmed likelihood estimate and the expectation–maximization (EM) methods, particularly in cases with small sample sizes.
Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience
Authors: Ando Tetsuya , Matsui Kazuhiro , Okamoto Yuto , Atsuumi Keita , Taniguchi Kazuhiro , Hirai Hiroaki , Nishikawa Atsushi
Published: October 2024

Introduction

Many studies have investigated the manipulation of a virtual upper arm using electromyogram (EMG); however, these studies primarily used a machine learning model or trigger control for this purpose. Furthermore, most of them could only display the constant motion of the virtual arm because the motion to be displayed was selected by pattern recognition or trigger control. In addition, these studies did not examine changes in the electromyographic signals after experiencing the virtual arm. By contrast, we propose a real-time, continuous, learning-free avatar that manipulates the virtual arm with electromyogram signals or physio-avatar EMG biofeedback (EB). The goal of the physio-avatar EB system is to induce physiological changes through experiential interactions.

Methods

We explored the possibility of changing motor control strategies by applying the system to healthy individuals as a case study. An intervention method that provided an experience of a body different from one’s own was conducted on seven participants using a time-invariant calculation algorithm to determine the joint angles of the avatar. Control strategies for an indicator of the equilibrium point in the baseline and adaptation phases were determined to evaluate the physio-avatar EB intervention effect. The similarity of these BL and adaptation control strategies compared to those used during the washout period was assessed using the coefficient of determination. The accuracy and reliability of the virtual reality (VR) system were evaluated by comparison with existing studies and the required specs.

Results and Discussion

Changes in motor control strategies due to the physio-avatar EB system were observed in four experiments, where the participants gradually returned to their pre-intervention control strategies. This result can be attributed to the aftereffects caused by error learning. This implies that the developed system influenced their motor control strategies. The number of EMG acquisition bits was 16 bits, and the sampling rate was 1,000 Hz. The refresh rate of the head-mounted display was 90 Hz, and its resolution was 1440×1600 for a single eye. Additionally, the simulation frame rate was 30 FPS. These values were adequate compared to existing studies and required specs. The essential contribution of this study is the development of an avatar that is controlled by a different method than has been used in previous studies and the demonstration of changes in a subject’s muscle activity after they experience an avatar. In the future, the clinical efficacy of the proposed system will be evaluated with actual patients.
Tribological and investigation study of a dlc-coated H13 friction drilling tool on AZ31B magnesium alloy
Authors: Alphonse Mathew , Raja V. K. Bupesh , Cepova Lenka , Salunkhe Sachin , Nasr Emad Abouel , Abdelgawad Abdelaty Edrees
Published: October 2024

Introduction

Friction drilling is an innovative method in hole-making for sheet metal applications, thin sheets of conventional structural alloy materials like copper, titanium, steel etc., even though there are other methods, such as thermal distortion for the welding of nuts, riveting of nuts, and threading. For the last hundred years, researchers have focused on studying the development of this technique to maintain strength, hole roughness, hole geometry, hardness etc. It is interested in finding solutions for wear, tool life, and plastic deformation.

Method

Friction drilling is also called a green hole-making process because this process uses the friction between the rotating tool and the workpiece. In this research, instead of regular HSS and Tungsten Carbide tools, the H13 tool steel is used, because the H13 steel tool has unique chemical compositions like chromium and molybdenum, which give high toughness, hot hardness, and wear resistance. Diamond-like-carbon (DLC) coating has been used in this research to enhance tool life and AZ31B magnesium alloy is used as the work material. Initially, in this research, the wear stability of the DLC-coated H13 tool was investigated, and later, the tool surface roughness and hole quality were verified.

Results and Discussion

The material loss observed for the DLC-coated H13 steel tool in the pin-on-disk test was 0.05 g. This investigation used two different diameter tools, namely 3 and 7 mm. Research has concluded that the 7 mm tool is better for friction drilling by seeing the roughness and hole quality. However, the conditions were that the spindle should rotate at 4,000 rpm and the feed rate of the tool to be at 200 mm/rev.
Effect of progesterone concentration on hCG trigger day on clinical outcomes after high-quality single blastocyst transfer in GnRH antagonist cycles
Authors: Jia Nan , Xu Jianing , Song Bingbing , Hao Haoying , Li Meng , Zhang Cuilian , Zhang Shaodi
Published: October 2024

Objective

To investigate whether progesterone levels on the human chorionic gonadotropin (hCG) trigger day are associated with clinical outcomes in fresh cycles and the first frozen–thawed cycles (the freeze-all strategy) following the transfer of a high-quality single blastocyst.

