Search Results (107)

Understanding the foot strike pattern (FSP) and impact force of running-related injuries is crucial for athletes and researchers. This study investigated a novel method for detecting FSP using the loadsol^® sensor insole during treadmill running. Twelve collegiate athletes ran at three different speeds (12, 15, and 20 km/h), with their FSP determined using both the kinematic method based on the foot strike angle and the loadsol^® method based on the plantar force applied to the rear-, mid-, and forefoot sensor areas. This study provides significant insights into FSP detection. Comparing the kinematic method to the loadsol^® method, the rearfoot, midfoot, and forefoot strike detection rates were 94.7%, 37.1%, and 81.8%, respectively. Moreover, the FSP was not uniform, even during treadmill running at a constant speed, with most participants exhibiting mixed patterns across different speeds. The loadsol^® sensor insole could offer a promising device for in-field measurement of FSP and impact forces, potentially helping researchers and athletes better understand and predict the potential running-related injury risks by monitoring step-to-step variations in running biomechanics. Full article

This research provides a detailed investigation into the mechanical properties and microstructural evolution of heat-resistant steel P92 subjected to both initial (i) welding procedures and simulated (ii) repair welding. The study addresses the influence of critical welding parameters, including preheating temperature, heat input, and post-weld heat treatment (PWHT), with a particular emphasis on the metallurgical consequences arising from the application of repair welding thermal cycles. Through the analysis of three welding probes—initially welded pipes using the PF (vertical upwards) and PC (horizontal–vertical) welding positions, and a PF-welded pipe undergoing a simulated repair welding (also in the PF position)—the research compares microstructure in the parent material (PM), weld metal (WM), and heat-affected zone (HAZ). Recognizing the practical limitations and challenges associated with achieving complete removal of the original WM under the limited (in-field) repair welding, this study provides a comprehensive comparative analysis of uniaxial tensile properties, impact toughness evaluated via Charpy V-notch testing, and microhardness measurements conducted at room temperature. Furthermore, the research critically analyzes the influence of the complex thermal cycles experienced during both the initial welding and repair welding procedures to elucidate the practical application limits of this high-alloyed, heat-resistant P92 steel in demanding service conditions. Full article

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article

Finding appropriate shaking parameters is crucial in designing effective mechanical harvesters. The maximum fruit removal can be achieved when the machine operator properly adjusts the amplitude and frequency for shaking each tree. This review covers the progress in research and development over the past decades on using mechanical harvesters for nut trees, such as almonds, pistachios, walnuts, and hickories, with a specific focus on the natural frequency of individual trees. Furthermore, the reported values of shaking frequency and amplitude from previous studies were discussed and compared, along with frequency calculation approaches based on various shaking mechanisms. Additionally, other parameters, such as clamping force, height, and shaking amplitude, were investigated to determine optimal values for minimizing tree damage. This review emphasizes that the tree’s diameter, height, and canopy morphology should be the primary factors considered when estimating the optimal shaking frequency for nut trees. It also highlights that, to date, the shaking amplitude, frequency, and duration set by field managers or machine operators tend to remain consistent for all trees, which can limit harvesting efficiency. The findings suggest that selecting these parameters uniformly across all trees may not result in efficient fruit removal for individual trees. However, with the assistance of modern computing technology and its adaptation for in-field applications, it is feasible to determine the optimal shaking frequency for each tree mathematically. This approach can maximize fruit removal rates while minimizing tree damage. Finally, the review suggests that improving existing harvesting machines by incorporating better vibratory patterns could offer benefits such as enhanced productivity, reduced labor costs, and decreased permanent tree damage. Full article

