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Abstract This paper presents an analytical and finite element modeling (FEM) investigation on the carbon fiber reinforced polymer concrete-filled polyvinyl chloride tube (CCFPT) concrete columns under axial eccentric compression. The study involved collecting experimental data from 32 CFPT columns confined with CFRP from literature and modeling them using FEM in ABAQUS. A parametric study was conducted on 260 CCFRP concrete columns, examining various parameters such as eccentricity, number of CFRP layers, thickness of PVC tube, column slenderness ratio, CFRP spacing, thickness of CFRP strips, confined concrete strength, and concrete core diameter. The effects of these parameters on the ultimate load and strain capacity were analyzed. Analytical models were developed to express the confined concrete strength and strain as functions of the constituent properties and dimensionless confinement parameters. The findings revealed that increasing eccentricity significantly reduced the ultimate load (up to 45%) and strain (up to 67%) capacities. Adding more layers of CFRP increased strength and strain capacities by 25% when going from 2 to 3 layers at a 20 mm eccentricity. Thicker PVC tubes increased load capacity by preventing buckling, but had inconsistent effects on strain. Higher slenderness ratios decreased both capacities, particularly strain. Six machine learning models were employed to predict the load-carrying capacity and confined ultimate strain. Various performance metrics, data visualization techniques, SHAP analysis, sensitivity analysis, and error characteristic curves were used to evaluate the prediction performance and analyze the impact of input parameters. The findings revealed that increasing eccentricity and CFRP layers lead to reduced ultimate load and strain capacities, while higher slenderness ratios result in increased ultimate loads. The study concluded that CCFPT columns with optimized CFRP wrapping can offer superior performance for eccentrically loaded columns.

Jensen's inequality, with its broad applications across various fields, presents an important subject for investigation and research. In this article, we introduce novel enhancements to Jensen's inequality by utilizing the convexity properties of a thrice differentiable function in the absolute sense. We conducted numerical experiments to compare our primary results with previously established findings. Additionally, we provided several applications of our major results to classical inequalities, means, and divergences.

IntroductionEnterococcus faecalis, a common inhabitant of the feline gastrointestinal tract, has emerged as a significant pathogen causing urinary tract infections (UTIs) in domestic cats. The rise of multidrug-resistant E. faecalis strains and their propensity to form biofilms pose significant challenges in treatment. This study investigated the antibacterial and antibiofilm activities of hexagonal zinc oxide nanoparticles (ZnONPs) alone and in combination with streptomycin and Moringa oleifera leaf extract (MOLe) against multidrug-resistant E. faecalis isolates from feline UTIs.MethodsAntimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method. Biofilm formation was assessed using the crystal violet assay, and biofilm-associated genes (sprE, gelE, fsrABC) were detected by PCR. ZnONPs, Str/ZnONPs (streptomycin-loaded ZnONPs), and Str/MOLe@ZnONPs (streptomycin and MOLe-loaded ZnONPs) were characterized using FTIR, DLS, TEM, and SEM. The antibacterial and antibiofilm activities of the synthesized nanoparticles were evaluated through time-kill assays, well diffusion assays, and gene expression analysis.ResultsA high prevalence of multidrug resistance was observed among the E. faecalis isolates, with significant resistance to ampicillin, vancomycin, and streptomycin. Characterization studies revealed the successful encapsulation of streptomycin and MOLe within the ZnONPs.In vitro assays demonstrated that Str/MOLe@ZnONPs exhibited potent antibacterial and antibiofilm activities against the tested E. faecalis strains, significantly reducing bacterial growth and biofilm formation.DiscussionThe emergence of multidrug-resistant E. faecalis strains necessitates the development of novel therapeutic strategies. This study demonstrates the promising potential of ZnONPs, particularly those loaded with streptomycin and MOLe, in combating biofilm-forming E. faecalis. The synergistic effects of the combined formulation may offer a novel approach to overcome antibiotic resistance and improve the treatment outcomes of E. faecalis UTIs in domestic cats.

Abstract Introduction There are limited data on the use of advanced therapies to treat psoriatic arthritis (PsA) in Russia. Guselkumab, an interleukin (IL)-23p19-subunit inhibitor, demonstrated efficacy in patients with PsA in the phase 3 DISCOVER-1 and -2, and COSMOS trials. This analysis evaluated the efficacy and safety of guselkumab in patients with PsA in Russia. Methods This post hoc analysis of DISCOVER-1 and -2 included 1002 biologic-naïve patients with active PsA from Russia (n = 317) and the rest of the world (RoW; n = 685). Patients received guselkumab 100 mg every 4 weeks (Q4W), or at week 0 and 4 then Q8W, or placebo then guselkumab Q4W at week 24 (Russian: n = 119, 88, and 110, respectively; RoW: n = 216, 246, and 223, respectively). Outcomes through week 52 were pooled (DISCOVER-1 and -2); outcomes from week 52 to 100 represent DISCOVER-2 only. Results In patients from Russia, ≥ 20% improvement in the American College of Rheumatology (ACR20) criteria response rates were higher with guselkumab vs. placebo at week 24, increased through week 52, and were consistent across all guselkumab-treated groups at week 100. Similar trends were generally observed for ACR50, ≥ 90% improvement in Psoriasis Area and Severity Index (PASI90), achievement of Disease Activity in Psoriatic Arthritis (DAPSA) low disease activity/remission and minimal disease activity, enthesitis and dactylitis resolution, ≥ 0.35 improvement in Health Assessment Questionnaire–Disability Index (HAQ-DI) score, improvement in patient-reported pain, and measures in patients with axial PsA (including Bath Ankylosing Spondylitis Disease Activity Index [BASDAI], Ankylosing Spondylitis Disease Activity Score [ASDAS], and patient-reported spinal pain). Efficacy responses were similar between patients from Russia and the RoW across all endpoints and timepoints. The safety profile of guselkumab in patients from Russia was consistent with previous findings. Conclusion This analysis demonstrated that the safety and efficacy profiles of guselkumab across all PsA domains and patient-reported outcomes in patients from Russia were similar to those in patients from the RoW. Trial Registration Numbers NCT03162796 and NCT03158285.

Objectives To prospectively evaluate the effect of guselkumab through 48 weeks across various clinical outcomes in subgroups of patients with psoriatic arthritis (PsA) and inadequate response to tumour necrosis factor inhibitors (TNFi-IR) from the phase 3b COSMOS trial. Subgroups were defined by baseline demographics, disease characteristics and prior/ongoing therapies.Methods Patients with active PsA (tender joint count (TJC) and swollen joint count (SJC) both ≥3) and TNFi-IR were randomised 2:1 to receive guselkumab 100 mg at week 0, week 4, then every 8 weeks through week 44 or to placebo with cross-over to guselkumab 100 mg at week 16 (early escape) or week 24 (planned). Guselkumab effect on joints (American College of Rheumatology (ACR) 20/50/70, enthesitis, dactylitis), skin (Psoriasis Area and Severity Index 90/100, Investigator’s Global Assessment 0/1), patient-reported outcomes (PROs) (Functional Assessment of Chronic Illness Therapy–Fatigue, Health Assessment Questionnaire–Disability Index) and composite outcome measures (PsA Disease Activity Score low disease activity, minimal disease activity) were evaluated by baseline patient age, sex, body mass index, SJC, TJC, PsA duration, %body surface area, C reactive protein, pain Visual Analogue Scale, number of prior TNFi and discontinuation reason, and conventional synthetic disease-modifying antirheumatic drug status. Results are descriptive only.Results Baseline characteristics were similar between guselkumab (n=189) and placebo (n=96) groups. The benefit of guselkumab over placebo in achieving ACR 20 (primary endpoint; 50% vs 28%) and ACR 50 (23% vs 8%) response at week 24 was observed within all subgroups. Furthermore, response rates in the guselkumab group increased between week 24 and week 48 within almost all subgroups. Similar response patterns at week 24 and through week 48 were observed across various clinical outcomes.Conclusions Guselkumab every 8 weeks led to consistent improvements through week 24 in joint, skin, PRO and composite outcomes versus placebo across diverse baseline-defined subgroups of TNFi-IR patients with PsA. Response rates increased or were durable through week 48 within most subgroups.

