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Malware analysis is the process of dissecting malicious software to understand its functionality, behavior, and potential risks. Artificial Intelligence (AI) and deep learning are ushering in a new era of automated, intelligent, and adaptive malware analysis. This convergence of AI and deep learning promises to revolutionize the way cybersecurity professionals detect, analyze and respond to malware threats. This paper proposed a Deep Neural Network (DNN) model built from features selected by ANalysis Of Variance (ANOVA) F-test (DNN-ANOVA) to increase accuracy by identifying informative features. ANOVA is a feature selection method used for numerical input data when the target variable is categorical. The top k most relevant features are those whose score values are greater than a certain threshold equal to the ratio between the sum of all features scores and the total number of features. Experiments are conducted on CIC-MalMem-2022 dataset. Malware Analysis is performed using binary classification to detect the presence or absence of malware and multiclass classification to detect not only the malware but also its type. According to the test results, DNN-ANOVA model achieves best values of 100 %, 99.99 %, 99.99 %, and 99.98 % in terms of precision, accuracy, F1-score and recall respectively for binary classification. In addition, DNN-ANOVA outperforms the current works with an overall accuracy rate of 85.83 %, and 73.98 % for family attacks and individual attacks respectively in the case of multiclass classification.

Abstract Musculoskeletal traumatic injuries (MTI) involve soft tissue lesions adjacent to a bone fracture leading to fibrous nonunion. The impact of MTI on the inflammatory response to fracture and on the immunomodulation of skeletal stem/progenitor cells (SSPCs) remains unknown. Here, we used single-nucleus transcriptomic analyses to describe the immune cell dynamics after bone fracture and identified distinct macrophage subsets with successive pro-inflammatory, pro-repair and anti-inflammatory profiles. Concurrently, SSPCs transition via a pro- and anti-inflammatory fibrogenic phase of differentiation prior to osteochondrogenic differentiation. In a preclinical MTI mouse model, the injury response of immune cells and SSPCs is disrupted leading to a prolonged pro-inflammatory phase and delayed resolution of inflammation. Macrophage depletion improves bone regeneration in MTI demonstrating macrophage involvement in fibrous nonunion. Finally, pharmacological inhibition of macrophages using the CSF1R inhibitor Pexidartinib ameliorates healing. These findings reveal the coordinated immune response of macrophages and skeletal stem/progenitor cells as a driver of bone healing and as a primary target for the treatment of trauma-associated fibrosis.

Open-loop synchronization techniques allow to overcome the stability issues to which closed-loop techniques are subject. Most open-loop techniques differ in their Quadratic Signal Generator and their frequency estimation method. In this paper, an overview of the main frequency estimation methods for single-phase power systems is presented. A description of each technique with their structure for numerical implementation is carried out. A comparative study of those frequency estimation methods is performed according to three test scenarios in order to evaluate the robustness of each method. Recommendations for the selection of the technique is given according to the simulation results.

This study evaluates the energy efficiency of an urban dairy farm in Tlemcen, Algeria, by assessing the feasibility of a grid-connected photovoltaic (PV)/wind hybrid energy system. Using HOMER and MATLAB software, the study explores the potential for replacing the farm's existing energy systems with a hybrid system integrated into a low-voltage electrical grid. The HOMER software determined the configuration that resulted in the lowest net present cost, energy cost in kWh, greenhouse gas emission mitigation, and renewable fraction (RF). The selected specifications of the renewable energy (RE) system components, power rates, and costs are based on the local market. The results indicate a net current cost of $106,117.90 and a levelized cost of energy of $0.0959/kWh, with a reduction in CO2 emissions by 594 kg/day. The system delivers 98 % RF with 4 kWh/m2/day medium solar radiation and 4 m/s wind speeds, and the ideal investment recovery takes 33 months. On the other hand, generation includes 933 kWh/year in grid buys and 42,488 kWh/year in sold-backs. The PV array generates 5457 kWh annually, the wind turbine produces 40,761 kWh/year, and an additional 939 kWh/year is purchased from the grid. Additionally, hybrid power systems in dairy farms reduce energy consumption by 90 % and increase milk production by 40 %, promoting sustainable agriculture. The findings highlight the importance of adopting RE systems in agricultural operations to achieve both economic and environmental sustainability.

