bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Private Television Crime Shows on Youth: Examining the Link Between Media Consumption and Aggressive Behaviour
Authors: Ahmad Bilal , Ali Muhammad Irfan , Ashraf Ali
Published: May 2024

The research paper investigates variation in the prevalence and distribution of ‘voice’ in the spoken (presentations/interviews) and written (essays/midterm exam scripts) academic registers of BS English students of Khyber Pakhtunkhwa province and the relation of this variation to the situational circumstances of the concerned registers. The learner corpus was constructed. The data was collected from BS English students of the twelve public sector universities of Khyber Pakhtunkhwa. All the texts were transcribed, coded, and then analysed through the computer software AntConc (3.5.8). The present study is unique in the sense that it has conducted a comparative analysis of the various spoken and written sub-registers of English in the academic non-native environment. This study has great implications for researchers in the field of register analysis, material design, and language teaching.
ЕФЕКТИВНІСТЬ ГАЛУЗЕВОГО ІНЖИНІРИНГУ ЯК ФУНКЦІЯ ІНТЕЛЕКТУАЛЬНОГО КАПІТАЛУ
Authors: ГОРАЛЬ Ліліана , ВОЙТКІВ Людмила , КОРНІЄНКО Андрій
Published: May 2024

Використовуючи базові визначення понять інжиніринг і бенчмаркінг, в статті зроблено акцент на ефективність галузевих особливостей їх застосування. Опираючись на пропозиції науковців розглядати основні інструменти забезпечення стійкого розвитку підприємств з позицій трьох складових, а саме як економічні, соціальні та екологічні інструменти, підкреслено комплексну оцінку рівня розвитку та визначення рівня надійності та ефективності системи управління. Наголошено, що використання українськими підприємствами практики бенчмаркінгу виявляється надзвичайно корисним. Класифікувати бенчмаркінг можна за допомогою таких параметрів як: складність поставлених завдань; спрямованість; рівень, на якому передбачено здійснювати бенчмаркінг. Розглянуто і класифікацію об'єктів бенчмаркінгу. Акцентовано, що наявність механізму взаємодії між рівнями управління підприємство підвищує  управлінську ефективність. Як приклад генерування доданої вартості для підприємства «Оператор газотранспортної системи України» завдяки застосуванню тотального інжинірингу в галузі наведено  зміну організаційної структури, доведення стану основних засобів до зменшення вдвічі коефіцієнту фізичного зношення,  збільшення витрат на оплату праці при зменшенні середньорічної чисельності працюючих. Подано коротку характеристику газотранспортного підприємства та його інноваційної інженерної діяльності в кризовому періоді. Підкреслено взаємодію персоналу підприємства з його менеджментом при вимушеному впровадженні інжинірингу компанії.  Запропоновано умову оцінювання галузевого інжинірингу через оцінювання якості персоналу підприємства: досягнення максимальної ефективності кожного з чинників. Прийнято умову ототожнення інтелектуального капіталу з людським капіталом при інших незмінних чинниках (ринкові активи; активи пов’язані з людським фактором; активи інфраструктури;. інтелектуальні цінності). Для оцінювання людського капіталу запропоновано  проводити розрахунок на конкретну особу. Пригадано, що в міжнародних компаніях застосовується методика оцінки людського капіталу – Звіт про людський капітал (HR Statement). Враховуючи вище наведене, запропоновано вважати, що загальна ефективність інжинірингу є функцією людського (інтелектуального) капіталу, бо поєднує в собі творче застосування наукових принципів у виробничих процесах, формуючи наукові, технологічні та управлінські основи розробки та реалізації інвестиційних проектів
DIRECTIONS FOR THE FORMATION OF AN ADAPTIVE CASH FLOW MANAGEMENT MECHANISM AT SMALL AND MEDIUM-SIZED BUSINESS ENTERPRISES UNDER CONDITIONS OF NATIONAL ECONOMY RECOVERY
Authors: VLASENKO Valentyn
Published: May 2024

