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Narrow width of optical absorption of conducting polymers and photons energy losses have been the challenges for fabricating highly efficient thin-film organic solar cell. Nickel-doped silver nanoclusters (Ni/Ag NCs) are employed here to capture more photons using polymers blend solar absorber medium to improve solar cell performances. The poly-3-hexylthiophene and (6-6)phenyl-C61-butyric acid methyl ester molecules blend were used a solar absorber layer in this investigation. The solar cells fabricated with NCs exhibited enhanced opt-electronic properties compared to the reference solar cell. Consequently, the experimental results suggest that the power conversion efficiency (PCE) has substantially increased with the incorporations of NCs in absorber layer, which is dependent on the concentrations of NCs in the medium. The maximum PCE achieved, in this work, is η = 6.2% at 2% of NCs by weight, which has exhibited to the lowest energy losses compared to other doping levels. This improvement in PCE is attributed to the occurrence of local surface plasmon resonance effect due to the inclusion of Ni/Ag NCs in polymer matrix. The results provide valuable insights on the use of Ni/Ag NCs for efficient photons capture in thin-film polymers blend medium.

Charge transport process is one of the most important factors that determine the performance of thin‐film organic solar cells. In this report, nanocore shells (NCSs) composed of a copper core and nickel as a shell (Cu@Ni) are successfully synthesized and used in the functional layer of thin‐film organic solar cell (TFOSC). The NCSs are doped in the hole‐selective material known as poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate at various concentrations from 0.1% to 0.5% by weight. Bulk heterojunction solar absorber design is used to fabricate new polymer solar cells using poly‐3‐hexylthiophene as a donor and [6,6] phenyl‐C61‐butyric acid methyl ester as an acceptor. The experimental results suggest that the device performances of the samples doped with Cu@Ni NCSs have significantly improved compared to the reference cell. The collection of high photocurrents is responsible for improved device performance as a result of better optical absorption and charge transport processes. Furthermore, the performances are found to be dependent on concentration of NCS in the transport layer. The best performance recorded in the study is found to be at the 0.2 wt% doping level. Such improvements in power conversion efficiency are attributed to the occurrence of local surface plasmon resonances on the NCS in the polymer transport layer. Organic solar cells have emerged as one of the possible solar cell technologies to fabricate low‐cost and lightweight solar panels. The use of metal nanoparticles in the functional layers of thin‐film organic solar cells is found to be beneficial to improve charge transport processes in the medium.

Recently, many authors investigated the existence of global attractors for different types of autonomous lattice dynamical systems. Within this work, we carefully study the existence of a uniform global attractor for a new class of first order non-autonomous lattice dynamical system in the Hilbert space l2l^{2}.

Many researchers examined the existence of global attractors for various types of first and second order lattice dynamical systems. Here we prove the existence of a global attractor for a new type of second order lattice dynamical systems in the Hilbert space l 2 × l 2 . For specific choices of the linear operators this system can be regraded as a spatial discretization of a continuous damped nonlinear Boussinesq equation on ℝ m , m ≥1.

For 𝜉 ∈ [1/2/, 3/4], the existence of the global attractor for the evolutionary equation corresponding to the following strongly damped nonlinear beam equation ( 1 + β A 1 / 2 ) u t t + δ A 1 / 2 u t + α A u + g ( ‖ u ‖ ξ − 1 / 4 2 ) A 1 / 2 u = f , t > 0 , has been studied in D H ( A ξ ) × D H ( A ξ − 1 / 4 ) . Such an equation is related to a nonlinear beam equation as well as Timoshenko's equation. The main difficulty of our work comes from the terms β A 1 / 2 u t t and g ( ‖ u ‖ ξ − 1 / 4 2 ) A 1 / 2 u , representing the rotational inertia of the beam and the tension within the beam due to its extensibility, respectively. We overcome the difficulty of introducing the solution, bounded absorbing set, and 𝑘-contracting property by carefully using the fractional power theory and suitable time-uniform a priori estimates.