Methods

This single-center retrospective analysis was conducted on patients undergoing in vitro fertilization with the gonadotropin-releasing hormone (GnRH) antagonist protocol from January 2017 to December 2023. The study included the first and second oocyte retrieval cycles with progesterone levels ≤2 ng/ml on hCG trigger day. Clinical pregnancy rates and early miscarriage rates were compared among groups using curve fitting, threshold effect analysis, and multivariable regression.

Results

When progesterone levels were between 1 and 2 ng/ml, the pregnancy rate in fresh cycles was only 51% of that in cycles with progesterone levels ≤1 ng/ml (95% CI: 0.33, 0.79, p = 0.0028). And the pregnancy rate decreased by 25% (95% CI: 0.51, 1.09) for frozen cycles, although there was no statistically significant (p = 0.1273). When cycle types were used as a binary variable in multivariate regression analysis, it was found that the clinical pregnancy rate in frozen cycles was 1.84 times higher than in fresh cycles (OR = 1.84, 95% CI: 1.38–2.47). For progesterone levels between 1 and 2 ng/ml, the clinical pregnancy rate in frozen cycles was 2.90 times that of fresh cycles (OR = 2.90, 95% CI: 1.59, 5.29, p = 0.0015). Progesterone levels on hCG day had no impact on the clinical pregnancy rate in thaw cycles, nor did they affect miscarriage rates in fresh or thaw cycles (p > 0.05).

Conclusion

When progesterone levels on hCG trigger day were between 1 and 2 ng/ml, the clinical pregnancy rate for frozen–thawed cycles of high-quality single blastocyst transfer using an GnRH antagonist protocol significantly surpasses that of fresh cycles, thus elective frozen embryo transfer after the freeze-all strategy is recommended.
Investigation of the impact of dual inoculations of arbuscular mycorrhizal fungi and plant growth-promoting rhizobacteria on drought tolerance of maize grown in a compost-amended field under Mediterranean conditions
Authors: Ouhaddou Redouane , Ech-chatir Lahoucine , Ikan Chayma , Soussani Fatima Ezzahra , Errouh Farid , Boutasknit Abderrahim , Rodrigez Julio Cesar , Er-Raki Salah , Duponnois Robin , Meddich Abdelilah
Published: October 2024

In the current context of rapid climate change, water scarcity and soil poverty are becoming increasingly alarming, leading to growing losses of 30–50% of global agricultural production. It is imperative to find environmentally-friendly approaches for improving plant tolerance to drastic conditions, particularly in arid and semi-arid Mediterranean regions. Biostimulants based on symbiotic microbes are emerging as effective strategies for improving tolerance and agricultural productivity. This study aims to evaluate the effects of single and double inoculation of arbuscular mycorrhizal fungi (My) and plant growth-promoting bacteria (Ba) on the growth, physiological and biochemical traits of maize crop grown in compost (Co) amended soil under two irrigation regimes: well-watered (WW: 100% of crop evapotranspiration [ETc]) and drought-stressed (DS: 50% ETc) using drip irrigation system. Reducing irrigation to 50% reduced shoot dry weight (SDW), root dry weight (RDW), 1,000-grains weight (TGW) and grain yield (Y). However, Ba alone increased SDW by 63%, while CoMyBa improved RDW, TGW and Y by 197, 43 and 175%, respectively compared with the control under DS conditions. Dual inoculation boosted root colonization intensity, normalized difference vegetation index (NDVI), total chlorophyll and leaf area of maize seedlings in compost-amended soil, compared to the controls. The application of Ba significantly reduced hydrogen peroxide and malondialdehyde by 46%, in maize seedlings grown in compost-amended soil, compared to the controls under DS. Our results indicated that My and Ba significantly boost the ability of maize to tolerate drought by improving water supply and physiology and stimulating the accumulation of organic and inorganic osmolytes, as well as improving the properties of soils such as cation exchange capacity particularly amended by Co. The dual inoculations were the most effective and represent an environmentally-friendly and relatively inexpensive approach to optimizing agricultural production and soil restoration programs in Mediterranean regions.
Alzheimer’s disease induced neurons bearing PSEN1 mutations exhibit reduced excitability
Authors: Maksour Simon , Finol-Urdaneta Rocio K. , Hulme Amy J. , Cabral-da-Silva Mauricio e Castro , Targa Dias Anastacio Helena , Balez Rachelle , Berg Tracey , Turner Calista , Sanz Muñoz Sonia , Engel Martin , Kalajdzic Predrag , Lisowski Leszek , Sidhu Kuldip , Sachdev Perminder S. , Dottori Mirella , Ooi Lezanne
Published: October 2024

