bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of stretching process for improve the textural characteristics of Kashar cheese
Authors: Çelebi M , Şimşek B
Published: April 2024

This study was aimed to optimize the stretching process (temperature, titration acidity, and NaCl concentration) and to determine the subsequent effects over the quality characteristics of Kashar cheese. The textural and meltability properties were studied in the optimization. Whey was used to set the acidity of stretching water. Textural, meltability, and sensory evaluation scores of matured Kashar cheese obtained from the marketplaces were used for target values ​​to optimize the stretching process. The response surface methodology was used to optimize conditions for stretching process, the arrangement of experimental design and evaluation of analysis results. The optimum conditions were found as follows: 80.20 °C for stretching temperature; 0.61 °SH for stretching water acidity; and 12.00 % for NaCl concentration. Under these conditions: 79.53 N for hardness, -292.24 g.sec for adhesiveness, 0.74 for cohesiveness, 51.20 N for gumminess, and 19.05 mm for meltability values were found in Kashar cheese that was produced for validation. The validation production results were within 95 % confidence intervals for each response obtained at optimal conditions. Thus, this model is successful to predict the textural and meltability values ​​of sample.
Socio-demographic analysis of destination selection factors for Himalayan Hill destinations
Authors: Badoni Manish , Rawat Babita , Aggarwal Megha
Published: April 2024

Background The towering peaks of the Himalayas lie in troves of captivating hill destinations, especially in India. Each destination aims to provide tourists with unique experiences and breath-taking landscapes. Understanding the tapestry of factors that weave the allure of these destinations and draw visitors from diverse backgrounds remains intriguing. Method This study delves into the socio-demographic tapestry of Himalayan hill destination selection, unraveling the complex interplay of demographic characteristics, social influences, and individual motivations that shape tourists’ choices. Results This study aims to answer why different tourists have different travel choices and what factors are the drivers behind such choices. The results show that destination selection factors are similar irrespective of respondents’ socio-demographic variabilities; however, for a few factors, the results are reversed. Conclusion The study has implications for policymakers and the limitations of the research discussed at the end.
Minimal clinically important difference in physical activity in patients with stroke
Authors: Hiragami Shogo , Yoshida Keishi , Otsuka Tsunehiro , Inoue Yu
Published: April 2024

Background Estimates of the minimal clinically important difference (MCID) for stroke-related outcomes are needed, but the MCID for physical activity is unknown. Objective To provide an anchor-based estimate of the MCID for physical activity in patients with stroke. Methods This study included 31 patients with stroke admitted to a hospital and discharged home. Physical activity, including the daily number of steps and metabolic equivalents (METs), was evaluated shortly after informed consent was obtained following admission (baseline) and discharge using an Active-style Pro HJA-750C with a triaxial accelerometer. We calculated the number of steps and time rate (%) of sedentary behavior (SB), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) per day. After discharge, the physical therapist rated each participant’s perceived amount of physical activity recovery on the Global Rating of Change scale (GRC). The mean change in each physical activity data point from baseline to after discharge in the group of participants who answered “a little better, meaningful” in the GRC was considered the MCID. Results Eighteen participants were included in the analysis. Participants’ physical function improved from baseline to at discharge during hospitalization, although mild motor paralysis persisted. MCID values for the step activity, SB, LPA, and MVPA were 1828 steps, -11.2%, 6.9%, 4.3% per day, respectively. Conclusion For researchers and clinicians, this study’s MCIDs provide a benchmark for interpreting changes in the effects of intervention studies, and specific guidelines for interventions in clinical practice. Further research with larger sample sizes is required to confirm these findings.
The genome sequence of the Atlantic cod, Gadus morhua (Linnaeus, 1758)
Authors: Jentoft Sissel , Tørresen Ole K. , Tooming-Klunderud Ave , Skage Morten , Kollias Spyridon , Jakobsen Kjetill S. ,   Wellcome Sanger Institute Tree of Life Management, Samples and Laboratory team,   Wellcome Sanger Institute Scientific Operations: Sequencing Operations,   Wellcome Sanger Institute Tree of Life Core Informatics team,   Tree of Life Core Informatics collective,   Darwin Tree of Life Consortium
Published: April 2024

