Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of the Ordinal Scale Classification Algorithm for Cyber Threat Intelligence Based on Deception Technology

Cyber deception technology plays an important role in monitoring attackers’ activities and detecting new attack types. However, in a deceptive environment, low-risk attack traffic, such as scanning, is included in large quantities and acts as noise. Therefore, even though high-risk traffic is actually present, it may be overlooked, or the analysis algorithm’s accuracy regarding traffic may be reduced, causing significant difficulties in intrusion detection and analysis processes. In this study, we propose a model that can identify and filter the ordinal scale risk of the source IP in deceptive environment-generated traffic. This model aims to quickly classify low-risk attacks, including information gathering and scanning, which are widely and repeatedly performed, as well as high-risk attacks, rather than classifying specific types of attacks. Most existing deceptive technology-based Cyber Threat Intelligence (CTI) generation studies have been limited in their applicability to real-world environments because data labeling, learning, and detection processes using AI algorithms that consume significant amounts of time and computing resources. Here, the Naive Bayes discriminant analysis-based ordinary scale classification model showed higher accuracy for low-risk attack classification, while consuming significantly fewer resources than the models presented in other studies do. The accuracy of the current active deceptive environment traffic analysis research may be enhanced by filtering low-risk traffic via preprocessing.
OPTIMAL DESIGN OF FAULT TOLERANT SENSOR NETWORKS

The selection of measurements is one of the most important problems in the design of process instrumentation. This paper is concerned with the design of sensor networks such that the observability of the variables which are necessav for the process control remains satisfied in the presence of sensor failures. Pseudo-minimal and minimal sensor sets are organized into an oriented graph which contains all the possible reconfiguration paths for which those variables remain observable. A bottom-up analysis of this graph allows to compute reliabiliw functions which evaluate the robustness of the observability properq with respect to sensor failures. The design of optimal sensor networh thus resumes to finding pseudo- minimal sensor sets such that the mean time bqore loosing the Observability property is larger than a predefined value.
Assessment of Circulating lncRNA H19 in Ankylosing Spondylitis Patients and Its Correlation with Disease Activity

Ankylosing spondylitis (AS) is a chronic inflammatory disease that results in severe pain and stiffness in the joints. The causes and pathophysiology of AS are still largely unknown. The lncRNA H19 plays key roles in the pathogenesis of AS by mediating inflammatory progression by acting in the axis of IL-17A/IL-23. The aims of this study were determining the role of lncRNA H19 in AS and assessing its clinical correlation. A case–control study was conducted and qRT-PCR was utilized to measure H19 expression. Comparing AS cases to healthy controls, it was found that H19 expression was significantly upregulated. For AS prediction, H19 demonstrated a 81.1% sensitivity, 100% specificity, and 90.6% diagnostic accuracy at a lncRNA H19 expression value of 1.41. lncRNA H19 had a significantly positive correlation with AS activity, MRI results, and inflammatory markers. lncRNA H19 seemed to be an independent predictor of AS (adjusted OR of 211 (95% CI: 4.7–939; p = 0.025)). After 3 months of clinical follow-up, seventeen patients (32.1%) showed minimal clinical improvement and fifteen patients (28.3%) showed major improvement. AS activity scores were significantly decreased in patients with high H19 expression. A significantly elevated lncRNA H19 expression was observed in AS cases compared with that in healthy controls. These results suggest that upregulation of lncRNA H19 expression may be involved in the pathogenesis of AS. The expression of the lncRNA H19 is related to the duration and activity of the disease. LncRNA H19 expression seems to be an independent predictor of AS.
Object Detection through Fires Using Violet Illumination Coupled with Deep Learning

Fire accidents threaten public safety. One of the greatest challenges during fire rescue is that firefighters need to find objects as quickly as possible in an environment with strong flame luminosity and dense smoke. This paper reports an optical method, called violet illumination, coupled with deep learning, to significantly increase the effectiveness in searching for and identifying rescue targets during a fire. With a relatively simple optical system, broadband flame luminosity can be spectrally filtered out from the scattering signal of the object. The application of deep learning algorithms can further and significantly enhance the effectiveness of object search and identification. The work shows that this novel optics–deep learning combined method can improve the object identification accuracy from 7.0% with the naked eye to 83.1%. A processing speed of 10 frames per second can also be achieved on a single CPU. These results indicate that the optical method coupled with machine learning algorithms can potentially be a very useful technique for object searching in fire rescue, especially considering the emergence of low-cost, powerful, compact violet light sources and the rapid development of machine learning methods. Potential designs for practical systems are also discussed.
First-Principles Study of Oxygen in ω-Zr