Alzheimer’s disease (AD) is a devastating neurodegenerative condition that affects memory and cognition, characterized by neuronal loss and currently lacking a cure. Mutations in PSEN1 (Presenilin 1) are among the most common causes of early-onset familial AD (fAD). While changes in neuronal excitability are believed to be early indicators of AD progression, the link between PSEN1 mutations and neuronal excitability remains to be fully elucidated. This study examined iPSC-derived neurons (iNs) from fAD patients with PSEN1 mutations S290C or A246E, alongside CRISPR-corrected isogenic cell lines, to investigate early changes in excitability. Electrophysiological profiling revealed reduced excitability in both PSEN1 mutant iNs compared to their isogenic controls. Neurons bearing S290C and A246E mutations exhibited divergent passive membrane properties compared to isogenic controls, suggesting distinct effects of PSEN1 mutations on neuronal excitability. Additionally, both PSEN1 backgrounds exhibited higher current density of voltage-gated potassium (Kv) channels relative to their isogenic iNs, while displaying comparable voltage-gated sodium (Nav) channel current density. This suggests that the Nav/Kv imbalance contributes to impaired neuronal firing in fAD iNs. Deciphering these early cellular and molecular changes in AD is crucial for understanding disease pathogenesis.
The role of vitamin D in diabetic foot ulcer; an umbrella review of meta-analyses
Authors: Liu Lu , Zhang Fan , Jamali Mehrdad , Guimarães Nathalia Sernizon , Radkhah Nima , Jamilian Parmida , Wang Qian
Published: October 2024

Background

Complications related to diabetic foot ulcers (DFU) due to diabetes are increasing. One of the factors influencing the management and treatment of complications related to DFU is the vitamin D serum levels of patients. Therefore, we sought to comprehensively review meta-analyses from randomized controlled trials and observational studies examining the link between serum vitamin D levels and DFU outcomes in diabetic patients.

Methods

We searched PubMed, Scopus, and ISI Web of Science until September 2024 and extracted the required data from related articles according to Inclusion criteria. The certainty of the evidence and the quality of conduct of the published meta-analyses were rated using the ASMTAR 2 tools, respectively.

Result

A total of 8 meta-analyses studies that met inclusion criteria were included. Based on the obtained results, it has been noted that individuals with DFU exhibit serum vitamin D levels significantly lower, ranging from −7.14 (5.44, 8.83) to −0.93 (95% CI: 0.17, 1.68) ng/ml, compared to those with diabetes but without DFU. Furthermore, individuals exhibiting severe vitamin D deficiency are found to be at least 1.82 times more susceptible to developing DFU. Conversely, administering varying doses of vitamin D supplementation has been shown to positively affect the size and number of ulcers in DFU patients.

Conclusion

This study suggests a potential link between lower levels of vitamin D in the blood and the risk of DFU, hinting at the benefits of vitamin D supplementation in improving outcomes associated with DFU. However, caution is warranted due to the potential bias present in the included studies.
Parental conflict and adolescents’ socially adverse emotions: the mediating role of family functioning
Authors: Kong Rong , Chen Ruihua , Meng Lingling
Published: October 2024

Objective

To examine the process of how parental conflict and family functioning influence adolescents’ socially adverse emotions (shyness and loneliness).

