Miracle-dz/UniFAR-FTUs huggingface dataset:
Paradise: Real-Time, Generalized, and Distributed Provenance-Based Intrusion Detection

Identifying intrusion from massive and multi-source logs accurately and in real-time presents challenges for today's users. This article presents Paradise, a real-time, generalized, and distributed provenance-based intrusion detection method. Paradise introduces a novel extract strategy to prune and extract process feature vectors from provenance dependencies at the system log level, and it stores them in high-efficiency memory databases. Using this strategy, Paradise does not depend on the specific operating system type or provenance collection framework. Provenance-based dependencies are calculated independently during the detection phase, thus, Paradise can negotiate all detection results from multiple detectors without extra communication overhead between detectors. Paradise also employs an efficient load-balanced distribution scheme that enhances the Kafka architecture to efficiently distribute provenance graph feature vectors to the detectors. The experimental results demonstrate that our method has a high detection accuracy with a low time overhead.
SPHT: A scalable and high‐performance hashing scheme for persistent memory

The evolution of persistent memory (PM) has significantly affected the design of today's indexing structures. Hashing‐based structures are widely used in storage systems to achieve fast query responses. Recently, several concurrent and failure‐atomic hashing schemes for PM have been proposed to improve the scalability. However, these works still suffer from limited scalability, especially under write‐intensive workloads or at a high number of threads. Our empirical study concludes three issues harm the scalability of PM hashing schemes: the NUMA effects, the resizing operations, and the inter‐thread interference overhead in PM. Based on the above scalability issues, we present SPHT, a scalable and persistent hashing scheme for hybrid DRAM‐PM memory. To eliminate the NUMA effects, SPHT maintains a hash subtable for every NUMA node and stores the key‐value items in the designated nodes. By doing so, all operations can be executed in the local memory without cross‐node communication. The hash subtable consists of two components: the search layer in DRAM for fast accesses and the data layer in PM for efficient persistence. The data layer is organized in a log‐structured way and its log chunks own separate PM space, thus avoiding the resizing operations in PM. Meanwhile, the compacted log structure also supports batching multiple small items, which effectively reduces the persistence overhead. Furthermore, to maximize concurrency, we assign threads to different partitions in the data layer to reduce the inter‐thread interference overhead. On Intel Optane DCPMM, our evaluations show that SPHT scales well and achieves up to 2.7 × higher performance than state‐of‐the‐art PM hashing schemes under YCSB workloads.
The COVID–19 Pandemic’s Impact on the Social Economy in European Countries

Since the outbreak of the COVID–19 pandemic, there have been 1 billion identified cases and more than 2 million deaths around the world. The current global problem of the pandemic, with the introduction of unpopular measures such as border closures and total quarantine, revealed a dilemma between economic growth and maintaining human health and intensifying traditional global problems such as hunger, poverty, and social inequality to new levels. Even countries with social economic models were confronted with the negative effects of the pandemic. The aim of the article is to examine the developmentof social economy model factors (Liberal, Continental, Scandinavian, Mediterranean, and Transitive) during a pandemic. The objects of study arecountries thatare centers of social economy models (Belarus, Slovakia, Ireland, Sweden, and Poland). The research method is factor modeling of global and national (local) social factors thathaddifferent consequences of the pandemic in the countries. Countries with different social economy models can use the results and recommendations to develop social policy to counter the pandemic.
Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network

Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field of the loaded object from the photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of the fringe pattern is affected by the geometry of the specimen and experimental configuration. When the loaded object produces complex fringe distribution, the traditional stress analysis methods still face difficulty in unwrapping. In this study, a deep convolutional neural network based on the encoder–decoder structure is proposed, which can accurately decode stress distribution information from complex photoelastic fringe images generated under different experimental configurations. The proposed method is validated on a synthetic dataset, and the quality of stress distribution images generated by the network model is evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and other evaluation indexes. The results show that the proposed stress recovery network can achieve an average performance of more than 0.99 on the SSIM.
Flexible power generation scenarios for biogas plants operated in Germany: impacts on economic viability and GHG emissions