This article explores the nonlinear dynamical analysis and control of a 2 − DOF system that emulates the lateral vibration of an asymmetric rotor model under external, multi-parametric, and mixed excitation. The linear integral resonant controller ( L I R C ) has been coupled to the rotor as an active damper through a magnetic actuator. The complete mathematical model, governing the nonlinear interaction among the rotor, controller, and actuator, is derived based on electromagnetic theory and the principle of solid mechanics. This results in a discontinuous 2 − DOF system coupled with two 1 / 2 − DOF systems, incorporating the rub-impact effect between the rotor and stator. The complicated mathematical model is investigated using analytical techniques, employing the perturbation method, and validated numerically through time response, basins of attraction, bifurcation diagrams, 0 − 1 chaotic test, and Poincaré return map. The main findings indicate that the asymmetric system model may exhibit nonzero bistable forward whirling motion under external excitation. Additionally, it can whirl either forward or backward under multi-parametric excitation, besides the trivial stable solution. Furthermore, in the case of mixed excitation, the rotor displays nontrivial tristable solutions, with two corresponding to forward whirling orbits and the other one corresponding to backward whirling oscillation. These findings are validated through the establishment of different basins of attraction. Finally, the performance of the L I R C in mitigating rotor vibrations and averting nonlinear catastrophic bifurcations under various excitation conditions. Furthermore, the rotor’s dynamical behavior and stability are explored in the event of an abrupt failure of one of the connected controllers. The outcomes demonstrate that the proposed L I R C effectively eliminates dangerous nonlinearities, steering the system to respond akin to a linear system with controllable oscillation amplitudes. However, the sudden controller failure induces a local rub-impact effect, leading to a nonlocal quasiperiodic oscillation and restoring the dominance of the nonlinearities on the system’s response.

The reactive two-dimensional steady laminar boundary layer flow over a wedge is examined in this work, with an emphasis on the mass and heat transfer of nanofluids. This study investigates the effects of three different chemical reactions—Arrhenius, bimolecular, and sensitized reactions—using Buongiorno’s model. Through similarity transformations, the system of partial differential equations (PDEs) is converted into ordinary differential equations, which are then solved by combining the shooting method with the Runge–Kutta–Fehlberg numerical technique. The findings show that the skin friction coefficient is greatly increased by raising the pressure gradient and stretching/contracting wedge parameters. On the other hand, as the thermophoresis parameter, Brownian motion parameter, activation energy, and Lewis number increase, the Nusselt number decreases, signifying a decrease in the efficiency of heat transfer. A higher Sherwood number, on the other hand, indicates increased mass transfer and is brought about by increases in the Lewis number, thermophoresis parameter, activation energy, and Falkner–Skan power-law parameter. These findings provide important information for maximizing heat and mass transfer in nanofluid systems. Key values for the skin friction coefficient, local Nusselt number, and the Sherwood number are given in tabular form, and the results are graphically represented.

Abstract While deep learning technologies have made remarkable progress in generating deepfakes, their misuse has become a well-known concern. As a result, the ubiquitous usage of deepfakes for increasing false information poses significant risks to the security and privacy of individuals. The primary objective of audio spoofing detection is to identify audio generated through numerous AI-based techniques. Several techniques for fake audio detection already exist using machine learning algorithms. However, they lack generalization and may not identify all types of AI-synthesized audios such as replay attacks, voice conversion, and text-to-speech (TTS). In this paper, a deep layered model, i.e., VGGish, along with an attention block, namely Convolutional Block Attention Module (CBAM) for spoofing detection, is introduced. Our suggested model successfully classifies input audio into two classes: Fake and Real, converting them into mel-spectrograms, and extracting their most representative features due to the attention block. Our model is a significant technique to utilize for audio spoofing detection due to a simple layered architecture. It captures complex relationships in audio signals due to both spatial and channel features present in an attention module. To evaluate the effectiveness of our model, we have conducted in-depth testing using the ASVspoof 2019 dataset. The proposed technique achieved an EER of 0.52% for Physical Access (PA) attacks and 0.07 % for Logical Access (LA) attacks.

In an active magnetic bearings (AMBs) model, every pair of opposite poles is aligned at an angle with the horizontal axis. In some configurations, there is a pair of poles which is in line with the horizontal axis. In other configurations, the same pair of poles might make a nonzero angle with the horizontal axis. This paper focused on the effect of changing such a configuration angle on the control process of an oscillatory rotor in an 8-pole active magnetic bearings model. Adopting the proportional-derivative (PD) control algorithm, the radial or Cartesian control techniques were applied. It was found that the rotor's oscillation amplitudes were not affected by the change in the configuration angle, even if its rotation speed and eccentricity were varied in the radial control scheme. However, the amplitudes were severely affected by the change in the configuration angle except at a specific angle in the Cartesian control scheme. The approximate modulating amplitudes and phases of the rotor's oscillations were extracted by the method of multiple-scales and a stability condition was tested based on the eigenvalues of the corresponding Jacobian matrix.

The current study is based on the mechanism of mixed convection and solar thermal radiation in Walters'-B fluid considering tera-hybrid nano-structures using convective boundary constraints (CBC) and (CHF) constant heat flux. The heat transmission phenomenon of the current study is taken into account under the influence of triple-suspended nanoparticles. The current problem has several potential applications, including improvements in solar thermal energy systems, nanofluids, aerospace, cooling processes, automotive engineering, and numerical modeling methods. A numerical approach, namely Crank-Nicolson, is utilized in the modeling of 3D Walter's B fluid past over a 3D circular cylinder whose radius varies sinusoidally for evaluation of velocity and temperature distributions. For mathematical modeling, the Cartesian coordinate system was used for the current study. Comparative analysis between constant heat flux (CHF) and convective boundary constraints (CBC) was demonstrated graphically against multifarious parameters towards the temperature profile and velocity profiles along the x-axis and in the y-axis. Moreover, comparative analysis for dissimilar parameters was manifested for Nusselt number through tables, and graphically for skin friction co-efficient and Nusselt number and has shown excellent accuracy. It was estimated that by enhancing values of Qsr, C, Hs and Ec, it was addressed that temperature curve increases for CHF and CBC cases.

Abstract This research explores the 3-D flow characteristics, entropy generation and heat transmission behavior of nanofluids consisting of copper and titanium in water as they flow across a bidirectional apparent, while considering the influence of magneto-hydrodynamics. The thermophysical properties of nanofluids are taken advantage of utilizing the Tiwari and Das demonstrate. The concept of the boundary layer has facilitated the comprehension of the physical ideas derived from it. By applying requisite transformations, the connected intricate sets of partial differential equation have been converted into ordinary differential equation. The modified model is calculated employing the widely recognized technique known as OHAM by using Mathematica program BVPh2.0 Software. For different dimensionless parameters computational and graphical investigations have been performed. It is notice that as fluid parameters change, they exhibit distinct responses in comparison to the temperature, velocity profiles and entropy generation. The results show that velocity profile rise with greater estimates of the magnetic parameter and the rate of entropy formation. Furthermore, thermal profiles become less significant as Eckert and Prandtl numbers increase.

