In this work, we obtained three new phosphorescent iridium complexes (Ir1-Ir3) of general stoichiometry [Ir(N^C)2(N^N)]Cl decorated with oligo(ethylene glycol) fragments to make them water soluble and biocompatible, as well as to protect them from aggregation with biomolecules such as albumin. The major photophysical characteristics of these phosphorescent complexes are determined by the nature of two cyclometallating ligands (N^C) based on 2-pyridine-benzothiophene, since quantum chemical calculations revealed that the electronic transitions responsible for the excitation and emission are localized mainly at these fragments. However, the use of various diimine ligands (N^N) proved to affect the quantum yield of phosphorescence and allows for changing the complexes sensitivity to oxygen, due to the variations in the steric accessibility of the chromophore center for O2 molecules. It was also found that the N^N ligands made possible to tune the biocompatibility of the resulting compounds. The wavelengths of the Ir1-Ir3 emission maxima fall in the range of 630-650 nm, the quantum yields reach 17% (Ir1) in deaerated solution and sensitivity to molecular oxygen, estimated as ratio of emission lifetime in deaerated and aerated water solution, displays the highest value 8.2 for Ir1. The obtained complexes feature low toxicity, good water solubility and the absence of a significant effect of biological environment components on the parameters of their emission. Of the studied compounds Ir1 and Ir2 have been chosen for in vitro and in vivo biological experiments aimed at estimation of oxygen concentration in cell lines and tumors. These sensors have demonstrated their effectiveness for mapping the distribution of oxygen and for monitoring hypoxia in biological objects studied.

The suspended test masses of gravitational-wave (GW) detectors require precise alignment to be able to operate the detector stably and with high sensitivity. This includes the continuous counter-acting of seismic disturbances, which, below a few Hertz, are not sufficiently reduced by the seismic isolation system. The residual angular motion of suspended test masses is further suppressed by the Angular Sensing and Control (ASC) system. However, in doing so, the angular motion can be enhanced by the ASC at higher frequencies where the seismic isolation system is very effective. This has led to sensitivity limitations between about 10 Hz and 25 Hz of the LIGO detectors in past observation runs. The observed ASC noise was larger than simple models predict, which means that more accurate detector models and new simulation tools are required. We present Lightsaber, a new time-domain simulator of the ASC in LIGO. The simulation is a nonlinear simulation of the optomechanical system consisting of the high-power cavity laser beam and the last two stages of suspension in LIGO including the ASC. The main noise inputs are power fluctuations of the laser beam at the input of the arm cavities, read-out noise of sensors used for the ASC, displacement noise from the suspension platforms, and noise introduced by the suspension damping loops. While the plant simulation uses local degrees of freedom of individual suspension systems, the control is applied on a global angular basis, which requires a conversion between the local and global bases for sensing and actuation. Some of the studies that can be done with this simulation concern mis-centering of the beam-spot (BS) position on the test masses, the role of laser power fluctuations for angular dynamics, and the role of the various nonlinear dynamics.

Quorum sensing (QS) describes a process by which bacteria can sense the local cell density of their own species, thus enabling them to coordinate gene expression and physiological processes on a community-wide scale. Small molecules called autoinducers or QS signals, which act as intraspecies signals, mediate quorum sensing. As our knowledge of QS has progressed, so too has our understanding of the structural diversity of QS signals, along with the diversity of bacteria conducting QS and the range of ecosystems in which QS takes place. It is now also clear that QS signals are more than just intraspecies signals. QS signals mediate interactions between species of prokaryotes, and between prokaryotes and eukaryotes. In recent years, our understanding of QS signals as mediators of algae–bacteria interactions has advanced such that we are beginning to develop a mechanistic understanding of their effects. This review will summarize the recent efforts to understand how different classes of QS signals contribute to the interactions between planktonic microalgae and bacteria in our oceans, primarily N-acyl-homoserine lactones, their degradation products tetramic acids, and 2-alkyl-4-quinolones. In particular, this review will discuss the ways in which QS signals alter microalgae growth and metabolism, namely as direct effectors of photosynthesis, regulators of the cell cycle, and as modulators of other algicidal mechanisms. Furthermore, the contribution of QS signals to nutrient acquisition is discussed, and finally how microalgae can modulate these small molecules to dampen their effects.

Acinetobacter baumannii is a nosocomial pathogen capable of causing serious infections associated with high rates of morbidity and mortality. Due to its antimicrobial drug resistance profile, A. baumannii is categorized as an urgent priority pathogen by the Centers for Disease Control and Prevention in the United States and priority group 1 critical microorganism by the World Health Organization. Understanding how A. baumannii adapts to different host environments may provide critical insights into strategically targeting this pathogen with novel antimicrobial and biological therapeutics. Exposure to human fluids was previously shown to alter the gene expression profile of a highly drug susceptible A. baumannii strain A118 leading to persistence and survival of this pathogen. Herein, we explore the impact of human pleural fluid (HPF) and human serum albumin (HSA) on the gene expression profile of a highly multi-drug resistant strain of A. baumannii AB5075. Differential expression was observed for ~30 genes, whose products are involved in quorum sensing, quorum quenching, iron acquisition, fatty acid metabolism, biofilm formation, secretion systems and type IV pilus formation. Phenotypic and further transcriptomic analysis using quantitative RT-PCR confirmed RNA-seq data and pointed out a distinctive role of HSA as the molecule involved in A. baumannii response.

It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

The present paper describes the development of a prosthetic hand based on the human hand anatomy. The hand phalanges are printed by using 3D printed with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables to create personalized joins that allow the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand grasping start. Their use may provide the prosthetic hand the possibility of classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate with more naturalness by adding conditions and classifications to the Electromyogram sensor.

Cellular communications play pivotal roles in multi-cellular species, but they do so also in uni-cellular species. Moreover, cells communicate with each other not only within the same individual but also with cells in other individuals belonging to the same or other species. These communications occur between two unicellular species, two multicellular species, or between unicellular and multicellular species. The molecular mechanisms involved exhibit diversity and specificity, but they share common basic features which allow common pathways of communication between different, and sometimes very different species. These interactions have been made possible by the high degree of conservation of the basic molecular mechanisms of interaction of many ligand-receptor pairs in evolutionary remote species. These inter-species cellular communications played crucial roles during Evolution and must have been positively selected, particularly when collectively beneficial in hostile environments. We think that communications between cells did not arise after their emergence but was part of the very nature of first cells. Synchronization of populations of non-living protocells through chemical communications may have been a mandatory step towards their emergence as populations of living cells and explain the large commonality of cell communication mechanisms among microorganisms, plants, and animals.

With the widespread of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose to augment the time series signals from inertia sensors with signals from ambient sensing to train deep convolutional neural networks (DCNN) models. DCNN provides the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertia and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors such as environment noise level and illumination, with an overall improvement of 5.3% accuracy.

Wi-Fi tethering using a mobile device (e.g., a smartphone or a tablet) as a hotspot for other devices has become a common practice. Despite the potential benefits of Wi-Fi tethering, the open source nature of mobile operating systems (e.g., Google Android) can be abused by a selfish device to manipulate channel-access parameters to gain an unfair advantage in throughput performance. This can cause serious performance problems within a well-planned Wi-Fi network due to an unauthorized selfish or misconfigured tethering device interfering with nearby well-planned access points (APs). In this paper, we demonstrate that the selfish behavior of a tethering node that adjusts the clear channel assessment (CCA) threshold has strong adverse effects in a multi-AP network, while providing the selfish node a high throughput gain. To mitigate this problem, we present a passive online detection scheme that identifies the network condition and detects selfish tethering nodes with high accuracy by exploiting the packet loss information of on-going transmissions. To the best of our knowledge, this is the first research to consider the problem of detecting a selfish tethering node in managed Wi-Fi networks.

Pseudomonas aeruginosa is an emerging opportunistic pathogen responsible for cystic fibrosis and nosocomial infections. In addition, empirical treatments are become inefficient due to their multiple-antibiotic resistance and extensive colonizing ability. Quorum sensing (QS) plays a vital role in the regulation of virulence factors in P. aeruginosa. Attenuation of virulence by QS inhibition could be an alternative and effective approach to control infections. Therefore, we sought to discover new QS inhibitors (QSIs) against LasR receptor in P. aeruginosa using chemoinformatics. Initially, a structure-based high-throughput virtual screening was performed using the LasR active site that identified 61404 relevant molecules. E-pharmacophore (ADAHH) screening of these molecules rendered 72 QSI candidates. In standard-precision docking, only 7 compounds were found as potential QSIs due to their higher binding affinity to LasR receptor (-7.53 to -10.32 kcal/mol compared to -7.43 kcal/mol of native ligands). The ADMET properties of these compounds were suitable to be QSIs. Later, extra-precision docking and binding energy calculation suggested ZINC19765885 and ZINC72387263 as the most promising QSIs. The dynamic simulation of the docked complexes showed good binding stability and molecular interactions. The current study suggested that these two compounds could be used in P. aeruginosa QS inhibition to combat bacterial infections.

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterization in plant leaves utilizing the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterization kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5\% to 22\%, and increased from 83.12\% to 99.33\% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical systems with inherent scale separation that impact the system dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the system resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, system dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.

