Integration Of Information Research Articles

Presubicular VIP expressing interneurons receive facilitating excitation from anterior thalamus

The presubiculum is part of the parahippocampal cortex and plays a fundamental role for orientation in space. Many principal neurons of the presubiculum signal head direction, and show persistent firing when the head of an animal is oriented in a specific preferred direction. GABAergic neurons of the presubiculum control the timing, sensitivity and selectivity of head directional signals from the anterior thalamic nuclei. However, the role of vasoactive intestinal peptide (VIP) expressing interneurons in the presubicular microcircuit has not yet been addressed. Here, we examined the intrinsic properties of VIP interneurons as well as their input connectivity following photostimulation of anterior thalamic axons. We show that presubicular VIP interneurons are more densely distributed in superficial than in deep layers. They are highly excitable. Three groups emerged from the unsupervised cluster analysis of their electrophysiological properties. We demonstrate a frequency dependent recruitment of VIP cells by thalamic afferences and facilitating synaptic input dynamics. Our data provide initial insight into the contribution of VIP interneurons for the integration of thalamic head direction information in the presubiculum.

BViT: Broad Attention-Based Vision Transformer.

Recent works have demonstrated that transformer can achieve promising performance in computer vision, by exploiting the relationship among image patches with self-attention. They only consider the attention in a single feature layer, but ignore the complementarity of attention in different layers. In this article, we propose broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer (ViT), which is called BViT. The broad attention is implemented by broad connection and parameter-free attention. Broad connection of each transformer layer promotes the transmission and integration of information for BViT. Without introducing additional trainable parameters, parameter-free attention jointly focuses on the already available attention information in different layers for extracting useful information and building their relationship. Experiments on image classification tasks demonstrate that BViT delivers superior accuracy of 75.0%/81.6% top-1 accuracy on ImageNet with 5M/22M parameters. Moreover, we transfer BViT to downstream object recognition benchmarks to achieve 98.9% and 89.9% on CIFAR10 and CIFAR100, respectively, that exceed ViT with fewer parameters. For the generalization test, the broad attention in Swin Transformer, T2T-ViT and LVT also brings an improvement of more than 1%. To sum up, broad attention is promising to promote the performance of attention-based models. Code and pretrained models are available at https://github.com/DRL/BViT.

ICT Integration in Secondary Schools: Evaluating Ease of Use and Perceived Benefits for Effective Teaching and Learning

This research investigates the integration of Information and Communication Technology (ICT) in secondary schools, focusing on educators' perceptions of its ease of use and perceived benefits for effective teaching and learning. Utilizing a quantitative approach with a survey methodology, data was collected from 60 secondary schools across Lahore and Gujranwala districts, involving 310 secondary school educators (144 males and 166 females). A proportionate stratified sampling technique was employed, and a modified version of the QICT (Questionnaire on the Integration of ICT) was used to assess teachers' views on ICT integration. Five experts validated the instrument with a reliability coefficient of 0.789. Results indicate that ICT integration is crucial for enhancing teaching and learning in secondary schools. Teachers with ICT-related qualifications demonstrated a more significant influence on classroom ICT usage. The study found that teachers generally perceived ICT integration as easy and beneficial, noting improved teaching effectiveness and student engagement. Educators with advanced ICT skills were more effective in incorporating technology into their teaching practices. Overall, the research underscores the importance of ICT in creating dynamic and effective learning environments in secondary education.

Process deviations, early sensemaking, and enabling operators: Thinking beyond the traditional alarm-based practice to enhance industrial resilience

In the dynamic landscape of modern industrial systems, there is an emerging important need for new thinking and practice to facilitate risk-informed decision-making, especially in high-risk industrial sectors. Such novel solutions should enable operators to proactively counteract and prevent unwanted events and incidents. Efforts to harness and convert relevant data to meaningful information with unique meanings, in fact represents new opportunities to create relevant knowledge of the actual context. This paper examines this through early sensemaking of process deviations, facilitating a seamless integration of information towards context-sensitive decision-making processes. Two industrial approaches are discussed in the paper: a conventional alarm-based practice, as well as an enhanced approach. The research underscores the application of such techniques through two industrial cases, aiming at fostering enhanced resilience, reliability, and safety within high-risk industrial settings.

