Search Results (736)

Hot-air drying is widely adopted for herbs because it is robust and easy to control, yet it is often energy-intensive and may operate far from optimal conditions when industrial dryers rely on fixed airflow paths and large air recirculation rates. This work investigates a conventional basket-type, adiabatic hot-air dryer through an instrumented 30 h drying campaign and a psychrometric energy analysis. The hot-air drier is designed to reduce the relative humidity of herbs from the environmental value (highly variable as a function of the species, the weather conditions, and, mostly, the seasonality) to 20%. Temperature and relative humidity were measured at four positions to characterize the shelf-by-shelf drying sequence and to identify process phases. A mass balance indicated that approximately 3.8 t of water was removed during the trial. Based on the measured thermodynamic states of the moist air and estimated airflow rates (35,000–53,000 m³/h), the baseline configuration was analyzed and an upgrade strategy was proposed to improve dehumidification and overall efficiency while preserving the conventional hot-air-drying concept. The alternative solution integrates a refrigeration-based dehumidification loop (heat pump) to decouple moisture removal from sensible heating; three plant layouts and seasonal boundary conditions (summer/winter) were simulated. For the most favorable configurations, the specific final–primary energy demand and the associated CO₂-equivalent emissions were reduced by about 70–85% compared with the baseline, depending on the airflow rate and recirculation strategy. The results highlight practical retrofit options for existing herb dryers and provide a transparent framework for translating measured psychrometric states into energy and emission indicators. The results, achieved and discussed in this study, were used to optimize the utilization of an already existing and operative hot-air dryer. Based on the proposed working configuration, the dryer now allows achieving the fixed target for herb mixtures of the previous configuration and, at the same time, reducing the energy consumption and associated equivalent CO₂ emitted, as well as achieving process completion in less time. Full article

This paper presents an action potential detector (APD) based on a high-order non-linear energy operator (HONEO). The APD consists of a HONEO, a positive threshold generator, a negative threshold generator, and an XOR. The APD is capable of detecting the half-width of an action potential since it can determine both the positive peak and the negative peak of the action potential by means of the HONEO and two threshold generators. In addition, the signal-to-noise ratio (SNR) of the APD can also be improved due to the two adaptive threshold generators. The circuit is designed in a standard 0.18 $\mathsf{\mu }$m CMOS process with a 1.8 V supply voltage. Pre-layout simulations are performed under typical conditions (TT process corner, 1.8 V supply, 27 ${}^{\circ }$C). The results show that the output amplitudes of the HONEO remain almost constant ($\pm 100$ mV) when the amplitude of the source signal varies from −10 mV to 30 mV at 1 kHz. Across temperature variations from $-20$${}^{\circ }$C to 80 ${}^{\circ }$C, the output amplitude remains within $\pm 12\%$ of the nominal value, demonstrating acceptable stability for the target implantable application. Compared to the conventional NEO, the APD achieves 14–$20\mathrm{dB}$ SNR improvement, a detection accuracy of $97\%$. The power consumption of the APD is approximately $62\mathsf{\mu }\mathrm{W}$. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article

Infrared thermography has been proposed as a non-invasive tool for mastitis detection in dairy ruminants; however, the extent of environmental confounding and diagnostic performance in small ruminants remain poorly characterized. This study evaluated udder thermography in dairy goats through correlation analysis under winter and summer conditions, and an experimental intramammary inflammation challenge using Staphylococcus aureus lipoteichoic acid, with receiver operating characteristic analysis using somatic cell count >1500 × 10³ cells/mL as the reference standard. Strong positive correlations between ambient temperature and udder surface temperatures intensified substantially from winter to summer, while surface temperatures showed weak or absent correlations with rectal temperature. Experimental inflammation induced a 12-fold increase in somatic cell count (305 vs. 3658 × 10³ cells/mL, p < 0.001); however, thermographic responses remained minimal and spatially inconsistent, with area under the curve values approximating 0.5 and weak correlations between thermographic measurements and somatic cell count. Environmental temperature variation and physiological thermoregulatory adjustments substantially exceeded the minimal thermal signal generated by intramammary inflammation, limiting diagnostic utility. Infrared thermography showed poor diagnostic utility for detecting experimentally induced intramammary inflammation in dairy goats under the tested conditions. Full article