The calcium-sensing receptor (CAS), as a chloroplast thylakoid membrane protein, involved in the process of [Ca2+] ext-induced [Ca2+]cyt increase (CICI) in the plant. However, the underlying mechanism regulating this process is lacking. Furthermore, recent evidence suggests that CAS may perform additional roles in the plant. Here, we provide an update covering the multiple roles of CAS in stomatal movement regulation and calcium signaling in the plant. We also analysis the possible phosphorylation mechanism of CAS by light and discuss the role of CAS in abiotic stress (drought, salt stress) and biotic stresses (plant immune signaling). Finally, we provide a perspective for future experiments which are required to fill gaps in our understanding of the biological function of CAS in the plant.

In this paper, the far field and near field optical responses of a gold nanoparticle are studied and simulated numerically. The electromagnetic field was excited by an electric dipole located near one end of the nanorod, which is used to model the emission of a quantum dot. Another excitation method was also simulated in which an incident plane wave is used. The excitation of dark plasmon modes of the gold nanorod is presented. The Poynting equation was solved numerically to study the influence of the gold nanorod on the dipole radiative power. In addition, the extinction cross section of the gold nanoparticle illuminated by the incident plane wave was calculated to estimate the amount of the scattered and absorbed light.

People localization is a key building block in many applications. In this paper, we propose a deep learning based approach that significantly improves the localization accuracy and reduces the runtime of Wi-Fi based localization systems. Three variants of the deep learning approach are proposed, a sub-task architecture, an end-to-end architecture, and an architecture that incorporates prior knowledge. The performance of the three architectures under different conditions is evaluated and the significant improvement of the three architectures over existing approaches is demonstrated.

Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into data-driven models. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches encode the physics into an underlying sparse basis space that spans the manifold to generate well-posedness. To achieve this, one commonly uses generic orthogonal Fourier basis or data specific proper orthogonal decomposition (POD) basis to reconstruct from sparse sensor information at chosen locations. Such a reconstruction problem is well-posed as long as the sensor locations are incoherent and can sample the key physical mechanisms. The resulting inverse problem is easily solved using $l_2$ minimization or if necessary, sparsity promoting $l_1$ minimization. Given the proliferation of machine learning and the need for robust reconstruction frameworks in the face of dynamically evolving flows, we explore in this study the suitability of non-orthogonal basis obtained from Extreme Learning Machine (ELM) auto-encoders for sparse reconstruction. In particular, we assess the interplay between sensor quantity and sensor placement for a given system dimension for accurate reconstruction of canonical fluid flows in comparison to POD-based reconstruction.

Aiming at the problems that large amount of video monitoring image data in underground coal mines leads to difficulties in transmission and storage, compressed sensing theory is introduced to encode and decode video images, and a new distributed video coding scheme is proposed. In order to obtain more sparse representation and more general applicability, a block-based adaptive sparse base scheme is proposed. For the acquisition of side information, fixed weight is usually used to synthesize side information and the correlation between different image blocks is neglected, a block-based classification weighted side information generation scheme is proposed. Experimental results show that the block-based classification codec scheme can make full use of inter-frame correlation. Under the appropriate sampling rate, the PSNR value of video reconstruction increases, which effectively improves the quality of video frame reconstruction.

Fiber optic sensors cannot measure damage; for getting information about damage from strain measurements, additional strategies are needed, and several alternatives have been proposed. This paper discuss two independent concepts: the first one is based on detecting the new strains appearing around a damage spot; the structure does not need to be under loads; the technique is very robust, damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers a wider coverage of the structure, it is based on identifying the changes caused by the damage on the strains field in the whole structure for similar external loads. Damage location does not need to be known a priori, detectability is dependent upon the sensors network density, damage size and the external loads. Examples of application to real structures are given.

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for Multiple Sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for Multiple Sclerosis patients.

Introduction: Rural Americans face significant health disparities due to limited access to healthcare services. Telehealth has emerged as a promising solution to mitigate these disparities by facilitating remote access to medical care. This paper comprehensively reviews the advantages, challenges, and implementation strategies of telehealth in rural areas.Method: A narrative review methodology was employed to synthesize existing literature and case studies on telehealth in rural settings. Databases such as PubMed, Google Scholar, and JSTOR were searched for relevant studies published in the last three decades. Keywords such as "telehealth," "rural health," and "healthcare disparities" guided the search process. The review focused on studies assessing the impact of telehealth on healthcare access, cost-effectiveness, and quality of care in rural areas.Results: The review findings indicate that telehealth significantly enhances healthcare access by reducing travel distances, increasing availability of specialist consultations, and promoting patient engagement. It improves chronic disease management and patient satisfaction by enabling convenient access to healthcare services at home. Despite initial setup costs and challenges such as broadband access and regulatory issues, telehealth demonstrates long-term cost savings through reduced travel expenses and improved healthcare efficiency. Quality of care is enhanced through improved communication between healthcare providers and patients, leading to better treatment adherence and health outcomes.Conclusion: Telehealth represents a transformative approach to address healthcare disparities in rural areas. It offers a cost-effective solution to improve access to healthcare services, enhance patient outcomes, and reduce healthcare costs in the long run. However, successful implementation requires addressing infrastructure challenges, refining reimbursement policies, and ensuring regulatory support. Future efforts should focus on expanding broadband infrastructure, refining policy frameworks, and conducting further research to optimize telehealth's impact on rural healthcare delivery.

Thoracic aortopathy encompasses a spectrum of conditions affecting the aorta, ranging from aneurysms to dissections, posing significant challenges in management and care. In recent years, remote care, facilitated by telemedicine and digital health technologies, has emerged as a promising approach to overcome barriers in delivering timely and efficient healthcare services. This paper explores the practical considerations and challenges associated with implementing remote care strategies in the management of thoracic aortopathy in India with the largest population in the world. By evaluating existing literature and drawing upon clinical experiences, we discuss the potential benefits, limitations, and best practices for incorporating remote care into the management of patients with thoracic aortopathy. Additionally, we address important factors such as patient engagement, technology integration, privacy concerns, and regulatory requirements to optimize the delivery of remote care in this specialized field.

Indigenous Peoples in Canada have shown great strength and resilience in maintaining their cultures and ways of life to date in the face of settler colonialism. Centering the water crises within Indigenous sovereignty and self-determination, we explore the impacts these crises are having on community members. Particularly, the continuous failure of the Canadian government to end the water crises in remote Indigenous communities, Star Blanket Cree Nation, is investigated in this paper. What implications have these water governance gaps had on Indigenous Peoples’ sovereignty and self-determination? We adopted an Indigenist theoretical framework to guide the study. Additionally, a community-based participatory research approach was adopted. To achieve our research goals of investigating the implications of the current water crises for Indigenous sovereignty and self-determination, specific methods of sharing circles were used to gather knowledge from community members, Elders, and knowledge keepers. The research findings strongly highlight the strength and resilience shown by remote Indigenous communities in the face of the current water crises and continuous government failure. Solving the current water crises will involve remote Indigenous communities taking charge of their own water governance through Indigenous-led water governance systems. Additionally, taking steps to rebuild trust through genuine reconciliation will be key. Therefore, listening to remote Indigenous communities and taking collaborative action is fundamental.

The advent of remote work has ushered in a new era of economic transformation, redefining the dynamics of local economies, and unleashing the multiplier effect on a national scale. This abstract explores the profound implications of remote work on local economies, dissecting how the multiplier effect operates within a nation's economic landscape. The multiplier effect is a fundamental economic concept that highlights how changes in spending and investment can create a ripple effect, leading to a magnified impact on an economy. This concept gains new significance in the context of remote work, as it triggers a chain reaction of economic activities that radiate from remote workers to their local communities. The increased flexibility and reduced geographical constraints of remote work have reshaped the allocation of resources. The continued reliance on remote work may lead to a reduction in demand for commercial real estate in urban centers, necessitating adaptive urban planning strategies. In conclusion, the rise of remote work has initiated a transformative era for local economies, driven by the multiplier effect. By amplifying the impacts of spending and investment, remote work fosters job diversification, innovation, and infrastructure development within communities. As remote work continues to reshape the landscape of work and life, understanding and harnessing its multiplier effect remains paramount for policymakers, businesses, and local communities alike. The multiplier effect is a concept in economics that illustrates how changes in spending can lead to larger impacts on a nation's economy through a chain reaction of economic activities. It demonstrates that an initial injection of spending, whether from government expenditure, investment, or consumption, doesn't just have a one-time impact. Instead, it sets off a series of interconnected spending and income-generation processes that result in a larger overall increase in economic activity. t's important to note that the multiplier effect can work in reverse as well. If there's a decrease in spending, it can lead to a decrease in economic activity and income through the same chain reaction process. This concept highlights the interconnected nature of economic activities and emphasizes the significance of changes in spending patterns on a nation's overall economic health. The magnitude of the multiplier effect is influenced by factors such as the marginal propensity to consume (the proportion of additional income that households spend), leakages (savings and taxes that reduce the multiplier effect), and the extent to which additional spending creates additional production capacity within the economy. Understanding the multiplier effect is essential for policymakers, as it helps them gauge the potential impacts of changes in fiscal and monetary policies on the economy.

A partial aperture onboard calibration method can solve the onboard calibration problems of some large aperture remote sensors, which is of great significance for the development trend of increasingly large apertures in optical remote sensors. In this paper, the solar diffuser reflectance degradation monitor (SDRDM) in the onboard calibration assembly (CA) of the FengYun-4 (FY-4) advanced geostationary radiance imager (AGRI) is used as the reference radiometer for measuring the partial aperture factor (PAF) for the AGRI onboard calibration. First, the linear response count variation relationship between the two is established under the same radiance source input. Then, according to the known bidirectional reflection distribution function (BRDF) of the solar diffuser (SD) in the CA, the relative reflectance ratio coefficient between the AGRI observation direction and the SDRDM observation direction is calculated. On this basis, the response count value of the AGRI and the SDRDM is used to realize the high-precision measurement of the PAF of the AGRI B1 ~ B3 bands by simulating the AGRI onboard calibration measurement under the illumination of a solar simulator in the laboratory. According to the determination process of the relevant parameters of the PAF, the measurement uncertainty of the PAF is analyzed; this uncertainty is better than 2.04% and provides an important reference for the evaluation of the onboard absolute radiometric calibration uncertainty after launch.

