Search Results (255)

This paper presents a compact wearable patch antenna operating in the 2.4 GHz ISM band for biomedical Internet of Things (IoT)-based healthcare monitoring applications. The proposed antenna is intended for integration with wearable biomedical sensors in order to support real-time physiological data transmission in remote patient monitoring systems. The antenna was designed on an FR4 substrate to achieve good impedance matching and stable radiation performance. The antenna showed good performance, with a reflection coefficient of −39.56 dB and a gain of 3.01 dB. SAR analysis confirmed compliance with IEEE and ICNIRP safety standards for wearable applications. In addition, the antenna prototype was fabricated and experimentally validated using a vector network analyzer (VNA), showing good agreement between simulated and measured results. Furthermore, the proposed system was implemented by integrating an ESP32 microcontroller with a MAX30100 physiological sensor, where the sensor is responsible for acquiring real-time biomedical data, including heart rate and blood oxygen saturation (SpO₂). The ESP32 processes the acquired data and enables wireless transmission through the proposed antenna to a smartphone and laptop using the Blynk IoT platform, which allows real-time remote monitoring and visualization of physiological parameters. The obtained results confirm the suitability of the proposed antenna for wearable biomedical devices, remote healthcare monitoring, and IoT-enabled healthcare applications. Full article

Background: The Taiwanese loach (Paramisgurnus dabryanus ssp. Taiwan, Dabry de Thiersant, 1872.) is an economically important aquaculture species in East Asia, and its body color directly affects its ornamental and market value. Our research group recently discovered a golden-red mutant, named “Gan Hong No. 1” (MR), within a wild-type (WT) population. During embryogenesis, MR individuals exhibit almost no melanophore deposition, and after hatching, xanthophores and erythrophores appear sequentially, suggesting that the body color variation likely originates from alterations in the gene regulatory network during early development. Objective: To systematically compare the transcriptomes of WT and MR-Taiwanese loach during early development, to identify the key regulatory pathways underlying red body color formation from a temporal perspective, to test whether the classical melanin synthesis pathway is impaired, and to provide a theoretical basis for selective breeding of body color traits. Methods: High-throughput transcriptome sequencing was performed on eight early developmental stages (0, 5, 10, 15, 20, 23, 28 and 43 h post-fertilization) of both loach types. Differential expression analysis, time-series trend analysis, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment were used to systematically characterize gene expression dynamics. Transcriptomic data validation was performed using real-time PCR. Results: In MR, the core transcription factor mitfa was significantly downregulated, whereas the expression of melanin synthesis genes such as kita and dct showed no significant difference, indicating that the impairment of melanogenesis is caused by mitfa downregulation. Trend analysis and pathway enrichment revealed that in MR embryos, pathways related to oxidative stress, unsaturated fatty acid biosynthesis, C-type lectin receptor signaling, p53 signaling, and apoptosis were significantly activated, while the thyroid hormone synthesis pathway was markedly upregulated. In WT, these pathways showed the opposite trend. qRT-PCR results were consistent with the transcriptome data. Conclusions: This study demonstrates that downregulation of mitfa serves as the initial trigger for red body color variation in the Taiwanese loach. This mutation impedes melanin synthesis and concurrently activates a coordinated regulatory network involving oxidative stress, immune inflammation, and thyroid hormone signaling. Accumulation of unsaturated fatty acids alleviates oxidative damage and supports carotenoid deposition, while immune signals eliminate aberrant melanocytes and promote compensatory generation of red and yellow chromatophores. The upregulated thyroid hormone further fine-tunes pigment cell differentiation. For the first time in a cobitid species, this study elucidates the mitfa-mediated, multi-pathway synergistic molecular mechanism driving the transition from melanin-based to carotenoid/pteridine-based red coloration in fish, thereby providing a theoretical reference for molecular breeding of body color in aquaculture. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