Fake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were trained on an extensive dataset of real and fake face images, incorporating steps like sampling, preprocessing, pooling, normalization, vectorization, batch processing and model training, testing-, and classification via output activation. The proposed work performs the comparative analysis of the three fusion models, which can be integrated with Generative Adversarial Networks (GAN) based on the performance evaluation. The Model-3, which contains the combination of DenseNet-201+ResNet-102+Xception, offers the highest accuracy of 0.9797, and the Model-2 with the combination of DenseNet-201+ResNet-50+Inception V3 offers the lowest loss value of 0.1146; both are suitable for the GAN integration. Additionally, the Model-1 performs admirably, with an accuracy of 0.9542 and a loss value of 0.1416. A second dataset was also tested where the proposed Model-3 provided maximum accuracy of 86.42% with a minimum loss of 0.4054.

In traditional Wireless Sensor Network routing protocols, data collected through timed interval sensing tends to have high temporal redundancy, which leads to unnecessary energy drain. To alleviate this problem and enable sensor networks to save energy to some extent, a practical solution is to utilize prediction-based data fusion methods. To this end, this paper first proposes a Low Energy Adaptive Clustering Hierarchy-Energy-Kopt-N algorithm, an optimization algorithm explicitly designed to address the cluster-head election phase of the Low Energy Adaptive Clustering Hierarchy protocol. Then, a data collection model using data prediction techniques – the Grey Data Prediction Model is formatted. Combining these improvements, a new data fusion algorithm that relies on data prediction, Grey-Clusters-Leach (GCL), is proposed. Simulation experiments demonstrate that the network energy drain of the GCL algorithm is reduced by 18%, 35%, 21.5% and 20%, and the network operation critical period life is extended by 3%, 35%, 22%, and 5% compared to the EQDC LEACH, LEACH-E, and SEP algorithms, respectively. GCL can effectively manage the size and number of clusters and reduce the number of packet transmissions by 20%.

Abstract The heat and mass transfer through the third grade fluid (TGF) flow over an inclined elongating sheet with the consequences of magnetic field and chemical reaction is reported. The impact of activation energy, heat source/sink, and thermal radiation is considered on the TGF flow. Fluid that demonstrate non-Newtonian (NN) properties such as shear thickening, shear thinning, and normal stresses despite the fact that the boundary is inflexible is known as TGF. It also has viscous elastic fluid properties. In the proposed model, the TGF model is designed in form of nonlinear coupled partial differential equations (PDEs). Before employing the numerical package bvp4c, the system of coupled equations are reduced into non-dimensional form. The finite-difference code bvp4c, in particular, executes the Lobatto three-stage IIIa formula. The impacts of flow constraints on velocity field, energy profile, Nusselt number and skin friction are displayed through Tables and Figures. For validity of the results, the numerical comparison with the published study is performed through Table. From graphical results, it can be perceived that the fluid velocity enriches with the variation of TGF factor and Richardson number. The heat source parameter operational as a heating mediator for the flow system, its influence enhances the fluid temperature.

Heart disease, globally recognized as a leading cause of death, has seen its impact magnified by the emergence of COVID-19. The heightened demand for early detection and diagnosis of heart disease has forced the development of innovative, intelligent systems. This research offers a novel approach by leveraging extended short-term memory networks (LSTM) and including COVID-19 as a significant parameter in cardiac arrest analysis. A comparative study is conducted between LSTM and other prevalent techniques, such as support vector machines (SVM), linear regression (LR), and artificial neural networks (ANN), focusing on accuracy and other prognostic criteria for heart disease. We aim to develop an intelligent system powered by LSTM to predict heart disease, thereby assisting healthcare professionals in making well-informed decisions about heart disease management, stroke prevention, and patient monitoring. Additionally, hyperparameter tuning has been performed to optimize the LSTM model's performance in cardiac arrest prediction. The results underscore that LSTM, especially when trained with COVID-19 as an input parameter, surpasses other established techniques in prediction accuracy. The proposed model underwent experimental testing, showcasing its proficiency in predicting cardiovascular disease.

This study targeted developing a novel Zinc oxide with alpha hematite nanoflowers (NFs)-loaded bee venom (Bv) (Bv-ZnO@αFe2O3 NFs) as a bio-natural product from bees to combine both the advantages of combination magnetic properties and the antimicrobial and anti-biofilm properties on isolated coliform bacteria from the effluent of wastewater treatment plants. About 24 isolates of treated wastewater isolates were multidrug resistant (MDR). The phylogenetic grouping of Escherichia coli (E. coli) and Klebsiella pneumonia (K. pneumonia) showed that the largest group was Group A, followed by Group B2 and Group B1. Fourier transform infrared (FTIR), The X-ray diffraction (XRD), and scanning electron microscopy-energy dispersive X-ray analysis (SEM− EDX) validated the coating operation’s contact with Bv onto ZnO@αFe2O3 NFs. According to high-resolution transmission electron microscopy (TEM) and selected area electron diffraction (SAED), pattern analyses for prepared nanoformulations exhibited a spherical shape of αFe2O3 (~9–15 nm), and floral needle shapes with uniform distribution of size with aggregation of ZnOαFe2O3 and Bv-ZnO@αFe2O3 NFs around (~100–200 nm). The toxicity of Bv-ZnO@αFe2O3 NFs was comparable up to 125 µg mL−1, when it reached 64.79% (IC50, 107.18 µg mL−1). The antibacterial activity showed different zones of inhibition against different isolates. The biofilm inhibitory activity of NPs and NFs showed a highly significant reduction (p < 0.001) in treated biofilms with ZnO@αFe2O3 and Bv-ZnO@αFe2O3. In essence, ZnO@αFe2O3 and Bv-ZnO@αFe2O3 NFs are promising antimicrobials for inhibiting the growth and biofilm of MDR E. coli and K. pneumonia isolates, thereby, biocontrol of wastewater.

Abstract Background Guselkumab is a selective interleukin (IL)-23 inhibitor targeting the IL-23p19 subunit. In the phase 3b COSMOS trial, guselkumab demonstrated efficacy in treating participants with active psoriatic arthritis (PsA) and inadequate response (IR; lack of efficacy or intolerance) to tumor necrosis factor inhibitors (TNFi). Methods Adults with active PsA (≥ 3 swollen joints, ≥ 3 tender joints) and IR to one or two TNFi (TNFi-IR) were randomized 2:1 to guselkumab at Weeks 0, 4, then every 8 weeks (Q8W) or placebo➔guselkumab Q8W at Week 24 with possible early escape at Week 16. Levels of serum cytokines, including interferon γ (IFNγ), IL-10, and tumor necrosis factor α (TNFα); T helper 17 (Th17) effector cytokines IL-17A, IL-17F, and IL-22; and acute phase proteins C-reactive protein (CRP), IL-6, and serum amyloid A (SAA), were assessed and compared with demographically matched healthy controls; guselkumab pharmacodynamics through Week 24 were also assessed. Associations between baseline biomarker levels and 1) baseline disease activity (28-joint disease activity score using CRP [DAS28-CRP], psoriasis area and severity index [PASI], and % body surface area [BSA] affected by psoriasis) and 2) clinical response (including ≥ 20% improvement in American College of Rheumatology criteria [ACR20] response) at Week 24 were assessed. Results Baseline serum levels of IL-6, IL-10, IL-17A, IL-17F, IL-22, TNFα, and IFNγ were significantly higher in COSMOS TNFi-IR participants than in healthy controls. Baseline IL-6, CRP, and SAA levels were associated with baseline DAS28-CRP. IL-17A and IL-17F levels were associated with baseline PASI score and psoriasis BSA. Baseline swollen or tender joint counts did not associate with baseline biomarker levels. At Week 24, significant decreases from baseline in CRP, SAA, IL-17A, IL-17F, and IL-22 levels were seen in guselkumab-, but not placebo-, treated participants. IL-17F and IL-22 levels at Week 24 in guselkumab-treated participants did not significantly differ from those of healthy controls. Guselkumab-treated participants achieving ACR20 response at Week 24 exhibited higher baseline IL-22 and IFNγ levels versus nonresponders. Conclusions Results from COSMOS participants with active, TNFi-IR PsA suggest guselkumab reduces levels of effector cytokines associated with the IL-23/IL-17 pathway, including those associated with baseline arthritis and skin disease activity. Trial registration ClinicalTrials.gov: NCT03796858.

