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In this work, the authors explored the interaction of a suite of fluorescent zinc complexes with H2S. The authors provide evidence that HS− binds the zinc center of all the complexes under investigation, allowing them to possibly function as sensors by a 'coordinative-based' approach. Naked-eye color changes occur when treating the systems with HS−, so the fluorescence responses are modulated by the presence of HS−, which has been related to a change in the energy level and coupling of excited states through a computational study. The results show the potential of the systems to function as HS−/H2S colorimetric and fluorescent sensors. Paper-strip-based sensing experiments foresee the potential of using this family of complexes as chemosensors of HS− in more complex biological fluids. [ABSTRACT FROM AUTHOR]

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2 % AUC and 1.25 % EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc. [ABSTRACT FROM AUTHOR]

Photochromic inks have shown disadvantages, such as poor durability and high cost. Self-healable hydrogels have shown photostability and durability. Herein, a viologen-based covalent polymer was printed onto a paper surface toward the development of a multi-stimuli responsive chromogenic sheet with thermochromic, photochromic, and vapochromic properties. Viologen polymer was created by polymerizing a dialdehyde-based viologen with a hydroxyl-bearing dihydrazide in an acidic aqueous medium. The viologen polymer was well immobilized as a colorimetric agent into a polyvinyl alcohol (PVA)/tricarboxy cellulose (TCC)-based self-healable hydrogel. The viologen/hydrogel nanocomposite films were applied onto a paper surface. The coloration measurements showed that when exposed to ultraviolet light, the orange layer printed on the paper surface switched to green. The photochromic film was used to develop anti-counterfeiting prints using the organic hydrogel composed of a PVA/TCC composite and a viologen polymer. Reversible photochromism with strong photostability was observed when the printed papers were exposed to UV irradiation. A detection limit was monitored in the range of 0.5–300 ppm for NH3(aq). The exposure to heat (70 °C) was found to reversibly initiate a colorimetric change. [ABSTRACT FROM AUTHOR]

Capacitive equipment refers to its insulation design using the principle of capacitance of electrical equipment, mainly by a variety of different capacitive components in series. Most of the equipment in the substation is capacitive equipment. Once an insulation failure occurs, it will lead to extremely serious consequences. Monitoring grid overvoltage and insulation degradation of capacitive equipment is an effective means to ensure the stable operation of the power system. Therefore, in order to enhance the health management of capacitive equipment, including transformers, bushings, and current transformers, and to mitigate the risk of severe failures, it is imperative to conduct broad-spectrum frequency-domain online monitoring of overvoltages, dielectric losses, and partial discharge. However, the current monitoring work requires the utilization of multiple detection apparatuses. Aiming at the disadvantage that the existing inspection is not well integrated and requires a combination of multiple devices. This paper proposes a smart grid overvoltage identification system that utilizes partial discharge (PD) signals in correlation with dielectric loss detection. The system achieves synchronous detection of dielectric loss and high-frequency partial discharge by synchronously and in real-time acquiring four current signals from the power grid, enhancing the integration level of the hardware system. [ABSTRACT FROM AUTHOR]

Sensor data fusion analysis is a crucial process that integrates data from multiple sensors to provide more accurate and comprehensive information. This analysis has various applications in fields such as industry, medicine, robotics, transportation, and information systems. The article highlights sixteen high-quality papers that explore different aspects of sensor data fusion, including facial detection, object detection, odometry systems, geospatial data evaluation, and fault detection. The papers demonstrate the potential of sensor data fusion in improving decision-making, efficiency, and productivity in various industries. Future research in this area will focus on miniaturization, multi-sensor systems, and addressing challenges related to data quality in mobile sensors. [Extracted from the article]

This paper describes a paper-based biochemical sensor that realizes redox cycling with close interelectrode distance. Two electrodes, the generator and collector electrodes, can detect steady-state oxidation and reduction currents when suitable potential is held at each electrode. The sensor has two gold plates on both sides of a piece of chromatography paper and defines the interelectrode distance by the thickness of the paper (180μm) without any micro-fabrication processes. Our proposed sensor geometry has successfully exhibited signatures of redox cycling. As a result, the concentration of ferrocyanide as reversible redox molecules was successfully quantified under the interference by ascorbic acid as a strong irreversible reducing agent. This was possible because the ascorbic acids are completely consumed by the irreversible reaction, while maintaining redox cycling of reversible ferrocyanide. This suggests that a sensor based on the redox cycling method will be suitable for detecting target molecules at low concentration. [ABSTRACT FROM AUTHOR]