This research introduces a new concept called Accelerating Virtual Rotor Control (AVRC) to address the challenges of low inertia and damping in a multi-source microgrid with combined Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR). While existing controllers have shown effectiveness, they often suffer from complexity and impracticality in real-world applications, the AVRC offers simplicity and effectiveness; therefore, it has been applied to low- inertia microgrids (MGs) by incorporating Superconducting Magnetic Energy Storage (SMES) device for improved microgrid response. Nevertheless, the Phase-Locked Loop (PLL) usually suffers from the low bandwidth, which affects the system response, and stability in some cases. Consequently, a Proportional-Integral (PI) controller has been integrated into the VRC system, where the effects of the measurement delays in the PLL are mitigated. In terms of SMES response, PI controller can create a static error resulting an over charging/discharging issue. To overcome this effect, an integral feedback loop is added into the AVRC, resulting in a comprehensive control strategy known as PI-AVRC/I. Additionally, to achieve a better optimization performance for the parameters of the proposed control strategy, a modification has been introduced to the Zebra Optimization Algorithm (MZOA) using Levy Flight motion to enhance its global search capability and avoid local optima. A Hardware-In-the-Loop is demonstrated using a Real-Time Digital Simulator (RTDS) with the aid of RSCAD software in order to evaluate the efficacy of the proposed control strategy under different scenarios such as step load perturbations with/without high Renewable Energy Sources (RESs) integration, random domestic loads fluctuation, and Communication Time Delay (CTD). The results affirm the robustness of the proposed control strategy in maintaining frequency and voltage deviation withing favorable limits, especially with high RESs penetration.

Introduction A proportion of people develop Long Covid after acute COVID-19, but with most studies concentrated in high-income countries (HICs), the global burden is largely unknown. Our study aims to characterise long-term COVID-19 sequelae in populations globally and compare the prevalence of reported symptoms in HICs and low-income and middle-income countries (LMICs).Methods A prospective, observational study in 17 countries in Africa, Asia, Europe and South America, including adults with confirmed COVID-19 assessed at 2 to

Abstract The purpose of this paper is to study the fundamental solution of the time-space bi-fractional diffusion equation incorporating an additional kinetic source term in semi-infinite space. The equation is a generalization of the integer-order model $${\partial _{{t}} {\rho (x,t)}} = {\partial ^2_{{x}} {\rho (x,t)}} - { \rho (x,t)}$$ ∂ t ρ ( x , t ) = ∂ x 2 ρ ( x , t ) - ρ ( x , t ) (also known as the Debye–Falkenhagen equation) by replacing the first-order time derivative with the Caputo fractional derivative of order $$0

The present study evaluated the potential of Salvia balansae from the Aures Mountains as a source of natural bioactive compounds. Leaves, flowers, and stems were extracted separately using methanol, ethanol, and acetone. Phenolic compounds were quantified colorimetrically and identified using liquid chromatography coupled with mass spectrometry (LC-MS). Antioxidant capacity was assessed using six different in vitro assays, 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt, 1,1-diphenyl-1-picrylhydrazyl, cupric reducing antioxidant capacity, ferric reducing antioxidant power, ferrous ion chelating, and phenanthroline assay, while the photo-protective capacity was evaluated using the sun protective factor. The methanolic flower extracts revealed the highest O-diphenol and phenolic levels (287.9 ± 0.50 and 147.87 ± 0.21 µg GAE/mg, respectively), whereas the acetonic and ethanolic leaf extracts contained the highest flavonoid (72.17 ± 0.12 μg QE/mg) and flavonol (35.28 ± 0.05 μg QE/mg) levels, respectively. LC-MS was used to identify 18 phenolics, including quinic acid (5.051–69.69 ppm), luteolin-7-o-glucoside (7.802–44.917 ppm), apigenin-7-o-glucoside (3.751–68.507 ppm), and cirsiliol (2.081–15.608 ppm), distinguishing this Aures taxon. Principal component analysis and unweighted pair-group method with arithmetic mean revealed variability in phytochemicals, antioxidant properties, and photo-protective activity influenced by biological activities and the compound content. Overall, S. balansae demonstrated promising photo-protective capacity, the presence of key bioactive compounds, and wide-ranging antioxidant potential, presenting this endemic plant as a valuable source of natural antioxidants and photo-protective agents with pharmaceutical and cosmetic applications.

