Search Results (227)

5G deployment in Malaysia is increasing the need for safe and efficient radio-frequency (RF) scanning at telecommunication towers, but service providers lack a clear, structured way to choose among available methods. This study develops a decision framework using a hybrid Delphi–Analytic Hierarchy Process (AHP) approach. A literature review identified criteria, sub-criteria, and six RF scanning alternatives. Ten experts then participated in three Delphi rounds: Rounds 1 and 2 confirmed five criteria and twenty-five sub-criteria, while Round 3 produced an expert ranking of the six alternatives, with drone-based and human-based scanning as the top priorities. Thirty practitioners subsequently completed AHP pairwise comparisons based on the Delphi-validated hierarchy. The AHP results show that Safety and Environment are the most important criteria, with ‘Fall’ and ‘Thunderstorm’ having the highest global weights. Drone-based scanning ranks highest, followed by human-based and ground-based methods, and the AHP ranking closely matches the expert ranking. The study provides a clear decision method for industry and policymakers to improve worker safety, guide inspection decisions, and strengthen telecommunication infrastructure in line with SDG 8 (Decent Work), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action). Full article

The Industry 5.0 paradigm places humans and the environment at the center. New communication methods based on virtual reality (VR) and brain–computer interfaces (BCIs) can improve system–operator interaction in multimedia communications, providing immersive environments where operators can more intuitively manage complex systems. The study was conducted through a systematic literature review combined with bibliometric and thematic analyses to map the current landscape of VR-BCI communication frameworks in IIoT environments. The methodology employed included structured resource selection, comparative assessment of interaction modalities, and cross-domain synthesis to identify patterns, gaps, and emerging technology trends. Key challenges identified include reliable signal processing, real-time integration of neural data with immersive interfaces, and the scalability of VR-BCI solutions in industrial applications. The study concludes by outlining future research directions focused on hybrid multimodal interfaces, adaptive cognition-based automation, and standardized protocols for evaluating human–cyber-physical system communication. VR interfaces enable operators to visualize and interact with network data in 3D, improving their monitoring and troubleshooting in real time. By integrating BCI technology, operators can control systems using neural signals, reducing the need for physical input devices and streamlining operation (including touchless technology). BCI-based protocols enable touchless control, which can be particularly useful in situations where operators must multitask, bypassing traditional input methods such as keyboards or mice. VR environments can simulate network conditions, allowing operators to practice and refine their responses to potential problems in a controlled, safe environment. Combining VR with BCI allows for the creation of adaptive interfaces that respond to the operator’s cognitive load, adjusting the complexity of the displayed information based on real-time neural feedback. This integration can lead to more personalized and effective training programs for operators, enhancing their skills and decision-making. VR and BCI-based solutions also have the potential to reduce operator fatigue by enabling more natural and intuitive interaction with complex systems. The use of these advanced technologies in multimedia telecommunications can translate into more efficient, precise, and user-friendly system management, ultimately improving service quality. Full article

This study examines the impact of Artificial Intelligence (AI)-enhanced Environmental, Social, and Governance (ESG) reporting on firm valuation in emerging markets. It aims to explore how AI integration enhances the interpretability and predictive accuracy of ESG metrics in shaping market perceptions and investor decisions. This study employs a panel dataset from 2018 to 2024, analysing publicly listed non-financial firms across five major sectors: manufacturing, energy, telecommunications, consumer goods, and industrials. This study contributed by employing AI-powered multimodal analysis with conventional ESG scoring methods and integrating Fixed-Effects Regression with machine learning (ML) algorithms including Extreme Gradient Boosting (XGBoost) and Random Forest to identify complex, non-linear relationships within ESG data and firm valuation. The results show empirical evidence that integrating ML enhances the explanatory power of ESG data. Findings indicate that ESG performance is positively correlated with higher market valuations, particularly in Environmental and Social dimensions. Governance metrics are more inconsistent, which may be due to heterogeneity in governance practices, regulatory enforcement and the challenges of quantifying governance quality beyond compliance indicators across the focus emerging markets. Firms identified in ESG controversies tend to face valuation penalties, which stresses market sensitivity to reputational risks. ML algorithms outperform conventional techniques in predictive accuracy, revealing complex, non-linear interactions within ESG data. This study contributes to both the academic literature and practice showing how next-generation ESG reporting can robust valuation models, address limitations of conventional ESG scoring, and ensure a reliable outlook for investors and policymakers of industries in emerging markets. Full article

