Miracle-dz/UniFAR-FTUs huggingface dataset:
Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.
A Novel Microcontroller-Based System for the Wheel-Running Activity in Mice

Abstract Voluntary wheel-running activity is a way to assess rodents’ circadian rhythm and motivation for exercise. Deficits in these behaviors are implicated in the pathophysiology of sleep and psychiatric disorders. Limited space in animal facilities can hamper long-term monitoring of running wheel activity outside of the home cage. To address this issue, we provide a stand-alone solution to monitor the wheel-running activity of mice in their home cage. This system, named the wheel-running activity acquisition (WRAQ) system, is based on a microcontroller driven by a lithium polymer battery. With the WRAQ, we can record the wheel-running activity and illumination data for at least 30 d. Applying the WRAQ to an endotoxemia mouse model robustly detected the altered wheel-running activity and its recovery. With wireless data transfer capability extension, the system also allows for online monitoring and reporting of the circadian time (CT). We used the online monitoring of wheel-running activity with this extended WRAQ system and observed a significant shift of the active period in the circadian rhythm following a temporal chemogenetic activation of the suprachiasmatic nucleus (SCN)-subparaventricular zone (SPZ). Together, these findings indicate that the WRAQ system is a novel and cost-effective solution for the analysis of wheel-running activity in mice.
Estimating the private value of industry-specific human capital from a dynamic portfolio choice model

Using CES earnings data, I estimate the private, risk-adjusted value of industry-specific human capital for employees in 72 industries. For each industry, I obtain the certainty equivalent income, which renders an individual indifferent between receiving and consuming this income and implementing the optimal consumption and portfolio choice decisions that solve the average Euler equations for employees in that industry. Depending on the industry of employment, every dollar initially earned is worth between 86 and 99 cents, when adjusted for the risks in industry-specific per-capita income growth, and the potential to hedge these risks with investments in the aggregate stock market. Inter-industry differences in certainty equivalent income per dollar of initial earnings can be explained by cross-sectional variation in the time series moments of the joint distribution of income growth and stock return. Cokurtosis, the tendency to generate left skewness in per-capita income growth in recessions, is as important as correlation.
Individual-based distributed epidemic spread model

This paper applied the theory of distributed computing and designed an individual-based distributed epidemic spread model according to the traditional SEIRS model. In the proposed spread model, microcosmic individuals interact and infect with each other through the spatial contact and social relationship contact, and influence the macrocosmic epidemic spread. The whole region is separated into the same regular sub-regions with quadtree coding, and the social relationship is classified into different types. Individuals were classified into corresponding groups according to the spatial locations or the social relationship types. The final region combination was implemented with quadtree decoding. The processes of interactions between different sub-regions and relationship types were emphasized. Every single step of distributed computing included the reseparation and re-combination of sub-regions, and the boundary process of sub-regions. The simulation experiment showed the proposed distributed model could save 80% and 50% runtime in spatial spread and relationship spread than single computing model approximately.
RT-LPDRnet: A Real-Time License Plate Detection and Recognition Network

Benefiting from the rapid development of deep learning, the accuracy of license plate (LP) detection and recognition has been greatly developed. However, the low computing power of embedded devices is difficult to complete this task in real-time. To tackle this problem, we propose a real-time license plate detection and recognition network in this paper. The detection head we designed can detect the bounding box and four corner points of the license plate, and then we apply the ROIAlign method to extract the features on the same backbone in order to perform license plate recognition. The resulting architecture, called RT-LPDRnet, outperforms all the SOTA methods on the large-scale license plate data set Chinese City Parking Dataset (CCPD), meanwhile with faster inference time than recent methods. Our code will be publicly available.
Multiple Biases-incorporated Latent Factorization of Tensors for Dynamic Network Link Prediction

The topological information of a dynamic network varies over time, making it crucial to capture its temporal patterns for predicting missing links accurately. A latent factorization of tensors (LFT)-based model has proven to be efficient to solve this problem, where a dynamic network is represented as a three-way high-dimensional and sparse (HiDS) tensor. However, currently LFT-based models do not consider multiple biases in analyzing an HiDS tensor for accomplishing dynamic link prediction. To address this issue, this paper proposes a multiple biases-incorporated latent factorization of tensors (MBLFT) model, which integrates short-term bias, preprocessing bias and long-term bias into an LFT model. Empirical studies on two large-scale dynamic networks from real applications show that compared with state-of-the-art predictors, an MBLFT model achieves higher prediction accuracy and computational efficiency for missing links in dynamic network.
Deep Multi-scale Recursive Residual Attention Network for Spectral Super Resolution

