Search Results (109)

Purpose: With the advent of sixth-generation (6G) communication technology, systematic mapping of its use cases to associated technical requirements has become essential for accelerating standardization, guiding R&D investment, and informing policy formulation. Methods: This study consolidated 65 use case scenarios from key academic and institutional 6G sources into 21 representative cases. A three-round Delphi-based expert assessment, employing a five-point Likert scale and interquartile-range-based consensus monitoring, was used to assign primary and secondary technical requirements across six core dimensions: immersive communication, massive communication, hyper-reliable low-latency communication, integrated sensing and communication, integrated artificial intelligence and communication (IAAC), and ubiquitous connectivity. A three-dimensional (3D) prism-based visualization framework was subsequently developed to represent the interdependencies among these requirements. Results: IAAC and massive communication emerged as the most critical requirements, each functioning as a primary or secondary driver across most use cases. The prism framework revealed hierarchical and complementary relationships among the six dimensions that conventional 2D wheel diagrams cannot adequately capture. Furthermore, a nine-criterion multidimensional classification framework, encompassing data transmission mode, decision-making mode, communication flow, interaction type, device type, deployment type, human activity innovation, user type, and personalization level, was developed, offering industry-specific guidance for service design. Collectively, the proposed framework supports user-centric design, informs strategic technology planning, and contributes to policy development while acknowledging existing limitations in quantitative mapping and economic analysis. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

This study assesses the presence and distribution of per- and polyfluoroalkyl substances (PFAS) and polybrominated diphenyl ethers (PBDEs) in the surface waters of the Itupararanga Reservoir and the Sorocaba River, Brazil. Samples collected during the dry and rainy seasons were analyzed to determine their composition, spatial distribution, and seasonal variability. Results indicate the ubiquitous presence of PFAS, with significantly higher concentrations in the dry season, suggesting point sources of contamination, such as industrial and domestic discharges. Perfluorobutanoic acid (PFBA), Perfluorooctane sulfonate (PFOS), and Perfluorooctanoic acid (PFOA) were the predominant compounds, while 6:2 Fluorotelomer sulfonate (6-2FTS) stood out for its abundance in areas with industrial activity. For PBDEs, marked seasonal variability was observed, with higher concentrations during the rainy season, suggesting the mobilization of these compounds by surface runoff. BDE-209 was the most abundant congener, representing over 58% of the total concentration of PBDEs detected. Concentrations of PFAS and PBDEs in the study area are comparable to those reported globally, although there are differences associated with industrial practices and local environmental dynamics. The increased presence of short-chain PFAS and Deca-BDEs highlights the need for ongoing monitoring and the implementation of regulatory measures to mitigate contamination in water sources used for human consumption. Full article

Microplastics (MPs)—synthetic polymer particles less than 5 mm in size—have emerged as ubiquitous contaminants in terrestrial and aquatic environments worldwide, raising concerns about their ecological and human health impacts. While research has predominantly focused on urban and marine settings, evidence shows that rural ecosystems are also affected, challenging assumptions of pristine conditions outside cities and coasts. This review synthesizes current knowledge on the presence, pathways, and impacts of MPs in rural environments, highlighting complex contamination dynamics driven by both local sources (agricultural plastics, domestic waste, rural wastewater, and road runoff) and regional processes (atmospheric deposition, hydrological transport, and sediment transfer). Key findings highlight that rural lakes, streams, soils, and groundwater systems are active sinks and secondary sources of diverse MPs, predominantly polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) in fibrous and fragmented forms. These particles vary in size, density, and color, influencing their transport, persistence, and bioavailability. Ecological effects include bioaccumulation in freshwater species, soil degradation, and potential food chain transfer, while human exposure risks stem from contaminated groundwater, air, and locally produced food. Despite these growing threats, rural systems remain underrepresented in monitoring and policy frameworks. The article calls for context-specific mitigation strategies, enhanced wastewater treatment, rural waste management reforms, and integrated microplastics surveillance across environmental compartments. Full article

Microplastics (MPs) are ubiquitous coastal contaminants that can induce oxidative stress, detoxification responses and inflammation in marine species. We evaluated MP occurrence and associated physiological responses in the digestive tract of the peacock wrasse Symphodus tinca (N = 28) from the northeastern coast of Ibiza (Balearic Islands, Spain). MPs occurred in 60.7% of the fish (58 items in total; mean 2.1 ± 0.5 items·fish⁻¹), dominated by fibres (75.9%). Polyester (38.1%) and polypropylene (23.8%) were the most frequent polymers in the subset of MPs analysed. Fish were grouped by median MP count (<2 vs. ≥2), and statistical differences and correlations were assessed. Individuals with ≥2 MPs showed significantly elevated activities of antioxidant enzymes (catalase, CAT; superoxide dismutase, SOD), the phase-II detoxification enzyme glutathione S-transferase (GST), and the pro-inflammatory enzyme myeloperoxidase (MPO). Production of reactive oxygen species (ROS) and oxidative-damage biomarkers, malondialdehyde (MDA) and protein carbonyls tended to be higher in the high-MP group, but differences were not statistically significant. MP exposure correlated positively with all biomarkers except protein carbonyls. In conclusion, higher MP loads in S. tinca are associated with activation of antioxidant, detoxification and inflammatory pathways, without clear evidence of widespread oxidative damage under the sampled conditions. These physiological responses suggest potential impacts on individual fitness that may signal early ecological effects in coastal fish populations, highlighting their value as early-warning indicators in coastal monitoring and environmental management. Full article