We present a genome assembly from an individual male Gadus morhua (the Atlantic cod; Chordata; Actinopteri; Gadiformes; Gadidae). The genome sequence is 669.9 megabases in span. Most of the assembly is scaffolded into 23 chromosomal pseudomolecules. Gene annotation of this assembly on Ensembl identified 23,515 protein coding genes.
Network-based System of Mechanical Vibration Fault Diagnosis
Authors: Zhang Qiuchen , Jin Xiaoxia
Published: April 2024

Nowadays researchers are investing in electrical machines fault diagnosis area. The users and manufacturers are strong for containing diagnostic features for reliability and scalability improvement. The regular monitoring enables machine faults early detection and hence helpful for automation by providing process control. The fault detection performance and machine-learning algorithms classification are highly dependent on features involved. The aim of this paper is to solve the network mechanical vibration problem and for that a research on mechanical vibration fault diagnosis is proposed. The first is to use the vibration signal receiving device to record the vibration signal of the target device. In the process of receiving the signal, the measurement point is related to the accuracy of the received signal, so it is necessary to prepare the measurement point. Secondly, the principle of fault detection based on vibration detection is introduced. The main purpose of this method is to identify the fault characteristics, simulate the fault with MATLAB, and obtain the error time-frequency diagram behavior. The feature vector dimension obtained by the idling confirmation example is the same as that of the rotor, which is 14, including 8 relative wavelet packet intensity entropy feature indexes and higher values, minimum value, peak-to-peak value, mean value, mean square error and variance. Finally, the deficiencies of the detected vibration faults are identified and similar improvements are proposed. Improvements only reduce signal vibration, disrupting feature isolation and identifying patterns.The observed percentage accuracy for classification of faults through proposed approach is 98.2%.
Performance Evaluation of Micro Automatic Pressure Measurement Sensor for Enhanced Accuracy
Authors: Zhu Shuiquan
Published: April 2024

The major objective of this research is to design sensitive components, conversion components, and various sensor circuits to achieve the miniaturization design for more accurate measurements. This article conducts performance testing on the designed miniaturized pressure sensor to determine whether it meets the qualified standards. The response time of designed sensor results increases with the increase of pressure under experimental conditions of different pressure application values (5MPa to 50MPa). The detection accuracy of the micro automatic pressure measurement sensor designed in this paper can reach 0.0452\%; the average pressure is 0.00364%, and the insulation resistance is 68.44 megohms, which meets reliability requirements. The sensitivity is 0.0582%; the nonlinearity is 0.0741%; the hysteresis is 0.0266%; the repeatability of 0.0625% meets the qualification standard for this instrument. Still, compared with traditional sensors, the sensor reduces the response time of results by about 60%. However, the author conducted the detection in an ideal environment. The actual working environment of sensors is relatively good. Therefore, the detection results obtained in this article may have some errors compared to the actual situation, and further analysis and testing are needed to optimize the performance of the designed sensor.
Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control
Authors: Zhou Shuai , Guo Jing
Published: April 2024

Currently, there are a series of problems in the management of the construction industry, such as resource waste, substandard quality, and low construction efficiency. In response to this phenomenon, the author proposes a multi-objective optimization control method for construction engineering management projects using deep learning algorithms. This method analyzes the relationship between cost, duration, and quality, and constructs an optimization management model for these three factors. At the same time, the improved SULSTM neural network algorithm is used to optimize the model parameters. The experimental results indicate that, when the value coefficient is 0.2211, the total investment cost and quality coefficient are 412700 yuan and 0.99496 yuan, respectively. When the value coefficient is 0.1976, the total cost and quality coefficient are 456300 yuan and 0.98798 yuan, respectively. When the value coefficient is 0.1990, the total cost and quality coefficient are 456300 yuan and 0.99496 yuan, respectively. Proved that the SUSTM neural network algorithm has faster convergence speed and lower loss values compared to the improved LSTM neural network algorithm. The cost of improving quality has a greater impact on the quality coefficient than the duration, and the total investment cost has a greater impact on the value coefficient than the quality coefficient.
Research on the Recommendation System of Music e-learning Resources with Blockchain based on Hybrid Deep Learning Model
Authors: Jin Shasha , Zhang Lei
Published: April 2024

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience.
A multi-level power grid enhanced identity authentication data management platform based on filtering algorithms
Authors: Nong Caiqin , Zou Hong , Zhang Jiafa , Liu Jiahao , Shao Pengfei , Jiang Jiawei , Xie Jiao
Published: April 2024

