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"Salah, Mohammed"
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On the Weighted Pseudo Almost-Periodic Solutions of Static DMAM Neural Network
This work is devoted to study a new class of static neural network. We propose a static multidirectional associative memory neural network with time-varying coefficients and delays. We assess the existence and global exponential stability of weighted pseudo almost-periodic solutions for the systems through exponential dichotomy theory, contraction mapping fixed point theorem, differential inequality techniques and weighted pseudo almost-periodic functions. The theoretical results obtained are supported with numerical simulations. To our knowledge, no paper in the literature has investigated the existence and the delay-independent stability of weighted pseudo almost-periodic solution for static delayed MAM neural network. The results obtained are new and complements that found in the literature.
Journal Article
SOBOLEV DIFFERENTIABLE STOCHASTIC FLOWS FOR SDES WITH SINGULAR COEFFICIENTS: APPLICATIONS TO THE TRANSPORT EQUATION
In this paper, we establish the existence of a stochastic flow of Sobolev diffeomorphisms $\\mathbb{R}^d\\ni x\\quad\\longmapsto\\quad\\phi_{s,t}(x)\\in \\mathbb{R}^d,\\qquad s,t\\in\\mathbb{R}$ for a stochastic differential equation (SDE) of the form $dX_t=b(t,X_t)\\,dt+dB_t,\\qquad s,t\\in\\mathbb{R},X_s=x\\in\\mathbb{R}^d.$ The above SDE is driven by a bounded measurable drift coefficient b: ℝ × ℝd → ℝd and a d-dimensional Brownian motion B. More specifically, we show that the stochastic flow ϕs,t(·) of the SDE lives in the space L2(Ω; W1,p (ℝd, w)) for all s, t and all p ∈ (1, ∞), where W1,p (ℝd, w) denotes a weighted Sobolev space with weight w possessing a pth moment with respect to Lebesgue measure on ℝd. From the viewpoint of stochastic (and deterministic) dynamical systems, this is a striking result, since the dominant \"culture\" in these dynamical systems is that the flow \"inherits\" its spatial regularity from that of the driving vector fields. The spatial regularity of the stochastic flow yields existence and uniqueness of a Sobolev differentiable weak solution of the (Stratonovich) stochastic transport equation $\\begin {cases}{\\displaystyle d_tu(t,x)+\\bigl(b(t,x)\\cdot Du(t,x)\\bigr)\\,dt+\\sum_{i=1}^de_i\\cdot Du(t,x)\\circ dB_t^i=0,\\cr u(0,x)=u_0(x),}\\end{cases}$ where b is bounded and measurable, u0 is $C_b^1$ and $\\{e_i\\}_{i=1}^d$ a basis for ℝd. It is well known that the deterministic counterpart of the above equation does not in general have a solution.
Journal Article
Pseudo Almost Automorphic Solutions of Recurrent Neural Networks with Time-Varying Coefficients and Mixed Delays
In this paper, existence, uniqueness and global exponential stability of pseudo almost automorphic solutions for a class of recurrent neural networks with time-varying coefficients and mixed delays are established by employing the fixed point theorem and differential inequality. Numerical example with graphical illustration is given to illuminate our main results.
Journal Article
Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis
In modern diagnostic approaches, the key step consists in generating the features related to fault type and severity. In fact, the generated features should be able to help the classifier to determine the health condition of the monitored system based on the measured signal. In this paper, in order to make an effective diagnosis about the rolling-element bearing failure, novel generated features that can maintain the physical meaning of the extracted vibration signal, while identifying its relationship to rolling bearing damage, are proposed using a wrapper model. For this purpose, based only on the Most Impulsive Frequency Bands (MIFBs) of the measured vibration signals for many bearing conditions, 33 feature parameters are proposed. Using a wrapper scheme, these parameters can be reduced until a set of them are found improving the efficiency of the diagnostic approach. The effectiveness of the proposed predictive features is analyzed by comparing it with some related works using many testing data for several bearing conditions. The experimental results reveal that the proposed procedure has obtained a high level of accuracy of 99.83%.
Journal Article
Alterations in skin microbiome mediated by radiotherapy and their potential roles in the prognosis of radiotherapy-induced dermatitis: a pilot study
Radiotherapy-induced dermatitis (RID) is an inflammatory cutaneous disorder that is acquired as an adverse effect of undergoing radiotherapy. Skin microbiome dysbiosis has been linked to the outcomes of several dermatological diseases. To explore the skin microbiota of RID and deduce their underlying impact on the outcome of RID, cutaneous microbiomes of 78 RID patients and 20 healthy subjects were characterized by sequencing V1-V3 regions of 16S rRNA gene. In total, a significantly apparent reduction in bacterial diversity was detected in microbiomes of RID in comparison to controls. Overall, the raised Proteobacteria/ Firmicutes ratio was significantly linked to delayed recovery or tendency toward the permanence of RID (Kruskal Wallis:
P
= 2.66 × 10
–4
). Moreover, applying enterotyping on our samples stratified microbiomes into A, B, and C dermotypes. Dermotype C included overrepresentation of
Pseudomonas, Staphylococcus
and
Stenotrophomonas
and was markedly associated with delayed healing of RID. Strikingly, coexistence of diabetes mellitus and RID was remarkably correlated with a significant overrepresentation of
Klebsiella
or
Pseudomonas
and
Staphylococcus
. Metabolic abilities of skin microbiome could support their potential roles in the pathogenesis of RID. Cutaneous microbiome profiling at the early stages of RID could be indicative of prospective clinical outcomes and maybe a helpful guide for personalized therapy.
