bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study of Online Teaching Strategies During Covid-19 of Secondary School Teachers
Authors: Saxena Preeti , Patel Dharmesh
Published: February 2024

During the advent of COVID-19, it became inevitable to switch from traditional face-to-face teaching to online teaching mode. Switching to the online teaching process was the only option left for uninterrupted learning. One of the ways to make the online teaching-learning experience was the ability to energize the class environment. Creating a classroom community where students can communicate after lectures and can share their ideas and thoughts. Recording of the lectures must be given to students so that they can see them later for better understanding. Effective teaching strategies implementation was utmost required to improve the online teaching and make online learning a successful experience for both teachers and the students. Descriptive survey method was used for the present study. Objectives were to study the role of teacher in online class during covid -19 pandemic, to find different types of educational strategies for making online classes, to study the role of parents and students in implementation during online classes. Sample was selected from the 7 Secondary Schools affiliated to the State Board of Gujarat in Vadodara city taking as random sampling technique which included 70 teachers. Self made questionnaire was used to achieve the objectives of the study. It has been found in present study that the maximum number of teachers were able to perform their role in helping students according to their needs, perform as a mentor, guide and friend, to inspire the students and the remaining have faced difficulty motivating their students. Able to maintain the proper interpersonal relationships, interactions were also possible between the teacher & students in online mode of teaching, but very few teachers face the difficulty in fulfilling or maintaining the interpersonal relationships. Different types of educational strategies were used in which maximum number of teachers were able to perform their interactive classroom though various activities. All the teachers were able to perform stories as strategies to clear the concept, majority of teachers are able to inspire the students and the remaining have faced difficulty with an inductive deductive approach & ICT based lesson plan in online mode of teaching. Majority of teachers were able to make good rapport with the students & Use teaching aids, various online quiz platforms in online mode of teaching, but very few teachers face the difficulty in fulfilling open forum or discussions & encourage Group feedback for engagement. The maximum number of teachers agreed that parents and students were found able to perform their roles, to perform online cultural programs, competitions & activities, to motivate online morning assemblies, dignity of online assessments, to connect in online parents and teacher meeting during online class.
Indícios tecnológicos e práticas nos cotidianos complexos e multirreferenciais da escola pública
Authors: Carmo Rogério Gusmão do , Aparecida Brito Barreto Denise , Vivien Carvalho de Oliveira Soares Claudia
Published: February 2024

Com o propósito de compreender a presença e influência das tecnologias no cotidiano escolar, assim como a visão dos educadores em relação às suas aplicações nas abordagens pedagógicas a partir das práticas cotidianas e dos fundamentos teóricos relacionados às tecnologias digitais no contexto educacional. A metodologia empregada baseou-se nas perspectivas teórico-metodológicas da pesquisa com os cotidianos e nos princípios da pesquisa exploratória através das entrevistas abertas como dispositivo. A amostra do estudo incluiu quatro professores do ensino fundamental, pertencentes a uma escola pública situada na região periférica de Vitória da Conquista, Bahia. Os resultados do estudo evidenciam que as principais dificuldades no uso das tecnologias digitais na educação são causadas pela falta de dois pilares fundamentais: formação adequada dos professores e infraestrutura. Além disso, é notável a influência dos desdobramentos da pandemia de COVID-19 nas percepções dos professores e em suas práticas educacionais, destacando os desafios enfrentados. Ademais, o estudo aponta para a necessidade de uma abordagem inovadora, que difira do que normalmente é proposto para a educação contemporânea, muitas vezes focando excessivamente na criação de produtos e estímulo ao consumo, em detrimento das reais necessidades da escola pública.
Duplication issues with the new interface of Scopus
Authors:   Rajagiri College of Social Sciences, Elango Bakthavachalam
Published: February 2024