In response to the optimal extraction of DCT coefficients in facial images, the author proposes a DCT coefficient extraction method based on discriminant analysis. Based on the discriminant analysis of DCT coefficients, the DCT coefficients with high discriminant values are selected as features. Comparing the DPA based discrete cosine coefficient selection method proposed by the author with the traditional Zigzag discrete cosine coefficient selection method, experiments were conducted on the ORL face database and the Yale face database, respectively. The recognition performance on the ORL face database was higher than that on the Yale face database, as the facial image expression and lighting changes in the ORL database were relatively few, making it suitable for extracting key features. In response to the problem that the speech parameter MFCC is greatly affected by noise and can only reflect the static characteristics of speech, the author extracted gamma pass filtering cepstrum coefficients with human auditory characteristics and gamma pass sliding differential cepstrum coefficients that can reflect the dynamic characteristics of speech based on gamma tone filters and sliding differential cepstrum. In the NUST603 speech database, under pure background, the recognition rate based on GFSDCC features reached 89.88%, and the recognition effect based on GFCC features was 87.52%, which is 4.66% and 2.36% higher than that based on MFCC features. In noisy environments, the average recognition rates of speaker recognition systems based on GFCC and GFSDCC are 56.06% and 59.07%, while the average recognition rates of speaker recognition systems based on MFCC speech features are 53.89%, 2.17% and 5.18% higher, respectively. The gain in this recognition effect comes from the characteristics of the auditory model, as the Gammatone filter effectively reflects the noise resistance of the human auditory system.
Design of Smart Home System based on Wireless Sensor Network Link Status Awareness Algorithm
Authors: Xu Rong
Published: April 2024

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect.
BlockFog: A Blockchain-based Framework for Intrusion Defense in IOT Fog Computing
Authors: VG Prasuna , Babu B Ravindra , Pydala Bhasha
Published: April 2024

In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.
Performance Comparison of Apache Spark and Hadoop for Machine Learning based iterative GBTR on HIGGS and Covid-19 Datasets
Authors: Sewal Piyush , Hari Singh
Published: April 2024

In the realm of distributed computing frameworks, such as Apache Spark and MapReduce Hadoop, the efficacy of these frameworks varies across diverse applications and algorithms contingent upon distinctive evaluation metrics and critical parameters. This research paper diligently scrutinizes the extant body of research that compares these two frameworks concerning said evaluation metrics and parameters. Subsequently, it conducts empirical investigations to authenticate the performance of these frameworks in the context of an iterative Gradient Boosting Tree Regression (GBTR) algorithm. Remarkably, the comparative analyses in previous studies encompass a spectrum of iterative machine learning regression and classification techniques, batch processing, SQL, and Graph processing algorithms. Furthermore, numerous investigations have explored the application of machine learning algorithms encompassing logistic regression, Page Rank, K-Means, KNN, and the HiBench suite. This paper presents the comparison between the two distributed computing platforms on iterative GBTR for classification task on the HIGGS dataset from the physics domain and for the regression task on the Covid-19 dataset from the healthcare domain. The empirical findings corroborate that Apache Spark exhibits superior execution speed in iterative tasks when the available physical memory significantly exceeds the dataset size. Conversely, Hadoop outperforms Spark when dealing with substantial datasets or constrained physical memory resources.
Enterprise Audit Risk Assessment and Prevention based on AHP Analysis
Authors: Sun Guoliang , Guan Bayi
Published: April 2024

If there are no auditing standards or auditing processes of big data, the audit risks of enterprises are increased. This paper first introduces the topic through the research background and literature review in order to ensure the integrity and accuracy of audit evidence to the maximum extent, and then analyses the causes of enterprise audit risk. When analysing the risk level of material misstatement, it is mainly the audit risk generated by the enterprise's unique business model, information system and financial management. Audit risk is mainly caused by the ability of auditors and audit process management. After quantitative analysis of the correctness of enterprise audit risk assessment indicators, this paper builds a multi-level comprehensive assessment model of enterprise audit risk on the basis of AHP analysis. At the same time, this paper puts forward specific measures to improve audit methods and audit processes and prevent audit risks in view of the actual problems encountered in the current audit risk of enterprises, so as to provide certain references for enterprise risk management and control.
Enhanced Feature-driven Multi-objective Learning for Optimal Cloud Resource Allocation
Authors: Rao I Uma Maheswara , Sastry JKR
Published: April 2024