With the rapid advance of digital technologies, the service industry has become a key driver of sustainable economic growth and the restructuring of international trade. Drawing on value-added trade flows for five pivotal service industries—construction, air transportation, postal telecommunications, financial intermediation, and education—over 2013–2021, this study examines the spatial evolution of the global service value chain (GSVC). Using social network analysis combined with a Temporal Exponential Random Graph Model (TERGM), we assess the dynamics of the GSVC’ core–periphery structure and identify heterogeneous determinants shaping their spatial networks. The findings are as follows: (1) Exports across the five industries display an “East rising, West declining” pattern, with markedly heterogeneous magnitudes of change. (2) The construction industry is Europe-centered; air transportation exhibits a U.S.–China bipolar structure; postal telecommunications show the most pronounced “East rising, West declining” shift, forming four poles (United States, United Kingdom, Germany, China); financial intermediation contracts to a five-pole core (China, United States, United Kingdom, Switzerland, Germany); and education becomes increasingly multipolar. (3) The GSVC core–periphery system undergoes substantial reconfiguration, with some peripheral economies moving toward the core; the core expands in air transportation, while postal telecommunications exhibit strong regionalization. (4) Digital technology, foreign direct investment, and manufacturing structure promote network evolution, whereas income similarity may dampen it; the effects of economic freedom and labor-force size on spatial network restructuring differ significantly by industry. These results underscore the complex interplay of structural, institutional, and geographic drivers in reshaping GSVC networks and carry implications for fostering sustainable services trade, enhancing interregional connectivity, narrowing global development gaps, and advancing an inclusive digital transformation. Full article

Electromagnetic interference (EMI) represents a growing challenge in the modern era, as electronic systems and wireless technologies become increasingly integrated into daily life. This review provides a comprehensive overview of EMI, beginning with its historical evolution over centuries, from early power transmission systems and industrial machinery to today’s complex environment shaped by IoT, 5G, smart devices, and autonomous technologies. The diverse sources of EMI and their wide-ranging effects are examined, including disruptions in electrical and medical devices, ecological impacts on wildlife, and potential risks to human health. Beyond its technical and societal implications, the economic dimension of EMI is explored, highlighting the rapid expansion of the global shielding materials market and its forecasted growth driven by telecommunications, automotive, aerospace, and healthcare sectors. Preventative strategies against EMI are discussed, with particular emphasis on the role of advanced materials. Carbon-based nanomaterials—such as graphene, carbon nanotubes, and carbon foams—are presented as promising solutions owing to their exceptional conductivity, mechanical strength, tunable structure, and environmental sustainability. By uniting perspectives on EMI’s origins, consequences, market dynamics, and mitigation strategies, this work underscores the urgent need for scalable, high-performance, and eco-friendly shielding approaches. Special attention is given to recent advances in carbon-based nanomaterials, which are poised to play a transformative role in ensuring the safety, reliability, and sustainability of future electronic technologies. Full article

This study investigates the impact of servant leadership on employee performance in mobile telecommunications providers, emphasizing the mediating role of organizational trust and its implications for organizational sustainability. Leadership effectiveness is particularly critical in environments where trust is limited, as it shapes both immediate performance and long-term organizational resilience. Using survey data from 375 employees across three telecom companies in Iraq, the results indicate that servant leadership is positively related to employee performance. Mediation analysis further demonstrates that organizational trust significantly transmits the effect of servant leadership on performance. These results extend current knowledge of leadership dynamics in the telecom sector and underscore the role of trust-based leadership in fostering sustainable organizational outcomes. Based on these insights, a practical framework was developed to integrate servant leadership principles into team-building initiatives, leadership development programs, and organizational systems. This framework not only supports the training of future leaders but also strengthens employee well-being, ethical culture, and long-term sustainability in the telecommunications industry. Full article

This study investigates the utilisation of AI tools, including Grammarly Free, QuillBot Free, Canva Free Individual, and others, to enhance learning outcomes for 180 s-year telecommunications engineering students at Universidad Politécnica de Madrid. This research incorporates teaching methods like problem-based learning, experiential learning, task-based learning, and content–language integrated learning, with English as the medium of instruction. These tools were strategically used to enhance language skills, foster computational thinking, and promote critical problem-solving. A control group comprising 120 students who did not receive AI support was included in the study for comparative analysis. The control group’s role was essential in evaluating the impact of AI tools on learning outcomes by providing a baseline for comparison. The results indicated that the pilot group, utilising AI tools, demonstrated superior performance compared to the control group in listening comprehension (98.79% vs. 90.22%) and conceptual understanding (95.82% vs. 84.23%). These findings underscore the significance of these skills in enhancing communication and problem-solving abilities within the field of engineering. The assessment of the pilot course’s forum revealed a progression from initially error-prone and brief responses to refined, evidence-based reflections in participants. This evolution in responses significantly contributed to the high success rate of 87% in conducting complex contextual analyses by pilot course participants. Subsequent to these results, a project for educational innovation aims to implement the AI-PBL-CLIL model at Universidad Politécnica de Madrid from 2025 to 2026. Future research should look into adaptive AI systems for personalised learning and study the long-term effects of AI integration in higher education. Furthermore, collaborating with industry partners can significantly enhance the practical application of AI-based methods in engineering education. These strategies facilitate benchmarking against international standards, provide structured support for skill development, and ensure the sustained retention of professional competencies, ultimately elevating the international recognition of Spain’s engineering education. Full article