The methodological and practical recommendations for forming an adaptive cash flow management mechanism at small and medium-sized business enterprises under conditions of the post-war recovery of the Ukrainian economy are substantiated. The author's interpretation of the category «cash flow» is proposed as a dynamic indicator that characterizes the constant movement of cash resources as a result of the operational, investment, and financial activities of the enterprise associated with the receipt (incoming cash flows) or spending (outgoing cash flows) of the enterprise's cash resources, which can have a cash and non-cash form, expressed in national or foreign currency and related to a certain point in time. This made it possible to determine the ontological content of the scientific category «mechanism of managing cash flows of the enterprise» and specify its components (management goals and tasks, management criteria, management methods, management levers, management factors, management principles, specifics and features of management, management tools, managerial functions, management resources, regulatory and legal support, and information support). The author's version of the stages of formation of an adaptive cash flow management mechanism at small and medium-sized business enterprises based on the criterion of their optimization is recommended. By the functional specialization of top managers and employees of the financial subsystem, the procedure for making a managerial decision regarding forming an adaptive cash flow management mechanism at small and medium-sized business enterprises is proposed. This clarified the functional responsibilities of employees of the financial subsystem in the cash flow management process. The expediency of using a payment calendar is substantiated, which will help to synchronize cash receipts and payments. A set of measures to increase (decrease) cash flow at small and medium-sized business enterprises has been formed.
Interpretación teológica: De los aportes de la enseñanza social de los pontífices de las dos primeras décadas del siglo XXI a la realidad geopolítica
Authors: Zuluaga Suárez, Pbro Hernando
Published: May 2024

Este ejercicio investigativo presenta la interpretación teológica de los aportes a la enseñanza social de la Iglesia en relación con el contexto geopolítico que realizan los pontífices de las dos primeras décadas del tercer milenio. La pregunta principal de investigación se ha formulado de la siguiente manera: ¿Cómo pueden interpretarse teológicamente los aportes de la enseñanza social de los pontífices de las dos primeras décadas del siglo XXI a la realidad geopolítica? El objetivo general del trabajo de investigación es el siguiente: Interpretar teológicamente, por medio de la hermenéutica de textos, los aportes de la enseñanza social de los pontífices de las dos primeras décadas del siglo XXI XXI a la realidad geopolítica. Los objetivos específicos son: • Identificar las características esenciales de la enseñanza social de los pontífices de las dos primeras décadas del siglo XXI referida a la realidad geopolítica. • Realizar la interpretación teológica de la enseñanza social del final del ministerio de San Juan Pablo II y del pontificado de Benedicto XVI con referencia a la realidad geopolítica. • Presentar la interpretación teológica acerca de la enseñanza social del Papa Francisco con aplicación a la realidad geopolítica. • Determinar los aportes —con la respectiva interpretación teológica— que la enseñanza social de los pontífices de las dos primeras décadas del siglo XXI puede aplicar al contexto geopolítico futuro.
Evaluation of propolis hepatotoxicity in male rats
Authors: SamieeRad Fatemeh , Sofiabadi Mohammad , Bahadoran Ensiyeh , Moradzadeh Mahshid , Gheibi Nematollah
Published: May 2024

Background: Propolis is a natural resinous combination that honeybees produce from the materials they gather from plant parts, buds, and exudates. Numerous favorable pharmacological qualities have been demonstrated for propolis.
Objectives: This study aimed to investigate the hepatotoxic effects of ethanolic propolis extract on the liver of male rats.
Methods: In this research, 40 male rats were divided randomly into five groups: 1. Control 2. Sham (solvent), and 3. Three experimental groups (ethanolic propolis extract at doses of 50,100 and 200 mg/kg). All materials were administered by oral gavage once daily for 13 consecutive days. On the 14th day, blood sampling was performed to measure serum levels of liver function enzymes and triglycerides. After deep induction of anesthesia, the liver of the rats was removed for histopathological studies. Data were analyzed using ANOVA and Tukey’s test (p <0.05).
Results: The data showed that the administration of propolis significantly increased the serum levels of aminotransferases in a dose-dependent manner and decreased triglycerides, accompanied by pathological changes.
Conclusions: The results of this study indicate that the administration of propolis is associated with liver toxicity, and it seems that it should be consumed more carefully.
Keywords: Alanine aminotransferase, Aspartate aminotransferase, Propolis, Hepatotoxicity Triglycerides
W 0 1 , p ( Ω ) × W 0 1 , p ( Ω ) versus C 0 1 ( Ω ) × C 0 1 ( Ω ) local minimizers
Authors: Da Silva João Pablo P.
Published: May 2024

