Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrimination between Atlantic Salmon (Salmo salar) of North American and European Origin using Restriction Analyses of Mitochondrial DNA

Twenty restriction endonucleases were used to study mitochondrial DNA (mtDNA) polymorphism in 11 hatchery strains of Atlantic salmon (Salmo salar) representing geographically separated populations in Europe and North America. The North American salmon mtDNAs studied were readily distinguished, by a minimum of seven restriction site differences, from fish of European origin. These results suggested that restriction analyses of mtDNA might provide a useful method for determining the proportions of European and North American Atlantic salmon caught in the West Greenland fishery. To test this proposition, we analyzed 328 salmon caught in the 1987 West Greenland fishery including 68 fish with coded wire or Carlin tags which provided the geographic source of the tagged salmon. We correctly identified the continent of origin for 67 of the 68 physically tagged salmon using two informative restriction endonucleases. This study provides a clear indication of the usefulness of mtDNA for discriminating between Europe.
Development and validation of a predictive index of elder self-neglect risk among a Chinese population

Abstract Objective To develop a predictive index that estimates the individual risk of incident self-neglect onset among the US Chinese older adults. Methods The study used two waves of longitudinal data from 2713 participants of the Population Study of Chinese Elderly (PINE). Data were collected during 2011–2015 in Chicago, Illinois, with approximately 2-year follow-up intervals. The main outcomes are incident self-neglect cases. Variables in 14 potential predictive domains were considered, which are (1) sociodemographic/socioeconomic, (2) neighborhood/community, (3) immigration and acculturation, (4) adverse events, (5) culture, (6) general wellbeing, (7) health behavior, (8) medical health, (9) health care, (10) physical function, (11) cognitive function, (12) social wellbeing, (13) violence, and (14) psychological wellbeing. Stepwise selection in multivariable logistical regression models and bootstrapping were used to develop and validate the predictive index. Results The 2-year self-neglect incidence rate was 237 (8.7%). A 19-item predictive model (with a c-statistic of 0.74) was developed. After correcting for overfitting by validating in 100 bootstrapping samples, the model demonstrated moderate predictive accuracy by a c-statistic of 0.68. A point-based risk index was developed and has an area under the receiver operating characteristic curve of 0.73. Discussion The study developed an efficient index with a moderate-to-good predictive ability of self-neglect. With further external validation, modification, and impact studies, the index could be a culturally relevant tool for practitioners to quantify the risk of self-neglect among the US Chinese older population.
A model of the spring migration into the North Sea by Calanus finmarchicus overwintering off the Scottish continental shelf

A particle tracking model was used to investigate the annual spring invasion of the North Sea by Calanus finmarchicus copepodites which overwinter in deep water off the Scottish continental shelf. Flow fields generated by a hydrodynamic model (HDM) were used to simulate the advection of zero drag particles representing the copepods. Particles were released simultaneously from a regular lattice of start positions at a given depth (D1), and ascended at a fixed rate (dD/dt) until they reached a final depth (D2) in the surface layers. The proportion of particles reaching target areas in the northern North Sea was relatively insensitive to a moderate degree of variation (±20%) around chosen default values of the vertical migration parameters (D1, D2 and dD/dt), derived from field data. The inclusion of horizontal diffusion velocities and diel vertical migration in surface layers did not significantly affect the results. Sensitivity to wind direction was investigated by applying flow fields from HDM runs with different wind forcing scenarios. For the default vertical migration parameters, only north-westerly winds resulted in particles entering the North Sea from release locations north of the Iceland–Scotland Ridge, where dense aggregations of overwintering copepods were encountered during field surveys. The particle tracking model predicted that the major routes for the spring Calanus invasion into the North Sea were the East of Shetland Atlantic Inflow and the Norwegian Trench Atlantic Inflow, which agreed with seasonal trends observed in Continuous Plankton Recorder data. Overall, despite its relative simplicity, particle tracking was confirmed as a robust tool to explore the causal mechanisms behind the annual invasion of the North Sea by C. finmarchicus emerging from diapause in the deep waters off the Scottish continental shelf.
A DDoS Attack Detection Method Based on Information Entropy and Deep Learning in SDN

Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment.
Relationships of Free Lipids with Quality Factors in Hard Winter Wheat Flours