Quantum cryptography protocols offering unconditional protection open great rout to full information security in quantum era. Yet, implementing these protocols using the existing fiber networks remains challenging due to high signal losses reducing the efficiency of these protocols to zero. The recently proposed quantum-protected control-based key distribution (QCKD) addresses this issue by physically controlling interceptable losses and ensuring that leaked quantum states remain non-orthogonal. Here, we present the first in-field development and demonstration of the QCKD over an urban fiber link characterized by substantial losses. Using information-theoretic considerations, we configure the system ensuring security and investigate the interplay between line losses and secret key rates. As an example, we present calculation for the communication distance 4 km, QCKD rate 490 bits per second, and find that the corresponding system’s total loss is about 1.628 decibels. Our results, backed by the statistical analysis of the secret key, confirm QCKD’s robustness under real-world conditions, and establish it as a practical solution for quantum-safe communications over existing fiber infrastructures. Full article

Precision Farming (PF) in vineyards represents an innovative approach to vine cultivation that leverages the advantages of the latest technologies to optimize resource use and improve overall field management. This study investigates the application of PF techniques in a vineyard, focusing on sensor-based decision-making for autonomous driving. The goal of this research is to define a repeatable methodology for virtual testing of autonomous driving operations in a vineyard, considering realistic scenarios, efficient control architectures, and reliable sensors. The simulation scenario was created to replicate the conditions of a real vineyard, including elevation, banking profiles, and vine positioning. This provides a safe environment for training operators and testing tools such as sensors, algorithms, or controllers. This study also proposes an efficient control scheme, implemented as a state machine, to autonomously drive the tractor during two distinct phases of the navigation process: between rows and out of the field. The implementation demonstrates improvements in trajectory-following precision while reducing the intervention required by the farmer. The proposed system was extensively tested in a virtual environment, with a particular focus on evaluating the effects of micro and macro terrain irregularities on the results. A key feature of the control framework is its ability to achieve adequate accuracy while minimizing the number of sensors used, relying on a configuration of a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU) as a cost-effective solution. This minimal-sensor approach, which includes a state machine designed to seamlessly transition between in-field and out-of-field operations, balances performance and cost efficiency. The system was validated through a wide range of simulations, highlighting its robustness and adaptability to various terrain conditions. The main contributions of this work include the high fidelity of the simulation scenario, the efficient integration of the control algorithm and sensors for the two navigation phases, and the detailed analysis of terrain conditions. Together, these elements form a robust framework for testing autonomous tractor operations in vineyards. Full article

Weeds remain one of the major limiting factors affecting agricultural production, causin significant yield loss globally. Spot spraying of resistant weeds as an alternative to broadcast applications provides the delivery of chemicals closer to the plant canopy. Also, wind speed can cause spray displacement and can lead to inefficient coverage and environmental contamination. To mitigate this issue, this study sought to evaluate drop nozzles configured to direct the spray closer to the target. A remotely piloted aerial application system was retrofitted with a 60 cm drop nozzle comprising a straight stream and a 30° full cone nozzle. A tracer spray solution was applied on 13 Kromekote cards placed in a grid configuration. The center of deposition for each spray application was determined using the Python (3.11) software. Regardless of nozzle angle, the drop nozzle produced ca. 76% lower spray displacement than the no drop nozzle. The no drop nozzles had a narrower relative span compared to the drop nozzles. This suggests that smaller, more driftable fractions of the spray did not deposit on the targets due to spray displacement. Additional research investigating in-field weed species under various meteorological conditions is required to move this technology forward. Full article

The problem of calibrating phased array antennas in a noisy environment using an autocorrelation algorithm is investigated and a mathematical model of the autocorrelation calibration method is presented. The proposed calibration system is based on far-field scanning of the phased array antenna in an environment with internal noise and external interference. The proposed method is applied to a phased array antenna and compared with traditional rotating-element electric-field vector methods, which involve identifying the maximum and minimum vector–sum points (REVmax and REVmin, respectively). The proposed calibration system is verified for a phased array antenna at 3 GHz. Experimental verification of the mathematical model of the proposed method demonstrates that the autocorrelation method is more accurate than the rotating-element electric-field vector methods in determining the amplitude and phase shifts. The measured peak gain of the combined beam in the E-plane increased from 7.83 to 8.37 dB and 3.57 to 4.36 dB compared to the REVmax and REVmin methods, respectively, and the phase error improved from 47° to 55.48° and 19.43° to 29.16°, respectively. The proposed method can be considered an effective solution for large-scale phase calibration at both in-field and in-factory levels, even in the presence of external interference. Full article

