Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Seasonal Effects of River Flow on Water Temperatures in an Agriculturally Dominated California River

Low streamflows can increase vulnerability to warming, impacting coldwater fish. Water managers need tools to quantify these impacts and predict future water temperatures. Contrary to most statistical models' assumptions, many seasonally changing factors (e.g., water sources and solar radiation) cause relationships between flow and water temperature to vary throughout the year. Using 21 yr of air temperature and flow data, we modeled daily water temperatures in California's snowmelt‐driven Scott River where agricultural diversions consume most summer surface flows. We used generalized additive models to test time‐varying and nonlinear effects of flow on water temperatures. Models that represented seasonally varying flow effects with intermediate complexity outperformed simpler models assuming constant relationships between water temperature and flow. Cross‐validation error of the selected model was ≤1.2°C. Flow variation had stronger effects on water temperatures in April–July than in other months. We applied the model to predict effects of instream flow scenarios proposed by regulatory agencies. Relative to historic conditions, the higher instream flow scenario would reduce annual maximum temperature from 25.2° to 24.1°C, reduce annual exceedances of 22°C (a cumulative thermal stress metric) from 106 to 51 degree‐days, and delay onset of water temperatures >22°C during some drought years. Testing the same modeling approach at nine additional sites showed similar accuracy and flow effects. These methods can be applied to streams with long‐term flow and water temperature records to fill data gaps, identify periods of flow influence, and predict temperatures under flow management scenarios.
Online Safety Monitoring Using Safety Modes

Robotic systems have to carry out more and more complex tasks, including ones where humans can be endangered. Residual design faults in such systems, as well as the inevitability of physical faults and interac- tion faults during operation, motivate the use of safety monitors to prevent catastrophic failures. In this paper, we consider the design of such safety monitors for multi- functional robotic systems. We present an approach and a formalization of the process for determining safety rules. It consists in identifying safety modes, according to the different tasks carried out by the monitored system. In practice, each safety mode is related to one or several functional modes and is speciﬁed by a permissiveness vector that deﬁnes the authorized domains of variation of key physical variables. The set of safety modes can be partially ordered according to their authorization vectors and can thus be represented as a directed acyclic graph. This graph is used to automatically build a model representing safety modes and their transitions, which can be implemented in an independent safety monitor. A case study has been carried out on a mobile manipulator robot, working in a factory alongside humans.
CHAPACIERIZING THE EFFECTS OF WEATHER ON COMMERCIAL BUILDING ENERGY USE

Energy service companies, whose returns are a function . of energy savings, have developed energy-normalization methods, based on degree-day measures of weather variation. True tests to determine the adequacy of these methods, however, require careful control of other determinants of building energy use. This paper uses a building energy simulation model to evaluate one of these methods for a large office building in Madison, WI using twelve years of actual weather data. Techniques for accounting for the effects of weather on building energy use based on degree-days are reviewed. A three parameter model, consisting of an intercept, slope, and vari able base temperature degree-day terms, is evaluated with simulations. Parameter estimates energy use were found to be sensitive to the year of data selected to develop the parameter. The resulting annual estimates of energy use tended to exceed DOE-2 predictions by up to ten per cent.
DIFFERENCES IN EXCLUSIONARY DISCIPLINE CONSEQUENCE ASSIGNMENT BY THE ECONOMIC STATUS OF BLACK STUDENTS DETERMINED TO BE AT-RISK: A TEXAS, STATEWIDE ANALYSIS

This study was conducted to determine the degree to which economic status was related to Discipline Alternative Education Program placement assignments for Grades 8 and 9 Black boys and Black girls who were at-risk during the 2017-2018 school year. Inferential statistical analyses of Texas statewide data revealed the presence of statistically significant differences in the number of Discipline Alternative Education Program placement assignments by the economic status of Black boys and Black girls at both grade levels. Grades 8 and 9 Black boys and girls who were at-risk and who were economically disadvantaged were assigned to statistically significantly more of these exclusionary discipline consequences than their counterparts who were not economically disadvantaged. Grade 8 Black boys who were economically disadvantaged were assigned to these exclusionary discipline consequences two times more often than Grade 8 Black boys who were not economically disadvantaged. Grade 8 Black girls who were economically disadvantaged were assigned to these exclusionary discipline consequences two-thirds more often than Grade 8 Black girls who were not economically disadvantaged. Grade 9 Black boys and Black girls who were economically disadvantaged were assigned these exclusionary discipline consequences one-third more often than their non-economically disadvantaged peers. Implications and recommendations for future research were discussed.
HIGH VALUES OF LIVER STIFFNESS PLAY AN IMPORTANT ROLE IN STRATIFYING THE RISK OF HEPATOCELLULAR CARCINOMA IN CIRRHOTIC HEPATITIS C PATIENTS.