Driven by the latest advancements in wireless technology, location-based services have attracted the interest of computing and telecommunication industries, as well as academia, to launch fast and accurate localization systems. The aim of this work is to propose a closed-loop localization framework for large-scale deployments, facilitating both the modeling and continuous monitoring of Activities of Daily Living (ADLs). The proposed system learns from a minimal set of Received Signal Strength Indicator (RSSI) samples, enriches them to cover unmeasured distances, and keeps recalibrating itself with live data. This method delivers a 0.5–0.8 m mean error, improving the error reported in recent studies by 65%. Furthermore, once reliable position estimation is achieved, the proposed framework can detect predefined Activities of Daily Living (ADLs) based on location patterns and movement behaviors, achieving 91% accuracy. This capability opens new opportunities for context-aware services and smart environment applications. Each module of the framework was individually tested and evaluated, demonstrating strong performance both in isolation and as part of the integrated system. Full article

Global navigation satellite systems (GNSSs) are an integral part of modern society and are used in various industries, providing users with positioning, navigation, and timing (PNT). However, their effectiveness is vulnerable to signal interference, since GNSSs are based on received satellite signals from space, and that can severely impact applications that rely on continuous and accurate data. Interference can pose significant risks to sectors dependent on GNSSs, including transportation, telecommunications, finance, geodesy, and others. For this reason, in parallel with the development of GNSSs, various interference protection techniques are being developed to enable users to receive GNSS signals without the risk of interference, which can cause various effects, such as reducing the accuracy of positioning, as well as completely blocking signal reception and making it impossible to obtain positioning. There are various sources and methods of interfering with GNSS signals, and the greatest consequences are caused by intentional interference, which includes jamming, spoofing, and meaconing. This study investigates the effects of jamming devices on static GNSS observations using high-accuracy devices through multiple controlled experiments using both single-frequency (SF) and multi-frequency (MF) jammers. The aim was to identify the distances within which signal interference devices disrupt GNSS signal reception and position accuracy. The research conducted herein was divided into several phases where zones within which the jammer completely blocked the reception of the GNSS signal were determined (blackout zones), as were zones within which it was possible to obtain the position (but the influence of the jammer was present) and the influence of the jammer from different directions/azimuths in relation to the GNSS receiver. Various statistical indicators of the jammer’s influence, such as DOP (dilution of precision), SNR (signal-to-noise-ratio), RMS (root mean square), and others, were obtained through research. The results of this study indicate that commercially available, low-cost jamming devices, when operated within manufacturer-specified distances, completely disrupt the reception of GNSS signals. Their impact is also evident at greater distances, where they significantly reduce SNR values, increase DOP, and decrease the number of visible satellites, leading to reduced measurement reliability and integrity. These results underline the necessity of developing effective protection mechanisms against GNSS interference and strategies to ensure reliable signal reception in GNSS-dependent applications, particularly as the use of jamming devices becomes more prevalent. Full article

Palladium (Pd) and titanium (Ti) exhibit opposite dielectric responses upon hydrogenation, with stronger effects observed in the near-infrared (NIR) region. Leveraging this contrast, we investigated Ti/Pd bilayer thin films as a platform for NIR hydrogen sensing—particularly at telecommunication-relevant wavelengths, where such devices have remained largely unexplored. Ti/Pd bilayers coated with Teflon AF (TAF) and fabricated via sequential electron-beam and thermal evaporation were characterized using optical transmission measurements under repeated hydrogenation cycles. The Ti (5 nm)/Pd (x = 2.5 nm)/TAF (30 nm) architecture showed a 2.7-fold enhancement in the hydrogen-induced optical contrast at 1550 nm compared to Pd/TAF reference films, attributed to the hydrogen ion exchange between the Ti and Pd layers. The optimized structure, with a Pd thickness of x = 1.9 nm, exhibited hysteresis-free sensing behavior, a rapid response time (${\mathrm{t}}_{90}$ < 0.35 s at 4% ${\mathrm{H}}_2$), and a detection limit below 10 ppm. It also demonstrated excellent selectivity with negligible cross-sensitivity to $\mathrm{C}{\mathrm{O}}_2$, $\mathrm{C}{\mathrm{H}}_4$, and CO, as well as high durability, showing less than 6% signal degradation over 135 hydrogenation cycles. These findings establish a scalable, room-temperature NIR hydrogen sensing platform with strong potential for deployment in automotive, environmental, and industrial applications. Full article