For decades, fruit maturity assessment in the field was challenging for producers, researchers, and food supply agencies. Knowing the maturity stage of the fruit is significant for precision production, harvest, and postharvest management. A prerequisite is to detect and classify fruit of different maturities from the background environment. Recently, deep learning technology has become a widely used method for intelligent fruit detection, due to it having higher accuracy, reliability, and a faster processing speed compared with traditional image-processing methods. At the same time, spectral imaging approaches can predict the maturity stage by acquiring and analyzing the spectral data of fruit samples. These maturity detection methods pay more attention to the species, such as apple, cherry, strawberry, and mango, achieving the mean average precision value of 98.7% in apple fruit. This review provides an overview of the most recent methodologies developed for in-field fruit maturity estimation. The basic principle and representative research output associated with the advantages and disadvantages of these techniques were systematically investigated and analyzed. Challenges, such as environmental factors (illumination condition, occlusion, overlap, etc.), shortage of fruit datasets, calculation, and hardware costs, were discussed. The future research directions in terms of applications and techniques are summarized and demonstrated. Full article

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards. Full article

Sustainable agricultural practices are needed to find a solution to the problem of soil erosion and decreased soil quality. A study was conducted during the 2021/2022 and 2022/2023 cropping seasons to evaluate the synergistic effect of the tillage system (TS) and manure rates (MR) on selected soil properties at the University of Limpopo Experimental Farm (Syferkuil) and University of Venda Experimental Farm (UNIVEN). The experiment had a split plot design with three replications. The main plots used conventional (CON) and in-field rainwater harvesting (IRWH) tillage systems, while subplots used poultry and cattle manure at rates of 0, 20, and 35 t ha⁻¹. Bulk density (BD), aggregate stability (AS), pH, total N, organic carbon (OC), available P, and exchangeable cations (Ca, Mg, and K) were determined. IRWH significantly increased AS in the 0–20 cm soil layer at Syferkuil. TS × MR interaction significantly influenced AS and total N in the 20–40 cm soil layer during the 2022/2023 season at Syferkuil. IRWH significantly increased Mg content in the 2021/2022 season and total N, OC, and Mg content in the 2022/2023 season at Syferkuil over CON. At UNIVEN, CON significantly increased total N, whereas IRWH increased available P in the 2022/2023 season. MR significantly increased AS, exchangeable Ca, Mg, and K at both sites. At Syferkuil, MR significantly increased total N, OC, and available P during both seasons, whereas at UNIVEN the significant increase was observed on OC and available P during both seasons and total N in the 2021/2022 season. It was found that IRWH and poultry manure (35 t ha⁻¹) improved most soil properties at both sites; however, this study recommends long-term experiments to investigate the combined effect of IRWH and manure rate on soil properties to validate the findings observed in this study. Full article

Objectives: Classic Kaposi’s sarcoma (CKS) is a rare malignancy with diverse clinical presentations, lacking a standard treatment. While localized therapies are commonly used for symptomatic lesions, radiotherapy (RT) has demonstrated effectiveness. This study aims to evaluate the efficacy of RT for treating skin lesions in CKS. Methods: A retrospective analysis was conducted on patients with KS treated between April 2012 and January 2024. In total, 69 lesions in 16 patients were included. Treatment response was defined as follows: complete response (CR) indicated the absence of clinically detectable skin lesions and symptoms; partial response (PR) was a reduction in lesion height by more than half or a lighter lesion color compared to before treatment. In-field recurrence was the appearance of new lesions within a previously irradiated field. Logistic regression analysis was used to investigate factors influencing response and in-field recurrence. Results: The median follow-up period was 52 months (range, 3–138 months). The overall response rate was 100%, with 92.8% of the patients achieving CR and 7.2% receiving PR. PR was observed in three patients with five lesions, all of which remained stable. In-field recurrence occurred in two patients with initially advanced disease, and all recurrent lesions responded to RT. No variables were significantly associated with response or in-field recurrence. Conclusions: RT for CKS showed a 100% response rate, with complete symptom relief in all cases. The effectiveness of RT was evident, even in cases involving disseminated lesions. Further research is needed to determine the optimal RT dose and fractionation. Full article

