Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Performance Indicators for Variable Rate Irrigation Implementation on Variable Soils

Decision support tools for precise irrigation scheduling are required to improve the efficiency of irrigation water use globally. This paper presents a method for mapping soil variability and relating it to soil hydraulic properties so that soil management zones for variable rate irrigation can be defined. A soil water balance is used to schedule hypothetical irrigation events based on (i) one blanket application of water to eliminate plant stress, (uniform rate irrigation, URI) and compares this to (ii) variable rate irrigation (VRI), where irrigation is tailored to specific soil zone available water holding capacity (AWC) values. The key performance indicators: irrigation water use, drainage water loss, nitrogen leaching, energy use, irrigation water use efficiency (IWUE) and virtual water content are used to compare URI and VRI at three contrasting sites using four years climate data for a dairy pasture and maize crop and two years climate data for a potato crop. Our research found that VRI saved 9 - 19 % irrigation water, with accompanying energy saving. Loss of water by drainage was also reduced by 25 - 45% using VRI, during the period of irrigation, which reduced the risk of nitrogen leaching. Virtual water content of these three primary products further illustrates benefits of VRI and shows that virtual water content of potato production used least water per unit of dry matter production.
Efficient Localization Algorithm With UWB Ranging Error Correction Model Based on Genetic Algorithm-Ant Colony Optimization-Backpropagation Neural Network

The development of wireless sensor network technology has extended the diverse range of tools available in location-based services (LBS). Indoor high-precision positioning is among the most popular topics in location tracking and positioning. Ultrawideband (UWB) double-sided two-way ranging (DS-TWR) is used widely because it provides a reliable ranging performance. In this study, the effect of the UWB DS-TWR ranging error was suppressed using a ranging error model to improve the reliability and accuracy of indoor positioning services. Based on the conditions described above, an optimized backpropagation neural network (BPNN) correction model that integrates both a genetic algorithm (GA) and the ant colony optimization (ACO) algorithm, forming the GA-ACO-BPNN model, is established and verified experimentally. In addition, under static and kinematic actual positioning conditions, improvements in the BPNN, GA-BPNN, ACO-BPNN, and GA-ACO-BPNN ranging error correction models in terms of their positioning performances are calculated and compared. The experimental results show that the proposed GA-ACO-BPNN model can reduce the impact of the ranging error on ranging and positioning effectively. The positioning accuracy and reliability of the UWB DS-TWR solutions are improved significantly after application of this model, which provides a reference point for solutions to subsequent fusion positioning problems, e.g., UWB and inertial measurement unit integration.
Research Progress in Light Environment Regulation of Facility Crops

Light intensity in the facility plays a key role in the growth and development of crops and their high-quality production, so the regulation of light environment largely determines the yield and economic benefits of facility crops. In order to make full use of relevant research results at home and abroad, and promote the research and application of light environment regulation technology in China’s facility agriculture, this paper summarizes the research progress of light environment regulation technology in facility agriculture from two aspects, namely, light supplement device and light environment regulation method. At the same time, aiming at the current research situation and existing problems in this field of China, this paper proposes some important scientific issues that need to be addressed in the future. This includes establishing a high-precision crop growth model under the influence of multiple environment factors, accurately obtaining the regulation target value of facility light environment, and effective regulation strategies for light environment. The solution of these problems can lay a certain foundation for the theoretical research and construction of intelligent regulation system of facility light environment.
Effect of substrate temperature on in-situ precipitation during laser powder bed fusion of Fe-TiB2 high modulus steel

ABSTRACT Fe-Ti-B high modulus steel (HMS) fabricated via laser powder bed fusion exhibits in-situ precipitation of nanostructured TiB2 particles within a ferritic Fe-matrix. However, porosity and cracking are common challenges associated with this process. This study systematically varies process parameters, specifically volume energy density and substrate temperature, to analyse macroscopic defects formation and propose methods to prevent their occurrence through detailed microstructure characterisation. For substrate temperatures of 400, 600, and 800 °C, an optimal combination of laser power and scan velocity was determined, resulting in minimised specimen porosity (< 1%). Yet, pronounced cracking occurred at 400 and 600 °C substrate temperature, most likely attributed to the presence of hard and brittle non-equilibrium microstructure constituents. Increasing the substrate temperature to 800 °C further reduces porosity and promotes the formation of the equilibrium constituents Fe-α and TiB2. These phases are desirable as they improve the stiffness-to-density ratio while reducing hardness and brittleness. By mitigating thermal gradient and resulting lower stresses, the successful fabrication of HMS samples with the desired microstructure and defect-free macrostructures becomes feasible. Potential future steps, such as incorporating in-situ heat treatments between layer depositions, are outlined and discussed as means to lower the substrate preheating temperature.
Self-Supervised Learning for Ultrasound Probe Angle Prediction in Plantar Fascia Images

