Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Programming and Greedy Heuristic in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN

Recently, a successful data taking in high energy physics experiments heavily relies on a stability of Data Acquisition System (DAQ). The paper deals with the Load Balancing (LB) problem of the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN and presents two approaches –Dynamic Programming (DP) and Greedy Heuristic (GH) – applied in finding LB optimal solution. DP represents a pure mathematical approach in terms of a mathematical optimization and GH is a representative belonging to a class of heuristics. Therefore, the mutual comparison is worth investigating. In order to demonstrate the performance of the proposed DP and GH approaches, three LB test cases are examined and the numerical results are compared with other LB solution methods.
Short-term Traffic Prediction Based on Genetic Improved Wavelet Neural Network

Efficient and accurate traffic prediction is the premise of the development of autonomous driving technology. In-depth research is made on the issue of short-term traffic speed prediction in autonomous driving systems. In view of the time-varying characteristics of the traffic main sentence, this paper designs and implements a traffic prediction system based on genetically improved wavelet neural networks. Through the training and learning of the historical average speed data of roads, it realizes the prediction of future road traffic conditions and helps the planning of travel routes. This algorithm circumvents the shortcomings of wavelet neural networks that easily fall into local minimums, and proposes to optimize the initial coefficients of wavelet neural networks by using the characteristics of global search of genetic algorithms to construct better neural networks. We have verified that the traffic speed prediction based on genetically improved wavelet neural network has a high degree of agreement with real data, and the effect is significantly better than the results of ordinary wavelet neural network, which has higher practical value.
Predictive Control of 3 DOF Helicopter Using a Kalman and Neural Network Estimator

This paper presents the design and simulation of two predictive controllers with different estimators on a 3 DOF helicopter. The work begins with the modeling of the helicopter, obtaining the equations using LaGrange method with a matrix form representation, including viscous friction and assuming a drive-motor with stationary behavior. With the dynamic equations defined, the next step was to obtain the identification of real parameters for the dynamic system. This was carried out using a grey box method applied in a real 3DOF Helicopter prototype built at the university. After verifying the model with the best fit-test, the controller design step begins. To have a comparison point of the advanced controller's performance, a classical PID was tuned. To use the Kalman estimator, a linearization process was achieved and verified with the respective simulation. To get the neural network estimator a NARX type of neural network was used with a layer size of 14 and with 2 delays per input and output. In the design of the MPC controller's the same weights and limitations were assumed, considering the real limitations of the prototype and keeping the reference input near to the linearization point. To get proper evaluation criteria the setting time, overshoot, noise rejection, computational time and ITAE index were the values used to determine the performance for each controller. Simulations were performed on the dynamic system, with step and random steps and tracking-trajectory. According to the obtained data in the step test, the three strategies were able to control the vehicle obtaining negligible differences. In the random step and trajectory tracking test was observed than MPC controller with a neural network estimator gets a better response without noise but, some noise levels were added and the Kalman filter gets a better rejection level. Analyzing the computation time, it was observed that the neural network estimator has the longest simulation times in comparison to the Kalman filter and the classical PID.
Afghanistan Vehicle Number Plate Detection and Recognition Using Image Processing and Convolutional Neural Networks

Although numerous research studies have been conducted concerning automatic vehicle number plate detection and recognition, various presented automated number plate recognition systems are devised for specific countries where number plates follow standard patterns. However, such systems cannot be applied in Afghanistan because of the different designs and the language. Moreover, due to the cursive nature, writing direction, and shape variation of the Pashto characters, the segmentation of words into isolated characters is a more complicated task. Hence, the Pashto optical character recognition is a less developed area. To date, no research study has been conducted for Afghanistan number plate detection and recognition. The details on the Afghanistan number plate include character, numbers, and each province's name. The paper presents the study of its type attempting to detect the number plate from the vehicle image and then recognize the province's name, characters, and numbers on the number plate. In particular, the new method incorporating four core steps. The first step is number plate detection applying canny edge detection based on user-defined thresholding and extracts the number plate involving several image processing techniques. The second phase is number plate adjustment using Randon transform-based techniques. The third stage is number plate segmentation isolating each character, number, and province name on the number plate using a scanning approach. The final step employs a convolutional neural network to classify the number plate's alphanumeric characters and provinces' names. In addition, two datasets have been created: the dataset for alphanumeric characters contains 2800 images of 14 classes, and the dataset for provinces' names contains 6800 images of 34 classes. The proposed models present 99.93 percent accuracy for provinces' names classification and 98.93 percent for alphanumeric characters' classification.
Cytochrome P450 enzymes— in vitro , in vivo , and in silico studies

