Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiplex PCR Assay for Simultaneous Identification of Five Types of Tuna (Katsuwonus pelamis, Thunnus alalonga, T. albacares, T. obesus and T. thynnus)

There is a need to identify the species of similar types of fish, especially those that are commercially sold. Particularly, the price of tuna varies depending on its type, which is difficult to determine as they are sold in cut or processed forms. This study developed a multiplex polymerase chain reaction (PCR) assay to identify the five most common tuna species: bigeye, skipjack, Atlantic bluefin, albacore, and yellowfin tunas. Newly designed species-specific primer sets for these five tuna species were created. Subsequently, the amplicon sizes obtained were 270, 238, 200, 178, and 127 base pairs for bigeye, skipjack, Atlantic bluefin, albacore, and yellowfin tunas, respectively. Each primer’s specificity was further tested using 15 other fish species, and no cross-reactivity was observed. To identify multiple targets in a single reaction, multiplex PCR was optimized to increase its resolution and accuracy. The detection levels of the multiplex PCR assay were confirmed to be 1 pg for all the five tunas. Additionally, it was successfully applied to 32 types of commercial tuna products. Therefore, this multiplex PCR assay could be an efficient identification method for various tuna species.
What to do when you do not see the data

What recourse exists when the teaching of Statistics by visual means is not part of the equation? This case study of a team from the University of Coimbra – one teacher, two members of the Media Production Center and a visually impaired student – took up the challenge of teaching a full semester in Statistics in such a way that all students would experience and comprehend fundamental concepts ranging from summarizing data numerically and graphically to making decisions in Statistics through understanding centrality, dispersion and hypothesis testing. A very pragmatic non-visual orientation required class planning to emphasize on contents received aurally or read in Braille, with activities and challenges designed to engage all students in a variety of formats. Materials used in class will be presented in which Statistical Thinking is explored using visual, aural and tactile senses. Future research opportunities will be discussed.
Overview of the Mechanisms of Drag Reduction by Means of Flexible Surfaces

The research of drag reduction technique have the advantages of saving energy and improving utilization rate, which behaves a tremendous value in economy and military, hence it is a hot issue for researchers all the time. Merging together with the method of bionic drag reduction, drag reduction mechanisms with flexible surface have realized its function by imitating the elastic characteristics of dolphin skin and using the interaction between flexible materials and boundary layer fluid. Development status of drag reduction technique with flexible surface was analysed, and its mechanisms were reviewed from two aspects of delayed transition and disturbance on boundary layer. In addition, drag reduction mechanisms with flexible surface were explored in terms of changing the structure in the boundary layer, moreover, its development tendency was prospected.
Untersuchungen über die histologische Struktur des Mandelkerngebietes

1 Diese Untersuchung geht von der von Brockhaus durchgeführten cytoarchitektonischen Gliederung des Mandelkerngebietes aus und hat sich die cytologische Vertiefung der bisher vorwiegend bei schwächerer bis zu 250facher-Vergrößerung erarbeiteten Cytoarchitektonik zum Ziel gesetzt . Die erste Mitteilung beschäftigt sich dabei mit dem Hauptkomplex des Mandelkerns, dem sogenannten Amygdaleum profundum nach Brockhaus, dessen Cytologie und Involution sie erschließt. Eine zweite Mitteilung wird dann das Problem des ventrodorsalen Entwicklungsgefälles des Amygdaleum Profundum zum Gegenstand haben. In einer weiteren Mitteilung soll die cytologische Gliederung einer vergleichendmorphologischen Untersuchung unterzogen werden .
Nanoparticles in Combating Neuronal Dysregulated Signaling Pathways: Recent Approaches to the Nanoformulations of Phytochemicals and Synthetic Drugs Against Neurodegenerative Diseases

