bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Recognition and Pre-alarm Model for Bird Hazards on Electric Transmission Lines Based on Algorithmic Optimization
Authors: Tao Wang , Hao Liu , Ningbo Liu , Jiantao Chen 
Published: December 2024

Abstract
The purpose of this paper is to study and develop an intelligent identification and pre-alarm model of bird damage in electric transmission line(ETL) based on algorithm optimization, so as to solve the security risks caused by bird activities in ETL. In order to achieve this goal, deep learning technology is adopted in the study, and the optimized EfficientNet network is used as the core model. In the aspect of data set construction, a rich data set containing 1600 bird damage images is constructed by combining the resources of on-site shooting images and network images. Through comparative experiments, the performance of the optimized algorithm on ETL bird pest recognition tasks was evaluated compared with traditional Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) algorithms. The results show that the precision, recall, and F1 score of this algorithm are significantly better than traditional algorithms. These excellent performance indicators validate the high accuracy and sensitivity of our algorithm in ETL bird pest recognition tasks. This model can achieve high-precision identification of ETL bird pests. The research results are of great significance for improving the safety management level of ETL and reducing power accidents caused by bird damage.
Energy Consumption Optimization in Data Centers Using LSTM-Based Load Prediction and Dynamic Resource Allocation
Authors: Dang Dujun 
Published: December 2024

Abstract
With the rapid growth of data centers, optimizing energy consumption has become a critical challenge. This paper proposes an energy management framework that integrates Long Short-Term Memory (LSTM) networks for load prediction with a dynamic resource allocation algorithm to optimize energy consumption in data centers. The LSTM model is employed to accurately predict future workloads based on historical data, enabling the proactive adjustment of resource utilization. The dynamic resource allocation algorithm, informed by LSTM-based predictions, dynamically manages server operations and cooling systems through techniques such as dynamic voltage and frequency scaling (DVFS) and server consolidation, ensuring that resources are allocated efficiently in real time. The proposed framework incorporates a multi-objective optimization approach to balance energy savings and system performance. We use genetic algorithms to fine-tune resource allocation based on the predicted load, optimizing for energy consumption and response time. Experimental results show that our method achieves significant energy savings compared to traditional static resource management approaches, with minimal impact on service quality. This research highlights the potential of machine learning-driven optimization algorithms in reducing the environmental footprint of data centers while maintaining operational efficiency.
The Optimization of Face Recognition Technology Based on Convolutional Neural Network
Authors: Song Yang 
Published: December 2024

Abstract
Personal identity authentication is the key to ensure personal information security, and it is widely used in banking, security inspection and other fields. However, due to the progress of technology, the security of some personal identity authentication methods has weakened in the past, and incidents such as forged ID cards frequently occur. The security of mobile phone dynamic authentication is also slowly declining. In order to avoid the above problems, face recognition technology came into being, and people paid extensive attention to it. In this paper, the development trend of CNN(Convolutional Neural Network) face recognition technology has been deeply studied and optimized. The research shows that the recognition rate is as high as 90% under normal light, left light and right light, and more than 80% under low light and strong light, which shows that the face recognition in this paper is robust to light changes. The main reasons for the failure of face recognition in low light and strong light are the unclear position of face feature points in the image and the poor quality of the collected image. The quality of the collected image can be judged in the collection stage to improve the recognition rate of this algorithm. The image quality evaluation and convolution kernel design optimization proposed in this study not only improve the accuracy of face recognition technology, but also enhance the stability of the system under different lighting conditions. Compared with the existing face recognition technology based on CNN, this method has achieved remarkable progress in technology through refined image quality screening and targeted convolution kernel design.
Multi-Microgrid Energy Trading Strategy Based on Multi-Agent Deep Deterministic Policy Gradient Algorithm
Authors: Qi Genhong , Fu Lingxiao , Wang Meng , Zhu Mingcheng , Yuan Shaoqing , Xu Jiang 
Published: December 2024

Abstract
Compared to individual microgrid, multi-microgrid (MMG) system can enhance the overall utilization of renewable energy, effectively improve the operational stability of local microgrids, and reduce the dependence on main grid. However, energy management of MMG encounters significant challenges due to the complex interaction between different microgrids. To tackle this issue, this paper introduces a non-cooperative gamebased optimal scheduling market trading model for MMG composed of various renewable energy sources, completing trade decisions while ensuring information independence. Considering the real-time changes in environmental transition functions and complex scheduling scenarios, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed, which modifies the experience replay mechanism and Markov process of the basic deep deterministic policy gradient (DDPG) algorithm. Compared to traditional multi-microgrid system scheduling algorithms, the method presented in this paper does not require individual predictions of state variables, achieves end-to-end training from agent states to actions, and ensures the information security and autonomous decision-making of each microgrid.
A Survey on the Current Situation of Social and Emotional Competence of Teacher Trainees in the Age of Artificial Intelligence and Exploration of Practice
Authors: Li Yan 
Published: December 2024