BACKGROUND Evaluate the role of liver stiffness measurement (LSM) by transient elastography (TE) as a risk factor for hepatocellular carcinoma (HCC) occurrence in a prospective cohort of Brazilian hepatitis C virus (HCV) patients with cirrhosis. METHODS A cohort of 99 consecutive HCV patients was included between 2011 and 2016 with baseline LSM ≥12 kilopascals (kPa). Baseline variables were evaluated and HCC occurrence was documented. Kaplan-Meier methods with a log-rank test and the use of cox univariate and multivariate analysis assessed the association between variables and clinical results. RESULTS The mean age was 57.8±10.6 years. In a follow-up over a mean of 3.3 years, 20 (20.2%) patients developed HCC. In univariate logistic regression analysis, variables associated with HCC occurrence were: lower platelet count (P=0.0446), higher serum alpha-fetoprotein (P=0.0041) and bilirubin (P=0.0008) values, higher Model for End-Stage Liver Disease (MELD) score (P=0.0068) and higher LSM (P=0.0354). LSM evaluated by TE was independently associated with HCC development, and the best cut-off value for higher HCC risk was >21.1 kPa (HR: 5.548; 95%CI: 1.244-24.766; P=0.025). CONCLUSION A high value of liver stiffness relates substantially to an increased risk for HCC occurrence in Brazilian patients with cirrhosis due to HCV.
Critical measures of success for a software project

The work presented here attempts to identify a set of software project performance measures and influence factors used by software development projects so that a valid comparison of performance can be made between completed projects. The performance measures identified in this document are core measures that are identified as a part of the set of critical measures of success since they address important attributes of a software development project. For forty small and medium sized software projects the performance measures and the influence factors were measured.
Polyphonic Sound Event Detection and Classification using Convolutional Recurrent Neural Network with Mean Teacher

Polyphonic sound event detection (SED) is research field which finds usefulness in cognitive IoT, security systems, voice assistants etc. This paper proposes a SED system, which makes use of semi supervised mean teacher approach, for DESED dataset, which has confined strongly classified data, and plethora of weakly labeled and unlabeled data. The sound data is processed by extracting the log mel spectrograms. A convolutional recurrent neural network (CRNN) model, which takes the log mel spectrograms as input, is used in first level of SED system. Then, the second level updates it further by using the mean teacher approach by learning from the weakly classified and unlabeled audio data. The proposed SED system is proved to be effective on the DESED dataset. Overall performance assessment is made with baseline as well as different top-ranked solutions. Event based F1-score is considered as performance evaluation metric. The experiments display that the proposed SED system with CRNN using mean teacher approach achieves F1-score of around 48.3%, which is significant improvement over baseline system.
Songs Recommendation using Context-Based Semantic Similarity between Lyrics

With the rapid growth of the internet, many songs and music are readily available for users on various platforms. The number, however, gets so huge that the user might get overwhelmed when it comes to selecting a follow-up song. A recommender system comes in handy in such situations, where users can choose a recommended piece based on their likes and dislikes. There can be various metrics in developing a song recommender system, lyrics being one of them. In this paper, a song recommendation system is proposed on English songs, which uses the contextual embeddings to extract the context out of the song lyrics, identifies semantic similarity between these lyrics, and gives the most similar songs to the user based upon his choice. The dataset is taken from musixmatch.com, containing around 3300 songs. Following the pre-processing of the data, the context is extracted from the lyrics using Google's Universal Sentence Encoder algorithm. The proposed methodology achieves an F1 score of 0.7700, which shows that the accuracy of the proposed model is better than the available models in the literature for the song recommendation system using the lyrics of the songs.
One-vs-All Methodology based Cassava Leaf Disease Detection

Cassava is the most important tropical root crop. Its starchy roots are a major source of dietary energy for more than 500 million people. It is known to be the highest producer of carbohydrates among staple crops. It is a very resistant crop however it is not immune to all viral and bacterial diseases. Cassava Mosaic Disease alone causes an annual loss of US$ 1.2 to 2.3 billion. It is a very concerning issue hence it is imperative to detect the disease at its early stages. Modern deep learning techniques can be very useful given the need for predicting diseases with high precision. We propose one-such deep learning-based solution involving Convolutional Neural Networks. Our work proposes a “One-vs-All” methodology for solving the task of classifying the four most prevalent types of cassava leaf diseases and healthy cassava leaves. For this we trained five different binary classifiers for the five different classes, each classifier used the Efficient Net B4 model as the base model followed by few fully connected layers. Our final multi-class classifier achieved an accuracy of 85.64% on highly skewed test data. Further, we deployed the model on Android using Android Studio, Java and XML for more accessibility of our classifier.
An Energy Storage System to support Cruise Ships Cold Ironing in the Port of Civitavecchia

