Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Algorithm for Solving 3 Dimensional Time Table Problem Based on Traveling Salesman Problem (TSP) Method

Scheduling problems are problems that are often faced by educational institutions, especially at the university level. This is because there are several obstacles in the preparation of the schedule, namely first, there should be no duplication of space, day, and hour. Second, there should be no duplication of lecturers on the same day and time, even though in different rooms and in different subjects. Third, there should be no duplication of group classes (study groups). Therefore, the purpose of this study is to obtain a genetic algorithm as a solution in overcoming the three constraints of preparing the schedule by using the Traveling Salesman Problem (TSP) method in the crossover process. To make it easier to organize the schedule, a 3-dimensional matrix is used with the x-axis representing (space, day, hour), the y-axis representing (courses, lecturers, credits) and the z-axis representing (classes). This study simulates the scheduling of 20 courses, 50 credits, 8 lecturers, and 19 classes. Chromosomes in this study are permutations of integers 1-20. Each gene in a chromosome represents a course package. From the scheduling results, the fitness function is 0.96 for 48 schedule slots (2 rooms x 3 days x 8 hours). For schedule slots greater than 50 (3 rooms x 3 days x 8 hours, 2 rooms x 4 days x 8 hours, and 2 rooms x 3 days x 9 hours), this algorithm is successful in getting fitness function 1. ©2018 JNSMR UIN Walisongo. All rights reserved.
Pancancer Analysis of the Oncogenic and Prognostic Role of NOL7: A Potential Target for Carcinogenesis and Survival

Despite growing evidence suggesting the critical function of NOL7 in cancer initiation and development, a systematic pancancer analysis of NOL7 is lacking. Herein, we present a comprehensive study of NOL7 which aimed to explore its potential role and detailed mechanisms across 33 human tumors based on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CATPAC) databases. As a result, both gene and protein levels of NOL7 were found to be increased in various tumor tissues, including breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), and head and neck squamous cell carcinoma (HNSC) as compared with corresponding normal tissues. Meanwhile, dysregulated NOL7 expression was found to be closely related to pathological stage and prognosis in several cancers, including LIHC, ovarian serous cystadenocarcinoma (OV), and bladder urothelial carcinoma (BLCA). The DNA methylation level of NOL7 was found to be decreased in most cancers and to be negatively associated with NOL7 expression. Furthermore, NOL7 expression was determined to be significantly associated with levels of infiltrating cells and immune checkpoint genes, including HMGB1. Analysis of NOL7-related genes revealed that RNA metabolism pathways, including “ribosome biogenesis”, “spliceosome”, and “RNA transport”, were mainly involved in the functional mechanism of NOL7 in human cancers. In summary, this pancancer study characterized the relationship between NOL7 expression and clinicopathologic features in multiple cancer types and further showed its potential regulatory network in human cancers. It represents a systemic analysis for further functional and therapeutic studies of NOL7 and highlights its predictive value with respect to the carcinogenesis and prognosis of various cancers, especially LIHC.
MSN: Mapless Short-Range Navigation Based on Time Critical Deep Reinforcement Learning

Automated vehicle(AV) based on reinforcement learning is an important part of the intelligent transportation system. However, currently, the performance of AV heavily that relies on the quality of maps and mapless navigation is one potential method for navigation in a strange and dynamic changing environment. Although many efforts are made on mapless navigation, they either need prior knowledge, rely on an exceptional constructed environment or simple feature fusion mechanism in the networks. In this paper, we proposed a deep reinforcement learning method, namely TC-DDPG, which is consisted of DDPG, multi-challenge deep learning networks and time-critical reward function. By comparing to existing approaches, TC-DDPG takes the cost of time into consideration and achieves better performance and converges more easily. A new open source simulator is proposed and extensive experiments are conducted to demonstrate the performance of the TC-DDPG, which outperforms comparing methods and achieves 62.9% less in time cost, 12.0% less in distance cost and about 90% fewer in numbers of model parameters.
Analysis of mm-Wave Multi-Stage Rectifier and Implementation