Abstract
In the context of the artificial intelligence era, paying attention to the development of the social and emotional competence of teacher-training students is of great practical significance for the healthy and comprehensive development of teacher-training students and their growth into high-quality technical and skilled talents. The study uses the correlation coefficient method, multiple linear regression, quantile regression, and other methods to carry out a questionnaire survey and practical exploration of the relevant data of students in a provincial teacher training university. The overall development of social and emotional competence of teacher education students is poor, and the overall social and emotional competence of teacher education students is only 3.91. Among them, teacher-student relationships and peer and parent-child interactions are the key factors affecting the development of social and emotional competence of teacher-education students, and the regression coefficients of these factors are 0.194, 0.161, and 0.146, respectively. The marginal effects of the teacher-student relationship and the social and emotional competence of teacher-education students are in an “inverted L-shape”. The marginal influence of the teacher-student relationship and teacher-student social and emotional competence showed an “inverted L-shaped” trend. Peer and parent-child interactions had the greatest positive impact on the social and emotional competence of teacher trainees around the median, which was roughly characterized by an “inverted U-shape”. The study suggests emphasizing social and emotional competence, promoting comprehensive development of teacher trainees, strengthening parent-child emotional care and peer communication, and seeking more scientific, rational and effective methods to improve students’ social and emotional competence.
Study on the Application of Machine Learning Technology in the Oilfield Investment Decision Making Process and the Long-term Impact on Economic Benefits
Authors: Gong Shanjun , Wang Yuegang 
Published: December 2024

Abstract
Oilfield investment planning is a large and complex systematic project which is an important part of oilfield development and production process management. This paper establishes a mathematical model for oilfield investment decision optimization based on the traditional Markowitz MV model and the factors of yield and economic cost that need to be considered in the oilfield investment decision process. The mathematical model is combined with the FT-QPNN model, and a finite time q -power neural network is introduced into the model to obtain the optimization model of oilfield investment decision-making based on improved machine learning technology. The long-term economic benefits in the oilfield investment decision-making process are analyzed using the economic benefit evaluation model. After using the model to optimize the investment decision of the LJ oilfield development project, the oil production forecast in 2027 is elevated to 25.09 MMbbl while reducing the operating cost of the oilfield project by 2,377,300 Yuan. In addition, after the optimization of the model in this paper, the average payback period of this oilfield project is reduced to 2.70 years, and the average financial internal rate of return of the project unit can be increased to 35.88%. This paper provides a decision-making basis for the realization of oilfield benefit maximization, which is a deepening of oilfield refinement management and is of general guiding significance for improving the level of oilfield development decision-making.
Research on the construction of player trajectory optimization model and its application in soccer tactics
Authors: Hou Kaikai , Jin Qiang , Li Jialiang , Zhao Qingqing 
Published: December 2024

Abstract
This paper builds up the optimization model of a soccer player’s movement trajectory, which provides good support and method for the player’s soccer tactics training. The player movement trajectory optimization model mainly covers two major functions of player movement trajectory extraction and optimization. The multi-target tracking problem in the soccer game video is transformed into a data association problem, and the similarity matrix is constructed by comprehensively considering the target’s motion information, detection features and apparent features, and the Hungarian algorithm is used to deal with the assignment problem and extract the motion trajectory. Using a genetic algorithm to plan and optimize the player’s movement trajectory, the path is optimized by three genetic operators, namely selection, crossover, and mutation, to find an optimal movement path. Taking the players of the Shandong soccer team of China University Games as the experimental objects to carry out soccer tactics training practice, the average number of passes and successful passes of the experimental group increased by 3.43 times and 21 times compared with the control group and showed stronger aggressiveness and mobility in the soccer playing test. Among the soccer tactical awareness indicators, the mean values of the experimental group were all greater than those of the control group, with the tactical knowledge indicator being the largest, greater than that of the control class by 0.837, showing a significant difference (P<0.05).
Stability Evaluation of Open-Pit Coal Mine Slopes Under Rainfall Infiltration Effects: A Case Study
Authors: Huang Yuejun , Hao Yupeng , Chen Guoyu , Zhang Lei , Zhao Yifan , Zhang Pengwei 
Published: December 2024

