Characterization of seasonal behaviors of naturally occurring radioactive materials in a groundwater borehole using an in situ monitoring system.

Understanding the temporal behaviors of naturally occurring radioactive materials is important for safeguarding groundwater as a secure water resource for drinking, agriculture, and industry usage. This study reports the vertical profiles of 238U concentration and 222Rn activity and the management of in situ monitoring systems during intensive field sampling of a national groundwater-monitoring borehole for seven years (2015-2021). The aim was to capture the seasonal characteristics of the 238U concentrations and 222Rn activity. Both factors were low in the rainy season and high in the winter season, reflecting the dilution effect of rainfall recharge. The 238U and 222Rn behaviors were associated with water-rock interactions of calcite dissolution in fracture zones filled with carbonate minerals. Furthermore, multilayer perceptron models estimated the 238U concentration and 222Rn activity with reasonable regression and classification accuracy. Hydrometeorological indicators (temperature and groundwater-level fluctuations) were more important estimators of 238U concentration and 222Rn activity than geochemical process indicators. The regression accuracy performance was higher at deeper sampling depths, where seasonality in the 238U and 222Rn behaviors dominated. From the predicted distributions of 238U concentrations and 222Rn activities, we could estimate the ranges of 238U concentrations and 222Rn activities emerging from groundwater boreholes. High exposure threats from 238U and 222Rn during groundwater usage were found in the winter season. When the multilayer perceptron models use the entire in situ monitoring data at refined temporal resolution, we can quickly determine the naturally occurring radioactive materials and further develop the national groundwater-monitoring borehole equipped with the in-situ monitoring system, supplementing the occasionally obtained field-measurement data.

Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals.

Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.

Sleep Disorders in Cancer-A Systematic Review.

INTRODUCTION: Sleep disorders, especially insomnia, are very common in different kinds of cancers, but their prevalence and incidence are not well-known. Disturbed sleep in cancer is caused by different reasons and usually appears as a comorbid disorder to different somatic and psychiatric diagnoses, psychological disturbances and treatment methods. There can be many different predictors for sleep disturbances in these vulnerable groups, such as pre-existing sleep disorders, caused by the mental status in cancer or as side effect of the cancer treatment. METHODS: A systematic literature review of 8073 studies was conducted on the topic of sleep and sleep disorders in cancer patients. The articles were identified though PubMed, PsycInfo and Web of Knowledge, and a total number of 89 publications were qualified for analysis. RESULTS: The identified eighty-nine studies were analyzed on the topic of sleep and sleep disorders in cancer, twenty-six studies on sleep and fatigue in cancer and sixty-one studies on the topic of sleep disorders in cancer. The prevalence of sleep disturbences and/or sleep disorders in cancer was up to 95%. DISCUSSION: Sleep disturbances and sleep disorders (such as insomnia, OSAS, narcolepsy and RLS; REM-SBD) in cancer patients can be associated with different conditions. Side effects of cancer treatment and cancer-related psychological dysfunctions can be instigated by sleep disturbances and sleep disorders in these patients, especially insomnia and OSAS are common. An evidence-based treatment is necessary for concomitant mental and/or physical states.

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.

The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage and monitor a person's stress. In this study, we propose an ensemble algorithm that can accurately determine mental stress states using a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture. When a person is exposed to stress, a displacement occurs in the electrocardiogram (ECG) signal. It is possible to classify stress signals by analyzing ECG signals and extracting specific parameters. To maximize the performance of the proposed stress classification algorithm, fast Fourier transform (FFT) and spectrograms were applied to preprocess ECG signals and produce signals in both the time and frequency domains to aid the training process. As the performance evaluation benchmarks of the stress classification model, confusion matrices, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves were used, and the accuracy achieved by the proposed model was 98.3%, which is an improvement of 14.7% compared to previous research results. Therefore, our model can help manage the mental health of people exposed to stress. In addition, if combined with various biosignals such as electromyogram (EMG) and photoplethysmography (PPG), it may have the potential for development in various healthcare systems, such as home training, sleep state analysis, and cardiovascular monitoring.