This paper presents a self-supervised learning (SSL) approach for estimating the angular offset of freehand ultrasound (US) probe movement relative to an ideal viewing angle. Proper US probe angle is essential for acquiring high-quality images for diagnostics. This can be challenging, however, due to the high variability in anatomy and the subjectivity in interpretation. The ability to guide a user to an optimal probe angle can greatly improve the quality of image acquired which in turn will facilitate an accurate assessment. Probe angle is particularly important for musculoskeletal US since the probe often needs to be perpendicularly aligned with the target anatomy (e.g., bone, tendon, or ligament). In this paper, we used plantar fascia (PF) as the use case to develop a probe angle guidance pipeline. It has an immediate application for PF thickness measurement, which is used for assessing plantar fasciitis. In this SSL pipeline, circularized versions of over 3,600 US images of PF were each rotated by a varied amount, and an encoder-support vector machine pair was trained to predict this known angle of rotation. In a cross-validation from 13 subjects, the model predicted rotation angle with an average error of 2.98 degrees (SD: 2.62), and the SSL-trained model accurately predicted the relative rotation angles of images acquired while rotating the US probe. The key contribution of this work is the SSL approach on synthetically generated image and probe angle pairs to emulate angular motion relative to a standard view. This approach for estimating angular offset without labelled data has the potential to be applied to future work guiding users to an optimal view for acquiring US images.
Random Quantum Neural Networks for Noisy Image Recognition

The application of classical Random Neural Networks (RNN) has been restricted to deterministic digital systems that generate probability distributions instead of the stochastic characteristics of random spiking signals. To optimize the utilization of the stochastic properties inherent in RNN, we propose a new category of supervised Random Quantum Neural Networks (RQNN) that incorporate a resilient training methodology. The RQNN under consideration utilizes a combination of classical and quantum algorithms, incorporating superposition state and angle encoding characteristics that draw inspiration from quantum information theory. Additionally, the model incorporates the stochastic random spiking property of neuron information encoding, which is known to exhibit spatial-temporal features similar to those observed in the brain. The proposed RQNN model has undergone thorough validation, relying on hybrid classical-quantum algorithms through a real IBM quantum processor. The proposed RQNN model is tested on the MNIST, Fashion-MNIST, and KMNIST datasets. The results indicate that it achieved an average classification accuracy of 59.6% when presented with unseen noisy test images. The experimental results demonstrate the efficacy and robustness of the proposed RQNN11The early version of the work is posted in arXiv: https://doi.org/10.48550/arXiv.2203. 01764. in classifying noisy images that have not been previously encountered.
Small-scale block defect detection of fabric surface based on SCG-NET

In contrast to the common small target detection problems, it is more difficult to locate and identify the small surface defects of fabric due to its own texture and complex background interference. Therefore, this paper proposes an effective detector for small-scale block defects on fabric surface by taking advantage of the backbone which integrates the Coordinate Attention module to enhance the acquisition of small-scale block defect location information. The FPN + PAN multi-scale detection structure is adopted to effectively integrate the feature information between different levels and deal with the multi-scale problem of defects. In the Neck section, a small target detection layer is set to expand the receptive field to prevent the loss of small-scale defect feature information. Moreover, we propose to use the GhostBottleneck module instead of the ordinary downsampling process to eliminate redundant convolutional calculations to improve the detection speed. The experimental results show that the optimal detection results of 0.855 and 0.912 are achieved in the detection recall and accuracy of fabric dataset, compared with other detectors, the result of small-scale defect detection rate is increased by at least 4.5% , and the detection process meets the real-time requirement of automatic defect detection, which verifies the effectiveness of our method.
Robust Mixed H∞ and Passive Event-triggered Reliable Control for Networked Control Systems under Denial of Service Attacks

This paper studies the event-triggered robust mixed H∞ and passive finite-time control for uncertain Networked Control systems (NCSs) with actuator failure and malicious denial of service (DoS) attacks. First, a time-delay system model is established based on a switching-like event-triggered scheme. Then, some sufficient conditions that can ensure finite-time bounded with mixed H∞ and passive performance for the nominal and uncertain NCSs are derived in the presence and absence of DoS attacks, respectively. Furthermore, the co-design of the robust reliable controller and event-triggered parameters is obtained in terms of linear matrix inequalities. Finally, a single-link robot arm example is given to illustrate the efficiency of the proposed method.
Adaptive traffic light control based on reinforcement learning under different stages of autonomy