In the mm-wave frequency band, the rectifier’s sensitivity is the bottleneck of a wireless-powered IoT transponder’s distance. At a meter’s distance, the received power is weak, and transistors in a front end of a wireless power receiver operate in the weak inversion region. This article presents a mathematical analysis of the mm-wave multi-stage rectifier in the low-power region. A design methodology of a 57–64-GHz fully integrated eight-stage rectifier with a bulk-drain connection in 40-nm CMOS technology is presented. Parasitic effects on a multi-stage rectifier are analyzed. Based on the analysis, an intrinsic threshold voltage modulation is applied to improve the sensitivity. A tree-type power splitter is also proposed for the RF power signal’s phase balance. With this method, the RF signal arrives at each stage with an equal phase, and its suppression at the output is improved by 16 dB. This mm-wave rectifier achieves −7.1-dBm input sensitivity at 57 GHz with 1-V dc output voltage. The overall sensitivity from 57 to 64 GHz is better than −4.5 dBm. Compared to other mm-wave silicon-based rectifier state of the arts, the proposed mm-wave rectifier achieves better sensitivity while maintaining a compact size.
Stability analysis of linear control systems with uncertain parameters

by YUGUANG FANG In this dissertation, we study stochastic stability of linear systems whose parameters are randomly varying in a certain sense. In particular, we present a new approach to stochastic stability analysis of systems whose system structure is randomly changing among a finite set of possibilities which capture the abrupt changes in systems parameters or sudden failures of system components. These types of systems are referred to as jump linear systems with a finite state Markov chain form process. We first investigate the properties of various types of moment stability for stochastic jump linear systems, and use large deviation theory to study the relationship between “lower moment” stability and almost sure stability. In particular, we have proved that the region for δ−moment stability is monotonically increasing as δ is decreasing to zero and asymptotically converges to the region for almost sure stability. Roughly speaking, this is equivalent to saying that almost sure stability is equivalent to δ−moment stability for sufficiently small δ > 0. Furthermore, we prove that although the top δ−moment Lyapunov exponent is, in general, not differentiable at zero, it is differentiable at zero from the right and its right derivative at zero is equal to the top Lyapunov exponent. This answers a long standing question in this area. Based on
Effects of Capacitor on the Hybrid Ignition System

This paper analyzed how capacitor has an effect on the hybrid ignition system; especially the accumulated energy. Matlab was used to simulate the primary current, self – induced emf, oscillation frequency and accumulated energy. The experiment data - primary current, self-induced emf, oscillation frequency and dwell time - was collected via PicoScope 4425 Automotive Oscilloscope. Simulation results show that the accumulated energy in capacitors is greater than the required ignition energy, which ensure hybrid system working fluently.
Evaluation of the performance of GaoFen-7 laser altimeter data for ground elevation retrieval over vegetated areas

ABSTRACT The Gao Fen-7 (GF-7) satellite employs a laser altimeter to provide the global measurement of ground elevation. This study aimed to evaluate the performance of GF-7 laser altimeter data in ground elevation retrieval over vegetated areas. First, GF-7 waveforms were processed to obtain the ground elevation. Second, we verified the ground elevation accuracy using airborne light detection and ranging (LiDAR) data. Finally, we analysed the influence of various factors (e.g., slope, signal-to-noise ratio (SNR), vegetation height) on ground elevation estimation. Results showed that the lowest Gaussian peak best represents the ground elevation, with root mean squared error (RMSE) and mean error of 2.30 m and 0.28 m, respectively. The laser 1 performs better than laser 2 in ground elevation retrieval as indicated by the lower RMSE. The ground elevation accuracy differed significantly across various land cover types and showed a pattern of lower estimation accuracy with more complex vertical structure. Additionally, results also indicated that the ground elevation accuracy decreased with increasing slope, vegetation height and vegetation coverage but increased with increasing SNR. Overall, our study found that GF-7 laser altimeter data have a strong capability to retrieve the ground elevation over vegetated areas.
Potential Coronaviral Inhibitors of the Nucleocapsid Protein Identified In Silico and In Vitro from a Large Natural Product Library