Breath analysis is considered to be an effective method for point-of-care diagnosis due to its noninvasiveness, quickness and simplicity. Gas sensors for breath analysis require detection of low-concentration substances. In this paper, we propose that reduction of the background current improves the limit of detection of enzymatic biogas sensors utilizing chromatography paper. After clarifying the cause of the background current, we reduced the background current by improving the fabrication process of the sensors utilizing paper. Finally, we evaluated the limit of detection of the sensor with the sample vapor of ethanol gas. The experiment showed about a 50% reduction of the limit of detection compared to previously-reported sensor. This result presents the possibility of the sensor being applied in diagnosis, such as for diabetes, by further lowering the limit of detection. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score. [ABSTRACT FROM AUTHOR]

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities. [ABSTRACT FROM AUTHOR]

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as smart dust. Several efforts focused primarily on computing and networking capabilities, but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data were processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios. [ABSTRACT FROM AUTHOR]

Glucose, as an essential substance directly involved in metabolic processes, is closely related to the occurrence of various diseases such as glucose metabolism disorders and islet cell carcinoma. Therefore, it is crucial to develop sensitive, accurate, rapid, and cost effective methods for frequent and convenient detections of glucose. Microfluidic Paper-based Analytical Devices (µPADs) not only satisfying the above requirements but also occupying the advantages of portability and minimal sample consumption, have exhibited great potential in the field of glucose detection. This article reviews and summarizes the most recent improvements in glucose detection in two aspects of colorimetric and electrochemical µPADs. The progressive techniques for fabricating channels on µPADs are also emphasized in this article. With the growth of diabetes and other glucose indication diseases in the underdeveloped and developing countries, low-cost and reliably commercial µPADs for glucose detection will be in unprecedentedly demand. [ABSTRACT FROM AUTHOR]

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. [ABSTRACT FROM AUTHOR]

Paper-based platforms can be a promising choice as portable sensors due to their low-cost and facile fabrication, ease of use, high sensitivity, specificity and flexibility. By combining the qualities of these 3D platforms with the optical properties of gold nanoparticles, it is possible to create efficient nanodevices with desired biosensing functionalities. In this work, we propose a new plasmonic paper-based dual localized surface plasmon resonance–surface-enhanced Raman scattering (LSPR-SERS) nanoplatform with improved detection abilities in terms of high sensitivity, uniformity and reproducibility. Specifically, colloidal gold nanorods (GNRs) with a well-controlled plasmonic response were firstly synthesized and validated as efficient dual LSPR-SERS nanosensors in solution using the p-aminothiophenol (p-ATP) analyte. GNRs were then efficiently immobilized onto the paper via the immersion approach, thus obtaining plasmonic nanoplatforms with a modulated LSPR response. The successful deposition of the nanoparticles onto the cellulose fibers was confirmed by LSPR measurements, which demonstrate the preserved plasmonic response after immobilization, as well as by dark-field microscopy and scanning electron microscopy investigations, which confirm their uniform distribution. Finally, a limit of detection for p-ATP as low as 10-12 M has been achieved by our developed SERS-based paper nanoplatform, proving that our optimized plasmonic paper-based biosensing design could be further considered as an excellent candidate for miniaturized biomedical applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel method of fabricating an enzymatic biosensor for breath analysis using chromatography paper as enzyme supporting layer and a liquid phase layer on top of screen printed carbon electrodes. We evaluated the performance with ethanol vapor being one of the breathing ingredients. The experimental results show that our sensor is able to measure the concentration of ethanol vapor within the range of 50 to 500 ppm. These results suggest the ability of detecting breath ethanol, and it can possibly be applied as a generic vapor biosensor to a wide range of diseases. [ABSTRACT FROM AUTHOR]

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area. [ABSTRACT FROM AUTHOR]