Soft and transparent triboelectric nanogenerator based E-skin for wearable energy harvesting and pressure sensing.

The demand for wearable sensor technologies has increased with the recent increase interactions humans-electronic device interactions. However, the supplying power for wearable sensors, such as E-skin and flexible electronics, remains a major technical challenge. Herein, we report a triboelectric nanogenerator (TENG)-based E-skin capable of biomechanical energy harvesting and self-pressure sensing without an external power source. PTFE-molded micro-patterned PDMS and a conductive yarn were combined to implement an E-skin with flexibility, elasticity, high sensitivity, and excellent stability. The manufactured E-skin generates a power of 154 mW m-2for an external force of 1 kgf and exhibits stable characteristics without deterioration of output even under 4500 cycles of repeated pressure. The E-skin can charge a capacitor and drive an electronic watch as well as monitor physiological signals, such as arterial pulses. The method used in this study can be extended to potential applications for power supply in wearable/soft electronics, medical monitoring, and human-machine interfaces.

Evaluation of optimal size of illite adsorbent for 137Cs removal in contaminated artificial lake.

Since the Chernobyl and the Fukushima Daiichi disasters, contamination caused by radioactive accidents has attracted increasing attention. The present study evaluated immediate cleanup of 137Cs dissolved in surface water reservoir using an illite adsorbent, simulating an event of 137Cs contamination at Lake Paldang, South Korea. The study was conducted in two parts: (1) calculation of the residence time (tr) of illite adsorbent, and (2) evaluation of the adsorption time (ta) of illite adsorbent. tr was calculated based on physical properties (e.g., density, diameter, shape, and roughness) of the illite adsorbent at designated depth of surface water. Subsequently, ta was measured for 4 illite adsorbents (Korea01-Illite, Korea02-Illite, USA-Illite, and China-Illite) at 100 and 100,000 µg/L Cs, via kinetic adsorption experiment. Upon spraying of illite adsorbents with 50-150 µm diameter to locations where lake depth was between 6.5 m and 25.5 m, tr ranged from 0.132 to 3.300 h ta of 4 illite adsorbents was shorter than 0.6 and 2.5 h, for respective tests using 100 and 100,000 µg/L Cs. Based on the two characteristic times (tr and ta), the optimal particle diameter for the 4 illite adsorbents were evaluated at available lake depths in Lake Paldang. The study revealed that the USA-Illite is the efficient adsorbent at 100 µg/L Cs; in contrast, China-Illite could serve as the effective adsorbent at 100,000 µg/L Cs. Also, it was suggested that adsorbent efficiency had seasonal variations; tr was 2 h longer in winter than summer. In general, the study suggests that in the event of 137Cs contamination at Lake Paldang, Korea01-Illite is likely the best adsorbent to remove 137Cs due to its removal efficiency and accessibility from the illite deposit in Korea.

Camera-Integrable Wide-Bandwidth Antenna for Capsule Endoscope.

This paper presents a new antenna design for a capsule endoscope. The proposed antenna comprises a camera hole and meandered line. These features enable the antenna to be integrated on the same side as the camera, within the capsule endoscope. Moreover, light-emitting diodes can be mounted on the surface of the antenna for illumination. The antenna achieves a wide bandwidth, despite the small size owing to its meandered line structure.

Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks.

Ultrasonic waves have good propagation in the human body and have been widely applied in biomedical device design without any reported side effects. Therefore, ultrasonic waves can provide an alternative method as an information carrier for body sensor networks (BSNs). This paper presents a novel wireless communication method that uses ultrasonic sound waves as a medium for healthcare systems. We investigated the feasibility of our proposal by testing it in a real digital communication experimental setup. To find an acceptable modulation method, the functionality of the proposed ultrasound-based digital communication approach was tested involving three principal modulation methods: amplitude shift keying (ASK), frequency shift keying (FSK), and phase shift keying (PSK). The modulated digital signals obtained from the experiments were compared with the simulated signals. Analysis of the results shows that ultrasonic waves are feasible and can be used for digital communication.