Abstract
Landslide disasters in open pit mining cause severe loss in engineering excavation and human safety. The stability evaluation of the open pit mine slope is difficult due to the complex lithology distribution and fluctuation of underground water. In addition to the inherent characteristics of the slope itself, rainfall may exacerbate and weaken its stability. In this work, a case study of the Shengli open-pit coal mine in Inner Mongolia is being considered. The high-precision formation profiles are established according to the data from the drilling hole. Then, a series of slope stability evaluations are conducted, considering the inclined and anti-inclined characteristics, the weak interlayer, and the dynamic rainfall infiltration of local hydrogeological and meteorological conditions. The results show that under natural conditions, all slopes of open-pit mines can meet the stability requirements. With the fluctuation of underground water levels, landslides may occur along the weak interlayer, and therefore, pumping water or controlling underground water levels is key in coal mine excavation. Rainfall infiltration has an impact on slope stability, especially when the top layer is saturated. While considering local rainfall conditions, all slopes meet the stability requirements. Finally, the slope of excavation is evaluated and a recommended safe angle for excavation is quantified. This study’s findings may provide guidance for the safe development and design of open-pit mines.
Gnathostoma turgidum – still a mysterious parasite
Authors: Nawa Y. , Delgado-Vargas F. , Díaz-Camacho S. P. 
Published: December 2024

Summary
The recent publication entitled “Possible zoonotic implications of the discovery of the advanced third stage larva of Gnathostoma turgidum (Spirurida: Gnathostomatidae) in a Mexican fish species” by Mosqueda-Cabrera et al., appeared in Helminthologia, 2023; 60(1): 112–116. was extremely interesting and informative showing potential zoonotic natue of G. turgidum. However, discovery of only one larva from one out of 17 fish rather indicate that this fish is an accidental host of G. turgidum. Whether this and other fish act as the second intermediate host in the life cycle of G. turgidum requires more field survey using both compression and digestion methods. Also, many biological events of G. turgidum such as intrahepatic maturation in the final hosts and the seasonal changes with self cure are remained unsolved. More extensive survey on intermediate and paratenic hosts for G. turgidum and biological research on unique features of G. turgidum is necessary.
Neurodevelopment of Critical ILL Neonates at the Age of 12 Months
Authors: Ristovska Sanja , Gucev Zoran , Dukovska Valentina 
Published: December 2024

Abstract
Critically ill neonates who survive are often left with dire consequences. Cerebral palsy, other neurological and motor deficiencies, intellectual disability, and various degrees of cognitive and behavioral deficiencies all result from neonatal critical diseases. We investigated psychomotor development in 20 children with hypoxic-ischemic encephalopathy (HIE), and as newborns often have multiple comorbidities, the following as well: HIE with respiratory distress syndrome (RDS), infections, hypo and hyperglycemia and hypocalcemia. Socio-demographic, pregnancy and delivery data together with appropriate staging tools in determining the severity of HIE or RDS were utilized in this evaluation. In addition, a physical examination, Apgar score, blood gas analyses, biochemical, microbiological and ultrasound data were also part of this study. A child's psychomotor development includes four main areas: motor skills, language, cognition, and social relationships. The Griffiths Mental Development Scale (GMDS) compares different developmental domains and enables early diagnosis of deficiencies and guidance for appropriate early intervention. Six children (30%) were diagnosed with cerebral palsy. GMDS showed that at the age of one year, 50% of the children had typical development, 5% had mild disability, 20% moderate disability and 25% had severe disability. The severity of HIE, Apgar score, weak muscle tone, seizures, disturbances in glucose homeostasis, comorbidities (sepsis, infection) statistically significantly impacted the outcome. Studies with a greater number of patients are needed to support these findings and enable early interventions to avoid severe consequences of critical illness neonates.
Trend of Kidney Replacement Therapy in North Macedonia from the Years 2015 Through 2020
Authors: Gjorgjievski Nikola , Karanfilovski Vlatko , Simjanovska Simona , Rushiti Emine , Cibrev Dragan , Dzekova-Vidimliski Pavlina , Memeti Ardian , Mexhiti Fatmir , Mucha Argjent , Poposka Elizabeta , Banskolieva Elena Babalj , Selami Zaku , Misovska Nadica , Kjulibrk-Nedelkovska Maja , Nedelkoska Mence , Gjorgjievska Gordana , Krecova Vasiliki , Stojceva Olivera , Spasovski Goce , Nikolov Igor 
Published: December 2024

Abstract

Introduction
Kidney replacement therapy (KRT) by dialysis or kidney transplantation represents the main treatment modalities for patients with kidney failure. Here we evaluate the trends in taking care of such patients in North Macedonia from 2015 through 2020.

Methods
The patients were analyzed according to age, sex, primary disease, and treatment modality. They were further subdivided into 3 groups: Group 1, prevalent patients; Group 2, incident patients with analysis starting at day 1 of KRT; and Group 3, incident patients with analysis starting at day 91 of KRT.