With the development of big data, artificial intelligence and vehicle road coordination, China intelligent transportation system is changing to autonomous transportation system. During the development of autonomous transportation, the traffic application scenarios will also change, which will affect the control efficiency of the same control strategy. In order to show the impact of the traffic control strategy on traffic in different stages of autonomous traffic, we use edge computing to simulate different stages of autonomous traffic, by establishing three scenarios of user-end computing, roadside-end computing and cloud computing, which respectively correspond to the three autonomous traffic stages of auxiliary autonomy, high autonomy and full autonomy. With the extensive application of reinforcement learning in the traffic field, deep Q-Learning network(DQN) has become a effective solution of traffic signal adaptive control. We use DQN to adaptively control the traffic lights, and through comparison of different scenarios, we show the differences of the same control strategy under different stages of autonomy. Through the experiment, we can conclude that the expected reward of user-end computing is 6.7% and 19.8% higher than roadside computing and cloud computing. Meanwhile, it shows that under the condition of full autonomy, the sensitivity and robustness of the control strategy are improved, effectively improving the traffic efficiency.
Sensorless Control of Electrically Excited Synchronous Motor Based on Pseudo Random High Frequency Signal Injection at Low-Speed Region

Nowadays, the cascaded H-bridge inverter-based high-voltage electrically excited synchronous motor (EESM) drive system has been widely used in the many industry fields. To reduce the hardware cost and improve the dynamic performance at the low-speed region, the field-oriented control (FOC)-based sensorless drive strategy is investigated in this work. At the low-speed region, a pseudo random high-frequency signal injection method is proposed to detect the rotor position and the speed for EESM drives. In details, a pre-designed pseudo high-frequency voltage is injected to the d-axis reference voltage. Then, a high-frequency mathematical model of EESM is established for the current response analysis. By using the dq- axis current response, the rotor position information can be extracted for low-speed operation. As compared to the conventional high-frequency signal injection, the noise pollution is mitigated by using random signal injection method. In simulation-based case studies, the effectiveness of noise mitigation and rotor position estimation for low-speed operation are verified.
A new fossil vent biota in the Ballynoe barite deposit, Silvermines, Ireland: Evidence for intracratonic sea-floor hydrothermal activity about 352 Ma

Considerable controversy exists as to the timing of the important Mississippian carbonate-hosted Irish-type Zn + Pb ± Ba ± Ag deposits. The Silvermines deposits have been defined as an end member of this style in that they have been interpreted to display textures indicative of sea-floor deposition. One of the strongest arguments in favor of this interpretation was the report of a hydrothermal vent field, including pyritic chimneys in the Ballynoe open-pit barite deposit. This paper adds to that body of evidence by describing a hydrothermal vent fauna from the same vent field, consisting of a delicately pyritized worm tube hosted by massive pyrite and hematized filaments of apparent microbial origin. The worm tube is remarkably similar to fossil worm tubes from modern and ancient volcanic-hosted massive sulfide deposits, and the filamentous microfossils have similarities to modern Fe-oxidizing bacteria. We have found no correlation between the worm tube and normal Mississippian fossils such as crinoids, whose replacement by pyrite in the immediately underlying Ballynoe footwall destroys original morphology. The sulfur isotope composition of the worm tube and host pyrite is essentially identical to that of the vent field pyrite and the main sulfide ore stage of Silvermines sulfides, all having a mean value about –20 per mil, indicating an open-system bacteriogenic sulfide source. These discoveries provide additional evidence for the exhalative nature of parts of the Silvermines orebodies, and imply that mineralization had begun in the Irish ore field by the late Tournaisian (~352 Ma).
Real-time Thermal Map Estimation for AMD Multi-Core CPUs Using Transformer

This paper presents a novel approach for real-time estimation of spatial thermal maps for the commercial AMD Ryzen 7 4800U 8-core microprocessor using a transformer-based machine learning method. The proposed method, called ThermTransformer, leverages real-time performance metrics of the AMD chip, provided by uProf 4.0, to accurately estimate transient thermal maps. These maps can be valuable for dynamic thermal, power, and reliability controls requiring higher accuracy. Unlike traditional Convolutional Neural Networks (CNN) designed for image data or Recurrent Neural Networks (RNN) suitable for transient data, ThermTransformer is based on a modified self-attention architecture. It takes time-series performance metrics information as input and directly generates transient thermal images. Our results demonstrate that this transformer-based method achieves the best of both worlds - surpassing CNN in prediction quality and performing well for transient data. Exper-imental results reveal that ThermTransformer achieves highly accurate predictions of power maps, with an RMSE of only 0.36°C or 0.8% of the full-scale error. Additionally, it outperforms the recently proposed GAN-based thermal map estimation method, ThermGAN, by 1.66x and the LSTM-based thermal prediction method, RealMaps, by 6.09x in terms of accuracy on average. Furthermore, the proposed approach can be efficiently deployed on the target chip, providing real-time estimation with a speed as fast as 14ms.
Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation: Development and External Validation of Three Novel (Machine Learning) Models

