Search Results (19)

The Gold-spotted pond frog (Pelophylax chosenicus (Okada, 1931)), classified as an endangered species in South Korea, has experienced significant population declines due to habitat loss, primarily driven by agricultural expansion and urbanization. This study aimed to assess the hibernation ecology of P. chosenicus using PIT tagging technology, focusing on its hibernation behavior, environmental conditions, and implications for conservation and restoration. Over a three-year period (2021–2024), PIT tags were implanted in 408 frogs, enabling continuous monitoring of hibernation sites and individual characteristics. The results revealed that hibernation depths ranged from 1 to 23 cm, with deeper burrowing observed during colder months, indicating a temperature-dependent survival strategy. The soil temperature at hibernation sites remained consistently higher than ambient air temperature, suggesting an adaptive mechanism to avoid freezing conditions. Additionally, frogs exhibited a strong preference for hibernation sites near water bodies, where soil moisture levels were high, highlighting the importance of preserving suitable microhabitats for successful overwintering. These findings provide valuable insights into the hibernation ecology of P. chosenicus and are critical for guiding effective habitat restoration initiatives. Future research should investigate physiological adaptations to varying hibernation environments and the potential impacts of climate change may impact the survival of this endangered species. Full article

This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna’s reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test. A calibration curve was established using reference materials—including low-density polyvinylchloride, polytetrafluoroethylene, polymethyl methacrylate, and polycarbonate—and validated through precise permittivity measurements. The system was subsequently applied to wood samples (fir, poplar, beech, and oak) at different humidity levels, revealing a sigmoidal relationship between moisture content and permittivity. The behavior was also confirmed using a portable and low-cost setup, consisting of a point-like coaxial sensor that could be easily moved and positioned as needed, enabling localized measurements on specific areas of interest of the sample, together with a miniaturized Vector Network Analyzer. These results underscore the potential of this portable, contactless, and scalable sensing platform for real-world monitoring of cultural heritage materials, enabling minimally invasive assessment of their structural and historical integrity. Moreover, by enabling the estimation of moisture content through dielectric permittivity, the system provides an effective method for early detection of water-induced deterioration in wood-based heritage items. This capability is particularly valuable for preventive conservation, as excessive moisture—often indicated by permittivity values above critical thresholds—can trigger biological or structural degradation. Full article

This article introduces a microwave sensor tailored for skin hydration monitoring. The design enables wireless operation by separating the sensing component from the reader, making it ideal for wearable devices like wristbands. The sensor consists of a semi-lumped $LC$ resonator coupled to an inductive coil reader, where the capacitive part of the sensing tag is in contact with the skin. The variations in the skin hydration level alter the dielectric properties of the skin, which, in turn, modify the resonances of the $LC$ resonator. Experimental in vivo measurements confirmed the sensor’s ability to distinguish between four hydration conditions: wet skin, skin treated with moisturizer, untreated dry skin, and skin treated with Vaseline, by measuring the resonance frequencies of the sensor. Measurement of the input reflection coefficient ($S_{11}$) using a vector network analyzer (VNA) revealed distinct reflection poles and zeros for each condition, demonstrating the sensor’s effectiveness in detecting skin hydration levels. The sensing principle was analyzed using an equivalent circuit model and validated through measurements of a fabricated sensor prototype. The results confirm in vivo skin hydration monitoring by detecting frequency shifts in the reflection response within the 50–200 MHz range. The measurements and data analysis show less than $0.037\%$ error in transmission zero ($f_z$) together with less than $1.5\%$ error in transmission pole ($f_p$) while being used to detect skin hydration status on individual human subjects. The simplicity of the detection method, focusing on key frequency shifts, underscores the sensor’s potential as a practical and cost-effective solution for non-invasive skin hydration monitoring. This advancement holds significant potential for skincare and biomedical applications, enabling detection without complex signal processing. Full article