Parabolic trough collectors (PTCs) are highly sensitive to receiver positioning accuracy; however, most existing studies report optical efficiency degradation without formally defining alignment tolerance limits. This study proposes a tolerance-based thermo-optical risk framework to quantify allowable receiver misalignment envelopes for reliable PTC operation. A Monte Carlo Ray Tracing (MCRT) methodology is employed to evaluate the impact of angular receiver misalignment on optical efficiency and circumferential heat flux redistribution. Beyond conventional efficiency metrics, normalized flux-based thermal non-uniformity indicators are introduced to assess thermo-mechanical risk without requiring full thermo-fluid modeling. The results reveal a nonlinear decoupling between optical acceptability and thermal safety. While optical efficiency remains above 0.80 up to approximately ±6°, pronounced flux localization and rapid growth of thermal stress indicators occur beyond ±4°, marking the onset of thermally critical behavior. The identified ±4° threshold corresponds to approximately twice the collector half-acceptance angle (θ_(crit)/δ ≈ 2), demonstrating geometry-dependent scaling characteristics. The proposed framework formalizes the optical–thermal decoupling phenomenon and transforms conventional efficiency-based evaluation into a reliability-informed alignment tolerance assessment tool applicable to manufacturing precision, installation control, and operational quality management in CSP systems. Full article

Under the strategic constraints of global carbon emission targets, how supply chain management can effectively drive enterprises’ low-carbon transformation has become an important issue. Based on China’s Supply Chain Innovation and Application Pilot Program (SCIAPP), this paper approaches it as a quasi-natural experiment to empirically investigate how supply chain management affects enterprises’ low-carbon technological innovation (LCTI). This paper uses the data from publicly listed companies in China. and the difference-in-differences approach to empirically test the policy effect of SCIAPP and determine its influencing path. The study finds that first, SCIAPP significantly enhances enterprises’ LCTI level by approximately 14.2%. Second, SCIAPP mainly achieves this through three mechanisms, including strengthening enterprises’ green management, promoting digital transformation, and improving operational efficiency. Third, the impact effect is stronger in enterprises with more robust environmental management systems, fewer financing constraints and higher capital intensity. Additionally, the LCTI driven by SCIAPP can further positively impact the supply chain resilience. This study innovatively incorporates pilot policies, supply chain management, and LCTI for analysis, providing theoretical evidence and empirical support for the government to optimize supply chain governance and achieve climate goals. Full article

The detection of plastic explosives in vapor form is extremely challenging due to the very low volatility of their primary components, such as RDX and PETN. To overcome this limitation, volatile chemical markers like 2,3-dimethyl-2,3-dinitrobutane (DMNB) are added to explosive formulations to enable indirect vapor detection. This study presents a rapid method for detecting and quantifying DMNB vapors using a handheld ion mobility spectrometer (IMS) operating near ambient temperature, ammonia-doped and equipped with a non-radioactive corona discharge ionization source. The instrument, model LCD-3.2E (Smiths Detection Ltd.), is based on a twin drift–cell time-of-flight configuration and simultaneously records ion mobility spectra in both positive and negative modes. DMNB generated distinct product ion peaks in both modes, with reduced mobility values (K₀) of 1.42 cm²V⁻¹s⁻¹ (positive) and 1.37 cm²V⁻¹s⁻¹ (negative). The method demonstrated high sensitivity, with limits of detection calculated at 1.4 ppb_v (10.2 × 10⁻³ mg m⁻³) in positive mode and 3.1 ppb_v (22.7 × 10⁻³ mg m⁻³) in negative mode. The IMS system provided rapid responses within seconds and covered a quantifiable concentration range of 5–3000 ppb_v, with saturation estimated to appear above approximately 5 ppm_v (36.6 mg m⁻³). The simultaneous dual-polarity response of the DT IMS enhances both the selectivity and reliability of identification. These findings confirm the capability of portable IMSs for fast trace vapor detection in DMNB, supporting its application in field-based screening scenarios such as luggage inspection or container interrogation, where indirect detection of plastic explosives is required. Full article

Under severe ice and snow weather, ice-covered pantograph–catenary arcs affect the safe operation of high-speed trains. This study investigates the impact of ice-covered arc electrical characteristics, plasma parameters, and material ablation mechanisms. By constructing a comprehensive pantograph–catenary icing experimental platform, arc voltage, current signals, high-speed dynamic images, and emission spectra were synchronously collected under different icing thicknesses ranging from 0 to 15 mm. Research indicates that ice coverture causes frequent “extinction–reignition” phenomena during the arc initiation stage due to the latent heat absorbed by melting ice, significantly reducing the initial stability of arc combustion. Spectral analysis confirms that the arc excitation temperature and energy density are positively correlated with the concentration of hydrogen ions produced by water vapor ionization, reaching a peak under the 5 mm icing condition. Experimental results show that the average energy density of ice-covered arcs is approximately double that of the non-iced condition, causing the ablation pits on the carbon strip to exhibit characteristics of greater depth and wider copper deposition zones. This study reveals the unique mechanisms and damage characteristics of icing pantograph–catenary arcs, providing an important basis for the safe design and maintenance of pantograph–catenary systems in high-cold railway environments. Full article