Bovine respiratory disease (BRD) is the costliest complex disease affecting the cattle industry worldwide, with significant economic losses. BRD pathogenesis involves several interactions between microorganisms, such as bacteria and viruses, and management factors. The present study aimed to characterize the nasal virome from 43 pooled nasal swab samples collected from Egyptian nonvaccinated cow-calf operations with acute BRD from January to February 2020 using metagenomic sequencing. Bovine herpesvirus-1 (BHV-1), first detection of bovine herpesvirus-5 (BHV-5), and first detection of bovine parvovirus-3 (BPV-3) were the most commonly identified in Egyptian cattle. Moreover, phylogenetic analysis of glycoprotein B revealed that the BHV-1 isolate is closely related to the Cooper reference strain (genotype 1.1), whereas the BHV-5 isolate is closely related to the reference virus GenBank NP_954920.1. In addition, the whole-genome sequence of BPV-3 showed 93.02% nucleotide identity with the reference virus GenBank AF406967.1. In this study, several DNA viruses, such as BHV-1 and first detection BHV-5, and BPV-3, were detected and may have an association with the BRD in Egyptian cattle. Therefore, further research, including investigating more samples from different locations to determine the prevalence of detected viruses and their contributions to BRD in cattle in Egypt, is needed.

There is a concerning lack of representative data on chronic obstructive pulmonary disease (COPD) awareness in Saudi Arabia, and a significant proportion of the population is vulnerable to developing a smoking habit, which is a major risk factor for the disease. Population-Based Survey of 15,000 people was conducted to assess the public knowledge and awareness of COPD across Saudi Arabia from October 2022 to March 2023. A total of 15002 responders completed the survey, with a completion rate of 82%. The majority 10314 (69%) were 18-30 year and 6112 (41%) had high school education. The most common comorbidities among the responders were depression (7.67%); hypertension (6%); diabetes (5.77%) and Chronic Lung Disease (4.12%). The most common symptoms were dyspnea (17.80%); chest tightness (14.09%) and sputum (11.19%). Among those who complains of any symptoms, only 16.44% had consulted their doctor. Around 14.16% were diagnosed with a respiratory disease and only 15.56% had performed pulmonary function test (PFT). The prevalence of smoking history was 15.16%, in which current smokers were 9.09%. About 48% of smokers used cigarette, 25% used waterpipe and around 27% were E-cigarette users. About 77% of the total sample have never heard about COPD. Majority of current smokers (73.5%; 1002), ex-smokers (68%; 619), and non-smokers (77.9%; 9911) are unaware of COPD, p value <0.001. Seventy five percent (1028) of the current smokers and 70% (633) of the ex-smokers have never performed PFT, p value <0.001. Male, younger age (18-30 years), higher education, family history of respiratory diseases, previous diagnosis of respiratory disease, previous PFT, and being an ex-smokers increases the odds of COPD awareness, p-value <0.05. There is a significantly low awareness about COPD in Saudi Arabia, especially among smokers. A nationwide approach must include targeted public awareness campaigns, continued healthcare professional education, community-based activities encouraging diagnosis and early detection, advice on smoking cessation and lifestyle changes, as well as coordinated national COPD screening programs.

Background Circulating microRNAs (miRNAs) are considered a hot spot of research that can be employed for monitoring and/or diagnostic purposes in coronary artery disease (CAD). Since different disease features might be reflected on altered profiles or plasma miRNAs concentrations, a combination of miRNAs can provide more reliable non-invasive biomarkers for CAD. Subjects and methods We investigated a panel of 14-miRNAs selected using bioinformatics databases and current literature searching for miRNAs involved in CAD using quantitative real-time PCR technique in 73 CAD patients compared to 73 controls followed by function and pathway enrichment analysis for the 14-miRNAs. Results Our results revealed three out of the 14 circulating miRNAs understudy; miRNAs miR133a, miR155 and miR208a were downregulated. While 11 miRNAs were up-regulated in a descending order from highest fold change to lowest: miR-182, miR-145, miR-21, miR-126, miR-200b, miR-146A, miR-205, miR-135b, miR-196b, miR-140b and, miR-223. The ROC curve analysis indicated that miR-145, miR-182, miR-133a and, miR-205 were excellent biomarkers with the highest AUCs as biomarkers in CAD. All miRNAs under study except miR-208 revealed a statistically significant relation with dyslipidemia. MiR-126 and miR-155 showed significance with BMI grade, while only miR-133a showed significance with the obese patients in general. MiR-135b and miR-140b showed a significant correlation with the Wall Motion Severity Index. Pathway enrichment analysis for the miRNAS understudy revealed pathways relevant to the fatty acid biosynthesis, ECM-receptor interaction, proteoglycans in cancer, and adherens junction. Conclusion The results of this study identified a differentially expressed circulating miRNAs signature that can discriminate CAD patients from normal subjects. These results provide new insights into the significant role of miRNAs expression associated with CAD pathogenesis.

