Search Results (27)

The transition from 5G to 6G wireless systems introduces new challenges at the physical layer, including the need for higher frequency operations, massive MIMO deployment, advanced beamforming techniques, and sustainable energy harvesting mechanisms. A plethora of feature articles, review and white papers, and roadmaps elaborate on the perspectives and research challenges of wireless systems, in general, including both unified physical and cyber space. Hence, this paper presents a comprehensive review of the technological challenges and recent advancements in wireless communication hardware that underpin the development of next-generation networks, particularly 6G. Emphasizing the physical layer, the study explores critical enabling technologies including beamforming, massive MIMO, reconfigurable intelligent surfaces (RIS), millimeter-wave (mmWave) and terahertz (THz) communications, wireless power transfer, and energy harvesting. These technologies are analyzed in terms of their functional roles, implementation challenges, and integration into future wireless infrastructure. Beyond traditional physical layer components, the paper also discusses the role of reconfigurable RF front-ends, innovative antenna architectures, and user-end devices that contribute to the adaptability and efficiency of emerging communication systems. In addition, the inclusion of application-driven paradigms such as digital twins highlights how new use cases are shaping design requirements and pushing the boundaries of hardware capabilities. By linking foundational physical-layer technologies with evolving application demands, this work provides a holistic perspective aimed at guiding future research directions and informing the design of scalable, energy-efficient, and resilient wireless communication platforms for the Future Internet. Specifically, we first try to identify the demands and, in turn, explore existing or emerging technologies that have the potential to meet these needs. Especially, there will be an extended reference about the state-of-the-art antennas for massive MIMO terrestrial and non-terrestrial networks. Full article

Service quality research has traditionally focused either on identifying Kano two-dimensional quality categories or detecting service quality deficiencies. However, integrating these perspectives remains a challenge due to the Kano model’s nonlinear characteristics and the importance-performance and gap analysis (IPGA) model’s linear approach. This study proposes the Kano-IPGA (KIPGA) model, incorporating mutual information (MI) to bridge the gap between these two models. The KIPGA model first employs moderated regression analysis to classify service attributes into Kano’s quality categories. MI is then used to calculate the relative importance (RI), while relative performance (RP) is determined using the original IPGA approach. The results are mapped into the KIPGA strategic matrix, categorizing service attributes into eight management strategies. An empirical analysis of Taiwan’s online insurance systems demonstrates the model’s effectiveness in simultaneously identifying Kano categories and prioritizing service quality improvements. The findings reveal that critical improvement and enhanced improvement regions require immediate attention. The proposed KIPGA model offers a systematic approach for service quality management, providing decision-makers with a structured framework to allocate resources effectively and enhance customer satisfaction. This study contributes to service quality research by offering an integrated model that accounts for both linear and nonlinear quality assessment perspectives. Full article

Background/Objectives: Routine immunisation (RI) coverage declines during the COVID-19 pandemic, from 2020 to 2022, are well-reported. With the declared end to the Public Health Emergency of International Concern in May 2023, and the cessation of most nonpharmaceutical interventions that were introduced to prevent or minimise COVID-19 spread, we (I) assess whether routine immunisation coverage has rebounded to the level of prepandemic trends and (II) seek to identify factors that help predict whether country performance has exceeded, maintained, or declined compared with expectations (based on time-series forecasting). Methods: We quantified global and country-level routine immunisation diphtheria–tetanus–pertussis (DTP) coverage trends postpandemic (2023) compared with prepandemic trends using time-series forecasting across 190 countries. We used discriminant analysis of principal components and random forests to identify relevant predictors of country-level coverage performance, including twenty-eight indicators of health system strength, health workforce, country income, pandemic containment, economic and health policies, and demographic aspects. Results: We show that mean global DTP third-dose coverage levels remained on average 2.7% [95% confidence intervals: 1.1–4.3%] lower than expected in 2023. However, once accounting for temporal demographic changes, we find that this translated to the total number of immunised children almost reverting to expected levels because of decreasing fertility reducing global-level immunisation target populations. At the country level, notable disruption remained in over thirty countries (16.8% of countries below expectations, 81.6% within expected ranges, and 1.6% above expectations). Neither predictive method performed well at identifying factors associated with coverage disruptions. Conclusions: Despite the end of COVID-19 pandemic measures, RI remains below expectations in about 20% of countries. No clear drivers of this continued disruption were identified. Further research is required to inform recovery efforts and prevent future epidemic and pandemic disruptions to routine health services. Full article