In cloud networks, especially those with distributed computing setups and data centers, one of the biggest obstacles is allocating resources. This is the key area, and this must be balanced between optimizing system performance on one side and affordability, stability (reliance) of operation, and energy efficiency. The importance of improving resource allocation methodologies in these complex cloud computing systems is recognized, and therefore this paper comes with an appropriate title--“Enhanced Feature-Driven Multi-Objective Learning for Optimal Cloud Resource Allocation” (OCRA), which integrates together both the latest machine learning techniques as well as traditional concepts from research into cloud computing. OCRA capably analyzes historical files on CPU, memory, disk and network usage. In addition to neatly assimilating large data sets such as that was the compliance rate with past SLAs or workload frequencies over certain time periods and resource allocations; even their patterns of service requests are an important piece of information for many busy people’s lives today the adaptive mechanism is one of the defining traits of the model. It can accurately anticipate changes in resource demand and immediately adjust supply, fully able to respond rapidly when fluctuations arise suddenly or unexpectedly. Multi-Objective Random Forests are at the very core of OCRA. Each tree for decision making is specially designed to meet a particular performance objective in mind. Combining these trees into a Random Forest ensemble increases not only the model's predictive accuracy but also its stability. Pareto optimization is wisely used to maintain a balance among performance indicators, without an excessive focus on one effect alone. OCRA is proven empirically through experimental studies where key performance indicators such as Resource Utilization Rate and Quality of Service (QoS) Adherence Rate are taken into account. OCRA is both energy-efficient, an important attribute in today's environmentally conscious world, and does not sacrifice performance. As far as speed, flexibility and overall efficiency are concerned, OCRA has always been superior to the other cloud resources allocation programs of its own day. While it's still not quite ready for users who don't have a firm background in computer science or programming skills (ocra is plotted on 0-x), with sufficient memory and dominant minutes turn into mechanical equipment without configuration services.
Research on Inventory Control Method Based on Demand Response of Power Big Data
Authors: Shi Huixuan , Gao Zhengping , Fang Li , Zhai Jiqing , Sun Hongzhi
Published: April 2024

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.
Scalable Video Fidelity Enhancement: Leveraging the state-of-the-art AI Models
Authors: Das Ankit , Paramaj Deven Prakash , BR Shambhavi
Published: April 2024

Improving visual quality is crucial as we navigate through the vast world of data. State-of-the-art (SOTA) artificial intelligence (AI) models provide highly effective solutions. Driven by the ever-growing demand for high-fidelity multimedia content, this research explores the groundbreaking capabilities of SOTA AI models to revolutionize video quality enhancement. Existing video capture methods often struggle with limitations in hardware, bandwidth, and compression, leading to subpar visual experiences. To address this challenge, we propose a novel Video Quality Enhancement Solution (VQES) that synergistically combines Google FILM for frame interpolation and Real-ESRGAN for image super-resolution. By applying these models to each video frame and integrating scalable post-processing techniques, a comprehensive VQES has been devised. Extensive experiments demonstrate that our VQES outperforms existing methods in terms of peak signal-to-noise ratio (PSNR) improvement and user-perceived visual quality. By advancing video fidelity, this research paves the way for consistently immersive, informative, and enjoyable visual experiences.
Energy Optimization of the Multi-objective Control System for Pure Electric Vehicles based on Deep Learning
Authors: Zhu Bubo
Published: April 2024

Advancements in information technology have revolutionized multiple sectors such as healthcare, industrial control, and environmental monitoring. With the advent of smaller, more sophisticated and wireless sensors, their applications have expanded across various industries. These sensors offer numerous advantages like cost-effectiveness, easy setup, reliable transmission, and high capacity for data processing. However, despite their benefits, there are certain limitations to consider. The primary constraint that affects their lifespan is energy availability, as replacing or recharging power sources for nodes can be challenging or infeasible. The reliance on batteries hampers data analysis by network nodes, hindering the exchange of information. Hence, prolonging the network's overall lifespan is crucial for optimizing its performance. The existing approaches, with their tried-and-tested practices and heterogeneity, require enhancements to address specific characteristics. In every application, two critical aspects are the duration of network operation and energy consumption for data routing. Through comparative analysis, it is evident that various algorithms and techniques can reduce energy usage to different extents. Based on these findings, a recommended strategy is to achieve a significant 70% reduction in energy consumption.
An Intelligent Network Method for Analyzing Corporate Consumer Repurchase Behavior Using Deep Learning Neural Networks
Authors: Lu Qiuping
Published: April 2024