ABSTRACT Hard winter wheat (Triticum aestivum L.) flours (n = 72) were analyzed for free lipids (FL) and their relationships with quality parameters. The two main glycolipid (GL) classes showed contrary simple linear correlations (r) with quality parameters. Specifically, kernel hardness parameters, flour yields, and water absorptions had significant negative correlations with monogalactosyldiglycerides (MGDG) but positive correlations with digalactosyldiglycerides (DGDG). MGDG showed negative correlations with gluten content but positive correlations with gluten index. The percentages of DGDG in FL had significant positive correlations among cultivars (n = 12) with mixograph and bake mix times (r = 0.71, P < 0.01 and r = 0.67, P < 0.05, respectively), mixing tolerance (r = 0.67, P < 0.05), and bread crumb grain score (r = 0.71, P < 0.01). These results suggest that increasing DGDG in FL could contribute to enhancing wheat quality attributes including milling, dough mixing, and breadmaking quality charact.
The molecular biology of plant acclimation to low temperature

In many temperate plant species, a period of exposure to a low positive temperature will acclimate the plants to withstand a subsequent freezing stress. A number of plant genes, which are up-regulated at steady-state mRNA levels by an acclimation treatment, have been isolated from both monocotyledon and dicotyledon species. Most of these genes are also responsive to a drought treatment and/or abscisic acid. The acclimation of plants to freezing stress is a complex process and although genetic studies in some species have identified genes with a major effect, in general, the inheritance of frost tolerance is multigenic. In view of this, it is not surprising that a range of different genes have been cloned. The precise function of the proteins encoded by these genes is unknown. However, analysis of predicted protein products and studies of recombinant proteins, together with detailed expression studies, are beginning to provide information about some of the genes. Both transcriptional and post-transcriptional controls have been shown to be involved in the expression of these genes. Although studies of RNA stabilizing systems are still in their early stages, a number of low temperature responsive promoters have been studied using reporter gene constructs. Other approaches to the molecular analysis of cold acclimation include the isolation of non-acclimating mutants and the production of transgenic plants.
High-Power Filter Design in Waveguide Technology: Future Generation of Waveguide Satellite Filters in Payloads Handling Increasing Bit Rates and Numbers of Channels

To design a filter for a particular application, many issues must first be considered. Which technology will be the most convenient? What design technique will provide better results for a particular set of frequency specifications? Once the device has been designed, will it fulfill all of the (not only electrical) requirements? It is not always easy to answer such questions in advance. In this article, we try to shed some light on these questions when our aim is the design of filters for high-power operation.
Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

While collaborative filtering (CF) is the most popular approach for recommendation systems, it only makes use of the ratings given to items by users and neglects side information about user attributes or item features. In this work, a natural language processing (NLP) technique is applied to generate a more consistent version of Tag Genome, a side information which is associated with each movie in the MovieLens 20M dataset. Subsequently, we propose a 3-layer autoencoder to create a more compact representation of these tags which improves the performance of the system both in accuracy and in computational complexity. Finally, the proposed representation and the well-known matrix factorization techniques are combined into a unified framework that outperforms the state-of-the-art models by at least 2.87% and 3.36% in terms of RMSE and MAE, respectively.
How Satellite Soil Moisture Data Can Help to Monitor the Impacts of Climate Change: SMAP Case Studies

Socially and economically costly extreme weather events have become more prevalent in the last decade. Monitoring and early warning systems could help mitigate the impact of such events by allowing people to better prepare themselves to manage their responses to these events. One significant element of an effective warning system is soil moisture because it is a key determinant of the exchange of water and heat energy between the land and atmosphere, the partitioning of precipitation between infiltration and runoff, and therefore has an influence on weather patterns and streamflow. In addition, soil moisture governs plant water availability – the key to crop yield forecasting. For these reasons, a wide range of organizations use soil moisture information to better predict and monitor climate and weather phenomena such as floods and droughts. By improving soil moisture estimates, it may be possible to improve the monitoring and early warning systems upon which these organizations rely, and hence better mitigate the impacts of extreme weather events. Through case studies, this article discusses several uses of soil moisture data products from NASA's Soil Moisture Active Passive (SMAP) mission to help improve soil moisture-related monitoring and early warning systems.
THE INTERACTION OF HIGHER PLANTS AND SOIL MICRO-ORGANISMS: I. MICROBIAL POPULATION OF RHIZOSPHERE OF SEEDLINGS OF CERTAIN CULTIVATED PLANTS