Biogas plants enable power to be generated in a flexible way so that variable, renewable energy sources can be integrated into the energy system. In Germany, the Renewable Energy Sources Act promotes flexible power generation in biogas plants. Two existing biogas plants in flexible operation were analyzed with respect to economic viability and greenhouse gas (GHG) emissions to assess the feasibility of flexible operation. To do this, a biogas technology simulation model was developed to reproduce the technical design of both biogas plants and to link this design with twelve flexibilization scenarios. The evaluation of the economic viability is based on a discounting method of investment appraisal. For assessing the level of GHG emissions, the life cycle assessment method has been applied. The results show that the profitability of flexibilization is contingent upon premium payments promoting flexibility and direct sales resulting from a higher electrical efficiency of new or additionally installed combined heat and power units. Overall, with respect to profitability, the results of the flexible power generation scenarios are dependent upon the properties of the technical plant, such as its power generation and gas storage capacities. Relative GHG emissions from flexible biogas plants show significantly lower values than for referenced fossil gas–steam power stations. Among the various scenarios, the results reveal that the level of GHG emissions especially depends on the number of operating hours of the additional combined heat and power unit(s). The results of the analyzed biogas plants showed no direct correlation between GHG emissions and the economic benefits. Overall, a flexible power generation of biogas plants may improve the economic viability as well as result in lower GHG emissions in comparison with a conventional base load operation. © 2016 The Authors. International Journal of Energy Research published by John Wiley & Sons Ltd.
Physiochemical Role of Nanoparticles in Solid Fuel Cells, Production and Applications in Physics and Chemical Sciences

Demand of fuel cells have been increased due to the high potential and incredibly efficient power sources. They have been wide range of applications such as portable fuel cells in laptops, up to huge stationary installations to power data centers. Ordinary cells are not providing good reliability and quality of power while on the other hand, fuel cells with efficient metal body allows the good reliability and quality of power provided does not degrade over time. Fuels cell that comprised of carbon nanotubes have been efficiently used for the fabrication process due to fine catalysis of adoption major barrier to many applications for power generation for commercial, industrial and residential buildings and in remote or inaccessible areas. The carbon-nanotubes are made up of smaller particles of carbon with allow the network of fine fibers while on the other hand, ordinary cells can be designed in storing hydrogen, as per the requirements. Porous Platinum nanotubes nanoporous have been designed to adopt the porosity and flow of the carbon particles for generation of energy. Different oxidants can be employed for fuel adjustments and other site reactions but oxygen is preferable due to their availability. It is easy to maintain the fuel cells adjustment through the use of nanotechnology to the industrials processes for manufacturing of variety of new materials.
A Randomized, Controlled Trial Exploring Collaborative Nursing Intervention on Self-Care Ability and Blood Glucose of Patients with Type 2 Diabetes Mellitus

Objective For determining the impacts of collaborative nursing intervention (CNI) on self-care ability and blood glucose (BG) of patients with type 2 diabetes mellitus (T2DM). Methods The study enrolled 72 T2DM patients, who are referred to our hospital between April 2017 and September 2019. Of them, 35 cases given routine nursing were set as the control group (CG) and 37 cases given CNI were set as the research group (RG). The Exercise of Self-Care Agency (ESCA) scale scores and the levels of fasting plasma glucose (FPG) as well as glycosylated hemoglobin (HbAlc) were observed pre- and postintervention. The scores of SAS and HAMD and Morisky pre- and postnursing intervention as well as postnursing SF-36 scores and patients' satisfaction toward the nursing content were recorded. Results After intervention, RG presented notably lower serum HbAlc and FPG levels than CG (P < 0.05); RG presented evidently lower SAS and HAMD scores while distinctly higher Morisky, SF-36, and ESCA scores than CG (P < 0.05); the nursing satisfaction in RG and CG was 97.30% and 51.43%, respectively. Conclusions In view of the fact that CNI can decrease HbAlc and FPG levels in patients with T2DM and enhance their self-care ability, it is worth popularizing in the clinic.
Clinical and radiographic evaluation of minimally invasive plate osteosynthesis (MIPO) in dogs with tibial fractures