In the intensive care unit (ICU), infection-related mortality is high. Although adequate antibiotic treatment is essential in infections, beta-lactam target non-attainment occurs in up to 45% of ICU patients, which is associated with a lower likelihood of clinical success. To optimize antibiotic treatment, we aimed to develop beta-lactam target non-attainment prediction models in ICU patients. Patients from two multicenter studies were included, with intravenous intermittent beta-lactam antibiotics administered and blood samples drawn within 12–36 h after antibiotic initiation. Beta-lactam target non-attainment models were developed and validated using random forest (RF), logistic regression (LR), and naïve Bayes (NB) models from 376 patients. External validation was performed on 150 ICU patients. We assessed performance by measuring discrimination, calibration, and net benefit at the default threshold probability of 0.20. Age, sex, serum creatinine, and type of beta-lactam antibiotic were found to be predictive of beta-lactam target non-attainment. In the external validation, the RF, LR, and NB models confirmed good discrimination with an area under the curve of 0.79 [95% CI 0.72–0.86], 0.80 [95% CI 0.73–0.87], and 0.75 [95% CI 0.67–0.82], respectively, and net benefit in the RF and LR models. We developed prediction models for beta-lactam target non-attainment within 12–36 h after antibiotic initiation in ICU patients. These online-accessible models use readily available patient variables and help optimize antibiotic treatment. The RF and LR models showed the best performance among the three models tested.
Frequency biases in predictions of aviation icing occurrence: What can we learn from climatologies?

The prediction of locations where aircraft are susceptible to the accumulation of ice due to the presence of supercooled liquid water is an important part of the hazard forecasts provided to aviators. We describe a method for quantitatively assessing the performance of different icing algorithms by using of model-derived climatologies and comparing them to climatologies derived from observations. This method helps identify frequency biases in potential new icing algorithms and helps in their subsequent development prior to testing in an NWP setting. Our icing-climatology evaluation method is illustrated by comparing the frequency-of-occurrence biases of a new icing algorithm to those of the index used operationally. Using the notion that the actual liquid water content (LWC) encountered by aircraft can be described using an probability density function describing the fluctuations of the LWC about the model-predicted grid-box-mean value our new index quantifies the likelihood of encountering local supercooled liquid water contents above any threshold. Our evaluation using climatologies then shows that the new index can be tuned to give unbiased predictions of icing occurrence from a climatological point-of-view. The new index is shown to capture the variations in icing frequency as a function of month, height and geographical location as well as the control icing index. However, the new index has the advantage that its formulation allows for the development of bespoke icing indices that decouple icing severity and risk. Additionally, the new index can be interpreted as a probability, allowing it to be incorporated into an ensemble-based probabilistic forecast.
Impact and associations of eosinophilic inflammation in COPD: analysis of the AERIS cohort

Eosinophilic inflammation in chronic obstructive pulmonary disease (COPD) predicts response to treatment, especially corticosteroids. We studied the nature of eosinophilic inflammation in COPD prospectively to examine the stability of this phenotype and its dynamics across exacerbations, and its associations with clinical phenotype, exacerbations and infection. 127 patients aged 40–85 years with moderate to very severe COPD underwent repeated blood and sputum sampling at stable visits and within 72 h of exacerbation for 1 year. Blood eosinophils ≥2% was prevalent at baseline, and predicted both predominantly raised stable-state eosinophils across the year (area under the curve 0.841, 95% CI 0.755–0.928) and increased risk of eosinophilic inflammation at exacerbation (OR 9.16; p<0.001). Eosinophils ≥2% at exacerbation and eosinophil predominance at stable visits were associated with a lower risk of bacterial presence at exacerbation (OR 0.49; p=0.049 and OR 0.25; p=0.065, respectively). Bacterial infection at exacerbation was highly seasonal (winter versus summer OR 4.74; p=0.011) in predominantly eosinophilic patients. Eosinophilic inflammation is a common and stable phenotype in COPD. Blood eosinophil counts in the stable state can predict the nature of inflammation at future exacerbations, which when combined with an understanding of seasonal variation provides the basis for the development of new treatment paradigms for this important condition. Blood eosinophil levels in COPD predict the nature of inflammation at future exacerbations and may guide therapy http://ow.ly/W10o30dNQiq