Earth system models (ESMs) are our key tools for analyzing the planet's existing state and predicting its evolution in the next continuing human-caused events. However, the use of artificial intelligence (AI) approaches to augment or even replace conventional ESM functions has expanded in recent years, raising hopes that AI will be able to overcome some of the major difficulties in climate research. We address the advantages and disadvantages of neural ESM neurons, as well as the unsolved question of whether AI will eventually replace ESMs. Dynamic geophysical events are the foundation of Earth and environmental studies. Given the widespread acceptance of AI and the growing amount of Earth data, the geoscientific community may wish to seriously explore using artificial intelligence (AI) approaches at a much deeper level. Although it is a tall ambition to integrate hybrid physics and AI approaches from a fresh perspective, geology has yet to figure out how to make such methods feasible. This research is an important step towards realising the concept of combining physics and artificial intelligence to address problems with the Earth's system.
Optimised ResNet50 for Multi-Class Classification of Brain Tumors
Authors: A Sravanthi Peddinti , Maloji Suman
Published: April 2024

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,
Detection Method of Tourist Flow in Scenic Spots based on Kalman Filter Prediction
Authors: Xu Xiaoyan , Zhang Li
Published: April 2024

The tourism industry has developed rapidly, but it is always limited by the environmental carrying capacity and cannot receive too many tourists at the same time. Therefore, it is very necessary to limit the number of tourists visiting at the same time based on traffic detection. To this end, the tourist scenic spot (TSS) traffic statistics system was designed. The system performed graying, binarization, image denoising, and morphological processing on the image. The pre-processed image used the background difference method based on mixed Gaussian background modeling to detect moving objects. The improved Hough transform circle detection method was used to identify the head target, and the Kalman filter (KF) was used to complete the target tracking. KF could predict the target trajectory accurately, and the improved Hough transform circle detection method could recognize the head under occlusion. The maximum missed detection rate of the statistical system was 3.2\%, the minimum is 0, and the overall detection accuracy was the highest. The error rate of inbound passenger flow was 4.10\%, and the error rate of outbound passenger flow was 3.0\%. Using this system can control the tourist flow (TF) in the scenic spot and avoid safety accidents due to excessive passenger flow. And it is conducive to the sustainable development of the scenic spot.
Application of Big Data Analysis and Intelligent Algorithm in Power System Operation Optimization
Authors: Jin Huichao , Huo Junyi , Wang Qingfen , Li Dexiong
Published: April 2024

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi-node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi-node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low-priority data streams because the delay of high-priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements.
Master Data Quality Management Framework: Content Validity
Authors: Ibrahim Azira , Mohamed Ibrahim , Satar Nurhizam Safie Mohd , Hasan Mohammad Kamrul
Published: April 2024

Organizations rely on high quality master data as a critical component in achieving their operational and strategic performance. To accomplish high quality master data, they need to be managed properly through a systematic and holistic framework. However, prevalent master data quality management frameworks lack in providing comprehensive management practice in assuring the quality of master data. Hence, stimulates the need to develop an improved master data quality framework. Prior to the development of the framework, the identification and validation of factors that contribute to the management of master data quality must be performed. Thus, this paper underlined four elements and seven factors affecting master data quality management. Further, the identified factors were validated using a questionnaire as the validation instrument. The questionnaire consists of 95 items representing the identified seven factors that were derived from previous studies in the domain of total quality management, data quality management, and master data. Since the items are derived from the different contexts of study, content validation is a need. Previous research has suggested several techniques for performing content validation, covering both quantitative and qualitative approaches. The quantitative approach employed objective assessment and the result was statistically analysed. While the qualitative approach adopted subjective assessment such as comments, ideas, or respond. In this paper, the quantitative approach is selected over the qualitative approach, considering the effort in analyzing several items (95 items) is less complex compared to the qualitative approach which is more difficult to interpret and account for biased results. The selected panel of experts validate the instrument using a three-point scale namely “1 = not relevant”, “2 = important (but not essential)”, and “3 = essential”. Later, using the technique proposed by Lawshe, the value of the content validity ratio (CVR) is calculated. As a result, 92 items are accepted, and 3 items are rejected. The elimination of the 3 items is due to the unsuitableness to be used in the context of the public sector. The validated items can be used as an instrument to validate the factors affecting master data quality management. The proposed factor would support the organization in managing master data quality more effectively.
EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework
Authors: Iqbal Anam , Qureshi Shaima , Chishti Mohammad Ahsan
Published: April 2024

