This study aimed to validate the digital transformation model in human resource management of the Civil Registration Organization of Iran. The research method was mixed (qualitative–quantitative) and employed a descriptive–analytical approach. In the qualitative phase, by applying grounded theory and conducting interviews with experts, the main and subcategories of digital transformation were extracted, followed by open, axial, and selective coding. Then, based on the qualitative findings, a questionnaire was designed and implemented in the quantitative phase with a sample of 380 managers and specialists. The data were analyzed using structural equation modeling (PLS-SEM) and validity and reliability tests. The reliability of the questionnaire was confirmed with Cronbach’s alpha above 0.7 and composite reliability above 0.8. Convergent validity was verified with AVE greater than 0.5, and discriminant validity was confirmed with HTMT less than 0.9. The findings indicated that digital transformation in human resource management is a multidimensional process influenced by causal conditions (β = 0.34), intervening conditions (β = 0.49), and contextual conditions (β = 0.56). Strategies such as organizational culture development, human resource empowerment, smart network development, and continuous monitoring were identified as key drivers. The outcomes of this transformation emerged at three levels — individual, organizational, and social — including enhanced employee performance, increased organizational agility, and improved social capital. The modeling results showed that all hypotheses were statistically significant (t > 1.96). Additionally, the coefficient of determination (R² = 0.20) and the global goodness-of-fit index (GOF = 0.411) indicated an acceptable model fit. Ultimately, the proposed model can serve as a comprehensive framework for digital human resource management in the Civil Registration Organization and similar organizations.

A Survey Non-Terrestrial Networks in 6G/ 7G Smart Network for 2035+ and Beyond

Background: The expanding scope of nuclear medicine, particularly with the rise of theranostics and personalized dosing regimens, demands a paradigm shift in radiation safety management. Traditional monitoring methods, while foundational, are often characterized by data latency and spatial gaps, failing to provide the dynamic and comprehensive oversight required in modern clinical environments. Methods: This perspective article synthesizes and conceptually analyzes current Internet of Things (IoT) architectures, wireless sensor technologies, and data processing frameworks. These technological capabilities were mapped onto the specific operational and safety requirements of a nuclear medicine department to propose a novel monitoring ecosystem. Results: A three-tier IoT enabled framework is proposed, comprising a perception layer of networked smart sensors, a network/ edge layer for data aggregation and immediate analysis, and an application layer for centralized visualization and analytics. This system conceptually enables real-time dose mapping, predictive exposure alerts, and enhanced workflow intelligence, as illustrated by hypothetical clinical use cases. Conclusion: The integration of IoT principles into radiation monitoring holds transformative potential. It can elevate safety protocols from passive, compliance-driven exercises to active, intelligent systems that support advanced therapeutic applications, optimize departmental operations, and provide unprecedented levels of safety assurance for staff and patients.

ABSTRACT Addressing sustainability transitions in tourism destinations requires a comprehensive understanding of path-dependance, lock-in factors, trigger events and path shaping processes. To initiate sustainability transitions in tourism destinations, it is crucial to analyze the roles of various governance actors, particularly destination management organisations (DMOs). While the role of DMOs is undisputable for sustainability in tourism, there is a need to pay more attention to their ability to harness knowledge and contribute to path shaping processes. Therefore, this study combines quantitative and qualitative methods, including importance-performance analysis, content analysis and social network analysis, to explore the role of DMOs as spatially embedded structures in sustainable transitions of destinations. The results show that in terms of lock-in factors in unsustainable development, DMOs face challenges related to data, management, and methodology issues. However, by triggering the sustainability transitions as leaders of stakeholders´ network, DMOs can break the historical development path and shift destinations to new paths. The study concludes that DMOs can adopt smart roles as data miners, hubs, and boundary spanners, managing knowledge distribution and leading sustainability transitions in tourism destinations. The findings provide contributions to the new roles of DMOs and link sustainability transitions to a smart network approach.