Metabolism is a major determinant of the pharmacokinetic properties of most drugs and is often behind bioavailability problems, drug-drug interactions, and metabolic idiosyncrasies. Cytochrome P450 (CYP) enzymes are a superfamily of microsomal hemoproteins catalysing the metabolic reactions of several exogenous compounds. The majority of crucial steps within drug metabolism are in connection with CYP enzymes. In the present study, in vivo, in vitro, and in silico approaches were applied and characterised to evaluate the effects of chemical entities on CYP-mediated metabolism. CYP2B6 was used as a target enzyme for these studies. For evaluation of the CYP inhibition potential of new chemical entities, a novel in vitro test system utilising the n-in-one approach was developed. This method proved to be robust and applicable to screening purposes. Validation of the n-in-one assay was done by comparing its performance to commonly used in vitro techniques using six structurally diverse drugs. All assay types yield remarkably similar results with the majority of the CYP forms tested. Several chemicals were screened in vitro and in silico in order to find potent and selective chemical inhibitors for CYP2B6. Ticlopidine, thioTEPA and 4-(4-chlorobenzylpyridine) were found to be highly effective inhibitors of CYP2B6. The selectivity of thioTEPA proved to be very high, whereas ticlopidine and 4-(4-chlorobenzylpyridine) also inhibited other CYPs. At a concentration level of 1 μM for ticlopidine and 0.1 μM for 4-(4-chlorobenzylpyridine), the inhibitory effect towards other CYPs was negligible. Due to wide clinical use and relevance, clopidogrel and ticlopidine were selected for further in vivo interaction studies. Both clopidogrel and ticlopidine significantly inhibited the CYP2B6catalysed bupropion hydroxylation and patients receiving either clopidogrel or ticlopidine are likely to need dose adjustments when treated with drugs primarily metabolised by CYP2B6. The effect of impaired kidney function on CYP2B6 activity and on bupropion pharmacokinetics was also explored. In patients with kidney disease, the bupropion AUC and Cmax were significantly higher and the apparent oral clearance of bupropion was notably lower compared to healthy controls. The present results indicate that the in silico and in vitro methods used are helpful in predicting in vivo drug-drug interactions. The effective utilisation of these models in the early phases of drug discovery could therefore help to target the in vivo studies and to eliminate metabolically unfavourable drug candidates.
Multistep Forecasting of New COVID-19 Cases Based on LSTMs Using Bayesian Optimization

The multistep prediction of new Corona Virus Disease (COVID-19) cases plays a vital role during the epidemic control period, and the Long Short-Term Memory (LSTM) based time series analysis model is the most frequently used among many prediction methods. But whether it is the cumulative error of the multistep prediction or the instability of the new case data of the COVID-19 make the performance of LSTM in this task not so good. In this paper, we selected three countries with more severe COVID-19 epidemics—India, Russia, and Chile, to predict new cases in the next 15 days with different multistep LSTM network models, and use Bayesian Optimization to explore the optimal hyperparameter space. The results show that: a) the performance of Recursive Prediction LSTM is the best (Mean Absolute Percentage Error, MAPE was reduced to 14.88%, 6.46%, and 16.31% for the three countries respectively), Encoder Decoder LSTM is second (15.52%, 19.61%, 19.87%), and the effect of vector output LSTM is the worst (23.55%, 26.82%, 19.57%); b) there are obvious extremely poor areas in the hyperparameter space, and the Bayesian Optimizer can focus on the good areas to avoid cost of tuning parameters based on bad hyperparameters; c) the data of new cases of COVID-19 in different countries have great differences in the hyperparameter expectations for the model. The bad area of hyperparameters and different expectations are likely to be one of the reasons why the COVID-19 data of different countries is hard to train jointly.
Generalized Projection Synchronization and Generalized Dislocation Synchronization of Fractional-order Financial Chaotic Systems with Different Orders

