Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Based Network Intrusion Detection Using Ensemble Classifiers

Network Intrusion Detection System is extensively utilized for protection and reducing the damages of information system. It protects threats and vulnerabilities in computer network. Due to the rapid growth of computer network communications, network intrusion is significantly increased and the intrusion detection is considered as a major issue in nowadays. For secure the communication, it is necessary to identify network attacks or malicious activities in network environment. To detect the intrusion in the network various methods have been proposed in past and effective analysis based on ensemble machine learning methods have been done to detect any types of anomalous events occurred in the flow of network traffic. In the learning process, ensemble methods are known to perform well. Investigating the best ensemble approach is crucial for creating an effective network intrusion detection system. In this paper, we used Bagged Naïve Bayes-Decision Tree (BNBDT) and Random Forest ensemble learning techniques and also used four base classification algorithms which are Naïve Bayes, KNN, Decision Tree and Logistic Regression on NSL-KDD network attack dataset for detecting the anomaly in network traffic and compared the performance of ensemble classifiers with the base classifiers. The proposed ensemble method provides better accuracy and relatively low false alarms rate than the other base classifiers.
Immunomodulatory Effects of the Pea Defensin Psd1 in the Caco-2/Immune Cells Co-Culture upon Candida albicans Infection

Candidiasis is one of the most common fungal diseases that can pose a threat to life in immunodeficient individuals, particularly in its disseminated form. Not only fungal invasion but also fatal infection-related inflammation are common causes of systemic candidiasis. In this study, we investigated in vitro immunomodulatory properties of the antifungal pea defensin Psd1 upon Candida albicans infection. Using the real-time PCR, we showed that Psd1 inhibited the antimicrobial peptide HBD-2 and pro-inflammatory cytokines IL-1 and IL-8 downregulation at mRNA level in epithelium cells caused by C. albicans infection. By using the Caco-2/immune cells co-culture upon C. albicans infection and the multiplex xMAP assay, we demonstrated that this pathogenic fungus induced a pronounced host defense response; however, the cytokine responses were different in the presence of dendritic cells or monocytes. We revealed that Psd1 at a low concentration (2 µM) had a pronounced immunomodulatory effect on the Caco-2/immune cells co-culture upon fungal infection. Thus, we hypothesized that the pea defensin Psd1 might be an effective agent in the treatment of candidiasis not only due to its antifungal activity, but also owing to its ability to modulate a protective immune response upon infection.
The Effect of Dopants on Structure Formation and Properties of Cast SHS Alloys Based on Nickel Monoaluminide

Alloys based on NiAl-Cr-Co (base) with complex dopants (base+2.5Mo-0.5Re-0.5Ta, base+2.5Mo-1.5Re-1.5Ta, base+2.5Mo-1.5Ta-1.5La-0.5Ru, base+2.5Mo-1.5Re-1.5Ta-0.2Ti, base+2.5Mo-1.5Re-1.5Ta-0.2Zr) were fabricated by centrifugal SHS metallurgy. The phase and impurity compositions, structure, mechanical properties, and the mechanism of high-temperature oxidation at T = 1150 °C were studied; the kinetic oxidation curves, fitting equations and parabolic rate constant were plotted. Al2O3 and Co2CrO4 were the major phases of the oxidized layer. Three layers were formed: I—the continuous Al2O3 layer with Co2CrO4 inclusions; II—the transitional MeN-Me layer with AlN inclusions; and III—the metal layer with AlN inclusions. The positive effect of thermo-vacuum treatment (TVT) on high-temperature oxidation resistance of the alloy was observed. The total weight gain by the samples after oxidative annealing decreased threefold (from 120 ± 5 g/m2 to 40 ± 5 g/m2). The phases containing Ru and Ti microdopants, which reduced the content of dissolved nitrogen and oxygen in the intermetallic phase to the values ∑O, N = 0.0145 wt. % for the base+2.5Mo-1.5Ta-1.5La-0.5Ru alloy and ∑O,N = 0.0223 wt. % for the base+2.5Mo-1.5Re-1.5Ta-0.2Ti alloy, were identified by transmission electron microscopy (TEM). In addition, with the significant high-temperature oxidation resistance, the latter alloy with Ti had the optimal combination of mechanical properties (σucs = 1644 ± 30 MPa; σys = 1518 ± 25 MPa).
Balancing desirability and promotion steadiness in partially stochastic manpower planning systems