This study explores the potential of inland navigation as a key component of Poland’s sustainable transportation strategy, focusing on the Opole–Szczecin route. It emphasizes inland waterways as an eco-friendly and efficient alternative to road and rail transport, potentially revitalizing local economies and reducing dependency on more traditional transport modes. The use of the River Information Service (RIS) system is highlighted as to its role in enhancing the logistical efficiency and safety of inland water transport. The research includes a comparative analysis of cargo transport on the Opole–Szczecin route, using road, rail, and inland-waterway options, revealing the advantages of inland-waterway transport in terms of cost-effectiveness and environmental impact. Finally, the paper discusses the challenges and opportunities for the development of inland navigation in Poland, advocating for greater integration with other transport branches through innovative technologies like RIS. Full article

Excessive levels of heavy metals in soil can significantly impact human health and ecological safety. Evaluating heavy metal pollution and identifying its sources are crucial for environmental management. This study investigates the status of heavy metal pollution in a southeastern region of China and aims to identify its sources using data from the first national soil pollution survey, which includes 282 sampling points. Indicators such as the geoaccumulation index (Igeo), the potential ecological risk index (RI), the hazard index (HI), and the total lifetime cancer risk (TLCR) were utilized to assess contamination levels. Geographic information systems (GIS), positive matrix factorization (PMF) receptor modeling, cluster analysis (ClusA) and human health risk assessments were integrated to analyze the sources of heavy metals. The results indicate that agricultural pollution sources have a minor impact on overall heavy metal contamination, with low ecological risk levels in the eastern and western regions. In contrast, the central region exhibited moderate risk, with areas of extremely high risk distributed across the central-west and central-south regions. PMF analysis identified pollution sources including natural origins, coal combustion, industrial emissions, and traffic, with contributions of 17.62%, 18.50%, 28.35%, and 35.56%, respectively. Overall, the carcinogenic risk in the study area is not high. Targeted recommendations were made in response to the pollution situation in the study area. This research enhances our understanding of heavy metal pollution in the soil of the study area and provides a reference for pollution source delineation in other regions. Full article

The research on high-precision and all-scenario localization using the millimeter-wave (mmWave) band is of great urgency. Due to the characteristics of mmWave, blockages make the localization task more complex. This paper proposes a cooperative localization system among user equipment (UEs) assisted by reconfigurable intelligent surfaces (RISs), which considers device-to-device (D2D) communication. RISs are used as anchor points, and position estimation is achieved through signal exchanges between UEs. Firstly, we establish a localization model based on this system and derive the UEs’ positioning error bound (PEB) as a performance metric. Then, a UE-RIS joint beamforming design is proposed to optimize channel state information (CSI) with the objective of achieving the minimum PEB. Finally, simulation analysis demonstrates the advantages of the proposed scheme over RIS-assisted base station positioning, achieving centimeter-level accuracy with a 10 dBm lower transmission power. Full article

This paper details the design, development, and evaluation of VulcanH, a computerized maintenance management system (CMMS) specialized in preventive maintenance (PM) and predictive maintenance (PdM) management for underground mobile mining equipment. Further, it aims to expand knowledge on trust in automation (TiA) for PdM as well as contribute to the literature on explainability requirements of a PdM-capable artificial intelligence (AI). This study adopted an empirical approach through the execution of user tests with nine maintenance experts from five East-Canadian mines and implemented the User Experience Questionnaire Plus (UEQ+) and the Reliance Intentions Scale (RIS) to evaluate usability and TiA, respectively. It was found that the usability and efficiency of VulcanH were satisfactory for expert users and encouraged the gradual transition from PM to PdM practices. Quantitative and qualitative results documented participants’ willingness to rely on PdM predictions as long as suitable explanations are provided. Graphical explanations covering the full spectrum of the derived data were preferred. Due to the prototypical nature of VulcanH, certain relevant aspects of maintenance planning were not considered. Researchers are encouraged to include these notions in the evaluation of future CMMS proposals. This paper suggests a harmonious integration of both preventive and predictive maintenance practices in the mining industry. It may also guide future research in PdM to select an analytical algorithm capable of supplying adequate and causal justifications for informed decision making. This study fulfills an identified need to adopt a user-centered approach in the development of CMMSs in the mining industry. Hence, both researchers and industry stakeholders may benefit from the findings. Full article

