Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Supported Clustered NOMA for 6G-Enabled Internet of Things: Trajectory Planning and Resource Allocation

The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air–space–ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article. Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it. First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed. Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.
Genomic evidence of genetic variation with pleiotropic effects on caterpillar fitness and plant traits in a model legume

Plant-insect interactions are ubiquitous, and have been studied intensely because of their relevance to damage and pollination in agricultural plants, and to the ecology and evolution of biodiversity. Variation within species can affect the outcome of these interactions, such as whether an insect successfully develops on a plant species. Whereas specific genes and chemicals that mediate these interactions have been identified, studies of genome-or metabolome-wide intraspecific variation might be necessary to better explain patterns of host-plant use and adaptation often observed in the wild. Here, we present such a study. Specifically, we assess the consequences of genome-wide genetic variation in the model plant Medicago truncatula for Lycaeides melissa caterpillar growth and survival (i.e., larval performance). Using a rearing experiment and a whole-genome SNP data set (>5 million SNPs), we show that polygenic variation in M. truncatula explains 9–41% of the observed variation in caterpillar growth and survival. We detect genetic correlations among caterpillar performance and other plant traits, such as structural defenses and some anonymous chemical features; these genetic correlations demonstrate that multiple M. truncatula alleles have pleiotropic effects on plant traits and caterpillar growth or survival (or that there is substantial linkage disequilibrium among loci affecting these traits). We further show that a moderate proportion of the genetic effect of M. truncatula alleles on L. melissa performance can be explained by the effect of these alleles on the plant traits we measured, especially leaf toughness. Taken together, our results show that intraspecific genetic variation in M. truncatula has a substantial effect on the successful development of L. melissa caterpillars (i.e., on a plant-insect interaction), and further point toward traits mediating this genetic effect.
From 3 to 38: diversity of blood groups in fish & Humans-A mini review

Blood group systems in animals arise due to the presence or absence of certain inherited antigens on the surface of their red blood cells. With the first discovery of blood groups in humans in 1900, several other blood groups have been discovered in different animals including fish. The blood group systems have been found in different fish species like goldfish, salmon, tuna, herring, cod, red crucian carp, trout and spiny dogfish. To date, there are shreds of evidence of only three blood groups in fishes: S system for pacific salmon, trout, cod, spiny dogfish and red crucian carp, Tg system for tuna, and Y system for skipjack tuna. In humans, 38 blood group systems are known to exist, 9 of which are considered to be major blood group systems; ABO being the most important. The genes of these blood groups are autosomal, except XG and XK which are X borne, and MIC2 which is present on both X and Y chromosomes. The present paper attempts to review the blood group systems found in fishes in comparison to the diverse blood group systems in humans.
A Dual-Sided Hybrid Excitation Eddy Current Damper for Vibration Suppression in Low Damping Linear Motor System

The linear motor system is normally a low-damping system to achieve high speed and high acceleration without mechanical transmission devices. Therefore, the vibration issue is serious especially in the high-speed start–stop motion process, which deteriorates the control precision and decreases work efficiency. This article proposes a dual-sided hybrid excitation eddy current damper (ECD) to suppress the vibration of the linear motor during decelerating and stopping stages. This ECD is composed of a copper plate and two E-shape hybrid excited electromagnets. The plate is sandwiched between these dual-sided electromagnets to generate the eddy current damping force when it moves and the ECD current is excited simultaneously. The analytical model of the ECD considering leakage flux is established and verified by FEA with the deviation less than 7%. Experiments are conducted on the prototype to verify the effectiveness of its vibration restraint performance. With the active damping, the peak amplitude of the position error and the settling time are reduced by 68.88% and 33.33%, respectively.
Micropollutant, Micropollutant Metabolites and Toxin Removals in a Drinking Water Treatment Plant Using Some Novel and Economical Removal Processes