Paper is a versatile, flexible, porous, and eco-friendly substrate that is utilized in the fabrication of low-cost devices and biosensors for rapid detection of analytes of interest. Paper-based sensors provide affordable platforms for simple, accurate, and rapid detection of diseases, in addition to monitoring food quality, environmental and sun exposure, and detection of pathogens. Paper-based devices provide an inexpensive technology for fabrication of simple and portable diagnostic systems that can be immensely useful in resource-limited settings, such as in developing countries or austere environments, where fully-equipped facilities and highly trained medical staff are absent. In this work, we present the different types of paper that are currently utilized in fabrication of paper-based sensors, and common fabrication techniques ranging from wax printing to origamiand kirigami-based approaches. In addition, we present different detection techniques that are employed in paper-based sensors such as colorimetric, electrochemical, and fluorescence detection, chemiluminescence, and electrochemiluminescence, as well as their applications including disease diagnostics, cell cultures, monitoring sun exposure, and analysis of environmental reagents including pollutants. Furthermore, main advantages and disadvantages of different types of paper and future trends for paper-based sensors are discussed. [ABSTRACT FROM AUTHOR]

This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper. [ABSTRACT FROM AUTHOR]

We report on the recent progress and development of research into cellulose-based electro-active paper for bending actuators, bioelectronics devices, and electromechanical transducers. The cellulose electro-active paper is characterized in terms of its biodegradability, chirality, ample chemically modifying capacity, light weight, actuation capability, and ability to form hybrid nanocomposites. The mechanical, electrical, and chemical characterizations of the cellulose-based electro-active paper and its hybrid composites such as blends or coatings with synthetic polymers, biopolymers, carbon nanotubes, chitosan, and metal oxides, are explained. In addition, the integration of cellulose electro-active paper is highlighted to form various functional devices including but not limited to bending actuators, flexible speaker, strain sensors, energy harvesting transducers, biosensors, chemical sensors and transistors for electronic applications. The frontiers in cellulose paper devices are reviewed together with the strategies and perspectives of cellulose electro-active paper and cellulose nanocomposite research and applications. [ABSTRACT FROM AUTHOR]

We describe the use of a paper-based probe impregnated with a vanadium-containing polyoxometalate anion, [PMo11VO40]5−, on screen-printed carbon electrodes for the electrochemical determination of chlorate. Cyclic voltammetry (CV) and chronocoulometry were used to characterize the ClO3− response in a pH = 2.5 solution of 100 mM sodium acetate. A linear CV current response was observed between 0.156 and 1.25 mg/mL with a detection limit of 0.083 mg/mL (S/N > 3). This performance was reproducible using [PMo11VO40]5−-impregnated filter paper stored under ambient conditions for as long as 8 months prior to use. At high concentration of chlorate, an additional catalytic cathodic peak was seen in the reverse scan of the CVs, which was digitally simulated using a simple model. For chronocoulometry, the charge measured after 5 min gave a linear response from 0.625 to 2.5 mg/mL with a detection limit of 0.31 mg/mL (S/N > 3). In addition, the slope of charge vs. time also gave a linear response. In this case the linear range was from 0.312 to 2.5 mg/mL with a detection limit of 0.15 mg/mL (S/N > 3). Simple assays were conducted using three types of soil, and recovery measurements reported. [ABSTRACT FROM AUTHOR]

A fully integrated paper microfluidic electrochemical device equipped with three different cation permeable films is developed to determine blood ions (Cl-, Na+, K+, and Ca2+) at a time. These blood ions that are normally dissolved in the real human blood stream are essential for cell metabolisms and homeostasis in the human body. Abnormal concentration of blood ions causes many serious disorders. The optimized microfluidic device working without any external power source can directly and effectively separate human blood components, and subsequently detect a specific blood ion with minimized interference. The measured sensitivity to Cl-, K+, Na+, and Ca2+ are -47.71, 45.97, 51.06, and 19.46 in mV decade-1, respectively. Potentiometric responses of the microfluidic devices to blood serum samples are in the normal ranges of each cation, and comparable with responses from the commercial blood ion analyzer Abbott i-Stat. [ABSTRACT FROM AUTHOR]