Background: Nutritional therapy is central in the management of chronic kidney disease (CKD) and cancer, yet these conditions impose partially conflicting requirements. The 2024 KDIGO guideline recommends a controlled protein intake (~0.8 g/kg/day) to reduce metabolic burden in non-dialysis CKD patients, whereas the ESPEN (European Society for Clinical Nutrition and Metabolism) guidelines support higher protein intake (≥1.0–1.5 g/kg/day) to prevent cancer-related malnutrition. Evidence guiding patients affected by both conditions is limited. We evaluated the effects of a Mediterranean-like controlled protein diet in onconephrological patients compared with CKD controls. Methods: In this retrospective study, 358 CKD patients (183 onconephrological, 175 controls) were followed at a tertiary center (2017–2024). Patients received a protein-controlled diet (0.6–1.0 g/kg/day) tailored to comorbidities and nutritional status. Nutritional assessment included bioelectrical impedance analysis and anthropometry. Renal function was evaluated using creatinine and cystatin C, and measured GFR by iohexol clearance at baseline and 12 months. Results: Baseline body composition was comparable between groups. After intervention, serum urea significantly decreased in both groups, without a decline in measured or estimated GFR. Fat mass and central adiposity indices were reduced, while lean mass and phase angle remained stable. No evidence of protein–energy wasting or catabolic activation emerged. Longitudinal analyses showed no significant time × cancer interaction for renal function or most bioimpedance-derived body composition parameters. However, at extended follow-up, arm circumference and tricipital skinfold thickness showed significant time × cancer interactions, suggesting different longer-term peripheral anthropometric trajectories according to cancer status. Conclusions: In this retrospective real-world cohort, structured nephro-nutritional management with an individualized Mediterranean-like controlled protein prescription was associated with preserved renal function and no evidence of overt nutritional deterioration in onconephrological patients. These findings support the feasibility and apparent safety of this approach in selected patients, while highlighting the need for prospective studies with objective dietary adherence assessment and longer-term evaluation of cancer-related anthropometric trajectories. Full article

This study investigates the dielectric relaxation and conduction mechanisms in $S{e}_{90}S{n}_{6}P{b}_4$ chalcogenide glassy material, which is of interest for applications in phase-change memory devices, optical memory, and thermoelectric sensors. Despite previous studies on chalcogenide glasses, the conduction mechanisms at varying temperatures and the role of correlated barrier hopping (CBH) remain unclear. Using impedance spectroscopy in the frequency range 1 Hz–1 MHz at temperatures from 288 K to 318 K, the real ($Z\prime$) and imaginary ($Z″$) parts of the complex impedance were recorded. The sample was also characterized by X-ray diffraction (XRD) to confirm its glassy nature, and X-ray photoelectron spectroscopy (XPS) to determine the surface chemical composition and oxidation states of the elements. Peaks in $Z″$ at each temperature were used to evaluate the relaxation time τ, revealing thermally activated processes with an activation energy of 0.62 eV. Nyquist plots showed semicircular behavior with decreasing radii at higher temperatures, indicating enhanced d.c. conductivity with an activation energy of 0.63 eV. A.C. conductivity analysis demonstrated frequency-dependent behavior consistent with the CBH model, with hopping energy calculated as 0.32 eV. The dielectric loss increased with temperature and decreased with frequency, stabilizing above 250 Hz at 318 K. These findings provide new insights into the dielectric and conduction properties of $S{e}_{90}S{n}_{6}P{b}_4$ glasses, supporting their optimization for practical electronic applications. Full article

Accurate quantification of glucose is vital for quality control in the food industry. While earth-abundant Cu has emerged as a promising candidate for non-enzymatic electrochemical sensing, conventional electrode fabrication relying on powder coating with polymeric binders inevitably buries active catalytic sites and impedes both electron transfer and mass transport. In this study, a binder-free, self-supporting Cu nanoparticles/Ni foam (Cu NPs/NF) electrode was developed via a facile one-step hydrothermal method. Benefitting from the enhanced charge-transfer efficiency and a substantially enlarged electrochemical active surface area, the Cu NPs/NF-based electrochemical glucose sensor exhibited a wide linear detection range (0.25–3310.52 μM), a high sensitivity of 7000 μA mM⁻¹ cm⁻², a low detection limit of 0.32 μM, and a rapid response time of 3 s. Furthermore, the developed Cu NPs/NF electrode displayed favorable reproducibility, storage stability, and high selectivity against common interferents present in food matrices, demonstrating its reliability for practical applications. The feasibility of the proposed sensor was successfully validated in real beverage samples. Given the simplicity of the one-step hydrothermal synthesis and the portability afforded by the self-supporting electrode architecture, this Cu NPs/NF electrode emerges as a highly attractive candidate for commercial glucose sensors. Beyond glucose, the design strategy can be readily extended to the detection of other electroactive food-quality markers, enabling the broader applicability of this electrode platform in comprehensive food analysis. Full article