In this work, we consider a functional I : W 0 1 , p ( Ω ) × W 0 1 , p ( Ω ) → R of the form I ( u , v ) = 1 p ∫ Ω ( | ∇ u | p + | ∇ v | p ) d x − ∫ Ω H ( x , u ( x ) , v ( x ) ) d x where Ω ⊂ R N is a smooth bounded domain, max { | ∂ s H ( x , s , t ) | , | ∂ t H ( x , s , t ) | } ⩽ C ( 1 + | s | σ 1 − 1 + | t | σ 2 − 1 ) a.e. x ∈ Ω, for some C > 0, ∀ t , s ∈ R, p < σ i ⩽ p ∗ : = N p / ( N − p ), i = 1 , 2, and 1 < p < N. We prove that a local minimum in the topology of C 0 1 ( Ω ) × C 0 1 ( Ω ) is a local minimum in the topology of W 0 1 , p ( Ω ) × W 0 1 , p ( Ω ). An important application of this result is related to the question of multiplicity of solutions for a class of systems with concave-convex type nonlinearities.
Sildenafil Reverses the Neuropathological Alzheimer’s Disease Phenotype in Cholinergic-Like Neurons Carrying the Presenilin 1 E280A Mutation
Authors: Giraldo-Berrio Daniela , Jimenez-Del-Rio Marlene , Velez-Pardo Carlos
Published: May 2024

Background: Familial Alzheimer’s disease (FAD) presenilin 1 E280A (PSEN 1 E280A) is characterized by functional impairment and the death of cholinergic neurons as a consequence of amyloid-β (Aβ) accumulation and abnormal phosphorylation of the tau protein. Currently, there are no available therapies that can cure FAD. Therefore, new therapies are urgently needed for treating this disease. Objective: To assess the effect of sildenafil (SIL) on cholinergic-like neurons (ChLNs) harboring the PSEN 1 E280A mutation. Methods: Wild-type (WT) and PSEN 1 E280A ChLNs were cultured in the presence of SIL (25μM) for 24 h. Afterward, proteinopathy, cell signaling, and apoptosis markers were evaluated via flow cytometry and fluorescence microscopy. Results: We found that SIL was innocuous toward WT PSEN 1 ChLNs but reduced the accumulation of intracellular Aβ fragments by 87%, decreased the non-physiological phosphorylation of the protein tau at residue Ser202/Thr205 by 35%, reduced the phosphorylation of the proapoptotic transcription factor c-JUN at residue Ser63/Ser73 by 63%, decreased oxidized DJ-1 at Cys106-SO3 by 32%, and downregulated transcription factor TP53 (tumor protein p53), BH-3-only protein PUMA (p53 upregulated modulator of apoptosis), and cleaved caspase 3 (CC3) expression by 20%, 32%, and 22%, respectively, compared with untreated mutant ChLNs. Interestingly, SIL also ameliorated the dysregulation of acetylcholine-induced calcium ion (Ca2+) influx in PSEN 1 E280A ChLNs. Conclusions: Although SIL showed no antioxidant capacity in the oxygen radical absorbance capacity and ferric ion reducing antioxidant power assays, it might function as an anti-amyloid and antiapoptotic agent and functional neuronal enhancer in PSEN 1 E280A ChLNs. Therefore, the SIL has therapeutic potential for treating FAD.
Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer’s Disease
Authors: Xie Linhui , Raj Yash , Varathan Pradeep , He Bing , Yu Meichen , Nho Kwangsik , Salama Paul , Saykin Andrew J. , Yan Jingwen
Published: May 2024

Background: There are various molecular hypotheses regarding Alzheimer’s disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.
Low-carbon building evaluation index system based on hierarchical analysis method
Authors: Zhou Ya , Fu Shaopeng
Published: May 2024