ABSTRACT Current manpower planning approaches focus on satisfying future personnel needs. In the mean time, there is a lack of contribution on managing the personnel work satisfaction on the long-term. In general, long-term work satisfaction can be achieved by maintaining the preferred promotion strategy. This paper aims at balancing the strategy to fulfill the future personnel requirements while at the same time maintaining the preferred promotion strategy. Moreover, stochastic wastage is considered to better capture the real world condition of voluntarily wastage. Illustration of the model shows that it enables obtaining favorable recruitment and promotion strategies.
Design of a Slotted Patch Circular Antenna Array for Breast Tumor Identification

Breast cancer has been one of the main causes of death in women in the last few decades, highlighting the need for more accurate methods of early detection that can facilitate more rapid and efficient treatment. X-ray mammography, magnetic resonance imaging (MRI), and ultrasound are all diagnostic tools for the early diagnosis of breast cancer. But they are constrained in some ways. For example, X-ray mammography, the most popular breast screening procedure, can miss cancer detection due to low tissue contrast and radiation exposure. In comparison to X-ray mammography, microwave imaging is safer for patients and offers higher detection rates. MBI utilizes differences in the electrical characteristics of fatty breast tissues and abnormal ones to diagnose breast cancer. This paper compares and contrasts four different microstrip patch antennas operating at 2.45 GHz for use in microwave breast imaging in the ISM frequency band. These antennas can identify tumors by analyzing and comparing their s-parameter, E-field, H-field, radiation pattern, and specific absorption rate (SAR). By placing each antenna separately on the breast skin and analyzing the field values and surface current density differences between the healthy and malignant tissue of the breast phantom designed and simulated in CST STUDIO SUITE, a single antenna is selected for the six-element antenna array to increase tumor detection sensitivity.
A Deep Reinforcement Learning Approach for Efficient, Safe and Comfortable Driving

Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the vehicle’s environmental context. We propose a data-centric application of Adaptive Cruise Control employing Deep Reinforcement Learning (DRL). Our DRL approach considers multiple objectives, including safety, passengers’ comfort, and efficient road capacity usage. We compare the proposed framework’s performance to traditional ACC approaches by incorporating such schemes into the CoMoVe framework, which realistically models communication, traffic, and vehicle dynamics. Our solution offers excellent performance concerning stability, comfort, and efficient traffic flow in diverse real-world driving conditions. Notably, our DRL scheme can meet the desired values of road usage efficiency most of the time during the lead vehicle’s speed-variation phases, with less than 40% surpassing the desirable headway. In contrast, its alternatives increase headway during such transient phases, exceeding the desired range 85% of the time, thus degrading performance by over 300% and potentially contributing to traffic instability. Furthermore, our results emphasize the importance of vehicle connectivity in collecting more data to enhance the ACC’s performance.
Selective classification considering time series characteristics for spiking neural networks

In this paper, we propose new methods for estimating the relative reliability of prediction and rejection methods for selective classification for spiking neural networks (SNNs). We also optimize and improve the efficiency of the RC curve, which represents the relationship between risk and coverage in selective classification. Efficiency here means greater coverage for risk and less risk for coverage in the RC curve. We use the model internal representation when time series data is input to SNN, rank the prediction results that are the output, and reject them at an arbitrary rate. We propose multiple methods based on the characteristics of datasets and the architecture of models. Multiple methods, such as a simple method with discrete coverage and a method with continuous and flexible coverage, yielded results that exceeded the performance of the existing method, softmax response.
Constant void ratio vs. constant confining pressure tests on partially saturated silty soil