Background: The transversus abdominis plane [TAP] block is a recognized method for blocking the neural afferents of the abdominal wall. Various adjuvants have been employed to enhance the quality and duration of local anesthetics.Aim of the work: This study aims to evaluate the effectiveness and safety of magnesium sulfate as an adjuvant to local anesthesia in ultrasound-guided TAP block for patients undergoing moderate-sized umbilical hernia repair under general anesthesia. The study will assess the postoperative pain and opioid consumption levels using the Visual Analogue Scale [VAS].Patients and Methods: This study included 60 patients who undergone surgical umbilical hernia repair. Patients have been randomly placed into two equal groups: Group I [M]: Ultrasound-guided Transversus abdominis plane block with Bupivacaine 0.25% [18 ml] [sunnybupivacaine] and 200 mg [2 ml] MgSO4, and Group II [S]: Ultrasound-guided Transversus abdominis plane block with Bupivacaine 0.25% 18 ml and [2 ml] normal saline.Results: There was a statistically significant decreased postoperative pain measured through VAS score all over the 24 hours postoperatively, and increased time of postoperative analgesia with less dose needed from analgesics in MgSO4 added to bupivacaine group compared to bupivacaine alone with p-value

This study utilized Aspergillus flavus to produce selenium nanoparticles (Se-NPs) in an environmentally friendly and ecologically sustainable manner, targeting several medicinal applications. These biosynthesized Se-NPs were meticulously characterized using X-ray diffraction (XRD), Fourier-transform infrared (FT-IR) spectroscopy, transmission electron microscope (TEM), and UV–visible spectroscopy (UV), revealing their spherical shape and size ranging between 28 and 78 nm. We conducted further testing of Se-NPs to evaluate their potential for biological applications, including antiviral, anticancer, antibacterial, antioxidant, and antibiofilm activities. The results indicate that biosynthesized Se-NPs could be effective against various pathogens, including Salmonella typhimurium (ATCC 14028), Bacillus pumilus (ATCC 14884), Staphylococcus aureus (ATCC 6538), Clostridium sporogenes (ATCC 19404), Escherichia coli (ATCC 8739), and Bacillus subtilis (ATCC 6633). Additionally, the biosynthesized Se-NPs exhibited anticancer activity against three cell lines: pancreatic carcinoma (PANC1), cervical cancer (Hela), and colorectal adenocarcinoma (Caco-2), with IC50 values of 177, 208, and 216 μg/mL, respectively. The nanoparticles demonstrated antiviral activity against HSV-1 and HAV, achieving inhibition rates of 66.4% and 15.1%, respectively, at the maximum non-toxic concentration, while also displaying antibiofilm and antioxidant properties. In conclusion, the biosynthesized Se-NPs by A. flavus present a promising avenue for various biomedical applications with safe usage.

Antimicrobial resistance is one of the largest medical challenges because of the rising frequency of opportunistic human microbial infections across the globe. This study aimed to extract chitosan from the exoskeletons of dead bees and load it with bee venom (commercially available as Apitoxin [Api]). Then, the ionotropic gelation method would be used to form nanoparticles that could be a novel drug-delivery system that might eradicate eight common human pathogens (i.e., two fungal and six bacteria strains). It might also be used to treat the human colon cancer cell line (Caco2 ATCC ATP-37) and human liver cancer cell line (HepG2ATCC HB-8065) cancer cell lines. The x-ray diffraction (XRD), Fourier transform infrared (FTIR), and dynamic light scattering (DLS) properties, ζ-potentials, and surface appearances of the nanoparticles were evaluated by transmission electron microscopy (TEM). FTIR and XRD validated that the Api was successfully encapsulated in the chitosan nanoparticles (ChB NPs). According to the TEM, the ChB NPs and the ChB NPs loaded with Apitoxin (Api@ChB NPs) had a spherical shape and uniform size distribution, with non-aggregation, for an average size of approximately 182 and 274 ± 3.8 nm, respectively, and their Zeta potential values were 37.8 ± 1.2 mV and − 10.9 mV, respectively. The Api@ChB NPs had the greatest inhibitory effect against all tested strains compared with the ChB NPs and Api alone. The minimum inhibitory concentrations (MICs) of the Api, ChB NPs, and Api@ChB NPs were evaluated against the offer mentioned colony forming units (CFU/mL), and their lowest MIC values were 30, 25, and 12.5 μg mL−1, respectively, against Enterococcus faecalis. Identifiable morphological features of apoptosis were observed by 3 T3 Phototox software after Api@ChB NPs had been used to treat the normal Vero ATCC CCL-81, Caco2 ATCC ATP-37, and HepG2 ATCC HB-8065 cancer cell lines for 24 h. The morphological changes were clear in a concentration-dependent manner, and the ability of the cells was 250 to 500 μg mL−1. These results revealed that Api@ChB NPs may be a promising natural nanotreatment for common human pathogens.

Abstract Background This post-hoc analysis of PsABio (NCT02627768) evaluated safety, effectiveness and treatment persistence in patients

Abstract Background To evaluate the real-world effect of the IL-12/23 inhibitor ustekinumab or of a tumour necrosis factor inhibitor (TNFi) on patient-reported outcomes (PRO) and their association with effectiveness endpoints in psoriatic arthritis (PsA) patients over 3 years. Methods In PsABio (NCT02627768), a prospective, observational study, patients with PsA that were prescribed first- to third-line ustekinumab or a TNFi, and remained on that drug for 3 years, were analysed for change in baseline in PROs (EuroQol-5 dimensions health state VAS [EQ-5D VAS], 12-item Psoriatic Arthritis Impact of Disease questionnaire [PsAID-12; range 0–10], Work Productivity and Activity Impairment for Psoriatic Arthritis questionnaire [WPAI; results expressed as a percentage for each domain]), and the association between PROs and WPAI with effectiveness endpoints, clinical disease activity index for psoriatic arthritis (cDAPSA), low disease activity (LDA)/remission, minimal disease activity (MDA) and very low disease activity (VLDA). Results In 437 patients (mean age 49.1 years, 47.8% female), at 3 years, ustekinumab and TNFi treatment led to comparable improvements in EQ-5D VAS; mean change from baseline (95% confidence intervals [CI]) was 11.0 (6.5; 15.4) and 18.9 (14.0; 23.9), respectively. Both groups improved PsAID-12 after 3 years; mean change from baseline (95% CI) was −2.9 (−3.2; −2.5) and −3.5 (−3.9; −3.2), respectively. At baseline, due to their PsA, TNFi-treated patients had lower work productivity compared to ustekinumab-treated patients; mean productivity reduction (95% CI) was 58.8 [52.4; 65.2] and 43.3 [35.6; 51.1]. Over 3 years, TNFi-treated patients had a greater improvement in work productivity compared to ustekinumab-treated patients, ultimately leaving work productivity to be comparable between groups; mean improvement (95% CI) was 44.5% (38.4; 50.6) and 24.9% (15.8; 34.0), respectively. A similar trend was observed in activity impairment. Patients in both treatment groups who achieved effectiveness endpoints, cDAPSA LDA/remission, MDA, and VLDA had greater improvement in PROs and WPAI than patients who did not achieve these endpoints. Conclusions At 3 years, improvements in PROs following ustekinumab or TNFi treatment were generally comparable; however, TNFi-treated patients achieved a greater improvement in work productivity, although this group started from a lower baseline. Achievement of effectiveness endpoints, independent of treatment group, also improved PROs. Trial registration ClinicalTrials.gov, NCT02627768. Registered on 11 December 2015

We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer $ \Delta $. A medical robotics system is described as combining computer science and machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.