Abstract Semiconductor films are crucial in photocatalysis applications, yet their controlled production remains challenging. Previous studies have mainly focused on deposition processes, heating rates, and doping of semiconductor oxides. In this paper, we introduce a novel method for fabricating tenorite (CuO) semiconductor films with varying precursor concentrations (0.01, 0.02, 0.04, 0.06, and 0.1 g/ml) using a dip-coating technique. We explore the impact of contact angles, 3D surface topography, and film thickness on photoactivation properties, areas with limited previous research focus. The results demonstrate that higher-concentration tenorite films (0.1 g/ml) exhibit rougher surfaces (77.3 nm), increased hydrophobicity (65.61°), improved light-harvesting ability, enhanced charge separation, and higher active oxygen output. The crystal sizes were within the range of 7.3–44.1 nm. Wettability tests show a 21.47% improvement in the 0.1 g/ml film surface under indirect sunlight compared to darkness. Transmittance rates in the 600 nm range were from 0.02 to 90.94%. The direct optical band gaps were 1.21–2.74 eV, while the indirect band gaps remained unaffected (0.9–1.11 eV). Surface morphology analysis reveals an increased presence of grains with higher concentrations. Regarding photocatalysis's impact on film morphology and copper content, SEM images reveal minimal changes in film structure, while copper content remains stable with slight variations. This suggests strong adhesion of tenorite to the film after photocatalysis. Tenorite thin films display exceptional photocatalytic efficiency, making them suitable for practical applications.

PurposeIn this study, we retrospectively reviewed the use of flow cytometry (FCM) in the diagnosis of inborn errors of immunity (IEIs) at a single center in Algeria. Sharing insights into our practical experience, we present FCM based diagnostic approaches adapted to different clinical scenarios.MethodsBetween May 2017 and February 2024, pediatric and adult patients presenting with clinical features suggestive of immunodeficiency were subjected to FCM evaluation, including lymphocyte subset analysis, detection of specific surface or intracellular proteins, and functional analysis of immune cells.ResultsOver a nearly seven-year period, our laboratory diagnosed a total of 670 patients (372 (55.5%) males and 298 (44.5%) females), distributed into 70 different IEIs belonging to 9 different categories of the International Union of Immunological Societies classification. FCM was used to diagnose and categorize IEI in 514 patients (76.7%). It provided direct diagnostic insights for IEIs such as severe combined immunodeficiency, Omenn syndrome, MHC class II deficiency, familial hemophagocytic lymphohistiocytosis, and CD55 deficiency. For certain IEIs, including hyper-IgE syndrome, STAT1-gain of function, autoimmune lymphoproliferative syndrome, and activated PI3K delta syndrome, FCM offered suggestive evidence, necessitating subsequent genetic testing for confirmation. Protein expression and functional assays played a crucial role in establishing definitive diagnoses for various disorders. To setup such diagnostic assays at high and reproducible quality, high level of expertise is required; in house reference values need to be determined and the parallel testing of healthy controls is highly recommended.ConclusionFlow cytometry has emerged as a highly valuable and cost-effective tool for diagnosing and studying most IEIs, particularly in low-income countries where access to genetic testing can be limited. FCM analysis could provide direct diagnostic insights for most common IEIs, offer clues to the underlying genetic defects, and/or aid in narrowing the list of putative genes to be analyzed.