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed. Full article

Harmonized and standardized collection, processing, and analysis of individual dietary data is essential for nutrition assessment and informed policy decision making. To underpin the harmonization of food consumption data collection methodologies and the development of a common, comprehensive European food consumption database, the European Food Safety Authority (EFSA) supported 36 child and/or adult dietary surveys in 18 EU Member States and four Balkan pre-accession countries through the EU Menu Project. Given the lack of relevant and harmonized research and data on food and nutrition in the Balkan region, CENM-IMR and CAPNUTRA scientists focused their activities on capacity building in nutrition research, particularly on the creation of a contemporary, harmonized research infrastructure (RI) that meets European standards. The EFSA EU Menu methodology has been implemented in the Balkans through the adaptation and use of an innovative, comprehensive tool for the standardized collection of food consumption and dietary intake assessment data, the Diet Assess and Plan (DAP). DAP has the essential features of an RI needed to strengthen public health surveillance, monitoring, evaluation, and nutrition research; this is a unique example of a standardized and harmonized tool for assessing dietary intake, i.e., collecting data on food and nutrition in the Balkan region and beyond. It is a concurrent tool for large-scale nutritional epidemiological studies and represents one of the new technologies for dietary intake assessment. National dietary surveys were conducted from 2017 to 2023 among adults aged 10 to 74 years (in Bosnia and Herzegovina, Montenegro, and Serbia) and children aged three months to nine years (in Montenegro, North Macedonia, and Serbia). The collected data on food consumption are internationally comparable with other European countries under the EU Menu Program. The data collected will be used for dietary and exposure risk assessment, establishment of national nutrient reference values, as a basis for the development of food-based dietary guidelines, a tool to provide evidence and infrastructure for public health nutrition policy decisions, and for tailored pathways to transform the food system in the Balkans towards a more nutrition-sensitive and sustainable system. Full article

Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to solving this issue and dealing with the dearth of subject-matter specialists in this area. Nonetheless, a major generalization obstacle is posed by the existence of small discrepancies between various classes of paddy diseases. Numerous studies have used features taken from a single deep layer of an individual complex DL construction with many deep layers and parameters. All of them have relied on spatial knowledge only to learn their recognition models trained with a large number of features. This study suggests a pipeline called “RiPa-Net” based on three lightweight CNNs that can identify and categorize nine paddy diseases as well as healthy paddy. The suggested pipeline gathers features from two different layers of each of the CNNs. Moreover, the suggested method additionally applies the dual-tree complex wavelet transform (DTCWT) to the deep features of the first layer to obtain spectral–temporal information. Additionally, it incorporates the deep features of the first layer of the three CNNs using principal component analysis (PCA) and discrete cosine transform (DCT) transformation methods, which reduce the dimension of the first layer features. The second layer’s spatial deep features are then combined with these fused time-frequency deep features. After that, a feature selection process is introduced to reduce the size of the feature vector and choose only those features that have a significant impact on the recognition process, thereby further reducing recognition complexity. According to the results, combining deep features from two layers of different lightweight CNNs can improve recognition accuracy. Performance also improves as a result of the acquired spatial–spectral–temporal information used to learn models. Using 300 features, the cubic support vector machine (SVM) achieves an outstanding accuracy of 97.5%. The competitive ability of the suggested pipeline is confirmed by a comparison of the experimental results with findings from previously conducted research on the recognition of paddy diseases. Full article