The presence of soil solids, water, and air complicates understanding unsaturated soil dynamics. A thorough understanding of unsaturated soil behavior is essential for the fruitful design of slopes, embankments, and retaining structures. A series of constant void ratio and constant confining pressure triaxial compression tests were carried out under constant water content conditions (pore air pressure drained and pore water pressure undrained) to study the shear strength and deformation characteristics of a partially saturated silty soil. Test specimens were prepared with a water content of 15%, 20% (Optimum water content), and 25%, with the corresponding degree of saturation of 37, 49, and 62.5%. The degree of compaction of all specimens was kept at 83% while the dry density was 1.29 g/cm3, and the void ratio was around 1.045. All the samples were isotropically consolidated under 500kPa by keeping deviatoric stress at 0kPa. The constant void ratio test results showed that the deviatoric stress reached a peak value followed by a sudden decrease within the axial strain of 0-1.5%. In contrast, the deviatoric stress increased continuously until reaching the critical state without depicting any peak for constant confining pressure tests. However, in both cases, the lower the water content at the time of specimen preparation, the higher the shear resistance.
Olfactory Obsessions: A Study of Prevalence and Phenomenology in the Course of Obsessive-Compulsive Disorder

Olfactory obsessions (OOs) are rarely described in the medical literature. The features of OOs appear consistent with characteristics of a typical obsession, but since they do not involve the realm of thought, it is questionable to term them obsessions per se. Olfactory Reference Syndrome (ORS) presents OOs inconsistently and is a distinctive diagnostic category related to OCD. Therefore, the primary objectives of our study were not only to assess the prevalence of OOs in OCD patients, but also to demonstrate their phenomenological consistency with other OCD symptoms. The study group consisted of 75 patients already diagnosed and treated for OCD. Hence, a comparison was made between OCD patients with and without OOs in terms of: symptom severity, level of insight and comorbidities. Olfactory obsessions (OOs) were found in 21.33% (n = 16). OOs induced compulsive behavior in more than 93% of subjects. The presence of OOs did not significantly differentiate the studied groups in terms of OCD severity (p = 0.876), level of insight (p = 0.680), depression (p = 0.746), mania (p = 0.525) and OCDP traits (p = 0.624). However, a comparison of the two groups showed that OOs patients presented higher levels of hostility (p = 0.036), cognitive impulsivity (p = 0.039), magic-type obsession (75% vs. 35.59%), and contamination obsession (87.50% vs. 67.80%). Conclusions: OOs frequently occur in the course of OCD, and their phenomenology is typical of this disorder. OOs are not a symptom of thought content disorders and are sensory in nature, which is not included in the definition of obsession. The presence of OOs in OCD provokes hostility and cognitive impulsivity. It can be assumed that the Olfactory Obsessions Questionnaire accurately identifies olfactory obsessions.
Hardware Implementation of Approximate Fixed-point Divider for Machine Learning Optimization Algorithm

Division operation is necessary for many applications, especially optimization algorithms for machine learning. Usually, a certain degree of loss is acceptable in calculating nonsignificant intermediate variables for a considerable speed improvement. This paper proposes a specialized divider to accelerate machine learning optimization algorithm implementation on hardware. Inspired by the fast inverse square root algorithm, we designed a hardware implementation method according to the algorithm, which generates an approximate division result with conversion between floating-point and fixed-point numbers and multiplication. This paper includes three versions of divider: fastDiv_accuracy, a conventional design with a 35% less delay and minimal error compared to delay-minimized standard divider from the Synopsys DesignWare library; fastDiv_area, an area-oriented design with a 67% less delay and acceptable error compared to the standard divider constrained to the same area size; fastDiv_speed, the fastest design with a 54% less delay compared to delay-minimized standard divider. All these three versions can be applied in deploying optimization algorithms in FPGA or ASIC design on demand.
Two categories of refutation decision procedures for classical and intuitionistic propositional logic