The aim of this study was to detect the levels of heptachlor (1, 4, 5, 6, 7, 8, 8-Heptachloro-3a, 4, 7, 7a-tetrahydro-4, 7-methano-1H-indene) and acetochlor (2-Chloro-N-(ethoxymethyl)-N-(2-ethyl-6-methylphenyl)-acetamide) Micropollutants (MK), their Metabolites (M) (heptachlor epoxide and fenthion and fenthion sulfoxide and some Toxins (T) (mycrocystin and aplysiatoxin from cyanotoxin) in the raw water of a drinking water treatment plant. Measurement procedures for the MK, M, and Ts have been developed. A sequential treatment process consisting of a Granulated Activated Carbon (GAC)/contact aerobic Membrane Bioreactor (MBR), Electro Fenton (EF) and NanoFiltration (NF) were found to be efficient in the treatment of MK, M and T. The maximum heptachlor and acetachlor removals in GAC by adsorption was 64% and 14%, respectively, while the Dissolved Organic Carbon (DOC) yields were recorded as 68% after 24 h. In the GAC/MBR contact aerobic reactor system, acetachlor and heptachlor yields were recorded as 89% and 27%, respectively. The heptachlor metabolite heptachlor epoxide was produced in this stage (30 ng/l) while the fenthion and fenthion sulfoxide metabolites of acetochlor were recorded as (120 ng/l) and (280 ng/l), respectively. These metabolites were removed with yields of 94.4%, 98%, and 94%, respectively, in the MBR. In NF the total yields of both pesticides and their metabolites varied between 96% and 98%. The toxin concentrations were reduced to (0.01 ng/l) and (0.02 ng/l) in the effluent of NF. The total cost of treating 1 m3 of raw drinking water was calculated as €1.98
Mobile edge cloud architecture for future low-latency applications

OF THE DISSERTATION Mobile Edge Cloud Architecture for Future Low-latency Applications by Sumit Maheshwari Dissertation Director: Dipankar Raychaudhuri This thesis presents the architecture, design, and evaluation of the mobile edge cloud (MEC) system aimed at supporting future low-latency applications. Mobile edge clouds have emerged as a solution for providing low latency services in future generations (5G and beyond) of mobile networks, which are expected to support a variety of realtime applications such as AR/VR (Augmented/Virtual Reality), autonomous vehicles and robotics. Conventional cloud computing implemented at distant large-scale data centers incurs irreducible propagation delays of the order of 50-100ms or more that may be acceptable for current applications but may not be able to support emerging real-time needs. Edge clouds considered here promise to meet the stringent latency requirements of emerging classes of real-time applications by bringing compute, storage, and networking resources closer to user devices. However, edge clouds are intrinsically local and have a smaller scale and are thus subject to significantly larger fluctuations in offered traffic due to factors such as correlated events and user mobility. In addition, edge computing systems by definition are distributed across multiple edge networks and hence are associated with considerable heterogeneity in bandwidth and compute resources. Considering these challenges, this thesis analyzes the requirements posed by the edge clouds and proposes specific techniques for control, network routing, data
A Scalable Deep Reinforcement Learning Approach for Traffic Engineering Based on Link Control

As modern communication networks are growing more complicated and dynamic, designing a good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the optimal traffic scheduling problem. Deep Reinforcement Learning (DRL) provides us with a chance to design a model-free TE scheme through machine learning. However, existing DRL-based TE solutions cannot be applied to large networks. In this article, we propose to combine the control theory and DRL to design a TE scheme. Our proposed scheme ScaleDRL employs the idea from the pinning control theory to select a subset of links in the network and name them critical links. Based on the traffic distribution information, we use a DRL algorithm to dynamically adjust the link weights for the critical links. Through a weighted shortest path algorithm, the forwarding paths of the flows can be dynamically adjusted. The packet-level simulation shows that ScaleDRL reduces the average end-to-end transmission delay by up to 39% compared to the state-of-the-art in different network topologies.
Soil, plant and animal relationship in northern districts of Tamil Nadu

Four blocks were randomly selected from each district. Samples of soil forage and blood serum of sheep in North Eastern Agro Climatic Zone of Tamil Nadu (Kanchipuram, Thiruvallur, Thiruvannamalai, Vellore and Villupuram). The samples were collected and analysed for different macro (Ca, P, and Mg) and micro (Zn, Cu and Fe) mineral contents to establish the mineral correlation among soil, forage and animals. The macro and micro mineral contents in soils of different (study area) districts were higher than their respective critical levels. Similarly, in forage all the mineral values were above critical level except magnesium lower than the critical value in all the districts and the copper lower than the critical level in Villupuram districts. The study also revealed the serum mineral values above the critical levels in all the districts and significant variation of minerals were observed except Zn and Fe. No significant correlation was observed between soil – plant relationship with respect to macro and micro minerals. Soil – animal relationship revealed highly significant negative (P≤0.01) correlation for calcium, zinc and copper and significant negative correlation (P≤0.05) correlation for iron and phosphorus. Plant – animal relationship revealed a highly significant negative correlation for Ca and Cu. Zinc had highly significant (P≤0.01) positive correlation. No significant correlation between soil-plant, soil-animal and plant – animal was observed for magnesium level. However, further, studies should need to get the clear idea about soil-plant-animal relationship.
Volatility Transmission and Volatility Impulse Response Functions in the Main and the Satellite Renminbi Exchange Rate Markets