Low-carbon building is an unavoidable development trend in the construction industry, especially in the critical moment of global warming, it is necessary to make a comprehensive evaluation of low-carbon buildings. At this stage, low carbon building has become an important direction in the construction field, and whether the low carbon building reaches the corresponding standards and the advantages played by the low carbon field need to be assessed with perfect evaluation indexes. Based on this, this paper constructs a low-carbon building evaluation system from the whole life cycle of the building using the hierarchical analysis method (AHP) and BP neural network method. Firstly, the definition and influencing factors of low-carbon buildings are analyzed, secondly, the evaluation index system of low-carbon buildings is constructed, and then the evaluation index system of low-carbon buildings is verified by using the hierarchical analysis method, and the results show that the evaluation results based on the hierarchical analysis and the BP neural network method are more accurate than those of the traditional hierarchical analysis method. The results show that the evaluation results based on hierarchical analysis and BP neural network are more accurate than the traditional hierarchical analysis method. It shows that the BP neural network method can effectively reduce the influence of subjective factors in the hierarchical analysis method and improve the objectivity of the evaluation results. On this basis, this paper proposes countermeasures to promote the development of low-carbon buildings, in order to provide a certain reference for the long-term development of low-carbon buildings.
The role of big data and IoT in logistics supply chain management of e-commerce
Authors: Ran Jingfei , Ma Hui , Ran Runyang
Published: May 2024

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.
Study on the digital transformation path of traditional media companies in the context of the Internet
Authors: Weng Minming
Published: May 2024

In the background of the Internet, traditional media companies are facing very serious challenges. This paper firstly gave a brief introduction to the development of traditional media, pointed out the necessity of digital transformation of traditional media companies, and analyzed Phoenix Media using political, economic, social and technological (PEST) analysis and strength, weaknesses, opportunity, and threat (SWOT) analysis. Moreover, the current development of the company was analyzed, and its digital transformation path was elaborated. Finally, the existing problems of the company were pointed out, and some suggestions were given. This study provides a reference for traditional media companies to carry out digital transformation.
Application of non parametric Bayesian methods in high dimensional data
Authors: Xia Yunqing
Published: May 2024

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.
LSTM network optimization and task network construction based on heuristic algorithm
Authors: Zhang Zhongpeng , Wang Guibao
Published: May 2024

This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies.
Optimal selection of manufacturing enterprise financialization based on simulated annealing investment portfolio theory
Authors: Yang Hanqi , Wang Xiaoyu
Published: May 2024

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices.
Multi scale evaluation and analysis of international trade objectives of port cities
Authors: Huang Xiumei , Jiang Dewei , Zhu Kexin
Published: May 2024

The analysis of the multi-scale evaluation of port city’s international trade goal is conducive to the sustainable development of port city’s international trade. In order to make a more in-depth study on the realization ability of port city’s international trade sustainable development goal, this paper proposes a new multi-scale evaluation method of port city’s international trade goal. This method selects the evaluation indexes, uses the improved normalization method to process the indexes, uses the combination of AHP and factor analysis method to form the subjective and objective combination weighting method, brings the processed indexes into the least square optimization combination evaluation model, calculates the index weight, and uses the fuzzy evaluation method to carry out multi-scale index evaluation on the international trade of port cities to realize its multi-scale evaluation and analysis. The results show that the standardized index of Shanghai’s foreign trade dependence is 0.0056, indicating its independence in international trade. In the comprehensive evaluation, the evaluation values of Shanghai, Tianjin, and Guangzhou are 92.56, 87.89, and 88.45, respectively, which are very close to the actual results, which shows that the accuracy of the evaluation method is high, and provides a theoretical basis for the sustainable development of international trade in port cities.
Intelligent fault diagnosis and security early warning method of new power system based on system network situation
Authors: Su Xuan , Gao Han
Published: May 2024

In the past few decades, China’s power demand has been increasing, and the power fiber plays a key role in ensuring the orderly dispatching of all links of the power system. The study used a wavelet decomposition and reconstruction method, which is a signal processing technique used to decompose complex optical power data into low-frequency and high-frequency signals with different frequency components. Through this decomposition, we can more clearly observe periodic fluctuations, trend changes, and noise components in optical power data. The study also examined different prediction models, including GRU, LSTM, ARMA), etc. The performance of these models in predicting optical power trends is then analyzed, taking into account their accuracy, stability, and computational efficiency. Finally, we carefully evaluated the GRU-ARMA combined prediction model and determined its superior performance in predicting optical power trends. The outcomes show that after adjusting the input data length of the gating cycle cell model and the relevant parameters of the autoregressive sliding mean model, the residual mean value was 0.0141. At the same time, the root mean square error calculated by the combined prediction model of the gating cycle unit-autoregressive moving mean model was 0.000618, which successfully improved the accuracy of predicting the optical power trend of power fiber. This research result provides an important reference for the aging state assessment of power fiber lines, and has an important practical application value for the maintenance of power fiber lines.
Machine learning based data governance methods for demand response databases
Authors: Wang Yu , Tang Bihong
Published: May 2024