Methods

Stratified cluster sampling was used to conduct a questionnaire survey among 1,100 junior high school students from three junior high schools in Beijing, Chongqing, and Shijiazhuang, China.

Results

(1) The overall experience of adolescents’ socially adverse emotions was at the moderate level; boys’ experience of shyness and loneliness was significantly higher than that of girls; the experience of shyness and loneliness in the second grade was significantly higher than that in the first grade; (2) Parental conflict was significantly negatively correlated with family functioning and significantly positively correlated with adolescents’ socially adverse emotions, while family functioning was significantly negatively correlated with adolescents’ socially adverse emotions; (3) Family functioning partially mediates the relationship between parental conflict and adolescents’ shyness and completely mediates the relationship between parental conflict and adolescents’ loneliness.

Conclusion

Compared to adolescents’ shyness, family functioning plays a more important mediating role in the relationship between parental conflict and adolescents’ loneliness.
Family function and adolescent altruistic behavior: the chain mediating role of extraversion personality and perceived social support
Authors: Gao Tao , Cui Hongbo , Bi Xiaoyan , Qing Zaihua , Chen Weiyu , Shi Keke , Song Meidan , Ma Yankun
Published: October 2024

Introduction

This study aimed to explore the relationship between family function and altruistic behavior in adolescents, while also investigating the potential mediating roles of extraverted personality and perceived social support.

Methods

Surveys were administered to high school students across various schools in Guangdong Province, utilizing the Family APGAR Questionnaire, Extraversion Personality Scale, Perceived Social Support Scale, and Altruistic Behavior Scale. A total of 972 valid questionnaires were collected for analysis.

Results

The findings revealed that family function did not directly predict altruistic behavior. However, extraverted personality and perceived social support were identified as mediators between family function and altruistic behavior. Furthermore, both extraverted personality and perceived social support acted as chain mediators in this relationship.

Discussion

These results emphasize the importance of considering family dynamics in understanding adolescent altruistic behavior and highlight the potential pathways through which family function influences altruistic tendencies. Promoting positive family environments and enhancing extraverted personality and perceived social support may facilitate the cultivation of altruistic behavior in adolescents.
Impacts from air pollution on respiratory disease outcomes: a meta-analysis
Authors: Su Jason G. , Aslebagh Shadi , Shahriary Eahsan , Barrett Meredith , Balmes John Randolph
Published: October 2024

Introduction

Air pollution is widely acknowledged as a significant factor in respiratory outcomes, including coughing, wheezing, emergency department (ED) visits, and even death. Although several literature reviews have confirmed the association between air pollution and respiratory outcomes, they often did not standardize associations across different studies and overlooked other increasingly impactful pollutants such as trace metals. Recognizing the importance of consistent comparison and emissions of non-exhaust particles from road traffic, this study aims to comprehensively evaluate the standardized effects of various criteria pollutants and trace metals on respiratory health.

Methods

We conducted a comprehensive meta-analysis of peer-reviewed journal articles on air pollution and respiratory outcomes published between 1 January 2000, and 1 June 2024. The study included children (age < 18 years), adults (age ≥ 18 years), and all age groups exposed to criteria pollutants established by the US Environmental Protection Agency National Ambient Air Quality Standards and over 10 trace metals. Using databases, such as PubMed, MEDLINE, Web of Science Core Collection, and Google Scholar, we identified 579 relevant articles. After rigorous screening and quality assessment using the Newcastle-Ottawa Scale, 50 high-quality studies were included. We converted various reported outcomes (e.g., odds ratios, relative risk, and percent increase) to a standardized odds ratio (OR) for comparability and performed meta-analyses using R 4.4.0 and related packages, ensuring the robustness of our findings.

Results

Our meta-analysis indicated significant associations between air pollutants and respiratory outcomes. For particulate matter with diameter ≤ 2.5 μm (PM2.5), the overall ORs for children, adults, and combined age groups were 1.31, 1.10, and 1.26, respectively, indicating a consistent positive association. Similar positive associations were observed for particulate matter with diameter ≤ 10 μm (PM10) and other pollutants, with children showing higher susceptibility than adults. The analysis of trace metals also showed significant associations; however, these findings require cautious interpretation due to the small number of studies.