This short communication discusses the duplication issue in Scopus's new interface. After searching for bibliographic records, the author noticed many duplicates in the downloadable file. This issue lasted a month, with 67% of records duplicated. After alerting Scopus, the new version was confirmed to have a duplication issue, prompting a recommendation to revert back to the old version. The study advises caution when utilizing Scopus data, particularly when exporting large files. Additionally, it highlights the novel attributes of the Scopus interface, including the ability to download 20,000 records at a time, a notable increase from the previous limit of 2,000.
IMPACT OF ACARICIDES ON VARROA DESTRUCTOR INFESTATION IN HONEY BEE COLONIES (Apis mellifera L.) AND THEIR HISTOLOGICAL EFFECTS ON HYPOPHARYNGEAL GLANDS
Authors: Salem Rasha A. , El-sayıed Hashish M. , Amro Abdulraouf , Abd Alla Asmaa
Published: February 2024

This study aimed to determine the random role of some acaricides, which are the most commonly used in Egypt for controlling Varroa destructor on Hypopharyngeal Glands (HPGs) of honey bees (Apis mellifera L.). The acaricides used in this study were Varroakiller®, Menthocaros®, Amitraz®, Varroby®, and formic acid. Results showed that the total number of fallen mites was 53.5, 47, 28.6, 26, and 24.5 for Varroakiller®, Menthocaros®, Amitraz®, Varroby®, and formic acid, respectively, compared to 16 mites in the control group. For sealed worker brood areas (SWBA), the treatments included Varroakiller® (327.98 cm2), formic acid (228.92 cm2), Varroby® (222.25 cm2), Menthocaros® (129.72 cm2), and Amitraz® (101.73 cm2), while the SWBA for control colonies was 44.83 cm2. Histological studies of the HPGs showed that although Varroakiller® effectively controlled Varroa mites and had no direct impact on worker bees or egg-laying areas, it considerably impacted the HPGs, which could ultimately affect the bee colony. Moreover, formic acid recorded a lower number of fallen Varroa with acceptable outcomes for egg-laying areas and sealed workers. This, coupled with the fact that formic acid was deemed the most significant due to its effectiveness over a short period, resulted in a reduction of 100% after 24 days.
An integrated system for managing technological processes for the efficient and safe production of dairy products, taking into account resource conservation
Authors: Rozbytska Tetyana , Tolok Halyna , Liu Xiaowei , Tyshchenko Liudmyla , Savchenko Oleksandr
Published: February 2024

The research results showed that the ISO 22000 and ISO 50001 standards are important standards of business management systems that do not contradict the principles of the organization and contribute to ensuring the high quality and safety of food products, as well as compliance with the principles of energy management if the requirements of these standards are fully met. The new versions of the standards are focused on the process approach as a key principle for the formation of integrated management systems. The article provides a structure of standards in the format of the Deming cycle (PDCA – plan, execute, check, act), which allows you to manage processes, determine and improve their capabilities. Therefore, the article theoretically substantiates and experimentally confirms the concept of a systemic approach to the management of the safety and quality of dairy products, based on the identification of influencing factors at various stages of their life cycle, optimization of production parameters, storage and sale of products by the requirements of the DSTU ISO 22000 and DSTU ISO 50001 standards. The article analyzes and systematizes research materials, including critical analysis and statistical data processing.

In addition, a model of an integrated system was developed. The conducted research was of great practical importance in the implementation of an integrated system of management of technological processes to ensure resource conservation and the safety of dairy products. Here are a few key aspects that highlight their importance: efficient use of resources, improving product safety, compliance with regulatory requirements, sustainability and environmental responsibility.

The implementation of an integrated system for managing technological processes to save resources and ensure the safety of dairy products has significant practical advantages for enterprises, consumers, and the environment. These advantages are manifested in the radical improvement of production efficiency, reduction of resource costs, improvement of product quality and safety, as well as in the preservation of natural resources and reduction of negative impact on the environment.
ОСОБЛИВОСТІ ПОБУДОВИ СТРАТЕГІЇ ЦИФРОВОГО МАРКЕТИНГУ В ОРГАНІЗАЦІЇ ЕЛЕКТРОННОГО БІЗНЕСУ
Authors: БОЛОТНА Оксана , ЛЯШЕВСЬКА Вікторія , СИВКІВ Дмитро
Published: February 2024