The aim of this study was a clinical and radiographic evaluation of the minimally invasive plate osteosynthesis (MIPO) method for treatment in dogs with tibial fractures. Ten dogs of different breeds, ages, and sex with tibial fractures were used as the subjects. A medial approach to the tibia was used in all the cases. The tibial tuberosity was palpated proximally and a short incision was made along the midline. After reduction of the fracture, proximal and distal insertion incisions were made to approach the tibia. The mean times for the MIPO operations were 21 minutes. The patients were able to stand and walk postoperatively on the first day and recovery was rapid for all of the dogs. The fracture healing was completed on day 45 based on the radiographic evaluations of these cases. Dogs with tibia fractures treated by MIPO healed rapidly without any complications (90%). Correct fracture selection is important for the use of MIPO to be successful. Further studies with a larger group of dogs should be considered to compare the efficacy of MIPO and other fixation techniques and for the long-term evaluation of the repaired tibial fractures.
Resident and transient coyotes exhibit differential patterns of movement behavior across heterogeneous landscapes in the southeastern United States

Abstract Coyotes (Canis latrans) are a highly adaptable canid species whose behavioral plasticity has allowed them to persist in a wide array of habitats throughout North America. As generalists, coyotes can alter movement patterns and change territorial strategies between residency (high site fidelity) and transiency (low site fidelity) to maximize fitness. Uncertainty remains about resident and transient coyote movement patterns and habitat use because research has reached conflicting conclusions regarding patterns of habitat use by both groups. We quantified effects of habitat on resident and transient coyote movement behavior using first passage time (FPT) analysis, which assesses recursive movement along an individual's movement path to delineate where they exhibit area‐restricted search (ARS) behaviors relative to habitat attributes. We quantified monthly movement rates for 171 coyotes (76 residents and 53 transients) and then used estimated FPT values in generalized linear mixed models to quantify monthly habitat use for resident and transient coyotes. Transients had greater movement rates than residents across all months except January. Resident FPT values were positively correlated with agricultural land cover during fall and winter, but negatively correlated with agriculture during spring. Resident FPT values were also negatively correlated with developed habitats during May–August, deciduous land cover during June–August, and wetlands during September–January except November. FPT values of transient coyotes were positively correlated with developed areas throughout much of the year and near wetlands during July–September. Transient FPT values were negatively correlated with agriculture during all months except June and July. High FPT values (ARS behavior) of residents and transients were generally correlated with greater densities of edge habitat. Although we observed high individual variation in space use, our study found substantive differences in habitat use between residents and transients, providing further evidence that complexity and plasticity of coyote habitat use is influenced by territorial strategy.
Prevalence and Risk Factors Analysis of Carotid Stenosis Among Ischaemic Heart Diseases Patient in Bangladesh: A Cross-Sectional Study

Purpose This study aimed to evaluate the prevalence of carotid stenosis among ischaemic heart disease (IHD) patients undergoing coronary artery bypass graft (CABG) surgery and identify risk factors associated with carotid stenosis at a tertiary-level hospital in Bangladesh. Patients and Methods This cross-sectional study examined 200 IHD patients scheduled for isolated and elective CABG surgery, and multivariate regression analysis was used to determine the impact of independent variables on carotid stenosis with coronary artery disease. A vascular surgeon and sonographer assessed carotid stenosis, and the severity of stenosis was classified according to the current Grayscale and Doppler US diagnosis models. Results We observed that the prevalence of carotid artery stenosis was 13.5%, and the male was significantly higher (85.2%) in the carotid stenosis group. A multivariate regression analysis observed that age (OR 1.79), dyslipidaemia (OR 2.19), uncontrolled hypertension (OR 2.38), uncontrolled DM (OR 2.51), multivessel coronary artery disease (OR 3.79), and multiple comorbidities (OR 4.46) are potential predictors of having significant carotid stenosis in a patient undergoing CABG surgery. Conclusion In Bangladesh, multivessel coronary artery disease, especially in elderly patients with multiple comorbidities, are 4 (four) times higher risk to have significant carotid artery stenosis. Preoperative carotid duplex screening should be performed to curtail the risk of postoperative adverse cerebrovascular events, particularly those who have carotid stenosis associated potential risk factors.
A hierarchical parametric analysis on Hopf bifurcation of an epidemic model

A common task in studying nonlinear dynamical systems is to derive the conditions on stability and bifurcations, which becomes difficulty when the system contain multiple parameters. In particular, finding the explicit conditions under which Hopf bifurcation can occur is not easy and becomes very involved even for simple models. In this paper, an epidemic model is presented to illustrate how to use a hierarchical parametric analysis for bifurcation study, in particular to demonstrate how to choose proper parameters as bifurcation parameters, how to deal with other "control" parameters, and how to derive the conditions on stability and Hopf bifurcation, which are explicitly expressed in terms of system parameters.
RTA-Assisted Type Well Construction in Montney Tight Gas Reservoir from Western Canada Sedimentary Basin

