Miracle-dz/UniFAR-FTUs huggingface dataset:
Diseases and Pests Identification of Lycium Barbarum Using SE-MobileNet V2 Algorithm

The prevention and control of crop diseases and insect pests is a key link in agricultural production. Accurate image recognition of diseases and insect pests is significant in agriculture and it has high efficiency, low cost and convenient operation. The traditional recognition method is difficult to recognize the pests relying on manual recognition, and it is easy to misjudge the pests. MobileNet V2 is an important algorithm for image recognition, which has high recognition accuracy and much lower parameter quantity than other algorithms. [1] This paper proposes a new SE-MobileNet V2 target recognition algorithm which improves the MobileNet V2 algorithm. The proposed algorithm embeds SE module between the last pooling layer and convolution layer of MobileNet V2 network, enhancing the receptive field of the network. It combines the advantages of MobileNet V2 and SENet, is more accurate than MobileNet V2 and is more suitable to recognize small target. In this paper, experiments are conducted based on data sets containing 11 kinds of lycium barbarum diseases and pests. Through experimental verification, the proposed algorithm has the highest recognition accuracy and strongest robustness than other algorithm. SE-MobileNet V2 target recognition algorithm improves the accuracy and speed of image recognition, is meaningful for accurate control of lycium barbarum diseases and pests.
Development and Content Validation of an Instrument to Measure Baseline Standards for Pediatric Oncology Nursing in Low- and Middle-Income Countries

The Nursing Working Group of the International Society of Pediatric Oncology developed baseline standards for pediatric oncology nursing care in low- and middle-income countries. The standards represent the foundational support required to provide quality nursing care and address barriers such as inadequate staffing, lack of support, limited access to education, and unsafe nursing environments. The purpose of the current study was to develop and validate an instrument to accurately measure the standards. Content validity was assessed by a panel of expert pediatric oncology nurses from all geographical regions of the World Health Organization. The experts were informed about the study’s purpose and provided the publications used to develop the instrument. The experts rated how well each criterion measured the corresponding standard by using a 4-point scale. A content validity index (CVI) was computed by using the percentage of total standards given a score of 3 or 4 by the experts. A CVI of . 98 was obtained from the panel’s evaluation. A CVI of more than . 80 is recommended for a newly developed instrument. On the basis of the panel’s recommendations, minor modifications were made to the instrument. We developed and validated the content of an instrument to accurately measure baseline standards for pediatric oncology nursing care. This instrument will aid future research on the effect of nursing standards on clinical outcomes, including mortality and abandonment of treatment, with the potential to influence health policy decisions and improve nursing support in low- and middle-income countries.
Dual-Band, Dual-Sense Textile Antenna With AMC Backing for Localization Using GPS and WBAN/WLAN

A wearable textile antenna with dual-band and dual-sense characteristics is presented in this work. It operates at the 2.45 GHz band for WBAN and WLAN applications, and at the 1.575 GHz band for Global Positioning System (GPS) applications. An antenna backing based on an artificial magnetic conductor (AMC) plane operating at 2.45 GHz band is introduced to reduce the backward radiation and to improve antenna gain. It consists of a $3\times 3$ array of square patch unit cells, where each unit cell is integrated with four square slits and a square ring. A square-shaped patch is then located on top of the substrate as its radiator. To enable dual-band operation, two corners of this radiator are truncated, with each of the four corners incorporated with a rectangular slit to enable its circular polarization characteristic in the GPS band. Simulation and experimental results are in good agreement and indicate proper antenna operation with linear polarization in the 2.45 GHz band and circular polarization in the 1.575 GHz band, with realized gain of 1.94 dBi and 1.98 dBic, respectively.
ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

Recently, MPI has become widely used in many scientific applications, including different non-computer science fields, for parallelizing their applications. An MPI programming model is used for supporting parallelism in several programming languages, including C, C++, and Fortran. MPI also supports integration with some programming models and has several implementations from different vendors, including open-source and commercial implementations. However, testing parallel programs is a difficult task, especially when using programming models with different behaviours and types of error based on the programming model type. In addition, the increased use of these programming models by non-computer science specialists can cause several errors due to lack of experience in programming, which needs to be considered when using any testing tools. We noticed that dynamic testing techniques have been used for testing the majority of MPI programs. The dynamic testing techniques detect errors by analyzing the source code during runtime, which will cause overheads, and this will affect the program’s performance, especially when targeting massive parallel applications generating thousands or millions of threads. In this paper, we enhance ACC_TEST to have the ability to test MPI-based programs and detect runtime errors occurring with different types of MPI communications. We decided to use hybrid-testing techniques by combining both static and dynamic testing techniques to gain the benefit of each and reduce the cost.
One-year real-life results on effect of intravitreal aflibercept in patients with diabetic macular oedema switched from ranibizumab