Collaborative research networks in Higher Education An integrative review

This study investigates the determining factors for the success of collaborative research networks in higher education, focusing on the formation, development, and operation of these networks. Despite the growing recognition of the importance of collaborative networks, there is a lack of studies that comprehensively identify the critical factors for their success. Utilizing an integrative literature review, the research was conducted in the ERIC, Web of Science (WOS), and Scopus databases. The main research question is: what factors influence the success of collaborative research networks in higher education? The findings indicate that the effectiveness of these networks is influenced by structural, functional, and evaluative factors. The integration of information and communication technologies (ICT) is essential for effective collaboration, allowing geographically dispersed teams to work together efficiently. The study concludes that public policies that value collaborative networks are necessary to promote an environment conducive to integration and scientific collaboration. This study contributes to the understanding of the elements that influence the success of collaborative research networks, highlighting the importance of integration, diversity, funding, and knowledge sharing, with significant implications for the formulation of policies and practices in higher education.

REVOLUTIONIZING POWER TRANSFORMER FAULT DIAGNOSIS THROUGH COGNITIVE ARTIFICIAL INTELLIGENCE AND DISSOLVED GAS ANALYSIS INTEGRATION

The research introduces a cognitive artificial intelligence (CAI) model that leverages dissolved gas analysis (DGA) to investigate power transformer faults. Conventional fault interpretation methods using DGA are limited in accuracy and uncertainty. In response, the proposed CAI model utilizes cognitive learning and direct interaction to achieve remarkably accurate fault identification without the need for supervised training. By extracting fault features through key gas ratio limitations. However, the CAI model also has a gap in data perception due to the information sensory challenges. Using gas ratios based on the conventional fault interpretation methods in the latest study still limited data perception of the CAI model to only three or four gas ratios. Thus, this study aims to increase data perception by extracting fault features through ten gas ratio limitations. The proposed CAI model's performance is validated, outperforming traditional methods like the Duval triangle method, Duval pentagon method, Doernenburg ratio method, and Roger ratio method, as well as common AI approaches including artificial neuron network, long short-term memory, nearest neighbor classifiers, support vector machine, ensemble classifiers, and decision trees. Notably, the CAI model's success rate in fault type identification stands at an impressive 98.04%. A distinctive trait of the CAI model is its autonomous knowledge accumulation and enhancement, enabled by inferring-fusion information and sensor-based knowledge integration. This intrinsic learning ability further contributes to its exceptional fault diagnosis accuracy. The proposed CAI model showcases promising potential for revolutionizing power transformer fault investigation and diagnosis, mitigating unplanned outages, and ultimately bolstering power system reliability.

Role of Agricultural Extension in Addressing Food Security

Agricultural extension services play a pivotal role in global efforts to achieve food security, acting as a critical link between research, farmers, and policymakers. This comprehensive review examines the multifaceted contributions of agricultural extension to food security goals, drawing on theoretical frameworks, empirical evidence, and case studies from diverse global contexts. This paper explores how extension services address the four dimensions of food security: availability, access, utilization, and stability. It analyses the evolution of extension models, from traditional top-down approaches to participatory and market-led systems, assessing their relative effectiveness in enhancing agricultural productivity and food security. This review paper highlights innovative practices, including the integration of information and communication technologies (ICT), community-based approaches, and public-private partnerships, which have shown promise in overcoming persistent challenges. The case studies from both developed and developing countries provide insights into successful strategies and their impacts on crop yields, farmer incomes, and nutritional outcomes. This paper also addresses critical challenges facing extension services, including financial constraints, technological barriers, and socio-cultural issues, proposing potential solutions and policy recommendations. By synthesizing current knowledge and identifying future research directions, this review underscores the indispensable role of agricultural extension in achieving global food security goals and emphasizes the need for continued innovation and adaptation in extension practices to meet evolving challenges in the agricultural sector.

QRGB+: Advanced QR Code Generator with RGB Color Method in Python to Expand Data Capacity

The present work entitled QRGB, consists of the development of an application in Python for the generation of QR codes using the additive color generation (RGB) method. This innovative method allows increasing the density of information stored in QR codes by using three color layers (red, green and blue), each representing a different set of data. QRGB offers an efficient and secure solution for storing and transmitting large amounts of information in limited spaces, significantly improving the capabilities of traditional black and white QR codes. By using three layers of colors, QRGB codes can store up to three times more information in the same space. This technique not only increases storage capacity but also improves information security, making it difficult to forge or manipulate the code. The overlay of multiple data layers allows redundancy to be implemented, increasing the robustness of the code against damage or reading errors. QRGBs are especially useful in applications that require the transmission of large amounts of data in limited spaces, such as in the packaging industry, digital business cards, and interactive advertising. Additionally, they have great potential in areas such as document and banknote security, where the authenticity and integrity of information are crucial. These points provide a solid foundation for understanding the innovation and advantages of colored QR codes (QRGB) compared to traditional QR codes, highlighting their applicability and potential in various sectors. This article presents a novel method for encoding and decoding information using a QRGB code, which involves the generation of three independent QR codes and their superimposition according to the additive color system (RGB). The research highlights the challenges encountered during the encoding and decoding processes due to the lack of specific libraries in Python, which required the creation of a custom solution using open source tools. The implementation takes advantage of Python and its libraries: qrcode[pil] to generate QR codes with the Pillow dependency for image manipulation, Pillow to open, manipulate and save different image formats, and opencv-python to perform tasks such as image processing and object detection. Despite facing issues with color mixing and accurate information retrieval, the proposed method demonstrates a significant increase in data density within a single QR code. Future work will focus on optimizing the algorithm and exploring potential applications in data security and high-density information storage. This Python script is designed to generate and decode QR codes with a logo overlay using a graphical user interface (GUI) built with Tkinter. The script combines several functionalities, such as creating QR codes, overlaying a logo, combining QR images of different colors, and manually decoding combined QR codes.