The increasing prevalence of IoT technology in smart homes has significantly enhanced convenience but also introduced new security and safety challenges. Traditional security solutions, reliant on sequences of IoT-generated event data (e.g., notifications of device status changes and sensor readings), are vulnerable to cyberattacks, such as message forgery and interception and delaying attacks, and fail to monitor non-smart devices. Moreover, fragmented smart home ecosystems require vendor cooperation or system modifications for comprehensive monitoring, limiting the practicality of the existing approaches. To address these issues, we propose IoTBystander, a non-intrusive dual-channel smart home security monitoring framework that utilizes two ubiquitous platform-agnostic signals, i.e., audio and network, to monitor user and device activities. We introduce a novel dual-channel aggregation mechanism that integrates insights from both channels and cross-verifies the integrity of monitoring results. This approach expands the monitoring scope to include non-smart devices and provides richer context for anomaly detection, failure diagnosis, and configuration debugging. Empirical evaluations on a real-world testbed with nine smart and eleven non-smart devices demonstrate the high accuracy of IoTBystander in event recognition: 92.86% for recognizing events of smart devices, 95.09% for non-smart devices, and 94.27% for all devices. A case study on five anomaly scenarios further shows significant improvements in anomaly detection performance by combining the strengths of both channels. Full article

Advancements in electronics and sensor technologies are driving the deployment of ubiquitous sensor networks across various applications, including asset monitoring, security, and networking. At the same time, ensuring the integrity and confidentiality of data collected by sensor nodes is crucial to prevent unauthorized access or modification. However, the limited resources f low-power sensor networks present significant challenges for securing innovative Internet-of-Medical Things (IoMT) applications in complex environments. These miniature sensing systems, essential for diverse healthcare applications, grapple with constrained computational power and energy budgets. To address this challenge, this study proposes a dynamic power management strategy within a resource-constrained FPGA-based cryptoprocessor core for secure IoMT networks. The sensor node design comprises two main modules: an 8-bit reduced instruction set computer (RISC) and a cryptographic engine. These modules collaboratively manage their power consumption during the operational stages of data acquisition, encryption, transmission, and sleep mode activation. The cryptographic engine employs a pseudorandom number generator to generate a keystream for data encryption, utilizing direct sequence spread spectrum (DSSS) encoding to ensure secure communication. The experimental results demonstrate the effectiveness of the proposed dynamic power management strategy within the resource-constrained cryptoprocessor core. The sensor node achieves an average power consumption of 0.1 mW while utilizing 2414 logic cells and 5292 registers. A comparative analysis with other state-of-the-art lightweight sensor nodes highlights the advantages of our dynamic power management approach within the cryptoprocessor sensing system. Full article

Naturally occurring radionuclides are ubiquitous at various levels of concentration, while exposure to ionizing radiation by humans is of global concern. Radiological health risk assessment due to the consumption of natural spring mineral water is critical for ensuring public health and safety. This study aims at investigating the radioactivity concentration levels of natural radionuclides ²²⁶Ra, ²³²Th and ⁴⁰K and the associated radiological health risk in commercial natural spring bottled water in South Africa. A total of 21 of the most-consumed bottled drinking water brands from grocery stores, were analysed using the HPGe gamma detector. The results indicate that the range of radioactivity concentrations is from 1.060 ± 0.067 to 2.571 ± 0.143 BqL⁻¹, with a mean of 1.766 ± 0.399 BqL⁻¹ for ²²⁶Ra; 1.736 ± 0.112 to 7.807 ± 0.099 BqL⁻¹, with a mean of 3.688 ± 1.371 BqL⁻¹ for ²³²Th and 149.000 ± 38.480 to 242.900 ± 59.700 BqL⁻¹ with a mean of 220.229 ± 22.297 BqL⁻¹ for ⁴⁰K. The potential radiological health risks evaluated show mean values for Ra_eq, D_Ab, AEID and AGED as 23.976 ± 0.446 BqL⁻¹, 12.232 ± 1.445 nGyh⁻¹, 0.060 ± 0.007 mSvy⁻¹ and 0.090 ± 0.027 mSvy⁻¹, respectively. The radiation dose based on age group is in the order of infants (≤1 year) > teenagers (12–17 years) > children (1–12 years) > adults (>17 years). The activity concentrations of radionuclides in bottled water are ranked in the order of ⁴⁰K > ²³²Th > ²²⁶Ra, with ²³²Th contributing the highest radiation dose, consistent with findings reported in previous studies. The findings reveal that the activity concentration levels and estimated radiological health risks are within the permissible limits set by UNSCEAR guidelines. Therefore, the consumption of bottled water is radiologically safe. However, the findings also suggest that 12 out of 1000 individuals may suffer cancer fatality, while 6 out of 1 million individuals may experience hereditary effects over their lifetime from the consumption of bottled water. Regular monitoring and stringent regulatory controls are recommended to ensure the radiological safety of bottled drinking water in South Africa. Full article

