Miracle-dz/UniFAR-FTUs huggingface dataset:
Fdsa-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction

With the development of transportation and the ever-improving of vehicle technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully dynamic self-attention Spatio-Temporal Graph Networks (Fdsa-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which testifies high model adaptability to the characteristics of the actual observed traffic flow (TF).
Security Assurance Model of Software Development for Global Software Development Vendors

The number of security attacks and the impact has grown considerably in the recent several years. As a result, new emerging software development models are required that assist in developing software that is secure by default. This article reviews the most widely used security software models. It proposes a new Security Assurance Model (SAM) for Software Development that is adaptable to all contemporary scenarios, emphasizing global software development (GSD) vendor companies. The SAM of Software Development was developed after studying 11 well-known development models and analyzing results obtained from a systematic literature review (SLR) and questionnaire survey. The SAM of Software Development consists of seven security assurance levels: Governance and Security Threat Analysis, Secure Requirement Analysis, Secure Design, Secure Coding, Secure Testing and Review, Secure Deployment, and Security Improvement. The security assurance levels of SAM of software development consist of 46 critical software security risks (CSSRs) and 388 practices for addressing these risks. The proposed SAM of Software Development was assessed based on a tool created by Motorola, which is used to evaluate the present state of a company’s software processes and find areas for improvement. We conducted 3 case studies on software development companies, using data from real software projects to examine the results of a practical experiment in each company. The results of the case studies indicate that the proposed SAM of Software Development helps measure the security assurance level of an organization. In addition, it can potentially serve as a framework for researchers to develop new software security measures.
Methods for Minimization and Management of Variability in Long Term Groundwater Monitoring Results

Abstract : High variability in groundwater monitoring results makes it difficult to accurately evaluate remedy effectiveness and to predict remediation timeframes. In order to determine the effect of sample collection method on monitoring variability, five methods for collecting groundwater samples from monitoring wells were compared at two sites in Texas and California with chlorinated VOC plumes. The five sample collection methods evaluated included three variations of low-flow purge sampling: low-flow with purge to parameter stability (Low-Flow Standard), low-flow with fixed small volume purge (3 Liters), and low-flow with fixed large volume purge (18 Liters), and two no purge methods using commercial devices: Active No Purge (HydraSleeve) and Passive No Purge (SNAP samplers). Eight monitoring wells were sampled at each site six times using each sampling method at approximately two month intervals, resulting in a total of 480 groundwater samples. For the dataset as a whole, the five sample methods yielded relatively small differences in VOC concentration ( /-20 ), and with the exception of the Active No Purge (HydraSleeve) method at the California site, only small differences in variability between sample events. For the purge methods tested, results indicate that low flow purge sampling using a fixed purge volume provides results of equal quality compared to standard low flow purge sampling based on field parameter stability. The overall results suggest that for most monitoring well sampling methods tested, data quality was independent of method. In that context, factors such as cost, ease of implementation, and sample volume requirements become prominent deciding factors in sample method selection.
Are Vaccinations Alone Enough to Curb the Dynamics of the COVID-19 Pandemic in the European Union?

I use the data on the COVID-19 pandemic maintained by Our Word in Data to estimate a nonstationary dynamic panel exhibiting the dynamics of confirmed deaths, infections and vaccinations per million population in the European Union countries in the period of January–July 2021. Having the data aggregated on a weekly basis I demonstrate that a model which allows for heterogeneous short-run dynamics and common long-run marginal effects is superior to that allowing only for either homogeneous or heterogeneous responses. The analysis shows that the long-run marginal death effects with respect to confirmed infections and vaccinations are positive and negative, respectively, as expected. Since the estimate of the former effect compared to the latter one is about 71.67 times greater, only mass vaccinations can prevent the number of deaths from being large in the long-run. The success in achieving this is easier for countries with the estimated large negative individual death effect (Cyprus, Denmark, Ireland, Portugal, Estonia, Lithuania) than for those with the large but positive death effect (Bulgaria, Hungary, Slovakia). The speed of convergence to the long-run equilibrium relationship estimates for individual countries are all negative. For some countries (Bulgaria, Denmark, Estonia, Greece, Hungary, Slovakia) they differ in the magnitude from that averaged for the whole EU, while for others (Croatia, Ireland, Lithuania, Poland, Portugal, Romania, Spain), they do not.
Carbon Metabolism of a Soilborne Mn(II)-Oxidizing Escherichia coli Isolate Implicated as a Pronounced Modulator of Bacterial Mn Oxidation

