Miracle-dz/UniFAR-FTUs huggingface dataset:
The Performance Analysis of Downlink NOMA in LEO Satellite Communication System

Non-orthogonal multiple access (NOMA) has the potential to provide higher throughput than conventional orthogonal multiple access (OMA), which has been considered as a key technology for 5G. NOMA in satellite communication system can provide anytime, anywhere access with improved spectral efficiency and system capacity because of its ubiquitous coverage. However, the characteristics of the satellite channels are different from that of the terrestrial network, i.e., huge time delay and Doppler shift. In this paper, different from the existing works, which mainly focus on the performance of NOMA in static terrestrial base stations and Geostationary orbit (GEO) scenarios, we investigate the performance analysis of downlink NOMA in dynamic low earth orbit (LEO) satellite communication system with Doppler shift considered. We combine NOMA and orthogonal frequency division multiplexing (OFDM) for better spectral efficiency. Our channel model includes both small scale model and large scale model. For simplify, we only consider two users in one spot beam. Besides, we express the performance analysis of downlink NOMA in LEO satellite communication system, like ergodic capacity, outage probability (OP) and mutual information. However, in traditional NOMA scheme, the proceeding error decision of the high power user will cause the deterioration of the subsequent detection performance. Therefore, a symmetrical coding (SC) scheme for different modulation mode is proposed for low power users to get better performance. Finally, simulation results validate the performance of the NOMA scheme is better than that of the OMA scheme. The proposed SC scheme can achieve a prominent increase performance contrasted to the traditional NOMA scheme.
Epidemiological Assessment of Hormesis in Studies with Low-Level Exposure

Despite its resurgence within toxicology and, specifically, risk assessment, the concept of hormesis remains peripheral to current epidemiological practice. In this paper we examine some reasons for this, focusing on applications within occupational and environmental epidemiology. Unclear in the existing literature is whether hormesis pertains to a single biological mechanism or response, or the aggregate effect of all correlates of exposure. Although J-shaped and U-shaped relationships between risk factors and disease endpoints have been identified epidemiologically, it is unclear whether such patterns reflect biological hormesis or a combination of factors resulting in a hormetic-looking relationship. Given the potential importance of assessing hormetic responses in epidemiological studies, we identify and discuss key limitations of epidemiology in validly detecting and interpreting hormesis. For example, most observational occupational and environmental studies lack the ability to determine the dose received by each individual, and therefore poor surrogates of exposure are frequently used, potentially introducing considerable systematic and random error. Further, because exposure is not randomly assigned to humans, the potential for confounding is great. Finally, using a simple simulation to assess the impact of ignoring hormesis in the analysis of epidemiological data containing mild hormesis, we demonstrate a resulting “hormetic bias,” in which relative risks at exposure levels above the hormetic region are systematically overestimated.
Cytofluorimetric Method for the Detection of the Cucumber Mosaic Virus

This study describes a method for the detection of cucumber mosaic virus (CMV) by flow cytometry. Extracts from leaves of healthy and virus-infected plants were incubated with latex particles, washed, and then incubated in succession with rabbit anti-CMV antibodies and anti-rabbit immunoglobulin antibodies labeled with fluorescein. Adsorption of CMV virions on the latex particles allowed the virions to become visible to the laser of the cytometer. When the threshold value for virus detection was set at three times that of healthy controls, the detection limit of the cytofluorimetric method was 10 pg/ml of purified virions and that of the enzyme-linked immunosorbent assay (ELISA) was 2.5 ng/ml. The measuring range of the assay described here was 5 ng to 10 pg of purified virions and that of ELISA was 20 to 2.5 ng. The cytofluorimetric method detected the coat protein of CMV in the extract of two transgenic plants at a dilution of 10 -3 , while ELISA detected the viral protein at a dilution of 5 x 10 -2 . The assay was also used to detect plum pox potyvirus and potato virus Y, alone as well as concurrently with CMV.
IoT Device to Detect Anemia: A Non-Invasive Approach with Multiple Inputs