ABSTRACT Internet of Things (IoT) has been diversified smart networks of connected things consisting of users, smart devices, and interwoven equipment that facilitate communication using wired and wireless mechanisms. The devices in IoT networks transfer data among each other to provide amenities to mankind. These networks have been in hot demand and use in the recent past and have scaled up to a large extent. Fog‐based IoTs have been maneuvered as a furtherance to cloud computing by accomplishing the processing of services faster and closer to end users. However, the rapid increase in the demand for services has raised various concerns like security and privacy in fog‐based IoTs, and therefore, the identification and removal of malicious devices have become a prominent challenge. To conquer this, a secure method called TRUFOG is presented in this article. TRUFOG is a task‐based dynamic trust mechanism. The proposed approach utilizes the following three major parameters: task success, task pending, and task failure ratio. Along with this recommendation credibility is used to estimate the trust of the fog node and average energy consumption is also tested. The security resistance of the TRUFOG mechanism is tested against black hole, gray hole, and energy consumption attacks. The proposed mechanism is extensively evaluated and compared with existing methods like TCF, TBMSC, and TRDTM in terms of accuracy, delivery ratio, energy consumption, and other metrics. The results have proven that TRUFOG has effectively recognized the malicious node in the network with a detection rate of 95.2%. The packet delivery ratio for TRUFOG under attack was found to be 4.5% better than TCF, 6.25% better than TBMSC, and 4.66% better than TRDTM. Furthermore, the average energy consumption of the TRUFOG method was found to be 13.6% lesser than TCF, 30.7% lesser than TBMSC, and 49.9% lesser than the TRDTM model.

The development of the Internet and artificial intelligence promotes smart home systems been used widespreadly in modern society, which effectively improves people's life quality. However, limitations in functionality, closed ecosystem, and high costs make smart home systems are very impractical for the elderly. This article conducts a comprehensive analysis of the smart home development globally, summarizes the characteristics of smart home systems in different regions in the world, then considers the background of Chinese future aging population, and points out the trend of smart home technology in China. This article systematically evaluated the advantages and disadvantages of smart home systems in Europe and Asia. As a response to the aging population situation in China, this research proposes the "Smart Network" residential system, an integrated all-house smart home framework. This system aims at providing a cost-effective solution for the elderly and those people with limited self-care ability. It achieves the goal through excellent compatibility and an efficient intelligent platform, demonstrating the potential to enhance the life quality by incorporating localized those care needs of old age, intelligent environment adaptation, and predictive fall detection systems.

Purpose – Purpose This is for explore interconnection strategic between system energy and defense in support resilience national with propose draft paradigm energy dual role paradigm This positioning energy No only as protected objects​ from threat external, but also as subject strategic support​ operation military through energy renewable, implementation network smart, and technology storage battery. Design/ methodology / approach – Research This use approach review library systematic (SLR) for synthesize findings academic and reports policy from three year lastly, which involves source journal national (SINTA indexed) and international (Elsevier, Springer, IEEE, etc.), with focus on intersection between resilience energy and defense strategy national. Findings – Review This disclose that infrastructure resilient energy​ increase capability defense, while system formidable defense​ ensure continuity supply energy national research​ This propose an integrative model consisting of from three layer strategic: integration policy national (between RUEN and RENSTRAHAN), development infrastructure green and digital, and development capacity source Power man together. Framework Work integrative This strengthen reciprocal resilience​ between sector energy and defense. Implications practical – The makers policies, institutions military, and stakeholders interest energy pushed for together implementing synchronized strategies, integration curriculum and investment in infrastructure digital energy use reach resilience national term long in face uncertainty geopolitics and environment. Originality / Value – Study This introduce framework Work new paradigm energy role double in defense national, which provides runway theoretical and practical for integrate transition energy sustainable with planning defense strategic. Study This contribute in a way conceptual for bridge gap between policy energy and defense strategy, especially in context threat hybrids and vulnerabilities system cyber-physical.

Speaking of monitoring and optimizing computer networks, knowing the traffic patterns is more than just a nice-to-have, it's an essential necessity. Network administrators have long faced the problems of identifying bottlenecks, unusual patterns and malicious activity, and now this research brings a brand-new network traffic monitoring system to the forefront. Our system is very much a game-changer and in real time, it breaks down, and makes a clear visual representation of the traffic that's coming and going through the network. It’s focused on bandwidth use, the sorts of protocols being used and how packets are moving. All it takes is a quick capture of packets and our system kicks out reports and charts that sum up what's really going on in the network, and shows that by keeping a close eye on traffic not only do we improve the performance of our networks, but we also catch cyber threats in their infancy.

