Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid detection of Phytophthora cinnamomi based on a new target gene Pcinn13739

Phytophthora cinnamomi causes crown and root wilting in more than 5,000 plant species and represents a significant threat to the health of natural ecosystems and horticultural crops. The early and accurate detection of P. cinnamomi is a fundamental step in disease prevention and appropriate management. In this study, based on public genomic sequence data and bioinformatic analysis of several Phytophthora, Phytopythium, and Pythium species, we have identified a new target gene, Pcinn13739; this allowed us to establish a recombinase polymerase amplification–lateral flow dipstick (RPA-LFD) assay for the detection of P. cinnamomi. Pcinn13739-RPA-LFD assay was highly specific to P. cinnamomi. Test results for 12 isolates of P. cinnamomi were positive, but negative for 50 isolates of 25 kinds of Phytophthora species, 13 isolates of 10 kinds of Phytopythium and Pythium species, 32 isolates of 26 kinds of fungi species, and 11 isolates of two kinds of Bursaphelenchus species. By detecting as little as 10 pg. µl−1 of genomic DNA from P. cinnamomi in a 50-µl reaction, the RPA-LFD assay was 100 times more sensitive than conventional PCR assays. By using RPA-LFD assay, P. cinnamomi was also detected on artificially inoculated fruit from Malus pumila, the leaves of Rhododendron pulchrum, the roots of sterile Lupinus polyphyllus, and the artificially inoculated soil. Results in this study indicated that this sensitive, specific, and rapid RPA-LFD assay has potentially significant applications to diagnosing P. cinnamomi, especially under time- and resource-limited conditions.
Observations of interaction regions and corotating shocks between one and five AU - Pioneers 10 and 11. [solar wind streams]

Interaction regions between adjacent solar wind streams have been identified between 1 and 5 AU using Pioneer 10 and 11 magnetic field and plasma measurements. Beyond 1 AU, a relatively large fraction of the interaction regions have been found to be accompanied by either forward shocks, reverse shocks, or shock pairs. The observations are consistent with previous theoretical proposals that the interaction between adjacent streams leads to the development of corotating interplanetary shocks.
COMPARATIVE STUDY OF MACHINE-LEARNING-BASED CLASSIFIERS FOR SOIL SALINIZATION PREDICTION USING SENTINEL-1 SAR AND SENTINEL-2 MSI DATA

Under arid and semi-arid climatic conditions, salinization, naturally induced by parental materials weathering or artificially caused by anthropogenic actions, depicts a severe land degradation form, which significantly impacts crop yield in arable lands affecting food security on local and regional scales. At the same time, its adverse effects on urban infrastructures extend to land subsidence, erosion, and groundwater quality deterioration. Hence, soil salinity and sodicity monitoring using remote sensing and GIS tools has been an attractive field of research for soil scientists and experts worldwide, mainly for promoting sustainable agriculture. In this context, using Sentinel-l SAR and Sentinel-2 MSI data acquired in September-October 2019 and soil salinity data provided by the Research Institute of Soil Sciences and Agricultural Chemistry (RISSAC), four machine learning classifiers, namely, random forest (RF), maximum likelihood (ML), minimum distance (MD), and nearest neighbors (KNN) were applied and compared in terms of classification accuracy. The results revealed that RF had outperformed other classifiers, with the highest overall accuracy of 97 % and a mean percentage error (MPE) of 0.028, followed by KNN (= 96 %, 0.041), ML (= 94.5 %, 0.054), and MD (= 86.5 %, 0.135). The importance score report based on the RF classifier showed that the VV backscatter coefficient, SWIRl, SWIR2, and VH backscatter coefficient are the most important features retrieved from remotely sensed data to estimate salinization risks. Despite its strong dependence on training data, random forest depicts the most suitable algorithm for salinization detection using radar and optical data. This study can provide a timeless reference for selecting efficient machine learning algorithms in future studies to predict salinization using data retrieved from multiple sensors.
Software-Defined Radio Network Positioning Technology Design. Transmitter Development

Software-Defined Radio (SDR) and Model-Based Design (MBD) are well known research and development (R&D) tools for rapid technology prototyping. Pioneering SDR hardware is Universal Software Radio Peripheral (USRP) boards from Ettus Research, and one of the most popular MBD software is Communications Toolbox from Mathworks with USRP Hardware Driver (UHD) support. World-wide spread and relative accessibility of SDR and MBD hardware-software revolutionized not only communication, but also network positioning technologies design and prototyping over the last decade. Increased interest to User Equipment (UE) location estimation is observed since 3GPP Release 9, which introduced Observed Time Difference of Arrival (OTDOA) in LTE with Positioning Reference Signals (PRS). Further evolution of PRS is motivated by the transition to 5G and beyond. The aim of SDR network positioning technology design is to substantiate physical layer technical solutions for UE positioning accuracy improvement in existing and emerging networks through laboratory measurements. Existing works focus mainly on receiver design for primary measurements accumulation and do not consider transmitter design. Significance of proposed approach lies in the possibility to investigate PRS transmitter pattern on primary measurement precision. The contribution of research is PRS transmitter development with USRP SDR hardware and Matlab software and its laboratory validation.
Hyperspectral Image Classification using Joint Spatial-Spectral Feature Extraction