Medium-term 5G/NB-IoT planning is made difficult by simultaneous uncertainty in device adoption and per-device traffic behavior because deterministic point forecasts do not quantify overload risk or support reliability-based capacity decisions. A diffusion-to-dimensioning workflow is proposed in which S-curve adoption modeling, bounded usage priors, scenario stress testing, and Monte Carlo uncertainty propagation are combined to generate predictive demand distributions, exceedance curves, and quantile-based capacity rules. The framework is applied to a Great Britain case study for 2025–2029 using smart meter deployment data and an M2M-based proxy for asset-tracking adoption. Analysis shows that planning-year upper-tail outcomes are driven primarily by asset-tracking usage uncertainty rather than by proxy scale alone. A ±30% perturbation of the AT adoption anchor changes $Q_{0.95}$ by approximately ±29.8%, whereas stressed AT usage increases $Q_{0.95}$ by 74.4%. Plausible positive dependence among key AT operational inputs further raises $Q_{0.95}$ by 18.3–22.5%. Limited hold-out evaluation provides strong out-of-sample support for the smart meter adoption stage and plausibility-only support for the shorter AT proxy. The framework is intended for medium-term, data-lean planning settings and is designed to support transparent risk-based capacity decisions rather than deterministic point sizing. Full article

Aging drinking water infrastructure and persistent underinvestment have increased the frequency of service disruptions across public water systems in the United States, yet empirical evidence on the financial implications of such disruptions for water utilities remains limited. This study examines the relationship between boil water advisory (BWA) exposure and operating costs incurred by public water utilities using a cross-sectional dataset of 239 publicly owned community water systems in West Virginia during the 2023 fiscal year. Utility costs are measured using operating revenue deductions, an accounting measure capturing operating expenses, taxes, and depreciation. Regression results indicate a statistically significant positive association between cumulative BWA exposure and utility costs. Specifically, a one-day increase in advisory exposure is associated with approximately a 0.08% increase in operating deductions, implying an average cost increase of $1020 per utility for each day under advisory. Duration-based measures of BWA exposure explain cost variation more consistently than simple advisory counts, highlighting the importance of capturing persistence rather than frequency alone. These findings demonstrate that service reliability disruptions impose financial burdens on public water utilities and highlight the need to incorporate reliability considerations into infrastructure investment decisions, rate setting, and long-term financial planning, particularly for small and resource-constrained systems. Full article

Outlier detection is a fundamental component of data preprocessing and quality monitoring across diverse scientific domains, including engineering, biomedical sciences, and finance. While many variables in controlled environments approximate a normal distribution, real-world data, particularly biological, environmental, and epidemiological measures, are frequently characterized by pronounced right-skewness. To address the shortcomings of conventional methods, this study introduces the Dynamic Threshold for Outlier Detection (DTOD), which reframes outlier detection as a concrete operational workflow. The DTOD framework dynamically adjusts detection thresholds based on a functional relationship between skewness and tail morphology. Validation through large-scale simulation experiments across light-, middle-, and high-skewness levels confirms the method’s versatility. The DTOD proves particularly effective at two ends of the spectrum: enhancing sensitivity for detecting subtle anomalies in light-skewed data while serving as a conservative, high-confidence screening tool that controls false positives in high-skewness environments. In real-world application to North American Association of Central Cancer Registries (NAACCR) data, the method successfully identified outliers with abnormally high unknown tumor size rates in colorectal cancer and maintained a low misclassification rate in highly skewed lung cancer data. Ultimately, the DTOD provides a promising, interpretable solution for improving data quality in skewed scenarios. Full article

This paper presents a comprehensive framework for the identification, state estimation, and robust control of a bistable Duffing–Holmes oscillator, validated through an experimental setup. First, to address parametric uncertainty, a Recursive Least Squares Method (RLSM) with a forgetting factor is applied to a filtered model representation, enabling accurate parameter convergence from noisy measurements. Subsequently, a Nonlinear Integral Extended State Observer (NIESO) is designed to reconstruct unmeasured states and estimate total disturbances. A key theoretical contribution is the derivation of explicit gain conditions that guarantee the observer’s stability, overcoming limitations of previous designs. For trajectory tracking, an observer-based backstepping controller is synthesized. Crucially, to bridge the gap between theory and practice, a drift-free integration scheme is implemented to generate feasible position commands for the shake table, preventing actuator saturation. Experimental results confirm the framework’s effectiveness, achieving a 3.7-fold reduction in RMS tracking error compared to open-loop operation, with the tracking error rapidly converging to a small neighborhood within approximately $0.2$ $\mathrm{s}$. Furthermore, the closed-loop system demonstrates superior energy efficiency, requiring significantly lower actuator voltage to sustain stable interwell oscillations. Full article