Results
The study included 10,395 person-years, mean age 59.2 ± 9.5 years, male sex 60.2%, hemodialysis therapy 84.7% in 2015 and 85.9% in 2020. Hypertension and diabetes mellitus were the leading causes of kidney disease. Group 1 showed an increase in KRT care of 10.3% (from 799 part per million population [pmp] in 2015 to 881 pmp in 2020). In Group 2 the number of patients was increasing with each successive year, the highest growth being observed in 2019. The patients of Group 3 showed a significant growth during this time period, from 126.5 ppm in 2015 to 162 pmp in 2019, but a subsequent decrease of 16.4% in 2020 (135.5 pmp), probably explained by the SARS-CoV-2 pandemic.

Conclusion
In the analyzed period, an increase in the number of patients with kidney failure in need of KRT is observed. We believe that this trend will continue in the following years, which should be taken into account when planning health policies in our country in the future.
Croup in Children and Risk Factors
Authors: Nejashmikj Valentina Risteska 
Published: December 2024

Abstract
Croup is a common respiratory illness in children under 5 years, primarily caused by viral infections. Symptoms include inspiratory stridor, barking cough, and hoarseness due to inflamed vocal cords. Diagnosis is based on symptoms difficulty breathing, stridor, chest in-drawing voice changes and head nodding. Severity ranges from mild to severe cases. This study aimed to explore variations in croup and factors contributing to its recurrence in children. This retrospective longitudinal study analyzed health records from the Pediatric Practice "Vitalino" in Veles, North Macedonia, from 2016 to 2022. It included children aged 0-5 years who had croup at least once. The sample was substantial and representative, allowing for a thorough examination of potential correlations and trends in croup occurrence. Among 707 studied children, 254 had croup, with a prevalence of 35.9% over seven years or 5.1% per year. Females had lower odds of recurrent croup, while males had higher odds, but the differences were not statistically significant. Atopic dermatitis and bronchiolitis exposure were significantly associated with croup in both genders. The study provides insights into the prevalence, distribution of croup and associated conditions in young children. The significant associations of atopic dermatitis and bronchiolitis suggest a link between these conditions and recurrent croup. Considering these strong associations, we can conclude that bronchiolitis and croup should be included in the trajectory of the atopic march.
Osimertinib-Induced Toxic Epidermal Necrolysis – A Case Report
Authors: Crvenkova Simonida , Popova Maja , Crvenkova Labina , Lazarevska Antonija 
Published: December 2024

Abstract

Purpose
To investigate the rare side effects of Osimertinib in a case of toxic epidermal necrosis.

Case Presentation
We report on a case of a 44-year old woman with lung adenocarcinoma harboring an EGFR-sensitizing mutation who was treated with Osimertinib as the second-line treatment. Ten days after Osimertinib initiation, a diffuse erythematous rash rapidly spread over the patient’s trunk along with vesicles and purpuric macules; furthermore, she developed erythema and exfoliation on the face and trunk and severe mucositis. Toxic epidermal necrolysis (TEN) is life-threatening dermatologic adverse event, caused by a delayed-type drug hypersensitivity reaction. Although skin toxicity is common during treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), Osimertinib-associated TEN is very rare.

Results
Treatment with systemic steroids and immunoglobulin as well as supportive treatment led to an improvement of her general wellbeing, followed by a remarkable recovery.

Conclusions
Although the clinical use of Osimertinib is becoming widespread, the side effects may not be fully understood. Clinicians should indeed pay more attention to the occurrence of side effects and be prepared to deal with them in timely manner.
Correlation Between Real-Time Shear Wave Elastography and Liver Serum Markers in Determining the Stage of Liver Fibrosis in Patients with Chronic Liver Diseases
Authors: Jusufi Arzana Hasani , Trajkovska Meri 
Published: December 2024

Abstract

Introduction: Non-invasive methods aim to predict the stage of liver fibrosis in line with histological findings via biopsy. Shear wave elastography and serum markers are proven as accurate non-invasive methods for determining liver fibrosis as a modern non-invasive methods compared to liver biopsy in staging hepatic fibrosis.

Aims: This study aims to determine the correlation between Shear Wave Elastography and indirect and direct serum markers of fibrosis when staging liver fibrosis.

Material and methods: The study was conducted in the Clinic of Gastroenterohepatology, the Institute of Immunology and Human Genetics, and the Institute of Pathology between 2021 and 2023. The study comprises 70 patients with liver lesions, diagnosed based on clinical results, laboratory tests, and ultra-sound imaging. All patients underwent liver biopsy, classified according to Ishak and Metavir score as a reference method for diagnosing liver fibrosis. Real-time shear wave elastography was also performed as a non-invasive method and serum markers were checked for liver fibrosis.

Findings: The statistical analysis indicated a positive correlation between the values of direct and indirect liver fibrosis markers and Shear Wave Elastography results.

Conclusion: Our study has demonstrated that shear wave elastography has a significant positive correlation with biochemical markers of liver lesions and serum markers of liver fibrosis, whereas it has a negative correlation with platelets.