The nucleocapsid protein (NP) is one of the main proteins out of four structural proteins of coronaviruses including the severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, discovered in 2019. NP packages the viral RNA during virus assembly and is, therefore, indispensable for virus reproduction. NP consists of two domains, i.e., the N- and C-terminal domains. RNA-binding is mainly performed by a binding pocket within the N-terminal domain (NTD). NP represents an important target for drug discovery to treat COVID-19. In this project, we used the Vina LC virtual drug screening software and a ZINC-based database with 210,541 natural and naturally derived compounds that specifically target the binding pocket of NTD of NP. Our aim was to identify coronaviral inhibitors that target NP not only of SARS-CoV-2 but also of other diverse human pathogenic coronaviruses. Virtual drug screening and molecular docking procedures resulted in 73 candidate compounds with a binding affinity below −9 kcal/mol with NP NTD of SARS-CoV-1, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-NL63, HoC-229E, and HCoV-HKU1. The top five compounds that met the applied drug-likeness criteria were then tested for their binding in vitro to the NTD of the full-length recombinant NP proteins using microscale thermophoresis. Compounds (1), (2), and (4), which belong to the same scaffold family of 4-oxo-substituted-6-[2-(4a-hydroxy-decahydroisoquinolin-2-yl)2H-chromen-2-ones and which are derivates of coumarin, were bound with good affinity to NP. Compounds (1) and (4) were bound to the full-length NP of SARS-CoV-2 (aa 1–419) with Kd values of 0.798 (±0.02) µM and 8.07 (±0.36) µM, respectively. Then, these coumarin derivatives were tested with the SARS-CoV-2 NP NTD (aa 48–174). Compounds (1) and (4) revealed Kd-values of 0.95 (±0.32) µM and 7.77 (±6.39) µM, respectively. Compounds (1) and (4) caused low toxicity in human A549 and MRC-5 cell lines. These compounds may represent possible drug candidates, which need further optimization to be used against COVID-19 and other coronaviral infections.
Approximate ML Detection for MIMO Phase Noise Channels

We consider the MIMO communication channel impaired by phase noises at the transmitter and receiver. We focus on maximum likelihood detection for uncoded single-carrier transmission. We derive an approximation of the likelihood function, and propose the so-called self-interference whitening (SIW) algorithm. While the exact ML solution is computationally intractable, we construct a simulation-based lower bound on the error probability of ML detection. Numerical experiments demonstrate that SIW is, in most cases of interest, very close to optimal with moderate phase noise. Surprisingly, such near-ML performance can be achieved by applying only twice the nearest neighbor detection algorithm.
A Research Participant and End-User Approach to Research Collaboration and Co-Development in the Torres Strait

Like many communities worldwide, those in the Torres Strait Islands face several severe social and environmental challenges. Torres Strait Islanders compete against the impacts of colonisation, globalisation and climate change to find sustainable solutions to ensure they survive and thrive in this rapidly changing world. This article describes the outcomes of workshops involving representatives from community-based non-government organisations on Thursday Island in the Torres Strait. It describes an approach to proactively kickstart research and identify grassroots innovations for complex social and environmental challenges. The findings suggest workshop participants obtained an increased awareness and understanding of research, the steps involved in research and their rights as participants. The researchers anticipate the findings from this project will contribute towards a better understanding of how to collaborate and co-develop research that is meaningful and beneficial to local contexts.
Subacromial Impingement Syndrome: A Systematic Review of Existing Treatment Modalities to Newer Proprioceptive-Based Strategies