This article analyzes the fundamental aspects of constructing a digital marketing strategy in organizations focused on electronic business. The impact of contemporary trends and challenges in electronic business on the strategic thinking of enterprises is described, particularly in the context of rapid technological changes and increased competition. The author explores key aspects of digital marketing strategy, defining their role in achieving success in electronic business. Specifically, the transition from traditional marketing methods to the use of online tools and internet platforms is examined. Special attention is given to creating an effective company image in the online environment and developing strategies aimed at building consumer trust. Proposed practical recommendations focus on improving segmentation, integrating electronic marketing, and creating personalized marketing messages for effective interaction with digital consumers. Proper construction of a digital marketing strategy enables the efficient utilization of digital marketing tools and internet platforms to enhance interaction, attract new customers, and retain existing ones. The overall goal is to improve the efficiency of marketing strategies, ensure high customer satisfaction, and increase the profitability of electronic business. This information can be utilized by top management, marketing departments, and electronic business specialists for the development and enhancement of marketing strategies. The emphasis is placed on the fact that an effective digital marketing strategy is a key success factor for organizations operating in the electronic business sector, highlighting the importance of continual adaptation to changes in the online environment to secure a sustainable competitive advantage through the use of personalized approaches and innovations.
ENHANCING ENTERPRISE MANAGEMENT SYSTEMS THROUGH CONFLICT MANAGEMENT TECHNOLOGIES
Authors: LEPEYKО Tetyana , LOHINOVA Kateryna , OMAROV Elvin
Published: February 2024

Modern communication, both at the level of society and an enterprise, is accompanied by conflicts. The nature of conflict is the incompatibility of the goals of one entity with the goals of another entity, which is a natural inherent feature of human relations. The way a person reacts to a conflict determines the negative or positive outcomes of any conflict. For this reason, it is business leaders who have the opportunity to resolve conflicts with the most significant positive effect if they have complete information about the nature of conflicts, their sources, ways of responding to them, and resolving them. Conflict presents challenges but also presents rich opportunities for improved relationships and problem-solving. Skillful management of conflicts is an invaluable skill for everyone in leadership positions.

For this reason, the study proposes to evaluate and consider the importance of communication with five conflict management styles in an organization according to the TKI model (Thomas Kilman Model). The research emphasizes the need for effective communication skills in managerial situations to resolve conflicts and optimize work. The research was based on a survey of twenty-two employees and managers with long-time experience in different organizations who have had their share of conflict and have a grasp on managing conflicts. The results of the investigation demonstrate that out of the five proposed conflict resolution styles according to the TKI model, the most significant number of respondents lean towards the collaborative style (working towards a goal), which is appropriate in conflict management, and that for collaboration to be practical, the need for fast and effective communication is critical. However, some organizations may focus less on conflict, which can have negative consequences and affect the morale of all stakeholders, including employees and employers. Based on the survey, the TKI model is the optimal conflict management approach. Consequently, developing practical communication skills is crucial for managers, administrators, and stakeholders to manage conflicts and enhance productivity.
Impact of the New Individual Income Tax Reform on Resident Consumption-Based on Empirical Data from Hubei Province
Authors: Zhou Yeye , Wang Kan
Published: February 2024

Tax reform, as an important measure of national macroeconomic regulation, is of great significance in stimulating resident consumption. The article selects data on individual income tax and resident consumption levels in Hubei Province from 2016 to 2021 as research samples, and uses SPSS software to empirically analyze the impact of the 2019 new individual income tax reform on resident consumption. The research results indicate that individual income tax reform can effectively promote resident consumption expenditure, and reducing residents’ tax burden is beneficial for promoting resident consumption. Based on this, the article proposes suggestions from three aspects: the individual income tax collection system, the individual income tax rate structure, and the tax regulatory system, providing theoretical reference for deepening the individual income tax reform.
Decoupling Elasticity Analysis of Transportation Structure and Carbon Emission Efficiency of Transportation Industry in the Context of Intelligent Transportation
Authors: Lu Shengrong , Liu Yao
Published: February 2024