An automatic theorem prover is a computer program that proves theorems without the assistance of a human being. Theorem proving is an important basic tool in proving theorems in mathematics, establishing the correctness of computer programs, proving the correctness of communication protocols, and verifying integrated circuit designs. This dissertation introduces two new categories of theorem provers, one for classical propositional logic and another for intuitionistic propositional logic. For each logic a container property and generalized algorithm are introduced. Many methods have been developed over the years to prove theorems in propositional logic. This dissertation describes and presents example proofs for five of these methods: natural deduction, Kripke tableau, analytic tableau, matrix, and resolution. Each of these methods uses refutation to prove a theorem. In refutation, the proposed theorem is assumed to be false. The theorem prover is successful, only if the analysis of this assumption leads to a contradiction. Each of these methods, except resolution, share a common algorithm. To prove this, the container is introduced. A data structure used by a method is a container, if it meets a set of properties. A generalized algorithm that proves theorems is introduced. Since each step in this algorithm uses only operations that are provided by the container. The steps it performs
To Pause With a Cliffhanger or a Temporary Closure? The Differential Impact of Serial Versus Episodic Narratives on Children’s Physical Activity Behaviors

Research has supported the effectiveness of narratives for promoting health behavior, but different narrative presentation formats (serial vs. episodic) have seldom been compared. Suspense theories suggest that serial narratives, which do not provide a full resolution at the end of an episode, may create higher motivation for continued engagement with a story. Forty-four 8 to 12-year-old children were randomly assigned to watch an animation series designed for an existing active video game in which the plot was delivered either continuously across multiple episodes (serial) or in multiple yet relatively independent self-contained episodes (episodic). Controlling for social desirability, children who watched the serial narrative had significantly more moderate to vigorous physical activity (MVPA) and step counts while the episodic group’s gameplay duration decreased, especially during later visits. There was no difference in self-reported narrative immersion or physical activity intention. Serial narratives can result in more time spent in MVPA behaviors than episodic narratives.
Novel Epoxidized Brazil Nut Oil as a Promising Plasticizing Agent for PLA

This work evaluates for the first time the potential of an environmentally friendly plasticizer derived from epoxidized Brazil nut oil (EBNO) for biopolymers, such as poly(lactic acid) (PLA). EBNO was used due to its high epoxy content, reaching an oxirane oxygen content of 4.22% after 8 h of epoxidation for a peroxide/oil ratio of 2:1. Melt extrusion was used to plasticize PLA formulations with different EBNO contents in the range of 0–10 phr. The effects of different amounts of EBNO in the PLA matrix were studied by performing mechanical, thermal, thermomechanical, and morphological characterizations. The tensile test demonstrated the feasibility of EBNO as a plasticizer for PLA by increasing the elongation at break by 70.9% for the plasticized PLA with 7.5 phr of EBNO content in comparison to the unplasticized PLA. The field-emission scanning electron microscopy (FESEM) of the fractured surfaces from the impact tests showed an increase in porosity and roughness in the areas with EBNO addition, which was characteristic of ductile failure. In addition, a disintegration test was performed, and no influence on the PLA biodegradation process was observed. The overall results demonstrate the ability of EBNO to compete with other commercial plasticizers in improving the ductile properties of PLA.
Predicting Factors of the Outcome of Cerebral AVMs Treated with Endovascular Embolization Followed by Gamma Knife Radiosurgery

BACKGROUND: Complete AVM obliteration could be achieved through microsurgery, endovascular techniques, or radiosurgery (focused radiation), in single or combined mannar. Each treatment option has associated risks and benefits AIM OF THE STUDY: Our clinical study presents a prospective study of cerebral AVMs treated with endovascular embolization followed with gamma knife radiosurgery. We analyzed the clinical and angioarchitectural outcomes as well as the complications. Finally we tried to find out the predicting factors for the success of this combined modality PATIENTS AND METHODS: Thirty nine patients with cerebral AVMs were treated endovascular embolization followed with gamma knife radiosurgery at The International Medical Centre in Cairo and studied analytically at THE Benha University Hospital from the beginning of January 2016 to the end of December 2021 . RESULTS: Factors that may predict the outcome clinically are: i
WSN-Based Smart Landslide Monitoring Device