Zirconium alloys, which are widely used as cladding materials in nuclear reactors, are prone to react with oxygen (O). Furthermore, the ω-Zr in zirconium alloys can significantly increase the strength and hardness of these alloys, but there is a lack of reports on the behavior of oxygen in ω-Zr in the current literature. To investigate their interactions, we have studied the behavior of O in ω-Zr using the first-principles approach. In this work, we examined the effects of vacancy and alloying elements (Nb, Sn) on the behavior of O in ω-Zr. The results show that O with a formation energy of −5.96 eV preferentially occupies an octahedral interstitial position in ω-Zr. A vacancy reduces the formation energy of O in a tetrahedral interstitial position in ω-Zr. Nb and Sn decrease the formation energy of O in the octahedral interstitial position by 6.16 eV and 5.08 eV. Vacancy effectively reduces the diffusion barrier of O around it, which facilitates the diffusion of O in ω-Zr. Nb and Sn preferentially occupy the 1b and 2d substitution sites in ω-Zr, respectively. Nb makes the diffusion barrier of O in ω-Zr lower and promotes the diffusion of O in ω-Zr. Moreover, Sn makes the diffusion of O around Sn difficult. It was further found that O is less prone to form clusters in ω-Zr and tends to independently occupy interstitial positions in ω-Zr. In particular, a single vacancy would make the binding energy between O atoms to be further reduced.
Experimental and Numerical Investigations of Bubble Dynamics in Porous and Non-Porous Media

In this study an experimental work is conducted to investigate the shape and speed of an air bubble in a pipe filled with different viscous fluids and porous media. The experimental results are also compared with the Computational Fluid Dynamics (CFD) simulation. Multiphase flows are complex due to the infinitely deformable nature of interface in gas/liquid flows. If one of the phases is gas acts as dispersed phase in the form of bubble, then the complexity will arise from the non-uniform distribution of bubbles in the pipe cross-section and axial distance. Inclusion of different viscous fluids simulating the industrial scale hydrocarbon properties brings added challenge in understating the bubble rise, coalescence and breakup dynamics. Moreover, bubble rise and change of shape of bubble in porous media will bring additional complexity in the flow dynamics. The pipe used in the experiment and CFD was 11.6 cm ID and a length of 100 cm. Three situations were tested: i) an air bubble rising in stagnant water, ii) an air bubble rising in moving water, and iii) an air bubble rising in a stagnant water but filled with porous media with porosity of 27%. Preliminary CFD results indicate that an air bubble has an average velocity of 0.2468 m/s and 0.2524 m/s in stagnant water and moving water, respectively, which is very close to experimental results.Copyright © 2015 by ASME
Decentralised Strategic Planning and Capacity Development as Drivers of Leadership Performance in Schools

The purpose of this article is to determine the association between decentralised strategic planning and capacity development as driving forces of leadership for improved performance in public high schools in Mpumalanga Province, South Africa. This study used pragmatism as a research paradigm and followed a parallel convergent mixed methods research approach. The sample comprised 170 respondents consisting of 130 school governing body (SGB) members and 40 Department of Education (DoE) officials. Quantitative data were collected from SGB members through a survey while qualitative data were collected from participants using focus group discussions (FGDs) and individual interviews. The qualitative data were analysed using thematic analysis while SPSS software was used to analyse quantitative data. In testing the hypothesis, it was proven that decentralised strategic planning capacity in SGBs was associated with the effectiveness of strategic planning in Mpumalanga’s high schools. The study revealed that the variables, capacity development and decentralised strategic planning, are well associated, and influence leadership for improved performance in high schools. The study also found that school leaders who can develop capacity are able to promote better performance in their schools. Meanwhile, the qualitative results concurrently supported the results of the quantitative study as the influence of capacity development on decentralised school planning proved to have improved school performance and effective decentralised school leadership.
Research on disturbing white‐box cryptography algorithm based on local differential privacy

White‐box cryptography protects the secret key extraction from software implementations of cryptographic primitives. The principle is to white‐box the key by using obfuscation and hide the key in the whole encryption process to ensure that the key cannot be traced and extracted. The idea of adding internal perturbed terms and additional random equations is given by Bringer et al. to implement white‐box encryption, which has been attacked due to the defect in structure. In this paper, we propose a new secure white‐box encryption method by combining the random perturbation mechanism of local differential privacy protection and adding internal interference term, which can protect the privacy of the intermediate secret key more securely. The further disrupted cipher can also obtain the correct result by voting without increasing the number of parallel systems, and also gives the practical white‐box representation to AEw/oS. Compared with the white‐box implementation proposed by Bringer et al., the memory space of the new white‐box scheme will be 44 bytes more, but in terms of security, it can resist traditional attacks and Mulder's attacks.
Driver Attention Detection Based on Improved YOLOv5