Abstract As the worldwide average life expectancy has grown, the prevalence of age-related neurodegenerative diseases (NDDs) has risen dramatically. A progressive loss of neuronal function characterizes NDDs, usually followed by neuronal death. Inflammation, apoptosis, oxidative stress, and protein misfolding are critical dysregulated signaling pathways that mainly orchestrate neuronal damage from a mechanistic point. Furthermore, in afflicted families with genetic anomalies, mutations and multiplications of α-synuclein and amyloid-related genes produce some kinds of NDDs. Overproduction of such proteins, and their excessive aggregation, have been proven in various models of neuronal malfunction and death. In this line, providing multi-target therapies carried by novel delivery systems would pave the road to control NDDs through simultaneous modulation of such dysregulated pathways. Phytochemicals are multi-target therapeutic agents, which employ several mechanisms towards neuroprotection. Besides, the blood–brain barrier (BBB) is a critical issue in managing NDDs since it inhibits the accessibility of drugs to the brain in sufficient concentration. Besides, discovering novel delivery systems is vital to improving the efficacy, bioavailability, and pharmacokinetic of therapeutic agents. Such novel formulations are also employed to improve the drug’s biodistribution, allow for the co-delivery of several medicines, and offer targeted intracellular delivery against NDDs. The present review proposes nanoformulations of phytochemicals and synthetic agents to combat NDDs by modulating neuroinflammation, neuroapoptosis, neuronal oxidative stress pathways and protein misfolding.
Improvement of LED-based photoacoustic imaging using sign coherence factor based on lag-delay-multiply-and-sum beamformer

Delay-and-sum (DAS) is a widely utilized beamforming algorithm in PAI because of its simplicity and real-time capabilities. However, it leads to strong sidelobes and low resolution. A beamformer called delay-multiplyand-sum (DMAS) was proposed, which improved the image quality compared to DAS in PAI. We introduce a weighting factor for DMAS called LAG-DMAS-SCF based on the phase correlation of signals in photoacoustic imaging (PAI). In LAG-DMAS-SCF, the pair-wised signals from DMAS are reorganized in terms of lag and the sign coherence factor (SCF) is applied based on the new signal to the output of DMAS. We have tested the performance of this technique using simulated and experimental data. We have used an LED-based PAI system for the experimental setup. Results show that the proposed algorithm improves the image quality compared to DAS and DMAS by suppressing sidelobes and reducing noise. In phantom imaging, the proposed algorithm showed improvement of around 45 dB and 30dB in Signal-to-noise ratio (SNR), and improvement of around 30% and 20% in full-width-half-maxima (FWHM) compared to DAS and DMAS, respectively. Hence, this algorithm can be an appropriate choice for improving image quality in the LED-based PAI system.
Impact of Prolonged Absence of Organized Training on Body Composition, Neuromuscular Performance, and Aerobic Capacity: A Study in Youth Male Soccer Players Exposed to COVID-19 Lockdown

The aim of this study is to examine how physical performance has changed after 15 weeks (109 days) long-term absence of organized training in youth soccer players imposed by the stay at home orders. A total of sixty-eight young male soccer players from different age categories (U15, U16, U17 and U19) voluntarily participated in the prospective cohort study. Body fat percentage (BF%), counter-movement jump (CMJ), 30 m sprint, change-of-direction (COD) and yo-yo intermittent recovery test level-1 (YYIRTL-1) were evaluated twice (before and after the detraining period). Subsequently, 2 × 2 repeated measures ANOVA was used to investigate group and time differences in repeated measurements. A significance level of p < 0.05 was implemented. CV and SWC values were calculated to test the reliability of the tests performed at different times. Statistical analysis was performed using the IBM SPSS statistics software (v.25, IBM, New York, NY, USA). Significant increments in BF%, 30 m sprint, and COD (left and right), and also significant decrements in CMJ and YYIRTL-1, were found after the detraining period. A long-term detraining period due to the stay at home orders has a detrimental effect on body composition, neuromuscular performances, and aerobic capacity in youth soccer players.
HARS: A High-Available and Resource-Saving Service Function Chain Placement Approach in Data Center Networks

Network function virtualization (NFV) is a promising technology that decouples network functions from hardware. Connecting virtual network functions (VNFs) in series to form a service function chain (SFC) can flexibly orchestrate and expand network functions. However, there are higher availability requirements for SFCs. This paper aims to solve the SFC placement problem under availability and resource constraints. This paper proposes the sideway cross (SC) backup model, which considers the availability of both VNFs and physical machines (PMs) in a data center. The SC model cross-arranges the backup instances of VNFs to guarantee availability and optimize resource consumption. Then, this paper proposes the heuristic meteor shower optimization (MSO) algorithm to place SFCs. Compared to traditional heuristic algorithms, MSO can improve the execution time by approximately 200%. Combined with the SC backup model, MSO can effectively improve the availability and resource overhead. The evaluation results show that the proposed approach can guarantee higher availability and consumes fewer resources. The proposed approach only needs 75% of the resources to achieve the same availability as the state-of-the-art models.
Analysis of the Existence of Environmental Kuznets Curve: Evidence from India