Mn(II)-oxidizing microorganisms are generally considered the primary driving forces in the biological formation of Mn oxides. However, the mechanistic elucidation of the actuation and regulation of Mn oxidation in soilborne bacteria remains elusive. Here, we performed joint multiple gene-knockout analyses and comparative morphological and physiological determinations to characterize the influence of carbon metabolism on the Mn oxide deposit amount (MnODA) and the Mn oxide formation of a soilborne bacterium, Escherichia coli MB266. Different carbon source substances exhibited significantly varied effects on the MnODA of MB266. A total of 16 carbon metabolism-related genes with significant variant expression levels under Mn supplementation conditions were knocked out in the MB266 genome accordingly, but only little effect on the MnODA of each mutant strain was accounted for. However, a simultaneous four-gene-knockout mutant (namely, MB801) showed an overall remarkable MnODA reduction and an initially delayed Mn oxide formation compared with the wild-type MB266. The assays using scanning/transmission electron microscopy verified that MB801 exhibited not only a delayed Mn-oxide aggregate processing, but also relatively smaller microspherical agglomerations, and presented flocculent deposit Mn oxides compared with normal fibrous and crystalline Mn oxides formed by MB266. Moreover, the Mn oxide aggregate formation was highly related to the intracellular ROS level. Thus, this study demonstrates that carbon metabolism acts as a pronounced modulator of MnODA in MB266, which will provide new insights into the occurrence of Mn oxidation and Mn oxide formation by soilborne bacteria in habitats where Mn(II) naturally occurs.
A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.
Machine Learning Models using Paprika Leaf Growth Forecast Based on Environmental and Energy Data

Paprika (Capsicum annuum L) is an extremely popular and widespread plant species in South Korea. The purpose of this paper is to develop the prediction method for Paprika growth and compare the leaf count of two areas of paprika (R1 and R2 mean Row planting) through investigation of the Greenhouse with the different environmental factors influencing its growth. The objectives of this paper are the following: (1) to use Machine Learning (ML) approach for crop growth prediction in greenhouse agriculture; (2) to research on the correlativity among different weather factors like input temperature, output temperature, wind speed, dew point, CO2, and humidity are connected to Paprika growth at the field level; and (3) to test growth data using the predictive machine-learning models Support Vector Machine (SVM), generic Random Forest (RF), Gradient Boosting Machines (GBM) and eXtreme Gradient Boosting (XGB). Compared to the principal component regression the machine learning models show the best skills in predicting Paprika growths. The Support Vector Machine method is used to provide the best performance in predicting Paprika growth. While measurements of one production period can predict crop development with sensible requirements, we need more attempts to allow this approach in several fields in the region.
Flexibility Improvement on Energy Consumption of Punching Machine Based on Green Manufacturing Method at PT Buana Intan Gemilang

These days, Green Manufacturing has established become a new technology for professional engineers and manufacturing workers to be an important part in the business world. Green Manufacturing was created to reflect the new manufacturing paradigm techniques to make something become more eco-efficient by reducing the energy consumption. PT Buana Intran Gemilang is one of Textile Company that produce curtain and prayer rug. In the production this company use a Jacquard punching machine designed to cut a hole in material such as card stock. The machine works manually with the flexibility less than 90 percent that led to the longest production time in meeting the demand, thus causing the electrical energy released for this machine is also very high. On the basis of these problems required a system to improve the flexibility of the machine in order to reduce energy consumption and make the company become greener environmentally friendly. From this research generated an automation system based on green manufacturing method which will be applied on a jacquard punching machine that can increase flexibility machine to be more than 90 percent. This system has saving the electricity consumption of 2092 KWh every making a complete fabric pattern.
Numerical investigation of the landing manoeuvre of a crew transfer vessel to an offshore wind turbine

ABSTRACT Crew transfer vessels are a fast and cost-effective means of transportation to bring service personnel and technical equipment to an offshore wind turbine. The landing manoeuvre of such service vessels is a complex coupled process involving the ship's motion in the seaway and the frictional contact between the fender and the boat landing. With the help of numerical simulations, we aim to identify the limit conditions under which a safe crew transfer can still be ensured, and to investigate innovative transfer concepts which serve to avoid severe accidents. In our research, we draw on a partitioned solution strategy which has already been successfully applied to other sophisticated multi-field phenomena and which allows to reuse specialised existing solvers for the solution of the involved subproblems.
L-carnitine Improves Atherosclerotic Plaques in Hyperlipidemic Animal Model (Hyperlipidemic Rabbits)

U SE of L-carnitine (L-c.) bears potentials of decreasing hyperlipidemia following fat diet feeding. 25 Animal (rabbits) were divided randomly into five groups . Normal group were kept on a plain chow diet for 75 days. HC classes were on a diet (hypercholesterolemic diet) for seventy-five days. Atorvastatin group were fed the same diet and received atorvastatin orally (20mg/kg/day) from day forty-five for thirty days. L-carnitine group were fed the same diet and intraperitoneal L-c. (250 mg/kg/day). Treatment group were fed the same diet and atorvastatin orally (l0 mg/kg/day) and L-carnitine intraperitoneally (125 mg/kg/day). Histology of aorta and biochemistry of serum were carried out. Foam cells with interrupted endothelial lining and transformed muscle fibers were observed in aorta wall in hypercholesterolaemic rabbits. Normal aorta wall was observed in rabbits of Atrovastatin and treatment groups. In rabbits of HC group triglyceride was significantly increased compared to that of other groups (P<0.05). The animals that received L-carnitine showed significantly reduced triglyceride (P<0.05). L-Carnitine reduces atherosclerotic plaques.
Standardised method for cardiomyocyte isolation and purification from individual murine neonatal, infant, and adult hearts