In response to negative impacts such as personal and property safety hazards caused by drivers being distracted while driving on the road, this article proposes a driver’s attention state-detection method based on the improved You Only Look Once version five (YOLOv5). Both fatigue and distracted behavior can cause a driver’s attention to be diverted during the driving process. Firstly, key facial points of the driver are located, and the aspect ratio of the eyes and mouth is calculated. Through the examination of relevant information and repeated experimental verification, threshold values for the aspect ratio of the eyes and mouth under fatigue conditions, corresponding to closed eyes and yawning, are established. By calculating the aspect ratio of the driver’s eyes and mouth, it is possible to accurately detect whether the driver is in a state of fatigue. Secondly, distracted abnormal behavior is detected using an improved YOLOv5 model. The backbone network feature extraction element is modified by adding specific modules to obtain different receptive fields through multiple convolution operations on the input feature map, thereby enhancing the feature extraction ability of the network. The introduction of Swin Transformer modules in the feature fusion network replaces the Bottleneck modules in the C3 module, reducing the computational complexity of the model while increasing its receptive field. Additionally, the network connection in the feature fusion element has been modified to enhance its ability to fuse information from feature maps of different sizes. Three datasets were created of distracting behaviors commonly observed during driving: smoking, drinking water, and using a mobile phone. These datasets were used to train and test the model. After testing, the mAP (mean average precision) has improved by 2.4% compared to the model before improvement. Finally, through comparison and ablation experiments, the feasibility of this method has been verified, which can effectively detect fatigue and distracted abnormal behavior.
The core technique and application of knowledge graph in power grid company administrative duty

Power knowledge graph of the great application potentials for the power utilities, has become one of the interesting research topics in academia and industry. The power big data onto the deployment of information management system, poses a challenge to the current power grid company's administrative duty system, at the same time, the opportunity for/in applying intelligent management notion into the power grid company has come into being. Based on these above descriptions , the application and exploration of knowledge graph in power grid company administrative duty has been put forward in this paper. Firstly, the on-duty text data is pre-processed; secondly, entity extraction and relationship extraction are carried out on the processed data; finally, the data is stored in the graph database to build the knowledge graph.
Cation Exchange Membranes and Process Optimizations in Electrodialysis for Selective Metal Separation: A Review

The selective separation of metal species from various sources is highly desirable in applications such as hydrometallurgy, water treatment, and energy production but also challenging. Monovalent cation exchange membranes (CEMs) show a great potential to selectively separate one metal ion over others of the same or different valences from various effluents in electrodialysis. Selectivity among metal cations is influenced by both the inherent properties of membranes and the design and operating conditions of the electrodialysis process. The research progress and recent advances in membrane development and the implication of the electrodialysis systems on counter-ion selectivity are extensively reviewed in this work, focusing on both structure–property relationships of CEM materials and influences of process conditions and mass transport characteristics of target ions. Key membrane properties, such as charge density, water uptake, and polymer morphology, and strategies for enhancing ion selectivity are discussed. The implications of the boundary layer at the membrane surface are elucidated, where differences in the mass transport of ions at interfaces can be exploited to manipulate the transport ratio of competing counter-ions. Based on the progress, possible future R&D directions are also proposed.
CERTIFICATION AS A MANAGERIAL TOOL IN TOURISM: IDENTIFYING THE EFFECTS ON SUSTAINABILITY AND COMPETITIVENESS

The environmental, economic and social problems that are encountered in tourism today are largely due to the neglect of purposeful management. The reflection of these negatives affects the environment, leads to a loss of identity, and provokes a decrease in interest in tourist destinations. Therefore, the research purpose is to explore the possibilities for tourism in the new realities of sustainability, within the potential of certification to promote management and competitiveness. A holistic approach is used, combining observation, synthesis, analysis on secondary data. As a result, recommendations for competitiveness in the context of sustainability, applicable in tourism practice, are derived.
A Portable Soft Exosuit to Assist Stair Climbing with Hip Flexion

Soft exosuits are an emerging class of robots that have been shown to significantly reduce the metabolic cost of daily human movement. However, most soft exosuits are currently being studied for walking and running, and relatively minor research has been carried out on exosuits for stair climbing. Numerous exoskeletons used for stair climbing have a more rigid structure and are heavier, which may result in a greater force required by the wearer to overcome the weight from the exoskeleton when ascending stairs, which can result in metabolic costs. As a result, a reduction in rigid structures can reduce the weight of the exoskeleton and further reduce metabolic costs during stair climbing. In this paper, a waist-wearable soft exosuit was designed that assists hip flexion to aid stair climbing in older adults, in order to demonstrate the importance of choosing to assist hip flexion during stair climbing. An admittance delayed feedback control method was also proposed to use the angular information measured by the IMUs to enable the exosuit to adapt to different staircases. Metabolic experiments have shown that people who use soft exosuits have an average decrease of 6.9% in metabolism when they climb stairs than those who do not. The muscle fatigue experiments demonstrated a reduction in muscle fatigue of approximately 9.35%, 38.75% and 9.65% for the rectus femoris, lateral femoris and gastrocnemius muscles, respectively, when compared to cases without the soft exosuit. The results show that assisted hip flexion during stair climbing is a reasonable approach to effectively reduce metabolic consumption and muscle fatigue.
MERS-CoV Found in Hyalomma dromedarii Ticks Attached to Dromedary Camels at a Livestock Market, United Arab Emirates, 2019