With the increasing level of grid intelligence and the related demand response database expanding, it is important to study a compound problem data governance method for demand response, while the traditional data governance methods have problems such as not considering data temporality and ignoring the impact of noise and duplicate data on data repair. As a result, this project will develop an anomaly data extraction and repair model based on two-way long and short memory networks, and repair the anomaly data by respective noise smoothing, missing data filling, and duplicate data cleaning. The paper also provides an adaptive moment estimation approach for optimisation to raise the model’s accuracy. The outcomes demonstrated that the study model’s precision for anomalous data extraction was 100% and its recall rate was 80%, which was a significant improvement over the previous state. In terms of anomalous data repair, the research model had the root mean square error value and lowest mean absolute percentage error value when compared with related models, at 0.0049 MPa and 1.375% respectively. Both the abnormal data extraction and repair performance of the research model are greatly improved over the related models, and have important value in the abnormal data governance of demand response databases.
Review on development prospect of operation scheduling strategies for flexible interconnected distribution network in renewable energy-penetrated power system
Authors: Ke Qingpai , Dong Di , Liu Tong , Xiong Shibin , Jiang Wei , Shi Xuntao , Lou Rongbo
Published: May 2024

As the renewable energy integrates in the power system, the interactions between source, load, and storage equipment in the distribution network become frequent. The original distribution network could not meet the growing demand of the power grid. The use of controlled power electronic devices for flexible interconnection transformation of the distribution network could help improve the controllability, reliability, and safety of the power system and promote the consumption of distributed power sources. It is an important means of evolving towards future intelligent distribution networks. Typical devices for flexible interconnection are discussed of the operating characteristics. In addition, the core technologies of flexible interconnected distribution network system are introduced from the view of point of dispatching. Finally, to achieve a wider application of flexible interconnected distribution networks, the development trend is prospected.
Detection and analysis of student behavior in college labor education courses based on YOLOv5 network
Authors: Zhang Ke
Published: May 2024

To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average accuracy of 90.1% on the test set, while the application test in the actual teaching environment showed that its average accuracy was 86.7%. This result is obviously superior to the existing technology, which proves the validity of the study and provides strong data support for the automatic detection of student behavior. In addition, in the teaching experiment, YOLOv5-O assisted teaching achieved the most significant teaching effect, and students’ achievement improved the most. The feasibility of this method is verified.
Evaluation of resource and environmental carrying capacity in Xiong’an New Area
Authors: Wang Qin , Zhang Feng
Published: May 2024

From four dimensions of resource, environment, ecology (green ecology) and social economy, correlation analysis method and principal component analysis method of SPSS are used to screen evaluation indexes to design a scientific and reasonable evaluation indexes system of resource and environmental carrying capacity in Xiong’an New Area in line with regional characteristics. Based on fuzzy comprehensive evaluation method, resource and environmental carrying capacity of the New Area are evaluated. The results show that: the resource and environmental carrying capacity of the New Area from 2014 to 2018 are 0.3229, 0.3556, 0.3521, 0.3859 and 0.4445 respectively, with an average annual growth rate of 8.32% and a steady growth trend. Then, some countermeasures to improve resource and environmental carrying capacity of the New Area are presented from three aspects of development and utilization of land resource, development and utilization of water resource and protection and utilization of water environment.
Smart urban management identification by integrating optical flow characteristics and HSV space
Authors: Chen Dongya
Published: May 2024

n smart cities, sanitation workers are the key to urban construction. Accurate targeting of sanitation workers can help managers better monitor and manage them. Pedestrian detection is the core and key component of object detection technology. The difficulty of pedestrian detection in the actual feature recognition is still how to quickly and accurately identify the identity in the complex video scene. To realize the effective detection of sanitation workers, the study designs an identity identification scheme conducive to the friendly management of smart urban management. Since the optical flow feature extraction method and HSV color space extraction method can not meet the actual detection efficiency, this study innovatively integrates the two methods to improve the detection accuracy of the mode. Meanwhile, the study also introduces PCA algorithm to identify the specific identity of sanitation workers. In the actual detection of sanitation workers, the identification rate of two sanitation workers is high, and the similarity is 98.61%. This technology greatly reduces the false detection rate of actual detection and improves the detection accuracy.
The spatial differentiation of human-environment relationship in mountain tourism destination, based on the image data of Landsat-8
Authors: Wang Juan , Ming Qingzhong , Qin Wen
Published: May 2024