Leaf moisture content (LMC) directly affects the life activities of plants and becomes a key factor to evaluate the growth status of plants. To explore a low-cost, real-time, rapid, and accurate method for LMC detection, this paper employs Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) sensor technology. By reading the tag information attached to the back of leaves, the parameters of the RSSI, phase, and reading distance of the tags are collected. In this paper, we propose an enhanced Multi-Feature Fusion algorithm based on Hyperdimensional Computing (HDC) called MFFHDC. In our proposed method, the real-valued features are encoded into hypervectors and then combined with Multi-Linear Discriminant Analysis (MLDA) for the feature fusion of different features. Finally, a retraining method based on Cosine Annealing with Warm Restarts (CAWR) is proposed to improve the model and further enhance its accuracy. Tests conducted in the experimental forest show that the proposed mechanism can effectively predict the LMC. The model’s Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) reached 0.0195, 0.0255, and 0.9131, respectively. Additionally, comparisons with other methods demonstrate that the presented system performs excellently in most aspects. As a lightweight model, this study shows great practical application value, particularly for the limited data volume and low hardware costs. Full article

The historical management of underground utilities such as communication lines, water and sewage pipes, and power lines is essential for their effective use. However, due to the nature of the buried facilities, detecting and tracking them are challenging, although various solutions are difficult to apply in the field, especially optical cables, which are mainly used for communication, making it more difficult to apply existing solutions. There has been limited research on the practicalities of solutions, especially on multilayer structures such as road pavements. Based on a literature review, we selected ultra-high-frequency radio frequency identification (UHF RFID), which is least affected by performance degradation or interference due to batteries. We experimented to identify the signal attenuation based on the medium by controlling the materials and moisture used in the road pavement as variables. The signal reached a depth of 68 cm and this was possible under conditions of asphalt and subgrade soil. In a comparative experiment for each variable, we recognized tags of sand and coarse aggregate up to a depth of 70 cm. When the moisture was considered, the signal attenuation difference was 1.8 dBm for each level. Based on the experimental results of this study, it was confirmed that UHF RFID can be used as a marker sensor to be attached to utilities installed under the road pavement. Depending on the structure and material of the pavement, the signal is sufficiently transmitted up to 40–50 cm, which is the target installation depth of the communication line. Full article

Moisture content (MC) detection plays a vital role in the monitoring and management of living trees. Its measurement accuracy is of great significance to the progress of the forestry informatization industry. Targeting the drawbacks of high energy consumption, low practicability, and poor sustainability in the current field of living tree MC detection, this work designs and implements an ultra-high-frequency radio frequency identification (UHF RFID) sensor system based on a deep learning model, with the main goals of non-destructive testing and high-efficiency recognition. The proposed MC diagnostic system includes two passive tags which should be mounted on the trunk and one remote data processing terminal. First, the UHF reader collects information from the living trees in the forest; then, an improved online sequential parallel extreme learning machine algorithm (OS-PELM) is proposed and trained to establish a specific MC prediction model. This mechanism could self-adjust its neuron network structure according to the features of the data input. The experimental results show that, for the entire living tree dataset, the MC prediction model based on the OS-PELM algorithm can identify the MC level with a root-mean-square error (RMSE) of no more than 0.055 within a measurement range of 1.2 m. Compared with the results predicted by other algorithms, the mean absolute error (MAE) and RMSE are 0.0225 and 0.0254, respectively, which are better than the ELM and OS-ELM algorithms. Comparisons also prove that the prediction model has the advantages of high precision, strong robustness, and broad applicability. Therefore, the designed MC detection system fully meets the demand of forestry Artificial Intelligence of Things. Full article

This study was to assess the nutritional quality and bioactive properties of black cumin (Nigella sativa L.) seeds and oil commonly found in the Chinese market. The results showed that black cumin seeds contain 5.02, 21.07, 39.02, 3.02, 6.01, and 25.86% moisture, crude proteins, crude fat, ash, fiber, and carbohydrates, respectively. It also contains substantial amounts of minerals, namely calcium, potassium, phosphorus, magnesium, sodium, iron, zinc, and copper. Glutamic acid (4.10 g/100 g protein) is the major amino acid of black cumin seeds. The major volatile components in black cumin seeds were thymoquinone (21.01%), o-cymene (18.23%), and β-thujene (17.22%). Cumin seed oil extracted by the soxhlet method contains high quantities of unsaturated fatty acids (UFA; 85.16%) and low amounts of saturated fatty acids (SFA; 15.02%). The major fatty acid of black cumin seed oil was linoleic acid (57.71%), followed by oleic acid (24.46%). The most prominent TAG of black cumin seed oils was oleoyl-dilinoleoyl-glycerol (OLL; 38.87%). In addition, the levels of α-tocopherol, β-tocopherol, γ-tocopherol, and total polyphenols in the black cumin seed oil were 25.59, 14.21, and 242.83 mg/100 g, and 315.68 mg GAE/kg, respectively, and possessed high antioxidant activity (DPPH IC_50%, of 4.02 mg/mL). These findings demonstrate that black cumin seeds are nutritionally rich with high potential applications in the food, pharmaceutical, and cosmetic industries. Full article

