This paper presents a bifunctional reconfigurable terahertz metamaterial device that integrates ultra-wideband absorption and high-efficiency broadband polarization conversion functionalities based on the phase-change material vanadium dioxide (VO2). By exploiting the reversible insulator-to-metal phase transition of VO2 near 68 °C, the device achieves dynamic switching between two distinct electromagnetic response mechanisms. When VO2 is in its insulating state, the device operates as a broadband linear cross-polarization converter. Upon transitioning to the metallic state, the device functions as an ultra-wideband perfect absorber. The physical mechanisms underlying the dual functionality are elucidated through surface current distribution analysis, electric field monitoring, impedance matching theory, and equivalent electromagnetic parameter extraction. Comprehensive parametric studies reveal the influence of key structural dimensions on device performance, while angular dependence analyses demonstrate excellent polarization insensitivity and wide-incidence-angle stability in both operational modes. Compared with recently reported terahertz devices, the proposed design exhibits superior bandwidth performance, compact structural dimensions, and dynamic functional reconfigurability, making it a promising candidate for applications in intelligent stealth systems, terahertz imaging, optical switching, and advanced communication technologies.

Monitoring enzyme structural dynamics is essential for elucidating catalytic mechanisms, yet transient conformational fluctuations on microsecond-to-millisecond timescales remain challenging to resolve with conventional techniques. Here, we investigate the catalytic dynamics of cytochrome P450 1A1 (CYP1A1) during benzo[a]pyrene (BaP) metabolism by measuring single-molecule protein conductance. We show that catalysis-induced α-helix structural rearrangements, together with redox transitions of the heme center, modulate charge-transport efficiency. A negative correlation between BaP concentration and conductance enables construction of a kinetic model, yielding an apparent Michaelis constant of 24.2-43.2 μM. Real-time conductance measurements resolve four distinct conductance states associated with catalytic intermediates, which are further assigned using metabolic intermediates as substrates. These results provide insight into competing detoxification and activation pathways of BaP metabolism. This work establishes protein conductance as a generalizable platform for probing transient enzymatic dynamics and kinetics at the single-molecule level.

In distributed renewable energy systems, load fluctuations caused by energy resources and energy storage increase the overload risk of distribution transformers, which may accelerate insulation aging and cause overheating, and undermine operational reliability. For transformer condition monitoring, this risk is reflected not by a single variable but by heterogeneous sensing observations acquired from electrical, thermal, and equipment status monitoring channels. Because full-scale inspection of latent defects is impractical under limited staffing and equipment resources, accurate overload risk prediction is important for sensor-driven maintenance allocation. With such motivations, this paper proposes a Transformer Overload Risk Assessment (TORA) approach for robust overload risk prediction under nonstationary load conditions. First, a feature matrix is constructed by jointly incorporating static features that capture long-term drift and dynamic features extracted from multisource sensing and supervisory signals that reflect short-term fluctuations. Then, static and dynamic features are assessed with Edge-based Static Feature Risk Assessment (E-SFRA) model and Cloud-based Dynamic Feature Risk Assessment (C-DFRA) model, respectively, according to their temporal and statistical characteristics. Next, a periodic calibration model (CE-PAA) is established through a cloud–edge loop, which uses low-latency edge updates and high-capacity cloud computation as feedback. Finally, risk score fusion (RSF) fuses generated static and dynamic risk scores to integrate cloud and edge strengths. The case study results indicate that TORA can transform heterogeneous monitoring signals into calibrated risk information in the studied single power plant scenario, providing useful support for multisource sensor data fusion, transformer condition monitoring, and maintenance decision making. Further validation using multi source field datasets is still needed to assess its cross scenario generalization ability.

Adhesive joints are widely used in structural applications. However, they are susceptible to degradation under service loads and adverse environmental conditions, leading to eventual catastrophic failure. Thus, the advancement of monitoring tools that can deliver real-time data on the deterioration of adhesive joints is crucial for enhancing the reliability of structures. This study investigated the feasibility of using electrical impedance responses to monitor integrity degradation under tensile and fatigue loading in single-lap adhesive joints in aluminum alloy and carbon fiber-reinforced polymer (CFRP) specimens. Previous works on electrical impedance monitoring of adhesive joint integrity invariably employed conductive adhesives. Theoretical considerations based on the concept of a capacitive system indicate that electrical impedance monitoring may still be feasible even if the joint is non-conductive. This has important implications as it suggests that the structural health of many existing ordinary adhesive joints may be amenable to impedance-based monitoring. To test this possibility, neat epoxy adhesive joints without the addition of conductive constituents were fabricated with aluminum and composite adherends. The specimens were subjected to tensile and fatigue degradation while the impedance responses under different excitation frequencies were monitored. The results showed that impedance monitoring is insensitive for detecting damage during tensile failure because the onset of debonding that produces a detectable impedance change occurs too close to the unstable final failure. For fatigue cycling, debonding developed at an early stage and evolved in a stable manner, and the impedance gradually increased with the number of fatigue cycles, reflecting the development of fatigue damage. These findings indicate that impedance-based monitoring on non-conductive adhesive joints has strong potential for tracking structural integrity degradation, particularly for fatigue loading.