Transportation industry is a pillar industry for the development of the economy of a nation, and is indispensable in improving the operational efficiency of national economy and enhancing social and economic benefits. However, transportation industry has characteristics of high energy consumption, pollution and emissions, it requires an effective balance between development and carbon reduction in the context of the “Carbon neutrality and carbon reduction Goals”. The problem of Poor transportation structure in China’s transportation industry is relatively serious, so optimizing the transportation structure has been urgent to be solved. Based on the TAPIO decoupling elasticity analysis theory, this article takes the Yangtze River Economic Belt region for example to analyze the decoupling elasticity between transportation structure and carbon emission efficiency. The results indicate that the overall decoupling elasticity of carbon emissions, carbon emission efficiency, and transportation structure is respectively characterized by strong negative decoupling and weak negative decoupling. In other words, when the proportion of iron and water converted turnover decreases, the carbon emissions increase, and carbon emission efficiency decreases in company.
Intelligent Measurement of Total Factor Carbon Emission Efficiency of Logistics Industry in Hubei Province Under Low Carbon Constraints
Authors: Liu Yao , Wu Zhaojun
Published: February 2024

Under the background of “dual carbon”, Hubei Province has always attached great importance to the low-carbon-logistics transformation and has made green and efficient the focus of logistics industry development. The article takes energy and unexpected output into account and takes unit carbon emission added value and unit carbon emission conversion turnover as output factors. It constructs an SBM-DEA model to measure the carbon emission efficiency of the logistics industry in Hubei China. Finally, based on the research results, a carbon emission reduction path for the logistics industry in Hubei Province is proposed, including strengthen policy guidance and reasonably set freight rates; transfer of high carbon emission transportation methods; extend the logistics value chain and carry out logistics technology innovation.The research conclusion can provide theoretical support for the government.
A Study on Leaving of Tibetan Cadres Under the Perspective of Psychological Contracts-Take Teachers and Civil Servants as an Example
Authors: Wang Ya’nan
Published: February 2024

The high turnover rate of Tibetan cadres has seriously affected the development of Tibet. Existing studies have mainly explored the turnover of rural teachers, and primary and secondary school teachers, and have paid less attention to the extra-occupational factors. In this paper, we take teachers and civil servants working on the Tibetan Plateau as examples to explore the turnover problems, and analyze their mediating and moderating mechanisms. The study finds that psychological contract breakdown significantly and positively affects the turnover intention of Tibetan cadres, while job satisfaction, job embeddedness, and perceived opportunities play a mediating role, and kinship responsibility moderates the relationship between job satisfaction, job embeddedness, and turnover intention. The results of this study expand the antecedents of job embeddedness theory and turnover modeling studies, enrich the mediation and boundary mechanisms of turnover research, explain the reason and internal mechanism of Tibetan cadres leaving and retention, and provide ideas for Tibetan cadres’ retention.
Disposal for the Pinching People and Objects Accidents in Fully Automated Operation Urban Rail Transit Lines Based on Fault Tree Analysis
Authors: Feng Jianrong , Shang Jie , Ibrahim Ahmad Nazrul Hakimi
Published: February 2024

The Fault Tree Analysis (FTA) method was used to analyze the accidents of high-speed trains with people and objects caught in fully automatic operation lines of urban rail transit. A fault tree was established to describe the top event, intermediate event, and basic event in the accident tree of high-speed trains with people and objects caught in train doors and platform doors. The minimum path set, and structural importance parameters of the accident were analyzed and calculated, and the equivalent tree of the accident tree was drawn. The four shortest path sets with shorter chains were selected for analysis, and effective measures were proposed to prevent the occurrence of high-speed train accidents involving people and objects.
Designing an Improved VMD-CNNs-DS Algorithm for the Port Crane Bearing Fault Diagnosis
Authors: Wu Shu , Lei Xinxin , Zhang Jinghua , Chen Xing
Published: February 2024