This study investigates the application of an eNose (electrochemical sensory array) device as a rapid and cost-effective screening tool to detect increasingly prevalent counterfeit electronic cigarettes, and those to which potentially hazardous excipients such as vitamin E acetate (VEA) have been added, without the need to generate and test the aerosol such products are intended to emit. A portable, in-field screening tool would also allow government officials to swiftly identify adulterated electronic cigarette e-liquids containing illicit flavorings such as menthol. Our approach involved developing canonical discriminant analysis (CDA) models to differentiate formulation components, including e-liquid bases and nicotine, which the eNose accurately identified. Additionally, models were created using e-liquid bases adulterated with menthol and VEA. The eNose and CDA model correctly identified menthol-containing e-liquids in all instances but were only able to identify VEA in 66.6% of cases. To demonstrate the applicability of this model to a commercial product, a Virginia Tobacco JUUL product was adulterated with menthol and VEA. A CDA model was constructed and, when tested against the prediction set, it was able to identify samples adulterated with menthol 91.6% of the time and those containing VEA in 75% of attempts. To test the ability of this approach to distinguish commercial e-liquid brands, a model using six commercial products was generated and tested against randomized samples on the same day as model creation. The CDA model had a cross-validation of 91.7%. When randomized samples were presented to the model on different days, cross-validation fell to 41.7%, suggesting that interday variability was problematic. However, a subsequently developed support vector machine (SVM) identification algorithm was deployed, increasing the cross-validation to 84.7%. A prediction set was challenged against this model, yielding an accuracy of 94.4%. Altered Elf Bar and Hyde IQ formulations were used to simulate counterfeit products, and in all cases, the brand identification model did not classify these samples as their reference product. This study demonstrates the eNose’s capability to distinguish between various odors emitted from e-liquids, highlighting its potential to identify counterfeit and adulterated products in the field without the need to generate and test the aerosol emitted from an electronic cigarette. Full article

Secondary growth in garlic depreciates its visual aspect and thereby renders the crop unviable for trade. Therefore, farmers commonly reduce fertilization and impose drought and oxidative stress caused by high-dose pesticides to reduce secondary growth in garlic plants. However, these procedures can be considered adverse, unhealthy, and environmentally inappropriate. To remedy this scenario, we investigated whether spraying growth inhibitors would prevent secondary growth in garlic plants. First, we evaluated the effects of abscisic acid, trinexapac-ethyl, chlormequat chloride, and paclobutrazol treatments on garlic plants grown in polyethylene tanks (250 m³). We then analyzed the effects of deficit irrigation combined with the application of trinexapac-ethyl (sprayed two or three times) and the application of trinexapac-ethyl, chlormequat chloride, or paclobutrazol alone (each sprayed two or three times) on garlic plants grown in the field, comparing them with the effects of deficit irrigation (control treatment) alone. The in-field experiment was replicated with the following treatments: control (deficit irrigation) and trinexapac-ethyl (sprayed two or three times) treatments. We analyzed the physiological, biometric, and production parameters affecting secondary growth in garlic plants. We observed that trinexapac-ethyl could efficiently regulate secondary growth without causing physiological disturbances in garlic plants. Our results provide valuable information that will contribute to the development of a sustainable technique to replace the current practices used by farmers to prevent secondary growth in garlic plants. Full article

This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments—NeoSpectra, TrinamiX, and AgroCares—when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar $R^2$ values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower $R^2$ values. This study underscores the importance of understanding the water effects on undried forage, as water‘s high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices. Full article