The vision of sustainable development goal 9 (SDG 9) is realized through the integration of innovative technologies in the cyber-physical system (CPS). This work focuses on a smart network meter (SNM) application, designed to manage the extensive big data analytics required for processing and analyzing vast amounts of aggregated data in a short period. To address these demands, an advanced explicitly parallel instruction computing (AEPIC) approach is employed, leveraging a multi-core hardware security module (HSM) built on the elliptic curve cryptography (ECC) algorithm. Implementing the algorithm on various field programmable gate arrays (FPGAs) ensures adaptability to different hardware configurations, delivering scalable and optimized performance for big data aggregation in SNM applications. The proposed module showcases exceptional performance in design analysis. The Virtex-7 FPGA demonstrates excellent suitability for big data analytics in smart network applications, with dynamic power consumption accounting for 55% of total power and an on-chip power of 0.542 watts.

Advanced Integration of Sensing and Communication With High DSP Compatibility for SMART Network

This study explores the transformative potential of network governance and smart specialization as foundational frameworks for rethinking managerial practices in the post-industrial epoch, where the saturation of information matrices demands new reflexive approaches to governance. The research positions networking not only as a technical instrument but also as a vital social practice that fosters connections, mutual support, and client acquisition, particularly under the extraordinary conditions of war in Ukraine, where traditional forms of communication and collaboration are restricted. Against the background of declining trust in hierarchical institutions and the growing complexity of public agency, the article argues that polycentric and participatory modes of coordination are indispensable for sustainable development. Methodologically, the work draws on a pluralistic set of approaches, combining inductive synthesis, comparative institutional analysis, synergistic modeling, and interpretive reflexivity in order to transcend the limitations of positivist inquiry. The findings highlight several actionable domains for implementing network governance in Ukraine, such as creating unified evaluation matrices, establishing longitudinal registers of civic initiatives, embedding territorially based partnership ecosystems, and adjusting project-oriented frameworks to organizational maturity.

Advanced Detection and Classification of Bot Attacks through Multi-Model Ensemble Learning for Enhanced Security in Smart Network Infrastructures

The software-defined networking (SDN) is a networking technology that employs the application programming interfaces (APIs) for communicating and managing the architecture of the hardware behind a connection to the internet. An energy-effective SDN enhances the utilization of network resources by dynamically adjusting the usage of power in response to demands. It reduces the operating expenses for networking structure enhances the effectiveness and provides a minimal negative environmental impact. The major drawbacks of the study include initial set-up costs, security concerns, and dependency on centralized administration that influences the responsiveness and flexibility of the systems. In this research, we proposed a novel technique known as multidimensional krill herd-based effective linear regression model (MKH-ELRM) for providing an energy effectiveness structure with SDN. Initially, we gathered the SDN dataset for this study. In addition, we employed a robust standardization for preprocessing. We use a linear discriminant analysis (LDA) method for feature extraction. Furthermore, in the feature extraction process, we split the dataset into testing and training sets. As a result, we utilize some metrics for existing and proposed methods to examine the efficiency of SDN energy. Recall (93%), precision (94%), f1-score (95%), and accuracy (92%) are the outcomes of our proposed MKH-ELRM technique. The result attains a superior result, which provides an accurate energy efficiency with SDN when compared to other existing methods. In conclusion, MKH-ELRM outcomes compare to other current approaches and demonstrate the capacity for providing sustainable and operationally effective SDN networks in reducing energy usage. The implemented framework ensures scalability, adaptability, and long-term sustainability, offering significant potential for future smart network applications with improved energy efficiency and environmental friendliness.