Abstract Purpose To assess visual and optical coherence tomography–derived anatomical outcomes of treatment with intravitreal aflibercept (Eylea®) for diabetic macular oedema in patients switched from intravitreal ranibizumab (Lucentis®). Design Retrospective, cohort study. Participants Ninety eyes (of 67 patients) receiving intravitreal anti–vascular endothelial growth factor therapy were included. Methods This is a retrospective, real-life, cohort study. Each patient had visual acuity measurements and optical coherence tomography scans performed at baseline and 12 months after the first injection of aflibercept was given. Main Outcome Measures We measured visual acuities in Early Treatment Diabetic Retinopathy Study letters, central foveal thickness and macular volume at baseline and at 12 months after the first aflibercept injection was given. Results Ninety switched eyes were included in this study. The mean (standard deviation) visual acuity was 63 (15.78) Early Treatment Diabetic Retinopathy Study letters. At baseline, the mean (standard deviation) central foveal thickness was 417.7 (158.4) μm and the mean macular volume was 9.96 (2.44) mm3. Mean change in visual acuity was +4 Early Treatment Diabetic Retinopathy Study letters (p = 0.0053). The mean change in macular volume was −1.53 mm 3 in SW group (p = 0.21), while the change in central foveal thickness was −136.8 μm (p = 0.69). Conclusion There was a significant improvement in visual acuity and in anatomical outcomes in the switched group at 12 months after commencing treatment with aflibercept for diabetic macular oedema.
Compliant Bimanual Aerial Manipulation: Standard and Long Reach Configurations

The ability of aerial manipulation robots to reach and operate in high altitude workspaces may result of interest in a wide variety of applications and scenarios that nowadays cannot be accessed easily by human operators. Consider for example the installation of sensors in polluted areas, the insulation of leaks in pipe structures, or the corrosion repair in power lines and wind turbines. This paper describes the application of a human-like dual arm aerial manipulator for the inspection of pipe structures, typical of chemical plants, involving the installation and retrieval of sensor devices. The goal is to reduce the time, cost, and risk with respect to conventional solutions conducted by human workers. Two configurations of the aerial robot are considered and compared: the standard, in which the arms are installed at the base of the multirotor, and the long reach configuration in passive pendulum, which extends the effective workspace of the manipulator and increases safety during the operation on flight. The kinematic and dynamic models of both configurations are derived, proposing a unified notation for the equations of motion, and a force/position control scheme that relies on the servo controller and the mechanical joint compliance. The paper also describes a simulation framework used for validating the execution of the aerial manipulation task before the realization of the real experiments, which contributes to reducing the probability of failure. The potential application of the standard and long reach configurations is evaluated in two sensor installation tasks carried out in an indoor testbed.
Cone-Angle Artifact Removal Using Differentiated Backprojection Domain Deep Learning

For circular trajectory conebeam CT, Feldkamp, Davis, and Kress (FDK) algorithm is widely used for its reconstruction. However, the existence of cone-angle artifacts is fatal for the quality when using this algorithm. There are several model-based iterative reconstruction methods for the cone-angle artifacts removal, but these algorithms usually require repeated applications of computational expensive forward and backward. In this paper, we propose a novel deep learning approach for cone-angle artifact removal on differentiated backprojection domain, which performs a data-driven inversion of an ill-posed deconvolution problem related to the Hilbert transform. The reconstruction results along the coronal and sagittal directions are then combined by a spectral blending technique to minimize the spectral leakage. Experimental results show that our method provides superior performance to the existing methods.
Case Reasoning Research of Emergency Network Opinion Based on Ontology

This paper introduces the construction process of the emergency network opinion case library. It uses ontology as the basic rule of knowledge representation and knowledge reasoning, it analyzes the field of emergency network opinion, and obtains the ontology model of emergency network opinion case. This paper discusses the process of case reasoning and the main method of case retrieval. It obtains the weighted sum of attribute similarity according to the categories of attributes, and realizes the application test of case reasoning of the emergency network opinion. (Abstract)
Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