Generalized projective synchronization and generalized dislocation projective synchronization are proposed for the synchronization of fractional-order chaotic systems with different orders. The synchronization problem of fractional-order systems with different orders is transformed into the synchronization problem of equal order systems. The synchronization of fractional-order financial chaotic systems with different orders is realized by using this method, and its feasibility is verified by theoretical calculation and numerical simulation.
The relationship between physical well-being and cognitive outcomes in 2–7 year old typically developing children: A systematic review

Objectives: The aim of this review was to provide an overview of researches presenting evidence for a relationship between physical well-being and cognitive outcomes in 2–7 year old typically developing children. Design: A systematic review. Methods: Articles involved in this review were from Science Direct, EBSCO and WoS. In total, 21 articles were included in this review. Two reviewers assessed the methodological quality independently. Results: The evidence for or against the relationship between physical well-being and cognitive outcomes was not sufficient. However, there was weak-to-strong evidence for some relationships between underlying categories of physical well-being and cognitive outcomes, including complex motor skills and higher order cognitive skills. Conclusions: There were some relationships between motor and cognitive skills. It offered insight into possible interventions designed to complex motor skills which may be beneficial for both motor and higher order cognitive skills of preschool children.
Distributed Latent Dirichlet Allocation on Streams

Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams, Streaming Variational Bayes (SVB) introduced Bayesian updating to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams: topic evolution, data turbulence, and real-time inference. In this article, we propose a novel distributed LDA algorithm—referred to as StreamFed-LDA—to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal, StreamFed-LDA is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of StreamFed-LDA allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency, StreamFed-LDA introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time, StreamFed-LDA also reduces the latency by orders of magnitudes in real-world datasets.
ELMarket: An E-Learning System Based on Blockchain

With the development of information technology in the era of big data, the traditional education model based on offline courses can no longer meet people's needs. Online education (i.e., E-Learning) has become a new type of education model. Blockchain has the characteristics of decentralization, traceability, collective maintenance, data disclosure, non-tampering and so on. Its characteristics make it an ideal solution for the existing problems of online education. In this paper, we proposed a new type of decentralized system which called ELMarket(ELM), aiming to solving the problems and deficiencies of existing online educational platforms, including that student user power is insufficient, participants are not interactive enough, and the copyright of educational resources is not protected well. ELM is based on Ethereum to build a private chain called "Intelligence Chain". We released circulating tokens which called ELStone(ELS) on the "Intelligence Chain" as an incentive to users. The system also proposed and widely applied the concept of "Smart Market". In order to protect teachers' creative rights and stimulate students' interests in learning, we have also added several mechanisms such as originality detection, review of users’ behavior and credits exchange. In addition, we combine IPFS and relational database to store, making up for the lack that blockchain cannot store large files. In the end, ELM established a safe, transparent, efficient, and non-tamperable online education ecosystem.
Hunting BGP zombies in the wild

As the key component of Internet's inter-domain routing, BGP is expected to work flawlessly. However, a recent study has revealed the presence of BGP zombies: Withdrawn prefixes that are still active in routing tables and that can cause routing issues. That study used experimental prefixes with scheduled withdrawals (BGP beacons). In this study we aim at detecting BGP zombies for any prefixes announced on the Internet. To that end we study characteristics of withdrawn messages, and devise a method to differentiate withdraw messages corresponding to local topological changes to those standing for prefixes withdrawn by their origin AS. Based on this classification we study the occurrence of zombies in the wild in six years of BGP data. We find over 6.5 millions zombies, among those we confirm that 94% report incoherent states and caused 468 potential routing loops. Our study also reveals that noisy prefixes, long AS paths, and ASes announcing a large number of prefixes are more prone to zombies.
A cache consolidation design of MLC STT-RAM for energy efficiency enhancement on cyber-physical systems