Background: In 2017, inflammatory bowel disease, including Crohn's disease (CD) and ulcerative colitis (UC) affected more than 6.8 million people worldwide, with increased incidence in newly industrialized countries. Although treatment options were previously limited to symptom reduction, current approaches benefit from disease-modifying biologics. In this study, we aimed to explore disease characteristics, treatment, and outcomes of patients with CD or UC treated with infliximab or golimumab in routine clinical practice in the Middle East and Northern Africa. Methods: HARIR was a prospective, observational, multicenter study (NCT03006198), in patients who were treatment naïve or who received two or fewer biologic agents. Observed data from routine clinical practice were presented descriptively. Results: Data from 86 patients enrolled from five countries (Algeria, Egypt, Kuwait, Qatar, and Saudi Arabia) were analyzed, 62 with CD and 24 with UC. All patients received infliximab. Clinically meaningful efficacy data were observed only for the CD group (up to Month 3) due to limited patient numbers. Crohn's Disease Activity Index (CDAI) scores at Month 3 indicated a positive response to treatment (reduced score of ≥70 and ≥25% compared with baseline) for 14/48 (29.2%) patients; notably, 28/52 (53.8%) patients had CDAI score

Portfolio theory underpins portfolio management, a much-researched yet uncharted field. This research suggests a collective framework combined with the essence of deep learning for stock selection through prediction and optimal portfolio formation through the mean-variance (MV) model. The CNN-LSTM model, proposed in Stage I blends the benefits of the convolutional neural network (CNN) and the long-short-term memory network (LSTM). The model combines feature extraction and sequential learning about temporal data fluctuations. The experiment considers thirteen input features, combining fundamental market data and technical indicators to capture the nuances of the wildly fluctuating stock market data. The input data sample of 21 stocks was collected from the National Stock Exchange (NSE) of India from January 2005 to December 2021, spanning two significant market crashes. Thus, the sample makes it possible to catch subtle market shifts for model execution. The shortlisted stocks with high potential returns are advanced to Stage II for optimal stock allocation using the MV model. The proposed hybrid CNN-LSTM outperformed the single models, i.e., CNN and LSTM, per the six-performance metrics and advocated by the 10-fold cross-validation technique. Furthermore, the statistical significance of the model is established using non-parametric tests followed by post hoc analysis. In addition, this method is validated by comparing the proposed model to four baseline strategies and relevant pieces of research, which it considerably outperforms in terms of cumulative return per year, Sharpe ratio, and average return to risk with and without transaction cost. These findings highlight the effectiveness of the hybrid CNN-LSTM approach in stock selection and portfolio optimization.

Driver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher insurance premiums, fines, and even criminal charges. The primary aim of our study is to detect driver behavior early with high-performance scores. The publicly available smartphone motion sensor data is utilized to conduct our study experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method is proposed for feature engineering. The proposed LR-RFC method combines the logistic regression and random forest classifier for feature engineering from the motion sensor data. The original smartphone motion sensor data is input into the LR-RFC method, generating new probabilistic features. The newly extracted probabilistic features are then input to the applied machine learning methods for predicting driver behavior. The study results show that the proposed LR-RFC approach achieves the highest performance score. Extensive study experiments demonstrate that the random forest achieved the highest performance score of 99% using the proposed LR-RFC method. The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents.

Plain Language Summary Psoriatic arthritis (PsA) is a type of arthritis associated with inflammation that occurs in almost one-third of patients with the inflammatory skin condition psoriasis. PsA can vary between individuals, and typically causes joint pain, swelling and stiffness, affecting both physical and social well-being. Over the past decade, several new PsA treatments have become available. However, there is currently a lack of agreement about the best treatment options. As PsA is a chronic (long-term) disease, the duration of time a patient continues taking a prescribed treatment (termed “treatment persistence”) is important. The randomised clinical trials used to determine if a treatment works use strict rules to select patients. Therefore, large studies from everyday practice are needed to better understand the effectiveness and safety of these PsA treatments for a wider range of patients. PsABIOnd is a real-life study that will compare guselkumab (an interleukin-23 inhibitor) and interleukin-17 inhibitors, which are two relatively new types of PsA treatments. The study will provide information about how long patients remain on these treatments and how effectively and safely they work over a 3-year period. PsABIOnd will also explore the impact of PsA on participants’ lives by collecting information about their quality of life, disease activity and treatment satisfaction. In addition, participants also taking part in the eDaily by PsABIOnd substudy will wear a watch-like device and use a smartphone-based app to record measurements including activity, sleep, pain and well-being to give a detailed picture of PsA and its impact on patients’ daily lives.

Abstract Background To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiography (FA) dataset and the reverse, to be used as a plausible alternative to either imaging technique in patients with diabetic macular edema (DME). Methods Original images of 1,195 eyes of 708 nonconsecutive diabetic patients with or without DME were retrospectively analyzed. OCT macular thickness maps and corresponding FA images were preprocessed for use in training and testing the proposed pix2pix GAN. The best quality synthesized images using the test set were selected based on the Fréchet inception distance score, and their quality was studied subjectively by image readers and objectively by calculating the peak signal-to-noise ratio, structural similarity index, and Hamming distance. We also used original and synthesized images in a trained deep convolutional neural network (DCNN) to plot the difference between synthesized images and their ground-truth analogues and calculate the learned perceptual image patch similarity metric. Results The pix2pix GAN-synthesized images showed plausible subjectively and objectively assessed quality, which can provide a clinically useful alternative to either image modality. Conclusion Using the pix2pix GAN to synthesize mutually dependent OCT color-coded macular thickness maps or FA images can overcome issues related to machine unavailability or clinical situations that preclude the performance of either imaging technique. Trial registration ClinicalTrials.gov Identifier: NCT05105620, November 2021. “Retrospectively registered”.

Background: Optical coherence tomography angiography (OCTA) is a new safe imaging technique that uses motion contrast imaging to high-resolution volumetric blood stream information to produce angiographic pictures in a matter of seconds. Objectives: We aimed to identify different patterns and types of CNV by OCTA and the relationship between these patterns and activity of CNV. Patients and Methods: Fifty eyes of 50 patients presented with CNV had a regular diagnostic evaluation that included FA and/or OCT. Suspected cases with CNV underwent scanning by OCTA in this study. Patients with media opacity or bad image quality had been excluded. Results: In 80% of patients, there is a statistically significant correlation between the patterns of CNV and its activity (P-value =0.0001). Conclusion: OCTA is a critical technique for better characterizing the various forms and patterns of CNV, as well as the link between these patterns and CNV activity.

