Quantifying tree biomass is a core element of forest inventory and a long-standing research priority at the Hubbard Brook Experimental Forest (HBEF). Because direct measurements are impractical, aboveground live tree biomass (AGB) must be inferred from allometric equations developed from limited destructive samples. The legacy HBEF biomass estimation models (BEMs) systematically underestimated AGB in young, even-aged stands, indicating limits to their generality. We developed a revised set of BEMs, the Hubbard Brook Forest Analytics (HBFA) models, using an expanded allometry dataset that incorporated additional species, size classes, and stand ages. Nonlinear fitting methods improved the representation of variance structure and diagnostic performance. Across species, HBFA models achieved high predictive skill (pseudo R² = 0.70–0.99) and low relative errors (rRMSE ≤ 0.27 for most species). When evaluated against direct harvest measurements, predicted AGB differed from observed values by only 1.6% in mature forests and remained within one standard error of locally derived estimates for early successional stands. Monte Carlo error decomposition showed that bias accounted for less than 6% of total prediction error, with residual and coefficient variability dominating. By integrating local and regional data with reproducible analytical procedures, the HBFA framework strengthens long-term biomass monitoring and supports uncertainty-quantified forest carbon assessments.

Effect of culture medium composition, incubation time, and temperature on the biofilm-forming ability of Aspergillus westerdijkiae.

This paper presents a feasibility and performance study of a Deep Operator Network (DeepONet) surrogate model for solving the neutron transport equation, potentially as a real-time solver to enable digital twin techniques for nuclear reactor autonomous control. In autonomous control, it requires real-time prediction of future system status under varying physical conditions. Conventional model-based methods, such as Monte Carlo and deterministic solvers, and artificial intelligence/machine learning–based partial differential equation solvers, such as physics-informed neural networks, require repeated simulations or retraining for new input boundary/initial conditions, limiting their practicality for real-time deployment. DeepONet addresses this by learning mappings between function spaces, enabling generalization to arbitrary input conditions and providing superfast predictions without retraining. The current study develops three distinct DeepONet models, each designed for isotropic, anisotropic and pure scattering regimes, to evaluate their predictive performance. Models were trained on diverse neutron source distributions generated using Gaussian random fields (GRFs) and tested across diverse scenarios, including GRFs with shifted statistical parameters, combined sources, and sinusoidal profiles. Results demonstrate that DeepONet consistently achieves high R2 score and low average relative error, showing excellent generalization performance and outperforming the developed feedforward neural network across all test cases. More importantly, it delivers substantial computational speedups, up to 80, 71, and 900 times, respectively, highlighting its efficiency and real-time potential. Additionally, a fourth DeepONet model is developed for parametric studies by incorporating scattering cross sections as parameter inputs that also achieves a higher prediction accuracy. The framework’s speed, accuracy, and adaptability make it a strong candidate for deployment in digital twin environments.

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is endemic in many pig-producing countries and poses significant health and economic challenges. Enhanced surveillance strategies are essential for effective disease management. This study aimed to evaluate and compare the performance of different time-series modeling techniques to predict weekly PRRSV-positive laboratory submissions in Ontario, Canada. Ten years of PRRSV diagnostic data were obtained from the Animal Health Laboratory at the University of Guelph and were processed into a weekly time series. The dataset was analyzed with autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), random forest (RF), and recurrent neural network (RNN) models. Two validation strategies were employed: a traditional train-test split and a simulated prospective rolling forecast. Model accuracy was evaluated using common predictive error metrics. Descriptive analysis indicated a gradual increase in PRRSV positive submissions over time, with no consistent seasonal pattern. ARIMA and ETS models generally overpredict case counts, while RF and RNN tended to underpredict them. Among the evaluated models, the RF regression model most accurately captured the underlying time-series dynamics and produced the lowest prediction errors across both validation approaches. Despite outperforming other models, the RF model’s high relative prediction errors limit its suitability for accurate forecasting of PRRSV-positive submissions in Ontario’s routine surveillance system. Further data refinement and algorithm improvements are warranted.