The Spectral super-resolution methods based on deep learning have achieved promising performance when the network models are well trained, most of which require sufficient training dataset. However, capturing enough paired color image and hyperspectral images is expensive or complex under existing conditions. Thus, the available training dataset is still small to some extent, which is prone to over-fitting as the network becomes deeper. To solve this problem, a novel method based on deep network is proposed for spectral super-resolution, which is named as multi-scale recursive residual attention network. Without increasing network parameters, the recursive learning can increase the depth and receptive field of network to strengthen nonlinear modeling capabilities. In addition, multi-scale residual attention block is proposed to exploit contextual information in different scale and adjust channel-wise features adaptively. Experiments on public hyperspectral datasets demonstrates that our method can achieve better reconstruction effects compared with other spectral reconstruction methods when there is small training dataset.
An Intelligent 3D Printing Path Planning Algorithm 3D Printing Path Planning Algorithm: An Intelligent Sub-Path Planning Algorithm

The path planning of large industrial parts in additive manufacturing directly affects manufacturing quality and efficiency. At present, the commonly used traditional path planning methods have many problems, such as turning and stacking of print head and the number of rise and fall of the print head, so they are not suitable for large industrial parts. Therefore, an intelligent path planning algorithm is proposed. Firstly, the two-dimensional plane obtained after slicing is concavely convexly decomposed into printing sub-areas, and then the sub-partition connection is treated as a traveling salesman problem (TSP) to reduce the number and length of printing paths. By comparing with traditional path planning algorithms, the proposed method significantly reduced the number of paths, the idle travel distance, and the number of print head lifts. The sub-partition-based intelligent path planning method provides a new idea for the additive manufacturing process of large industrial parts.
Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study

The present article explores the impacts of climate change on precipitation at station scale in the Minab basin, Iran. The data used for evaluation were large‐scale input (predictor) parameters extracted from the reanalysis data set of the National Center for Environmental Prediction and National Center for Atmospheric Research to downscale monthly precipitation. In this research, four approaches were applied to downscale precipitation, including an implementation on extreme learning machine (ELM) for single‐hidden layer feedforward neural network, artificial neural network, genetic programming, and quantile mapping. The results indicated that the ELM approach outperformed all other approaches in downscaling the large‐scale global climate model atmospheric variables to monthly precipitation at station scale. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1853–1862, 2018
THE ROLE OF AGILE SOFTWARE ARCHITECT IN THE AGE OF DIGITAL DISRUPTION AND TRANSFORMATION

: The consequence of the increasing development and use of digital technologies, in every segment of society, is the emergence of digital disruption - a powerful external pressure that is changing the way business is done in all industries. Businesses are responding to digital disruption by digital transformation, which involves organizational change, redefining and align - ing digital and business strategies, new business models, increased agility of software develop - ment and delivery processes, migration and/or integration of legacy systems using cloud-based platforms and ecosystems. In such a context, one of the key responsibilities of a software architect is to maintain the agility of the organization by defending the flexibility of digital strategy and IT resources so that the enterprise is able to transform and respond adequately and rapidly to the effects of digital disruption. In this regard, the question arises as to how digital disruption and business transformation affect the change in the role, importance, competence and agility of a software architect, especially in the context of the development of complex business software systems. This paper aims to present the role of an agile software architect in the era of digital disruption and transformation, by integrating the results of theoretical and empirical research. A systematic literature review identifies the role, importance, and competencies of a software architect in implementing agile architecture. In other hand, empirical research, based on a case study in a large enterprise, provides a better understanding of the importance of software archi - tect for aligning business and digital strategy, as well as its contribution to increasing the agility of the process of developing, delivering and integrating complex business software systems.
SENTIMENT ANALYSIS USING N-GRAM ALGO AND SVM CLASSIFIER