Journal Article
Enhancing the backstepping control approach competencies for wind turbine systems using a dual star induction generator
The goal of this study is to present a novel and improved backstepping control (BC) technique for a dual-star induction generator (DSIG) powered by a wind turbine. This approach relies on the ant lion optimization (ALO), which is employed to determine the optimal parameters of the BC approach and improve the performance of the wind conversion energy system. The ALO approach enhances the robustness of the DSIG, enabling faster dynamic responses, greater accuracy, and consistently improved effectiveness. The fitness function of the ALO approach integrates both integral time absolute error and integral time squared error criteria, ensuring the fulfillment of effectiveness objectives. The performance of the BC-ALO approach is validated through MATLAB. The results of the tests show that the new approach reduces total harmonic distortion, minimizes stator energy fluctuations, and improves dynamic efficiency compared to the BC approach. Additionally, the method can handle uncertainties in model parameters, making it versatile and practical. Simulation results show that the BC-ALO method reduces the total harmonic distortion value compared to the BC method by percentages estimated at 29.45%, 50.44%, and 43.10% in all tests. Also, this approach improves the overshoot value of DSIG power compared to the traditional BC strategy by an estimated 100% in all tests. The proposed approach improves the response time value of the reactive power compared to the conventional BC strategy by percentages estimated at 97.65%, 97.78%, and 95.23% in all tests. The DC link voltage ripples are low if the proposed approach is used, with ratios estimated at 63.31%, 71.38%, and 71.89% in all tests. These results make the proposed approach interesting in other applications such as photovoltaic systems.
Journal Article
Stability and Bifurcation Analysis of a Discrete Tumor-Immune System with Allee Effects
Differential equations are usually employed to accurately represent the ongoing relationships between tumor cells and immune effector populations, enabling scientists to discover how variation in growth and response rates affects tumor development or elimination. The essential objective of this work is to analyze the dynamical development of a discrete tumor-immune interaction model, with a particular focus on finding out how the combined effects of tumor growth and immune response influence tumor progression. The forward Euler approach is effectively used to discretize the governed system. The bifurcation theory is used to establish the fixed points of the considered system, the stability about the fixed points, and Neimark–Sacker and period-doubling bifurcations. We identify parameter domains that result in tumor existence, restricted oscillations, or full-tumor elimination utilizing stability evaluation, bifurcation examination, and computational simulations. In addition, the 0–1 test is presented. Chaos control is also developed. This article successfully discusses some numerical simulations to verify the results obtained. In general, the research gives an overall insight into this interaction and highlights the circumstances under which the immune system is capable of suppressing or removing tumor cells.
Journal Article
Hopf bifurcation and chaos in fractional-order modified hybrid optical system
In this paper, a chaotic fractional-order modified hybrid optical system is presented. Some basic dynamical properties are further investigated by means of Poincaré mapping, parameter phase portraits, and the largest Lyapunov exponents. Fractional Hopf bifurcation conditions are proposed; it is found that Hopf bifurcation occurs on the proposed system when the fractional-order varies and passes a sequence of critical values. The chaotic motion is validated by the positive Lyapunov exponent. Finally, some numerical simulations are also carried out to illustrate our results.
Journal Article
Irrigation water quality shapes soil microbiomes: a 16 S rRNA-based biogeographic study in arid ecosystems
Soil microbiome plays a crucial role in ecosystem; however, the responses of the soil microbiome to nonconventional irrigation water sources remain poorly understood. This study employed 16 S rRNA sequencing to investigate microbial community shifts in soil samples collected from four geographically distinct locations affected by different irrigation water sources: saline ground water affected by seawater (SW), a brackish water lake (BW), a wastewater drain (WW), and a freshwater canal that receives inflows from multiple agricultural drains (FW). Our findings revealed distinct microbial signatures shaped by water quality, with Firmicutes dominating WW soils (49.2%) due to metal resistance (DESeq2,
p =
3.67 × 10
− 4
), whereas
Chloroflexi
and
Cyanobacteria
thrived in BW environments (LEfSe, LDA > 4,
p =
8.23 × 10
− 6
), reflecting adaptations to chloride-rich conditions. FW soils enriched Acidobacteria and Verrucomicrobia, which are associated with moderate salinity and nutrient cycling, whereas SW samples harbored halotolerant
Actinobacteria
and Deinococcus-Thermus (DESeq2,
p =
1.47x
− 05
). Statistical analyses revealed key potential biomarkers, including
Streptococcus
(WW, DESeq2
p =
3.67x
− 24
),
RB41
(BW, LEfSe
p =
1.62x
− 13
), and
Candidatus_Udaeobacter
(SW, DESeq2
p =
1.47x
− 05
). Physicochemical drivers such as salinity (R² =0.319,
p =
0.00041) and heavy metals (Pb/Mn in WW) strongly influence community structure. Notably, WW irrigation reduced alpha diversity (Shannon index: 4.79–5.41 vs. 6.65–7.43 in FW; Kruskal-Wallis
p =
0.0056), highlighting pollutant-induced stress. These findings highlight the balance between water reuse and soil health, offering a foundation for microbiome-driven bioremediation approaches in arid environments. By utilizing native, stress-resilient microbial communities, our research promotes sustainable agricultural practices in water-limited regions.
Journal Article