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is endemic in many pig-producing countries and poses significant health and economic challenges. Enhanced surveillance strategies are essential for effective disease management. This study aimed to evaluate and compare the performance of different time-series modeling techniques to predict weekly PRRSV-positive laboratory submissions in Ontario, Canada. Ten years of PRRSV diagnostic data were obtained from the Animal Health Laboratory at the University of Guelph and were processed into a weekly time series. The dataset was analyzed with autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), random forest (RF), and recurrent neural network (RNN) models. Two validation strategies were employed: a traditional train-test split and a simulated prospective rolling forecast. Model accuracy was evaluated using common predictive error metrics. Descriptive analysis indicated a gradual increase in PRRSV positive submissions over time, with no consistent seasonal pattern. ARIMA and ETS models generally overpredict case counts, while RF and RNN tended to underpredict them. Among the evaluated models, the RF regression model most accurately captured the underlying time-series dynamics and produced the lowest prediction errors across both validation approaches. Despite outperforming other models, the RF model's high relative prediction errors limit its suitability for accurate forecasting of PRRSV-positive submissions in Ontario's routine surveillance system. Further data refinement and algorithm improvements are warranted.

Child labor remains a critical concern in Indonesia, including in Bali Province, which exhibits a higher prevalence than the national average. However, efforts to formulate effective local policies are often hindered by the unreliability of child labor statistics at the regency/municipality level, primarily due to high Relative Standard Error (RSE) values. This study seeks to estimate more reliable proportion of child labor at the regency level in Bali through the application of Small Area Estimation (SAE). The analysis utilizes data from the August 2024 Sakernas survey, supplemented with contextual variables from the 2024 PODES dataset. The SAE approach employed was the Hierarchical Bayes method with a Beta distribution (HB-Beta). The findings indicate that the HB-Beta model yields better accurate estimates, as evidenced by RSE values below 25% across all regencies. This demonstrates the potential of the HB-Beta model produces more accurate estimates than direct estimates, as it can better reflect differences between regency and help design more effective local policies to reduce child labor.

Vegetation vertical structure refers to the 3D distribution of vegetation aboveground biomass. Vegetation vertical structure of tropical forests influences other ecological and environmental variables that are essential for the functioning of the ecosystems. Integrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020. We mapped canopy height, the height of half the cumulative returned energy from GEDI (RH50), total canopy cover, foliage height diversity, and total plant area index. The resulting maps tended to have the highest errors in the Amazon and Andean regions. Total cover had the highest relative error. Interrelationship curves between forest structural metrics of GEDI footprints are maintained across mapped metrics, indicating that the predictive models preserve structural relationships observed in GEDI data. Due to the medium-high spatial resolution and national coverage of the forest structural maps presented in this work, these maps will be useful for evaluating and mapping other ecological variables and conservation priorities in Colombia.

ABSTRACT Pyrimethanil [PMT] is one of the most preferred fungicides for controlling grey mould, wilt, leaf scab, and blight diseases in fruits and vegetables. A disposable, inexpensive, simple, and sensitive modified silver nanoparticles/poly-L-cysteine pencil graphite electrode [AgNPs/poly(L-cys)/PGE] was developed using an electrodeposition technique to study the electrochemical behaviour and determination of the PMT in pH 4.0 phosphate-citric acid buffer solution. The surface morphology and surface area of the sensor were elucidated using scanning electron microscope [SEM] and cyclic voltammetry [CV], respectively. To study the electrochemical behaviour and quantitative determination of PMT by square wave voltammetry [SWV] on AgNP/poly(L-cys)/PGE, parameters such as pH, frequency, step potential, and pulse amplitude, which have significant effects on the current and potential, were optimised separately at first time. Under optimum experimental conditions, the linear dynamic range [LDR] was 20.0–500.0 μg/L, and the limit of detection [LOD] and limit of quantification [LOQ] values were highly sensitive at 20.24 μg/L and 67.47 μg/L, respectively. Additionally, the interference effects of some pesticides were studied, and PMT was analysed with high recovery and low relative error. Finally, the analytical application of the proposed method and modified AgNPs/poly(L-cys)/PGE was successfully demonstrated in real samples, such as the commercial drug Mythos® and tap water. Accordingly, 3.0 µM PMT prepared from MYTHOS® drug was found to be 2.98 ± 0.03 µM at 95% confidence level (n = 3) and was determined with 99.56% recovery, 0.39% relative standard deviation and ̶ 0.43% relative error. Ultimately, a fully validated, simple, inexpensive, sensitive, reproducible, environmentally friendly, miniature, and disposable nanosensor for the determination of PMT was developed.