In this article, the structure of the normalized error-based proportional-integral (PI) controller is presented and its application to the DC–DC buck converter is discussed. The main motivation is to overcome the drawbacks of saturation and the limited range of controller gains offered by the traditional PI controller. Initially, the theoretical structure and advantages of an improved PI controller are shown. Next, the problem of regulation of the step-down DC–DC converter is addressed using the proposed controller. The objective is to keep the load voltage constant even when the converter parameters vary. The averaged state-space model of the converter is presented and a detailed stability analysis based on the Lypunov indirect method is carried out. The results show an improved range of controller parameters when the proposed controller is employed. Finally, some simulation results are shown to illustrate the effect of controller parameter variations on the output response. These results also verify the ability of the proposed controller to handle the changes in the load, input voltage, and reference voltage of the converter. Moreover, a comparative simulation study validates the superior transient response of the proposed normalized error-based PI controller over the traditional PI controller.

The performance of photovoltaic (PV) modules worsens due to increasing their operating temperature. In the current study, a passive cooling technique comprising an aluminum metal foam (AMF) with PCM for thermal regulation of a PV system is performed. Outdoors experiments are conducted comprising two modules: PV without any modifications and PV with AMF embedded in PCM denoted as PV/PCM/AMF. The temperature distribution on the surface of the PV panels, PCM temperature, open-circuit voltage, and output power produced were recorded during the winter period. The results revealed that the PV surface temperature of the PV-PCM/AFM system was 4 %, 7.4 %, and 13.2 % lower than that of conventional PV, and the power produced from PV-PCM/AFM system was 1.85 %, 3.38 %, and 4.14 % higher than that of conventional PV in December, January, and February, respectively.

In recent investigations, secondary bacterial infections were found to be strongly related to mortality in COVID-19 patients. In addition, Pseudomonas aeruginosa and Methicillin-resistant Staphylococcus aureus (MRSA) bacteria played an important role in the series of bacterial infections that accompany infection in COVID-19. The objective of the present study was to investigate the ability of biosynthesized silver nanoparticles from strawberries (Fragaria ananassa L.) leaf extract without a chemical catalyst to inhibit Gram-negative P. aeruginosa and Gram-positive Staph aureus isolated from COVID-19 patient’s sputum. A wide range of measurements was performed on the synthesized AgNPs, including UV–vis, SEM, TEM, EDX, DLS, ζ -potential, XRD, and FTIR. UV-Visible spectral showed the absorbance at the wavelength 398 nm with an increase in the color intensity of the mixture after 8 h passed at the time of preparation confirming the high stability of the FA-AgNPs in the dark at room temperature. SEM and TEM measurements confirmed AgNPs with size ranges of ∼40-∼50 nm, whereas the DLS study confirmed their average hydrodynamic size as ∼53 nm. Furthermore, Ag NPs. EDX analysis showed the presence of the following elements: oxygen (40.46%), and silver (59.54%). Biosynthesized FA-AgNPs (ζ = −17.5 ± 3.1 mV) showed concentration-dependent antimicrobial activity for 48 h in both pathogenic strains. MTT tests showed concentration-dependent and line-specific effects of FA-AgNPs on cancer MCF-7 and normal liver WRL-68 cell cultures. According to the results, synthetic FA-AgNPs obtained through an environmentally friendly biological process are inexpensive and may inhibit the growth of bacteria isolated from COVID-19 patients.

Background: Early rehabilitation after knee arthroscopic surgery is needed to improve functional recovery which is achieved by ideal analgesia.The aim of the work: This study aimed to compare between safety and efficacy of fascia iliaca block [FIB] and adductor canal block [ACB] using ultrasound guidance for post-operative analgesia and motor affection of quadriceps muscle after knee arthroscopic surgeries.Patients and Methods: Sixty patients of American Society of Anesthesiologist [ASA] I and II were included. They were scheduled for elective knee arthroscopic surgeries between April 2017 and October 2018. They were selected from Al-Azhar University Hospital [Damietta, Egypt]. They were randomly allocated to receive either FIB or ACB by bupivacaine 0.25 with ultrasound guidance. Their pain, and sensory block were assessed before and after procedure. The time for first analgesic request and total consumed analgesics were documented.Results: At 30 minutes, 1, 2, 4, 6, 8, 12 and 24 hours, there were no significant differences among the mean value of Numerical Rating Scale [NRS] between FIB and ACB. The first time to introduce morphine and total morphine consumption showed no significant differences between FIB and ACB. There was significant motor affection of FIB in comparison with ACB.Conclusion: FIB and ACB provided effective and safe postoperative analgesia for patients scheduled for knee arthroscopic surgeries, with sparing of quadriceps muscle strength and early ambulation in ACB patients.

In the realm of data protection strategies, differential privacy ensures that unauthorized entities cannot reconstruct original data from system outputs. This study explores discrete event systems, specifically through probabilistic automata. Central is the protection of state data, particularly the initial state privacy of multiple starting states. We introduce an evaluation criterion to safeguard initial states. Using advanced algorithms, the proposed method counters the probabilistic identification of any state within this collection by adversaries from observed data points. The efficacy is confirmed when the probability distributions of data observations tied to these states converge. If a system’s architecture does not meet state differential privacy demands, we propose an enhanced supervisory control mechanism. This control upholds state differential privacy across all initial states, maintaining operational flexibility within the probabilistic automaton framework. Concluding, a numerical analysis validates the approach’s strength in probabilistic automata and discrete event systems.

In this work, we present a framework that facilitates the sharing of EHRs among the community of health-care providers (HCPs). However, the sharing might be obstructed by patients’ privacy and the controlling legislation. Nevertheless, our sharing scheme of EHRs strives to respect patients’ privacy and comply with relevant legislator guidelines, e.g., HIPPA. The proposed work introduces two services while sharing the EHRs: privacy and non-repudiation. To this end, we introduce the partners and the role of each during the course of exchanging of EHRs. The principle of sharing EHRs among HCPs has to be reinforced to save patients’ lives while cryptographic primitives have to be employed to serve this purpose. In this paper, we are motivated to introduce the notion of non-repudiation private membership test (NR-PMT). In NR-PMT, we help patients receive medical services with great flexibility while maintaining their privacy and thwarting all possible threats to disclose their identities. In addition, a formal security analysis based on Kailar’s accountability framework has been used to analyze the proposed framework. Also, a complexity analysis has been conducted.

Because of their unique features, nanomaterials have been proposed and have gained acceptance in postharvest applications in fruit. Increasing the storage life and improving the quality of Valencia oranges was investigated using nano-chitosan. A chitosan nanoparticle was prepared by using high-energy ball milling. Chitosan nanoparticles were characterized by Dynamic light scattering, FTIR spectroscopy and Surface morphology by transmission electron microscopy. Fully mature Valencia oranges were harvested and then coated with one of these concentrations (0.2, 0.4, and 0.8% nano-chitosan) and control. The fruits were stored under room storage conditions for 75 days. The quality parameters (fruit weight losses, fruit decay percentage, fruit firmness, total acidity, total soluble solids percentage and T.S.S./acid ratio, ascorbic acid content) were taken in biweekly intervals after 0, 15, 30, 45, 60, and 75 days. Beside the in vitro testing of antifungal activity of chitosan nanoparticles. According to the findings of the two succeeding seasons, the nano-chitosan 0.8% treatment showed the best effects and had the lowest rate of fruit weight loss, fruit deterioration, and T.S.S./acid ratio in comparison to the other treatments in both seasons. Furthermore, the 0.8% nano-chitosan reveled the highest levels of fruit hardness and fruit pulp firmness. Fruit weight loss, fruit deterioration, TSS, and TSS/acid ratio, as well as other metrics, were steadily elevated prior to the storage time. The best results were obtained when Valencia oranges fruits were treated with 0.8% nano-chitosan for 75 days at room temperature.

