Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism for Extracting Features Using Particle Swarm Optimization for Intrusion Detection Systems

The task of ensuring cyber-security has grown increasingly challenging given the concerning expansion of Computing connection and furthermore, there are a large number of computer-related applications available. It also needs a strong defense mechanism towards various cyber-attacks. Identifying irregularities and dangers in such a computer Security measures (IDS) have been established to aid with information security. Particularly, ML approaches are a subset of artificial intelligence (ai). (AI), a useful data-driven anti - malware system was developed. Two alternative intrusion detection (ID) classification reaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach ware used for reduce dimensionality. The classification meets used to characterize network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO plus Decision Tree and PSO plus K-Nearest Neighbor are the three classifiers used. The Knowledge discovery in databases 99 datasets was used to corroborate the identification techniques' findings. On the result of the implementation, successful metrics like as the following metrics were used to analyze cyber-security databases for various kinds of cyber-attacks: specific, recall, f1-score, correctness, accuracy, and constancy. The two's respective precision, detection rate (DR), and totally bogus rate were also compared to see which one outperforms the other (FPR). The solution was then contrasted with the IDS that was already in place. In terms of detecting network anomalies, The outcomes show that PSO+ANN outperforms the PSO+KNN and PSO+DT classifier algorithms.
Computationally Eﬀicient Blood Vessels Segmentation in Fundus Image on Shared Memory Parallel Machines

: Blood vessels segmentation in fundus image is a requiring step in order to detect retinopathies. A higher performing segmentation was been proposed in [12]. It consists at three dependent stages: Provide two binary images to extract wide vessels, compute features of the remaining pixels on binary images in order to extract fine vessels, and then combine both wide and fine vessels. The segmentation execution time is about 3-12 seconds when it is performed with fundus image having resolutions between 768*584 and 999*960. These latest resolutions are quite smaller than ones provided by actual retinograph, which leads to a higher rise on execution time. In this paper, we propose a parallelism strategy of the segmentation approach for implementation in Shared Memory Parallel Machine (SMPM). First, both binary images are provided in parallel. Thereafter, features processing is split according to their computational complexities. At the later stage, wide vessels and fine vessels images are subdivided adequately in the objective of a parallel combination. The parallel strategy is implemented using OpenCV and then assessed on STARE public data sets. Experimental analyses of execution time and efficiency are presented and discussed.
Identi�cation and Computational Validation of TolC Derived B-and T-Cell Epitopes for the Development of Pan-Rickettsial Vaccine

Introduction: Rickettsia is vector-borne causing Rickettsial infections with high mortality. Currently, no vaccines are available for the prevention of rickettsioses. The study aims to identify B-and T-cell epitopes to peptides derived from TolC protein of rickettsial group of pathogens. Immunogenic linear B-cell epitopes and MHC class I and II T-cell epitopes predicted from the consensus sequences of TolC protein for each rickettsial species. The putative antigenic epitopes may trigger an effective immune response and prove to be a novel vaccine that protects against all rickettsial infections. Methods: MHC class-I binding T-cell epitopes were predicted along with TAP binding e�ciency and proteasomal cleavage sites. Molecular docking study was carried out to investigate the binding a�nity between the MHC-I protein molecules with the peptides. The MHC-peptide complex with the highest RMSD score was further used for molecular dynamic simulation study. Results: Three B-cell candidate peptide epitopes were selected, of which epitope IYPEGGAQYSRIRSAKNQTRNSA/VVQ is conserved across both typhus group and spotted fever group of rickettsial species was considered best suitable for candidate vaccine. The peptide epitope KLYEAKITR had a good RMSD score as well as promiscuous binding to 28 different HLA allele supertypes and was considered suitable for the development of subunit peptide vaccine. Conclusion: The B-and T-cell candidate peptide epitopes identi�ed in our study would elicit B-and T-cell immune responses respectively and serve as a novel candidate peptide vaccine.
Applying direct manipulation concepts: direct manipulation dik operating system (DMDOS)

Software engineers are often called upon to design user interfaces, but strategies and guidelines are only beginning to emerge. Shneiderman (1983) introduced the term "Direct Manipulation" to describe user interfaces which have:1) continuous representation of the objects of interest.2) physical actions (movement and selection by mouse, joystick, touch screen, etc.) or labeled button presses instead of complex Syntax.3) rapid, incremental, reversible operations whose impact on the object of interest is immediately visible.4) layered or spiral approach to learning that permits usage with minimal knowledge.The concepts of direct manipulation has been applied in some distinctive systems such as XEROX STAR and APPLE Macintosh, and many application software products such as spread sheets, word processors, drawing tools, desk-top managers, etc.However, the basic software of personal computers, the operating system, is still often based on command language concepts. This paper describes DMDOS (Direct Manipulation Disk Operating System), that we designed by applying the concepts of direct manipulation. to MS-DOS on the IBM PC.
EVALUATION OF DIFFERENT ATTACKS TOWARD LORAWAN AND SECURITY SUGGESTIONS