Abstract Hymexazole is a systemic fungicide in the oxazole family that is widely used to control a variety of diseases caused by fungi, such as A phanomyces cochlioides , Fusarium, and Pythium . In this study, a simple, selective, disposable, and sensitive electrochemical poly(aniline) pencil graphite electrode (PANI/PGE) sensor was developed for the first time to study the behavior of hymexazole and quantitatively analyze by cyclic voltammetry (CV) and square wave voltammetry (SWV) for the first time on PANI/PGE. The PANI/PGE exhibited more effective catalytic performance than PGE, and the sensitivity of hymexazole was increased by nearly 70%. The electroactive surface area and surface morphology of the sensor were elucidated, and the pH effect, scan rate effect, electron transfer kinetics, and sensitivity were investigated in the determination of hymexazole. In addition, the oxidation mechanism of hymexazole was proposed for the first time. The SWV exhibited satisfactory dynamic linearity for hymexazole within 5.08–49.5 µg/mL with a detection limit of 1.53 µg/mL. The selectivity of the proposed modified electrode and hence the method for the determination of hymexazole was investigated in the presence of various pesticides. Finally, electrochemical determination of hymexazole was successfully performed in real samples with high recovery and low relative error on the modified PANI/PGE.

Continuous monitoring of large structures is crucial to ensure their optimal functionality. This paper presents a comprehensive study on dam monitoring using the interferometric synthetic aperture radar (InSAR) technique and prediction time series based on InSAR data. Two types of dams were the focus of the study: rock-fill Atatürk Dam, the largest dam in Türkiye, located in the eastern part of the country, and earth-fill Büyükçekmece Dam in Istanbul. In our analysis, we applied the compressed InSAR approach, which provides a higher density of persistent scatter for InSAR analysis. Unlike other studies on dam monitoring using InSAR methods, we aimed to predict displacement using time series derived from both ascending and descending InSAR results, yet this aspect has received little attention. For this purpose, we employed the long short-term memory (LSTM) neural network deep learning method. Moreover, we conducted experiments in both dams with different training and testing ratios acquired in both ascending and descending orbits to evaluate the importance of sampling number. The maximum displacements observed were −15 mm/year for Büyükçekmece Dam and −7 mm/year for Atatürk Dam. For Atatürk Dam, the root-mean-square error (RMSE) is consistently less than 0.9 mm, with percent root-mean-square error (%RMSE) ranging between 6.9% and 26%. In the case of Büyükçekmece Dam, we observed an RMSE of less than 1.3 mm, with %RMSE values ranging between 9.3% and 36.5% for different training and testing scenarios. Our LSTM results demonstrated that as the training percentage increased, the %RMSE values generally lose as well. This indicates a considerably higher relative error when less training data are used, highlighting the importance of data quantity in the predictive accuracy of our model. The results demonstrated that the LSTM estimation method can be effectively applied to health monitoring of large structures, such as dams.

Continuous monitoring (CM) solutions can facilitate faster detection and repair of emissions compared to traditional survey methods. This study tested 13 CM solutions over 12 weeks using single-blind controlled testing. Controlled release rates ranged from 0.08 to 6.75 kg CH4 h−1 and lasted 18 min to 8 h. Six solutions demonstrated 90% method detection limits (DL90s) ranging from 0.5 [0.3, 0.6] kg CH4 h−1 to 6.7 [5.9, 8.0] kg CH4 h−1. Of the 6 solutions, 5 had low False Positive (FP) rates (7.8%–18.9%), and 4 had low False Negative (FN) rates (8.0%–34.1%). Similar to Ilonze et al., the results show that the tested solutions balance method sensitivity with low FP and FN rates. All scanning/imaging solutions achieved high localization precision and accuracy (≥40%) at the equipment unit level. Single quantification estimates exhibited high relative quantification errors, ranging from 33 [0.9, 66]%, 95% confidence interval (CI) to 1326 [1003, 1648]%, 95% CI for small emissions (between 0.1 and 1 kg CH4 h−1) and from 3 [−20, 26]%, 95% CI to 3578 [−2832, 9988]%, 95% CI for large emissions (>1 kg CH4 h−1). The mean detection time for all solutions ranged from 5 h to 5 days. Relative to previous studies, errors in quantification estimates decreased, as did FN and FP rates, with improved DL90s for 2 of the 4 retested solutions. However, the mean detection times increased for 2 solutions, remained constant for one solution, and decreased for 1 of the 4 retested solutions. These findings highlight that continuous, rigorous testing enhances solution performance, with notable improvements observed across multiple testing programs using the same test protocol.