Intramammary infections, which cause mastitis, can increase treatment and labor costs, decrease milk production, and affect milk quality. Meters that measure quarter somatic cell count (SCC) could be used to make more informed dry cow therapy decisions. The objective of this study was to compare the RT-10 iPhone adapter (RT-10; Dairy Quality Inc., Newmarket, ON, Canada), DeLaval Cell Counter (DSCC; DeLaval, Gurnee, IL, USA), Porta Check Quick Test (PortaCheck, White City, OR, USA), California Mastitis Test (ImmuCell, Portland, ME USA), pH meter (Hanna Instruments, Smithfield, RI, USA), electrical conductivity meter (OHAUS, Parsippany, NJ, USA), and the dual laser infrared temperature thermometer (Klein Tools, Lincolnshire, IL, USA) for measuring SCC in individual Holstein mammary quarters in comparison to a reference standard, the Fourier Transform Spectrometer 600 Combi System (Combi; Bentley Instruments, Chaska, MN, USA). Meters were evaluated using 658 individual cow quarter samples and 100 bulk-tank samples to measure SCC. Individual quarter milk samples from 160 cows from four commercial dairy herds were collected just before dry off and tested within 4 h of collection. To test bulk-tank SCC, 100 bulk-tank milk samples (25 mL) were collected from UC Davis Veterinary Medicine Teaching and Research Milk Quality Lab. Meter SCC values were regressed on observed Combi SCC. Goodness of fit was then evaluated by partitioning the mean square predicted error (MSPE). For individual quarter SCC, RT-10 had the highest coefficient of determination (R² = 0.86), lowest MSPE, and highest proportion of MSPE due to random variation (96%). Both the RT-10 and DSCC had the highest sensitivity and specificity for identifying quarter SCC above and below 200,000 cells/mL. For bulk-tank SCC, DSCC had the highest coefficient of determination (R² = 0.45), lowest MSPE, and highest proportion of MSPE due to random variation (80%). The RT-10 and DSCC could be used to measure individual quarter SCC to determine which cows to treat at dry off potentially reducing antibiotic use. Full article

Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries’ attention, constituting a vital part of the global geopolitical chessboard since humanity’s energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords “biofuel” and “nanocatalyst” were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms’ oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx). Full article

Addressing the challenges of internet-based 5G technology, namely increasing density through micro-cell systems, frequency spectrum, and reducing resource costs, is needed to meet the use of IoT-based 6G technology with the goal of high-speed, high-capacity, and low-latency communication. In this research, we considered the coverage performance and ergodic capacity of the Reconfigurable Intelligent Surface (RIS)-aided cooperative nonorthogonal multiple-access network (NOMA) of an IoT system. This enables the upgrading of 5G- toward 6G-technology-based IoT systems. We developed a closest-form formula of near and far user coverage probabilities as a function of perfect channel statistical information (p-CSI) using only a single-input single-output (SISO) system with a finite number of RIS elements under the Nakagami-m fading channel. We also define ergodic capacity as a simple upper limit by simplifying the use of symbolic functions and it could be used for a sustained period. The simulation findings suggest that RIS-assisted NOMA has a reduced risk of outage than standard NOMA. All of the derived closed-form formulas agree with Monte Carlo simulations, indicating that the distant user’s coverage probability outperforms the nearby user. The bigger the number of RIS parts, however, the greater the chance of coverage. They also disclose the scaling law of the number of phase shifts at the RIS-aided NOMA based on the asymptotic analysis and the upper bound on channel capacity. In both arbitrary and optimum phase shifts, the distant user’s ergodic capacity outperforms the near user. Full article

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the “golden record” that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality. Full article

Inland navigation is hardly ever used to transport cargo in cities. In most urban areas, it is performed almost exclusively via road transport, with the virtual exclusion of rail and inland transport. Research and implementation projects in several European cities have shown that employing inland navigation is a viable alternative for road transport in urban areas. The research involved a case study of transporting the same number of 40-foot containers by inland waterway and road and then comparing the results in terms of transport time, transport costs, and carbon dioxide emissions between two metropolitan areas in Poland. The article shows that River Information Services (RIS) system can contribute to improving freight transport efficiency not only on longer routes, but also in urban and inter-urban conditions. The findings were that inland shipping is much cheaper and more environmentally friendly, but transport takes much longer and is not always possible due to insufficient waterway infrastructure. The paper can be used as a road map to proceed with new approach to planning urban and inter-urban logistics, with the use of inland navigation supported by the RIS system. The study delivers evidence that the main benefits of using RIS for urban logistics are: optimization of the cargo route, improved supervision and control of cargo transport, optimization of inter-branch transport, optimization of the use of fleet, more efficient use of technical infrastructure of waterways, combination of many recipients/senders into one transport, and reduction of administrative barriers. Full article