Drought is one of the significant threats to the agricultural sector. However, there is limited knowledge on plant response to drought stress and post-drought recovery. Pandanus amaryllifolius, a moderate drought-tolerant plant, is well-known for its ability to survive in low-level soil moisture conditions. Understanding the molecular regulation of drought stress signaling in this plant could help guide the rational design of crop plants to counter this environmental challenge. This study aimed to determine the morpho-physiological, biochemical, and protein changes of P. amaryllifolius in response to drought stress and during recovery. Drought significantly reduced the leaf relative water content and chlorophyll content of P. amaryllifolius. In contrast, relative electrolyte leakage, proline and malondialdehyde contents, and the activities of antioxidant enzymes in the drought-treated and recovered samples were relatively higher than the well-watered sample. The protein changes between drought-stressed, well-watered, and recovered plants were evaluated using tandem mass tags (TMT)-based quantitative proteomics. Of the 1415 differentially abundant proteins, 74 were significantly altered. The majority of proteins differing between them were related to carbon metabolism, photosynthesis, stress response, and antioxidant activity. This is the first study that reports the protein changes in response to drought stress in Pandanus. The data generated provide an insight into the drought-responsive mechanisms in P. amaryllifolius. Full article

The use of Key Enabling Technologies (KET), in the definition of innovative systems, is a crucial point for smart industries and sustainability. The proposed work combines innovations from different fields, including industrial sustainability on the one hand, and smart electronics on the other. An innovative multifunctional panel is presented, produced with waste resulting from the industrial processing of paper and cardboard; the panel can be used for the control of safety in processing factories and for the monitoring of environmental conditions in the area, as well as the energy improvement of the building envelope. Several sensors are embedded in the panel for monitoring temperature, moisture, and localization by means of an RFID tag. In addition, the panel is battery–free, thus enhancing the installation and realization of the system. The power supply is provided by the tag reader as irradiated power, thus realizing a very low power application. Panels have been already realized and experimental tests have been performed in the laboratory. Full article

Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used. Full article

Herein, a passive low-profile moisture sensor design based on radio frequency identification (RFID) technology is proposed. The sensor consists of an LC resonant loop, and the sensing mechanism is based on the fringing electric field generated by the capacitor in the circuit. A standard planar inductor and a two-layer interdigital capacitor (IDC) with a significantly higher fringing capacitance compared to that of a conventional parallel plate capacitor (PPC) are used, resulting in improved frequency offset and sensitivity of the sensor. Furthermore, a sensor tag was designed to operate at an 8.2 MHz electronic article surveillance (EAS) frequency range and the corresponding simulation results were experimentally verified. The IDC- and PPC-based capacitor designs were comprehensively compared. The proposed IDC sensor exhibits enhanced sensitivity of 10% in terms of frequency offset that is maintained over time, increased detection distance of 5%, and more than 20% increase in the quality factor compared to sensors based on PPC. The sensor’s performance as a urine detector was experimentally qualified. Additionally, it was shown experimentally that the proposed sensor shows a faster response to moisture. Both simulation and experimental data are presented and elucidated herein. Full article