Accurate diagnosis of bearing faults is crucial for detecting faults in rotating machinery. However, the variability and complexity of recorded vibration signals present a significant challenge in distinguishing unique characteristic fault features. To address this issue, we proposed VMD-CNNs-DS, a novel bearing fault diagnosis algorithm based on data-level and decision-level bipolar fusion network. The adaptive variational mode decomposition (VMD) method based on parameter optimization is employed to transform multiple sensor raw vibration signals into time-frequency analysis. The convolutional neural network (CNN) takes as input a graph of the features of the vibration signals from the two sensors. Improve DS theory for decision blending. An extensive evaluation of the fault diagnosis performance of VMD-CNNs-DS on various data sets shows that VMD-CNNs-DS achieves better results than a single CNN network model by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.
Challenges and Innovations in Teaching Business Administration Majors in the Digital Economy Era
Authors: Xiong Wenjie
Published: February 2024

On the basis of exploring the teaching requirements and changes of business management majors in application-oriented undergraduate colleges under the new situation, this paper analyzes many problems and their causes in professional teaching and talent cultivation, explores the new challenges posed by the digital economy era to the teaching of business management majors in application-oriented undergraduate colleges, and proposes innovative teaching methods and approaches for business management majors in application-oriented undergraduate colleges in the digital economy era.
A cross-model hierarchical interactive fusion network for end-to-end multimodal aspect-based sentiment analysis
Authors: Zhong Qing , Shao Xinhui
Published: February 2024

For the aspect-based sentiment analysis task, traditional works are only for text modality. However, in social media scenarios, texts often contain abbreviations, clerical errors, or grammatical errors, which invalidate traditional methods. In this study, the cross-model hierarchical interactive fusion network incorporating an end-to-end approach is proposed to address this challenge. In the network, a feature attention module and a feature fusion module are proposed to obtain the multimodal interaction feature between the image modality and the text modality. Through the attention mechanism and gated fusion mechanism, these two modules realize the auxiliary function of image in the text-based aspect-based sentiment analysis task. Meanwhile, a boundary auxiliary module is used to explore the dependencies between two core subtasks of the aspect-based sentiment analysis. Experimental results on two publicly available multi-modal aspect-based sentiment datasets validate the effectiveness of the proposed approach.
Creating an Unprotected Class: Addressing Legal Risks in the Era of Biologically-Defined Alzheimer’s Disease
Authors: Vaishnav Neil , Gonzalez Rosa , Karunungan Krystal , Tyler Ana , Zheng William , Arias Jalayne J.
Published: February 2024

Background: Documentation of preclinical biomarker tests for Alzheimer’s disease (AD) in the medical record may expose patients to employment and insurance discrimination risks. There is a gap in research describing clinicians’ approaches to documenting biomarker results. Objective: To evaluate discrimination risks faced by patients undergoing biomarker testing for AD through a qualitative analysis of clinician documentation practices. Methods: Semi-structured interviews using hypothetical patient scenarios. The qualitative analysis focused on interviewees’ responses related to documentation and disclosure of results. Results: We collected and analyzed 17 interviews with dementia experts; and identified three approaches to documenting biomarkers as: an association with active AD, noninformative, and an increased susceptibility for AD. Those who associated biomarkers with active disease were more likely to favor disclosure to employers and insurers, which could increase discrimination risks. Conclusions: This study demonstrates the variety of documentation and disclosure practices likely to emerge for preclinical AD biomarker tests and highlights a need for guidelines in this area.
Narcissistic Personality Disorder as Prodromal Feature of Early-Onset, GRN-Positive bvFTD: A Case Report
Authors: Michelutti Marco , Urso Daniele , Gnoni Valentina , Giugno Alessia , Zecca Chiara , Vilella Davide , Accadia Maria , Barone Roberta , Dell’Abate Maria Teresa , De Blasi Roberto , Manganotti Paolo , Logroscino Giancarlo
Published: February 2024