Musculoskeletal pain is a common reason for primary care visits, with many visits for shoulder pain due to subacromial impingement syndrome (SIS). Current treatments lack evidence for effective management, showing only temporary outcomes. This systematic review evaluates existing modalities in comparison to the use of more permanent proprioceptive-based strategies. Specifically, this meta-analysis compared the use of kinesiology tape, myofascial trigger point release (MPTR), scapular stabilization exercises (SSE), and resistance training. PubMed, BioMedCentral, and ScienceDirect databases were queried for studies evaluating proprioceptive-based exercises in the last nine years. In total, 48 studies met the inclusion and exclusion criteria. After removing duplicates, a total of 14 level 1 studies were left. Kinesiology tape use demonstrated a statistically significant reduction in pain-free range of motion. MPTR improved in all pain scores and the disability scores index. SSE also reduced pain; however, mixed results were seen for range of motion. Finally, resistance training not only reduced pain but improved proprioception and joint position sense. Even though all techniques showed some promise in treating SIS, further large-scale studies exploring related outcomes are needed.
An automatic cost optimization method for wide field small aperture telescope arrays

Time domain astronomy requires continuous observations to capture series of images of celestial object. To satisfy observation requirements, scientists need to place several telescopes in different sites or in the same site to form a telescope array. In recent years, several telescope array projects have been proposed. Design of telescope arrays is quite different from that of a single telescope. We need to make trade off between the image quality, the aperture size, the field of view and the number of telescopes to satisfy the scientific requirement with minimal overall cost. In this paper, we will introduce our method to optimize the design of a telescope array which would consider detail design of telescopes as well as the overall cost for building and maintaining of these telescopes. This paper provides a useful tool for future telescope array projects.
Enhance Rating Prediction for E-commerce Recommender System Using Hybridization of SDAE, Attention Mechanism and Probabilistic Matrix Factorization

: E-commerce is essential application in world wide. In everyday live, we cannot escape from e-commerce transaction. E-commerce requires intelligent machine to deliver product information to customer. Intelligent machine popular called recommender system developed by matrix factorization. Rating is representation of customer expression for satisfied product or service. Unfortunately, number of ratings is too sparse due to majority customer lazy to give rating for e-commerce product. Number of sparse rating matrix have impact in matrix factorization in rating prediction. Moreover, extreme sparse rating has impact degrade performance significantly. Many researchers consider to enhance matrix factorization using customer and product information such as customer demographics information, customer testimony, product review, product description, and etc. In this research, we consider to incorporating Stack Denoising Auto Encoder (SDAE), attention mechanism aims to enhance product review document understanding representation and matrix factorization based on Probabilistic Matric Factorization (PMF) to produce rating prediction. According to experiment report, our model superior over previous work based on hybrid model between PMF, Convolutional Neural Network (CNN) and SDAE that popular called PHD in 1%, and superior over hybrid model between PMF, Long Short Term Memory (LSTM) and SDAE that popular called DDL-PMF in 0.9%, and achieved significantly in 8% over traditional PMF based on RMSE evaluation metrics.
Visual Task Classification using Classic Machine Learning and CNNs

- Our eyes actively perform tasks including, but not limited to, searching, comparing, and counting. This includes tasks in front of a computer, whether it be trivial activities like reading email, or video gaming, or more serious activities like drone management, or flight simulation. Understanding what type of visual task is being performed is important to develop intelligent user interfaces. In this work, we investigated standard machine and deep learning methods to identify the task type using eye-tracking data - including both raw numerical data and the visual representations of the user gaze scan paths and pupil size. To this end, we experimented with computer vision algorithms such as Convolutional Neural Networks (CNNs) and compared the results to classic machine learning algorithms. We found that Machine learning-based methods performed with high accuracy classifying tasks that involve minimal visual search, while CNNs techniques do better in situations where visual search task is included.
Functional assumptions of virtual system to improve shooting training and intervention tactics of services responsible for security (VirtPol)