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite concentrations, metabolic fluxes and enzyme levels. Nevertheless, determining the kinetic parameters that underlie cellular physiology poses notable obstacles to the widespread use of these mathematical representations of metabolism. Here we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration of diverse omics data and other relevant information, including extracellular medium composition, physicochemical data and expertise of domain specialists, RENAISSANCE accurately characterizes intracellular metabolic states in Escherichia coli. It also estimates missing kinetic parameters and reconciles them with sparse experimental data, substantially reducing parameter uncertainty and improving accuracy. This framework will be valuable for researchers studying metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnology.

Exploring age-related functional brain changes during audio-visual integration tasks in early to mid-adulthood

The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into age-related AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audio-visual integration task with a specific focus on assessing the brain maturation effects in various age groups, particularly in early-mid adulthood. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18–25 years), Transition (26–33 years), and Middle (34–50 years) age cohort healthy participants. We utilized source-based features for the classification of these age groups. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings unveiled diminished levels of brain activation among middle-aged individuals, which escalate when exposed to AVI stimuli. Lower frequency bands showed substantial changes with increasing age during AVI. Our results demonstrated that implementing the k-means elbow method during the AVI task successfully categorized brain regions into five distinct brain networks. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.

Reducing Cluster Overlap in Movie Recommendations with IKSOM and Silhouette Clustering

The exponential growth of online material requires the implementation of effective and precise recommendation systems in order to optimize the user experience. Nevertheless, conventional approaches frequently encounter problems such as cluster overlap, which reduces the accuracy of suggestions. This paper presents a new method for minimising the overlap between clusters in movie recommendation systems. It achieves this by combining Improved Kohonen Self-Organizing Maps (IKSOM) with Silhouette Clustering. The proposed method utilises IKSOM to efficiently represent high-dimensional user-item interactions in a two-dimensional space, enabling the formation of distinct and meaningful clusters. Subsequently, Silhouette Clustering is utilised to enhance the separation and cohesion of clusters, hence reducing overlap. The experimental findings show that proposed hybrid model works much better than the baseline techniques, obtaining an RMSE of 0.423 and MAE of 0.216. Additionally, it improves precision (93.6%), recall (94.2%) and F1-score (93.4%). Additionally, the proposed technique demonstrates a high level of accuracy (97.3%) with a precision rate of 95.8%. These results emphasise the method's efficacy in minimising errors and enhancing the overall performance of the recommendation system. The results indicate that combining IKSOM with Silhouette Clustering can improve the precision and dependability of movie recommendation systems by resolving cluster overlap and offering more individualised user experiences. Subsequent research will investigate the implementation of this method in different fields and the integration of supplementary contextual information to enhance the accuracy of recommendations.

A rotated object detection strategy for remote sensing images using misaligned cross-fusion structures

Remote sensing image object detection is a challenging task with a focus on detection accuracy. In the object detection task of remote sensing images, scenes often contain objects with varying scales and dense arrays, leading to issues such as low accuracy, missed detections, and false alarms. To address these challenges, this paper introduces MCSC-Net, a rotated object detector, designed to enhance detection performance for objects of different scales in densely arranged scenarios. The proposed detector features a novel neck network named MCFN-V3, which incorporated a cross-fusion structure. MCFN-V3 facilitates simultaneous feature extraction in upper and lower layers with feedback to the middle layer. Through the cross-fusion of feature layers, it achieves the integration of multi-scale feature information, thereby enhancing the network's detection capabilities for objects at multiple scales. Additionally, the paper presents the STC module to address the problem of poor feature correlation in high-resolution image processing by CNN networks. This module improves feature expressiveness and range, strengthening inter-feature relationships within layers. Furthermore, in the target positioning stage, the paper transforms the angle regression problem into a classification task, making the network more suitable for detecting rotated objects in dense scenes. To assess the effectiveness of the algorithm, experiments were conducted on two publicly available remote sensing datasets, DOTA and UCAS-AOD. Our method achieved a multiclass average precision (mAP) of 77.4 % on DOTA and an outstanding performance of 97.0 % on UCAS-AOD. These experimental results validate the effectiveness of the proposed approach.