Detection of diseases is a key aspect when considering the world today. Diseases if not cured early it gets worse day by day. Therefore, early detection of the disease is a key aspect that needs to be considered. In this project, the team aims to develop a device to detect Anemia which is a disease caused due to the lack of Fe3+ ions in the body. This is a disease that would even lead to organ failure eventually leading to a heart attack or death. Anemia is causing the red intensity of blood to reduce. Therefore, the team is using that factor to detect the disease. The team has built this device using a key symptom where the intensity of red in the blood is measured and the output is sent to a central server, whilst the server also gets input from a questionnaire that is built in-app. Using this data, the server will decide whether the user is suffering from anemia or not. When tested against the anemia patient there is an accuracy rate of almost 83% that is obtained and when crossed checked against the actual testing done, it qualifies the taken accurate result.
Forestry application of the AHP by use of MPC© software

We present an example of the application of the AHP decision-making approach to forest management, by use of MPC© 2.0 software. The example considered is that of a forest services company interested in buying a timber harvester. The company had preselected four different machines as possible alternatives, and established 11 different criteria involved in the decision, grouped into four categories (economic, environmental, social and technical). The decision-making process was undertaken using MPC© 2.0 software tools, which enable establishment of criteria on two levels, independent pairwise comparison of criteria (first phase) and of alternatives under each criterion (second phase), repetition of the decision-making process by the same or different users, graphical display of the results on the computer screen, and sensitivity analysis.
Towards Robust Pattern Recognition: A Review

The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance. From the perspective of accuracy, pattern recognition seems to be a nearly solved problem. However, once launched in real applications, the high-accuracy pattern recognition systems may become unstable and unreliable due to the lack of robustness in open and changing environments. In this article, we present a comprehensive review of research toward robust pattern recognition from the perspective of breaking three basic and implicit assumptions: closed-world assumption, independent and identically distributed assumption, and clean and big data assumption, which form the foundation of most pattern recognition models. Actually, our brain is robust at learning concepts continually and incrementally, in complex, open, and changing environments, with different contexts, modalities, and tasks, by showing only a few examples, under weak or noisy supervision. These are the major differences between human intelligence and machine intelligence, which are closely related to the above three assumptions. After witnessing the significant progress in accuracy improvement nowadays, this review paper will enable us to analyze the shortcomings and limitations of current methods and identify future research directions for robust pattern recognition.
Multi-Cloud Container Communication Using Software Defined Networks

Over the last decade, cloud computing has changed the IT world, mainly in the ways of storing, accessing and securing the users' data. However, there is no single cloud management platform in the market that can be used to manage different clouds (ex: AWS and AZURE), together with container communication between the clouds, to cater different cloud user requirements. We have proposed a platform for cloud service providers that automates the multi-cloud management related services with following modules: (1) communication, (2) resource allocation and (3) log view. Our results show that, using our proposed modules, the multi-cloud management related services can be automated efficiently and reliably.
Parallel Speciation by Natural Selection

Natural selection plays a prominant role in most theories of speciation. Mayr (1963) and others viewed selection as one of the causes of genetic divergence between allopatric populations that yielded reproductive isolation as an incidental by-product. Selection is also invoked in models of parapatric (Endler 1977), reinforcement (Dobzhansky 1951), founder event (Barton 1989), and sympatric (Diehl and Bush 1989) speciation. Yet there is surprisingly ittle evidence from nature in support of its role in any scenario. Our purpose here is to introduce an unappreciated form of evidence for natural selection that we call "parallel speciation. " This we define as the repeated independent evolution of the same reproductive isolating mechanism. Below, we explain the phenomenon, list criteria needed to demonstrate it, and identify several possible examples from nature. We discuss broad implications of parallel speciation and review several mechanisms that can produce it. Throughout we use the concept of biological species (Mayr 1963): populations belong to the same species only if they are not reproductively isolated. We add the proviso that reproductive isolation between good species may be imperfect.
Constitutive activation of PI3K‐Akt and NF‐κB during prostate cancer progression in autochthonous transgenic mouse model

Cancer progression is usually facilitated by independent growth signals that may lead to increased cell survival and evasion of apoptosis. Phosphatidylinositol 3′‐OH kinase (PI3K)‐Akt and transcription factor NF‐κB are important signaling molecules and key survival factors involved in the control of cell proliferation, apoptosis, and oncogenesis. Although PI3K‐Akt and NF‐κB have been implicated in the development and progression of prostate cancer, expression of these molecules during progression of autochthonous disease has not been elucidated.
THE ROLE OF ANTIGEN PRESENTATION IN THE REGULATION OF CLASS-SPECIFIC (TH1/TH2) IMMUNE RESPONSES