Nanoparticles (NPs) supplementation to biodigesters improves the digestibility of biowaste and the generation of biogas. This study investigates the impact of innovative nanoadditives on the microbiome of biodigesters. Fresh cow manure was anaerobically incubated in a water bath under mesophilic conditions for 30 days. Three different NPs (zinc ferrite, zinc ferrite with 10% carbon nanotubes and zinc ferrite with 10% C76 fullerene) were separately supplemented to the biodigesters at the beginning of the incubation period. Methane and hydrogen production were monitored daily. Manure samples were collected from the digesters at different time points and the microbial communities inside the biodigesters were investigated via real‐time PCR and 16 S rRNA gene amplicon‐sequencing. The results indicate that zinc ferrite NPs enhanced biogas production the most. The microbial community was significantly affected by NPs addition in terms of archaeal and bacterial 16 S rRNAgene copy numbers. The three ZF formulations NPs augmented the abundance of members within the hydrogenotrophic methanogenic phyla Methanobacteriaceae. While Methanomassiliicoccacaea were enriched in ZF/C76 supplemented biodigester due to a significant increase in hydrogen partial pressure, probably caused by the enrichment of Spirochaetaceae (genus Treponema). Overall, NPs supplementation significantly enriched acetate‐producing members within Hungateiclostridiaceae in ZF/CNTs, Dysgonomonadaceae in ZF and Spirochaetaceae ZF/C76 biodigesters. Zinc ferrite nanoparticles enhanced biogas production the most. The microbial community was significantly affected by nanoparticles addition in terms of archaeal and bacterial 16S rRNAgene copy numbers.

Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output. This research introduces a novel approach utilizing deep learning techniques to address these limitations. A U-Net architecture is employed to segment solar panels from background elements in thermal imaging videos, facilitating a comprehensive analysis of cooling system efficiency. Two predictive models—a 3-layer Feedforward Neural Network (FNN) and a proposed Convolutional Neural Network (CNN)—are developed and compared for estimating cooling percentages from individual images. The study aims to enhance the precision and reliability of heat mapping capabilities for non-invasive, vision-based monitoring of photovoltaic cooling dynamics. By leveraging deep regression techniques, the proposed CNN model demonstrates superior predictive capability compared to traditional methods, enabling accurate estimation of cooling efficiencies across diverse scenarios. Experimental evaluation illustrates the supremacy of the CNN model in predictive capability, yielding a mean square error (MSE) of just 0.001171821, as opposed to the FNN’s MSE of 0.016. Furthermore, the CNN demonstrates remarkable improvements in mean absolute error (MAE) and R-square, registering values of 1.2% and 0.95, respectively, whereas the FNN posts comparatively inferior numbers of 3.5% and 0.85. This research introduces labeled thermal imaging datasets and tailored deep learning architectures, accelerating advancements in renewable energy technology solutions. Moreover, the study provides insights into the practical implementation and cost-effectiveness of the proposed cooling efficiency monitoring system, highlighting hardware requirements, integration with existing infrastructure, and sensitivity analysis. The economic viability and scalability of the system are assessed through comprehensive cost-benefit analysis and scalability assessment, demonstrating significant potential for cost savings and revenue increases in large-scale PV installations. Furthermore, strategies for addressing limitations, enhancing predictive accuracy, and scaling to larger datasets are discussed, laying the groundwork for future research and industry collaboration in the field of photovoltaic thermal management optimization.