Increasingly, Cold Ironing (CI) for Cruise ships has become a highly diffused practice as it allows to cut vessels emissions and comply with the always more stringent regulations on ships air pollution. This research draws upon the Port of Civitavecchia, one of the world’s most visited from cruise ships, thus calling for the necessity of adopting the appropriate infrastructure. The harbour’s grid is only supplied by Medium and Low Voltage Substations and has a total capacity of nearly 9.3 MW and is not enough to satisfy large cruise ships hotelling power demand which can reach up to 11 MW or more. This study suggests as a solution to the above-mentioned issue, implementing an Energy Storage System (ESS) and increasing the use of local Renewable Energy Sources (RES). Moreover, the ESS besides providing the necessary power to cruise ships at berth, if properly sized, it can also improve the port’s grid reliability. Doing so, enhances a more efficient use and contribution of the local RES. This research first introduces the practice of Cold Ironing for cruise ships. It will focus on the systems infrastructure requirements and the reasons behind its increased popularity. It follows an analysis of the port of Civitavecchia MV power capacity and infrastructure. This study proposes the implementation of ESS and RES as a solution that allows a sustainable provision of electricity from shore to cruise ships at berth. Finally, an evaluation of the possibility of embedding ESS and new RES in the port's current electrical infrastructure is performed.
Neural Network Applications: A Literature Review

Abstract : Neural networks have been applied to a very broad range of tasks in many different disciplines. The focus of the discussion is on those areas closely related to potential applications in the Air Force personnel system. Similarities between the neural network applications and existing or potential personnel problems were described. Results of other researchers were examined in areas with problems similar to the personnel system and their specific architectures discussed. Artificial networks, Neural networks, Personnel research.
A Classification of Augmented Reality Approaches for Spatial Data Visualization

The field of Augmented Reality (AR) has seen a rapid expansion within the past decades, with many sectors nowadays applying AR methodologies for their own particular visualization needs. One field in particular that has adopted AR visualization techniques is the geospatial sciences, as they have identified the inherent spatial nature of AR as particularly applicable for visualizing (geo)spatial relationships in systems of interest. However, while we can find multiple classification schemes for AR applications in AR literature, to our knowledge no such classification exists that focuses specifically on the spatial aspects of AR applications, namely the visualization size, scale, and acknowledgement of and alignment with the user’s physical environment. Therefore no AR classification exists that is specifically applicable to the geospatial sciences. In this paper we present an initial classification of AR approaches for spatial data visualization, highlighting the different spatial characteristics that are of particular relevance to the geospatial sciences. We expect that the initial classification presented here will help researchers working with AR visualizations in organizing their application design with regard to its spatial characteristics.
Coverage Enhance in Boundary Deployed Camera Sensor Networks for Airport Surface Surveillance

Unlike the conventional coverage issue in camera sensor networks, the airport surface surveillance brings new challenges for achieving the desirable coverage. One of the biggest challenges comes from the deployment of camera sensors, i.e. due to the consideration of safety, the cameras are always installed on the land-side facilities in airports. In this paper, we propose a coverage enhancing method by deploying additional cameras for the boundary deployed heterogeneous camera networks. We convert the area coverage problem to maximize the barrier coverage in the area of interest by a boundary deployed camera network. The proposed method is divided into three phases: detecting the uncovered gaps in barriers; finding the optimal positions for deploying additional cameras; and optimizing the orientation of the additional cameras to cover the uncovered gaps. The simulation results confirm that our algorithm has better performance in coverage improvement and additional camera usage compared with other related methods.
Optimal Planning Strategy for Electric Vehicle Charging Station integrated with Battery Backed Solar Photovoltaic System in Distribution Network

This study presents a planning methodology for embedding electric vehicle charging stations (EVCS) integrated with battery backed solar photovoltaic distributed generators (BBSPVDG) in the 33 bus radial distribution system (RDS) using a combinational multi-objective function based on priori method. To solve the problem, the genetic algorithm (GA) and whale optimization algorithm (WOA) were used. The findings indicate that when an energy management strategy is correctly implemented, significant technological and economical gains can be realised. Additionally, the study demonstrates that combining solar photovoltaic (SPV) and battery energy storage systems (BESS) with EVCS decreases line losses and improves the voltage profile of the system.
Assisting Patients of Contagious Disease