The microbial population in the rhizosphere of wheat, oats, alfalfa, and peas was studied and the relative abundance of different types of micro-organisms recorded. By means of the plating method it was found that bacteria and actinomycetes were 7 to 71 times greater in the rhizosphere than in the soil distant from the roots, whereas fungi were but 0.75 to 3.1 times more numerous.Different varieties of plants affected the activity of the various groups of soil micro-organisms differently. Thus fungi were more numerous in the rhizosphere of oats, and bacteria in the rhizosphere of alfalfa. Seventeen genera of fungi were represented in isolates from the rhizosphere of seedlings and the soil distant from the roots. However, no marked difference was observed in the types isolated from the rhizosphere of different varieties of seedlings. A fungus isolated from the rhizosphere of alfalfa proved to be a new species and the genus Spicularia Persoon was amended to include it.
An Overview of the MPEG-5 Essential Video Coding Standard [Standards in a Nutshell]

Since the 1970s, various image and video coding techniques have been explored, and some of them have been included in the video coding standards issued by the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) Motion Pictures Expert Group (MPEG) and International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) Video Coding Experts Group (VCEG). MPEG is the most successful standards development organization (SDO) for multimedia compression standardization. In particular, most of the widely deployed video coding standards of the past 30 years have been developed within this working group. One of the first, standardized in 1996, was the MPEG-2 standard [1], which is still in use as a digital TV standard in many countries.
A Model for Tracking People and Property in Crowds

Getting lost or loosing personal belongings in a crowded event is a common problem whose frequency depends on the level of congestion of the crowding. There are many regular crowded evens where this problem persists and creates personal and social upheaval, which, if not resolved, may tarnish the image of the organizers and administrators. One such event is the annual pilgrimage of Hajj, which attracts millions of pilgrims from different parts of the globe to Makkah (Mecca) in Saudi Arabia. Although there are other regular events which attracts much more participants but none of them is as complex as the Hajj. Besides Hajj, Makkah also witnesses large crowding of pilgrims in the holy month of Ramadan. During these events, some of the pilgrims get lost and are unable to reach back to their places of stay. Many of the pilgrims loose their belongings including documents like passport etc. In this article we propose an integrated management model to solve this problem. The proposed model is based on a web system and a mobile application that use maps and location service, in addition to the use of smart IoT tools, especially RFID Tags and Readers for monitoring and tracking. The system would help individuals to search and retrieve their missing items from the service centers easily, or navigate them to the nearest center for reporting and claiming found items. The proposed model will add a new service to be provided by the event management to help the pilgrims in the sacred journey. This model can be adapted to any crowded event be it regular or irregular.
Model Compression Based on Knowledge Distillation and Its Application in HRRP

In High Resolution Range Profile (HRRP) target recognition approach, ultra-deep network, huge amount of parameter, and the time and space consumption for model deployment are the problems of conventional one-dimensional Convolutional Neural Network (CNN). To solve these problems, a model compression method based on knowledge distillation is proposed in this paper. In this method, a high accurate complex network is trained firstly, and then a conventional cross-entropy loss function and a loss function based on softening processing of output value is combined to build and train a simple network. Finally a lightweight and accurate recognition model is obtained. The experimental results of actual dataset show that the method proposed in this paper not only reduces the parameter amount and calculation consumption of the lightweight model greatly, but also approaches the recognition performance of the complex network.
Designing a Bangla Stemmer using rule based approach

Stemming is a preprocessing task for natural language processing that involves normalizing inflected words representing the same concept of the original word. Steaming is a process of text normalization that has many applications. There are many techniques for steaming of inflected words for different languages but very few works for Bangla word steaming. Therefore, stemming Bangla word is a unsolved problem. There are many different situations that can occur in Bangla language for word steaming. In this paper, we present a rule based algorithm to stem Bangla words. We developed the rules for infection detection for verb inflection (বিভক্তি), number inflection (বচন), and others. Using our rules, we developed a system to find the root word of Bangla words and found good performance. Sufficient examples are provided to explain the proposed system.
Handwritten Bengali Number Detection using Region Proposal Network