In this research, we explore the computational and experimental optimization of compliant constant-torque mechanisms (CTMs) fabricated via injection molding using polymeric materials. We investigate how geometric variations influence the torsional strength of CTMs through numerical simulation, experimental validation, and artificial neural network (ANN) modeling. Four different geometries with the same overall dimensions were designed and analyzed to quantify their mechanical performance. The results reveal that the geometric configuration significantly affected the torsional behavior of the CTMs, with circular cross-sections demonstrating superior strength. Moreover, the ANN model exhibited a high prediction accuracy and minimal relative errors, closely aligning with the experimental outcomes. Despite this, discrepancies between our numerical and experimental results suggest that further refinements in material modeling and manufacturing processes are warranted. In this paper, we emphasize the importance of integrating computational (CAE), artificial neural networks (ANNs) and experimental techniques for optimizing polymer-based CTMs. CAE simulations for Model 4 showed a constant-torque section from 23–44 degrees with 0.142 N·m torque, while experimental and ANN results indicated a longer range (20–45/46 degrees) with higher torque values (0.164 N·m). Experimental and ANN predictions for Model 4 showed an approximate 97% similarity. While these findings represent a foundational step, the characteristics of polymer CTMs suggest potential relevance for advancing applications in precision engineering, biomedical devices, and soft robotics, pending further application-specific validation.

The objective of this study was to characterize austenitic stainless steel 310 produced by Wire and Arc Additive Manufacturing (WAAM), addressing a gap in the literature regarding this alloy. Microstructural, chemical, and mechanical analyses were performed. Optical and electron microscopy revealed a predominantly columnar grain structure with characteristic tracks along the deposition direction. Point and mapping EDS analyses indicated a homogeneous distribution of iron, chromium, and nickel; however, point measurements suggested a possible underestimation of nickel, likely due to high relative error. Tensile tests demonstrated anisotropic mechanical behavior, with yield strength meeting standards at 45° and 90°, but lower at 0°. Ultimate tensile strength and elongation were below conventional requirements, with a maximum elongation of 15% at 90°. Additionally, the sample exhibited a total porosity of approximately 0.89%, which contributes to the reduction in mechanical properties, especially in the direction parallel to the deposition tracks. Overall, the WAAM-produced 310 stainless steel presented a microstructure similar to hot-rolled and annealed AISI 310 steel, but with distinctive features related to the additive process, such as mechanical anisotropy and microstructural directionality. These limitations highlight the need for process optimization to improve mechanical performance but reinforce the alloy’s structural potential in additive manufacturing.

BACKGROUND: Establishing the postmortem interval of a deceased individual with the highest possible accuracy is critical for the objective investigation of criminal homicide. It is well known that instrumental error in measuring a parameter used in calculations is one of the primary sources of error in any computational method. However, in body temperature-based methods for estimating postmortem interval, the accuracy of temperature measurement adequate for forensic medicine has not been established. AIM: The work aimed to develop practical recommendations for selecting a measuring instrument for postmortem examination based on the impact of body temperature measurement accuracy and environmental factors on the error in calculated postmortem interval. METHODS: Using the phenomenological mathematical model by Henssge et al., changes in postmortem body temperature were predicted in various diagnostic zones (brain, liver, rectum) at ambient temperatures of 0 °C and 20 °C. The method’s instrumental error was calculated for these conditions using thermometers with accuracies of 1 °C, 0.1 °C, and 0.01 °C. The results were expressed as both absolute and relative errors (%), which were calculated as the ratio of error to the postmortem interval at which it was obtained. RESULTS: The highest relative errors were reported during the early phase of postmortem cooling and during the stage of temperature equalization between the body and the environment. In all cases, using thermometers with an accuracy of 1 °C resulted in a relative error of more than 15% of the postmortem interval value. Moreover, the use of thermometers with an accuracy of 0.1 °C or 0.01 °C ensured a relative instrumental error of no more than 5% throughout the modeling period (2–24 hours). CONCLUSION: According to biomedical research guidelines, satisfactory results can only be achieved when body and ambient temperatures are measured with an accuracy of 0.1 °C or 0.01 °C. For practical convenience, we implemented the algorithm for calculating the instrumental error in postmortem interval estimation in the form of a Microsoft Excel spreadsheet.