This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

Recently, the use of nanofertilizers has received a great deal of attention in managing plants under biotic and abiotic stresses. However, studies that elucidate the role of silicon dioxide nanoparticles (SiO2NPs) in regulating maize tolerance to drought stress are still at early stages of development. In this study, plants that were treated with SiO2NPs (0.25 g/L as foliar spray) displayed considerable improvement in the growth indices, despite being subjected to drought stress. In addition, the action of SiO2NPs led to a considerable rise in the levels of chlorophylls, proline, cell membrane integrity, leaf water content, and antioxidant enzymes (superoxide dismutase (SOD), catalase (CAT), and guaiacol peroxidase (G-POX)). In contrast, an inverse trend was seen in the oxidative injury, the total amount of soluble sugars, and the activity of ascorbate peroxidase (APX). At the same time, carotenoids were unaffected in SiO2NPs-treated and non-treated plants under drought stress. The results of the molecular investigation that was conducted using qRT-PCR showed that the relative expression of the D2 protein of photosystem II (PsbD) was elevated in SiO2NPs-treated plants in response to drought stress, while the expression of the osmotic-like protein (OSM-34) and aquaporin (AQPs) was downregulated in SiO2NPs-treated plants in response to drought stress. This research could pave the way for further investigations into how SiO2NPs boost plant resistance to drought stress.

ABSTRACT Surface-growing antibiotic-resistant pathogenic bacteria such as Escherichia coli and Staphylococcus aureus are emerging as a global health challenge due to dilemmas in clinical treatment. Furthermore, their pathogenesis, including increasingly serious antimicrobial resistance and biofilm formation, makes them challenging to treat by conventional therapy. Therefore, the development of novel antivirulence strategies will undoubtedly provide a path forward in combatting these resistant bacterial infections. In this regard, we developed novel biosurfactant-coated nanoparticles to combine the antiadhesive/antibiofilm properties of rhamnolipid (RHL)-coated Fe3O4 nanoparticles (NPs) with each of the p-coumaric acid (p-CoA) and gallic acid (GA) antimicrobial drugs by using the most available polymer common coatings (PVA) to expand the range of effective antibacterial drugs, as well as a mechanism for their synergistic effect via a simple method of preparation. Mechanistically, the average size of bare Fe3O4 NPs was ~15 nm, while RHL-coated Fe3O4@PVA@p-CoA/GA was about ~254 nm, with a drop in zeta potential from −18.7 mV to −34.3 mV, which helped increase stability. Our data show that RHL-Fe3O4@PVA@p-CoA/GA biosurfactant NPs can remarkably interfere with bacterial growth and significantly inhibited biofilm formation to more than 50% via downregulating IcaABCD and CsgBAC operons, which are responsible for slime layer formation and curli fimbriae production in S. aureus and E. coli, respectively. The novelty regarding the activity of RHL-Fe3O4@PVA@p-CoA/GA biosurfactant NPs reveals their potential effect as an alternative multitarget antivirulence candidate to minimize infection severity by inhibiting biofilm development. Therefore, they could be used in antibacterial coatings and wound dressings in the future. IMPORTANCE Antimicrobial resistance poses a great threat and challenge to humanity. Therefore, the search for alternative ways to target and eliminate microbes from plant, animal, and marine microorganisms is one of the world’s concerns today. Furthermore, the extraordinary capacity of S. aureus and E. coli to resist standard antibacterial drugs is the dilemma of all currently used remedies. Methicillin-resistant S. aureus (MRSA) and vancomycin-resistant S. aureus (VRSA) have become widespread, leading to no remedies being able to treat these threatening pathogens. The most widely recognized serotypes that cause severe foodborne illness are E. coli O157:H7, O26:H11, and O78:H10, and they display increasing antimicrobial resistance rates. Therefore, there is an urgent need for an effective therapy that has dual action to inhibit biofilm formation and decrease bacterial growth. In this study, the synthesized RHL-Fe3O4@PVA@p-CoA/GA biosurfactant NPs have interesting properties, making them excellent candidates for targeted drug delivery by inhibiting bacterial growth and downregulating biofilm-associated IcaABCD and CsgBAC gene loci.

This paper proposes a novel high voltage gain structure of DC-DC converter with soft-switching ability for photovoltaic (PV) applications. A small size coupled inductor with one magnet core is utilized to improve the voltage conversion ratio in the proposed converter. The converter has one active MOSFET with low conducting resistance ( $R_{DS}-_{ON}$ ), which in turn reduces the conduction losses and complexity of the control section. Due to the low input current ripple, the lifetime of the input PV panel is increased, and the maximum power point (MPP) of the PV panel can be easily tracked. The MOSFET’s zero-voltage and zero-current switching and diodes are the other countenance of the proposed converter, which improve its efficiency. Additionally, an improved Perturb and Observe MPP tracking (IP&O MPPT) algorithm is introduced to boost the extracted power of the input PV sources. To validate the performance of this converter, the operation modes principle, steady-state and efficiency survey, and comparison results with other same family converters are carried out. Finally, an experiential prototype is built with 20 V input, 200 V output, power rate of 200 W, and 50 kHz operating frequency to validate the mathematical analysis and effectiveness of the proposed structure. The efficiency of the proposed converter was estimated by over 95% at various power levels.

For the automatic tracking of unknown moving targets on the ground, most of the commonly used methods involve circling above the target. With such a tracking mode, there is a moving laser spot on the target, which will bring trouble for cooperative manned helicopters. In this paper, we propose a new way of tracking, where an unmanned aerial vehicle (UAV) circles on one side of the tracked target. A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle. This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements. A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV. The transition path algorithm can generate waypoints that meet the flight ability. In this paper, we analyze the entire method and detail the scope of applications. We formulate an observation angle as an evaluation index. A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.

There is currently a global COVID-19 pandemic caused by the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) and its variants. This highly contagious viral disease continues to pose a major health threat global. The discovery of vaccinations is not enough to prevent their spread and dire consequences. To take advantage of the current drugs and isolated compounds, and immediately qualifying approach is required. The aim of our research is evaluation the potency for natural antiviral compounds against the SARS CoV-2 Mpro. Molecular docking of four phenolic compounds from Phillyrea angustifolia leaves with SARS-CoV-2 Mpro has been conducted. Similarly, the stability of selected ligand–protein interactions has been determined using MD simulations. Moreover, the quantitative structure–activity relationship (QSAR), MMGBSA binding energies, pharmacokinetics, and drug-likeness predictions for selected phenolic have been reported. The selected phenolic compounds (Luteolin-7-O-glucoside, Apigenin-7-O-glucoside, Demethyl-oleuropein, and Oleuropein aglycone) revealed strong binding contacts in the two active pockets of a target protein of SARS-CoV-2 Mpro with the docking scores and highest binding energies with a binding energy of −8.2 kcal/mol; −7.8 kcal/mol; −7.2 kcal/mol and −7.0 kcal/mol respectively. Both Demethyloleoeuropein and Oleuropein aglycone can interact with residues His41 and Cys145 (catalytic dyad) and other amino acids of the binding pocket of Mpro. According to QSAR, studies on pharmacokinetics and drug-like properties suggested that oleuropein aglycone could be the best inhibitor of SARS-CoV-2 for new drug design and development. Further in vivo, in vitro, and clinical studies are highly needed to examine the potential of these phenolic compounds in the fight against COVID-19.