Preparation of DMMTAV and DMDTAV Using DMAV for Environmental Applications: Synthesis, Purification, and Confirmation.

Dimethylated thioarsenicals such as dimethylmonothioarsinic acid (DMMTAV) and dimethyldithioarsinic acid (DMDTAV), which are produced by the metabolic pathway of dimethylarsinic acid (DMAV) thiolation, have been recently found in the environment as well as human organs. DMMTAV and DMDTAV can be quantified to determine the ecological effects of dimethylated thioarsenicals and their stability in environmental media. The synthesis method for these compounds is unstandardized, making replicating previous studies challenging. Furthermore, there is a lack of information about storage techniques, including storage of compounds without species transformation. Moreover, because only limited information about synthesis methods is available, there may be experimental difficulties in synthesizing standard chemicals and performing quantitative analysis. The protocol presented herein provides a practically modified synthesis method for the dimethylated thioarsenicals, DMMTAV and DMDTAV, and will help in the quantification of species separation analysis using high performance liquid chromatography in conjunction with inductively coupled plasma mass spectrometry (HPLC-ICP-MS). The experimental steps of this procedure were modified by focusing on the preparation of chemical reagents, filtration methods, and storage.

Development of a Telemetric, Miniaturized Electrochemical Amperometric Analyzer.

In this research, we developed a portable, three-electrode electrochemical amperometric analyzer that can transmit data to a PC or a tablet via Bluetooth communication. We performed experiments using an indium tin oxide (ITO) glass electrode to confirm the performance and reliability of the analyzer. The proposed analyzer uses a current-to-voltage (I/V) converter to convert the current generated by the reduction-oxidation (redox) reaction of the buffer solution to a voltage signal. This signal is then digitized by the processor. The configuration of the power and ground of the printed circuit board (PCB) layer is divided into digital and analog parts to minimize the noise interference of each part. The proposed analyzer occupies an area of 5.9 × 3.25 cm² with a current resolution of 0.4 nA. A potential of 0~2.1 V can be applied between the working and the counter electrodes. The results of this study showed the accuracy of the proposed analyzer by measuring the Ruthenium(III) chloride ( Ru III ) concentration in 10 mM phosphate-buffered saline (PBS) solution with a pH of 7.4. The measured data can be transmitted to a PC or a mobile such as a smartphone or a tablet PC using the included Bluetooth module. The proposed analyzer uses a 3.7 V, 120 mAh lithium polymer battery and can be operated for 60 min when fully charged, including data processing and wireless communication.

Design of a Wireless Sensor System with the Algorithms of Heart Rate and Agility Index for Athlete Evaluation.

Athlete evaluation systems can effectively monitor daily training and boost performance to reduce injuries. Conventional heart-rate measurement systems can be easily affected by artifact movement, especially in the case of athletes. Significant noise can be generated owing to high-intensity activities. To improve the comfort for athletes and the accuracy of monitoring, we have proposed to combine robust heart rate and agility index monitoring algorithms into a small, light, and single node. A band-pass-filter-based R-wave detection algorithm was developed. The agility index was calculated by preprocessing with band-pass filtering and employing the zero-crossing detection method. The evaluation was conducted under both laboratory and field environments to verify the accuracy and reliability of the algorithm. The heart rate and agility index measurements can be wirelessly transmitted to a personal computer in real time by the ZigBee telecommunication system. The results show that the error rate of measurement of the heart rate is within 2%, which is comparable with that of the traditional wired measurement method. The sensitivity of the agility index, which could be distinguished as the activity speed, changed slightly. Thus, we confirmed that the developed algorithm could be used in an effective and safe exercise-evaluation system for athletes.

Corrugated Textile based Triboelectric Generator for Wearable Energy Harvesting.

