Hasil untuk "Cellular telephone services industry. Wireless telephone industry"

Ling-Lu Chen, Lei Chang, Jian-Qiang Zhang

A wideband folded dipole antenna with stable radiation patterns and a compact size is proposed. A folded bowtie dipole and a reflector with two vertical metal plates are applied to achieve compact dimensions. By introducing a parasitic dipole orthogonal to the folded bowtie dipole and a cylindrical substrate placed on these two dipoles, the impedance matching is improved over the entire operating bandwidth. A parasitic patch loaded above the two dipoles enhances the directional radiation capability at the high end of the operating bandwidth, and it is also used to improve the impedance matching in the middle of the operating bandwidth. The measured impedance bandwidth (IBW) for VSWR ≤ 2 is 135.8% (0.65–3.4 GHz). Experimental results also indicate that the broadside gain ranges from 4.1 to 7.4dBi, and stable, smooth radiation patterns with wide half-power beamwidths (HPBWs) are achieved. The design achieves a compact size of 0.29λL×0.22λL×0.11λL (λL is the free-space wavelength at the starting frequency).

Felix Glawe, Laura Kremer, Luisa Vervier et al.

Collaborative robots (cobots) are a core technology of Industry 4.0. Industry 4.0 uses cyber-physical systems, IoT and smart automation to improve efficiency and data-driven decision-making. Cobots, as cyber-physical systems, enable the introduction of lightweight automation to smaller companies through their flexibility, low cost and ability to work alongside humans, while keeping humans and their skills in the loop. Industry 5.0, the evolution of Industry 4.0, places the worker at the centre of its principles: The physical and mental well-being of the worker is the main goal of new technology design, not just productivity, efficiency and safety standards. Within this concept, human trust in cobots and human autonomy are important. While trust is essential for effective and smooth interaction, the workers' perception of autonomy is key to intrinsic motivation and overall well-being. As failures are an inevitable part of technological systems, this study aims to answer the question of how system failures affect trust in cobots as well as human autonomy, and how they can be recovered afterwards. Therefore, a VR experiment (n = 39) was set up to investigate the influence of a cobot failure and its severity on human autonomy and trust in the cobot. Furthermore, the influence of transparent communication about the failure and next steps was investigated. The results show that both trust and autonomy suffer after cobot failures, with the severity of the failure having a stronger negative impact on trust, but not on autonomy. Both trust and autonomy can be partially restored by transparent communication.

Antoine Houssard

In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practices, the way of choosing, investing, and succeeding across multiple and concurrent artifacts. Our results show that, although both actors write papers and code, their practices and the norms guiding them differ greatly. Nevertheless, it appears that the presence of industrials in academic studies leads to practices leaning toward the industrial side, but also to greater success in both artifacts, suggesting that if convergence is, then it is passing through those mixed teams rather than through pure academic or industrial studies.

Changfu Xu, Jianxiong Guo, Jiandian Zeng et al.

The Artificial Intelligence Generated Content (AIGC) technique has gained significant traction for producing diverse content. However, existing AIGC services typically operate within a centralized framework, resulting in high response times. To address this issue, we integrate collaborative Mobile Edge Computing (MEC) technology to reduce processing delays for AIGC services. Current collaborative MEC methods primarily support single-server offloading or facilitate interactions among fixed Edge Servers (ESs), limiting flexibility and resource utilization across all ESs to meet the varying computing and networking requirements of AIGC services. We propose AMCoEdge, an adaptive multi-server collaborative MEC approach to enhancing AIGC service efficiency. The AMCoEdge fully utilizes the computing and networking resources across all ESs through adaptive multi-ES selection and dynamic workload allocation, thereby minimizing the offloading make-span of AIGC services. Our design features an online distributed algorithm based on deep reinforcement learning, accompanied by theoretical analyses that confirm an approximate linear time complexity. Simulation results show that our method outperforms state-of-the-art baselines, achieving at least an 11.04% reduction in task offloading make-span and a 44.86% decrease in failure rate. Additionally, we develop a distributed prototype system to implement and evaluate our AMCoEdge method for real AIGC service execution, demonstrating service delays that are 9.23% - 31.98% lower than the three representative methods.

Breno Alves de Andrade, Rodrigo Siqueira, Lidiane Gomes et al.

Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed, implemented, and evaluated within this domain. Objective: This study aims to map the current state of research on corporate training in software engineering in industry settings, using Eduardo Salas' training framework as an analytical lens. Method: A systematic mapping study was conducted involving the selection and analysis of 26 primary studies published in the field. Each study was categorized according to Salas' four key areas: Training Needs Analysis, Antecedent Training Conditions, Training Methods and Instructional Strategies, and Post-Training Conditions. Results: The findings show a predominance of studies focusing on Training Methods and Instructional Strategies. Significant gaps were identified in other areas, particularly regarding Job/Task Analysis and Simulation-based Training and Games. Most studies were experience reports, lacking methodological rigor and longitudinal assessment. Conclusions: The study offers a structured overview of how corporate training is approached in software engineering, revealing underexplored areas and proposing directions for future research. It contributes to both academic and practical communities by highlighting challenges, methodological trends, and opportunities for designing more effective training programs in industry.