The convolutional neural network (CNN) is commonly used in visual recognitions and classifications. However, CNN can also be applied as a forecaster that can extract features from spatiotemporal data. This paper proposes a 24h ahead electricity price forecasting method, which integrates CNN with an evolutionary algorithm and utilizes spatiotemporal data. The optimal structure of the CNN network for the locational marginal price (LMP) forecasting was obtained using a genetic algorithm (GA). A gene mapping scheme was initially encoded to represent the search space and the process of selection, mutation, and crossover eliminated structures that did not satisfy the validation fitness function and then competitive individuals were generated. The evolution process uses the root mean square error (RMSE) as the validation fitness function, which is optimzed by training the created CNN network. The proposed gene mapping scheme can be used to design an optimal CNN structure once the mapping between gene binary bits and parameters/hyperparameters of CNN is given. Day-ahead LMP and demand datasets from Pennsylvania-New Jersey-Maryland (PJM) power market were used to demonstrate the evolutionary capability of the proposed method and the finding of optimal CNN structures. Each studied dataset was grouped into 4 subsets corresponding to various seasonal characteristics (different types of situations in real life). Experimental results revealed that the proposed GA-CNN always yielded a higher forecasting accuracy and lower error rates than other forecasting methods.
First report of Amblyomma boeroi Nava, Mangold, Mastropaolo, Venzal, Oscherov and Guglielmone (Acari: Ixodidae) from the Chacoan Peccary, Catagonus wagneri (Artiodactyla: Tayassuidae), in Paraguay

Abstract All stages of the hard tick Amblyomma boeroi Nava, Mangold, Mastropaolo, Venzal, Oscherov and Guglielmone were found for the first time on two carcasses of the Chacoan peccary, Catagonus wagneri, in Paraguay, one from a site located 100 km west of Mariscal Estigarribia (S 21°29′, W 61°22′), and the other from Destacamento Teniente Martinez (S 20°55′, W 59°50′), Boquerón Department.
SERENADE: A Parallel Iterative Algorithm for Crossbar Scheduling in Input-Queued Switches

Most of today’s high-speed switches and routers adopt an input-queued crossbar switch architecture. Such a switch needs to compute a matching (crossbar schedule) between the input ports and output ports during each switching cycle (time slot). A key research challenge in designing large (in number of input/output ports N) input-queued crossbar switches is to develop crossbar scheduling algorithms that can compute “high quality” matchings – i.e., those that result in high switch throughput (ideally 100%) and low queueing delays for packets – at line rates. SERENA is one such algorithm: it outputs excellent matching decisions that result in 100% switch throughput and reasonably good queueing delays. However, since SERENA is a centralized algorithm with O(N) time complexity, it cannot support switches that both are large and have a very high line rate per port. In this work, we propose SERENADE (SERENA, the Distributed Edition), a parallel iterative algorithm that provably precisely emulates SERENA in only O(logN) iterations between input ports and output ports, and hence has a time complexity of only O(logN) per port.
Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN

Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at the baseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overall Quality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). In this paper, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). To further improve, two new strategies are proposed and tested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectively.
The Extra Mile: Special Consideration of Atrial Fibrillation in Older Adults with Endurance Athletic History

Patient: Male, 80-year-old Final Diagnosis: Ischemic stroke Symptoms: Altered mental status • hematoma • hemiparesis • hyperreflexia Medication:— Clinical Procedure: — Specialty: Cardiology Objective: Challenging differential diagnosis Background: Aerobic exercise is uniformly accepted as one of the most important modifiable factors to improve cardiovascular health, but endurance athletic training poses a significant risk factor for development of atrial fibrillation (AFib) in middle-aged and older adults. Ubiquitous risk assessment tools (CHADS2 and CHA2DS2-VASc) and US Preventive Services Task Force guidelines do not presently account for this association. A case is presented which illustrates a dire outcome of undiagnosed AFib in an elderly male patient who had run many marathons. Case Report: An 80-year-old male with well-controlled hypertension and hypercholesterolemia and a history significant for running many marathons throughout his life was brought in via ambulance after being found down with head trauma by his wife at home. A short run of AFib was recorded on telemetry and electrocardiogram (ECG) and a review of previous ECGs revealed evidence of interatrial block (Bayés Syndrome), though the patient had no history of AFib or anticoagulation. This coupled with imaging indicated thromboembolic stroke to the left middle cerebral artery leading to right-sided hemiplegia and a subsequent fall to the right, causing right-sided head trauma and intracranial hemorrhage. His clinical course did not improve, and on his fifth day of admission he was transferred to comfort care, extubated, and succumbed to his injuries. Conclusions: This case and the accompanying summary of evidence strongly encourage further investigation and a higher index of suspicion for AFib in asymptomatic older adults with a history of endurance athletic training.
Detection of Renibacterium salmoninarum in salmonid kidney samples: a comparison of results using double‐sandwich ELISA and isolation on selective medium