Nowadays, emerging trends in the field of technology related to big data, cognitive computing, and the Internet of Things (IoT) have become closely related to people's lives. The Internet of Things networks consist of a huge number of interconnected devices and sensors that process and transmit data. Such Activities require efficient energy to be performed at the highest quality and range, hence the concept of Long-Range Wide Area Network (LoRaWAN) introduced, which concerns about delivering lower energy consumption, supporting large networks and mobility. LoRaWAN is a protocol that is designed to connect to operated things to the internet in regional, global networks and target internet of things. LoRaWAN can help make the life of people easier, but it has problems regarding the security such as replay attack, bit-flipping, and others. In this article, the security mechanisms in LoRaWAN will be evaluated as well as simulating security attacks that aims to know how to improve security in LoRaWAN. We discuss security features, cryptographic, key management, message acknowledgement and activation methods of LoRaWAN. Simulation is done under virtual environment using many tools and most used is Mbed simulator. We did the simulation, and the result is the attacks breached to LoRaWAN and got to the network server. Finally, we highlight the mitigation of these attacks.
Brain Tumor MRI Identi�cation and Classi�cation Using DWT, PCA, and KSVM

Background Classi�cation, segmentation, and the identi�cation of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. The optimum classi�cation technique helps make the proper choice and delivers the best therapy. Despite several signi�cant efforts and encouraging discoveries in this subject, precise segmentation and classi�cation remain challenging tasks.
EMOTION RECOGNITION IN ARABIC: A BERT-BASED TRANSFER LEARNING APPROACH LEVERAGING SEMANTIC INFORMATION OF ONLINE COMMENTS

With a wide range of practical applications, such as the diagnosis of mental health disorders and the detection of suspicious online behavior, the recognition of emotions in textual data is a crucial task. However, Arabic emotion recognition remains under-addressed in comparison to other languages, primarily due to the scarcity of labeled data. Pre-trained language models and transfer learning offer promising techniques to overcome the scarcity of labeled data for downstream tasks, such as emotion recognition. In this paper, we comprehensively explore the utilization of pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for Arabic fine-grained emotion recognition. We further propose a straightforward yet effective method for enhancing transfer learning in emotion recognition through the integration of semantic information, specifically, information extracted based on sentiment analysis and named entity recognition. To evaluate our proposed method, we conduct experiments on an existing benchmark dataset using pre-trained BERT models. The results indicate that our proposed approach of integrating the named entity information with the BERT model yielded a high weighted-average F1 score of 81.60%. Compared to the existing studies on Arabic emotion recognition in the literature, our proposed method outperforms the state-of-the-art approach, yielding of 9.80%, 9.72%, and 9.60% in weighted-average F1 score, precision, and recall respectively.
Perfectionism and Life Satisfaction in Gifted Students

This study examines the relationship between gifted students' perfectionism and life satisfaction. It also aims to understand how perfectionism and life satisfaction differ according to variables such as gender, parental education level, number of siblings, and grade level for gifted students. The relational survey method, one of the quantitative research methods, was used. The sample of the study consists of 395 gifted 5th–8th grade students. The Positive and Negative Perfectionism Scale and the Life Satisfaction Scale were used to collect data within the scope of this research. Some findings show that life satisfaction and positive perfectionism were high and negative perfectionism low among gifted secondary school students. When their positive perfectionism increases, their life satisfaction increases, and when their positive perfectionism decreases, their life satisfaction decreases. The life satisfaction of gifted students does not differ according to their gender, and negative perfectionism was significantly higher in girls. The life satisfaction of the gifted student who was the only child in the family was higher than that of the gifted student who had two or more siblings. The life satisfaction of the gifted student whose mother had a master's degree or above was higher than that of others. As gifted students move from 5th to 8th grade, their life satisfaction decreases and their negative perfectionism increases.
Particle trapping with optical nanofibers: a review [Invited]

Optical trapping has proven to be an efficient method to control particles, including biological cells, single biological macromolecules, colloidal microparticles, and nanoparticles. Multiple types of particles have been successfully trapped, leading to various applications of optical tweezers ranging from biomedical through physics to material sciences. However, precise manipulation of particles with complex composition or of sizes down to nanometer-scales can be difficult with conventional optical tweezers, and an alternative manipulation tool is desirable. Optical nanofibers, that is, fibers with a waist diameter smaller than the propagating wavelength of light, are ideal candidates for optical manipulation due to their large evanescent field that extends beyond the fiber surface. They have the added advantages of being easily connected to a fibered experimental setup, being simple to fabricate, and providing strong electric field confinement and intense magnitude of evanescent fields at the nanofiber’s surface. Many different particles have been trapped, rotated, transported, and assembled with such a system. This article reviews particle trapping using optical nanofibers and highlights some challenges and future potentials of this developing topic.
Challenges in Coagulation Management in Neurosurgical Diseases: A Scoping Review, Development, and Implementation of Coagulation Management Strategies