Primary cardiomyocytes are invaluable for understanding postnatal heart development. However, a universal method to obtain freshly purified cardiomyocytes without using different agedependent isolation procedures and cell culture, is lacking. Here, we report the development of a standardised method that allows rapid isolation and purification of high-quality cardiomyocytes from individual neonatal through to adult C57BL/6J murine hearts. Langendorff retrograde perfusion, which is currently limited to adult hearts, was adapted for use in neonatal and infant hearts by developing an easier in situ aortic cannulation technique. Tissue digestion conditions were optimised to achieve efficient digestion of hearts of all ages in a comparable timeframe (<14 min). This resulted in a high yield (1.56-2.2×106 cells/heart) and viability (~70-100%) of cardiomyocytes post-isolation. An immunomagnetic cell separation step was then applied to yield highly purified cardiomyocytes (~95%) as confirmed by immunocytochemistry, flow cytometry, and qRT-PCR. For cell-type specific studies, cardiomyocyte DNA, RNA, and protein could be extracted in sufficient yields to conduct molecular experiments. We generated transcriptomic datasets for neonatal cardiomyocytes from individual hearts, for the first time, which revealed nine sex-specific genes (FDR<0.05) encoded on the sex chromosomes. Finally, we also developed an in situ fixation protocol that preserved the native cytoarchitecture of cardiomyocytes (~94% rodshaped post-isolation), and used it to evaluate cell morphology during cardiomyocyte maturation, as well as capture spindle-shaped neonatal cells undergoing cytokinesis. Together, these procedures allow molecular and morphological profiling of high-quality cardiomyocytes from individual hearts of any postnatal age.
Phase coherence of 0.1 Hz microvascular tone oscillations during the local heating

The origin of the mechanisms of blood flow oscillations at low frequencies is discussed. It is known that even isolated arteriole demonstrates oscillations with the frequency close to 0.1 Hz, which is caused by the synchronous activity of myocyte cells. On the other hand, oscillations with close frequency are found in the heart rate, which are associated with quite different mechanism. The main purpose of this work is to study phase coherence of the blood flow oscillations in the peripheral vessels under basal and perturbed conditions. Local heating which locally influences the microvascular tone, as one of currently elucidated in sufficient detail physiological test, was chosen. During such provocation blood flow though the small vessels significantly increases because of vasodilation induced by the local synthesis of nitric oxide. In the first part of the paper microvascular response to the local test is quantified in healthy and pathological conditions of diabetes mellitus type 1. It is obtained that regardless of the pathology, subjects with high basal perfusion had lower reserve for vasodilation, which can be caused by the low elasticity of microvascular structure. Further synchronization of pulsations of the heated and undisturbed skin was evaluated on the base of wavelet phase coherency analysis. Being highly synchronised in basal conditions 0.1 Hz pulsations became more independent during heating, especially during NO-mediated vasodilation.
Design and Analysis of a Magnetic Field Screw Based on 3-D Magnetic Field Modulation Theory

This paper investigates the 3-D magnetic field modulation theory of the magnetic screw transmission. A magnetic field modulation screw (MFMS) is proposed to prove this theory. The MFMS consists of three parts, including the inner part and outer part carrying the helically permanent-magnet (PM) with different pole-pairs. The intermediate part has a set of helically ferromagnetic pole-pieces, which modulate the helical PM magnetic field. The innovation of this MFMS is that it can achieve fully helical magnetic coupled, which satisfies the 3-D modulation in the circumferential and axial direction, respectively. Moreover, the intermediate part can move in both rotation and linear motion without PMs. Therefore, it is very suitable for long-stroke, high force, and two-degree of freedom applications. Afterward, the prototype of a magnetic screw is built to verify the magnetic transmission capability. The effectiveness of the proposed approach is verified by both simulation and experiment.
Bayesian Estimation for Kumaraswamy Shanker Distribution with applications of COVID-19 data

Abstact In this paper, a new three-parameter distribution called the Kumaraswamy Shanker (Kw-Sh) distribution is proposed and studied. The reliability (survival) function, hazard rate function, reversed hazard rate function r and the cumulative hazard rate function of the new distribution is obtained. Some mathematical properties of this distribution such as moments, moments generating function, incomplete moments, quantile function, entropies (Renyi and Shannon) and mean deviation are derived. The method of maximum likelihood and Bayesian estimation method are used to estimate the distribution parameters. Finally, two real datasets, related to Covid-19, were used to examine the performance of the proposed distribution compared to Shanker and exponential distributions based on Akaike information criterion (AIC). The results showed that the new distribution is more proper in fitting data than other distributions. Also, the estimation of parameters using the Bayesian method is better than the maximum likelihood method