Objectives: Internet of Things (IoT) could be described as the pervasive and global network which aids and provides a system for the monitoring and control of the physical world through the collection, processing and analysis of generated data by IoT sensor devices. This study investigates the performance of downward routing in RPL-based protocols within Wireless Networked Smart Infrastructure (WNSI) environments, particularly in smart city contexts. The aim is to assess scalability, reliability, and throughput, and to identify routing inefficiencies under dense and heterogeneous traffic conditions. Methods: Key metrics were computed using the NS3 simulator, three techniques—SPPB-RPL, DODAG, and HTDO-RPL were evaluated based on downward routing success rate, node scalability, and WNSI efficiency. Findings: In the proposed technique, two novel attacks can be discussed and the hybrid technique for downward RPL (HTDO-RPL) is proposed to monitor the network scenario in an effective manner and focuses about the downward routing methodology to improve the scalability of the nodes and to prevent the RPL network from topology attacks. HTDO-RPL demonstrated superior performance in downward routing, node scalability, and overall WNSI efficiency. SPPB-RPL showed limitations due to static topology assumptions, while DODAG provided moderate gains through dynamic data aggregation. HTDO-RPL achieved the highest throughput and lowest routing failure rates, making it the most effective technique for smart city deployments. Novelty: This work introduces a comparative technique for evaluating downward RPL routing protocols using WNSI-specific metrics and realistic traffic models. By integrating simulation-based analysis with infrastructure-aware performance indicators, the study offers a novel lens for optimizing IoT routing in smart City environments. The proposed technique increased the scalability by 35% for the range of 500 meters when compared with standard DODAG and by 25% for the range of 500 meters when compared with SPPB-RPL due to HTDO-RPL efficiency by including safe re-routing and storing mode. Keywords: Downward RPL, Scalability, Smart City, WNSI, Storage, Routing

The scientific monitoring and reliable telecommunications (SMART) initiative, led by a joint task force including ITU, aims to integrate electronic sensors into undersea telecommunications cables for real-time and high-sensitivity subsea monitoring. The current integrated sensing and communication (ISAC) solution to the SMART application still relies on wavelength band multiplexing of sensing information via Ethernet switches, leading to optical communication bandwidth waste. To achieve the dense and low-interference ISAC for SMART network applications, we demonstrate the co-transmissions of coherent optical digital subcarrier modulation (DSCM) communication signals and temperature sensing information in the SMART system. Besides the co-transmission capability, the special design of the sensing transmission format is made to enable the compatible DSP with DSCM communications, which shares the same wavelength channel. Moreover, due to the different physical locations of the in-line sensing joints to the communication transceivers in the SMART system, it is hard to align the wavelength of the communication laser and the sensing one, which cannot ensure the precise allocation of sensing information into the frequency blanks of DSCM communication signals. To deal with these two issues, the sensing information at inline joints is proposed to be modulated in the manner of optical single-sideband (SSB) modulation and frequency modulation (FM), and then the precise allocation into the frequency blanks of DSCM communication signals can be realized, along with the full compatibility to demodulate the sensed temperature using the traditional frequency offset estimation in coherent DSP. Experiments on a two-span repeatered single-mode fiber link validate the integration of 20 GBaud optical DP-QAM16 transmissions and real-time temperature sensing at a sensing joint. Advanced communications are enabled by implementing space-time coding on DSCM communication signals, with 0.2 dB Q factor improvement. As for the sensing functionality, the temperature sensing resolution at 0.0625°C, which reaches the limitation of the employed electronic thermometer DS18B20, has been obtained by using the communication-compatible DSP. We believe the proposed ISAC scheme along with the corresponding DSP flowchart makes sense for monitoring the subsea via the advanced SMART cables.

ABSTRACTDual crosslinking (both multiple physical and chemical crosslinks) approach was adopted to prepare double network (DN) smart hydrogels wherein PVA–borax complex having reversible physical crosslinking serves as first network, and chemical crosslinked copolymer of 2‐hydroxyethyl methacrylate and acrylic acid as second network. Such DN hydrogels not only exhibited pH‐responsive behavior but also good mechanical and self‐healing properties. The degree of swelling of DN hydrogels was observed to be lower than that of the single polymer network, which was further decreased with increasing PVA–borax content in the DN hydrogels. On the other hand, the mechanical properties of the hydrogels were improved with increasing concentration of PVA–borax in the DN hydrogels. A DN hydrogel having 10 wt% of PVA–borax with 25 wt% water content exhibited ~143% higher tensile strength (0.85 MPa) than that of the single network hydrogel (0.35 MPa). The toughness of the DN hydrogel was observed to be 1.244 N/mm2. Moreover, such tough hydrogels were made conductive by in situ polymerization of aniline within the porous structure of the DN hydrogels. The electrical conductivity of such a dry hydrogel (CDNH‐2) was observed to be 1.04 × 10−3 S cm−1, whereas it was 1.53 × 10−3 S cm−1 in its swollen state. Such multifunctional mechanically tough, pH‐responsive, and conductive hydrogels may have several applications wherein self‐healing ability would become an added advantage.