Bleeding and thromboembolic (TE) complications in neurosurgical diseases have a detrimental impact on clinical outcomes. The aim of this study is to provide a scoping review of the available literature and address challenges and knowledge gaps in the management of coagulation disorders in neurosurgical diseases. Additionally, we introduce a novel research project that seeks to reduce coagulation disorder-associated complications in neurosurgical patients. The risk of bleeding after elective craniotomy is about 3%, and higher (14–33%) in other indications, such as trauma and intracranial hemorrhage. In spinal surgery, the incidence of postoperative clinically relevant bleeding is approximately 0.5–1.4%. The risk for TE complications in intracranial pathologies ranges from 3 to 20%, whereas in spinal surgery it is around 7%. These findings highlight a relevant problem in neurosurgical diseases and current guidelines do not adequately address individual circumstances. The multidisciplinary COagulation MAnagement in Neurosurgical Diseases (COMAND) project has been developed to tackle this challenge by devising an individualized coagulation management strategy for patients with neurosurgical diseases. Importantly, this project is designed to ensure that these management strategies can be readily implemented into healthcare practices of different types and with sustainable integration.
Response of fish biota to dams in the Lower Colorado River Basin: empirical findings and utility for predicting responses to climate and water use change

Over the past century, development of the Colorado River Basin for water resources changed a dynamic river into a managed system. Seasonality in flow, temperature, and turbidity is reduced, and formerly riverine habitats are now reservoirs. These changes have played out in the fish fauna. Native species have declined and non-natives have increased. The key alteration, relative to the past, is increased competition and predation, especially at juvenile stages, which is likely mediated by more stable flow and resource regimes. A dataset (SONFISHES) assembled by W. L. Minckley and covering 150 years of fish occurrence in the Lower Colorado River (below Glen Canyon Dam) has provided insight into patterns in extirpation among native fishes and range expansion among non-native fishes. Several studies have combined these data with measures of extinction risk to explain how range fragmentation and species’ traits correlate with observed extinctions or threats in native species. Another study analyzed range shifts within a strategy space of potential fish life histories (originally introduced by K. O. Winemiller and K. A. Rose) to understand how human activity has created and removed ecological niches. Together, these studies support a causal link between environmental and faunal change. This strongly suggests that continuing the status quo will result in further extirpation and extinction of native fishes. I argue, however, that current knowledge is not yet sufficient to quantitatively forecast the magnitude and timescale of the response in fish biodiversity to any projected future environment. Such forecasting requires a model that quantitatively relates the environment to persistence in the strategy-space of fish life histories. Unfortunately, we do not need such a model to make the qualitative prediction: threatened “big-river” fishes will eventually go extinct without intervention.
Active Neurodynamics at Home in Patients with Knee Osteoarthritis: A Feasibility Study

The aim was to evaluate the feasibility of a home-based neurodynamic programme for patients with knee osteoarthritis (KO). Thirty participants (70% women) ≥ 50 years old with KO (Kellgren–Lawrence grades I–II) were included. Active mobilisation of the femoral nerve was performed at home over a period of 6–8 weeks. The feasibility of the programme was assessed using a survey that included questions related to understanding of the activity; adherence to the intervention; the burden caused by the intervention; self-perceived effects on the participant; follow-up; the barriers; and facilitators. Pain intensity, using the numerical rating scale (NRS); pressure pain thresholds (PPT); temporal assessment; pain modulation; Knee Injury and Osteoarthritis Outcome Score (KOOS), 12-item Short Form Survey questionnaire (SF-12), and the Central Sensitization Inventory questionnaire (CSI) were also collected, before and after the intervention. All patients performed the intervention, completed at least 42 days of activity, and considered the exercise adequate, with 28 participants (93.3%) reporting that the intervention was good for them. Statistically significant values (p < 0.05) were found for NRS, elbow PPT, external knee PPT, internal knee PPT, elbow CPM, CSI, and KOOS. Home-based active neurodynamic treatment has been shown to be a feasible and safe intervention for KO patients. In addition, this intervention has shown positive effects on pain and function.
Challenges of Using Social Media as a Teaching Tool in Secondary Schools

The COVID-19 pandemic resulted in a significant shift towards the use of social media platforms to support teaching with educators being responsible for integrating social media into their pedagogical practices. Yet its use has many challenges. This research aimed to describe the challenges secondary school teachers shared about using social media as a teaching tool. This descriptive multiple case study followed an interpretive, inductive, and qualitative approach. Four secondary schools were selected as cases and teachers who used social media as a teaching tool were interviewed. Multiple challenges are described that impact teachers and students. Many are due to the features of social media, and some are due to school infrastructure and policies. Practically this research can help teachers have a better understanding of these challenges before using social media for their own teaching. Policies, curricula, applications, and pedagogies can also be adapted and changed based on the finding of this research.
On which timescales do gas transfer velocities control North Atlantic CO2 flux variability?