A brain tumor refers to an abnormal growth of cells in the brain that can be either benign or malignant. Oncologists typically use various methods such as blood or visual tests to detect brain tumors, but these approaches can be time-consuming, require additional human effort, and may not be effective in detecting small tumors. This work proposes an effective approach to brain tumor detection that combines segmentation and feature fusion. Segmentation is performed using the mayfly optimization algorithm with multilevel Kapur’s threshold technique to locate brain tumors in MRI scans. Key features are achieved from tumors employing Histogram of Oriented Gradients (HOG) and ResNet-V2, and a bidirectional long short-term memory (BiLSTM) network is used to classify tumors into three categories: pituitary, glioma, and meningioma. The suggested methodology is trained and tested on two datasets, Figshare and Harvard, achieving high accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of a comparative analysis with existing DL and ML methods demonstrate that the proposed approach offers superior outcomes. This approach has the potential to improve brain tumor detection, particularly for small tumors, but further validation and testing are needed before clinical use.

In years with unfavorable weather, winter frost during the blossoming season can play a significant role in reducing fruit yield and impacting the profitability of cultivation. The mango Naomi cultivar Mangifera indica L. has a low canopy that is severely affected by the effects of frost stress. As a result of the canopy being exposed to physiological problems, vegetative development is significantly inhibited. The current investigation aimed to study the influence of spraying nitric oxide and fogging spray systems on Naomi mango trees grafted on ‘Succary’ rootstock under frost stress conditions. The treatments were as follows: nitric oxide (NO) 50 and 100 μM, fogging spray system, and control. In comparison to the control, the use of nitric oxide and a fogging system significantly improved the leaf area, photosynthesis pigments of the leaf, the membrane stability index, yield, and physical and chemical characteristics of the Naomi mango cultivar. For instance, the application of 50 μM NO, 100 μM NO, and the fogging spray system resulted in an increase in yield by 41.32, 106.12, and 121.43% during the 2020 season, and by 39.37, 101.30, and 124.68% during the 2021 season compared to the control, respectively. The fogging spray system and highest level of NO decreased electrolyte leakage, proline content, total phenolic content, catalase (CAT), peroxidases (POX), and polyphenol oxidase (PPO) enzyme activities in leaves. Furthermore, the number of damaged leaves per shoot was significantly reduced after the application of fogging spray systems and nitric oxide in comparison to the control. Regarding vegetative growth, our results indicated that the fogging spray system and spraying nitric oxide at 100 μM enhanced the leaf surface area compared to the control and other treatments. A similar trend was noticed regarding yield and fruit quality, whereas the best values were obtained when the fogging spray system using nitric oxide was sprayed at a concentration of 100 μM. The application of fogging spray systems and nitric oxide can improve the production and fruit quality of Naomi mango trees by reducing the effects of adverse frost stress conditions.

Cyber-physical systems are characterized by the intrinsic combination of software and physical components that usually include (wired and wireless) communication devices, sensors, actuators, and control processing units. Some wireless devices communicate over insecure channels, rendering cyber-physical systems at risk of malicious attacks that might lead to catastrophic damage. This paper touches upon the problem of sensor deception attacks in supervisory control of discrete-event systems, where an attacker can insert, delete, or replace sensor readings to mislead the supervisor and induce system damage. We model potential attacks using nondeterministic finite-state transducers and then introduce a new defence strategy that utilizes insertion functions. Insertion functions are a type of monitoring interface that alters the system’s behaviour by adding extra observable events. Finally, we construct a nondeterministic finite-state transducer called a supervisor filter that recovers the original language generated by the plant by handling the altered language. The insertion function and the supervisor filter cooperate to control the system and confuse the intruder without confusing the supervisor.

ABSTRACT The purpose of this study was to assess the bacteriological quality in some domestic bottled waters marketed in Al Anbar Province of Iraq. In total, 120 samples were collected from 20 different domestic bottled water companies. The current study findings demonstrated that the positive total bacterial count for aerobic bacteria was 20 CFU/ml (16.6%) out of 120 samples. From 120 tested samples, coliform bacteria had a much lower count of 13 CFU/ml (10.8%). The bacteriological analysis tests of this study showed that the brand bottled water of Alhilwa had the highest mean of total bacterial count at 485 CFU/ml, followed by Alwafi and Araco, which found at mean of 283 and 196 CFU/ml, respectively. The other brands of bottled waters included Sawa and Izmir, which had given lower mean of bacterial count at 87 and 58 CFU/ml, respectively, while all other tested brands of bottled waters had zero content of total bacterial count. According to the biomedical tests and Vitek2 system employed for this study, the isolated bacterial species as contaminants in bottled waters were Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae. The results of this study showed that Pseudomonas aeruginosa was sensitive to all tested antibiotics, but the Escherichia coli was resistance to amoxicillin, azithromycin, ceftazidime, and cefixime. The Klebsiella pneumonia demonstrated sensitivity to all tested antibiotics except the cefixime. Therefore, antibiotics belonging to the types of penicillin, carbapenem, and quinolones can be considered the best medicine for treating infections caused by the bacteria diagnosed in this study. In conclusion, the findings of this study showed that some domestic bottled waters sold in markets and shops in Al Anbar Province have bacteriological contents that are within permitted ranges for Iraqi and WHO standards. IMPORTANCE Researchers analyzed how lifestyle factors affect the overall health of people with bacterial infections from the water. The article describes significance of the research because many people do not have access to clean, safe drinking water where this water is essential to life, and many die of waterborne bacterial infections. So, the purpose of the article is to draw attention to the major factors of the most dangerous bacteria transmitted through water marketed in Al Anbar Province of Iraq: Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae. Furthermore, our specific significant contribution has been to show the most important treatments for treating infections caused by the bacteria diagnosed in this study.

This work investigated the effects of the bioflavonoid hesperetin-7-rhamnoglucoside isolated from Citrus uranium fruit peel on Helicobacter pylori (H. pylori). Separation and purity, crystalline state, and urease inhibition assays were carried out. Then, molecular docking and molecular dynamics (MD) simulations were conducted with urease as the target protein. Hesp was isolated from citrus peel with a purity of 95.14 µg mg−1 of dry raw material. X-ray diffraction analysis, hydrogen-1 nuclear magnetic resonance, Fourier transform infrared spectroscopy, and differential scanning calorimetry revealed that pure Hesp had the same crystallinity rating as the Hesp standard. The kinetic inhibition study demonstrated that Hesp inhibited H. pylori urease in a competitive and concentration-dependent manner with jack bean urease. In addition, bioimaging studies with laser scanning confocal microscopy and scanning electron microscopy illustrated that Hesp interacted with bacterial cells and induced membrane disruption by creating holes in the outer membranes of the bacterial cells, resulting in the leakage of amino acids. Importantly, molecular docking and 20 ns MD simulations revealed that Hesp inhibited the target protein through slow-binding inhibition and hydrogen bond interactions with active site residues, namely, Gly11 (O⋯H distance = 2.2 Å), Gly13 (O⋯H distance = 2.4 Å), Ser12 (O⋯H distance = 3.3 Å), Lys14 (O⋯H distance = 3.3 Å), and Arg179 (O⋯H distance = 2.7 Å). This work presents novel anti- H. pylori agents from natural sources.

Diabetic retinopathy (DR) is one of the major complications caused by diabetes and is usually identified from retinal fundus images. Screening of DR from digital fundus images could be time-consuming and error-prone for ophthalmologists. For efficient DR screening, good quality of the fundus image is essential and thereby reduces diagnostic errors. Hence, in this work, an automated method for quality estimation (QE) of digital fundus images using an ensemble of recent state-of-the-art EfficientNetV2 deep neural network models is proposed. The ensemble method was cross-validated and tested on one of the largest openly available datasets, the Deep Diabetic Retinopathy Image Dataset (DeepDRiD). We obtained a test accuracy of 75% for the QE, outperforming the existing methods on the DeepDRiD. Hence, the proposed ensemble method may be a potential tool for automated QE of fundus images and could be handy to ophthalmologists.