Background: Legionella species are the causative agent of Legionnaires’ disease and, as ubiquitous waterborne bacteria, are prone to antimicrobial resistance gene (ARG) acquisition and dissemination due to the antimicrobial contamination of natural environments. Given the potential health risks associated with ARGs, it is crucial to assess their presence in the Legionella population. Methods: The ARGs lpeAB and tet56 were detected in 348 samples, isolates, and DNA extracts using conventional PCR. In a subset of lpeAB-positive isolates, azithromycin (AZT) MIC values were obtained using the EUCAST protocol and LpeAB activity was evaluated through an efflux pump inhibition assay. Results: The lpeAB gene was found in 19% (66/348) of samples, with higher detection rates in the L. pneumophila and L. pneumophila sg1 subgroups, at 30% and 41%, respectively. A positive association between lpeAB and L. pneumophila sg1 was found. The MIC values of the lpeAB-positive isolates ranged from 0.064 to 2 mg/L. LpeAB inhibition resulted in 2- and 4-fold MIC reductions in 10 of the 13 isolates analyzed. One sample each of L. longbeacheae and L. bozemanae was found to possess the tet56 gene. Conclusions: The lpeAB gene is predominant in L. pneumophila sg1. A few isolates with the lpeAB gene exhibited MIC values below the EUCAST tentative highest MIC values for wild-type isolates. Expanding ARG monitoring in Legionella is essential to assess the public health risk of Legionnaires’ disease. Full article

With the compelling popularity of integrated sensing and communication (ISAC), Wi-Fi sensing has drawn increasing attention in recent years. Starting from 2010, Wi-Fi channel state information (CSI)-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition, and vital sign monitoring. In this paper, we retrospect the latest achievements of Wi-Fi sensing using commodity-off-the-shelf (COTS) devices from the past 5 years in detail. Specifically, this paper first presents the background of the CSI signal and related sensing models. Then, recent studies are categorized from two perspectives, i.e., according to their application scenario diversity and the corresponding sensing methodology difference, respectively. Next, this paper points out the challenges faced by Wi-Fi sensing, including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical Wi-Fi sensing in real-life applications. Full article

Hyperspectral imaging is currently under active development as a method for remote sensing, environmental monitoring and biomedical diagnostics. The development of hyperspectral sensors is aimed at their miniaturization and reducing the cost of components for the purpose of the widespread use of such devices on unmanned aerial vehicles and satellites. In this review, we present a broad overview of recent work on the development of hyperspectral devices’ configurations, studies aimed at modifying sensors and the possibility of reducing the cost of components of such devices. In addition, we will present the main trends in the development of hyperspectral device configurations for ubiquitous applications. Full article

The antibiotic resistance crisis has seriously jeopardized public health and human safety. As one of the ways of horizontal transfer, transformation enables bacteria to acquire exogenous genes naturally. Bisphenol compounds are now widely used in plastics, food, and beverage packaging, and have become a new environmental pollutant. However, their potential relationship with the spread of antibiotic resistance genes (ARGs) in the environment remains largely unexplored. In this study, we aimed to assess whether the ubiquitous bisphenol S (BPS) could promote the transformation of plasmid-borne ARGs. Using plasmid pUC19 carrying the ampicillin resistance gene as an extracellular ARG and model microorganism E. coli DH5α as the recipient, we established a transformation system. Transformation assays revealed that environmentally relevant concentrations of BPS (0.1–10 μg/mL) markedly enhanced the transformation frequency of plasmid-borne ARGs into E. coli DH5α up to 2.02-fold. Fluorescent probes and transcript-level analyses suggest that BPS stimulated increased reactive oxygen species (ROS) production, activated the SOS response, induced membrane damage, and increased membrane fluidity, which weakened the barrier for plasmid transfer, allowing foreign DNA to be more easily absorbed. Moreover, BPS stimulates ATP supply by activating the tricarboxylic acid (TCA) cycle, which promotes flagellar motility and expands the search for foreign DNA. Overall, these findings provide important insight into the role of bisphenol compounds in facilitating the horizontal spread of ARGs and emphasize the need to monitor the residues of these environmental contaminants. Full article

Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. Full article

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm² with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively. Full article

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. Full article