Developing an inexpensive, sensitive, and point-of-use biosensor for pesticide detection is becoming an important area in sensing. Such sensors can be used in food packaging, agricultural fields, and environmental monitoring of pesticides. The present investigation has developed a zinc oxide (ZnO)-based biosensor on porous, flexible substrates such as carbon paper and carbon cloth to detect organophosphates such as paraoxon (OP). Here, the influence of morphology and underlying substrate on biosensor performance was studied. The biosensors were fabricated by immobilizing the acetylcholinesterase (AChE) enzyme on ZnO, which is directly grown on the flexible substrates. The ZnO biosensors fabricated on the carbon cloth demonstrated good performance with the detection limit of OP in the range of 0.5 nM–5 µM, higher sensitivity, and greater stability. [ABSTRACT FROM AUTHOR]

High-precision measurement of temperature value distributions in production scenarios is of great significance for industrial production, but traditional temperature field reconstruction algorithms rely on the design of manual feature extraction methods with high computational complexity and poor generalization ability. In this paper, we propose a high-precision temperature field reconstruction algorithm based on deep learning, using an efficient adaptive feature extraction method for temperature field reconstruction. We design an improved temperature field reconstruction algorithm based on the ResNet18 neural network; introduce the CBAM attention mechanism in the model; and design a feature pyramid, using M-FPN, a multi-scale feature aggregation network fusing PAN and FPN, to make the extracted feature information propagate multi-dimensionally among different layers to improve the feature characterization ability. Finally, the mean square error is used to guide the model to optimize the training so that the model pays more attention to the data and reduces the large error to ensure that the gap between the predicted value and the real value is small. The experimental results show that the reconstruction accuracy of the improved algorithm presented in this paper is significantly better than that of the original algorithm in the case of typical peaked temperature field distributions. [ABSTRACT FROM AUTHOR]

Railway transportation has been integrated into people's lives. According to the "Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System" issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution. [ABSTRACT FROM AUTHOR]

The paper "Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions" (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model's root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones. [ABSTRACT FROM AUTHOR]

We report a battery-powered, paper-based electrochromic array for visualized electrochemical sensing. The paper-based sensing system consists of six parallel electrochemical cells, which are powered by an aluminum-air battery. Each single electrochemical cell uses a Prussian Blue spot electrodeposited on an indium-doped tin oxide thin film as the electrochromic indicator. Each electrochemical cell is preloaded with increasing amounts of analyte. The sample activates the battery for the sensing. Both the preloaded analyte and the analyte in the sample initiate the color change of Prussian Blue to Prussian White. With a reaction time of 60 s, the number of electrochemical cells with complete color changes is correlated to the concentration of analyte in the sample. As a proof-of-concept analyte, lactic acid was detected semi-quantitatively using the naked eye. [ABSTRACT FROM AUTHOR]

Paper-based surface-enhanced Raman scattering (SERS) sensors have garnered much attention in the past decade owing to their ubiquity, ease of fabrication, and environmentally friendly substrate. The main drawbacks of a paper substrate for a SERS sensor are its high porosity, inherent hygroscopic nature, and hydrophilic surface property, which reduce the sensitivity and reproducibility of the SERS sensor. Here, we propose a simple, quick, convenient, and economical method for hydrophilic to hydrophobic surface modification of paper, while enhancing its mechanical and moisture-resistant properties. The hydrophobic paper (h-paper) was obtained by spin-coating diluted polydimethylsiloxane (PDMS) solution onto the filter paper, resulting in h-paper with an increased contact angle of up to ≈130°. To complete the h-paper-based SERS substrate, gold nanoparticles arranged on graphene oxide (AuNPs@GO) were synthesized using UV photoreduction, followed by drop-casting of AuNPs@GO solution on the h-paper substrate. The enhancement of the SERS signal was then assessed by attaching a rhodamine 6G (R6G) molecule as a Raman probe material to the h-paper-based SERS substrate. The limit of detection was 10 nM with an R2 of 0.966. The presented SERS sensor was also tested to detect a thiram at the micromolar level. We expect that our proposed AuNPs@GO/h-paper-based SERS sensor could be applied to point-of-care diagnostics applications in daily life and in spacecraft. [ABSTRACT FROM AUTHOR]