Owing to the energy-constraint nature of cyber-physical systems (CPS), energy efficiency has become a primary design consideration for CPS. On CPS, owing to the high leakage power issue of SRAM, the major portion of its energy consumption comes from static random-access memory (SRAM)-based processors. Recently, with the emerging and rapidly evolving nonvolatile Spin-Transfer Torque RAM (STT-RAM), STT-RAM is expected to replace SRAM within processors for enhancing the energy efficiency with its near-zero leakage power features. The advances in Magnetic Tunneling Junction (MTJ) technology also realize the multi-level cell (MLC) STT-RAM to pack more cells with the same die area for achieving the memory density. However, the write disturbance issue of MLC STT-RAM prevents STT-RAM from properly resolving the energy efficiency of CPS. Although studies have been proposed to alleviate this issue, previous strategies could induce additional management overhead due to the use of counters or lead to frequent swap operations. Such an observation motivates us to propose an effective and simple strategy to combine direct and split cache mapping designs to enhance the energy efficiency of MLC STT-RAM. A series of experiments have been conducted on an open-source emulator with encouraging results.
Merfin: improved variant filtering and polishing via k-mer validation

Read mapping and variant calling approaches have been widely used for accurate genotyping and improving consensus quality assembled from noisy long reads. Variant calling accuracy relies heavily on the read quality, the precision of the read mapping algorithm and variant caller, and the criteria adopted to filter the calls. However, it is impossible to define a single set of optimal parameters, as they vary depending on the quality of the read set, the variant caller of choice, and the quality of the unpolished assembly. To overcome this issue, we have devised a new tool called Merfin (k-mer based finishing tool), a k-mer based variant filtering algorithm for improved genotyping and polishing. Merfin evaluates the accuracy of a call based on expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller’s internal score. Moreover, we introduce novel assembly quality and completeness metrics that account for the expected genomic copy numbers. Merfin significantly increased the precision of a variant call and reduced frameshift errors when applied to PacBio HiFi, PacBio CLR, or Nanopore long read based assemblies. We demonstrate the utility while polishing the first complete human genome, a fully phased human genome, and non-human high-quality genomes.
Temporary Storage of the Human Nasal Tissue and Cell Sheet for Wound Repair

Temporary storage of nasal tissues and nasal cell sheets, which entails transportation between hospitals and cell culture facilities, is an important issue in regenerative medicine. Herein, we investigated the preservation of chilled and frozen nasal tissues and expiry dates of ready-to-use nasal cell sheets. Although the cell number in preserved tissues was lower than that in fresh tissue, nasal cell sheets could be fabricated from tissues that had been refrigerated for 5 days and frozen–thawed over 5 days. Moreover, the nasal mucosal cell sheets were preserved in a non-hazardous buffer. The cell number, viability, and structure were not maintained in saline containing E-cadherin for 2 days; however, these were maintained in Hank’s balanced salt solution for 2 days, but not for 5 days. To assess the proliferation capacity of cells in the stored cell sheets, we performed cell sheet grafting assays in vitro. Cell sheets stored in Hank’s balanced salt solution for 2 days adhered to collagen gel and expanded normally. Our results show that nasal tissues can be stored temporarily in refrigerators or deep freezers, and Hank’s balanced salt solution can be used for preservation of ready-to-use cell sheets for a few days.
A Novel Probable Pathogenic PSEN2 Mutation p.Phe369Ser Associated With Early-Onset Alzheimer's Disease in a Chinese Han Family: A Case Report

The pathogenesis of Alzheimer's disease is complex, and early-onset Alzheimer's disease (EOAD) is mostly influenced by genetic factors. Presenilin-1, presenilin-2 (PSEN2), and amyloid precursor protein are currently known as the three main causative genes for autosomal dominant EOAD, with the PSEN2 mutation being the rarest. In this study, we reported a 56-year-old Chinese Han proband who presented with prominent progressive amnesia, aphasia, executive function impairment, and depression 5 years ago. The 3-year follow-up showed that the patient experienced progressive brain atrophy displayed on magnetic resonance imaging (MRI) and dramatic cognitive decline assessed by neuropsychological evaluation. This patient was clinically diagnosed as EOAD based on established criteria. A heterozygous variant (NM_000447.2: c.1106T>C) of PSEN2 was identified for the first time in this patient and her two daughters. This mutation causing a novel missense mutation (p.Phe369Ser) in transmembrane domain 7 encoded by exon 11 had not been reported previously in 1000Genomes, ExAC, or ClinVar databases. This mutation was predicted by four in silico prediction programs, which all strongly suggested that it was damaging. Our results suggest that this novel PSEN2 Phe369Ser mutation may alter PSEN2 protein function and associate with EOAD.