It is important to distinguish between natural earthquakes and those induced by CO2 injection at carbon capture and storage sites. For example, the 2004 Mw 6.8 Chuetsu earthquake occurred close to the Nagaoka CO2 storage site during gas injection, but we could not quantify whether the earthquake was due to CO2 injection or not. Here we simulated changes of pore pressure during CO2 injection at the Nagaoka site and compared them with estimated natural seasonal fluctuations of pore pressure due to rainfall and snowmelt as well as estimated pore pressure increases related to remote earthquakes. We clearly distinguished changes of pore pressure due to CO2 injection from those due to rainfall and snowmelt. The simulated local increase in pore pressure at seismogenic fault area was much less than the seasonal fluctuations related to precipitation and increases caused by remote earthquakes, and the lateral extent of pore pressure increase was insufficient to influence seismogenic faults. We also demonstrated that pore pressure changes due to distant earthquakes are capable of triggering slip on seismogenic faults. The approach we developed could be used to distinguish natural from injection-induced earthquakes and will be useful for that purpose at other CO2 sequestration sites.

Gene editing tools such as CRISPR-Cas9 have created unprecedented opportunities for genetic studies in plants and animals. We designed a course-based undergraduate research experience (CURE) to train introductory biology students in the concepts and implementation of gene editing technology as well as develop their soft skills in data management and scientific communication. We present two versions of the course that can be implemented with twice-weekly meetings over a five-week period. In the remote-learning version, students perform homology searches, design guide RNAs and primers, and learn the principles of molecular cloning. This version is appropriate when access to laboratory equipment or in-person instruction is limited, such as closures that have occurred in response to the Covid-19 pandemic. In the in-person version, students design guide RNAs, clone CRISPR-Cas9 constructs, and perform genetic transformation of the model plant Arabidopsis thaliana. The highly parallel nature of the CURE makes it possible to target dozens to hundreds of genes, depending on the number of course sections available. Applying this approach in a sensitized mutant background enables focused reverse genetic screens for genetic suppressors or enhancers. The course can be readily adapted to other organisms or projects that employ gene editing.

Near-field (NF) integrated sensing and communication (ISAC) has the potential to revolutionize future wireless networks. It enables simultaneous communication and sensing operations on the same radio frequency (RF) resources using a shared hardware platform, maximizing resource utilization. NF-ISAC systems can improve communication and sensing performance compared to traditional far-field (FF) ISAC systems by employing the unique propagation characteristics of NF spherical waves with an additional distance dimension. Despite its potential, the NF-ISAC literature covers just a few specialized topics. A comprehensive survey encompassing all aspects of NF-ISAC systems has thus far been lacking. To this end, this paper systematically explores the prodigious potential of NF-ISAC technology. Specifically, an in-depth analysis of both NF and FF systems is provided, investigating their applicability in communication and sensing scenarios. Various channel model scenarios for NF and FF are discussed, emphasizing their distinguishing features. The advantages and philosophies of ISAC are further explored, opening opportunities to investigate both narrow-band and wide-band systems within NF ISAC. Case studies and simulation examples for each NF-ISAC design philosophy are provided to gain deeper insights into these system designs. An extensive literature review of existing NF-ISAC studies is conducted, exploring various methodologies, potentials, prospects, and conclusions. Finally, prospective research areas, remaining challenges, and applications of future NF-ISAC systems are discussed.

The objective of this study, which is divided into two parts, is twofold: to address long standing challenges in the sensing of atmospheric turbulence-induced wavefront aberrations in strong scintillation conditions via comparative analysis of several basic scintillation resistant wavefront sensing (SR-WFS) architectures and iterative phase retrieval (IPR) techniques (Part I, this paper), and to develop a framework for the potential integration of SR-WFS techniques into practical closed-loop non-astronomical atmospheric adaptive optics (AO) systems (Part II). In this paper we consider basic SR-WFS mathematical models and phase retrieval algorithms, tradeoffs in sensor design and phase retrieval technique implementation, and methodologies for WFS parameter optimization and performance assessment. The analysis is based on wave-optics numerical simulations imitating realistic turbulence-induced phase aberrations and intensity scintillations, and optical field propagation inside SR-WFSs. Several potential issues important for practical implementation of SR-WFS and IPR techniques such as requirements for phase retrieval computational grid resolution, tolerance with respect to optical element misalignments, and the impact of camera noise and input light non-monochromaticity are also considered. Results demonstrate that major wavefront sensing requirements desirable for AO operation in strong intensity scintillations can potentially be achieved by transitioning to novel SR-WFS architectures based on iterative phase retrieval techniques.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting existing terrestrial fiber infrastructure. Employing a Waveplate model, we integrate real ground displacement data from seven earthquakes, magnitudes ranging from four to six, to simulate the strains within fiber cables and collect large set of light’s polarization evolution data. These simulations help to enhance a Machine-Learning model that is trained and validated to detect Primary waves arrivals that precede earthquakes’ destructive Surface waves. The validation results show that the model achieves over 95% accuracy. The Machine-Learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates by a triangulation method and calculate fibers to epicenter distance. This setup allows early warning generation to municipalities close to epicenter location, and progressing to those further away. The model testing shows 98% of accuracy in detecting primary wave and one-second of detection time, affording nearby areas 21-seconds to take countermeasures, and an extended 57-seconds in more distant areas.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

The electronic nose (e-nose) is an innovative device that mimics the human olfactory system, allowing for the detection and identification of numerous scents. This abstract gives an in-depth examination of the current status of electronic nose technologies, with an emphasis on sensing mechanisms, applications, and recent breakthroughs. The research delves into the many uses of e-nose, such as food quality control, environmental monitoring, medical diagnostics, and industrial operations. It also covers the problems and potential future applications of electronic nose technology. The literature review highlights the importance of this technology in improving our capacity to evaluate complex scents in several domains.

The Internet of Things (also known as IoT), a revolution, has enhanced the relevance of mobile employees' job efficiency in mobile crowd sensing (MCS). Incentives based on reputation for mobile workers (MW) play an important role in increasing service utilization, motivating mobile employees, and developing confidence in the service. The cell phone is the most exploited entity in mobile crowd-sensing MCS and cloud computing. In the underlying investigation, we observed that they had not evaluated the complexity level of a task, resulting in a strong reputation for executing multiple basic tasks. A person who completes a tough task may have a lower reputation score. Complexity levels of tasks (CLT) were developed for reputation evaluation on a crowd-sensing network, which would be utilized to evaluate the MW reputation. We offered simple and complex levels of challenges to assess reputation scores. Tasks receive incentives based on their reputation. By measuring reputation on (CLT) complexity level of task, this research will help the system to Maintain the reputation of the mobile worker so that entities of the system get the maximum benefit out of it, by hiring well-reputed mobile workers and MW receive an incentive on it. Furthermore, we conduct a comparative analysis between our scheme and various machine learning algorithms to identify the algorithm that best performs in evaluating task complexity. Considering both the simple and complex reputation scores, the comparison suggests that after analyzing the data, it is clear that the CLT (proposed) scheme and the Linear regression scheme not only outperformed the Neural Network scheme in terms of Complex Reputation Scores but also in terms of Simple Reputation Scores. We also apply statistical tests, such as p-tests and t-tests, to determine the significance of the results obtained from different algorithms. Considering MW 308, MW 345, and MW 1045's efforts and abilities, the MCS system may offer incentives and awards to further motivate and consider their continuing contributions. This reputation-based incentivization technique not only encourages strong competition among mobile workers, but also makes sure the delivery of high-quality services inside the MCS system.

The study aims to evaluate the Quorum Sensing (QS) system inhibition against some Gram-positive and Gram-negative bacteria detected by molecular modeling of R. cathartica L. plant extract. R. cathartica L. methanol extract was evaluated in vitro. The research was carried out for the possible role and mechanism of R. cathartica L. The antibacterial activity of R. cathartica L. seed extract on some Gram-positive and Gram-negative strains was evaluated using culture-based and computational analysis. Gentamicin (CN; 30 µg/ml, Bioanalyse) was used as an antibacterial control of the extract on the production potential of QS-dependent virulence factors on Pseudomonas aeruginosa PAO1 was investigated. Phytochemical analysis resulted in the identification of 23 phenolic phytochemicals. R. cathartica L. extract was observed to have antimicrobial activity against Gram-positive and Gram-negative bacteria at different rates (11.3 mm-16 mm). The inhibitory effect of R. cathartica L. extract on P. aeruginosa PAO1 (elastase 61%, pyocyanin 18%, and biofilm 61%) was recorded moderately. Phytochemical analysis revealed that the main component of the extract is kaempferol. Therefore, the binding potential of kaempferol on QS system receptors was investigated via molecular docking. Computational analysis showed that kaempferol can inhibit the QS system by preventing competitive ligands from connecting to LasR. This study explored the activity and mechanism of action for a new green alternative against resistance. might provide a mechanism for R. cathartica L. seed extract components and thus provide a new avenue for using R. cathartica L. seed extract as a green antibacterial agent.