To prevent progressive and fatal expansion of an infectious agent, it is critical for the host to develop the appropriate immune response. The induction of a given set of effector cells is determined by the activation of T helper lymphocytes. These CD4 T cells can be classified into TH1 and TH2 lines according to their cytokine release pattern and their regulatory functions. Each subclass regulates a different set of immune effectors and they are, most of the time, mutually exclusive. Since not all immune effectors are effective against a given pathogen, the choice of a class response is thus a crucial event. We propose a qualitative model for understanding the dynamics of the TH1-TH2 network. In our model, the induction of a TH1 or a TH2 response is a function of the physical features of the pathogen, of the site of infection and of the efficiency of the ongoing response. One of our predictions is that anergy, in addition to its role in the mechanisms of peripheral tolerance, could have an important influence on the regulation of the TH1-TH2 balance. In summary, our model accounts for several important experimental observations in terms of transitions between multiple steady states. It emphazises the role of antigen presentation in directing the choice of a class-specific response.
Plus Go: Intelligent Complementary Ride-Sharing System

Currently the world population is gathering to the cities making huge traffic congestion throughout the day. This has drawn serious attention to the society incurred to implement smart solutions for traffic management. One of the prominent problems for traffic congestion is the number of vehicles entering the cities is high. It is a popular fact that the solitary travelers coming to a defined destination make the vehicles underutilized. Therefore, this study proposes a solution to implement a new ride-sharing platform: Plus Go, to reduce this underutilization. Plus Go matches the travelers by considering the designation, traveler preferences, shortest path details, and the ratings of the users. Moreover, Plus Go intelligently estimates the traveling cost based on the fuel consumption of the vehicle, distance traveled, and the time taken to reach the destination. The proposed solution matches the travelers with 98% accuracy ensuring that ride-sharing is an effective solution to reduce the number of vehicles entering the cities.
Compact Dual Band LowPass-BandPass Filter

This paper presents the design of a compact dual band lowpass-bandpass filter (LPF-BPF) for GSM applications. The idea behind this design is to apply multiple miniaturization techniques on the dual band to achieve compact dimensions with optimal performance. Miniaturization is achieved by using lumped capacitors along with DGS and meandering techniques. The designed filter shows dual behavior: lowpass behavior with cutoff frequency at 0.9 GHz and bandpass behavior with center frequency at 2.4 GHz. The achieved size reduction when applying the three miniaturization techniques is 70%. Measurement results validate the proposed design. Moreover, the design is also compatible with integrated circuit process.
Analysis of Error Impact for Batch Handover Authentication Protocols in Mobile Wireless Networks

The handover authentication protocol controls the access of multiple access points in a mobile wireless network (MWN). PairHand is a handover authentication protocol that uses identity-based public key cryptography based on bilinear pairing and provides batch verification to efficiently control multiple accesses. However, there may be errors in the messages transmitted via a wireless channel; such errors can affect the batch verification process. Previous studies involving PairHand have calculated the verification delay without considering errors. We have classified the message verification methods into two types: individual verification, in which the messages are individually verified and batch verification, in which the messages are verified all at once. We formulate the verification delay as a function of the bit error probability and visualize it. In our graph, the batch verification delay rapidly increases as the bit error probability increases and then forms an intersection with the individual verification delay. This intersection can be a guideline for whether to use batch verification or individual verification, depending on the bit error rate. We also classify the previously proposed handover authentication protocols into pairing-based protocols and pairing-free protocols and analyze the computation and communication costs of each. Based on the analysis results, we propose a mini-batch method to reduce the batch verification delay in an error-prone MWN.
Phase-Aware Cache Partitioning to Target Both Turnaround Time and System Performance