This paper presents a comparative analysis of three outdoor wave propagation models—ITU-R P.1546-6, the SUI model, and ITU-R P.452-17—benchmarked against the deterministic Parabolic Equation Modeling (PEM) method at 3.6 GHz and 6 GHz. The evaluation focuses on prediction accuracy (RMSE, MAE, bias, relative error), terrain sensitivity, and computational efficiency. At 3.6 GHz, ITU-R P.1546-6 shows poor terrain responsiveness and high relative errors, while ITU-R P.452-17 demonstrates strong terrain sensitivity and low errors in flat areas, but decreased accuracy over hilly terrain. At 6 GHz, the SUI model consistently underestimates field strength and exhibits weak terrain sensitivity, limiting its use to rough estimations. In contrast, ITU-R P.452-17 maintains good terrain correlation and acceptable accuracy, although it slightly overestimates field strength in complex environments. The results confirm that prediction accuracy, terrain sensitivity, and bias are highly model- and frequency-dependent. ITU-R P.452-17 emerges as the most reliable and computationally efficient alternative to deterministic methods when terrain effects must be considered without significant computational overhead.

Combustible gas leakage remains a critical safety concern in industrial and indoor environments, necessitating the development of detection systems that are both accurate and practically deployable. This study presents a wireless gas detection system that integrates a gas sensor array, a low-power microcontroller with Zigbee-based communication, and a Back Propagation (BP) neural network optimized via a sequential hybrid strategy. Specifically, Particle Swarm Optimization (PSO) is employed for global parameter initialization, followed by Dung Beetle Optimization (DBO) for local refinement, jointly enhancing the network's convergence speed and predictive precision. Experimental results confirm that the proposed PSO-DBO-BP model achieves high correlation coefficients (above 0.997) and low mean relative errors (below 0.25%) for all monitored gases, including hydrogen, carbon monoxide, alkanes, and smog. The model exhibits strong robustness in handling nonlinear responses and cross-sensitivity effects across multiple sensors, demonstrating its effectiveness in complex detection scenarios under laboratory conditions within embedded wireless sensor networks.

Effective rehabilitation after orthopaedic surgery is critical. The early post-operative phase is increasingly managed in outpatient settings, necessitating objective measures such as step counts to monitor rehabilitation progress. However, it remains unclear if commercially available wearables or accelerometers using simple algorithms can accurately count steps in early post-operative conditions. We hypothesised that only accelerometers could accurately determine the number of steps under these conditions. This case series involved 20 healthy subjects, 7 female and 13 males, walking in a circle at varying speeds under partial loading with three different walking aids (forearm crutches, walking frame and rolling walker) and four wearables (Vivofit 4, Fenix 3HR, Fitbit Charge 3 and Omron HJ-325) and one accelerometer (AX6) worn on the wrist, hip and ankle. The two-point and modified three-point gait patterns commonly used post-operatively were simulated. The primary end point was the relative error (RE), defined as RE = (manual count - automated count)/manual count, of each wearable measurement compared to visual and video step counting, the gold standard. The RE of AX6 and Fitbit was less than 0.1 for all walking aids except the rolling walker, with AX6 showing the lowest standard deviation (SD) compared to other wearables. Other wearables had significantly higher RE. Increased gait speed generally improved accuracy, reducing RE in most devices, except for the AX6, which showed the opposite trend. At 0.6 m/s, only AX6 achieved an RE below 0.1. The ankle was identified as the best measuring location. During the early post-operative period, commercial wearables can only accurately count steps under specific conditions and should be used cautiously for monitoring steps in the early post-operative phase. However, accelerometers with appropriate coding appear suitable for this purpose. Level III diagnostic study.