As Internet of Things (IoT) devices become more integrated into critical infrastructures, they bring vulnerabilities to Denial-of-Service (DoS) attacks, necessitating robust strategies for detection and mitigation. Nowadays, more and more things are connected to the Internet, and the development of new devices is accelerating. The entire communication ecosystem requires security solutions at different levels since these networked smart items can interact with each other in unprotected environments. IoT technology differs from conventional networks in that it has its own set of characteristics, such as different resource limits and network protocol needs. The attacker exploits many security flaws in the IoT system to initiate various attacks. Given the rise in attacks, it is critical to address the implications of the Internet of Things. This paper introduces a novel Hybrid Intelligent System specifically designed to detect and mitigate DoS attacks effectively in IoT environments. Utilizing the CIC-Distributed Denial-of-Service (DDoS) 2019 dataset, the system integrates comprehensive pre-processing techniques, including min-max normalization and an improved SMOTE algorithm, to address the data imbalance. Feature extraction comprises the extraction of raw attributes and statistical measures, standard deviation, median, mean, and variance, Information Gain and Improved Holo-entropy using Spearman's Rank correlation. Classification is performed using a hybrid model that combines Improved LinkNet (ILN) and Long Short-Term Memory (LSTM) architectures, leveraging Cone activation functions to preserve spatial information and enhance training efficiency. Upon detection of attacks, the system identifies and mitigates attacker nodes using threshold-based methods. Practically used to improve security, adjust to new attack techniques, and reduce false alarms in smart cities, smart homes, industrial IoT, and healthcare. The ILN + LSTM Scheme exhibits an accuracy of 0.963, which is superior to the findings of the existing techniques.

The internet of things (IoT) is a scalable network of interconnected smart devices that aims to improve quality of life, business growth, and efficiency across multiple sectors. Since the IoT is an expanding network, a large amount of data is generated, collected, and exchanged. However, most of this data is personal data that contains private or sensitive information, which makes it a target for several cyber threats due to poor encryption, weak authentication mechanisms, and insecure communications. Therefore, ensuring the privacy and confidentiality of sensitive information remains a critical challenge. This paper presents a comprehensive literature review focusing on privacy and confidentiality issues within the IoT ecosystem. It categorizes existing research into privacy-preserving techniques, authentication and trust mechanisms, and machine learning-based solutions. Beginning by detailing the review methodology employed to gather and analyze relevant research. The review then explores recent research work related to privacy concerns and authentication and trust mechanisms, emphasizing various approaches and solutions developed to address these challenges. The paper further delves into machine learning-based solutions that offer innovative methods for enhancing privacy and confidentiality.

This article demystifies multi-cloud AI infrastructure, providing an accessible overview of distributed high-performance architectures essential for modern artificial intelligence systems. It explores the fundamental challenges of efficient data transfer between environments, addressing speed limitations, security vulnerabilities, and cost concerns. The discussion examines hybrid and multi-cloud architectures that combine on-premises systems with multiple cloud providers to optimize AI workloads. The article highlights emerging solutions, including direct physical connectivity options, Software-Defined Networking (SDN), and Smart Network Interface Cards (SmartNICs). Through detailed case studies and practical implementation considerations, it reveals how organizations can achieve substantial improvements in performance, security, and cost-efficiency while maintaining regulatory compliance. The article further explores future trends including edge computing for real-time inference and AI-driven network optimization, illustrating how these technologies will shape the next generation of AI infrastructure.

Abstract The growing prevalence of cyberattacks poses a significant threat to the confidentiality and integrity of sensitive data transmitted over networks. Effective Network Intrusion Detection Systems (NIDS) are essential to detect and mitigate these threats. This study introduces a novel model, Network Intrusion Detection System using Stacked Ensemble Learning Technique (NIDSSELT), which addresses key challenges in existing NIDS solutions, including class imbalance, detection accuracy, and false alarm rates. The proposed NIDSSELT framework integrates Extra Trees (ET) and Xtreme Gradient Boosting (XGB) as base classifiers with Logistic Regression (LR) as the meta-classifier, leveraging a Stacked Ensemble Learning Technique for robust predictions. To address class imbalance, the model employs K-means SMOTE for oversampling and representation of minority classes. Comprehensive evaluation using accuracy and F1-score, validated through 10-fold cross-validation, demonstrates the superior performance of NIDSSELT compared to existing single-classifier and ensemble-based models, offering a promising solution for enhanced network security.