In this article, we present a smart monitoring wireless sensor networking (WSN) node device, referred to as “SMARTMODE. ” The indigenously developed SMARTMODE is energy-efficient, context-adaptive, reliable, and accurate. We have custom-designed it to monitor landslide movement and causative parameters and to ensure the system’s longevity, reliability, and robustness in harsh environmental conditions. When in sleep mode, the SMARTMODE is designed in such a way that it consumes less power (i.e., 0.034 mA at 4.2 V) while is still able to detect minor slope variations. This is achieved by enabling low-power interrupt-based sensing in the SMARTMODE. The SMARTMODE is also capable of automatically varying its data acquisition frequency based on the severity of the environmental context, thereby making it context-adaptive. Furthermore, we designed the SMARTMODE in such a way that it can detect and filter faults such as noise and outliers in the acquired sensor’s data. In addition, to enhance the reliability, SMARTMODE comprises energy harvesting and backup approach to deal with power outage issues. This approach enables automatic recharging of SMARTMODE’s battery even during nighttime to ensure reliable and continuous landslide monitoring. Finally, we evaluated the performance of the proposed SMARTMODE by deploying it in an actual landslide location.
Fabrication of Poly Dopamine@poly (Lactic Acid-Co-Glycolic Acid) Nanohybrids for Cancer Therapy via a Triple Collaboration Strategy

Breast cancer is a common malignant tumor among women and has a higher risk of early recurrence, distant metastasis, and poor prognosis. Systemic chemotherapy is still the most widely used treatment for patients with breast cancer. However, unavoidable side effects and acquired resistance severely limit the efficacy of treatment. The multi-drug combination strategy has been identified as an effective tumor therapy pattern. In this investigation, we demonstrated a triple collaboration strategy of incorporating the chemotherapeutic drug doxorubicin (DOX) and anti-angiogenesis agent combretastatin A4 (CA4) into poly(lactic-co-glycolic acid) (PLGA)-based co-delivery nanohybrids (PLGA/DC NPs) via an improved double emulsion technology, and then a polydopamine (PDA) was modified on the PLGA/DC NPs’ surface through the self-assembly method for photothermal therapy. In the drug-loaded PDA co-delivery nanohybrids (PDA@PLGA/DC NPs), DOX and CA4 synergistically induced tumor cell apoptosis by interfering with DNA replication and inhibiting tumor angiogenesis, respectively. The controlled release of DOX and CA4-loaded PDA@PLGA NPs in the tumor region was pH dependent and triggered by the hyperthermia generated via laser irradiation. Both in vitro and in vivo studies demonstrated that PDA@PLGA/DC NPs enhanced cytotoxicity under laser irradiation, and combined therapeutic effects were obtained when DOX, CA4, and PDA were integrated into a single nanoplatform. Taken together, the present study demonstrates a nanoplatform for combined DOX, CA4, and photothermal therapy, providing a potentially promising strategy for the synergistic treatment of breast cancer.
Design of Control System for Suppression of Neoclassical Tearing Modes with Electron Cyclotron Heating and Current Drive on EAST

Abstract This paper discusses a control system for the Experimental Advanced Superconducting Tokamak (EAST) that suppresses neoclassical tearing modes (NTMs) (3/2 and 2/1) by applying electron cyclotron (EC) heating and current drive. It allows the magnetic island to be detected and localized in real time and the EC beam to be deposited exactly on the island position by stepwise tuning of the steerable launch mirror. The mode features are identified with temperature perturbation to develop the algorithm for real-time island location. The necessary parameters for the design of the control system are discussed and determined based on simulation of the nonlinear island growth of NTMs with application of EC current drive. Although similar work has been done on many other devices, this is the first attempt on EAST.