Microfluidic paper combines pump-free water transport at low cost with a high degree of sustainability, as well as good availability of the paper-forming cellulosic material, thus making it an attractive candidate for point-of-care (POC) analytics and diagnostics. Although a number of interesting demonstrators for such paper devices have been reported to date, a number of challenges still exist, which limit a successful transfer into marketable applications. A strong limitation in this respect is the (unspecific) adsorption of protein analytes to the paper fibers during the lateral flow assay. This interaction may significantly reduce the amount of analyte that reaches the detection zone of the microfluidic paper-based analytical device (µPAD), thereby reducing its overall sensitivity. Here, we introduce a novel approach on reducing the nonspecific adsorption of proteins to lab-made paper sheets for the use in µPADs. To this, cotton linter fibers in lab-formed additive-free paper sheets are modified with a surrounding thin hydrogel layer generated from photo-crosslinked, benzophenone functionalized copolymers based on poly-(oligo-ethylene glycol methacrylate) (POEGMA) and poly-dimethyl acrylamide (PDMAA). This, as we show in tests similar to lateral flow assays, significantly reduces unspecific binding of model proteins. Furthermore, by evaporating the transport fluid during the microfluidic run at the end of the paper strip through local heating, model proteins can almost quantitatively be accumulated in that zone. The possibility of complete, almost quantitative protein transport in a µPAD opens up new opportunities to significantly improve the signal-to-noise (S/N) ratio of paper-based lateral flow assays. [ABSTRACT FROM AUTHOR]

The high incidence of Diabetes Mellitus in low-income regions has promoted the development of low-cost alternatives to replace blood-based procedures. In this work, we present a bienzymatic paper-based sensor suitable for the naked-eye detection of glucose in saliva samples. The sensor was obtained by a stamping procedure and modified with chitosan to improve the colorimetric readout. The bienzymatic reaction of GOx-HRP coupled with 2,4,6-tribromo-3-hydroxy benzoic acid was applied for the detection of glucose within a range from 0 to 180 mgdL–1 in buffer and artificial saliva solutions. The visual readout was perceived by the naked eye and registered with an office scanner to evaluate the analytical performance. The results showed a limit of detection of 0.37 mgdL–1 (S/N = 3) with an R.S.D. of 1.69% and a linear range from 1 to 22.5 mgdL–1 with an R2 of 0.99235. The analysis of human saliva samples was performed without pre-processing, achieving recoveries from 92 to 114%. The naked-eye detection was evaluated under two different light settings, showing average recoveries of 108.58 and 90.65% for standard and low illumination. The proposed device showed potential for easy-to-use, sensitive, low-cost, fast, and device-free detection of salivary glucose suitable for untrained personnel operation and limited facilities. [ABSTRACT FROM AUTHOR]

Energy-efficient Buildings (EeB) are demanded in today’s constructions, fulfilling the requirements for green cities. Pre-fab buildings, which are modularly fully-built in factories, are a good example of this. Although this kind of building is quite new, the in situ inspection is documented using traditional tools, mainly based on paper annotations. Thus, the inspection process is not taking advantage of new technologies. In this paper, we present the preliminary results of the SIRAE project that aims to provide an Augmented Reality (AR) tool that can seamlessly aid in the regular processes of pre-fab building inspections to detect and eliminate the possible existing quality and energy efficiency deviations. In this regards, we show a description of the current inspection process and how an interactive tool can be designed and adapted to it. Our first results show the design and implementation of our tool, which is highly interactive and involves AR visualizations and 3D data-gathering, allowing the inspectors to quickly manage it without altering the way the inspection process is done. First trials on a real environment show that the tool is promising for massive inspection processes. [ABSTRACT FROM AUTHOR]

We report on the development of an electrochemical probe for the trace analysis of 2,4,6-trinitrotoluene (TNT) in soil samples. The probe is a combination of graphite electrodes, filter paper, with ethylene glycol and choline chloride as the solvent/electrolyte. Square wave chromatovoltammograms show the probes have a sensitivity for TNT of 0.75 nA/ng and a limit of detection of 100 ng. In addition, by taking advantage of the inherent paper chromatography step, TNT can be separated in both time and cathodic peak potential from 4-amino-dinitrotolene co-spotted on the probe or in soil samples with the presence of methyl parathion as a possible interferent. [ABSTRACT FROM AUTHOR]

This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context. [ABSTRACT FROM AUTHOR]