We analyse volatility spillovers between the on- and offshore (CNY and CNH) Renminbi exchange rates towards the US dollar (USD). The volatility impulse response (VIRF) methodology introduced by Hafner and Herwatz (2006) is applied to several shocks between January 2012 and December 2019. Furthermore, we propose a novel way of estimating VIRFs based on Bayesian estimation of the MV-GARCH BEKK model. A simple Independence Chain Metropolis-Hastings algorithm allows drawing VIRFs in an efficient manner, allowing to analyse the significance and persistence of volatility shocks and associated volatility spillovers. The VIRF results show that the CNH exchange rate promptly reflects the global market demand and supply, while the CNY exchange rate reacts with a time lag. The VIRF results also show the existence of spillovers between the two markets as the co-volatility increases in response to shocks.
ATrie Group Join: A Parallel Star Group Join and Aggregation for In-Memory Column-Stores

This article presents a new holistic and efficient approach to big data analysis. We introduce a new parallel algorithm, known as ATrie Group Join (ATGJ), that integrates join, grouping and aggregation operations to accelerate big data analytical workloads in in-memory column-stores. ATGJ performs a single scan of the fact columns and uses a mixture of data and task parallelism for the optimal use of computing resources. It uses a novel technique to perform group-by and aggregation realising the grouping attributes as a tree shaped deterministic finite automation known as Aggregate Trie or ATrie. ATrie facilitates grouping of attributes and processing of data in tight loops that significantly improves the performance on modern hardware. Unlike other competing algorithms, use of ATrie avoids the creation of multiple data structures with the increasing number of dimension tables and grouping attributes. Also, we demonstrate that ATGJ performs efficiently even when the ATrie becomes bushy. We evaluated the algorithm using Star Schema Benchmark (SSBM) to show that it is significantly faster and scales better than other algorithms for the number of concurrent threads, the number of group-by attributes, the data set size and the query complexity.
Detailed analysis of chick optic fissure closure reveals Netrin-1 as an essential and conserved mediator of epithelial fusion during vertebrate embryogenesis

Epithelial fusion underlies many vital organogenic processes during embryogenesis. Disruptions to these cause a significant number of human birth defects, including ocular coloboma. We provide robust spatial-temporal staging and unique anatomical detail of optic fissure closure (OFC) in the embryonic chick, including strong evidence for roles of apoptosis and epithelial remodelling. We performed complementary transcriptomic profiling and show that Netrin-1 (NTN1) is precisely expressed in the chick fissure margin at the fusion plate but is immediately downregulated after fusion. We further provide a combination of protein localisation and phenotypic evidence in chick, humans, mice and fish that Netrin-1 has an evolutionarily conserved and essential requirement for OFC, and is likely to have a major role in palate fusion. Our data reveal that NTN1 is a new locus for human coloboma and other multi-system developmental fusion defects, and that chick OFC is a powerful model for epithelial fusion research.
Treatment of mine water to remove manganese

Typical acid mine water contains 1-8 mg/L Mn, but concentrations of 50-100 mg/L are not uncommon. At present, most mine operators with manganese problems use excess alkalinity to raise the pH of water to 10 or higher to facilitate manganese oxidation and precipitation. Excess alkalinity, although it increases treatment costs 20-100 pct, was determined by the Bureau of Mines to be less expensive than NaOCl and KMnO/sub 4/ (chemical oxidants). However, laboratory tests indicate that the manganese precipitated by excess alkalinity is easily resolubilized, while that produced by the oxidizing agents is relatively stable. The Bureau also studied two novel approaches to the manganese problem. One was an inexpensive pipeline treatment system device known as the ILS, which was developed by the Bureau to aerate and neutralize mine water. At three mines with influent Mn concentrations of 10-68 mg/L, use of the ILS reduced Mn concentrations to less than mg/L at pH values of only 6.8-7.3. Another novel approach to mine water treatment is the construction of wetlands on mined sites. Results from ongoing tests indicate that this biological treatment can be engineered to optimize either iron or manganese removal. Moderate Mn concentrations (14-30 mg/L) have been reduced to 2-6more » mg/L in constructed wetlands at four mine sites in western Pennsylvania. 25 references, 3 figures, 3 tables. « less
Encoding Justice: A Quest for Facilitating Access to Justice by e-Handling of Cross-Border Litigation. The Example of the European Uniform Procedures