Elucidating the structural evolution of molecules during photoinduced aggregation remains a central challenge in molecular science, particularly in solution where aggregation processes occur dynamically. Here, we present an electrical strategy to characterize the evolution of photoinduced aggregation at the single-molecule level. Using persulfurated arenes as a model system, photoexcitation enhances intermolecular interactions that drive molecular assembly and induce conformational stabilization within aggregated species once the irradiation is halted. These stabilized conformations generate distinct ratiometric conductance signatures that can be reliably detected using scanning tunneling microscope-break junction measurements. Time-dependent irradiation experiments reveal the progressive evolution of aggregation, enabling electrical monitoring of structural changes during the assembly process. Complementary theoretical analyses further reveal the correlation between molecular geometry and conductance response. This work establishes a direct link between photoinduced aggregation evolution and single-molecule conductance, providing an electrical perspective for investigating structural dynamics in photoresponsive molecular assemblies.

Information units are progressively approaching the fundamental physical limits of integration density, including in terms of extremely small sizes, multistates and probabilistic traversal. However, simultaneously encompassing all of these characteristics in a unit remains elusive. Here, via real-time in situ electrical monitoring, we clearly observed stochastic alterations of multiple conductance states in Sc2C2@C88. The true random bit sequence generated exhibited an autocorrelation function whose confidence interval fell within ±0.02, demonstrating high-quality randomness. The alterations of multiple conductance states are controllable, that is, whose probability distributions could traverse from 0 to 1, enabling us to factorize 551 into its prime factors. Furthermore, we proposed a matrix-chain multiplication scheme and experimentally verified the multiplication of two 4 × 4 state-transition matrices with a small maximum error of <0.05. Combined with theoretical calculations, the stochastic but controllable multistates are probably attributed to the rich energy landscape, which could be stepwise changed by the electric field. Our findings reveal extremely small multilevel probabilistic bit for matrix multiplication, which pave the way for ultra-compact intelligent electronic devices.

Battery health prediction typically relies on electrical signals such as capacity, voltage, and impedance, which are chemically nonspecific and can lag the onset of failure. Here we show that operando gas evolution measured by online electrochemical mass spectrometry (OEMS) provides SOC-resolved chemical signatures that enable earlier degradation-stage identification and long-horizon capacity forecasting. Using intermittently sampled CO, CO2, and C2H4 profiles, we develop two complementary models: (i) a label-free PCA-K-means stage classifier that distinguishes linear and accelerated nonlinear degradation, and (ii) a single-shot forecaster that fuses a short capacity/SOH history with one OEMS-measured cycle to predict future capacity trajectories without autoregressive rollout. On held-out test cells, the stage classifier exhibits a 3.6% misclassification rate (5/136 OEMS-measured cycles) and flags the nonlinear transition 32.0 ± 17.2 cycles before the Bacon-Watts knee (18.0 ± 8.1% of lifetime). On held-out cells closest to the training protocol, the forecaster achieves 1.7-2.9% RMSE at 100-cycle horizons and reduces long-horizon drift relative to capacity-only baselines. These results establish gas evolution as a chemically grounded complement to electrical monitoring and motivate future integration with compact off-gas sensing for cycle-level battery health management.