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck et al.

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Lukas Walter, Clemens Sauerwein, Daniel W. Woods

Security conferences are important venues for information sharing, where academics and practitioners share knowledge about new attacks and state-of-the-art defenses. Despite their importance, researchers have not systematically examined who shares information and which security topics are discussed. To address this gap, our paper characterizes the speakers, sponsors, and topics presented at prestigious academic and industry security conferences. We compile a longitudinal dataset containing 9,728 abstracts and 1,686 sponsors across four academic and six industry conferences. Our findings show limited information sharing between industry and academia. Conferences vary significantly in how equitably talks and authorship are distributed across individuals. The topics of academic and industry abstracts display consistent coverage of techniques within the MITRE ATT&CK framework. Top-tier academic conferences, as well as DEFCON and Black Hat, address the governance, response, and recovery functions of the NIST Cybersecurity Framework inconsistently. Commercial information security and insurance conferences (RSA, Gartner, Advisen and NetDiligence) more consistently cover the framework. Prevention and detection were the most common topics in the sample period, with no clear temporal trends.

Franziska Greinert, Malte S. Ubben, Ismet N. Dogan et al.

The transition of second-generation quantum technologies from a research topic to a topic of industrial relevance has led to a growing number of quantum companies and companies that are exploring quantum technologies. Examples would include a start-up building a quantum key distribution device, a large company working on integrating a quantum sensing core into a product, or a company providing quantum computing consultancy. They all face different challenges and needs in terms of building their quantum workforce and training in quantum concepts, technologies and how to derive value from them. With the study documented in this paper, we aim to identify these needs and provide a picture of the industry's requirements in terms of workforce development and (external) training and materials. We discuss, for example, the shortage of engineers and jobs relevant to the quantum industry, the challenge of getting people interested in quantum, and the need for training at different levels and in different formats - from awareness raising and self-learning materials to university courses in quantum systems engineering. The findings are based on 34 semi-structured interviews with industry representatives and a follow-up questionnaire to validate some of the issues raised in the interviews. These results have influenced activities in EU projects, including an update of the European Competence Framework for Quantum Technologies.

Md Hadisur Rahman, Md Rabiul Hasan, Nahian Ismail Chowdhury et al.

In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems.

Limor Hatsor, Artyom Jelnov

The premise of industrial symbiosis IS is that advancing a circular economy that reuses byproducts as inputs in production is valuable for the environment. We challenge this premise in a simple model. Ceteris paribus, IS is an environmentally friendly approach; however, implementing IS may introduce increased pollution into the market equilibrium. The reason for this is that producers' incentives for recycling can be triggered by the income gained from selling recycled waste in the secondary market, and thereby may not align with environmental protection. That is, producers may boost production and subsequent pollution to sell byproducts without internalizing the pollution emitted in the primary industry or the recycling process. We compare the market solution to the social optimum and identify a key technology parameter - the share of reused byproducts that may have mutual benefits for firms, consumers, and the environment.

Atif Hussain, Rana Rizwan

This paper argues for the strategic treatment of artificial intelligence as a key industry within broader industrial policy framework of Pakistan, underscoring the importance of aligning it with national goals such as economic resilience and preservation of autonomy. The paper starts with defining industrial policy as a set of targeted government interventions to shape specific sectors for strategic outcomes and argues for its application to AI in Pakistan due to its huge potential, the risks of unregulated adoption, and prevailing market inefficiencies. The paper conceptualizes AI as a layered ecosystem, comprising foundational infrastructure, core computing, development platforms, and service and product layers, supported by education, government policy, and research and development. The analysis highlights that AI sector of Pakistan is predominantly service oriented, with limited product innovation and dependence on foreign technologies, posing risks to economic independence, national security, and employment. To address these challenges, the paper recommends educational reforms, support for local AI product development, initiatives for indigenous cloud and hardware capabilities, and public-private collaborations on foundational models. Additionally, it advocates for public procurement policies and infrastructure incentives to foster local solutions and reduce reliance on foreign providers. This strategy aims to position Pakistan as a competitive, autonomous player in the global AI ecosystem.