The carotid body is a major peripheral chemoreceptor that senses changes in arterial blood oxygen, carbon dioxide, and pH, what is important for regulation of breathing and cardiovascular function. The mechanisms by which the carotid body senses O2 and CO2 are well known; conversely, the mechanisms by which it senses pH variations are almost unknown. Here we used immunohistochemistry to investigate how the human carotid body contributes to the detection acidosis analyzing whether it expresses acid-sensing ion channels (ASIC) ion channels, and to know if these channels are in the cells chemosensory glomic cells or in the afferent nerves. ASIC1, ASIC2 and ASIC3, and to a much lesser extent ASIC4, immunoreactivity was detected in subpopulations of type I glomus cells, as well as in the nerves of the carotid body. In addition, immunoreactivity was found for all ASIC subunits in neurons of the petrosal and superior cervical sympathetic ganglia, where afferent and efferent neurons are located, respectively, innervating the carotid body. This study reports for the fist time the occurrence of ASIC proteins in the human carotid body, demonstrating that they are present in glomus chemosensory cells (ASIC1 < ASIC2 > ASIC3 > ASIC4) and nerves, presumably both afferent an efferent supplying the organ. These results suggest that the detection of acidosis by the carotid body can be mediated by ASIC ion channels present in the type I glomus cells or directly by sensory nerve fibers.

In current work, we illustrate the effect of adding small amount of Carbon to very common Co2MnSi Heusler alloy based-glass coated microwires. A significant change in the magnetic and structure structural properties has observed for the new alloy Co2MnSiC compared to the Co2MnSi alloy. The magneto-structural investigations have performed to clarify the main phys-ical parameters i.e., structural & magnetic at a wide range of measuring temperature. The XRD analysis illustrated the well-defined crystalline structure with average grain size (Dg = 29.16 nm) and a uniform cubic structure with A2-type compared to the mixed L21 and B2 cubic structures for Co2MnSi-based glass coated microwires. The magnetic behaviour has investigated at a tem-perature range (5 to 300 K) and an external applied magnetic field (50 Oe to 20 kOe). The adding of small amount of Carbon to the Co2MnSi matrix enhance the magnetic thermal stability, where the thermomagnetic behaviour of Co2MnSiC glass-coated microwires show a perfect stable be-haviour for a temperature range from 300 K to 5 k, the differences between the coercivity value is only 0.3 Oe compared to 4 Oe for Co2MnSi-sample. In addition, M-H loops measured at tempera-ture below 50 K show unsaturated loops; meanwhile the Co2MnSi loops shows a strong antifer-romagnetic coupling for the loops measured below 50 K. By studying the field cooling (FC) and field heating (FH) magnetizations curves at a wide range of external applied magnetic field we detected a critical magnetic field (H = 1 kOe) where FC and FH curves have a stable magnetic behavior for Co2MnSiC sample, such stability does not find in Co2MnSi sample. We proposed a phenomenal expression to estimate the magnetization thermal stability, ΔM (%), of FC and FH magnetization curves and the maximum value is detected at the critical magnetic field where ΔM (%) ≈ 98 %. The promising magnetic stability of Co2MnSiC glass-coated microwires with tem-perature is due to the changing of the microstructure induced by adding Carbon, as the A2-type structure show a unique stability by variation the temperature and the external magnetic field. In addition, a unique internal mechanical stress, which induced during the fabrication process and plays on controlling magnetic behavior with temperature and external magnetic field. The ob-tained results make Co2MnSiC promising candidate for magnetic sensing devices based Heusler glass-coated microwires.

Aldehydes are natural volatile aroma compounds generated by the Maillard reaction of sugars and amino acids in food and affect the flavor of food. They have been reported to exert taste-modifying effects, such as increases in taste intensity at concentrations below the odor detection threshold. The present study examined the taste-enhancing effects of short-chain aliphatic aldehydes, such as isovaleraldehyde (IVAH) and 2-methylbutyraldehyde, attempted to identify the taste receptors involved. The results obtained revealed that IVAH enhanced the taste intensity of taste solutions even under the condition of olfactory deprivation by a noseclip. Furthermore, IVAH activated the calcium-sensing receptor CaSR in vitro. Receptor assays on aldehyde analogues showed that C3-C6 aliphatic aldehydes and methional, a C4 sulfur aldehyde, activated CaSR. These aldehydes functioned as a positive allosteric modulator for CaSR. The relationship between the activation of CaSR and taste-modifying effects was investigated by a sensory evaluation. Taste-modifying effects were found to be dependent on the activation state of CaSR. Collectively, these results suggest that short-chain aliphatic aldehydes function as taste modulators that modify sensations by activating orally expressed CaSR. We propose that volatile aroma aldehydes may also partially contribute to the taste-modifying effect via the same molecular mechanism as kokumi substances.

The rapid development of Deep Learning brought novel methodologies for 3D Object Detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, new technologies, and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it became crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

A nonlinear MEMS multimass sensor is investigated. The MEMS design consists of an array of nonlinear microcantilevers clamped to a shuttle mass which, in turn, is attached to a linear spring and a dashpot. The microcantilevers are made of a polymeric hosting matrix reinforced by aligned carbon nanotubes (CNT) with different CNT volume fractions in each cantilever so as to suitably tune the frequency bandwidth of the device. The advantages of a single input-single output (SISO) system are here exploited for the design, actuation and detection principles as in previous literature.

Smart flexible materials with piezoresistive property are increasingly used in sensors field. When embedded in structures, they would allow an in-situ structural health monitoring and damage assessment of impact loading such as crash, bird strikes and ballistic impacts. However, this could not be achieved without a deep characterization of the relation between piezoresistivity and mechanical behavior. The aim of this paper is to study the potential use of the piezoresistivity effect of a conductive foam made of a flexible polyurethane matrix filled with active carbon for integrated Structural Health Monitoring (SHM) and damage assessment applications. To do so, PolyUrethane Foam filled with Active Carbon, namely PUF-AC, are manufactured and tested under quasi-static compression tests and under Dynamic Mechanical Analyser (DMA) with in-situ measurement of its resistivity during tests. A relation is proposed for describing the evolution of the resistivity versus strain and stress. In addition, a demonstrative experiment of a PUF-AC sample subjected to low velocity impact assesses the interest of this type of materials for damage assessments.

In current study we have obtained Co2FeSi-glass coated microwires with different geometrical aspect ratio, ρ = d/Dtot (diameter of metallic nucleus, d and total diameter, Dtot). The structure and magnetic properties are investigated at a wide range of temperature. XRD analysis illustrates a notable changing in the microstructure by increasing the aspect ratio of Co2FeSi glass coated microwires. Amorphous structure is detected for the sample with the lowest aspect ratio (ρ = 0.23), whereas a growth of crystalline structure is observed in the other samples (aspect ratio ρ = 0.30 and 0.43). This change at the microstructure properties correlates with dramatic changing in magnetic properties. For the sample with the lowest ρ -ratio, non-perfect square loops are obtained with low normalized remanent magnetization. A notable enhancement in the squareness and coercivity are obtained by increasing ρ -ratio. Changing the internal stresses strongly affects the microstructure, resulting in a complex magnetic reversal process. The thermomagnetic curves show large irreversibility for the Co2FeSi with low ρ -ratio. Meanwhile, if we increase the ρ -ratio, the sample shows perfect ferromagnetic behavior without irreversibility. The current result illustrates the ability to control the microstructure and magnetic properties of Co2FeSi-glass-coated microwires by changing only their geometric properties without performing any addition heat treatment. The modification of geometric parameters of Co2FeSi glass-coated microwires allows to obtain microwires which exhibit an unusual magnetization behavior that offers opportunities to understand the phenomena of various types of magnetic domain structures, which is essentially helpful for designing sensing devices based on thermal magnetization switching.

Inositol poly- and pyrophosphates (InsPs and PP-InsPs) are central eukaryotic messengers. These very highly phosphorylated molecules can exist in two distinct conformations, a canonical one with five phosphoryl groups in equatorial positions, and a “flipped” axial conformation. Using 13C-labeled InsPs / PP-InsPs, the behavior of these molecules was investigated by 2D-NMR under solution conditions reminiscent of a cytosolic environment. Remarkably, the most highly phosphorylated messenger InsP8 readily adopts both conformations at physiological conditions. Environmental factors - such as pH, metal cation composition, and temperature - strongly influence the conformational equilibrium. Thermodynamic data revealed that the transition of InsP8 from the equatorial to the axial conformation is, in fact, an exothermic process. The speciation of InsPs and PP-InsPs also affects their interaction with protein binding partners; addition of Mg2+ decreased the binding constant Kd of InsP8 to an SPX protein domain. The results illustrate that PP-InsP speciation reacts very sensitively to solution conditions, suggesting it might act as an environment-dependent molecular switch.