Electrical monitoring of brain activity can be performed discreetly and continuously over long periods of time using intra-auricular electroencephalography (intra-auricular EEG), a promising technique suitable for subjects who are difficult to monitor, such as newborns or patients with neurological conditions requiring discreet but long-term neurophysiological assessment. The concept of intra-aural EEG can be realized through the development of systems that include wearable sensors, whose performance critically depends on the development of biocompatible electrode materials that exhibit low impedance and can maintain and provide stable contact between the electrode and the epithelial tissue. Based on our previous work on carbon nanotube (CNT)-based hydrogel composites for intra-aural EEG electrodes, this study focuses on the electrochemical characterization of hydrogels initially prepared from gelatin methacrylate (GelMA)/2-hydroxyethyl methacrylate (HEMA) doped with varying concentrations of CNTs (0–3 wt%). In the present study, the materials obtained in the first stage were evaluated using electrochemical impedance spectroscopy (EIS) under both liquid and dry conditions, supplemented by measurements of hydration capacity. The results show that the composite with 3% CNT content exhibits suitable properties, making the material making the 3 wt% CNT formulation a promising platform for the further development of 3D-printable hydrogel electrodes for intra-aural EEG applications. Equivalent circuit modeling reveals improved ionic and electronic conductivity compared to the undoped hydrogel, attributed to better CNT dispersion and polymer crosslinking. This work provides insights into the structure–property relationships of CNT–hydrogel composites and lays the foundation for the further development of a 3D-printed and in vitro/in vivo validated prototype of intra-aural EEG sensors.

The increasing demand for intelligent operation and maintenance in hydropower plants highlights the limitations of conventional inspection approaches in real-time performance and reliability, especially under edge-computing constraints. This paper presents DRR-YOLOv11s, a lightweight object detector for electrical equipment fault detection and worker safety monitoring. By optimizing the model structure for efficient inference, DRR-YOLOv11s reduces parameters by 37.58% and computational cost by 28.64% compared with the baseline, resulting in 5.88 M parameters and 15.2 GFLOPs. Experiments on the Electrical Equipment Failure dataset show a precision of 92.50%, recall of 89.42%, mAP@0.5 of 93.46%, and mAP@0.5:0.95 of 73.45%, while achieving 160.22 FPS. Cross-dataset evaluation on a Personal Protective Equipment dataset further indicates good generalization. Overall, DRR-YOLOv11s achieves a favorable trade-off between accuracy and efficiency, supporting practical deployment for real-time monitoring in hydropower plants.

Corona discharge at the tip of buildings in a thunderstorm environment is an important factor causing changes in the near-ground electric field, but the influence of a quadratic growth law and quantitative research on the parameters is still rare. Therefore, based on the three-dimensional corona discharge model, this paper studies the influence of positive and negative symmetrical triangular wave electric fields with different amplitudes on the corona discharge of an independent lightning rod. Studies have shown that the corona current is synchronized with the peak of the background electric field. Studies have shown that the corona current is synchronized with the peak of the background electric field. When the polarity of the electric field changes from positive to negative, the positive charge accumulated in the positive half-cycle promotes the subsequent negative corona, so the negative corona starts in advance when the polarity reverses. Compared with unipolar discharge, the amplitude of the negative current and the number of negative charges have significantly improved. However, due to the counteraction of neutralization between positive and negative charges, the total corona charge is at a low level, which shows a net negative polarity result. The corona current and the amount of charge increase nonlinearly with an increase in the background electric field amplitude. Under the symmetrical triangular wave electric field, the quantitative fitting relationship between the peak value of the negative corona current in the second half-cycle and the amount of charge is established for the 5 m high independent lightning rod, which is I− = −0.0532 − 0.153 E − 0.0682 E2, Q− = −3.18 × 10−3 + 7.762 × 10−4E − 4.671 × 10−5 E2, respectively. The increase in the background electric field amplitude will aggravate the disturbance of the corona discharge to the near-surface electric field. When the direction of the electric field has reverted to zero, the existence of the space charge will lead to a significant change in the strength and polarity of the ground electric field. When the thunderstorm background electric field changes from positive to negative, the corona effect reverses the polarity of the ground electric field in advance, and the larger the peak value of the background electric field, the larger the advance. The corona interference mechanism revealed by this study can provide an important reference for correcting the electric field monitoring data and improving the accuracy of lightning warnings.

Accurate time synchronization is essential in distributed electrical signal monitoring, where phase coherence and event correlation depend on precise timing agreement between acquisition nodes. Conventional approaches often rely on a single synchronization source, typically internet-based Network Time Protocol (NTP) or GPS-disciplined clocks, which is impractical in isolated, offline, or cost-sensitive scenarios. This paper introduces an autonomous offline synchronization architecture for multi-node monitoring systems built on Raspberry Pi 5 (RPI5) platforms connected to a private Ethernet network. Instead of depending on one timing method, the system integrates several complementary mechanisms: battery-backed RTC persistence via the J5 interface, deterministic orchestration through systemd services, automated boot time recovery, chrony-managed NTP discipline, and Precision Time Protocol (PTP) hardware timestamping using PTP Hardware Clock (PHC). Synchronization performance is validated through continuous multi-day measurements of long-term stability, inter-node phase coherence, and short-term jitter. Controlled power-loss scenarios are also included to verify recovery behavior. The system maintains sub-microsecond alignment between nodes using only commodity hardware and no external time source. To further confirm inter-node timestamp alignment at the signal level, both hardware-based reference signal injection and software-based synchronized signal emulation are employed, providing ground-truth validation alongside scalable and reproducible evaluation. The results show that low-cost embedded hardware can support reliable, long-duration synchronization in fully offline installations.