Hyperspectral images (HSI) are playing a dominant role in many applications as they contain high spectral and spatial information. The HSIs contain hundreds of attached spatial-spectral bands. The extraction of joint spectral-spatial features becomes a difficult task due to the correlation between bands. The deep learning models provide a solution to the extraction of spatial-spectral features by introducing various kernels. Thus, this article is focused on the implementation of deep learning convolutional neural network (DLCNN) based feature extraction with a bio-optimization-based feature selection mechanism. Initially, enhanced guided image filtering (EGIF) is developed to extract the spatial features. In addition, Residual Network 50 (ResNet50) is introduced to extract the color-based three-dimensional spectral features from the HSI dataset. Finally, DLCNN is applied to classify the various bands of HSI. The subjective and objective performance of proposed hybrid network resulted in superior performance as compared to state-of-art approaches in terms of overall accuracy (OA), average accuracy (AA), precision, recall and F1-score.
Collaborative Sensing in Internet of Things: A Comprehensive Survey

Collaborative sensing leverages the cooperation of a collection of sensors to complete a large-scale sensing task in Internet of Things (IoT). Although some previous studies have reviewed the literature of collaborative sensing in sensor networks, there still lacks a systematic and holistic overview with the consideration of practical application needs in IoT. In this article, we highlight that sensing application is coupled with sensing mechanism and sensing collaboration, and comprehensively survey the sensing mechanism, collaboration and applications in the context of IoT. Specifically, we first give an introduction of sensing technologies widely employed in IoT applications. Then, we systematically expatiate on more design issues for collaborative sensing, i.e., sensing models, deployment methods, sensing scheduling strategies and metrics for sensing quality. Next, the network-wide sensing collaboration is expatiated through three fundamental types of coverage problems as well as their solutions and insightful observations. Furthermore, we discuss promising sensing applications and elaborate how the sensing problem is established from the realistic application requirements and how the sensing quality impacts the application performance. Finally, we discuss several inspiring future research directions.
Malicious Impact Mitigation by Index-Modulation for Decisions Gathering in Wireless Sensor Networks

In target detection applications, Wireless Sensor Networks (WSNs) report binary decisions about the absence/presence of a target to a Sink Node (SN) in order to make a global decision and take an appropriate action. However, as these networks are extremely vulnerable to malicious attacks, their performance could be significantly degraded. Index-Modulation (IM) based approach seems to own a security merit where nodes' decisions are not entirely exposed, but hidden with the indices of transmitting devices. This, to a large extent, will limit malicious nodes from deceiving local decisions and therefore mitigate their overall impact on the global decision. In this paper, IM as a secured physical layer transmission approach in WSNs is introduced. Simulation and analytical results have highlighted the robustness of IM schemes against different numbers and variations of malicious attacks comparing to their counterparts of non-IM schemes.
A guided walking program in urban environment for an healthy, active community: the experience of the University of Ferrara

Background. The health benefits of physical activity are well known. Improving participation in health-enhancing physical activity is a public health issue of urgent concern. The aim of our project was to promote the practice of physical activity in sedentary adult through a guided walking program and to check its long-term maintenance. Methods. 650 sedentary subjects (442 women, 208 men) were admitted to the project. Body weight, body mass index (BMI), waist circumference, systolic and diastolic blood pressure, and walking speed were determined at enrollment and redetermined after 1 year in the subjects completing the project. Daily groups of guided walking were organized in the urban environment of the city of Ferrara in Italy. Results. In the 326 subjects completing the project, highly significant reduction in body weight, BMI, waist circumference, systolic and diastolic blood pressure and increase in walking speed were documented. Habit to walk was verified in the 326 finishers after 16 months and 7 years from the end of the walking program. An active lifestyle was maintained by the 79% and 69% of the participants respectively. Conclusion. Guided walking was followed by highly significant reduction in risk factors for cardiovascular diseases. A high percentage of previously sedentary adults enrolled kept the habit of walking up to seven years after the end of the project, indicating that guided walking programs may be effective in permanently changing lifestyle.
Data-driven Interpretable Policy Construction for Personalized Mobile Health