The study aims to estimate the Environmental Kuznets Curve (EKC) in the Indian context for the period of 1991-2018 by considering the role of economic growth, renewable energy, foreign direct investment, stock market size, energy intensity, and private investment in the energy sector with the help of autoregressive distributed lag (ARDL) cointegration technique. The results confirm the existence of an inverted U-shaped EKC relationship between economic growth and CO2 emission level. However, the estimation of turnaround point shows that CO2 levels have kept increasing past the turnaround point, indicating that income disparity is a decisive factor in determining emissions and is a better indicator than national income. Furthermore, results confirm the negative impact of economic growth and stock market size on the environment. In contrast, renewable energy, foreign direct investment, and energy intensity positively impact the environment in the long run. However, the impact of private investment is insignificant. Though the Indian economy continued to grow during the last few decades, the minimal investment towards renewable energy may not help to reduce the environmental problems. Hence, the nation's income growth alone does not solve the ecological issues in the long run. In addition, it requires an increase in private investment towards the development of India's renewable energy sector.
Technologies to Relief Un-uniformity of Power Batteries Used in Electrical Vehicles

The un-uniformity of power batteries used in electrical vehicles (EVs) shorten battery service life,reduce battery system performance,and limit battery packs application. To relief the growth of un-uniformity during battery application, the authors structured a battery management system (BMS) for designing single cell protection circuit,and proposed battery pack state estimation. The technologies to resolve the problem could be as a good thermal management in design stage, an efficient electric protection with cell voltage monitoring and cell balancing typically in using stage, and an appropriate cell classification in assembling stage.
DLS: A Fast and Flexible Neural Network Training System with Fine-grained Heterogeneous Device Orchestration

Neural network accelerators (e.g., TPUs) have become mainstream devices in computing systems. Unfortunately, the existing accelerator-based systems for neural networks fail to fully leverage the acceleration opportunities due to the limited flexibility. Specifically, the majority of the accelerators focus on only the compute-intensive operations of neural networks (e.g., convolution and fully-connected layers). However, we identify that sub-optimal handling of auxiliary operations such as embedding and compression can incur non-trivial loss in terms of accuracy, training speed, and adaptability to new domains. The problem persists considering that recent advancements in neural networks often come from auxiliary operations. To effectively handle rapidly evolving auxiliary operations and maximize acceleration opportunities, we propose DLS, a holistic neural network acceleration system using heterogeneous computing devices. The key idea is to distribute compute-intensive operations on highly specialized ASICs for maximum performance, and auxiliary operations on flexible devices (e.g., FPGA, GPU) for better adaptability. We emphasize that a naive integration of different devices fails to deliver high performance due to high communication overheads. To address this communication inefficiency, we propose an efficient FPGA-based device orchestration utilizing direct device-to-device communication and fine-grained operation scheduling. In this way, our system alleviates the communication overhead between heterogeneous devices by removing expensive kernel stack traversal and leveraging computation units and communication links in parallel. The evaluation using popular neural networks with emerging auxiliary operations shows that our system achieves both flexibility and high performance for various cases from single-accelerator training to distributed training (2.6-8.9x speedup).
A Community Outbreak of Legionnaires’ Disease with Two Strains of L. pneumophila Serogroup 1 Linked to an Aquatic Therapy Centre

An outbreak of Legionnaires’ disease affected 18 people in Montpellier, a town of the south of France, between December 2016 and July 2017. All cases were diagnosed by a positive urinary antigen test. No deaths were reported. Epidemiological, environmental and genomic investigations (nested Sequence-Based Typing (nSBT) and whole genome sequencing) were undertaken. For the cases for which we had information, four had a new isolate (ST2471), one had a different new isolate (ST2470), one had a genomic pattern compatible with the ST2471 identified by nSBT (flaA = 3), and one had a genomic pattern not compatible with two previous identified STs (pilE = 6). The analysis conducted on the pool of an aquatic therapy center revealed seven isolates of Legionella pneumophila. Whole genome analysis confirmed the link between the environmental and clinical isolates for both ST2470 and ST2471. As the outbreak occurred slowly, with several weeks between new cases, it was not possible to immediately identify a common source. The sixth case was the first to report having aquatic therapy care. Of the 18 cases, eight had attended the aquatic therapy center and the other 10 were inhabitants who lived, worked or walked close to the center. The main cause for this outbreak was the lack of facility maintenance. This investigation highlights the risk to public health of aquatic therapy centers for users and nearby populations, and emphasizes the need for risk reduction measures with specific guidelines to improve health and safety in aquatic facilities.
Implementation of surface electromyography controlled prosthetics limb based on recurrent neural network