A total of 1239 kidney samples from four species of salmonid fish, Atlantic salmon, Salmo salar L. , brown trout, Salmo trutta L. , Arctic charr, Salvelinus alpinus (L.), and rainbow trout, Oncorhynchus mykiss (Walbaum), were screened for Renibacterium salmoninarum using double-sandwich ELISA and bacterial isolation. For bacterial isolation, samples were homogenized, washed, plated onto S-KDM and incubated for 12 weeks. Samples for ELISA were kept frozen until tested. After thawing, 25% homogenates in PBS were heated at 100°C for 15 min in the presence (2.5% v/v) of HemoDe solvent (terpene and butylated hydroxyanisole) and then centrifuged. The supernatant was tested with polyclonal antibodies against whole bacterium in a double-sandwich ELISA. In seven out of 12 groups tested, all samples were negative in both tests. Positive ELISA results occurred in five groups. Renibacterium salmoninarum was isolated on SKDM from samples in four out of these five groups. The ELISA test gave significantly higher numbers of positive samples in three out of the four groups showing positive results in both tests.
Water quality of runoff from flat panel display glass concrete during modified accelerated aging plus leaching tests

Flat panel display glass (FPDG) from plasma televisions is difficult to recycle due to their heavy metal composition. When crushed, flat panel display glass has similar physical properties to sand and could be used as a recycled aggregate in concrete, if the potentially harmful metals in FPDG can be encapsulated. To replicate leaching from environmental exposure, at periodic points during freeze–thaw tests, samples of concrete with 0%, 10%, and 20% FPDG were exposed to simulated rainwater and the runoff water was analyzed. For the metals detected (arsenic, barium, chromium, and lead), lead concentrations in the leachate/runoff from 20% FPDG samples exceeded the acute ambient water quality criteria after approximately 50–60 cycles; however, no samples exceeded the toxic characteristic leaching procedure limits at any point during freeze–thaw testing. Increases in lead and chromium concentrations over time indicate a need for stabilization, especially since lead exceeded the acute water quality criteria.
Improving Interpretability of 2-D Ultrasound of the Lumbar Spine

Ultrasound-guided anesthesia uses a safe, portable imaging modality to provide visual feedback during the needle injection. Widespread adoption of ultrasound-guided anesthesia has been primarily limited by a lack of access to advanced ultrasound technology and a lack of ultrasound training for anesthesiologists. We sought to address these limitations by introducing a method that aids the interpretability of cross-sectional ultrasound from conventional (2D) machines. We propose a constrained registration of a 3D active shape model constructed from computerized tomography (CT) scans of the lumbar spine to a specific set of targets automatically extracted from 2D B-mode ultrasound images with machine learning models. The registration results in an overlay of the entire bone cross-section of the lumbar spine onto the ultrasound image. Our proposed registration achieved a mean squared error of $1.4 \pm 0.3$ mm on a set of 43 ultrasound images, which is smaller than the key anatomical features, suggesting that the overlay is suitable for interpretation.
Multi-Echelon Inventory optimizations for Divergent Networks by Combining Deep Reinforcement Learning and Heuristics Improvement

Mathematical solutions of obtaining an optimal policy of multi-echelon inventory are often computationally intractable for supply chain systems of realistic sizes. To deal with supply chain environment dynamics and the very large space of planning variables, this paper proposes a novel simulation-optimization approach for dynamic optimization of a continuous review (s, Q) multi-echelon inventory policy by combining deep reinforcement learning approach with local search metaheuristics. We compare the performance of a scatter search, a deep Q-learning and our proposed combined approach. The simulation outcomes show that the combined approach improves decision performances evidently in terms of profitability, adaptability and solution time efficiency.
FGB: Feature Guidance Branch for Organ Detection in Medical Images

In this paper, we propose a novel method that detects and locates different abdominal organs from CT images. We 1) utilize the distributions of organs on CT images as a prior to guide object localization; 2) design an efficient guidance map and propose an interpretable scoring method, feature guidance branch(FGB) to filtrate low-level feature maps by scoring for them; 3) establish effective relations among feature maps by visualization to enhance interpretability. Evaluated with three public datasets, the proposed method outperforms the baseline model on all tasks with a remarkable margin. Furthermore, we conduct exhaustive visualization experiments to verify the rationality and effectiveness of our proposed model.