Tight gas reservoirs are mainly developed by multistage hydraulic fracturing horizontal wells (MSHFHWs). A type well provides average production profiles based on real well data and can be constructed from multiple wells to investigate the behavior of the reservoir. For unconventional reservoirs, type wells are the key to reserve calculations and medium- and long-term field development planning. Both geological and completion parameters are key factors affecting single well performance of MSHFHWs. Based on the drilling, hydraulic fracturing, and production data for over 1,800 MSHFHWs in the Montney tight gas reservoir in the Groundbirch region of the Western Canada Sedimentary Basin (WCSB), the main hydraulic fracturing factors affecting the production performance of MSHFHWs were investigated. A rate transient analysis- (RTA-) assisted workflow for type well construction is proposed based on existing production data and considering the geological and engineering factors. Based on the field data, the main hydraulic fracturing factors that affect the production performance of the MSHFHWs in Montney are lateral length, proppant tonnage, and the number of stages. Base type wells are predicted from the P50 wells, which are selected from the wells with normalized lateral length and the same fracturing technique and proppant tonnage. The base type well represents the well performance for a specific drilling and completion background. RTA was introduced to scale up the base type well to predict the type well of new completion design. The new workflow predicts both the base type well with a specific drilling and completion background and the upgraded type well, which uses new completion design. It is highly meaningful and provides a valuable reference to practical studies involving type well prediction in unconventional gas reservoirs.
Special Education Teacher Preparation, Literacy Instructional Alignment, and Reading Achievement for Students With High-incidence Disabilities

We used survey and administrative data from Washington State to assess the degree to which special education teacher preparation, district literacy instructional practices, and the alignment between preparation and practice were associated with the reading test score gains of students with high-incidence disabilities taught by early-career special education teachers in Grades 4 to 8. These students tended to have larger reading gains when their districts used evidence-based literacy decoding practices (e.g., phonological awareness, phonics, and reading fluency) and when their special education teacher graduated from a teacher education program that also emphasized these practices. Students with high-incidence disabilities in districts that used balanced literacy practices tended to have lower reading gains. Finally, students with high-incidence disabilities taught by early-career special education teachers tended to have larger reading gains when their teacher’s student teaching placement was supervised by a more experienced cooperating teacher.
Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market.The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007–2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.
SENMQTT-SET:An Intelligent Intrusion Detection in IoT-MQTT Networks Using Ensemble Multi Cascade Features

Recently, the number of Internet of Things (IoT) networks has been grown exponentially, which results in more data sharing between devices without appropriate security mechanisms. Since huge data management is involved, maintaining the time constraints between the devices in IoT networks is another significant issue. To address these issues, an intelligent intrusion detection system has been adapted to recognize or predict a cyber-attack using Elite Machine Learning algorithms (EML), and a lightweight protocol is used to manage the time-constrained issue. The experimental analysis of work is done on a testbed setup with the hardware and sensors connected using a lightweight Message Queue Telemetry Transport (MQTT) protocol. This comprises three parts: (i) collection of data with the help of a sensor for three different scenarios called SEN-MQTTSET; (ii) multi-context feature generation using an ensemble statistical multi-view cascade feature generation algorithm from the SEN-MQTTSET dataset; and (iii) evaluating the dataset using ML algorithms. The SEN-MQTTSET dataset has been created from the three scenarios, such as normal, attack on a subscriber, and attack on a broker. The multi-context feature is generated from the raw dataset using an ensemble statistical multi-view cascade feature generation algorithm. The EML is proposed to select the best model for intrusion detection among ML algorithms such as Logistic Regression, K-Nearest Neighbour, Random Forest, Naive Bias, Support Vector Machine, Gradient Boosting, and Decision Tree by the performance metrics such as accuracy, prediction time, F1-score, and others. The proposed dataset is validated and the accuracy is found to be above 99considered system model. Different quality parameters have been carried out for legitimate and attack traffic features to calculate the delay between the IoT-MQTT network.