This study investigated the effects of ultrasound-assisted marination on NaCl diffusion in pork using multiphysics simulation and evaluated the accuracy of electrical impedance spectroscopy for predicting NaCl content during marination. The results showed that short-term ultrasonic treatment did not significantly enhance moisture diffusion from brine into pork tissue. However, multiphysics simulation demonstrated that ultrasound significantly accelerated NaCl penetration, enabling a reduced brine concentration without compromising the final salt content, as further confirmed by thermogravimetric analysis, which showed higher residual NaCl and mass in treated samples. Electrical impedance properties exhibited systematic changes with increasing ultrasonic marination time, including decreased impedance, increased phase angle, and a reduced Cole–Cole arc radius, reflecting enhanced NaCl diffusion and structural modifications in muscle tissue. A strong linear correlation between impedance parameters and NaCl content was established, and validation results confirmed that impedance spectroscopy can accurately predict NaCl levels during marination. These findings highlight the potential of combining ultrasound-assisted marination with impedance-based techniques for real-time, non-destructive monitoring of salt content in meat processing. Full article

Obesity remains a major global health concern associated with increased cardio-metabolic risk and healthcare burden. L-Carnitine plays a central role in mitochondrial fatty acid transport and has been investigated as a potential adjunct in weight management. This study evaluated the real-world effectiveness and tolerability of L-Carnitine L-Tartrate supplementation in adults with overweight and class I obesity. In this prospective, single-center, uncontrolled observational study, 50 adults (BMI 25–35 kg/m²) newly initiated on L-Carnitine L-Tartrate 2000 mg/day were followed for 8 weeks in a non-comparative, real-world setting, alongside standard lifestyle advice. The primary outcome was the mean change in body weight from baseline. Secondary outcomes included anthropometric measures, body composition parameters assessed by bioelectrical impedance analysis, quality of life using the RAND 36-Item Health Survey, global satisfaction, and safety. Over 8 weeks, mean body weight in this cohort decreased from 73.69 ± 7.73 kg at baseline to 67.36 ± 7.87 kg at Week 8 (mean reduction: 6.33 kg; 8.59%; p < 0.001. Over the follow-up period, we observed reductions in waist circumference (−2.38 cm), hip circumference (−2.96 cm), total fat mass (−3.90 kg), and visceral fat (−39.91%) (all p < 0.001) in within-subject analyses. Quality of life shows progressive improvement over time. No adverse events or treatment discontinuations were reported. In this exploratory, single-arm, real-world observational study, initiation of L-Carnitine L-Tartrate supplementation alongside routine lifestyle advice was associated with reductions in body weight, central adiposity, and improvements in patient-reported outcomes over 8 weeks. While the uncontrolled study design warrants cautious interpretation, these findings provide supportive real-world evidence and generate a basis for future controlled studies to further evaluate the therapeutic potential of L-Carnitine L-Tartrate. Full article

This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level or segment-level labels for real-time filtering and offline dataset curation. The framework is quantitatively validated exclusively on ECG data. Its modular architecture is designed to extend to further non-stationary periodic biomedical time-series signals including photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) through modality-specific parameter adaptation; however, this broader applicability currently reflects architectural extensibility rather than experimentally validated performance. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency domain SNR estimation, and baseline wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels from 20 healthy participants demonstrates high methodological classification accuracy (98.1%), achieving approximately a 98% F1-score, 99% sensitivity, and 97% specificity. Prospective validation on patient populations with cardiovascular pathology is identified as a necessary step toward clinical deployment. This modular approach improves the reliability of downstream analysis by preventing corrupted data from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems. Full article