The North Atlantic is an important basin for the global ocean's uptake of anthropogenic and natural carbon dioxide (CO2), but the mechanisms controlling this carbon flux are not fully understood. The air‐sea flux of CO2, F, is the product of a gas transfer velocity, k, the air‐sea CO2 concentration gradient, ΔpCO2, and the temperature‐ and salinity‐dependent solubility coefficient, α. k is difficult to constrain, representing the dominant uncertainty in F on short (instantaneous to interannual) timescales. Previous work shows that in the North Atlantic, ΔpCO2 and k both contribute significantly to interannual F variability but that k is unimportant for multidecadal variability. On some timescale between interannual and multidecadal, gas transfer velocity variability and its associated uncertainty become negligible. Here we quantify this critical timescale for the first time. Using an ocean model, we determine the importance of k, ΔpCO2, and α on a range of timescales. On interannual and shorter timescales, both ΔpCO2 and k are important controls on F. In contrast, pentadal to multidecadal North Atlantic flux variability is driven almost entirely by ΔpCO2; k contributes less than 25%. Finally, we explore how accurately one can estimate North Atlantic F without a knowledge of nonseasonal k variability, finding it possible for interannual and longer timescales. These findings suggest that continued efforts to better constrain gas transfer velocities are necessary to quantify interannual variability in the North Atlantic carbon sink. However, uncertainty in k variability is unlikely to limit the accuracy of estimates of longer‐term flux variability.
The Potential of Bioaugmentation-Assisted Phytoremediation Derived Maize Biomass for the Production of Biomethane via Anaerobic Digestion

Anthropogenic behaviors are causing the severe build-up of heavy metal (HM) pollutants in the environment, particularly in soils. Amongst a diversity of remediation technologies, phytoremediation is an environmentally friendly technology that, when coupling tolerant plants to selected rhizospheric microorganisms, can greatly stimulate HM decontamination of soils. Maize (Zea mays) is a plant with the reported capacity for HM exclusion from contaminated soil but also has energetic importance. In this study, Zea mays was coupled with Rhizophagus irregularis, an arbuscular mycorrhizal fungus (AMF), and Cupriavidus sp. strain 1C2, a plant growth-promoting rhizobacteria (PGPR), as a remediation approach to remove Cd and Zn from an industrial contaminated soil (1.2 mg Cd kg−1 and 599 mg Zn kg−1) and generate plant biomass, by contrast to the conservative development of the plant in an agricultural (with no metal pollution) soil. Biomass production and metal accumulation by Z. mays were monitored, and an increase in plant yield of ca. 9% was observed after development in the contaminated soil compared to the soil without metal contamination, while the plants removed ca. 0.77% and 0.13% of the Cd and Zn initially present in the soil. The resulting biomass (roots, stems, and cobs) was used for biogas generation in several biomethane (BMP) assays to evaluate the potential end purpose of the phytoremediation-resulting biomass. It was perceptible that the HMs existent in the industrial soil did not hinder the anaerobic biodegradation of the biomass, being registered biomethane production yields of ca. 183 and 178 mL of CH4 g−1 VS of the complete plant grown in non-contaminated and contaminated soils, respectively. The generation of biomethane from HM-polluted soils’ phytoremediation-derived maize biomass represents thus a promising possibility to be a counterpart to biogas production in an increasingly challenging status of renewable energy necessities.
A Robust Markovian Block Sparse Adaptive Algorithm With Its Convergence Analysis

In this brief, a robust Markovian adaptive filter is proposed for block sparse system identification problem. To make Markovian adaptive filter robust against impulsive noise, a Generalized Gaussian Distribution (GGD) model is utilized for the impulsive noise. Then, a Maximum A Posteriori (MAP) adaptive estimator of the system impulse response is devised in the presence of GGD impulsive noise. A moment-based parameter estimation method is also presented for estimating the scale parameter of GGD noise. Moreover, the convergence analysis of the suggested robust Markovian algorithm is derived. Simulation results show the effectiveness of the proposed robust algorithm compared to some state-of-the-art algorithms in the literature, especially from the computational complexity viewpoint.