Hepatitis B virus (HBV) remains a major global public health challenge, and its early screening is essential for controlling transmission and improving treatment outcomes. We analyzed serum samples from 422 participants via Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to establish a screening model for hepatitis B surface antigen (HBsAg)-positive status. Following multi-bin preprocessing and single-sample spectral aggregation, we assessed three machine learning algorithms—random forest, deep neural network, and light gradient boosting machine (LightGBM). Among them, the LightGBM model achieved the best performance, with an optimized F1 score of 0.87 and an area under the receiver operating characteristic curve (AUC) of 0.94. A 100-iteration ensemble feature stabilization strategy identified twelve distinct m/z peaks as stable biomarkers for HBsAg-positive screening. Independent validation yielded sensitivity of 77.7% and specificity of 76.0%—insufficient for individual diagnosis but potentially suitable for population-level surveillance programs combined with confirmatory testing, particularly in resource-limited settings where conventional methods are impractical. Notably, the method offers a detection time of approximately one minute, a per-sample cost of ~$0.14. In conclusion, the combination of MALDI-TOF MS and machine learning enables a rapid, low-cost screening tool for large-scale HBV detection. Full article

Digital Twins for cultural heritage buildings commonly depend on high-fidelity 3D scanning, detailed onsite surveys, and unrestricted data acquisition. In many countries, however, legal, regulatory, and conservation constraints render such methods inaccessible or explicitly prohibited, significantly limiting the deployment of digital-heritage technologies in real settings. This paper introduces HeritageTwin Lite, a regulation-compliant workflow for constructing low-detail yet operational Digital Twins of protected cultural heritage buildings using only publicly permissible data sources. The proposed approach relies on a GIS-based 2D application through which users select a site and manually delineate building footprints and structural outlines. These 2D sketches are combined with satellite imagery, publicly available photographs, archival records, and open datasets to generate a massing-level 3D model. Building height and volumetric characteristics are estimated using contextual cues such as surrounding structures, known architectural typologies, and scale references derived from people or urban elements. The resulting Digital Twin prioritizes geometric plausibility over fine architectural detail, enabling simulation, analysis, and decision-support tasks, such as environmental modeling, airflow and CFD approximation, and high-level Heritage BIM integration, while fully respecting cultural heritage restrictions. Three case studies illustrate the proposed workflow and systematically identify which components of conventional smart-building and Digital Twin pipelines remain feasible and which become infeasible under heritage regulations. The results demonstrate a practical and scalable path toward compliant Digital Twins for protected buildings, positioning low-detail modeling not as a limitation but as a regulatory necessity. Full article

Photosynthetic algae–microbial fuel cells (PAMFCs) are attractive for energy-positive wastewater treatment and carbon mitigation. However, PAMFC performance under continuous flow is often constrained by limited cathodic electron-acceptor supply and unstable photosynthetic biofilms, while the extent to which cathode interfacial engineering can stabilize diurnal power output and assimilative NH₄⁺–N removal remains unclear. In this study, the sponge-like and petal-like ZnO_0.2-NiO@rGO-modified carbon fibers (ZnO_0.2-NiO@rGO-pCFs and ZnO_0.2-NiO@rGO-pCFp) and pre-fabricated carbon felt (pCF) were used as cathode materials to construct three sets of PAMFC systems. Under light–dark cycling, the engineered cathodes reached steady operation within about 6.5 d and increased the steady-state voltage to approximately 0.35 V, compared with approximately 0.08 V for pCF. Under continuous-flow conditions, cathodic NH₄⁺–N removal exhibited a stable diurnal rhythm, with higher removal during illumination at about 43–51% than in the dark at about 29–30%, consistent with algal assimilation as the primary nitrogen sink, while cathode modification mainly improved the cathodic microenvironment and response stability. Compared with pCF, the ZnO_0.2–NiO@rGO cathode enriched a more even, Chlorophyta-dominated algal biofilm with an approximate relative abundance of 80%, indicating that its selective interfacial environment favors biofilm stabilization and sustains in situ oxygen production and cathodic electron-acceptor supply. Consequently, the composite cathode enhanced voltage output and stabilized light-enhanced, assimilative NH₄⁺–N removal under aeration-free operation, while establishing an interpretable link between electrochemical performance and 18S rDNA-derived community assembly features, thereby providing a low-cost cathode design basis for nitrogen removal in wastewater treatment. Full article