Background: Behavioral variant frontotemporal dementia (bvFTD) typically involves subtle changes in personality that can delay a timely diagnosis. Objective: Here, we report the case of a patient diagnosed of GRN-positive bvFTD at the age of 52 presenting with a 7-year history of narcissistic personality disorder, accordingly to DSM-5 criteria. Methods: The patient was referred to neurological and neuropsychological examination. She underwent 3 Tesla magnetic resonance imaging (MRI) and genetic studies. Results: The neuropsychological examination revealed profound deficits in all cognitive domains and 3T brain MRI showed marked fronto-temporal atrophy. A mutation in the GRN gene further confirmed the diagnosis. Conclusions: The present case documents an unusual onset of bvFTD and highlights the problematic nature of the differential diagnosis between prodromal psychiatric features of the disease and primary psychiatric disorders. Early recognition and diagnosis of bvFTD can lead to appropriate management and support for patients and their families. This case highlights the importance of considering neurodegenerative diseases, such as bvFTD, in the differential diagnosis of psychiatric disorders, especially when exacerbations of behavioral traits manifest in adults.
Causal Association Between Allergic Diseases and Dementia: Evidence from Multivariate Mendelian Randomization Study
Authors: Wang YuanYing , Wang ShiHao , Wu JiaXin , Liu XinLian , Zhang LuShun
Published: February 2024

Background: The link between allergic diseases and dementia remains controversial, and the genetic causality of this link is unclear. Objective: This study investigated the causal relationship between allergic diseases and dementia using univariate and multivariate Mendelian randomization (MR) methods. Methods: We selected genome-wide association studies including 66,645 patients with allergic diseases and 12,281 patients with dementia, with statistical datasets derived from the FinnGen Consortium of European origin. After a rigorous screening process for single nucleotide polymorphisms to eliminate confounding effects, MR estimation was performed mainly using the inverse variance weighting method and the MR-Egger method. Sensitivity analyses were performed using Cochran’s Q test, MR-PRESSO test, MR Pleiotropy residuals and leave-one-out analysis. Results: Univariate and multivariate MR together demonstrated a causal relationship between atopic dermatitis and reduced vascular dementia (VaD) risk (OR = 0.89, 95% CI: 0.81–0.99, p = 0.031; OR = 0.85, 95% CI: 0.76–0.95, p = 0.003). MVMR confirmed asthma was associated with a reduction in the risk of Alzheimer’s disease (AD) (OR = 0.82, 95% CI: 0.71–0.94, p = 0.005) and may be associated with a reduction in the risk of VaD (OR = 0.80, 95% CI: 0.65–0.99, p = 0.042); allergic rhinitis may be causally associated with an increased risk of AD (OR = 1.16, 95% CI: 1.00–1.35, p = 0.046) and VaD (OR = 1.29, 95% CI: 1.03–1.62, p = 0.027). In sensitivity analyses, these findings were reliable. Conclusions: MR methods have only demonstrated that allergic rhinitis dementia is associated with an increased risk of developing dementia. Previously observed associations between other allergic diseases and dementia may be influenced by comorbidities and confounding factors rather than causality.
Effects of Intranasal Dantrolene Nanoparticles on Brain Concentration and Behavior in PS19 Tau Transgenic Mice
Authors: Vera Robert , Hong Nicholas , Jiang Bailin , Liang Ge , Eckenhoff Maryellen F. , Kincaid Halle J. , Browne Veron , Chellaraj Vinolia , Gisewhite Douglas , Greenberg Michael , Ranjan Sudhir , Zhu Gaozhong , Wei Huafeng
Published: February 2024