Quantitative nuclear magnetic resonance (qNMR) spectroscopy could potentially be used for environmental microplastic analyses, provided the challenges posed by mixed polymer samples with varying concentrations and overlapping signals are understood. This study investigates the feasibility of qNMR as a reliable and cost-efficient method for quantifying synthetic polymers in mixtures of low and varying concentrations, addressing key challenges and limitations. Polymer mixtures were analysed using deuterated chloroform (CDCl3) and deuterated tetrahydrofuran (THF-d8) as solvents, with polystyrene (PS), polybutadiene-cis (PB), polyisoprene-cis (PI), polyvinyl chloride (PVC), polyurethane (PU), and polylactic acid (PLA) as selected polymers. Mixtures contained either low and high concentrations of each polymer or equal concentrations of all six polymers. Polymer concentrations were measured using the internal standard method. The method showed low relative errors for low concentrations of PS in CDCl3 and PVC in THF-d8, with values of -5% and 0%, respectively, while PB and PI in CDCl3 show relative errors of +5% and -3%, respectively. We observe significant linearity between nominal and measured concentrations with R 2 values ranging from 0.9655 to 0.9981, except for PU, which had high relative errors and poor linearity (R 2 = 0.9548). Moreover, simultaneous quantification of six polymers in THF-d8 proves effective at intermediate concentrations. However, overlapping proton signals are observed, causing high-concentration polymers to mask low-concentration ones. While this study demonstrates low limit of quantification (LOQ) and advances in simultaneous polymer quantification, further research is needed to improve qNMR accuracy for mixed polymer samples and environmentally relevant concentrations.

Accurate prediction of pressure drops in multi-phase flow systems is essential for optimizing processes in industries such as oil and gas, where operational efficiency and safety depend on reliable modeling. Traditional models often need help with the complexities of multi-phase flow dynamics, resulting in high relative errors, particularly under varying flow regimes. In this study, we simulate a comprehensive multiphase flow experimental data collected from the lab. This study presents innovative methods for accurately modeling pressure drops in multi-phase flow systems. It also studies the complicated dynamics of multi-phase flows, which are flows with more than one phase at the same time. It does this by using two different data-driven models, nonlinear ARX and Hammerstein-Wiener, instead of neural networks (NNs), so that the models don’t get too good at fitting environments with lots of changes and little data. Our research applies system identification approaches to the intricacies of this domain, providing new insights into choosing the best appropriate modeling strategy for multi phase flow systems, taking into account their distinct properties. The experimental results show that the nonlinear Hammerstein-Wiener and ARX models were much better than other methods, with fitting accuracy rates of 81.12% for the Hammerstein-Wiener model and 86.52% for the ARX model. This study helps the creation of more advanced control algorithms by providing a reliable way to guess when the pressure drops and showing how to choose a model that fits the properties of the multi-phase flow. These findings contribute to enhanced pressure management and optimization strategies, setting a foundation for future studies on real-time flow control and broader industrial applications.

In recent years, interest in airships for cargo transport and stratospheric platforms has increased, necessitating accurate dynamic modeling for stability analysis, autopilot design, and mission planning, specifically through the calculation of stability derivatives, like added mass and inertia. Despite the several CFD methods and analytical solutions available to calculate added masses, experimental validation remains essential. This study introduces a novel methodology to measure these in a wind tunnel, comparing the results with prior studies that utilized towing tanks. The approach involves designing the test model and a crank-slider mechanism to generate motion within the wind tunnel, considering load cell sensitivity, precision, frequency range, and Reynolds numbers. A revolution ellipsoid model, made from extruded polystyrene, was used to validate analytical solutions. The test model, measuring 1 m in length with an aspect ratio of 6, weighing 482 g, was moved along rails by the crank-slider system. By increasing the motion frequency, structural vibrations affecting load cell measurements were minimized. Proper signal processing, including high-pass filtering and second-order Fourier series fitting, enabled successful virtual mass calculation, showing only a 2.1% deviation from theoretical values, significantly improving on previous studies with higher relative errors.