Mountain is the most traditional tourism destination in China. On the basis of analyzing the particularity and its spatial differentiation of human-environment relationship in mountain tourism destination, this paper conducts empirical analysis in Yulong Snow Mountain by land use type classification. Firstly, ENVI is used to preprocess the Landsat-8 images of the research area. Then the visual interpretation is performed by ArcGIS to classify land use types. Based on the above, the differentiation status of human-environment relationship is analyzed. It concluded that: (1) Under the influence of natural factors such as piedmont, climate, altitude, gradient, slope, geological environment, and geomorphic characteristics, the uneven spatial distribution of human-environment relationship in mountain tourism destination is greatly affected by vegetation, rainfall, heat distribution, wind direction, suitability for human activities, and especially the topography; (2) The Yulong Snow Mountain forms three distinct levels of spatial differentiation in human-environment relationships between the east and west slopes, between high, medium and low elevations, and between different gradients.
Key technologies of end-side computing power network based on multi-granularity and multi-level end-side computing power scheduling
Authors: Wang Hengjiang , Cui Fang , Ni Mao , Zhou Ting
Published: May 2024

With the development of modern society, business organizations have higher and higher requirements for the efficiency of cloud computing services. In order to improve the comprehensive computing capability of cloud computing network, it is very important to optimize its end-side computing power. This research takes the Hadoop platform as the computing end-side cloud computing network structure as the research object, and designs a Hadoop end-side multi-granularity and multi-level multi-level network that integrates the Graphics processing unit (GPU) and the information transfer interface (Multi Point Interface, MPI). Hierarchical computing power optimization scheduling model and improved microservice deployment s11trategy that integrates multi-level resources. The performance verification experiment results show that the mean value of all node balance ratios of the original strategy and the improved strategy on computing resource-oriented, memory resource-oriented, and disk resource-oriented microservices are 0.13 and 0.12, 0.21 and 0.17, and 0.22 and 0.19, respectively. The value of the service instance cost in the scheme using the critical path optimization scheduling strategy is always at a low level, while the instance cost value of the native strategy is significantly higher than the former. It can be seen that the end-side computing power optimization scheduling model designed in this study can indeed play a role in improving the computing performance of the end-side computing power network.
Weak signal detection method based on nonlinear differential equations
Authors: Guan Zhanrong
Published: May 2024

With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of -25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as -40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability.
Cooperative control method of multiple spacecraft formation based on graphtheory
Authors: Li Li , Du Lei , Hu Yixuan
Published: May 2024

Spacecraft cluster is a new way of multi-vehicle cooperation and a major problem in the current space distribution system. A large number of core technologies of spacecraft cluster technology need to be simulated, analyzed and verified on the ground. On this basis, this article proposes the construction of a spacecraft system model and a dynamic model, and designs a path planning method that combines the spacecraft system model with the a* algorithm. And use the Morphin search tree algorithm to improve the A-star algorithm, and then simulate and analyze the spacecraft formation control scheme. The simulation results of trajectory planning show that after running the model for 21 ms, the improved A* algorithm achieved the expected error. Its MAE value is 10 - 4, achieving good path planning effect. The research plan can effectively achieve control of spacecraft formation and meet the needs of collaborative tasks and navigation route planning.
Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network
Authors: Chen Zhiliang , Wang Juan , Wei Miao
Published: May 2024

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.
Gut Microbiota as a Modifier of Huntington’s Disease Pathogenesis
Authors: Khoshnan Ali
Published: May 2024