The smart readiness indicator (SRI) is a new common European scheme for rating a building and assessing its capacity to accommodate smart-ready services. The primary goal is to secure the proper indoor environmental quality in accordance to the occupant needs, with low energy use, utilizing control strategies with building domains including HVAC, domestic hot water, lighting, envelope components, use of electricity, electric vehicle charging, monitoring and control. The paper presents an overview of the structure and outlines the SRI calculation steps and the generic technical framework. It provides a synopsis of the default catalogues that include smart-ready services for existing buildings with simple installations and new buildings with more complex installations. Furthermore, it summarizes the overall process to aggregate the calculations into a single SRI score. Following the available method and tool, several field studies have been performed in Hellenic commercial buildings. The results are elaborated to reveal the relation of the buildings’ energy performance and SRI in the existing condition and the resulting improvements following different renovation measures. The main findings reveal that the overall assessment method and tool are easy to implement. However, the user needs some expertise to interpret the building characteristics and features of its technical installations to the prescribed different levels. The initial SRI scores for the investigated case studies ranged from only 4.1% with an overall low energy performance (ranked at energy class-D), up to 42.9% with a high energy performance (ranked at energy class-B+). The assessment of various renovation scenarios for improving the building controls increased the SRI to reach a score of 24.8% (from 4.1%) up to 68.1% (from 35.6%). The associated energy savings ranged from 12% to 18%, respectively.

Background: Interstitial cystitis (IC) is a debilitating lower urinary tract condition characterised by chronic inflammation of the bladder. As the aetiology remains unknown, current treatments are symptomatic, aiming to reduce inflammation and pain. Cannabidiol (CBD), the most common cannabinoid in industrial Cannabis sativa (hemp), is one of the most important pharmacologically active cannabinoids used in medicine due to its anti-inflammatory and antioxidant effects without psychoactive properties. While other cannabinoids have shown beneficial effects in animal models of IC, the impact of CBD on the urinary bladder and overall animal well-being has not been elucidated. Methods: Using a cyclophosphamide (CYP)-induced mouse model of IC, we investigated the effects of intraperitoneally administered CBD on bladder structure, function, inflammation, and animal behaviour. A multimodal approach was applied, including light and electron microscopy, immunolabeling, qPCR, transepithelial electrical resistance (TEER) measurements, behavioural testing, and monitoring of animals. Results: CBD treatment promoted the restoration of damaged urothelial structure and improved the integrity of the blood-urine barrier. Additionally, CBD exerted an anti-inflammatory effect, reducing oedema and infiltration of inflammatory cells in the bladder wall with chronic cystitis. Finally, the increased burrowing activity of CBD-treated mice suggests a benefit of CBD on overall well-being. Conclusions: Our findings suggest that CBD has a beneficial effect on the inflamed urinary bladder and could potentially serve as an adjunct treatment for patients with IC in the future.

Quantifying solute back-diffusion in heterogeneous dual-permeability systems: effects of flow velocity and matrix hydraulic conductivity using electrical resistivity monitoring

Microbiologically influenced corrosion dynamics and technological innovations in monitoring and control.

Energy efficiency is a strategic measure to support government budget savings as mandated by Presidential Instruction Number 1 of 2025. The Class I Sleman Geophysical Station Technical Implementation Unit (UPT) Yogyakarta, as part of the Meteorology, Climatology, and Geophysics Agency (BMKG), still faces limitations in monitoring electrical energy consumption due to manual recording using conventional kWh meters, resulting in data that are not real-time and less effective for evaluating energy usage. This study aims to design and develop a three-phase electrical energy consumption monitoring system based on a microcontroller that is capable of monitoring electricity usage in real time and in an integrated manner. The research methodology includes the stages of needs identification, hardware and software system design, system implementation, performance testing, and evaluation. The system is developed using an ESP32 microcontroller, a PZEM-004T sensor for measuring electrical parameters, LCD I2C 16x2 for data display, and an Ethernet W5500 module for data communication through a Local Area Network (LAN). Electrical energy consumption data are stored in a database and presented through a web-based application developed using HTML, CSS, JavaScript, PHP, and MySQL in the form of an informative visual dashboard. The test results indicate that the developed monitoring system operates reliably and demonstrates a high level of accuracy, with an average measurement deviation of 0.02 kWh compared to the official PLN electricity meter. The system is capable of providing real-time and historical energy consumption information, thereby facilitating evaluation processes and supporting decision-making related to energy efficiency. Consequently, the developed system is considered effective in improving electrical energy management and contributing to operational budget savings within the Class I Sleman Geophysical Station UPT environment.