Chronic Obstructive Pulmonary Disease (COPD) poses a significant global healthcare challenge. It is a progressive lung disease that causes breathing difficulties and can significantly impact a person's quality of life. COPD is primarily caused by smoking, but other factors, such as air pollution and genetic predisposition, can also contribute to its development. This paper introduces a novel Context-Aware Framework for the Diagnosis and Personalized Management of COPD. We discuss the limitations of traditional COPD management, highlighting the importance of early detection and remote monitoring. Early detection and remote monitoring are crucial in managing COPD as they allow for timely interventions and better disease management. In this paper, we propose a framework based mostly on contextual data and other parameters of COPD as put forth by the World Health Organization (WHO) in the form of the International Classification of Functioning, Disability, and Health. Ontologies drive this architecture and incorporate dynamic contextual information from patient environments, user profiles, and sensor data. In addition to the various obvious data items like patient personal details (gender, contact, medical history) and COPD risks and symptoms, the COPD ontology also considers the details about the caregiver and healthcare professional. This is in addition to the contextual data processed separately using the Context Ontology. The ontology we constructed using Protégé serves as the framework for the structured representation and logical inference of contextual information. By harnessing dynamic contextual data, our ontology enables real-time decision-making tailored to individual patient requirements. It empowers healthcare professionals to make informed choices and deliver timely interventions, enhancing healthcare services by offering proactive care to detect early signs of health deterioration and suggest preventive measures. This approach improves patient experiences and optimizes resource allocation within the healthcare system. To uphold ethical standards and prioritize the needs of patients, we emphasize the significance of safeguarding data, obtaining informed permission, and recognizing data ownership. The ontology-based approach presented in this study offers a scalable and flexible framework that can be readily incorporated into existing healthcare systems, redefining the management of COPD in response to evolving demands. Security poses one of the biggest threats in context-based environments due to the different data formats acquired by the diverse sensors. Another essential consideration is confidentiality because the data in hand is sensitive patient information.
Prediction of English Teacher Career Development Based on Data Mining and Time Series Model
Authors: Fan Liping
Published: April 2024

With the gradual growth of the teaching profession, the teaching profession is facing new trends in reform and development, and the same dilemma exists for English teachers’ career development and planning. To this end, the study first uses a modified K-means clustering method to cluster and analyse the factors affecting English teachers’ professional development, forming a system of indicators on English teachers’ professional development. The Long Short-Term Memory (LSTM) network employed the time-series features to create a time-series model, and the Support Vector Machine (SVM) was used to forecast the course of English teachers’ career development. To assess the current career status of English teachers and their impact on people and organizations, this study proposes a career prediction model for English teachers. This model utilizes data mining and time series modeling to provide accurate predictions. In accordance with the experimental findings, the precision of the upgraded K-Means model was 98.58%, and the error between the projected sample data and the actual sample data for the training of the trend prediction model for English teachers’ career growth was 0.032. It was able to accurately predict teachers’ career development and explore the specific factors affecting English teachers’ career development, so as to solve the problems in teachers’ career development.
Performance Analysis for Optimized Light Weight CNN Model for Leukemia Detection and Classification using Microscopic Blood Smear Images
Authors: Alkhouli Mahmoud Saed , Joshi Hiren
Published: April 2024

The objective of this work is to create a diagnostic tool for the early diagnosis of leukaemia which is a serious type of cancer affecting bones and blood. Acute lymphoblastic leukemia (ALL) is the most dangerous form of leukemia. Doctors diagnose it by blood samples under powerful microscopes with enhanced lenses which can be slow and is sometimes affected by disagreements among experts. Therefore, the purpose of this work was to create a profound diagnostic tool for the early diagnosis of leukaemia.We proposes an Optimized Light Weight CNN to detect ALL at the early stage. Fragmentation and classification based on preprocessing are the two main components of the suggested method. Artificial images are created during the segmentation process and then tamed by chromatic modification. The proposed model is used to extract the best deep features from every blood smear image to predict the presence of ALL. The work was tested by two lymphoblastic leukaemia image databases (ALL_IDB1 and ALL_IDB2). Deep-learning (DL) models-based segmentation and classification techniques have recently been introduced for detecting ALL; however they still have certain drawbacks. The proposed approach was assessed with few DL parameters like accuracy, F1 score, precision, recall and area under the curve. In comparison to the most recent research studies already published; the suggested strategy produced exceptional classification accuracy as 99.56%, F1 score as 99.53%.
PROFESSIONAL EDUCATION OF STUDENTS IN EDUCATIONAL INSTITUTIONS OF THE MINISTRY OF INTERNAL AFFAIRS OF RUSSIA
Authors: Lebedeva Maria , Kutenkova Oksana
Published: April 2024