Managing digital records within Nigeria's regulatory framework

Nigeria has a complicated legal and regulatory environment. This complicated history brings to mind the oft-quoted Biblical phrase, “There was no king in Israel, and every man did what was right in his own eyes.” (New King James Version Bible, 1982 Judges 17:6) As the technological landscape began to change, strong legislative frameworks are now required to guarantee the security, accessibility, and integrity of digital information. Since enacting the Nigeria Data Protection Regulation (NDPR) in 2019, and the subsequent passing into law of the Nigeria Data Protection Act (NDPA) 2023, and establishing the Nigeria Data Protection Commission (NDPC), Nigeria has reached landmark milestones in Nigeria's journey toward improving data protection and privacy. This paper provides a comprehensive analysis of the recent developments in Nigeria's data protection landscape and implications for digital records management in government agencies and businesses. This paper further tries to examine the implications of these developments for digital records management within Government Agencies and Businesses. It will also provide a brief history of digital records in Nigeria. Furthermore, it will analyze key provisions of the NDPA and the NDPC's strategic priorities and identify opportunities and challenges for aligning records management practices with the new framework. The paper also reviews the relationship between records management and organizational credibility in Nigeria, discussing how proper record management is crucial for the credibility of public institutions and organizations in Nigeria. Additionally, it attempts to show the benefit of data-driven regulatory approaches to assist organizations in navigating Nigeria's complex regulatory landscape. This paper offers recommendations for businesses, agencies, and regulators to utilize the NDPA and its strategies to enhance the adoption of digital records management. The methodology for selecting and analyzing sources involved a comprehensive literature review and policy analysis, focusing on key legal documents, academic articles, and reports from relevant agencies.

The interplay of multiple unconditioned stimuli in evaluative conditioning: A weighted averaging framework for attitude formation via stimulus co-occurrences.

Evaluative conditioning (EC) is a key effect in attitude formation, leading to changes in the liking of neutral attitude objects due to their pairing with positive or negative stimuli. Despite EC's significance, current theories and most empirical findings are limited to stimulus pairings with a single affective stimulus at a time. In contrast, social environments often involve more complex combinations of affective stimuli. In this article, we introduce a novel framework grounded in information integration research to understand how conditioned attitudes develop in the presence of multiple affective stimuli. Through 10 experiments with different designs, measures, materials, and pairing procedures, we find that individuals' conditioned attitudes follow the average valence of all affective stimuli present with a stronger weighting of negative stimuli. This weighted averaging rule bears two implications for EC in more complex stimulus combinations. First, EC effects are nonmonotonous, such that additional stimuli of the same valence do not produce incremental EC effects. Second, EC effects are interdependent, such that the impact of one stimulus is weakest when accompanied by another negative stimulus and strongest when no other affective stimulus is present. We examine different cognitive processes underlying this weighted averaging rule, including potential differences in pairing memory or changes in the affective stimuli's valence when other stimuli are present. Our findings present a novel theoretical perspective on EC and offer valuable insights into attitude change from stimulus co-occurrences in stimulus-rich environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhancing Quality of School Education through DTH Channels in Andhra Pradesh

The role of technology has expanded to such an extent that ICT is prevalent in every aspect of life. ICT promotes integration of information for imparting easily accessible, affordable and quality education. There is an inconsistency between the widely accepted promise of ICT to transform teaching and learning and its actual implementation. The National education policy 2020 aspires and aim to make our country global knowledge superpower .The policy built on the foundational pillars of Access, Equity ,Quality ,Affordability and Accountability has been aligned to the 2030 agenda for sustainable development and aims to transform India into a vibrant knowledge society and global knowledge superpower by ensuring equitable and universal access to the highest quality education for all learners regardless of social and economic background.

USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES TO IMPROVE PRACTICAL SKILLS OF ANESTHESIOLOGISTS

The aim – to explore the role of information and communication technologies in the professional development of anesthesiologists, focusing on simulation training, electronic medical records, telemedicine, and electronic learning resources. Materials and Methods. A review of scientific literature, along with an analysis of publications in medical journals and databases such as PubMed, Scopus, and Google Scholar, to identify key trends and results of using of information and communication technologies in anesthesiology was conducted. Results. Simulation training allows anesthesiologists to practice complex clinical scenarios in a safe environment, significantly enhancing their readiness for real emergency situations. Electronic medical records simplify access to patient data, ensuring the accuracy and continuity of medical information, which is crucial for preoperative preparation and postoperative care. Telemedicine opens new possibilities for consultation and training, allowing anesthesiologists to receive advice from colleagues worldwide and participate in remote learning sessions. Electronic learning resources provide continuous access to the latest knowledge, research, and clinical guidelines, fostering ongoing professional development. Conclusion. The integration of information and communication technologies in the training and practice of anesthesiologists enhances the quality of medical care, patient safety, and the professional development of specialists.

An efficient model for small object detection in the maritime environment

Environmental perception is crucial for autonomous ships realizing autonomous navigation, in particular, the high-precision and low-latency detection of small objects on the sea surface is a key and challenging issue. To address this problem, this paper presents a model that improves the detection accuracy and delivers excellent real-time performance for autonomous ship navigation. The backbone of the proposed model enhances the modelling capabilities by expanding deformable convolution and introducing the self-designed attention mechanism. Additionally, an enhanced feature fusion structure is designed by the pixel shuffle based on super-resolution reconstruction to keep the integrity of feature information for small objects. This paper also presents an optimized model quantization strategy that alleviates the problem of low model efficiency caused by the limited resources onboard the ship. Compared to the earlier model, the present one has increased the mean average precision on the Rizhao Zhuimeng-3# maritime optical dataset by 4.5 % and by 21 % for small object detection. Furthermore, the real-time detection for high-resolution images can now reach a speed of 67 frames/s. Moreover, the present model outperforms existing methods concerning accuracy when the frames/s are similar. The results indicate that the proposed model can be potentially applied to autonomous ships.

Deciphering the Genetic Landscape: Exploring the Relationship Between HLA-DQA1, HLA-DQB1, and HLA-DRB1 Genes in Diabetes Mellitus

Abstract: Diabetes mellitus (DM) is a complex and multifactorial metabolic disorder with a significant genetic component. The human leukocyte antigen (HLA) genes, specifically HLA-DQA1, HLA-DQB1, and HLA-DRB1, have been implicated in the susceptibility and pathogenesis of DM. This review delves into the intricate interplay of these HLA genes, seeking to unravel the genetic tapestry that contributes to the development and progression of diabetes. We begin by providing an overview of the HLA system and its critical role in immune regulation. Subsequently, we explore the current state of knowledge regarding the association between HLA-DQA1, HLA-DQB1, and HLADRB1 polymorphisms and susceptibility to both type 1 and type 2 diabetes. Emphasis is placed on recent advancements in genetic research methodologies, including genomewide association studies and next-generation sequencing, that have provided deeper insights into the genetic architecture of DM. The review also scrutinizes the functional implications of specific HLA alleles in modulating immune responses and the potential mechanisms by which they contribute to the autoimmune processes observed in type 1 diabetes. Additionally, we examine the role of HLA genes in the context of insulin resistance and beta-cell dysfunction in type 2 diabetes, shedding light on the shared and distinct genetic underpinnings of these two major forms of DM. Furthermore, we discuss the clinical implications of HLA genotyping in predicting disease risk, prognosis, and personalized treatment strategies. The integration of genetic information into clinical practice holds promise for precision medicine approaches in diabetes management.

Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis

Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization’s fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately.

Smart industrial information integration: a lightweight privacy protection model in an intelligent manufacturing architecture

In the industrial ecosystem, both the manufacturing and edge clouds collaborate via virtualisation technology, playing a crucial role in the efficient aggregation and processing of vast data from endpoints, offering computational and analytical support for enhanced decision-making in all industrial operations. Consequently, ensuring the reliability of virtualised manufacturing environments is paramount. Remote attestation offers a viable solution, however, current schemes are plagued by inefficiencies and privacy leakage issues. To address these concerns, we propose an efficient remote attestation approach. The approach actively conceals sensitive industrial information in measurement logs by using a hash algorithm, thereby preventing privacy breaches. Furthermore, it streamlines log entries with a log compression algorithm, enhancing remote verification efficiency in the industrial information architecture. Extensive experiments were conducted in an authentic cloud environment by applying this approach across both mesh and hierarchical remote verification structures, reducing time overhead by approximately 81.32%. Additionally, it marginally impacts server performance with only a 1.49% reduction. The results affirm the proposed approach's exceptional efficacy in decreasing verification durations and mitigating remote verification attacks, demonstrating its promising utility and feasibility in the intelligent industrial platform.