Electrical monitoring of subseabed CO₂ sequestration: Recent advances and prospects

Impedimetric biosensors are useful for pathogen detection as they combine electrical impedance spectroscopy with the specificity of immunological reactions. These devices can be engineered to detect minute changes in electrical impedance caused by interactions between immobilized recognition elements and target antigens in a sample. They are advantageous in allowing for label-free and real-time detection, with the ability to operate without electroactive materials. Herein, we report an impedimetric biosensor containing nanoyeast expressing SARS-CoV-2 antibody fragments as the active layer. Using nanoyeast offers key advantages such as biocompatibility and stability. The single-chain antigen-binding fragment (scFab) against receptor binding domain of SARS-CoV-2 was mutated according to in silico predictions. It was expressed in Saccharomyces cerevisiae fused to the agglutinin 2 (Aga2), where the binding to Aga1 on the yeast cell wall displays the scFab on the surface of nanofragmented yeast (NY). Electrical impedance monitoring confirmed the successful immobilization of NY onto an adsorbed chitosan layer. This biosensor architecture detected SARS-CoV-2 spike protein with a limit of detection (LoD) of 5 × 10⁻¹⁸ g/mL. It distinguished viral concentrations ranging from 0.3 to 80 plaque-forming units per milliliter (PFU/mL) and demonstrated selectivity for SARS-CoV-2 over H1N1 influenza and Dengue virus. These findings suggest that this biosensing technology could be further adapted for other biomedical and clinical analyses, being promising to improve current pathogen detection methods.

This study aimed to evaluate the hydrodynamic behavior of a vertical subsurface flow constructed wetland (VSSF-CW) treating domestic sewage by applying a saline tracer, comparing system performance in operational Years 3 (NR-3) and 5 (NR-5), and assessing the influence of a rainfall event (R-5). Electrical conductivity monitoring was used to construct residence time distribution (RTD) curves for all tests, enabling detailed characterization of hydraulic behavior. As a result, the system exhibited highly dispersed flow (d > 1.21; N < 2.07) with a tendency toward continuous stirred tank reactor (CSTR) behavior. A comparison between NR-3 and NR-5 tests revealed significant differences (p < 0.05, t test) in the hydrodynamic parameters. The rainfall event (R-5) had a statistically significant effect (p < 0.05, t test), decreasing hydraulic retention time, increasing dilution, and enhancing dispersive flow within the treatment unit. These findings highlight the importance of long-term hydrodynamic monitoring in VSSF-CW systems and demonstrate how operational conditions and external factors such as rainfall can influence treatment performance.

The increasing demand for reliable and efficient power management in industrial and commercial environments necessitates advanced monitoring solutions. Traditional methods of voltage and current monitoring, which rely on analog meters and manual data collection, suffer from limitations including high costs, lack of real-time capabilities, and limited scalability. This paper presents the design and implementation of a smart, IoT-based three-phase voltage and current monitoring system that addresses these challenges through the integration of modern sensor technology, microcontroller-based data processing, and cloud computing platforms. The proposed system utilizes three PZEM-004T sensors—one dedicated to each phase—to measure critical electrical parameters including voltage, current, power, and energy consumption. An Arduino Mega microcontroller serves as the central processing unit, acquiring data from all three sensors simultaneously via serial communication. The processed data is transmitted wirelessly using an ESP8266 Wi-Fi module to the ThingSpeak cloud platform, enabling real-time visualization, historical data analysis, and remote monitoring through a web-based interface. The prototype was tested under various load conditions to evaluate measurement accuracy, system responsiveness, and data transmission reliability. Results demonstrate voltage and current measurement accuracy within ±1%, successful real-time data logging with 15-second update intervals, and reliable cloud integration with minimal data loss. The total system cost remains significantly lower than commercial alternatives while offering comparable functionality, making it suitable for industrial plants, commercial buildings, educational institutions, and renewable energy applications. This work contributes a cost-effective, scalable, and accessible solution for three-phase electrical monitoring, with potential extensions to predictive maintenance, power quality analysis, and integration with building management systems.