Conclusion

Our study supports associations between air pollutants, including non-exhaust trace metals, and respiratory outcomes across different age groups. The findings underscore the need for stringent environmental health policies and further research, especially in regions with higher pollution levels. The future studies should consider long-term and short-term exposures separately and include diverse populations to improve the accuracy and generalizability of the results.
The effects of work–family conflict, work engagement, and job burnout on self-rated health of public health emergency responders in Jilin Province, China, in the context of the COVID-19
Authors: Hu Bingqin , Yang Guofeng , Ma Jingyu , Chen Yitong , Cui Peiyao , Liang Yifang , He Xin , Li Jinghua
Published: October 2024

Introduction

Amid sudden public health crises, preserving the well-being and optimal working states of frontline healthcare professionals is imperative for efficaciously managing the emergences. However, there is a paucity of research investigating the health status of frontline healthcare professionals through the perspective of work–family conflict. This study sought to elucidate the complex interrelations between work–family conflict, work engagement, job burnout, and self-rated health among public health emergency responders within the context of the COVID-19 pandemic.

Methods

A convenience sampling method was employed to survey 1,309 public health emergency responders at the Jilin Provincial Center for Disease Control and Prevention. An online survey was administered utilizing a self-constructed questionnaire. The hypothesized relationships between the variables were tested using structural equation modeling.

Results

The direct impact of work–family conflict on self-rated health is not significant. The association between work–family conflicts and self-rated health was significantly mediated by work engagement and job burnout, respectively. Meanwhile, work engagement and job burnout had a chain mediating effect on work–family conflict and self-rated health.

Conclusion

Work–family conflict plays a critical role in shaping the health and work status of public health emergency responders during public health crises. Organizations and managers should, in their workplace management practices, focus not only on work-related factors but also give due consideration to family-related factors. Supportive policies, including family-friendly initiatives, should be developed to safeguard the health and work engagement of public health emergency responders.
Bovine reproductive tract and microbiome dynamics: current knowledge, challenges, and its potential to enhance fertility in dairy cows
Authors: Gupta Deepshikha , Sarkar Antisa , Pal Yash , Suthar Vishal , Chawade Aakash , Kushwaha Sandeep Kumar
Published: October 2024

The cattle production system focuses on maintaining an animal-based food supply with a lower number of cattle. However, the fecundity of dairy cows has declined worldwide. The reproductive tract microbiome is one of the important factors which can influence bovine fecundity. Therefore, reproductive tract microbiomes have been explored during the estrus cycle, artificial insemination, gestation, and postpartum to establish a link between the micro-communities and reproductive performance. These investigations suggested that microbial dysbiosis in the reproductive tract may be associated with declined fertility. However, there is a scarcity of comprehensive investigations to understand microbial diversity, abundance, shift, and host-microbiome interplay for bovine infertility cases such as repeat breeding syndrome (RBS). This review summarizes the occurrence and persistence of microbial taxa to gain a better understanding of reproductive performance and its implications. Further, we also discuss the possibilities of microbiome manipulation strategies to enhance bovine fecundity.
Towards robust visual odometry by motion blur recovery
Authors: Luan Simin , Yang Cong , Qin Xue , Chen Dongfeng , Sui Wei
Published: October 2024

Introduction

Motion blur, primarily caused by rapid camera movements, significantly challenges the robustness of feature point tracking in visual odometry (VO).

Methods

This paper introduces a robust and efficient approach for motion blur detection and recovery in blur-prone environments (e.g., with rapid movements and uneven terrains). Notably, the Inertial Measurement Unit (IMU) is utilized for motion blur detection, followed by a blur selection and restoration strategy within the motion frame sequence. It marks a substantial improvement over traditional visual methods (typically slow and less effective, falling short in meeting VO’s realtime performance demands). To address the scarcity of datasets catering to the image blurring challenge in VO, we also present the BlurVO dataset. This publicly available dataset is richly annotated and encompasses diverse blurred scenes, providing an ideal environment for motion blur evaluation.
Basketball self-evaluation matrix: discrepancy between self-confidence and decision-making performance on psychological profiling of players
Authors: Liu Mengru , Kong Anthony , Lau Newman , Feng Zeping , Liu Xi
Published: October 2024

Background

In basketball training, self-evaluation plays a crucial role in the decision-making and execution of movements of players. The self-evaluation of players is influenced by their perception of own basketball ability and self-confidence state. This study aimed to explore potential discrepancies between self-confidence levels of players and their decision-making performance, while also characterizing different types of players.