Recently it is observed that the research advancement is very significant in the field of deployable robots for healthcare sector. Considering the current pandemic situation where social distancing is must- healthcare robots can be deployed very easily to perform the manual task resulting in reduced human efforts. In this paper, the design and implementation of an affordable, versatile system is proposed. The system is IoT enabled to meet the current demand and it can be controlled from any platform. The prototype can follow through lines and recognize hospital beds, can autonomously distribute medicines, record patient vital signs, and also has obstacle avoidance capabilities. There's also a 5-DOF arm, which is controllable over wireless medium by hand gestures. The IoT website can control the robot, record medicines, and view patient vitals' data. This work is intended to leave an impact on the healthcare sector of developing countries.
Recommendation of Product Value by Extracting Expiry Date using Deep Neural Network

The quality of food products plays a vital role in maintaining the standards of human health. A large quantity of expired food products is thrown away as it is not safe to consume. Proper labeling of expiry dates ensures both the safety and the quality of food. In most of the packaged food products, the manufacturing date of the product is available along with the shelf-life period of the product. In this paper, we propose an artificial intelligence-based approach to extract and perceive the expiry date in packaged food for estimating its remaining shelf life by fetching the relevant fields from the inventory database. Based on the remaining shelf-life discount rates are calculated for the food products to promote the sales with attractive discount rates for the consumer and reduced food wastage for the seller. The proposed approach performs two-level processing for date recognition from the product's scanned images. Initially, it performs object detection using the Single Shot Detector MobileNet (SSD MobileNet) model to extract the boundaries of the date from the scanned image. Following the extraction of date, the text in it is recognized using the Attention OCR model. On extensive evaluation, the proposed method of date extraction shows better performance than other models with nearly 3% improved accuracy.
Simulation of a continuous lignite excavation system

A discrete-event simulation model using the GPSS/H simulation language has been developed for a excavation system at a multi- level terrace mine. The continuous excavation system consists of five bucket wheel excavators and a network of 22 km of belt conveyors. Ways of dealing with the continuous material flow and frequent changes of material type are considered. The principal model output variables are production and arrival rate at the transfer point of mineral and waste. Animation and comparison with previous production data have been used to validate the model. 14 refs., 6 figs., 1 tab.
Design and Performance Analysis of Optimized Fractional Order PID Controller for Magnetic Levitation System

Incorporation of fractional order calculus in conventional integer order control system design algorithm, makes the existing control system more efficient & robust. Fractional order PID (FOPID) controllers possess two additional design parameters to better adjust and control the dynamical properties of a complex control system. The objective of this work is to compare the performances of FOPID with the integer order PID (IOPID) while controlling the position of a metallic ball in a Magnetic Levitation System (MAGLEV), which is unstable & highly non-linear system. To explore a suitable control option, the parameters of both FOPID & IOPID have been optimized by three different metaheuristic optimization algorithms namely, Bird Swarm Algorithm (BSA), Elephant herding Optimization (EHO) & Grey wolf Optimization (GWO). The simulation & real time results show that optimized FOPID controllers exhibit better transient and steady-state responses than conventional IOPID controllers irrespective of optimization methods used for tuning. It can also be concluded that GWO based control algorithm performs better than other two algorithms.
Automatic Diseases Detection and Classification in Maize Crop using Convolution Neural Network

Maize is a major crop in Pakistan and it plays an important role in the economy of the country. However different types of plant diseases could affect both the quality and quantity of maize production. To cope with such issues, the majority of the farmers still depend on traditional methods, which are expensive, time-consuming, laborious, and not very effective. To address the issues, we proposed a Convolution Neural Network (CNN) based solution for the detection and classification of different types of maize diseases. We used a publicly available free dataset of 4000 images. The images were classified into four categories. The first three categories represent the Common rust, Cercospora leaf spot grey, and Northern leaf blightand diseases, while the last category represents normal leaves. To test, implement, and evaluate the performance of our proposed method, we used a MATLAB simulation environment. We also compared our results with two other solutions, available in the literature. Our solution achieved 96.53 % accuracy. From the results, we concluded that the proposed method could be used for the automatic detection and classification of different types of maize diseases.
Switch Fault Detection and Localization for T-Type Converter

Over the past decade, multilevel converters have received considerable interest in medium voltage applications. A large number of active switches in a multilevel converter increases the probability of switch failure and decreases the reliability of the system. An open switch fault can lead to a malfunction of the whole system and should be diagnosed as soon as possible. In this paper, an open-switch fault diagnosis method for a five-level H-Bridge T-type converter is presented. The proposed fault detection method and the fault localization algorithm are based on the switching patterns and the converter output voltage level observation. This approach is capable to detect and localize an open circuit fault for all switches. First, it is validated by simulations in Matlab Simulink and Simscape toolbox environments. Experimental results are also presented and discussed to validate the simulation results.