To promote healthy behaviors, many mobile health applications provide message-based interventions, such as tips, motivational messages, or suggestions for healthy activities. Ideally, the intervention policies should be carefully designed so that users obtain the benefits without being overwhelmed by overly frequent messages. As part of the HeartSteps physical-activity intervention, users receive messages intended to disrupt sedentary behavior. HeartSteps uses an algorithm to uniformly spread out the daily message budget over time, but does not attempt to maximize treatment effects. This limitation motivates constructing a policy to optimize the message delivery decisions for more effective treatments. Moreover, the learned policy needs to be interpretable to enable behavioral scientists to examine it and to inform future theorizing. We address this problem by learning an effective and interpretable policy that reduces sedentary behavior. We propose Optimal Policy Trees + (OPT+), an innovative batch off-policy learning method, that combines a personalized threshold learning and an extension of Optimal Policy Trees under a budget-constrained setting. We implement and test the method using data collected in HeartSteps V2N3. Computational results demonstrate a significant reduction in sedentary behavior with a lower delivery budget. OPT + produces a highly interpretable and stable output decision tree thus enabling theoretical insights to guide future research.
Patient Reported Outcomes in a Practice Changing Randomized Trial of Bevacizumab in the Treatment of Advanced Cervical Cancer: An NRG Oncology/Gynecologic Oncology Group Study

Background— To analyze patient reported outcomes (PROs) in GOG 240, the practice-changing, randomized phase 3 trial that concluded that chemotherapy (cisplatin-paclitaxel or topotecan-paclitaxel) plus bevacizumab significantly improves overall survival (OS), progression-free survival (PFS), and response rates compared to chemotherapy alone in advanced cervical cancer. Trial registration number: NCT00803062. Methods— Patients were assessed pre-cycle 1, 2, and 5 and at 6 and 9 months post-cycle 1 with the Functional Assessment of Cancer Therapy-Cervix Trial Outcome Index (FACT-Cx TOI), and items from the FACT/GOG-Neurotoxicity (Ntx) subscale, and a worst pain item from the Brief Pain Inventory (BPI). Differences in FACT-Cx TOI scores were assessed using a linear mixed model adjusting for baseline score and age. A mixed effects mixed distributions model was fitted to evaluate treatment differences of likelihood to report neurotoxicity and pain, and severity of these symptoms, once reported. The association between baseline health-related quality of life and survival was analyzed using Cox proportional hazards models. Findings— Among 390 evaluable patients, PRO completion rates declined from 96% (baseline) to 63% (9 months post-cycle 1). Completion rates were not significantly associated with OS (HR 0.80; 95% CI 0.74, 0.87; p<0.001) and PFS (0.88; 95% CI: 0.83, 0.95; p<0.001). Interpretation— Improvements in OS and PFS attributed to the incorporation of bevacizumab in the treatment of advanced cervical cancer were not accompanied by any significant deterioration in health-related quality of life. Study supported by NIH funding.
Weighted Naive Bayes Approach for Imbalanced Indoor Positioning System Using UWB

The accuracy and reliability of an Ultra- WideBand (UWB) Indoor Positioning System (IPS) are compromised owing to the positioning error caused by the Non-Line-of-Sight (NLoS) signals. To address this, Machine Learning (ML) has been employed to classify Line-of-Sight (LoS) and NLoS components. However, the performance of ML algorithms degrades due to the disproportion of the number of LoS and NLoS signal components. A Weighted Naive Bayes (WNB) algorithm is proposed in this paper to mitigate this issue. The performance of the proposed algorithm is compared with conventional state-of-the-art ML algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT) using the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC). The results prove that the WNB classifier can significantly reduce the impact of the limited number of NLoS components that are available for training the model. The proposed WNB algorithm also maintains a high classification accuracy and robustness in mixed LoS/NLoS conditions.
Localizing and Explaining Faults in Microservices Using Distributed Tracing

Finding the exact location of a fault in a large distributed microservices application running in containerized cloud environments can be very difficult and time-consuming. We present a novel approach that uses distributed tracing to automatically detect, localize and aid in explaining application-level faults. We demonstrate the effectiveness of our proposed approach by injecting faults into a well-known microservice-based benchmark application. Our experiments demonstrated that the proposed fault localization algorithm correctly detects and localize the microservice with the injected fault. We also compare our approach with other fault localization methods. In particular, we empirically show that our method outperforms methods in which a graph model of error propagation is used for inferring fault locations using error logs. Our work illustrates the value added by distributed tracing for localizing and explaining faults in microservices.
Impact Assessment Of Energy Conservation Measures On Building Energy Consumption, Carbon Emission, And Adaptation Cost Using Future Weather Data