As a seventh most spoken native language, Bengali needs a robust and accurate optical character recognition (OCR) especially for number detection. As there is no publicly available well-organized dataset for Bangla number OCR, a synthesized dataset was generated to fill the lack of available data. The recent advancement in artificial intelligence using deep neural networks easily outperforms prior hand selected feature-based machine learning approaches. As the region proposal networks (RPN) in deep neural networks perform very well in detecting objects, it can be used for digit detection in an image. So, in this work a very robust Bengali handwritten number detection system is presented where with the help of deep neural networks and a very well-organized, unbiased generated dataset we achieved state of the art result in handwritten Bangla number detection. This system beats any related prior works by a large margin while considering a real world dataset for benchmarks. The overall detection accuracy was 97.8%. The processing can be done real-time with about 35 images per second using a GPU. Also, while implementing the solution is completely based on python, the framework used for deep learning is Google’s Tensorflow and the dependencies, all of which are publicly available.
Research on Network rumor spreading model Based on Thermal Radiation Mechanism

Exploring the law of the spread of online rumors is an important way to rumor governance. In the analogy of thermal radiation process, this paper constructs a thermal radiation model of network rumor propagation. First, an analogy analysis of the thermal radiation process and the rumor propagation process, and the parameters involved in the process of thermal radiation are analyzed and corresponding to various influencing factors in the process of Network rumor propagation. Then the UTS rumor propagation model is constructed. Finally, the experimental simulation results verify the rationality of the UTS model, which provides an important basis for network rumor governance.
Distributed Learning in the Nonconvex World: From batch data to streaming and beyond

Distributed learning has become a critical enabler of the massively connected world that many people envision. This article discusses four key elements of scalable distributed processing and real-time intelligence: problems, data, communication, and computation. Our aim is to provide a unique perspective of how these elements should work together in an effective and coherent manner. In particular, we selectively review recent techniques developed for optimizing nonconvex models (i.e., problem classes) that process batch and streaming data (data types) across networks in a distributed manner (communication and computation paradigm). We describe the intuitions and connections behind a core set of popular distributed algorithms, emphasizing how to balance computation and communication costs. Practical issues and future research directions will also be discussed.
Effect of Symmetrical Voltage Sag on Induction Motor Considering Phase-Angle Factors Based on a New 2-D Multi-Slice Time-Stepping Finite Element Method

Voltage sag is one of the most common power quality disturbance in industry, which causes huge inrush current and inrush torque of induction motors, and has serious impacts on the safe and stable operation of induction motor. To further study the detailed transient characteristics of induction motors during sag events, an improved 2-D multi-slice field-circuit-motion coupling time-stepping finite element method (FEM) is proposed. Then, taking a 5.5 kW, a 55 kW and a 135 kW induction motors as examples, the influence of phase-angle jumps and initial point-on-wave on the stator current peak, torque peak, speed loss and critical clearing time of the induction motor are discussed by using the proposed model. The simulation results show that stator current peak and torque peak increase obviously, while speed and critical clearing time decrease apparently, when considering the phase angle jump. And different initial points have little influence on these operation indicators. Finally, the proposed method is verified by comparing the measured and calculation data of the 5.5 kW three-phase squirrel-cage motor, respectively. The results show that the proposed method can reach higher precision with considering nonlinear characteristics.
A Study of Solar Method Azimuth

The antenna performance parameter of weather radar, an extremely significant part of radar constant, which directly influences the measurement accuracy of radar echo intensity. Therefore, antenna test always be considered as an important step in radar inspection and calibration. But, a high placement position required to the new generation weather radar and a iron tower installation needed to the far-field test, are always both brings great inconvenience in the regular or irregular examination of antenna. This paper discusses the method of testing weather radar antenna by using the sun as a signal source, relevant principles or methods in detail, and algorithm of solar optical angular diameter. Improves the solar method azimuth beam width correction algorithm through analyzing the phonomenon that the azimuth beam width changed with the solar elevation angle in solar method. Analyzes the measurement error and accuracy of the sun method by measuring a antenna of X-band weather radar in Chengdu University of Information Technology. The results show that the antenna beam width measurement becomes more stable and the measurement accuracy shows greatly improved by using the azimuth beam width correction algorithm in solar method. Compared with the antenna far-field test, the solar method presents the advantages of low cost, simple operation and high measurement accuracy, and has a wide application prospect in the field of weather radar antenna testing.