Michael Mayr, Georgios C. Chasparis, Josef Küng

Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of industrial-relevant tasks like process monitoring, predictive control or decision support. The backbone of a DT, i.e. the concrete modelling methodologies and architectural frameworks supporting these models, are complex, diverse and evolve fast, necessitating a thorough understanding of the latest state-of-the-art methods and trends to stay on top of a highly competitive market. From a research perspective, despite the high research interest in reviewing various aspects of DTs, structured literature reports specifically focusing on unravelling the utilized learning paradigms (e.g. self-supervised learning) for DT-creation in the process industry are a novel contribution in this field. This study aims to address these gaps by (1) systematically analyzing the modelling methodologies (e.g. Convolutional Neural Network, Encoder-Decoder, Hidden Markov Model) and paradigms (e.g. data-driven, physics-based, hybrid) used for DT-creation; (2) assessing the utilized learning strategies (e.g. supervised, unsupervised, self-supervised); (3) analyzing the type of modelling task (e.g. regression, classification, clustering); and (4) identifying the challenges and research gaps, as well as, discuss potential resolutions provided.

Jie Guo, Shaoqing Zhang, Chong-Zhi Han et al.

In this article, an 8 × 8 MIMO antenna system with multidecoupling structures is proposed for the fifth-generation (5G) terminal applications. First, an antenna element consisting of an L-shaped open slot with dimensions of 5.6 × 5 mm2 and two bent-L-shaped monopoles with dimensions of 12.7 × 6.1 mm2 is introduced. Due to coupling feed technology, the initial open slot owns a wide bandwidth from 3.62 GHz to 4.82 GHz. For broadening the bandwidth of the initial antenna element, two bent-L-shaped monopoles are added to the open slot to adjust the input impedance. By optimizing the parameters of monopoles, the proposed antenna element has been finally determined and the bandwidth of the antenna element has been broadened (2.82 GHz to 5.23 GHz). Second, a dual antenna pair is constructed by placing the two antenna elements with an edge-to-edge distance of 27 mm in a mirror image placement; the 8 × 8 MIMO antenna system is brought about by symmetrically disposing four such antenna pairs with an edge-to-edge distance of 47 mm. Moreover, the isolation of two closely placed antenna elements is achieved by the parasitic strip-loading technique and defective ground techniques. Due to the decoupling techniques used in the 8 × 8 MIMO antenna system, the average isolation has been optimized from 13.2 dB to 21.9 dB and the total efficiency of each antenna element has been improved from a worst 20% to over 45%. Besides, the calculated mean efficiency gain is less than 1 dB, and the calculated envelop correlation coefficient (ECC) is lower than 0.01 for desired frequency bands. Furthermore, the proposed 8 × 8 MIMO antenna system has the measured broadband bandwidth from 3.28 GHz to 5.05 GHz (covering N77, N78, and N79 bands) and a compatible dimension for ultra-thin mobile phones. The simulated results of this work are all obtained from EM software CST STUDIO SUITE 2019. In general, the proposed 8 × 8 MIMO antenna system with a high-isolation property provides a hopeful solution to 5G ultra-thin mobile phones.

Abdullahi Saka, Ridwan Taiwo, Nurudeen Saka et al.

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.

Anne de Bortoli, Maxime Agez

Industries struggle to build robust environmental transition plans as they lack the tools to quantify their ecological responsibility over their value chain. Companies mostly turn to sole greenhouse gas (GHG) emissions reporting or time-intensive Life Cycle Assessment (LCA), while Environmentally-Extended Input-Output (EEIO) analysis is more efficient on a wider scale. We illustrate EEIO analysis usefulness to sketch transition plans on the example of Canada s road industry - estimation of national environmental contributions, most important environmental issues, main potential transition levers of the sector, and metrics prioritization for green purchase plans). To do so, openIO-Canada, a new Canadian EEIO database, coupled with IMPACT World plus v1.30-1.48 characterization method, provides a multicriteria environmental diagnosis of Canada s economy. The road industry generates a limited impact (0.5-1.8 percent) but must reduce the environmental burden from material purchases - mainly concrete and asphalt products - through green purchase plans and eco-design and invest in new machinery powered with cleaner energies such as low-carbon electricity or bioenergies. EEIO analysis also captures impacts often neglected in process-based pavement LCAs - amortization of capital goods, staff consumptions, and services - and shows some substantial impacts advocating for enlarging system boundaries in standard LCA. Yet, pavement construction and maintenance only explain 5 percent of the life cycle carbon footprint of Canada s road network, against 95 percent for the roads usage. Thereby, a carbon-neutral pathway for the road industry must first focus on reducing vehicle consumption and wear through better design and maintenance of roads (...)

Avez Syed, Muntasir Sheikh, Mohammad Tariqul Islam et al.