Low‐cost traditional prosthetic legs, available worldwide, can make walking and stair climbing possible but still difficult. This article presents the hardware implementation of the surface electromyography (sEMG) powered prosthesis actuation (PPA) system using a learned neural network algorithm based on recurrent neural network (RNN), which is used to train sEMG benchmark databases, and predict joint angle. This implementation was created based on sEMG signal measurements. The data were collected from three benchmark datasets describing different subjects during performance, and analyzing various gait patterns were used to construct the neural network and reduce significant model errors in a real‐time setting. Processing circuits, interfacing the output with the controller board, signal amplification, motor driving circuits, and single‐board computer programming are included in the implementation.
A Pilot Randomized Control Trial of Teens Taking Charge: A Web-based Self-management Program for Adolescents with Cancer

Background: There is a lack of self-management tools for adolescents with cancer (AWC). This study evaluated the feasibility of Teens Taking Charge Cancer, a web-based self-management program. Methods: A pilot randomized control trial (RCT) was conducted across 4 pediatric oncology clinics. AWC (12–18 years) and their caregivers were randomized to either the intervention or control group. All were asked to complete 12 website modules over 12 weeks (at their own pace) and received monthly calls from health coaches. The intervention website was based on cognitive behavioral principals, designed as an interactive self-guided online program, while the control consisted of education and included links to 12 general cancer websites. Outcome assessments occurred at enrollment and 12 weeks post-intervention. The primary outcomes included rate of accrual and retention, adherence to the protocol, acceptability and satisfaction with intervention using questionnaire and semi-structured interviews, adverse events and engagement with the intervention. Results: Eighty-one teen-caregiver dyads were enrolled with a retention rate of 33%. In the intervention group 46% (n = 18) logged in at least once over the 12-week period. A mean of 2.4 of 12 modules (SD 3.0) were completed; and no one completed the program. Thirty-three percent of caregivers in the intervention logged into the website at least once and none completed the full program. Discussion: The results from this pilot study suggest that the current design of the Teens Taking Charge Cancer RCT lacks feasiblity. Future web-based interventions for this group should include additional features to promote uptake and engagement with the program.
Traveling Waves Measurement with Distribution Transformers

A new method using distribution transformer to measure traveling waves is proposed to solve the problem of difficulty of catching traveling wave signals in feeders' terminal.The requirements of fault location signals to measurement transformers are analyzed.Equivalent circuit of transformer for analyzing transfer characteristic of traveling waves is built,and the transfer characteristic is studied using parameters of practical transformers.The results show that,distribution transformers can transfer traveling waves surge effectively and meet the needs of fault location based on traveling waves.The transfer characteristics and rules of three-phase distribution transformers are further investigated.The results show that although line- to-line voltage of stable state after single-phase to-earth fault in the neutral non-effective grounding system keeps invariable, transient process of the fault produces aerial mode component of traveling waves that can be transferred effectively to transformers' low-voltage side.Based on this,the idea is proposed that traveling waves generated by different type of faults can be measured effectively with only two line-to-line-voltage signals in transformers' low-voltage side.Experimental tests verify the feasibility and correctness of the proposed method.
Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier.
A Model of Scientific Data Reasoning

Data reasoning is an essential component of scientific reasoning, as a component of evidence evaluation. In this paper, we outline a model of scientific data reasoning that describes how data sensemaking underlies data reasoning. Data sensemaking, a relatively automatic process rooted in perceptual mechanisms that summarize large quantities of information in the environment, begins early in development, and is refined with experience, knowledge, and improved strategy use. Summarizing data highlights set properties such as central tendency and variability, and these properties are used to draw inferences from data. However, both data sensemaking and data reasoning are subject to cognitive biases or heuristics that can lead to flawed conclusions. The tools of scientific reasoning, including external representations, scientific hypothesis testing, and drawing probabilistic conclusions, can help reduce the likelihood of such flaws and help improve data reasoning. Although data sensemaking and data reasoning are not supplanted by scientific data reasoning, scientific reasoning skills can be leveraged to improve learning about science and reasoning with data.