Method

Data was collected from 20 youth basketball players who participated in a decision-making video task and a self-confidence assessment. Based on data from their self-confidence and decision-making awareness assessment, the K-means cluster analysis was used to categorize the players into different groups. Then, ANOVA and post hoc Scheffe test were conducted to compare these clusters.

Results

The cluster analysis identified four distinct profiles of players and the results of the ANOVA and post hoc Scheffe tests revealed significant differences between the four clusters. The “High Self-confidence & Low Decision-making Awareness” players might display an overconfident mindset, while the “High Self-confidence & High Decision-making Awareness” players potentially demonstrated the better performance and maintained a consistent and confident attitude. The “Low Self-confidence & High Decision-making Awareness” players appeared to lack confidence and needed to foster greater faith in their abilities. Finally, the “Low Self-confidence & Low Decision-making Awareness” players required a long-term and comprehensive training program to improve their skills.

Discussion

These preliminary findings informed the development of a self-evaluation matrix, designed to help coaches better understand player profiles and design tailored interventions. Moreover, this study contributes on sport calibration and enhances understanding of the behavioral and psychological states of players.
Linear Discriminant Analysis for Predicting Net Blotch Severity in Spring Barley with Meteorological Data in Finland
Authors: Ruusunen Outi , Jalli Marja , Jauhiainen Lauri , Ruusunen Mika , Leiviskä Kauko
Published: October 2024

Predictive information on plant diseases could help to reduce and optimize the usage of pesticides in agriculture. This research presents classification procedures with linear discriminant analysis to predict three possible severity levels of net blotch in spring barley in Finland. The weather data utilized for classification included mathematical transformations, namely features of outdoor temperature and air humidity with calculated dew point temperature values. Historical field observations of net blotch density were utilized as a target class for the identification of classifiers. The performance of classifiers was analyzed in sliding data windows of two weeks with selected, cumulative, summed feature values. According to classification results from 36 yearly data sets, the prediction of net blotch occurrence in spring barley in Finland can be considered as a linearly separable classification task. Furthermore, this can be achieved with linear discriminant analysis by combining the output probabilities of separate binary classifiers identified for each severity level of net blotch disease. In this case, perfect classification with a resolution of three different net blotch severity levels was achieved during the first 50 days from the beginning of the growing season. This strongly suggests that real-time classification based on a few weather variables measured on a daily basis can be applied to estimate the severity of net blotch in advance. This allows application of the principles of integrated pest management (IPM) and usage of pesticides only when there is a proven need.
Perspectives on the Regional Strategy for Implementation of National Action Plans on Antimicrobial Resistance in the WHO African Region
Authors: Yahaya Ali Ahmed , Fuller Walter , Kithinji Dennis , Mazengiya Yidnekachew Degefaw , Gahimbare Laetitia , Bishikwabo-Nsarhaza Kizito
Published: October 2024

Background: The WHO Regional Office for Africa developed a Member States (MS)-endorsed regional strategy to fast-track the implementation of MS’ national action plans (NAP) on Antimicrobial resistance (AMR). This study explored the perspectives of AMR’s national focal points in MS on the implementation of the priority interventions of the regional strategy in their countries. Methods: An online survey consisting of ratings and discussions covering the implementation of the six priority interventions was conducted. Sums of the scores per priority intervention were obtained, and their percentage to the total possible scores were calculated to categorize the implementation as inadequate (0–25%), basic (26–50%), intermediate (51–75%), or advanced (76–100%). Results: Thirty-six of the forty-seven national AMR focal points responded to the survey between 12 November 2023 and 8 January 2024. The implementations were rated as 37–62% (basic-to-intermediate), with the multisectoral coordination and collaboration committee receiving the highest overall rating (62%, 421/684), while the promotion of sustainable investment for the NAP on AMR received the least overall rating (37%, 257/700). The focal points mainly recommended awareness campaigns, capacity building, and regulations and guidelines to improve the implementation of the AMR strategy. Conclusions: The survey revealed a need to enhance awareness campaigns, support the establishment and functioning of AMR evaluation and monitoring systems, and build the capacity of AMR staff with cost-benefit analysis and budgeting skills. It also showed the necessity to improve awareness and conduct education on AMR, streamline evidence generation through One Health Surveillance systems, integrate initiatives to reduce hospital-acquired infections in the antimicrobial stewardship programs, and enhance regulations and guidelines to optimize the use of antimicrobials.
Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview
Authors: Han Binglan , Susnjak Teo , Mathrani Anuradha
Published: October 2024