Background: Repurposing dantrolene to treat Alzheimer’s disease has been shown to be effective in amyloid transgenic mouse models but has not been examined in a model of tauopathy. Objective: The effects of a nanoparticle intranasal formulation, the Eagle Research Formulation of Ryanodex (ERFR), in young adult and aged wild type and PS19 tau transgenic mice was investigated. Methods: The bioavailability of intranasal ERFR was measured in 2 and 9–11-month-old C57BL/6J mice. Blood and brain samples were collected 20 minutes after a single ERFR dose, and the plasma and brain concentrations were analyzed. Baseline behavior was assessed in untreated PS19 tau transgenic mice at 6 and 9 months of age. PS19 mice were treated with intranasal ERFR, with or without acrolein (to potentiate cognitive dysfunction), for 3 months, beginning at 2 months of age. Animal behavior was examined, including cognition (cued and contextual fear conditioning, y-maze), motor function (rotarod), and olfaction (buried food test). Results: The dantrolene concentration in the blood and brain decreased with age, with the decrease greater in the blood resulting in a higher brain to blood concentration ratio. The behavioral assays showed no significant changes in cognition, olfaction, or motor function in the PS19 mice compared to controls after chronic treatment with intranasal ERFR, even with acrolein. Conclusions: Our studies suggest the intranasal administration of ERFR has higher concentrations in the brain than the blood in aged mice and has no serious systemic side effects with chronic use in PS19 mice.
Machine learning and evolutionary optimization approach for solving the flexible job-shop scheduling problem
Authors: Guo Hong , Yang Jin , Yang Jun
Published: February 2024

This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP.
Business investment decision-making based on mathematical model and risk analysis
Authors: Qi Ruijuan , Liu Chang , Zhang Qiwen , Gu Lingzi
Published: February 2024

Business investments are prone to market risks, so pre-analysis is mandatory. The type of risk, its period, sustainability, and economic impact are the analyzable features for preventing loss and downfall. In recent years, mathematical models have been used for representing business cycles and analyzing the impacting risks. This article introduces a Decisive Risk Analytical Model (DRAM) for identifying spur defects in business investments. The proposed risk analytical model exploits the investments, returns, and influencing factors over the various market periods. The risk model is tuned for identifying the influencing factors across various small and large investment periods. The model is tuned to adapt to different economic periods split into a single financial year. In the process of tuning and training the mathematical analysis model, deep learning is used. The learning paradigm trains the risks and modifying features from expert opinion and previous predictions. Based on these three factors, the risk for the current investment is forecasted. The forecast aids in improving the new investment feasibilities with minimal risks and model modifications. The frequent market status is identified for preventing unnecessary risk-oriented forecasts using the training performed. Therefore, the proposed model is reliable in identifying risks and providing better investment recommendations.
New improved model for joint segmentation and registration of multi-modality images: application to medical images
Authors: Badshah Noor , Begum Nasra , Rada Lavdie , Ashfaq Muniba , Atta Hadia
Published: February 2024

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively.
HVAC energy consumption prediction based on RJITL deep neural network model
Authors: Li Xiaoli , Du Linhui , Yu Xiaowei , Wang Kang , Hu Yongkang
Published: February 2024

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.
Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm
Authors: Qu Ying , Wang Xuming
Published: February 2024

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.
Lattice Isomorphism and L-homeomorphism
Authors: Tomy Navin , Johnson T.P.
Published: February 2024

This paper deals with lattice isomorphic L-topological spaces. We are concerned with a question: Under what conditions will a lattice isomorphic L-topological spaces be L-homeomorphic. We give contributions to this question in three different ways.
Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm
Authors: Gul Rimsha , Bashir Maryam
Published: February 2024

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.
Authorization of Aadhar data using Diffie Helman key with enhanced security concerns
Authors: Karthika K. , Rangasamy Devi Priya
Published: February 2024

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.
RETRACTED: SFL-MDrone: Synchronous federated learning enabled multi drones
Authors: Sharma Itika , Gupta Sachin Kumar
Published: February 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
DBSCAN-based energy users clustering for performance enhancement of deep learning model
Authors: Aurangzeb Khursheed
Published: February 2024

Background: Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology: In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results: The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption.
Pedestrian trajectory prediction method based on automatic driving
Authors: Huang Mengtao , Wang Jiaxuan
Published: February 2024

Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system’s comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively.
RETRACTED: Alzheimer’s disease classification algorithm based on fusion of channel attention and densely connected networks
Authors: Shengbin Liang , Haoran Sun , Fuqi Sun , Hongjian Wu , Wencai Du
Published: February 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.