In industrial paper production, online monitoring of a range of quality parameters is essential for ensuring that the performance and appearance of the final product is suitable for a given application. In this article, two optical sensing techniques are investigated for non-destructive, non-contact characterization of paper thickness, surface roughness, and production defects. The first technique is optical coherence tomography based on a mid-infrared supercontinuum laser, which can cover thicknesses from ~20–90 μm and provide information about the surface finish. Detection of subsurface voids, cuts, and oil contamination was also demonstrated. The second technique is terahertz time domain spectroscopy, which is used to measure paper thicknesses of up to 443 μm. A proof-of-concept thickness measurement in freely suspended paper was also demonstrated. These demonstrations highlight the added functionality and potential of tomographic optical sensing methods towards industrial non-contact quality monitoring. [ABSTRACT FROM AUTHOR]

This study aimed to design and fabricate planar constricted–expanded structures that are integrated into paper-based channels in order to enhance their chaotic advection and improve their mixing performance. Chromatography papers were used to print paper-based microfluidics using a solid-wax printer. Three different constricted–expanded structures—i.e., zigzag, crossed, and curved channels—were designed in order to evaluate their mixing performance in comparison with that of straight channels. A numerical simulation was performed in order to investigate the mixing mechanism, and to examine the ways in which the planar constricted–expanded structures affected the flow patterns. The experimental and numerical results indicated that the proposed structures can successfully mix confluents. The experimental results revealed that the mixing indices (σ) rose from the initial 20.1% (unmixed) to 34.5%, 84.3%, 87.3%, and 92.4% for the straight, zigzag, curved, and cross-shaped channels, respectively. In addition, the numerical calculations showed a reasonable agreement with the experimental results, with a variation in the range of 1.0–11.0%. In future, we hope that the proposed passive paper-based mixers will be a crucial component in the application of paper-based microfluidic devices. [ABSTRACT FROM AUTHOR]

In recent years paper-based microfluidic systems have emerged as versatile tools for developing sensors in different areas. In this work; we report a novel physical sensing principle for the characterization of liquids using a paper-based hygro-mechanical system (PB-HMS). The PB-HMS is formed by the interaction of liquid droplets and paper-based mini-structures such as cantilever beams. The proposed principle takes advantage of the hygroscopic properties of paper to produce hygro-mechanical motion. The dynamic response of the PB-HMS reveals information about the tested liquid that can be applied to characterize certain properties of liquids. A suggested method to characterize liquids by means of the proposed principle is introduced. The experimental results show the feasibility of such a method. It is expected that the proposed principle may be applied to sense properties of liquids in different applications where both disposability and portability are of extreme importance. [ABSTRACT FROM AUTHOR]

Biosensors are devices composed of a biorecognition part and of a transduction part. A review is conducted on the progress of the use of low-cost sensors in healthcare monitoring, the algorithms used to process sensor data and the wireless communication techniques [[11]]. [Extracted from the article]

This paper presents a shock detection device for packages in the supply chain. The primary purpose is to identify package damage during storage, delivery, and handling. Additionally, products are likely to be damaged if dropped from a certain height, which sometimes does not appear on the package. By continuously measuring package vibrations and detecting shocks in the supply chain, consumers can gain an insight into the state of the product upon delivery. This paper presents the Smart Sticker implementation for ultra-low-power shock detection in the supply chain. The overall energy consumption must be kept as low as possible while continuously sensing the presence of shock to ensure that the Smart Sticker's battery lasts as long as possible. The Smart Sticker functions in three modes to meet the established constraints: low-power, active, and data transfer mode. While detecting the shock, the low-power mode uses the least amount of energy needed. If the shock exceeds the threshold, the Smart Sticker enters active mode, stores the detected g force value in memory, and then switches back to low-power mode. Finally, employing Near Field Communication (NFC) and energy harvesting, the data transfer mode allows the consumer to read the recorded data. The results show that the Smart Sticker for shock detection performs according to set requirements and successfully monitors and detects shock for packages in the supply chain. [ABSTRACT FROM AUTHOR]