The transepidermal water loss (TEWL) and the skin wettedness factor (SWF) are considered parts of a key perspective related to skincare. The former is used to determine the loss of water content from the stratum corneum (SC), while the latter is used to determine the human skin comfort level. Herein, we developed two novel approaches: (1) determination of the TEWL and the SWF based on a battery-free humidity sensor, and (2) the design of a battery-free smart skincare sensor device tag that can harvest energy from a near field communication (NFC)-enabled smartphone, making it a battery-free design approach. The designed skincare device tag has a diameter of 2.6 cm and could harvest energy (~3 V) from the NFC-enabled smartphone. A series of experimental tests involving the participation of eight and six subjects were conducted in vivo for the indoor and outdoor environments, respectively. During the experimental analysis, the skin moisture content level was measured at different times of the day using an android smartphone. The TEWL and SWF values were calculated based on these sensor readings. For the TEWL case: if the skin moisture is high, the TEWL is high, and if the skin moisture is low, the TEWL is low, ensuring that the skin moisture and the TEWL follow the same trend. Our smart skincare device is enclosed in a 3D flexible design print, and it is battery-free with an android application interface that is more convenient to carry outside than other commercially available battery-based devices. Full article

Stable water isotopologues in paleoclimate archives ( ${\mathsf{\delta }}^{18}\mathrm{O}$ ) have been widely used as an indicator to derive past climate variations. The modern observed spatial ${\mathsf{\delta }}^{18}\mathrm{O}$ -temperature relation in the middle and high latitudes has been used to infer the paleotemperatures changes from ice core data. However, various studies have shown that the spatial slope is larger than the temporal slope at the drill site by a factor of 2. Physically, the different spatial and temporal slope has been suggested to result from the amplified local surface air temperature cooling in the polar region at Last Glacial Maximum (LGM), according to the slope ratio equation derived in our previous study. To explicitly confirm the “polar amplification” effect in understanding the differences between temporal and spatial isotope–temperature relations, here we use the same isotope-enabled atmospheric general circulation model with a moisture-tracing module embedded to quantitatively estimate the contributions of different sources to the precipitated heavy oxygen isotopes in the middle and high latitudes. Our results show that the major sources of ${\mathsf{\delta }}^{18}\mathrm{O}$ in precipitation over middle and high latitudes are from oceans where the sea surface temperature cooling at Last Glacial Maximum (LGM) is less than −2 $°\mathrm{C}$ , while the local moisture sources with a higher cooling can be also relevant for polar regions, such as north Greenland. Additionally, the neglect of the strengthened local inversion layer strength at LGM could be the main cause for the overestimated source temperature cooling by the slope ratio equation, especially for the polar regions in the Northern Hemisphere. Full article

A smart sensor label based on the integration of ultra high frequency (UHF) radio frequency identification (RFID) technology and sensors is presented. The label is composed of a semi-active system that measures temperature, light, relative humidity and gravimetric water content (GWC) in the soil. The deployed system provides a simple, cost effective solution to monitor and control the growing of plants in modern agriculture and is intended be a part of a smart wireless sensor network (WSN) for agricultural monitoring. This paper is focused on analysis and development of a moisture sensor to measure GWC. It is based on a capacitance measurement solution, the accuracy of which is enhanced using several sensor driving frequencies. Thanks to the cancellation of supply voltage variations, the modeling of the GWC sensor and readout circuit was correct. The results we measured were close to modeled values. The maximum measurement resolution of the capacitive moisture sensor was 0.07 pF. To get the GWC from measured capacitance, a scale was used to weigh the mass of water in the soil. The comparison between capacitance measurement and calculated soil GWC is presented. The RFID measurement system has energy harvesting capabilities and an ultra-low power microcontroller, which uses embedded software to control the measurement properties. The microcontroller has to choose the appropriate model depending on the measured amplitude and chosen frequency to calculate the actual voltage on the sensing capacitor. Full article

A novel and miniaturized semi-elliptical 20-bit fully passive chipless RFID sensor tag is proposed in this article. The realized sensor tag is made up of semi-elliptical shaped open-end slots within the compact size of 25 mm × 17 mm. The multi-substrate analysis of the proposed tag is examined using non-flexible and flexible materials. The articulated tag configuration is capable of monitoring moisture levels when the largest resonator is covered by a heat-resistant sheet of Kapton HN (DuPontTM). The proposed tag functions in the operational frequency band of 4.1 GHz–16 GHz and possesses the overall bit density of 4.70 bit/cm². The structure is composed of a thin passive substrate layer topped with an active layer of conductive path and is considered as a potential candidate for low-cost identification of the tagged objects. In addition to that, its moisture sensing property and flexible nature make it a reliable smart sensor for conformal applications. Full article