This article presents printed log periodic antennas with metamaterials for use in microwave imaging. A single layer of epsilon negative (ENG) metamaterial (MTM) array (1 × 6) of the unit cell is on the radiating patch. Adding a single negative metamaterial structure enhances the properties of far-field antennas, such as radiation pattern and gain, both of which are vital for breast imaging. Two frequency bands exhibit negative permittivity: 3–3.3 GHz and 3.6–4.5 GHz. In the operating band, the proposed antennas have achieved a maximum gain of 5.5 dBi and impedance bandwidth of 3 GHz (2–5 GHz) with a reflection coefficient less than −10 dB. At the lowest operating frequency of 2 GHz, the electrical dimensions of this designed antenna are 0.34λ × 0.26λ × 0.01λ. All 16 transceiver antennas are arranged vertically in a circular pattern around the phantom, each acting as a transmitter and the rest as receivers. The system design is carried out with the electromagnetic simulators CST and HFSS. After receiving the extracted data, the data are postprocessed using the MATLAB software and the delay multiply and sum (DMAS) imaging algorithm. Based on the reconstructed image, it is evident that the MTM-loaded antenna-based imaging system can detect many undesired tumors inside the breast phantom.

Sara Nilsson Tengstrand, Piotr Tomaszewski, Markus Borg et al.

The Scaled Agile Framework (SAFe) is a framework for scaling agile methods in large organizations. We have found several experience reports and white papers describing SAFe adoptions in different banks, which indicates that SAFe is being used in the banking industry. However, there is a lack of academic publications on the topic, the banking industry is missing in the scientific reports analyzing SAFe transformations. To fill this gap, we present a study on the main challenges with a SAFe transformation at a large full-service bank. We identify the challenges in the bank under study and compare the findings with experience reports from other banks, as well as with research on SAFe transformations in other domains. Many of the challenges reported in this paper overlap with the generic SAFe challenges, including management and organization, education and training, culture and mindset, requirements engineering, quality assurance, and systems architecture. However, we also report some novel challenges specific to the banking domain, e.g., the risk of jeopardizing customer relations, stability, and trust of external stakeholders. This study validates several SAFe-related challenges reported in previous work in the banking context. It also brings up some novel challenges specific to the banking industry. Therefore, we believe our results are particularly useful to practitioners responsible for SAFe transformations at other banks.

Umut Demirhan, Ahmed Alkhateeb

Cell-free massive MIMO systems have promising data rate and uniform coverage gains. These systems, however, typically rely on optical fiber based fronthaul for the communication between the central processing unit (CPU) and the distributed access points (APs), which increases the infrastructure cost, leads to high installation time, and limits the deployment flexibility and adaptability. To address these challenges, this paper proposes two architectures for cell-free massive MIMO systems based on wireless fronthaul that is operating at a \textit{higher-band} compared to the access links: (i) A wireless-only fronthaul architecture where the CPU has a wireless fronthaul link to each AP, and (ii) a mixed-fronthaul architecture where the CPU has a wireless link to each cluster of APs that are connected together via optical fibers. These dual-band architectures ensure high-data rate fronthaul and provide high capability to synchronize the distributed APs. Further, the wireless fronthaul reduces the infrastructure cost and installation time, and enhances the flexibility, adaptability, and scalability of the network deployment. To investigate the achievable data rates with the proposed architectures, we formulate the end-to-end data rate optimization problem accounting for the various practical aspects of the fronthaul and access links. Then, we develop a low-complexity yet efficient joint beamforming and resource allocation solution for the proposed architectures based on user-centric AP grouping. With this solution, we show that the proposed architectures can achieve comparable data rates to those obtained with optical fiber-based fronthaul under realistic assumptions on the fronthaul bandwidth, hardware constraints, and deployment scenarios. This highlights a promising path for realizing the cell-free massive MIMO gains in practice while reducing the infrastructure and deployment overhead.

Guilu Wu, Hongyun Chu

An increasing number of vehicles make spectrum resources face serious challenges in vehicular cognitive small-cell networks. The means of spectrum sharing can greatly alleviate this pressure. In this paper, we introduce a supermodular game theoretic approach to analyze the problem of spectrum sharing. The small-cell BS (primary service provider, PSP) and the vehicle (secondary service provider, SSP) can share the spectrum, where the PSP can sell idle spectrum resources to the SSP. This is taken as a spectrum trading market, and a Bertrand competition model is considered to depict this phenomenon. Different PSPs compete with each other to maximize their individual profits. The Bertrand competition model can be proved as a supermodular game, and the corresponding Nash equilibrium (NE) solution is provided as the optimal price solution. Hence, an improved genetic simulated annealing algorithm is designed to achieve NE. Simulation results demonstrate that the NE point for the price of the primary service provider exists. The change of the exogenous variable is also analyzed on the equilibrium point.

Ziheng Zhang, Jiaoyan Chen, Xi Chen et al.

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.