The article examines the problems of the pedagogical component of professional education in the framework of training at the St. Petersburg University of the Ministry of Internal Affairs of Russia. The purpose of the work is to determine the optimal scenario for the teacher's work aimed at professional development and self-determination, i.e. which means and methods of education, as well as organizational forms of classes, should be used in teacher–student interaction in order to achieve the set goal - the formation of a high level of development of professional and moral qualities of students. 102 cadets of the St. Petersburg University of the Ministry of Internal Affairs of Russia aged from 19 to 21 years took part in the study. The sample consisted of 33 boys and 69 girls. The questionnaire consisted of 9 questions, assuming open detailed answers. The results were processed using content analysis. The cadets express their opinion on the mutual influence of the teacher-student on the learning process, noting also the personal characteristics of the students, which may hinder the perception of information, the formation of professional self-identification and the development of professionally oriented thinking, and the content component of the discipline taught. The importance of the discipline "Professional education of students in educational organizations of the Ministry of Internal Affairs of Russia", according to the cadets, is due to the applied importance of the information received in the classroom, which is necessary both in future professional activities and for the formation of skills of interaction with classmates and permanent staff, as well as a more in-depth study of official etiquette and professional ethics, which is also relevant in the process of adaptation to the conditions of study in a departmental educational organization. According to the cadets, the most effective forms and methods of conducting classes are those that involve a creative approach to completing tasks, business games, watching movies, and field trips to territorial authorities.
Exploring of the relationship aggressive behavior and propensity to bullying in adolescents
Authors: Volkova Tatiana , Kaygorodova Nadezhda , Rybin Denis
Published: April 2024

This article is devoted to the study of a current phenomenon of modern psychology - aggressiveness, which can take different forms from physical violence to hidden verbal insults. The purpose of this study is to identify the characteristics of aggressive behavior of adolescents in the context of their tendency to engage in bullying. The sample consisted of 50 teenagers, of which 29 were girls and 21 boys, students of a rural school in the district center of the Altai Territory. The study was carried out using a questionnaire for the study of aggressiveness (A. Bass and A. Darkie), a questionnaire for the level of aggressiveness (A. Bass and M. Perry), an aggressiveness test (L.G. Pochebut), and the method “Identification of the bullying structure” (E. G. Norkina). Bullying structures were identified and the relationships between each structure and manifestations of aggressiveness were determined. The conducted mathematical and statistical analysis using the determination of the arithmetic mean deviation, the Spearman correlation coefficient, and the Wilcoxon T-test coefficient showed significant differences in the characteristics and degree of manifestation of aggressiveness before and after the correctional and developmental training program with adolescents. Based on the results obtained, correctional and developmental training was developed and conducted, which helped reduce the manifestations of aggression and hostility among adolescents. The results obtained can be applied to provide psychological support to adolescents of various bullying structures in educational organizations.
Public Intimacy on social media -extimacy as connected presence.
Authors: Mateus Samuel
Published: April 2024

The modern experience of intimacy and sociality is being radically transformed, impacting not just the quality of social relationships but also the experience of the self and the borders between private and public expressions of the self. Social media are prime aspects of today’s intimacy practices. They fulfil a need and desire for (remote) communications and a feeling of (mediated) connection. The contemporary transformations regarding intimacy are presented in this paper. It is stated as a hypothesis that a reordering of the private and public realms has been happening since the 20th century, requiring a new theory of publicity and publicness. Only a reconceptualization of the modern notions of “public” and “private” enables us to fully understand the mutations in public intimacy provoked by social media. The objective is to demonstrate that media modulates intimacy, and concepts such as extimacy and connected presence are needed to fully comprehend and evaluate it. This work begins by recounting the transformations in modern intimacy and its contemporary redefinition. Then, a characterization of the contemporary articulations of intimacy is drawn, and the implications of extimacy for the individual in the context of highly mediatized societies are presented. It is argued that extimacy involves a complete reformulation of modern intimacy in direction or a malleable, fluid, and negotiated, public intimacy. It is even more important today as growing differentiation and performativity in social life demand the management of social relations.