: The sentiment analysis is the technique which can analyze the behavior of the user. Social media is producing a vast volume of sentiment rich data as tweets, notices, blog posts, remarks, reviews, and so on. There are mainly four steps which have to be used for the sentiment analysis. The data pre-processing is done in first step. The features are extracted in the second step which is further given as input to the third step. For the sentiment analysis, data is classified in the third step. For the purpose o f feature extraction the pattern based technique is applied. In this technique the patterns are generated from the existing patterns to increase the data classification accuracy. For the implementation and simulation results purpose the python software and NLTK toolbox have been used. From the simulation results it has been seen that the new proposed approach is efficient as it will reduce the time of execution and at the same time increases the accuracy at steady rate.
EFFECT OF LEAD AND CADMIUM IN IRRIGATION WATER AND FOLIAR APPLIED MALIC ACID ON VEGETATIVE GROWTH, FLOWERING AND CHEMICAL COMPOSITION OF SALVIA SPLENDENS PLANTS (B) EFFECT OF CADMIUM

Received: 4/11/2020 Accepted: 14/12/2020 ABSTRACT: The present study was carried-out at Antoniadis Botanical Garden Research Branch, Horticultural Research Institute, A.R. C. Alexandria, Egypt during the two successive seasons of 2018 and 2019. The aim of this study was to evaluate the effects of irrigation water contaminated with cadmium on Salvia splendens plants grown in a sandy soil, for possibilities of using malic acid spray treatments to overcome the effects of lead pollution. Seedlings of Salvia splendens were individually planted in plastic pots (20 cm diameter) filled with 5 kg of sandy soil. Four concentrations of cadmium 0,100, 200 and 300 mg/l were applied in the irrigation water. The plants were sprayed with malic acid at concentrations of 0, 250 and 500 mg/l at monthly internals in both seasons. The results showed that for vegetative and flowering growth parameters, there was no significant interaction between cadmium concentrations and foliar spray by malic acid, while significant reduction were observed in all parameters after irrigation with contaminated water with cadmium and significant increase in vegetative and flowering growth parameters were observed after 250 mg/l malic acid application. For chlorophyll and carbohydrate content the significantly highest value was obtained from plants irrigated with tap water and sprayed with 500 mg/l malic acid while the significantly highest cadmium content in leaves, stem and roots was obtained due to the treatment of 300 mg/l without application of malic acid.
Assessing the Impact of Increased Effluent Discharge into Cape Cod Canal

The Town of Wareham is considering a significant expansion of its wastewater treatment facility (WWTF) that would enable it to serve additional neighborhoods in Bourne, Plymouth, and Wareham. Currently, the treated effluent from the Wareham WWTF is discharged into the Agawam River, a shallow (order 1‐m mean depth at high water) branch of the Wareham River. As part of the plan to expand its WWTF, the Town of Wareham is considering relocating its effluent outfall to the western end of Cape Cod Canal, at or near the site of the sewage outfall of the Massachusetts Maritime Academy (MMA; Figure 1). This is deemed to be a more suitable location for effluent discharge than the Agawam River due to the vigorous tidal flows, and attendant tidal flushing, in the canal. The volume of water passing through the canal on a given flood or ebb tide is estimated to vary through the neap‐spring cycle over a range of 14‐20 billion gallons (see the Appendix for details), which equates to a daily through‐canal flow of roughly 56‐80 billion gallons (four times the volume passed on each ebb or flood tide).
Design and Development of Magneto Rheological Fluid Base Damper

Over the past three decades, a great deal of interest has been generated regarding the use of structural protective systems to mitigate the effects of dynamic environmental hazards, such as vibration, on mechanical engineering structures and automobile sectors for human comfort, earthquakes and strong wind, on civil engineering structures. These systems usually employ supplemental damping devices to increase the energy dissipation capability of the protected structure. One of the most promising new devices proposed for structural protection is magneto rheological (MR) fluid dampers. MR fluids possess rheological properties, which can be changed in a controlled way. These rheological changes are reversible and dependent on the strength of an excitation magnetic field. MR fluids have potentially beneficial applications when placed in various applied loading (shear, valve and squeeze) modes. Magneto rheological dampers, or as they are more commonly called, MR dampers, are being developed for a wide variety of applications where controllable damping is desired. MR fluid dampers have the capability of changing their effective damping force depending on the current input to the damper. These applications include dampers for automobiles, heavy trucks, prosthetic limbs, gun recoil systems, bicycles, and possibly others related to mechanical discipline like brake, clutch etc. Magneto rheological (MR) fluid dampers having very good mechanical simplicity, high dynamic range, low power requirements, large force capacity, and robust ness, this class of devices has been shown to mesh well with application demands and constraints to offer an attractive means of protecting infrastructure systems. This work issues the design and analysis of the linear magneto rheological damper. Basic information concerning the characteristics of the typical magneto rheological fluid and the damper incorporating it, were presented with the detail description of the applied fluid developed in our premises. With reference to the computations, the prototype damper was designed, manufactured and tested under different operating conditions. Performed calculations were verified with the experimental results and their accuracy was evaluated. The conclusions and observations from the research were compiled in the summary.
Willingness to Pay Analysis of First Fuel in Yogyakarta