Quorum sensing (QS) is a form of intra- and inter-species communication system which is employed by bacteria to regulate their collective behavior in a cell population-dependent manner. QS has been implicated in the virulence of several pathogenic bacteria. This work aimed to investigate the anti-quorum sensing (anti-QS) potential of ethanolic extracts of eight aromatic plants of Cyprus namely, Origanum vulgare subsp. hirtum, Rosmarinus officinalis, Salvia officinalis, Lavendula spp., Calendula officinalis, Melissa officinalis, Sideritis cypria, and Aloysia citriodora. We initially assess the effects of the extracts on autoinducer 2 (AI-2) signaling activity, using Vibrio harveyi BB170 as a reported strain. We subsequently assess the effect of the ethanolic extracts on QS-related processes including biofilm formation and swarming and swimming motilities of Escherichia coli MG1655. Of the tested ethanolic extracts those of Origanum vulgare subsp. hirtum, Rosmarinus officinalis, and Salvia officinalis were the most potent AI-2 signaling inhibitors while the extracts from the other plants exhibited low to moderate inhibitory activity. The three ethanolic extracts also inhibited the biofilm formation (>60%) of E. coli MG1655, as well as its swimming and swarming motility in a concentration-dependent manner. These extracts may consider true anti-QS inhibitors because they disrupt QS-related activities of E. coli MG1655 without affecting bacterial growth. The results suggest that plants from the unexplored flora of Cyprus could serve as a source to identify novel anti-QS inhibitors to treat infectious diseases caused by pathogens resistant to antibiotics

Electromagnetic sensing has been used for diverse applications of non-destructive testing, including the surface inspection, measurement of properties, object characterization. However, the measurement accuracy could be significantly influenced by the lift-off between sensors and samples. To address the issue caused by lift-offs, various strategies have been proposed for the permeability measurement of ferromagnetic steels, which mainly involves different sensor designs and signal features (e.g., the zero-crossing feature). In this paper, a single high-frequency scenario for the permeability retrieval is introduced. By combining the signal of two sensing pairs, the retrieval of magnetic permeability is less affected by the lift-off of sensors. Unlike the previous strategy on reducing the lift-off effect (directly taking the phase term out of the integration) using the Dodd-Deeds analytical method, the proposed method is based on a high-frequency linear feature of the phase term. Therefore, this method has the merit of high accuracy and fast processing for the permeability retrieval (a simplified version of Dodd-Deeds analytical formulas after the integration). Experimental measurement has been carried out on the impedance measurement of designed sensors interrogating ferromagnetic dual-phase steels. For sensor lift-offs of up to 10 mm, the error of the permeability retrieval is controlled within 4 % under the optimal frequency.

Atomtronics is a relatively new subfield of atomic physics that aims to realize the device behavior of electronic components in ultracold atom-optical systems. The fact that these systems are coherent makes them particularly interesting since, in addition to current, one can impart quantum states onto the current carriers themselves or perhaps perform quantum computational operations on them. After reviewing the fundamental ideas of this subfield, we report on the theoretical and experimental progress made towards developing externally-driven and closed loop devices. The functionality and potential applications for these atom analogs to electronic and spintronic systems is also discussed.

Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well-suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection will be discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays will be discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.

In this paper, we present and analyze a compact inner-wall grating slot microring resonator (IG-SMRR) with the footprint of less than 13 μm × 13 μm on the SOI platform for label-free sensing, which comprises a slot microring resonator (SMRR) and inner-wall grating (IG). Its detection range is significantly enhanced without the limitation of the free spectral region (FSR) owing to the combination of SMRR and IG. Structural parameters of IG and SMRR are investigated and optimized for favorable transmission properties. The simulation results shows that the IG-SMRR has an ultra-large quasi-FSR of 84.6 nm, and the concentration sensitivities of sodium chloride solutions and D-glucose solutions are up to 960.61 pm/% and 933.06 pm/%, respectively. The investigation on the combination of SMRR and IG is a valuable exploration of label-free sensing application for ultra-large detection range and ultra-high sensitivity in future.

Visual Evoked Potentials (VEPs) are used in clinical applications in ophthalmology, neurology and extensively in brain computer interface (BCI) research. BCI literature covers steady state VEP (SSVEP) and code modulated VEP (c-VEP) BCIs along with sophisticated methods to improve information transfer rates (ITR). There is a gap of knowledge regarding the VEP adaptation dynamics, physiological generation mechanisms and relation with BCI performance. A simple dual display VEP switch was developed to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50 and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits Quasi-Steady-State VEPs (QSS-VEPs) that are utilized for simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs and it is assumed that it shares the similar generators of the SSVEPs. Eight subjects were recorded and the performance of the overall system was analyzed by means of Receiver Operating Characteristic (ROC) curves, accuracy plots and ITRs. In summary QSS-VEPs performed better than transient VEPs. It was found that in general 32rps stimulation had the highest ROC area, accuracy and ITRs in general. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated. Moreover, QSS-VEPs were found to lead to higher accuracy by the template matching compared to SSVEPs at 10rps and 32rps.

Food preservatives are compound that are used for the treatment of food to improve the shelf life. In the food industry, is necessary to monitor all processes, for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. In the present work, is reported the fabrication of a novel sensor array based on SnO2, CuO and WO₃ nanowires deposited on top of commercial μHPs, provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide and ozone. Results are very promising: the sensors array was able to operate for long time consuming less than 50mW for each single sensor, and PCA analysis confirms that the device was able to discriminate between different compounds.

The increasingly common occurrence of antibiotic-resistant bacteria has become an urgent public health issue. There are currently some infections without any effective treatment, which require new therapeutic strategies. An attractive alternative is the design of compounds capable of disrupting bacterial communication known as quorum sensing (QS). In gram-negative bacteria, such communication is regulated by acyl-homoserine lactones (AHLs). QS allows bacteria to proliferate before expressing virulence factors. Our group previously reported that hexyloxy phenyl imidazoline (9) demonstrated 71% inhibitory activity of QS at 100 µM (IC50=90.9 µM) in Chomobacterium violaceum, a gram-negative bacterium. The aim of the present study was to take 9 as a lead compound to design and synthesize three 2-imidazolines (13-15) and three 2-oxazolines (16-18), to be evaluated as quorum sensing inhibitors on C. violaceum CV026. We were looking for compounds with a higher affinity towards the Cvi receptor of this bacterium and the ability to inhibit QS. The binding mode of the test compounds on the Cvi receptor was explored with docking studies and molecular dynamics. It was found that 8-pentyloxyphenyl-2-imidazoline 13 reduced the production of violacein (IC50=56.38 µM) without affecting bacterial growth, suggesting inhibition of quorum sensing. Indeed, compound 13 is apparently one of the best QS inhibitors known to date. Molecular docking revealed the affinity of compound 13 for the orthosteric site of the N-hexanoyl homoserine lactone (C₆-AHL) on the CviR protein. Ten aminoacid residues in the active site of C₆-AHL interacted with 13, and 7 of these are the same as those interacting with AHL. Contrarily, 8-octyloxyphenyl-2-imidazoline 14, 8-decyloxyphenyl-2-imidazoline 15 and 9-decyloxyphenyl-2-oxazoline 18 bound only to an allosteric site and thus did not compete with C₆-AHL for the orthosteric site.

Localizing the source of a signal is often as important as deciphering the signal’s message. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. Comparing these strategies across modalities and model systems allows a broader understanding of the general principles shaping spatial processing. In this review we focus on the electrosensory system of knifefish and provide an overview of our current understanding of spatial processing in this system, in particular, localization of conspecific signals. We argue that many mechanisms observed in other sensory systems, such as the visual or auditory systems, have comparable implementations in the electrosensory system. Our review therefore describes a field of research with unique opportunities to provide new insights into the principles underlying spatial processing.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion as it not only offers extra information on the event described but also enhances trust on the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for IoT-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. Moreover, we have successfully assessed their performance using network simulations with realistic urban traces.

Diabetic patients need long-term and frequent glucose monitoring to assist in insulin intake. The current finger-prick devices are painful and costly which make noninvasive glucose sensors highly demanded. In this review paper, we discuss several advanced electromagnetic (EM) wave based technologies for noninvasive glucose measurement, including infrared (IR) spectroscopy, photoacoustic (PA) spectroscopy, Raman spectroscopy, fluorescence, optical coherent tomography (OCT) and microwave sensing. Development and progress of each method are discussed regarding fundamental principle, system setup and experimental results. Despite the promising achievements reported previously, there is no established product to obtain FDA approval or survive marketing test. Limitations and prospects of these techniques are discussed at the end of this review.

Plasmonic gold nanorods play important roles in nowadays state-of-the-art plasmonic sensing techniques. Most of the previous studies and applications focused on gold nanorods with relatively small aspect ratios, where the plasmon wavelengths are smaller than 900 nm. Gold nanorods with large aspect ratios are predicted to exhibit high refractive-index sensitivity (Langmir 2008, 24, 5233–5237.), which therefore should be promising for developing of high-performance plasmonic chemical- and bio-sensors. In this study, we developed gold nanorods with aspect ratios over 7.9, which exhibit plasmon resonances around 1064 nm. The refractive index (RI) sensitivity of these nanorods have been evaluated by varying their dielectric environment, whereby a sensitivity as high as 473 nm/RIU can be obtained. Furthermore, we have demonstrated the large-aspect-ratio nanorods as efficient substrate for surface enhanced Raman spectroscopy (SERS), where an enhancement factor (EF) as high as 9.47×10⁸ was measured using 4-methylbenzenethiol (4-MBT) as probe molecule. Finally, a type of flexible SERS substrate is developed by conjugating the gold nanorods with the polystyrene (PS) polymer. The results obtained in our study can benefit the development of plasmonic sensing techniques utilized in the near-infrared spectral region.