Traditional hyperspectral cameras transmit full data cubes to host computers, creating severe bandwidth and storage bottlenecks that impede real-time analysis. We present a Zynq-7035-based intelligent camera using hardware–software codesign to enable on-board processing and transmit only actionable results. This intelligent camera is designed for high-throughput edge-sensing tasks, prioritizing rapid detection and information extraction over exhaustive raw data acquisition. The processing system (PS) handles command scheduling while the programmable logic (PL) implements a row-parallel pipeline for image acquisition, preprocessing, and spectral matching; all modules are decoupled through a unified DDR3 interface to support flexible algorithm integration. Push-broom experiments on leaf samples demonstrate Euclidean distance-based spectral matching executed entirely within the camera. Raw data and classification maps are uploaded via User Datagram Protocol (UDP). Results confirm accurate identification of diseased regions with two orders of magnitude data reduction, validating the architecture for real-time hyperspectral processing. Full article

Heap bioleaching is widely used to extract copper from low-grade sulfide ores thanks to its operational simplicity, low cost, and environmental sustainability. However, current control strategies rely primarily on single-factor optimization and often overlook the synergistic interactions of multiple key parameters, such as ore particle size, pore structure, pH, temperature, microbial activity, and oxygen transfer efficiency. As a result, issues such as low recovery rates, extended leaching periods, and high operational costs persist. Moreover, the “gray-box” nature of heap systems impedes real-time monitoring of internal physical, chemical, and biological processes. In addition, empirical multi-parameter optimization is time-consuming and inadequate for capturing complex interdependencies. This review was conducted to systematically examine the key factors influencing heap bioleaching efficiency and critically evaluate recent advances in numerical simulation and intelligent control strategies. As a result, we identified a major research gap: the existing models—including microscale shrinking core models (SCMs), mesoscale pore-network models based on CT reconstruction, and macroscale continuum models—have inherent limitations. SCMs assume idealized spherical particles with uniform mineral distribution while neglecting pore structure evolution and biofilm dynamics. Mesoscale models offer detailed pore characterization but lack robust multi-physics coupling (thermal–hydro–mechanical–chemical–biological, or THMCB). Macroscale models rely on homogenization assumptions that oversimplify spatial heterogeneity and temporal variations in permeability. This analysis covers the relevant literature from 1985 to 2025, with a focus on three methodological scales (micro, meso, and macro) and their integration with machine learning approaches. A notable finding is that hybrid neural network models (e.g., BP and RBF architectures) outperform purely physics-based models in predicting leaching kinetics under varying operational conditions. However, their accuracy depends heavily on high-quality field data—a limitation rarely addressed in prior reviews. By clearly delineating these model-specific limitations and scale-dependent trade-offs, this review makes two unique contributions: a structured framework for selecting and coupling numerical methods according to process requirements and a roadmap for integrating artificial neural networks with multi-physics simulations to achieve real-time intelligent control of heap bioleaching. The findings offer both theoretical guidance and practical references for optimizing the processing of low-grade copper sulfide ores. Full article

This study presents a rapid, eukaryotic impedimetric biosensor that applies the yeast Saccharomyces cerevisiae as a robust, cost-effective biorecognition element for monitoring the acute toxicity of two representative pharmaceuticals, naproxen and isoniazid, in aquatic systems. The biosensor utilizes a previously developed three-electrode system made from type 316 stainless steel. Yeast cells seeded onto these electrodes serve as the biosensing element. By monitoring changes in electrical impedance, the system quantifies the cellular stress induced by pharmaceutical exposure. Electrochemical Impedance Spectroscopy (EIS) revealed a concentration-dependent decrease in both resistance and capacitance, attributed to cell death and subsequent desorption from the working electrode surface. These findings were validated through optical density at 600 nm (OD₆₀₀) growth curve analysis and methylene blue viability staining, which confirmed metabolic inhibition and membrane damage. Results indicate a linear response for naproxen within the 2.5 mM to 20 mM range, with a LOD of 0.509 mM, and for isoniazid within the 10 mM to 100 mM range, with a LOD of 0.684 mM. Naproxen demonstrated a more pronounced cytotoxic effect, with cell viability dropping to 41.08% at 10 mM compared to 68.79% for isoniazid. While conventional analytical methods focus on chemical quantification, this proof-of-concept biosensor provides a rapid toxic/non-toxic signal, offering a biologically relevant tool for real-time monitoring of industrial waste streams and acute environmental contamination. Full article