This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.
Historical Elite Differential Evolution Based on Particle Swarm Optimization Algorithm for Texture Optimization with Application in Particle Physics
Authors: Martínez-Guerrero Emmanuel , Lagos-Eulogio Pedro , Miranda-Romagnoli Pedro , Noriega-Papaqui Roberto , Seck-Tuoh-Mora Juan Carlos
Published: October 2024

Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called “HE-DEPSO” to obtain chi-squared values that are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.
Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network
Authors: Le An Thanh , Shakiba Masoud , Ardekani Iman , Abdulla Waleed H.
Published: October 2024

This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications.
A Comparative Study for Evaluating the Groundwater Inflow and Drainage Effect of Jinzhai Pumped Storage Power Station, China
Authors: Wu Jian , Zhou Zhifang , Wang Hao , Chen Bo , Wang Jinguo
Published: October 2024

Various hydrogeological problems like groundwater inflow, water table drawdown, and water pressure redistribution may be encountered in the construction of hydraulic projects. How to accurately predict the occurrence of groundwater inflow and assess the drainage effect during construction are still challenging problems for engineering designers. Taking the Jinzhai pumped storage power station (JPSPS) of China as an example, this paper aims to use different methods to calculate the water inflow rates of an underground powerhouse and evaluate the drainage effect caused by tunnel inflow during construction. The methods consist of the analytical formulas, the site groundwater rating (SGR) method, and the Signorini type variational inequality formulation. The results show that the analytical methods considering stable water table may overestimate the water inflow rates of caverns in drained conditions, whereas the SGR method with available hydro-geological parameters obtains a qualitative hazard assessment in the preliminary phase. The numerical solutions provide more precise and reliable values of groundwater inflow considering complex geological structures and seepage control measures. Moreover, the drainage effects, including a seepage-free surface, pore water pressure redistribution, and hydraulic gradient, have been accurately evaluated using various numerical synthetic cases. Specifically, the faults intersecting on underground caverns and drainage structures significantly change the groundwater flow regime around caverns. This comparative study can not only exactly identify the capabilities of the methods for cavern inflow in drained conditions, but also can comprehensively evaluate the drainage effect during cavern construction.
Automated Test Creation Using Large Language Models: A Practical Application
Authors: Hadzhikoleva Stanka , Rachovski Todor , Ivanov Ivan , Hadzhikolev Emil , Dimitrov Georgi
Published: October 2024

The article presents work on developing a software application for test creation using artificial intelligence and large language models. Its main goal is to optimize the educators’ work by automating the process of test generation and evaluation, with the tests being stored for subsequent analysis and use. The application can generate test questions based on specified criteria such as difficulty level, Bloom’s taxonomy level, question type, style and format, feedback inclusion, and more, thereby providing opportunities to enhance the adaptability and efficiency of the learning process. It is developed on the Google Firebase platform, utilizing the ChatGPT API, and also incorporates cloud computing to ensure scalability and data reliability.
Online Self-Learning-Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development
Authors: Zhang Xianxia , Wang Lufeng , Tang Shengjie , Zhao Chang , Yao Jun
Published: October 2024

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process coupled with the rotary hearth furnace system’s reliance on human labor results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for proportioning raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the local outlier factor (LOF) method to detect any abnormal values, using Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience demonstrates good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and significantly enhances batching efficiency and product quality.