Despite predictions of the paperless office, global demand for printing and writing paper remains strong, and paper appears to be here to stay for some time. Not only firms, but also governments, libraries, and archives are in possession of large collections of legacy documents that still must be sorted and scanned. In this study, terahertz-based techniques are demonstrated to address several routine tasks related to the automated paper handling of unsorted legacy documents. Specifically, we demonstrate terahertz-based counting of the number of sheets in unconsolidated paper stacks, as well as locating stapled documents buried in paper stacks. [ABSTRACT FROM AUTHOR]

Next, the paper "Whole-Body Adaptive Functional Electrical Stimulation Kinesitherapy Can Promote the Restoring of Physiological Muscle Synergies for Neurological Patients" [[9]] introduces a novel treatment approach, Adaptive Functional Electrical Stimulation Kinesitherapy (AFESK™), for neurological patients using whole-body adaptive functional electrical stimulation kinesitherapy. In the paper "Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps" [[2]], researchers explore the potential of electromyography (EMG) in detecting hand osteoarthritis at an early stage. The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation. The review highlights the potential of cortico-muscular interactions for improving rehabilitation approaches in patients with impaired locomotor functions, paving the way for innovative rehabilitation strategies. [Extracted from the article]

Shortage of labor and increased work of young people are causing problems in terms of care and welfare of a growing proportion of elderly people. This is a looming social problem because people of advanced ages are increasing. Necessary in the fields of care and welfare, pneumatic artificial muscles in actuators of robots are being examined. Pneumatic artificial muscles have a high output per unit of weight, and they are soft, similarly to human muscles. However, in previous research of robots using pneumatic artificial muscles, rigid sensors were often installed at joints and other locations due to the robots' structures. Therefore, we developed a smart actuator that integrates a bending sensor that functions as a human muscle spindle; it can be externally attached to the pneumatic artificial muscle. This paper reports a smart artificial muscle actuator that can sense contraction, which can be applied to developed self-monitoring and robot posture control. [ABSTRACT FROM AUTHOR]

Aircraft flight simulators have good cost efficiency, high reusability, and high flight safety. All airlines and aircraft manufacturing companies choose it as sophisticated training equipment for ground simulation, effectively reducing pilot training costs, ensuring personnel safety and aircraft wear and tear. The new simulator proposed in this paper combines a digital motion-cueing algorithm, flight software and motion platform to make pilots feel as if they are in the real world. By using EtherCAT technology to drive the motion-cueing platform, it can improve the data transmission speed of the simulator as well as the strong anti-interference ability of communication and the control operation efficiency. Therefore, the simulated flight subjects can perform long-distance and large-angle training. Next, a set of measurement systems was established to provide monitoring items including attitude, velocity and acceleration, which can be displayed on the screen and recorded on the computer in real time and dynamically. Finally, seven training subjects were implemented to demonstrate the feasibility and correctness of the proposed method. [ABSTRACT FROM AUTHOR]

An inkjet-printed, fully passive sensor capable of either humidity or gas sensing is presented herein. The sensor is composed of an interdigitated electrode, a customized printable gas sensitive ink and a specialized dipole antenna for wireless sensing. The interdigitated electrode printed on a paper substrate provides the base conductivity that varies during the sensing process. Aided by the porous nature of the substrate, a change in relative humidity from 18% to 88% decreases the electrode resistance from a few Mega-ohms to the kilo-ohm range. For gas sensing, an additional copper acetate-based customized ink is printed on top of the electrode, which, upon reaction with hydrogen sulphide gas (H2S) changes, both the optical and the electrical properties of the electrode. A fast response time of 3 min is achieved at room temperature for a H2S concentration of 10 ppm at a relative humidity (RH) of 45%. The passive wireless sensing is enabled through an antenna in which the inner loop takes care of conductivity changes in the 4-5 GHz band, whereas the outer-dipole arm is used for chipless identification in the 2-3 GHz band. [ABSTRACT FROM AUTHOR]