Huntingtin (HTT) protein is expressed in most cell lineages, and the toxicity of mutant HTT in multiple organs may contribute to the neurological and psychiatric symptoms observed in Huntington’s disease (HD). The proteostasis and neurotoxicity of mutant HTT are influenced by the intracellular milieu and responses to environmental signals. Recent research has highlighted a prominent role of gut microbiota in brain and immune system development, aging, and the progression of neurological disorders. Several studies suggest that mutant HTT might disrupt the homeostasis of gut microbiota (known as dysbiosis) and impact the pathogenesis of HD. Dysbiosis has been observed in HD patients, and in animal models of the disease it coincides with mutant HTT aggregation, abnormal behaviors, and reduced lifespan. This review article aims to highlight the potential toxicity of mutant HTT in organs and pathways within the microbiota-gut-immune-central nervous system (CNS) axis. Understanding the functions of Wild-Type (WT) HTT and the toxicity of mutant HTT in these organs and the associated networks may elucidate novel pathogenic pathways, identify biomarkers and peripheral therapeutic targets for HD.
J-PARC linac and RCS – operational status and upgrade plan to 2 MW
Authors: Yamamoto Kazami , Moriya Katsuhiro , Okita Hidefumi , Yamada Ippei , Chimura Motoki , Saha Pranab Kumar , Shobuda Yoshihiro , Tamura Fumihiko , Yamamoto Masanobu , Morishita Takatoshi , Hotchi Hideaki , Liu Yong
Published: May 2024

The linac and the 3 GeV rapid-cycling synchrotron (RCS) at the Japan Proton Accelerator Research Complex (J-PARC) were designed to provide 1-MW proton beams to the following facilities. Due to the improvement of the accelerator system, we accelerated a 1-MW beam with a small beam loss. The lack of anode current in the radiofrequency (RF) cavity, rather than beam loss, limits the RCS beam power. Recently, we developed a new acceleration cavity that can accelerate a beam with a low anode current. This new cavity enables us to reduce the requirement for the anode power supply and accelerate a beam of more than 1 MW. We considered how to achieve beam acceleration beyond 1 MW. So far, a beam of up to 1.5 MW is expected to be accelerated after replacing the RF cavity. We also studied to achieve an up to 2 MW beam in J-PARC RCS.
Role of MEK1 and DIAPH3 expression in colorectal adenoma-carcinoma sequence
Authors: Foda Abd AlRahman Mohammad , El-Hawary Amira Kamal , Elnaghi Khaled , Eldehna Wesal M. , Enan Eman T.
Published: May 2024

BACKGROUND:

It is well established that most colorectal carcinomas arise from conventional adenomas through the adenoma-carcinoma sequence (ACS) model. mitogen-activated protein kinases (MAPKs) pathway has been reported as a crucial player in tumorigenesis. The MAPK signaling pathway is activated by different extracellular signals involving the “mitogen-activated/extracellular signal-regulated kinase 1 (MEK1)”, and this induces the expression of genes involved in proliferation and cellular transformation. Diaphanous-related formin-3 (DIAPH3) acts as a potential metastasis regulator through inhibiting the cellular transition to amoeboid behavior in different cancer types.

OBJECTIVE:

The aim of the study was to investigate the pattern of immunohistochemical expression of MEK1 and DIAPH3 in colorectal adenoma (CRA) and corresponding colorectal carcinoma (CRC) specimens.

METHODS:

The immunohistochemical expression of DIAPH3 and MEK1 was examined in 43 cases of CRC and their associated adenomas using tissue microarray technique.

RESULTS:

MEK1 was overexpressed in 23 CRC cases (53.5%) and in 20 CRA cases (46.5%). DIAPH3 was overexpressed in 11 CRA cases (about 29%) which were significantly lower than CRC (22 cases; 58%) ( P = 0.011). Both MEK1 and DIAPH3 overexpression were significantly correlated in CRC ( P = 0.009) and CRA cases ( P = 0.002). Tumors with MEK1 overexpression had a significantly higher tumor grade ( P = 0.050) and perineural invasion ( P = 0.017).

CONCLUSIONS:

Both MEK1 and DIAPH3 are overexpressed across colorectal ACS with strong correlation between them. This co- expression suggests a possible synergistic effect of MEK1 and DIAPH-3 in colorectal ACS. Further large-scale studies are required to investigate the potential functional aspects of MEK1 and DIAPH3 in ACS and their involvement in tumor initiation and the metastatic process.