Gulf of El Zayt wind farm is one of the giant projects for wind stations in Egypt, as its production is expected to reach up to 580 MW. Sliding Mode Control (SMC) is characterized by its ability to achieve rapid control of systems which suffer from the impact of uncertainty variables. This paper presents an improved method to enhance the effect of Seconded Order Sliding Mode Control (SSMC) on the applied systems. This improved method consists of a combination of the SSMC and proportional integral controller (PI) which is named as an optimized Seconded Order SMC-PI Control (OSSMC-PI). The Slime Mould Algorithm (SMA) and the particle swarm optimization (PSO) methods are applied to assign the optimal gains of proposed OSSMC-PI controller. To clear up the achievement of this improved method, its impact on the performance of Gulf of El Zayt wind farm stage one - 120 MW (GZWF-120) during a three-phase fault is compared with the impact of different control technologies such as the proportional integral controller (PI), optimized PI controller and PI-SMC controller the performance of GZWF-120. The system performance is enhanced considerably with the application of the suggested OSSMC-PI controller compared to the classical SM controller and PI controller.

Background/Objectives: Staphylococcus aureus and Staphylococcus pseudintermedius are major opportunistic pathogens in both humans and dogs. In pets, the dissemination of methicillin-resistant isolates (MRSA or MRSP) is problematic for the treatment of animals and is a public health issue due to their zoonotic potential. MRSA and MRSP may also harbor virulent genes that increase their dangerousness. This study aimed to assess the prevalence of (MR)SA and (MR)SP in healthy dogs and their owners in Algeria. Methods: Swabs were collected from various body sites of healthy dogs (n = 88) and from the nose of their owners (n = 38). Antimicrobial susceptibility testing was performed by antibiograms according to the disc diffusion method, and clonality was assessed using Pulsed-Field Gel Electrophoresis (PFGE). All methicillin-resistant isolates were short-read whole-genome sequenced using the Illumina technology. Results: 26 S. aureus and 17 S. pseudintermedius isolates were respectively collected from 13 dogs (13/88, 14.8%). No MRSP isolate was detected, while MRSA was found in six dogs (6.8%). Isolates belonged to ST1 (n = 3), ST 80 (n = 1), and ST 22 (n = 2, including the single-locus variant ST7118). All MRSA displayed the immune evasion cluster (IEC) type E. The ST80 isolate presented the Panton–Valentine toxin, and the ST22/ST7118 isolates carried the tst gene coding for the toxic shock syndrome toxin. Conclusions: The epidemiology of MRSA in healthy Algerian dogs mirrors the one in Algerian people. This poses a zoonotic and public health concern due to the virulence and resistance genes displayed by these isolates. Our results indicate the need for developing One Health strategies to avoid a large-scale dissemination of MRSA in Algerian dogs.

Chagas disease and leishmaniasis are two neglected tropical diseases that affect millions of people in low- and middle-income tropical countries. These diseases caused by protozoan parasites pose significant global health challenges, which have been exacerbated by the recent COVID-19 pandemic. There is an urgent need for novel therapeutics as current treatments are limited by toxicity and drug resistance. Nucleoside analogues, which have been extensively studied and successfully applied in antiviral and antitumor therapies, hold potential that has yet to be fully explored for treating these neglected diseases. In this review, we discuss the use of nucleoside analogues as promising therapeutic agents for Chagas disease and leishmaniasis. After briefly examining the pathology, progression, and current treatment options for these diseases, we provide a comprehensive analysis of the status of nucleoside analogues and explore their prospects. By outlining the current landscape and future directions, this review aims to guide research and development efforts towards more effective nucleoside-based treatments for Chagas disease and leishmaniasis.