In this study, a flexible strain sensor is devised using corrugated poly(3-hexylthiophene) (P3HT) thin film. In the previous studies, the P3HT-based photoactive thin film was shown to generate direct current (DC) under broadband light, and the generated DC voltage varied with applied tensile strain. Yet, the mechanical resiliency and strain sensing range of the P3HT-based thin film strain sensor were limited due to relatively more brittle thin film constituent—poly(3,4-ethylenedioxythiophene)-polystyrene(sulfonate) (PEDOT:PSS) conductive thin film as a bottom electrode. To address this issue, it is aimed to design mechanically resilient strain sensor using corrugated thin film constituents. Buckling is induced to form corrugation in the thin films by applying pre-strain to the substrate, where the thin films are deposited, and releasing the pre-strain afterwards. It is known that corrugated thin film constituents exhibit different optical and electronic properties from non-corrugated ones. Therefore, to optimize design of the flexible strain sensor, it was studied to understand how the applied pre-strain and thickness of the PEDOT:PSS thin film affect the optical and electrical properties. Also, pre-strain effect on light absorptivity of the corrugated P3HT-based thin films was studied. In addition, strain effect was investigated on the optical and electrical properties of the corrugated thin film constituents. Finally, flexible strain sensors are fabricated by following the design guideline, which is suggested from the studies on the corrugated thin film constituents, and DC voltage strain sensing capability was validated. As a result, flexible strain sensor exhibited tensile strain sensing range up to 5% at frequency up to 15 Hz with maximum gage factor ~7.

This is the second of a two-paper series describing a multi-scale modeling approach developed to simulate crack sensing in polymer fibrous composites by exploiting interruption of electrically conductive carbon nanotube (CNT) networks. The approach is based on the finite element (FE) method. FE models at three different scales, namely the micro-scale, the meso-scale and the macro-scale, have been developed using the ANSYS PDL environment. In the present paper, the meso- and macro-scale analyses are described. In the meso-scale, a two-dimensional model of the CNT/polymer matrix reinforced by carbon fibers is used to develop a crack sensing methodology from a parametric study which relates the crack position and length with the reduction of current flow. In the meso-model, the effective electrical conductivity of the CNT/polymer computed from the micro-scale is used as input. In the macro-scale, the final implementation of the crack sensing methodology is performed on a CNT/polymer/carbon fiber composite volume using as input the electrical response of the cracked CNT/polymer derived at the micro-scale and the crack sensing methodology. Analyses have been performed for cracks of two different lengths. In both cases, the numerical model predicts with good accuracy both the length and position of the crack. These results highlight the prospect of conductive CNT networks to be used as a localized structural health monitoring technique.

Metal oxide p-n heterojunction nanofibers (NFs) are among the most promising approaches to enhancing the efficiency of gas sensors. In this paper, we report the preparation of a series of p-NiO-loaded n-ZnO NFs, namely (1 − x) ZnO-xNiO (x = 0.03, 0.05, and 0.1 wt%), for hydrogen gas sensing experiments. Samples were prepared through the electrospinning technique followed by a calcination process. The sensing experiments showed that the sample with 0.05 wt% NiO loading resulted in the highest sensing performance at an optimal sensing temperature of 200 °C. The sensing mechanism is discussed in detail and contributions of the p-n heterojunctions, metallization of ZnO and catalytic effect of NiO on the sensing enhancements of an optimized gas sensor are analyzed. This study demonstrates the possibility of fabricating high-performance H₂ sensors through the optimization of p-type metal oxide loading on the surfaces of n-type metal oxides.

A new type of gold nanoparticles was synthesized by using benzoyl pyrazolone as a capping agent. The synthesized AuNPs exhibited a spherical shape and a monodisperse and fluorescence emission characteristic peak at 650 nm. The AuNPs –BMPBP was demonstrated as sensitive and selective fluorescent chemosensor for detection of Al ³⁺ ion. In the presence of Al ³⁺ ion, the fluorescent emission of BMPBP-AuNPs at 650 nm, increased with an increasing concentration of Al ³⁺ ion with a low detection limit (2 µM), the proposed method provided to apply determination of Al ³⁺ ion of tap water with satisfactory results.

In this study, a graphene-doped porous silicon (G-doped/p-Si) substrate for low ppm H₂ gas detection by an inexpensive synthesis route was proposed as a potential noble graphene-based gas sensor material and to understand the sensing mechanism. The G-doped/p-Si gas sensor was synthesized by a simple capillary force-assisted solution dropping method on p-Si substrates, whose porosity was generated through an electrochemical etching process. G-doped/p-Si was fabricated with various graphene concentrations and exploited as a H₂ sensor operated at room temperature. The sensing mechanism of the sensor with/without graphene decoration on p-Si was proposed to elucidate the synergetic gas sensing effect generated from the interface between the graphene and p-type silicon.

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improving data storage, transmission and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. By introducing the idea of compressive sensing in compressed reconstruction, the spectral adaptive grouping distributed compressive sensing algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The experimental results showed that comparing with orthogonal matching pursuit(OMP) and gradient projection reconstruction(GPSR), the proposed algorithm can significantly improve the visual effect of image reconstruction in the spatial domain. The PSNR in low sampling rate(sampling rate is lower than 0.2) increases by 13.72dB than OMP and 1.66dB than GPSR. In the spectral domain, the average normalized root mean square error、the mean absolute percentage error and the mean absolute error of the proposed algorithm is35.38%，31.83% and 33.33% lower than GPSR respectively.. Therefore, the proposed algorithm can achieve relatively high reconstructed efficiency.

To help reduce the impact of geohazards, an innovative landslide early-warning technology based on an energy demodulation-based fiber optic sensing (FOS-LW for short) technology, is introduced in this paper. FOS-LW measures the energy change in a sensing fiber at the segment of micro-bending, which can be caused by landslide movements, and automatically raises an alarm as soon as the measured signal intensity in the fiber reaches a pre-set threshold. Based on the sensing of micro-bending losses in the fiber optics, a two-event sensing algorithm has been developed for the landslide early-warning. The feasibility of the FOS-LW technology is verified through laboratory simulation and field tests. The result shows that FOS-LW has some unique features such as the graded alarm, real-time responses, remote monitoring, low cost and passive optical network, and can be applied in the early-warning of landslides.

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of the portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2,000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2,000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. Follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad application in preventative health care.

We present an autonomous system that remotely monitors the state of reinforced concrete structures. This system performs the real-time follow-up of the corrosion rate of rebars (iCORR), along with other relevant parameters, such as temperature, corrosion potential (ECORR) and electrical resistance of concrete (RE), at many of structures’ control points by using embedded sensors. Its corrosion sensor is stressed, to which a novel electrochemical polarization technique is applied. The custom electronic system manages the sensor network, consisting of a measurement board per control point connected to a central single-board computer in charge of processing measurement data and uploading results to a server via 4G connection. In this work, we report the results obtained after implementing the sensor system into a reinforced concrete wall, where two well-differentiated representative areas were monitored. The obtained corrosion parameters showed consistent values. Similar conclusions are obtained with ECORR recorded in rebars. With the iCORR follow-up the corrosion penetration damage diagram is built. This diagram is particularly useful for identifying critical events during the corrosion propagation period and to be able to estimate structures’ service life. Hence the system is presented as a useful tool for the structural maintenance and service life predictions of new structures.

(1) Background: Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information, becoming a valuable tool for medicine, particularly for the operating room and surgical learning; (2) Methods: A systematic review was conducted following the PRISMA guidelines to answer the research questions established using the PICO framework; (3) Results: Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes; (4) Conclusion: Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The possibility of integration of distinct medical imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses need consideration. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

Background: Sickle cell disease (SCD) is a public health problem in the Democratic Republic of Congo. While reference sickle cell centers have been implemented in capital cities of African countries and have proven to be beneficial for SCD patients, they have never been set up in rural areas for families with very low sources of income. Method: A cohort of 143 children with SCD aged 10 years old (IQR (interquartile range): 6–15 years) (sex ratio male/female = 1.3) were clinically followed for 12 months without any specific intervention aside from the management of acute events, and then for 12 months with a monthly medical visit, biological follow-up, and regular prophylaxis. Results: The median age of patients at the diagnosis of SCD was 2 years (IQR: 1–5). The implementation of standardized and regular follow-ups in a new sickle cell referral center in a remote city showed an increase in the annual mean hemoglobin level from 50 to 70 g/L (p = 0.001), and a decrease in the lymphocyte count and spleen size (p < 0.001). A significant decrease (p < 0.001) in the average annual number of hospitalizations and episodes of vaso-occlusive crises, blood transfusions, infections, and acute chest syndromes were also observed. Conclusions: Creation of a sickle cell referral center and the regular follow-up of children with SCD are possible and applicable in the context of a remote city of an African country and represent simple and accessible measures that can reduce the morbimortality of children with sickle cell disease.

Technology advancements are evident in the healthcare field; numerous new opportunities and applications have emerged during the last years based on embedded intelligence and related to real-time diagnosis of medical issues, tele-care and telemedicine, remote monitoring of patients, computer-assisted “smart” transportation in case of emergencies, as well as new types of remotely controlled surgical operations. This paper aims to provide an overview of the application of embedded intelligence in the field of healthcare; it gathers the main critical requirements, related technologies and research advancements up to date, and it presents the most important challenges that are yet to be faced. The main focus is given in the development and optimization of Body Area Networks (BANs) based on advanced embedded sensing devices, the optimization of smart gateways in such networks, and the provision of holistic scalable and secure solutions in the healthcare domain. In addition, the paper presents two principal use cases that stem from the combination of novel information and communication technologies with classic healthcare practices, explaining the functional and non-functional requirements, as well as the mixed criticality characteristics of the associated systems.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, computational model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Gas leakage can occur for various reasons, including a gas leak in the distribution line, damaged regulators, service connection failures, fluctuations in inlet gas pressure, and other factors leading to millions in losses to the state. The objective is to develop a general and intelligent model for tracking and monitoring LPG distribution based on the Internet of Things and Business Intelligence technology. Devices such as sensors and actuators reduce risks and prevent accidents. IoT can also access automatic control of machines and infrastructure, and sensors’ data helps make intelligent decisions. The quantitative approach is used in the systematic review methodology based on the PRISMA statement; the architecture design is based on analytical research that studies the feasibility of a measure through empirical evidence; and the architecture description uses a qualitative approach. The Y.4908 Standard evaluates an IoT network’s interoperability, usability, and security. A targeted group of thirty IT professionals was surveyed to assess the Business Intelligence model. Forty scientific papers were selected for analysis. A model with IoT components and a four-level business intelligence system was found. At the overall average level, 49% strongly agree, 38% agree, 12% neither agree nor disagree, and 1% disagree. In other words, the model has an overall average approval rating of 87%.