This study aims to measure the value of willingness to pay and analyze the factors that affect consumer willingness to pay toward Pertamax fuel. This study's dependent variable is income, number of owned vehicles, the frequency of vehicle use, and product literacy. In this study sample of 100 Pertamax fuel users in Yogyakarta were selected using the Purposive Sampling method. This study using multiple linear regression or Ordinary Least Square (OLS). The results show that the value of consumer willingness to pay toward Pertamax fuel is Rp. 10,545. The factors that influence willingness to pay are income, frequency of vehicle use, and product literacy. These three variables are positive and have a significant influence on consumer willingness to pay toward Pertamaxfuel. While the variable number of the owned vehicle.
Advanced analysis in epidemiological modeling: Detection of wave

Some mathematical concepts have been used in the last decades to predict the behavior of spread of infectious diseases. Among them, the reproductive number concept has been used in several published papers for study the stability of the spread. Some conditions were suggested to predict there would be either stability or instability. An analysis was also suggested to determine conditions under which infectious classes will increase or die out. Some authors pointed out limitations of the reproductive number, as they presented its inability to fairly help understand the spread patterns. The concept of strength number and analysis of second derivatives of the mathematical models were suggested as additional tools to help detect waves. In this paper, we aim at applying these additional analyses in a simple model to predict the future. Keywords: Strength number, second derivative analysis, waves, piecewise modeling.
Effect of Amlodipine Combined with N-acetylcysteine on Crohn’s Disease Model in Rats

This study highlights the effect of Amlodipine combined with N-acetylcysteine on Crohn's disease model in rats. Model of Crhon's disease was induced by subcutaneous Indomethacin administrated at a dose rate of 9 mg/kg for two days at 24h intervals. N-acetylcysteine (500mg/Kg), Amlodipine (10 mg/kg) and N-acetylcysteine (500 mg/kg) combined with Amlodipine (5mg/kg) were administrated for seven consecutive days beginning 24 h after the first Indomethacin injection. The small intestinal injury was assessed by small intestine weight/length ratio, macroscopic damage, histological study, as well as by biochemical measurement of reduced glutathione, lipid peroxides, and superoxide dismutase activity. The results showed that Administration of Amlodipine combined with N-acetylcysteine decreased small intestine weight/length ratio, macroscopic and microscopic small intestinal damage scores. Superoxide dismutase activity was increased; lipid peroxidation was decreased, but reduced glutathione levels were decreased in the combination group. No statistical significance was observed when previous findings were compared with Indomethacin control group (p>0.05). This study also showed that administration Amlodipine combined with N-acetylcysteine significantly decreased macroscopic score comparing with N-acetylcysteine administration alone. Conclude that combination of Amlodipine with N-acetylcysteine didn't produce obvious enhancement in the intestinal injury induced by Indomethacin (rat model of Crhon's disease), but this combination attenuated the injury comparing with N-acetylcysteine alone in this model of Crhon's disease.
DESKTOP BASED APPLICATION FOR NUMBERPLATE AND LICENSE RECOGNITION USING DEEP LEARNING

: In this research, we look at detecting and recognizing car license plate’s problem in natural scene photographs. In a single forward transfer, here We introduce a deep neural network that could simultaneously locate license number plates and identify letters. The entire network could be trained from start to end. Unlike current techniques, which consider license plate detection and identification as two separate tasks that must be solved one at a time, our system incorporates the two tasks into one network that can solve them both simultaneously. It not only prevents intermediate errors from building up