Triboelectric energy harvesting has been applied to various fields, from large-scale power generation to small electronics. Triboelectric energy is generated when certain materials come into frictional contact, e.g., static electricity from rubbing a shoe on a carpet. In particular, textile-based triboelectric energy-harvesting technologies are one of the most promising approaches because they are not only flexible, light, and comfortable but also wearable. Most previous textile-based triboelectric generators (TEGs) generate energy by vertically pressing and rubbing something. However, we propose a corrugated textile-based triboelectric generator (CT-TEG) that can generate energy by stretching. Moreover, the CT-TEG is sewn into a corrugated structure that contains an effective air gap without additional spacers. The resulting CT-TEG can generate considerable energy from various deformations, not only by pressing and rubbing but also by stretching. The maximum output performances of the CT-TEG can reach up to 28.13 V and 2.71 µA with stretching and releasing motions. Additionally, we demonstrate the generation of sufficient energy from various activities of a human body to power about 54 LEDs. These results demonstrate the potential application of CT-TEGs for self-powered systems.

Stretchable Triboelectric Fiber for Self-powered Kinematic Sensing Textile.

Stretchable fiber and yarn triboelectric nanogenerator are sought for such applications as wearable sensing system such as cloth communication devices, electronic textiles, and robotic sensory skin. Unfortunately, previously reported triboelectric fiber and yarn are difficult to have stretchable property. We introduce here a new type of stretchable and weavable triboelectric fibers with microdiameter dimensions. The stretchable triboelectric fibers can be reversibly stretched up to 50% in tensile direction while generating voltage output proportional to the applied tensile strain. The reversible distance change induced by the Poisson's ratio difference between the core fiber (silver-coated nylon/polyurethane) and the shell (wrinkled polyvinylidene fluoride-co-trifluoroethylene/carbon nanotube layer) during tensile deformation is the key working principle for electrical generation. Owing to exceptional structural stability, the stretchable triboelectric fibers show high performance retention after 10,000 times repeated stretching/releasing cycle. Furthermore, the stretchable triboelectric fibers are mechanically strong to be woven into a commercial textile for textile based sensors, which can detect magnitude as well as direction of the motion.

Kinetics of Dimethylated Thioarsenicals and the Formation of Highly Toxic Dimethylmonothioarsinic Acid in Environment.

Dimethylmonothioarsinic acid (DMMTAV) is a highly toxic, thiolated analogue of dimethylarsinic acid (DMAV). In comparison, a further thiolated analogue, dimethyldithioarsinic acid (DMDTAV), and DMAV both exhibit lower toxicity. To understand the environmental conditions responsible for forming DMMTAV, the kinetics of DMAV thiolation are examined. The thiolation of DMAV is pH-dependent and consists of two consecutive first-order reactions under excess sulfide conditions. The first thiolation of DMAV to form DMMTAV is faster than the second one to DMDTAV. DMMTAV is therefore an intermediate. The first reaction is first-order in H2S at pH 6.0 and 20 °C; therefore, the overall reaction is second-order and the rate coefficient in this condition is 0.0780 M-1 s-1. The rate coefficient significantly decreases at pH 8.0, indicating that H2S(aq) triggers the thiolation of DMAV. The second reaction rate is significantly decreased at pH 2.5; therefore, reaction under strongly acidic conditions leads to accumulation of highly toxic DMMTAV in the early stages of thiolation. The transformation of DMDTAV to DMMTAV is catalyzed in the presence of ferric iron. Formation of DMMTAV should be considered when assessing risk posed by arsenic under sulfidic or sulfate reducing conditions.

Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network.

Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.

Chitosan-Polypyrrole Fiber for Strain Sensor.

A chitosan/polypyrrole composited fiber as bio-compatible materials for artificial muscles is investigated. The chitosan/polypyrrole fiber (CPF) is fabricated by in-situ chemical polymerization of pyrrole monomer solution using FeCl3 as an oxidant. The electrical resistivity of the fiber is changed according to the strain variation applied to the both ends of the specimen. The sensor built by using the CPF has a higher gauge factor (4) compared to conventional metal strain gauges (~2) indicating a suitable material for delicate force control in sensing work.