Introduction 1. 474 Access to Justice and ICT 2. 477 The European Uniform Procedures 3. 482 Digital Justice via Portals and European Uniform Procedures 4. 485 e-Justice Portal 4.1. 486 e-CODEX 4.2. 488 Integrating e-Communication Solutions within the EU 4.3. 492 Sources of Complexity in Handling EU Procedures Digitally 5. 493 Interrelating Technical and Procedural Requirements 5.1. 494 National Technical and Organisational Infrastructures 5.2. 496 Interplay between National and European Rules 5.3. 497 Language and Legal Semantics 5.4. 498 Reaching the Critical Level of Knowledge and Users 5.5. 498 In Search of Simplicity: Towards a Different Approach to e-Justice? 6. 500 Concluding remarks 7. 504
Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning

Grant-free multiple access (GFMA) is a promising paradigm to efficiently support uplink access of Internet of Things (IoT) devices. In this paper, we propose a deep reinforcement learning (DRL)-based pilot sequence selection scheme for GFMA systems to mitigate potential pilot sequence collisions. We formulate a pilot sequence selection problem for aggregate throughput maximization in GFMA systems with specific throughput constraints as a Markov decision process (MDP). By exploiting multiagent DRL, we train deep neural networks (DNNs) to learn near-optimal pilot sequence selection policies from the transition history of the underlying MDP without requiring information exchange between the users. While the training process takes advantage of global information, we leverage the technique of factorization to ensure that the policies learned by the DNNs can be executed in a distributed manner. Simulation results show that the proposed scheme can achieve an average aggregate throughput that is within 85% of the optimum, and is 31%, 128%, and 162% higher than that of acknowledgement-based GFMA, dynamic access class barring, and random selection GFMA, respectively. Our results also demonstrate the capability of the proposed scheme to support IoT devices with specific throughput requirements.
Classifying Student’s Duration of Study in Faculty of Science and Technology UNAIR Using Naïve Bayes and Neural Network Classifiers

Timely graduation is one of the essential criteria for a university in the accreditation program. The objective of this study is to predict the duration of study based on several factors related to students. The data in this study were the data of Faculty of Science and Technology (FST) graduates for 11 years (2008-2018) but limited to the undergraduate degree. The department in FST includes Mathematics, Statistics, Information System, Chemistry, Biology, Physics, Biomedical Engineering, and Environmental Sciences and Technology. The attributes in this work are department, address, gender, high school status, high school national exam score, admission program, department selection order, parents' income, GPA and ELPT. The dependent variable, study duration, is divided into two categories, which are a timely graduate (less or equal to 4 years) and untimely graduate (more than 4 years). The classification methods in predicting the period of study are Naïve Bayes and Neural Network. In this study, various percentages of training data and testing data will be compared. The results reveal that Naïve Bayes outperforms Neural Network in classification accuracy, even in the smaller sample.and their difference is statistically significant.
A Common-Ground Quazi-Z-Source Single-Phase Inverter Suitable for Photovoltaic Applications

In this article, a coupled inductor common-ground single-phase inverter, well-suited for photovoltaic (PV) applications, is presented. The proposed PV inverter is constructed based on a two-stage topology. At the first stage, a high gain DC-DC structure converts low PV voltage to the high bipolar output voltages. The produced bipolar output voltages are the inputs for the conventional half-bridge inverter in the second stage. It is noteworthy that the parasitic capacitors between the PV panel and its ground provide the path for circulating the leakage current in conventional configurations. This problem is tackled in the proposed topology by making a common-ground structure. Furthermore, the first stage contains only one switch with low voltage stress, which leads to a significant suppression in conduction losses. The proposed DC-AC structure has the ability to reduce the input current ripple and also recycle the leakage energy caused by the coupled inductor. The operation principles of the proposed structure are analyzed in detail, and finally, the experimental results are accomplished to confirm the accuracy of the performed analysis.
The use of a series of string quartet live music concert to reduce anxiety in first-year dental students

The objective of this study was to investigate the use of live music concerts to reduce anxiety in first-year dental students at the University of Michigan School of Dentistry. University of Michigan dental students pose moderate to high anxiety, being the first-year dental students mostly affected. Music interventions are a well-established alternative approach to reduce anxiety in adult populations. First-year dental students volunteered to participate in this study. The baseline anxiety and post-concert anxiety levels were assessed using the STAI-6 questionnaire. All live concerts took place during regular course sessions. Students’ perceived experience with the live music concerts was also assessed. Our results demonstrated a significant anxiety reduction of 30% in first-year dental students. More importantly, live music concerts obtained an overwhelming 93% student approval rate. We conclude that music is a promising, feasible, cost-effective, fast acting and low-risk approach to reduce anxiety in first-year dental students.