In this research, a security paper based on frequency selective structure technologies was designed and fabricated using selective wave reflection characteristics to prevent the offline leakage of confidential documents. Document leakage detection systems using security papers detect security papers using transceiving antenna gates. For the application of such systems, the structure must be designed with excellent reflection performance and stability at the angle of incidence. For this purpose, a loop and patch-type frequency selective structure based on a four-legged element structure was designed to have X-band frequency reflection characteristics. This design was based on optimized variables and was realized through the screen printing method using silver ink on A4 paper. It was verified that both the design and simulation results matched well. To verify its actual applicability, a detector module operable at 10 GHz was manufactured to observe both the security paper detection range in relation to distance with a signal strength of −10 dBm and the detection area in relation to the number of times that the security paper had been folded. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for monocular depth estimation using the hourglass neck module. The proposed method has the following originality. First, feature maps are extracted from Swin Transformer V2 using a masked image modeling (MIM) pretrained model. Since Swin Transformer V2 has a different patch size for each attention stage, it is easier to extract local and global features from images input by the vision transformer (ViT)-based encoder. Second, to maintain the polymorphism and local inductive bias of the feature map extracted from Swin Transformer V2, a feature map is input into the hourglass neck module. Third, deformable attention can be used at the waist of the hourglass neck module to reduce the computation cost and highlight the locality of the feature map. Finally, the feature map traverses the neck and proceeds through a decoder, comprised of a deconvolution layer and an upsampling layer, to generate a depth image. To evaluate the objective reliability of the proposed method in this paper, we used the NYU Depth V2 dataset to compare and evaluate the methods published in other papers. As a result of the experiment, the RMSE value of the novel method for monocular depth estimation using the hourglass neck module proposed in this paper was 0.274, which was lower than those published in other papers. The lower the RMSE value, the better the depth estimation method; therefore, its efficiency compared to other techniques has been proven. [ABSTRACT FROM AUTHOR]

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue on the topic of the integration of blockchain and Internet of Things (IoT) technologies. The special issue includes 11 papers that explore various applications and challenges related to this integration. The papers cover topics such as blockchain applications in cultural heritage preservation, energy trading, educational record-keeping, and collaborative manufacturing. Additionally, the papers discuss smart contract security and vulnerability detection, security and privacy in blockchain technology, and the integration of blockchain in wireless networks. Overall, the papers contribute innovative ideas and methodologies that have the potential to enhance the security, privacy, and efficiency of IoT-enabled blockchain ecosystems. [Extracted from the article]

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%. [ABSTRACT FROM AUTHOR]

The precise segmentation of retinal vasculature is crucial for the early screening of various eye diseases, such as diabetic retinopathy and hypertensive retinopathy. Given the complex and variable overall structure of retinal vessels and their delicate, minute local features, the accurate extraction of fine vessels and edge pixels remains a technical challenge in the current research. To enhance the ability to extract thin vessels, this paper incorporates a pyramid channel attention module into a U-shaped network. This allows for more effective capture of information at different levels and increased attention to vessel-related channels, thereby improving model performance. Simultaneously, to prevent overfitting, this paper optimizes the standard convolutional block in the U-Net with the pre-activated residual discard convolution block, thus improving the model's generalization ability. The model is evaluated on three benchmark retinal datasets: DRIVE, CHASE_DB1, and STARE. Experimental results demonstrate that, compared to the baseline model, the proposed model achieves improvements in sensitivity (Sen) scores of 7.12%, 9.65%, and 5.36% on these three datasets, respectively, proving its strong ability to extract fine vessels. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field. [ABSTRACT FROM AUTHOR]

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency–response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor's sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor. [ABSTRACT FROM AUTHOR]

Tuning the sensing properties of spiropyrans (SPs), which are one of the photochromic molecules useful for colorimetric sensing, is important for efficient analysis, but their synthetic modification is not always simple. Herein, we introduce an alkyne-functionalized SP, the modification of which would be easily achieved via Cu-catalyzed azide-alkyne cycloaddition ("click reaction"). The alkyne-SP was conjugated with a bis(triethylene glycol)-benzyl group (EG-BtSP) or a simple benzyl group (BtSP), forming a triazole linkage from the click reaction. The effects of auxiliary groups to SP were tested on metal-ion sensing and cyanide detection. We found that EG-BtSP was more Ca2+-sensitive than BtSP in acetonitrile, which were thoroughly examined by a continuous variation method (Job plot) and UV-VIS titrations, followed by non-linear regression analysis. Although both SPs showed similar, selective responses to cyanide in a water/acetonitrile co-solvent, only EG-BtSP showed a dramatic color change when fabricated on paper, highlighting the important contributions of the auxiliary groups. [ABSTRACT FROM AUTHOR]