Stretchable, weavable coiled carbon nanotube/MnO2/polymer fiber solid-state supercapacitors.

Fiber and yarn supercapacitors that are elastomerically deformable without performance loss are sought for such applications as power sources for wearable electronics, micro-devices, and implantable medical devices. Previously reported yarn and fiber supercapacitors are expensive to fabricate, difficult to upscale, or non-stretchable, which limits possible use. The elastomeric electrodes of the present solid-state supercapacitors are made by using giant inserted twist to coil a nylon sewing thread that is helically wrapped with a carbon nanotube sheet, and then electrochemically depositing pseudocapacitive MnO2 nanofibers. These solid-state supercapacitors decrease capacitance by less than 15% when reversibly stretched by 150% in the fiber direction, and largely retain capacitance while being cyclically stretched during charge and discharge. The maximum linear and areal capacitances (based on active materials) and areal energy storage and power densities (based on overall supercapacitor dimensions) are high (5.4 mF/cm, 40.9 mF/cm(2), 2.6 µWh/cm(2) and 66.9 µW/cm(2), respectively), despite the engineered superelasticity of the fiber supercapacitor. Retention of supercapacitor performance during large strain (50%) elastic deformation is demonstrated for supercapacitors incorporated into the wristband of a glove.

All-solid-state carbon nanotube torsional and tensile artificial muscles.

We report electrochemically powered, all-solid-state torsional and tensile artificial yarn muscles using a spinnable carbon nanotube (CNT) sheet that provides attractive performance. Large torsional muscle stroke (53°/mm) with minor hysteresis loop was obtained for a low applied voltage (5 V) without the use of a relatively complex three-electrode electromechanical setup, liquid electrolyte, or packaging. Useful tensile muscle strokes were obtained (1.3% at 2.5 V and 0.52% at 1 V) when lifting loads that are ∼25 times heavier than can be lifted by the same diameter human skeletal muscle. Also, the tensile actuator maintained its contraction following charging and subsequent disconnection from the power supply because of its own supercapacitor property at the same time. Possible eventual applications for the individual tensile and torsional muscles are in micromechanical devices, such as for controlling valves and stirring liquids in microfluidic circuits, and in medical catheters.

Flexible supercapacitor made of carbon nanotube yarn with internal pores.

Electrochemical deposition of MnO2 onto carbon nanotube (CNT) yarn gives a high-performance, flexible yarn supercapacitor. The hybrid yarn's blended structure, resulting from trapping of MnO2 in its internal pores, effectively enlarges electrochemical area and reduces charge diffusion length. Accordingly, the yarn supercapacitor exhibits high values of capacitance, energy density, and average power density. Applications in wearable electronics can be envisaged.

Free-standing nanocomposites with high conductivity and extensibility.

The prospect of electronic circuits that are stretchable and bendable promises tantalizing applications such as skin-like electronics, roll-up displays, conformable sensors and actuators, and lightweight solar cells. The preparation of highly conductive and highly extensible materials remains a challenge for mass production applications, such as free-standing films or printable composite inks. Here we present a nanocomposite material consisting of carbon nanotubes, ionic liquid, silver nanoparticles, and polystyrene-polyisoprene-polystyrene having a high electrical conductivity of 3700 S cm(-1) that can be stretched to 288% without permanent damage. The material is prepared as a concentrated dispersion suitable for simple processing into free-standing films. For the unstrained state, the measured thermal conductivity for the electronically conducting elastomeric nanoparticle film is relatively high and shows a non-metallic temperature dependence consistent with phonon transport, while the temperature dependence of electrical resistivity is metallic. We connect an electric fan to a DC power supply using the films to demonstrate their utility as an elastomeric electronic interconnect. The huge strain sensitivity and the very low temperature coefficient of resistivity suggest their applicability as strain sensors, including those that operate directly to control motors and other devices.