The sixth Intergovernmental Panel on Climate Change assessment report shows a 1.5° Celsius increase in the earth’s temperature by 2040 which could lead to catastrophic weather events. Keeping this projection to the lower range through mitigation efforts is therefore of paramount importance. Buildings account for a significant amount of carbon emissions. Therefore, predicting buildings’ energy consumption and carbon emission in advance and implementing the most cost and energy-efficient strategies accordingly can help to combat climate change. This paper analyzed the energy consumption and carbon emission of an office building in 16 US climate zones now and in 2050 using future weather data. Also, the impact of three energy conservation measures and their implementation cost has been assessed in the cities with higher future energy use to suggest the most energy and cost-effective building upgrade that can offset the additional energy consumption caused by global warming with a reasonable payback period.
FLNet: Graph Constrained Floor Layout Generation

In this work, we propose a generative-based approach, FLNet, to synthesize floor layout plans guided by user constraints. Our approach considers user inputs in the form of boundary, room types, and spatial relationships and generates the layout design satisfying these requirements. We evaluated our approach on floor plans data, RPLAN, consisting of 80,000 vector-graphics floor plans of residential buildings designed by professional architects. We perform both qualitative and quantitative analysis along three metrics - Layout generation accuracy, Realism, and Quality to evaluate the generated layout designs. We compare our approach with the existing baselines and outperform on all these metrics. The layout designs generated by our approach are more realistic and of better quality.
Simultaneous measurement of forearm muscle activity, vibrational transfer and grip strength during the tennis forehand stroke using a novel wearable device - a pilot study

Elbow tendinopathy injuries are very common in tennis players. One of the commonly accepted theories describing the development of elbow tendinopathy is based on stiffness of the forearm skeletal muscle units, their repetitive overuse and vibrational transfer from impact in tennis. The objective of this study was to design, develop and test a novel microcontroller based wearable device which could simultaneously measure a player's grip strength, forearm electromyographic activity and vibrational transfer under realistic playing conditions. The device was tested on four tennis players of various playing levels (a previously WTA player, a qualified tennis coach and 2 recreational players) who were required to hit forehands cross-court with different spin levels (flat and topspin) into the back diagonal square following a specific height guideline. The results indicated that the device could record unique player technical profiles of vibrational transfer, racket orientation angle, grip strength and forearm muscle activity during the stroke. This study demonstrates the potential benefits of a wearable device to measure kinematic parameters associated with elbow injury development in tennis players, as well as provide coaching recommendations for potentially reducing the risk of developing elbow tendinopathy.
Explanatory item response models : a generalized linear and nonlinear approach

1 A framework for item response models. - 2 Descriptive and explanatory item response models. - 3 Models for polytomous data. - 4 An Introduction to (Generalized (Non)Linear Mixed Models. - 5 Person regression models. - 6 Models with item and item group predictors. - 7 Person-by-item predictors. - 8 Multiple person dimensions and latent item predictors. - 9 Latent item predictors with fixed effects. - 10 Models for residual dependencies. - 11 Mixture Models. - 12 Estimation and software. - Afterword.
Secrecy Outage Probability in Cooperative NOMA based 5G Wireless Networks in Nakagami-m Fading Environment

This paper investigates the application of a cooperative non-orthogonal multiple access (NOMA) network with two down-link users in Nakagami-m fading scenario. The near user acts as a decode-and-forward full-duplex relay for the far user. An eavesdropper is also present in the network, which tries to access messages intended for legitimate users. In full-duplex mode, the near user will send jamming signals and receive desired signals during the first phase. During the second phase, the base station sends jamming signals. Analytical expressions for the secrecy outage probabilities and the overall system secrecy outage probability (SOP) are derived. Also, the optimal power allocation coefficient that minimizes the overall SOP is determined using the simulated annealing (SA) algorithm. We establish that using optimal power allocation coefficients can reduce the overall outage probability significantly as compared to the random selection of the power allocation factor.
An IoT-enabled Portable System for Field-based Endurance Run Testing

Nowadays, aerobic capacity tests are recurrently used to determine the maximum oxygen consumption (VO2max) of an individual. The VO2max expresses the maximum aerobic capacity and is considered the best cardio-respiratory fitness index. A widely used test in a school context for this purpose is the PACER (Progressive Aerobic Cardiovascular Endurance Run) test, which consists of a shuttle race at a stage of 20 meters under a certain pace that is increased over time. When performed in a school context, where several individuals perform the test at the same time, some problems arise that may affect the reliability of the test. Problems such as human error, in measurements and lap counting, or lack of perception of the rhythm for the tested individual can affect the reliability of the results obtained. It is also difficult to repeat the test indefinitely, since it requires more than one test for the results to have the desired credibility. This research activity involves the development and validation of an electronic digital prototype for setting the pace of the PACER test. This apparatus will need to be portable for using in all possible settings (field and laboratory); price affordable; will have the possibility to be used in other paced tests; it will be controlled via a digital App (Smartphone) and will allow collecting inline data.