The Last Level Cache (LLC) plays a key role in the system performance of current multi-cores by reducing the number of long latency main memory accesses. The inter-application interference at this shared resource, however, can lead the system to undesired situations regarding performance and fairness. Recent approaches have successfully addressed fairness and turnaround time (TT) in commercial processors. Nevertheless, these approaches must face sustaining system performance, which is challenging. This work makes two main contributions. LLC behaviors regarding cache performance, data reuse and cache occupancy, that adversely impact on the final performance are identified. Second, based on these behaviors, we propose the Critical-Phase Aware Partitioning Approach (CPA), which reduces TT while sustaining (and even improving) IPC by making an effective use of the LLC space. Experimental results show that CPA outperforms CA, Dunn and KPart state-of-the-art approaches, and improves TT (over 40 percent in some workloads) over Linux default behavior while sustaining or even improving IPC by more than 3 percent in several mixes.
FEW-NNN: A fuzzy entropy weighted natural nearest neighbor method for flow-based network traffic attack detection

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor (FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm (NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1) the feature weights of samples are calculated based on fuzzy entropy values, 2) the fuzzy memberships of samples are determined based on affinity among samples, and 3) K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class; that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.
Deep Learning based Flood Prediction and Relief Optimization

Flood is a major natural disaster that occurs recurrently in Sri Lanka. It is important to stay on alert and get early preparations to avoid unnecessary risks that cause damage to both life and property. This project developed a flood assistance application “DHARA” to support early flood preparation and flood recovery process. DHARA mobile application facilitates river water level prediction, safest evacuation route suggestion and provides relevant warnings and alert notifications and the web application provides affected area detection, victim and relief estimation to assist flood recovery management. This system is developed as a mobile application and a web application. A recurrent neural network architecture named Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), a path finding algorithm namely A star (A*) algorithm and a clustering technique named Fuzzy Clustering are used for the development of the system. The system is verified with sample data related to “Wellampitiya” and “Kaduwela” area based on river “Kelanl”. The river water level prediction model has successfully predicted the water level 4 hours in advance. The verification results of the river water level prediction showed a satisfactory agreement between the predicted and real records with 85.4% accuracy.
Using a Genetic mixture model to study Phenotypic traits: Differential fecundity among Yukon river Chinook Salmon

Abstract Fecundity is a vital population characteristic that is directly linked to the productivity of fish populations. Historic data from Yukon River (Alaska) Chinook salmon Oncorhynchus tshawytscha suggest that length-adjusted fecundity differs among populations within the drainage and either is temporally variable or has declined. Yukon River Chinook salmon have been harvested in large-mesh gill-net fisheries for decades, and a decline in fecundity was considered a potential evolutionary response to size-selective exploitation. The implications for fishery conservation and management led us to further investigate the fecundity of Yukon River Chinook salmon populations. Matched observations of fecundity, length, and genotype were collected from a sample of adult females captured from the multipopulation spawning migration near the mouth of the Yukon River in 2008. These data were modeled by using a new mixture model, which was developed by extending the conditional maximum likelihood mixture model that.
Effect of infection by Ustilago bullata on vegetative growth of Bromus catharticus

Abstract Glasshouse experiments have shown that Ustilago bullata Berk. can severely reduce seedling survival, tillering, and herbage and root dry matter production of Bromus catharticus Vahl. Infected plants produced increased numbers of flowering tillers. These effects were not apparent in the field. A field experiment indicated that infection can prevent B. catharticus plants from surviving beyond flowering. An unreported leaf blade deformation symptom of infected plants is described.
Actin‐mediated chloroplast movement in Griffithsia pacifica (Ceramiales, Rhodophyta)

Chloroplast banding occurs in Griffithsia pacifies Kylin in a daily rhythm. At the start of the light period, chloroplasts have a uniform distribution. During the light period chloroplasts move away from the ends of the cell leaving two chloroplast‐free regions by mid‐afternoon. Later in the light period these bands disperse as chloroplasts return to the ends of cell where they remain throughout the dark period. After enzyme treatment with β‐glucuronidase to permeabilize cell walls, non‐banded plants did not form bands after the addition of 5 μg ml−1 cytochalasin B in dimethyl sulfoxide (DMSO). When enzyme‐grown plants with chloroplast bands were treated with cytochalasin B, bands were retained for at least three days. Plants grown in media supplemented with β‐glucuronidase or DMSO alone gave banding consistent with untreated controls. Staining of microfilaments with rhodamine‐phalloidin before and after treatment with cytochalasin B gave results consistent with chloroplast movement studies. This is the first report of actin‐mediated organelle movement in red algae.