Virtual system to improve shooting training and intervention tactics of services responsible for security is a teaching tool that responds to the training needs of taking interventional activities. The system will enable activation of scenarios with a set of variants that are high quality videos. A module within the system will measure psychophysical parameters to provide better training outcomes. Development of a simulator will be achieved due to the latest advances in the field of technology and IT. Its modular design, both in terms of structure and software, will allow it to be easily upgraded and developed in the future.
TDLens: Toward an empirical evaluation of provenance graph-based approach to cyber threat detection

To combat increasingly sophisticated cyber attacks, the security community has proposed and deployed a large body of threat detection approaches to discover malicious behaviors on host systems and attack payloads in network traffic. Several studies have begun to focus on threat detection methods based on provenance data of host-level event tracing. On the other side, with the significant development of big data and artificial intelligence technologies, large-scale graph computing has been widely used. To this end, kinds of research try to bridge the gap between threat detection based on host log provenance data and graph algorithm, and propose the threat detection algorithm based on system provenance graph. These approaches usually generate the system provenance graph via tagging and tracking of system events, and then leverage the characteristics of the graph to conduct threat detection and attack investigation. For the purpose of deeply understanding the correctness, effectiveness, and efficiency of different graph-based threat detection algorithms, we pay attention to mainstream threat detection methods based on provenance graphs. We select and implement 5 state-of-the-art threat detection approaches among a large number of studies as evaluation objects for further analysis. To this end, we collect about 40GB of host-level raw log data in a real-world IT environment, and simulate 6 types of cyber attack scenarios in an isolated environment for malicious provenance data to build our evaluation datasets. The crosswise comparison and longitudinal assessment interpret in detail these detection approaches can detect which attack scenarios well and why. Our empirical evaluation provides a solid foundation for the improvement direction of the threat detection approach.
Handbook of X-ray Astronomy

1. X-ray astronomy optics Daniel A. Schwartz 2. Proportional counters and other detector techniques Richard J. Edgar 3. CCDs for x-ray astronomy Catherine E. Grant 4. Data reduction and calibration Keith A. Arnaud and Randall K. Smith 5. Data analysis Randall K. Smith, Keith A. Arnaud and Aneta Siemiginowska 6. Archives, surveys, catalogues and software Keith Arnaud 7. Statistics Aneta Siemiginowska 8. Analysis of extended emission K. D. Kuntz Appendices Index.
H α control strategy of motor torque ripple in hybrid electric vehicles: an experimental study

In this study, the authors propose a novel control strategy of torque ripple reduction in hybrid electric vehicles (HEVs). By its periodic cycle of power production, the internal combustion engine used in a hybrid vehicle provides a torque that fluctuates constantly. This is problematic for vehicle manufacturers since this torque ripple creates uncomfortable vibrations and mechanical damage. To achieve this active control, the problem is reformulated in complex notation in the frequency domain with respect to the main order of the fluctuations. A permanent magnet synchronous machine is mounted on the crankshaft in order to generate a torque sequence opposing ripple torque. The control strategy is based on a static output feedback synthesis under & H ∞ performance specification. A complete modelling of the hybrid propulsion from an experimental test bench is achieved. The model is performed at low speed (900 rpm). In fact, at this speed, the ripple is very restrictive. Experimental results highlight the control approach interest.
Conceptualising and evaluating inclusive economic development: a productivity perspective

ABSTRACT Although scholarly attention towards inclusive economic development is emergent, its concept and evaluation remain ambiguous since few attempts have been made to elaborate on its definitions and goals. In this study, we conceptualize and define inclusive economic development, propose a productivity perspective for evaluating it, and construct an inclusive total factor productivity index based on data envelopment analysis. According to the estimation results, we report and map the major findings of our index. The results demonstrate that although inclusive economic development is progressing globally, significant regional disparities persist.