The field of fluorescence sensing, leveraging various supramolecular self-assembled architectures constructed from macrocyclic pillar[n]arenes, has seen significant advancement in recent decades. This review comprehensively discusses, for the first time, the recent innovations in the synthesis and self-assembly of pillar[n]arene-based supramolecular architectures (PSAs) containing metal coordination sites, along with their practical applications and prospects in fluorescence sensing. Integrating hydrophobic and electron-rich cavities of pillar[n]arenes into these supramolecular structures endow the entire system with self-assembly behavior and stimulus responsiveness. Employing the host–guest interaction strategy coupled with complementary coordination forces, PSAs exhibiting both intelligent and controllable properties are successfully constructed. This provides a broad horizon for advancing fluorescence sensors capable of detecting environmental pollutants. This review aims to establish a solid foundation for the future development of fluorescence sensing applications utilizing PSAs. Additionally, current challenges and future perspectives in this field are discussed.

We use COMSOL Multiphysics software to model optical processes in media and the effects of light propagation in whispering gallery mode resonators (WGMRs). Although plane wave irradiating on resonator does not create resonances, it was shown that they can be obtained when focused tangentially on the perimeter of the resonator. We demonstrate that resonances can be excited in resonators by prism coupling when the incident angle of the light is larger than the angle of total internal reflection. We determined the optimal distance between the prism and the resonator to get the strongest resonances. Bio-sensing principle of WGMRs is shown by demonstrating that the shift of resonances increases with a larger number of "bio-molecules" attached to the surface of WGMR. Surface functionalization and surface roughness were explored.

In this paper, a buck DC-DC converter with the proposed twin frequency control scheme (TFCS) and accurate load current sensing (ALCS) have been designed and implemented with 0.18µm CMOS technology for a supply voltage ranging from 2.0-3.0V which is compatible with state-of-the-art batteries (NiCd/NiMH: 1.1-2V, Li-Ion: 2.5-4.2V). The proposed converter yields a peak efficiency of about 92.7% with a load current of 30mA. Furthermore, it only occupies a silicon area of 1.3mm2. The proposed buck converter is dedicated for smartphone applications whereby it spends most of its time in idle, low load conditions.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising focuses in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues, for conforming to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation，while traditional medical sensors cannot meet such requirements. In addition, in order to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. On the basis of the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

Of late, the focus has been shifted towards quorum sensing inhibitors which reduce bacterial virulence and lower the probability of resistance and refining infections. In this work, meta-bromo-thiolactone (mBTL), a potent quorum and virulence inhibitor against Staphylococcus aureus and MRSA phenotypes, was formulated in chitosan nanoparticles (ChNPs) using ionic gelation method. mBTL-loaded-ChNPs were characterized for particle size, polydispersity index, zeta potential. Morphology was visualized by Transmission Electron Microscopy (TEM), drug release profile and antibiofilm analysis using Confocal Laser Scanning Microscope (CLSM) and Scanning Electron Microscopy (SEM) were performed. Synthesized mBTL-loaded-CNPs showed homogenized nano-size particles ranging from 158+1.3 to 284+5.6 nm with spherical particles that exhibited sustainable release profile over 48 hr at 37 °C. These findings revealed successful preparation of mBTL-loaded-ChNPs, that further showed effective antibiofilm activity at MIC50 (0.5 mg/mL) where all strains displayed reduced biofilm formation compared to untreated strains. CLSM results showed a significant reduction in the number of viable cells, indicating the effectiveness of m-BTL as an antibacterial agent. SEM analysis permitted visualization of biofilm structure in relation to the spatial localization of important biofilm matrix components, the formed biofilms were clearly distinguished in the SEM images. Bacterial cells in the control group were enclosed in thick biofilms. In contrast, there was a considerable reduction in biofilm production when mBTL was present, where bacterial cells seemed less ordered and more scattered with no detectable biofilms. In conclusion, mBTL-loaded-ChNPs is a potential alternative treatment to overcome antimicrobial resistance and condensed MRSA infections.

A novel technique using a W-band metasurface for the purpose of transmissive fine powder layer sensing is presented. The proposed technique may allow for the detection, identification, and characterization of inhomogeneous ultrafine powder layers which are effectively hundreds of times thinner than the incident wavelengths used to sense them. Such a technique may be useful during personnel screening processes (i.e., at an airport) and in industrial manufacturing environments where early detection and quantization of harmful airborne particulates can be a matter of security or safety. To our knowledge, the requisite science involved in such a novel W-band metasurface sensing technique has not been fully explored.

Sensing Technology (ST) plays a key role in Structural Health Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems or smart materials that monitor a wide variety of materials properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and allowing continuous and permanent measurements of the structural integrity. The integration of ESs is limited to the processing technology to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material composition, either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM employing ESs, focusing on the actual developments and innovation of these, as well as analysing the challenges that these technologies present, to build a path that allows a connected world through distributed measurement systems.

Fiber distributed optical fiber acoustic sensor (DAS) is generally used in distributed long-distance acoustic/vibration measurement. We found that DAS with excellent performance of low self-noise and anti-fading in combination with the plastic structure in daily life as an acoustic transducer can achieve high-sensitivity acoustic measurement at a single point rather than designing a state-of-the-art acoustic transducer or using special enhanced scattering fiber. The simple acoustic transducer we proposed for DAS can reach the sensitivity level of -106.5dB re. 1rad/μPa at a sensing range of 5.1 km, which can meet many demands on the industrial site.

The aim of this work is to propose two different and complementary sensors for the structural health monitoring of concrete beams. In particular, a diffused sensing element and a split ring resonator network are presented. The first sensor is able to detect the variation of the dielectric properties of the concrete along the whole beam length, for a diffuse monitoring both during the important concrete curing phase and also for the entire life cycle of the concrete beams. The resonators instead work punctually, in their surroundings, allowing an accurate evaluation of the permittivity both during the drying phase and after. This allows the continuous monitoring of any presence of water both inside the concrete beam and in points that can be critical, in the case of beams in dams, bridges or in any case subject to a strong presence of water which could lead to deterioration, or worse, cause serious accidents. Moreover, the punctual sensors are able to detect the presence of cracks in the structure and to localize them.

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

Biofilm formation is important in the free-living and symbiotic lifestyles of Sinorhizobium meliloti. Here we show that the S. meliloti NspS-MbaA system is homologous to the c-di-GMP modulating NspS-MbaA system described in Vibrio cholerae and modulates biofilm formation, exopolysaccharide (EPS) production and motility in S. meliloti in response to exogenous polyamines. Biofilm and EPS production in quorum sensing-deficient wild type strain 1021 decreased in cultures containing putrescine, 1,3-diaminopropane or spermine, but were unaffected in a 1021 nspS mutant (1021 nspS). Decreases in biofilm formation caused by these polyamines paralleled decreases in EPS production and increases in nspS transcription. Cell aggregation did not differ markedly between strain 1021 and 1021 nspS. The S. meliloti quorum sensing-proficient Rm8530 wild type strain and nspS mutant (Rm8530 nspS) produced similar levels of biofilm under control conditions and 3.2- and 2.2-fold more biofilm in cultures with norspermidine, but these changes did not correlate with EPS production. Rm8530 nspS aggregated up to several fold more than the wild type under most conditions. Changes caused by polyamines in swimming and swarming motility were not generally dependent on NspS-MbaA in either 1021 or Rm8530. We speculate that the S. meliloti NspS-MbaA system could modulate biofilm formation in response to environmental polyamines.

Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average (MA) colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher-order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV) to map pasture biomass yield and nutrient status, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed predictive models of both pasture yield and pasture nutrient status. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements of pasture yields, to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict pasture yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of pasture yield and on average indicated an error of 0.8 t ha-1 in grasslands and 1.3 t ha-1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale, however further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

This paper introduces a closed-loop control strategy for maintaining consistency of liquid temperature in commercial Drop on Demand (DoD) inkjet printing. No additional sensors or additional actuators are installed in the printhead while achieving the consistency in liquid temperature. To this end, this paper presents a novel in situ sensing-actuation policy at every individual liquid-nozzle, where the jetting mechanism has three distinct roles. It is used for jetting liquid droplet onto the print media based on the print-job. It is used as a soft sensor to estimate the real-time liquid temperature of the jetting nozzle. While not jetting liquid, it is used as a heating actuator to minimize the gradient of liquid temperature among nozzles. The soft sensing based in situ controller is implemented in an experimentally validated digital twin that models the thermo-fluidic processes of the printhead. The digital twin is scalable and flexible to incorporate an arbitrary number of liquid-nozzles, making the control strategy applicable for future designs of the printhead.

Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes a number of simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100~dB, with the mean difference of less than 2~dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

In Taiwan, remote healthcare was initially limited to telemedicine services, and the systematic development of remote care was relatively delayed. It was not until 2007 that the Department of Health of the Executive Yuan first initiated the 'Remote Care Pilot Program,' followed by the 'Remote Care Service Improvement and Quality Enhancement Plan' in the subsequent year. In 2010, Taiwan officially launched the 'Remote Health Care Services Development Plan' and established a dedicated project office, marking the comprehensive start-up of remote care services. This stage saw significant contributions from artificial intelligence aids and ICT remote transmission technologies, greatly promoting the application of smart healthcare in home care. This study aims to explore the strategic gaps in the application of smart healthcare in home remote care. By analyzing gaps in 'legal regulations,' 'economic factors,' 'user behavior habits,' 'policy and culture,' and 'environment and technology,' this study employs a mixed-methods approach. Particularly, it uses the Analytic Hierarchy Process (AHP) to construct a weighted ranking of these strategic gaps. Through in-depth interviews with doctors and smart healthcare experts and the collection of 16 valid AHP questionnaire samples, which cover both industry and academia with a balanced distribution, the survey results are ensured to be representative and reliable. The findings indicate that 'legal regulations' are considered the most critical strategic gap that needs to be addressed first, followed by 'user behavior habits.' This suggests that when designing smart healthcare applications, greater consideration needs to be given to user acceptance and habits. By systematically analyzing and addressing these strategic gaps, this study not only provides solutions to issues in the application of smart healthcare in home remote care but also offers valuable references for future research and practice, aiming to make home remote care services more comprehensive and efficient.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stay and amputation. We suggest a remote-monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, wound characteristics, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10), categorized into low, medium, and high risk for unfavorable outcomes, and visually represented using color-coded radar plots. The initial results of case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

Technological advancements in the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and novel technological tools have transformed disaster management practices from localized approaches to a global framework. These innovations necessitate a unified understanding and application of diverse methodologies at an international level, ensuring they are accessible and operational across different sectors. This integration is crucial not only for technical personnel but also for medical teams, who play a pivotal role in disaster response and management. By fostering a comprehensive grasp of these methods, we can enhance coordination, efficiency, and effectiveness in addressing disasters on a global scale. The authors aimed to provide a comprehensive overview of emerging techniques that are driving shifts in strategic approaches and treatment technologies. Recognized as a domain of continuous research and development, this overview, by necessity, remains an incomplete snapshot of an evolving field. It highlights the dynamic nature of technological and strategic advancements, underscoring the importance of ongoing investigation and innovation to stay abreast of current trends and future directions. The article also highlights the importance of international collaboration beyond societal and national boundaries A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

We explored the impacts of the remote and return to in-person work periods on sleep and well-being as reported by faculty (n = 22) and non-teaching staff (n=21) with and without disabilities. Our results show that contrary to expectations, the Covid-19 remote teaching/working period resulted in better sleep, as well as greater well-being, than the return to in-person. With respect to sleep, faculty members had slightly more negative outcomes than staff, most evident in heightened anxiety and work aspects. Faculty with disabilities had somewhat worse sleep and well-being during the remote period than faculty without disabilities. During the return to in-person work, both faculty and non-teaching staff reported more negative than positive sleep and well-being outcomes. In particular, during the in-person period faculty members experienced slightly more negative sleep outcomes related to anxiety and work, while staff members experienced slightly more negative sleep outcomes related to the need to commute and lifestyle. Our findings show that there were benefits and disadvantages to both remote and in-person work periods, suggesting a hybrid work schedule should be considered in more detail, particularly as an optional reasonable accommodation for faculty and staff with disabilities. Our study highlights that training to keep faculty abreast of the latest technological innovations, ways to promote work-life balance and steps to remedy classroom size and building ventilation to prevent the spread of disease all need urgent attention.

Femtosecond laser-induced fluorescence (FLIF) and femtosecond laser-induced optical breakdown spectroscopy (FIBS) are important tools for remote diagnostics of atmospheric aerosols using LiDAR techniques. They are based on light emission excitation in disperse medium via the multiphoton nonlinear processes in aerosol particles induced by high-power optical pulses. To date, the main challenge restraining the large-scale application of the FLIF and FIBS in atmospheric studies is the lack of valued theory of the stimulated light emission in liquid microparticles with sufficiently broad range of sizes. In this paper, we fill this gap and present the theoretical model of dye water droplets emission under high-intense laser exposure that adequately simulates the processes of multiphoton excited fluorescence and optical breakdown plasma emission in microparticles and gives quantitative estimates of the angular and power characteristics of the nonlinear emission. The model is based on the numerical solution to the inhomogeneous Helmholtz equations for the stimulating (primary) and nonlinear (secondary) waves provided by the random nature of molecule emission in particles. We show that droplet fluorescence stimulated by the multiphoton absorption generally becomes more intensive with increasing particle size. Moreover, far-field plasma emission from liquid particles demonstrates larger angular diversity when changing droplet radius in comparison with the multiphoton excited fluorescence, which is mainly due to the excitation of the internal optical field resonances in spherical particles.

The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

This article presents the development of the theoretical background and the design of an electronic device for monitoring the condition of a gapless Metal Oxide Surge Arrester (MOSA). The device is intended to be used online. Due to the inaccessibility and possible remote location of most surge arresters, it is equipped with a communication system, allowing the device to convey the measurement of the surge arrester characteristics under any conditions. By gathering measurements of the surge arrester’s resistive component of leakage current, it is possible to determine the condition of the MOSA. After interpreting the results, these can be sent via a data transfer unit to a server, which, in turn, sends it onward to the authorised personnel through the surge arrester control centre.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A Random Forest model worked best with 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

This work introduces a neuromorphic sensor based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed sensor integrates the FSR in the schematic of spiking neuron in order to make the sensor to generate spikes with frequency that depends on the applied force. The performance of the proposed sensor is evaluated for the control of a SMA-actuated ro-botic finger by monitoring the force during steady state when the finger pushes on a tweezers. For comparison purposes, we performed similar evaluation when the SNN receives input from a widely used compression load cell (CLC). The results show that the proposed FSR based neuro-morphic sensor has very good sensitivity to low forces and the function between the spiking rate and the applied force is continuous with good variation range. However, when compared to the CLC, the proposed sensor has lower linearity of the response, but it benefits from two orders of magnitude lower power consumption. These aspects imply that the neuromorphic sensor based on FSR are useful for bioinspired control of humanoid robotics representing a low power and low cost alternative the widely used sensors.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

This study introduces a low-cost solution provided by a specialized source measure unit (SMU), offering an alternative to conventional source meter units. Through precise IV curve measurements for graphene-channel FETs, the SMU demonstrates its capability in accurately characterizing these devices with minimal noise and high accuracy. This cost-effective solution presents a promising avenue for researchers and developers seeking reliable tools for sensor development and characterization, providing valuable insights into the behavior of graphene-channel FETs and the effect of surface illumination without the need for expensive equipment. Additionally, the SMU was validated with known passive and active components, along with probe station integration for microscale connection, ensuring its effectiveness in capturing accurate measurements. The SMU’s focus on collecting IV curves, coupled with its ability to identify device defects, such as Schottky junctions, contributes to its utility in quality testing for semiconductor devices. Its low-cost nature makes it accessible for various research endeavors, enabling efficient data collection and analysis for graphene-based sensor applications.

An original approach has been proposed for designing a nanofibrous (NFs) layer using UV-cured polyvinylpyrrolidone (PVP) as a matrix, incorporating mesoporous graphene carbon (MGC) nanopowder both inside and outside the fibres, creating a sandwich-like structure. This architecture is intended to selectively adsorb and detect acetic acid vapours, which are known to cause health issues in exposed workers. The nanocomposite MGC-PVP-NFs layer was fabricated through electrospinning deposition onto interdigitated microelectrodes (IDEs) and stabilised under UV-light irradiation. To enhance the adhesion of MGC onto the surface of the nanocomposite polymeric fibres, the layer was dipped in a suspension of polyethylenimine (PEI) and MGC. The resulting structure demonstrated promising electrical and sensing properties, including rapid responses, high sensitivity, good linearity, reversibility, repeatability, and selectivity towards acetic acid vapours. Initial testing was conducted in a laboratory using a bench electrometer, followed by validation in a portable sensing device based on consumer electronic components (by ARDUINO®). This portable system was designed to provide a compact, cost-effective solution with high sensing capabilities. Under room temperature and ambient air conditions, both laboratory and portable tests exhibited favourable linear responses, with detection limits of 0.16 and 1 ppm, respectively.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

In this study, a composite film was developed for electrochemical sensing of tryptophan (Trp). Porous reduced graphene oxide (PrGO) was utilized as the electron transfer layer, and a C-undecylcalix[4] resorcinarene Langmuir-Blodgett (CUCR-LB) film served as the molecular recognition layer. Atomic force microscopy (AFM), transmission electron microscopy (TEM), Raman spectroscopy, scanning electron microscopy (SEM), and electrochemical experiments were employed to analyze the characteristics of the CUCR-LB/PrGO composite film. The electrochemical behavior of Trp on the CUCR-LB/PrGO composite film was investigated, revealing a linear response range of Trp was 1.0×10-7 to 3.0×10-5 mol L-1 and a detection limit of 3.0×10-8 mol L-1. Furthermore, the developed electroanalytical method successfully determined Trp content in an amino acid injection samples. This study not only introduces a rapid and reliable electrochemical method for determination of Trp but also presents a new strategy for constructing high-performance electrochemical sensing platforms.