The main mode of transmission of Middle East respiratory syndrome-related coronavirus (MERS-CoV) between dromedaries is likely via the respiratory route. However, there must be other modes to explain how the infection is brought to MERS-CoV-negative closed herds, such as transmission by ticks. Here, we present a study performed at three different locations in the United Arab Emirates (UAE) involving 215 dromedary camels (Camelus dromedarius) and the ticks attached to them. We tested the camels and ticks via RT-(q)PCR for the presence of MERS-CoV nucleic acids, as well as flaviviruses that may be present in the region (e.g., Alkhumra hemorrhagic fever virus). Camel sera were additionally analyzed for evidence of previous exposure to MERS-CoV. In total, 8 out of 242 tick pools were positive for MERS-CoV RNA (3.3%; Ct 34.6–38.3), 7 of which contained Hyalomma dromedarii ticks, and one contained a Hyalomma sp. tick (species not identified). All of the virus-positive ticks’ host camels were also positive for MERS-CoV RNA in their nasal swab samples. Short sequences established in the N gene region from two positive tick pools were identical to viral sequences from their hosts’ nasal swabs. In total, 59.3% of dromedaries at the livestock market had MERS-CoV RNA in their nasal swabs (Ct 17.7–39.5). While dromedaries at all locations were negative for MERS-CoV RNA in their serum samples, antibodies were detected in 95.2% and 98.7% of them (tested by ELISA and indirect immunofluorescence test, respectively). Given the probably transient and/or low level of MERS-CoV viremia in dromedaries and the rather high Ct values observed in the ticks, it seems unlikely that Hyalomma dromedarii is a competent vector for MERS-CoV; however, its role in mechanical or fomite transmission between camels should be investigated.
THE MEDIATING EFFECT OF EASE OF DOING BUSINESS ON THE RELATIONSHIP BETWEEN PUBLIC DEBT AND INFRASTRUCTURE IN ARAB COUNTRIES

This study examines how the ease of doing business in Arab countries mediates the relationship between public debt and infrastructure. According to previous research, one way to encourage the start-up of new infrastructure projects that boost economic growth and reduce debt build-up is to improve the business environment. The results showed that the relationship between public debt and infrastructure is fully mediated by the ease of doing business, using panel data for 19 Arab countries from 2000 to 2020. Based on the general least squares method, infrastructure was statistically negatively affected by public debt. By reducing the harmful effects of public debt on infrastructure, the study confirms that the ease of doing business leads to a more positive relationship between the two. This is due to the fact that a business-friendly environment can attract investment and boost the economy, which can help counteract the harmful effects of public debt on infrastructure. The paper provides recommendations for policymakers in Arab countries to create a favorable business environment that increases investment in infrastructure and reduces the effects of public debt. They need to promote good governance, which would curb corruption, strengthen institutions, and implement reforms to improve the ease of doing business, as some Arab countries still lag behind in many aspects of doing business, hindering business prosperity, proper infrastructural investment, and debt sustainability.
First- and second-order forcing expansions in a lattice Boltzmann method reproducing isothermal hydrodynamics in artificial compressibility form

Abstract The isothermal Navier–Stokes equations are determined by the leading three velocity moments of the lattice Boltzmann method (LBM). Necessary conditions establishing the hydrodynamic consistency of these moments are provided by multiscale asymptotic techniques, such as the second-order Chapman–Enskog expansion. However, for simulating incompressible hydrodynamics the structure of the forcing term in the LBM is still a discordant issue as far as its correct velocity expansion order is concerned. This work uses the traditional second-order Chapman–Enskog expansion analysis to demonstrate that the truncation order of the forcing term may depend on the time regime in this case. This is due to the fact that LBM does not reproduce exactly the incompressibility condition. It rather approximates it through a weakly compressible or an artificial compressible system. The present study shows that for the artificial compressible setup, as the incompressibility flow condition is singularly perturbed by the time variable, such a connection will also affect the LBM forcing formulation. As a result, for time-independent incompressible flows the LBM forcing must be truncated to first order whereas for a time-dependent case it is convenient to include the second-order term. The theoretical findings are confirmed by numerical tests carried out in several distinct benchmark flows driven by space- and/or time-varying body forces and possessing known analytical solutions. These results are verified for the single relaxation time, the multiple relaxation time and the regularized collision models.