Currently, optical fibers play a leading role in telecommunications, serve as special transmission components for industrial applications, and form the basis of highly sensitive sensor elements. One of the most commonly used modifications is the reduction in the initial dimensions of the cladding and core to a few or several micrometers, allowing the evanescent wave emerging from the tapered region to interact with the surrounding environment. As a result, the microfiber formed in this way is highly sensitive to any changes in its surroundings, making it an ideal sensing element. This article primarily focuses on reviewing the latest trends in science involving various types of optical microfibers, including tapers, rings, loops, coils, and tapered fiber Bragg gratings. Additionally, it discusses the most commonly used materials for coating fiber optic elements—such as metals, oxides, polymers, organic materials, and graphene—which enhance sensitivity to specific physical factors and enable selectivity in the developed sensors. Full article

Networked microgrids (NMG) are gaining popularity as an example of smart grids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware, such as Cisco 4331 Integrated Services Routers (ISR). We evaluate QoD in NMG control data by measuring RTT under varying relative network congestion levels. The results reveal that high-variance background traffic leads to greater RTTs, surpassing the industrial communication response time requirement specified by the European Telecommunications Standards Institute (ETSI) by over 25 times. Full article

This paper presents two innovative wireless network designs for the automation system of the Sof-Algeen water station in Zintan, addressing the challenge of connecting field instruments—such as pressure switches, solenoid valves, and differential pressure sensors—over distances of up to 4 km. Due to high costs, limited flexibility, and scalability concerns, traditional hardwired solutions are impractical for such distances. A comprehensive analysis of various Industrial Internet of Things (IIoT) network designs determined that the IEEE 802.11 standard and Phoenix Contact’s Trusted Wireless technology best meet the project’s requirements for long-distance connectivity, real-time data acquisition, system compatibility, and compliance with national telecommunications regulations. This study proposes optimal network designs using the IEEE 802.11 standard and a hybrid mesh and star network for Trusted Wireless, and evaluates these technologies based on performance, reliability, and infrastructure compatibility using simulation. The network designs were validated using the Radio Mobile tool, considering the water station’s specific terrain and wireless module parameters. The findings indicate distinct differences in structure, operation, and cost-effectiveness between the two proposed solutions, highlighting the benefits of each in achieving optimal link feasibility for robust water station automation. Full article

The Knapsack Problem belongs to the best-studied classical problems in combinatorial optimization. The Multiple-choice Knapsack Problem (MCKP) represents a generalization of the problem, with various application fields such as industry, transportation, telecommunication, national defense, bioinformatics, finance, and life. We found a lack of survey papers on MCKP. This paper overviews MCKP and presents its variants, solution methods, and applications. Traditional operational research methods solving the knapsack problem, such as dynamic programming, greedy heuristics, and branch-and-bound algorithms, can be adapted to MCKP. Only a few algorithms appear to have solved the problem in recent years. We found various related problems during the literature study and explored the broad spectrum of application areas. We intend to inspire research into MCKP algorithms and motivate experts from different domains to apply MCKP. Full article

Machine learning (ML) in queueing theory combines the predictive and optimization capabilities of ML with the analytical frameworks of queueing models to improve performance in systems such as telecommunications, manufacturing, and service industries. In this paper we give an overview of how ML is applied in queueing theory, highlighting its use cases, benefits, and challenges. We consider a classical $GI/G/K$-type queueing system, which is at the same time rather complex for obtaining analytical results, consisting of K homogeneous servers with an arbitrary distribution of time between incoming customers and equally distributed service times, also with an arbitrary distribution. Different simulation techniques are used to obtain the training and test samples needed to apply the supervised ML algorithms to problems of regression and classification, and some results of the approximation analysis of such a system will be needed to verify the results. ML algorithms are used also to solve both parametric and dynamic optimization problems. The latter is achieved by means of a reinforcement learning approach. It is shown that the application of ML in queueing theory is a promising technique to handle the complexity and stochastic nature of such systems. Full article