Metal–insulator–metal capacitors based on electrosprayed silica nanoparticles exhibit exceptionally high effective permittivity. However, their dielectric response is highly sensitive to ambient humidity, which can compromise data reliability. This study analyzes impedance characteristics of two silica nanoparticle-based MIM capacitors: (i) one measured under ambient conditions (0.1 Hz to 2 MHz) at 100/500 mV, and (ii) another under controlled relative humidity (RH) (40%, 70% and 90%) at 500 mV. Impedance consistency is rigorously assessed via Kramers–Kronig (KK) transforms. The first capacitor shows excellent KK consistency for real part Z′ (NRMSE = 3.3%), compatible with linear time-invariant assumptions. The second capacitor exhibits strong humidity-dependence deviations; NRMSE for Z′ rises from 14.5% at 40% RH to 141.2% at 90% RH, indicating linearity/causality breakdown from moisture-induced ionic conduction and interfacial polarization. These findings demonstrate that while increased humidity amplifies the effective dielectric response, it simultaneously introduces non-idealities that invalidate standard KK assumptions. Due to inherent microstructural variability between devices, humidity-dependent conclusions are derived from controlled intra-device analysis. Transmission Line Modeling confirms moisture enhances ionic network connectivity. Thus, KK analysis serves as a sensitive probe of environmental effects on nanostructured dielectrics, offering a framework to diagnose non-ideal behavior without a priori equivalent circuit models. Full article

Various global precipitation datasets have been used in precipitation-related fields such as hydrology, meteorology, climatology, and ecology to achieve different research objectives. Error analysis is an integral part before applying them to operational fields. However, the growing number of precipitation products and the absence of comprehensive error comparison research jointly impede users in distinguishing product-specific error patterns and constrain developers from enhancing precipitation estimation accuracy. To address this issue, we performed error analysis and comparison of thirteen global precipitation products—categorized as delayed time (DT), near real-time (NRT), and real-time (RT) types—across mainland China. Results revealed that GSMaP-Gauge (Gauge-adjusted Global Satellite Mapping of Precipitation) performed best in terms of detection indicators, while MGP (Multi-source merged global precipitation product) performed best in estimating precipitation accuracy. However, IMERG-Final (Integrated Multisatellite Retrievals for Global Precipitation Measurement Final Run) proved ineffective in reducing the overestimations of both storm and light precipitation events in regions of complex topography. Furthermore, two DT products (i.e., ERA5 (Fifth generation of ECMWF atmospheric reanalyses of the global climate) and MGP) overestimated the frequency of light precipitation events, with relative rainfall occurrence biases exceeding 80%. This bias is attributable to both false detections and the misclassification of high intensity rainfall as light precipitation. Although GSMaP-NOW (based exclusively on passive microwave data) detected precipitation more effectively than the infrared-only PDIRNow (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)—Dynamic Infrared Rain Rate (Now)), it achieved lower accuracy. This discrepancy reflects the tradeoff between the higher precipitation sensitivity of passive microwave observations and their sparse temporal sampling, compared with the continuous coverage provided by infrared data. Finally, our findings indicated that current evaluation approaches do not reliably determine the optimal precipitation product, since product superiority is contingent upon the selected error metric. This underscores the urgent need to develop theoretically grounded and operationally reliable methods for selecting optimal precipitation products to support data users in deriving robust and reliable conclusions in hydrology, meteorology, and ecology. Full article

To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles. Full article