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The rising global cost of housing demands innovative solutions to improve construction materials and reduce expenses, making affordable housing more attainable. This study investigates the synergistic benefits of eco-friendly additives—kenaf fiber and glass powder—in masonry applications aimed at affordable housing solutions. The aim is to identify the effect of different kenaf fiber and glass powder proportions on lateritic brick. Kenaf fiber was incorporated into a cement-lateritic clay mix at varying percentages (0.5 %, 1.0 %, 1.5 %, and 2.0 %), while glass powder replaced cement at levels of 10 %, 20 %, 30 %, and 40 %. The reference mix was prepared as 20 % cement, 20 % sand, and 60 % laterite clay while the water/cement ratio was maintained at 0.4. Results indicate that kenaf fiber and glass powder significantly improve compressive, flexural, and splitting strengths compared to the reference mix. However, kenaf fiber exhibits a reduction in ultrasonic velocity. The dosage of kenaf fiber and glass powder profoundly influences the hybrid material properties. The study has revealed that 4 % of glass powder and 1 % of kenaf fiber highly improved the compressive strength, flexural strength and ultrasonic velocity of the mixes. On the other hand, 4 % of glass powder and 1.5 % of kenaf fiber highly improved the splitting strength of the material. These results were supported by a predictive model validated against experimental data. Optimization reveals an optimum mix with 20.44 % cement replacement (comprising 15.91 % cement, 4.08 % glass powder, and 1.19 % kenaf fiber), 20 % sand and 58.80 % clay, offering balanced performance for masonry bricks in affordable mass housing construction.

The strengthening of masonry infill wall panels (MIWPs) in reinforced concrete (RC) frame structures is of prime importance due to their failure in the in-plane and out-of-plane direction, as evidenced by past earthquakes. This research study applies an innovative approach of dual-strengthening materials to individual MIWPs in the RC frame structures to improve their seismic performance. Ferrocement overlay (FCO) and engineered cementitious composites (ECC) materials are applied to different MIWPs in a half-scaled two-story, two-by-two-bay reinforced concrete frame structure with different arrangements of infill walls. The strengthened masonry infill wall panels (SMIWPs) in the RC frame structure were tested using the quasi-static testing protocol. The testing results are compared with conventional masonry infill wall panels (CMIWPs) walls in RC frame structure in terms of damage pattern and backbone curve to evaluate the effect of the strengthening strategies applied to the MIWPs. The lateral Strength, stiffness, and ultimate ductility were enhanced by 166%, 40%, and 60%, respectively. The strengthening strategy effectively improved the damage propagations, lateral Strength, stiffness, and ductility.

AbstractThis study focused on current research on creative accounting and external auditors as well as prospective topics for future research. In the Web of Science Core Collection, a bibliometric and visualization tool for the Web of Science and viewer-based literature, 465 research articles dealing with creative accounting and external auditors authored between 1981 and 2022 were analyzed. Cluster analysis, all-keyword co-occurrence analysis, and bibliographic coupling mapping are all investigated in the study. This research enables us to propose future research paths that may be useful in reflecting on the significant impact that technology will have on the growth of creative accounting and external auditors. The survey discovered four main research trends: the first trend shown is ‘The Future of External Auditing and Financial Reporting Quality’ (red cluster). The second trend focuses on the future of auditors in detecting creative accounting (the green cluster). The third trend is Financial Statement Audit Quality Management for Earnings Management and Fraud Detection (blue and purple clusters). The fourth trend is related to Preventing the Next Financial fraud: A Global Creative Accounting (yellow cluster).

AbstractThis paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into sub-categories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models’ performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA).

A CSP Plant and in particular the solar field has hundreds of thousands of components and the corresponding process parameters. This needs to be monitored and evaluated every day to make sure that the plant is working efficiently. A performance assessment with a production simulation is done daily where the expected and actual power production are compared. Such evaluation is usually done with the plant performance model and the necessary data is compiled in calculation programs like Excel. The work is repetitive, time consuming and human errors may easily occur. Typically, the evaluations are done from different parties with deviating results and conclusions. ACWA Power has developed an online simulation platform to automatize the data processing to increase the data reliability and reduce the time effort. The application has been implemented in the Noor Energy 1 hybrid solar plant in Dubai and shall be presented in this paper.

Recent advances in artificial intelligence (AI) and computing technologies are currently disrupting the modeling and design paradigms in photonics. In this work, we present our perspective on the utilization of current AI models for photonic device modeling and design. Initially, through the physics-informed neural networks (PINNs) framework, we embark on the task of modal analysis, offering a unique neural networks-based solver and utilizing it to predict propagating modes and their corresponding effective indices for slab waveguides. We compare our model’s predictions against theoretical benchmarks and a finite differences solver. Evidently, using 349 analysis points, the PINN approach had a relative percentage error of 0.69272% compared to the finite differences method, which had a percentage error of 1.28142% with respect to the analytical solution, indicating that the PINN approach was more accurate in conducting modal analysis. Our model’s continuity over the entire solution domain enhances its performance and flexibility while requiring no training data due to its guidance by Maxwell’s equations, setting it apart from most AI approaches. Our model design also flexibly enables simultaneous prediction of multiple modes over any specified intervals of effective indices. In addition, we present a novel reinforcement learning (RL)-based paradigm, employing an actor–critic model for inverse design. We utilize this paradigm to optimize the transmittance of a grating coupler by manipulating the device geometry. Comparing the obtained design to that obtained using the Particle Swarm Optimization (PSO) algorithm, our RL-based approach effectively produced a significant enhancement of 34% in 14 iterations only over the initial design compared to the PSO, which prematurely scored 27% enhancement in 30 iterations, proving that our model navigates the design space more efficiently, achieving a better design than PSO and resulting in a superior design. Based on these approaches, we discuss the future of AI in photonics in forward modeling and inverse design and the untapped potential in bringing these worlds together.

Background: Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations. Methods: We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504). Results: Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the GFI1 gene (6 CpGs with p0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (–0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (–0.043±0.013 kg, p=0.0011) in the combined cohorts. Conclusions: This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birth weight in newborns, in both White European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers. Funding: This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.

Abstract Childhood immunization has contributed significantly to child survival globally. Ghana adopted the Expanded Program on Immunization (EPI) in the year 1972, and since then Ghana's immunization program has chalked huge successes in disease prevention, particularly the drastic reduction in the incidence of the six childhood killer diseases. Despite these successes, there are shortfalls that affect childhood vaccinations in Ghana. Here, we look at the evolution of childhood vaccine adoption in Ghana, the disease burden in the pre‐vaccination era and the vaccination era, the benefits of the immunization program, and the identified shortfalls. More importantly, the impact of childhood immunization on the current state of vaccine‐preventable diseases and the prospects the future holds for vaccine development and disease prevention in Ghana were discussed. Undoubtedly, Ghana has made significant progress in vaccine adoption and expansion of immunization program, resulting in a significant reduction in vaccine‐preventable deaths particularly in children. However, challenges pertaining to vaccine coverage, periodic shortages of vaccine, untimely immunization, and logistics constraints persist, which need redress. Moreover, there is currently no continuous post‐vaccine surveillance to evaluate long‐term vaccine impact. Additionally, Ghana lacks the technology and skill to manufacture its own vaccines. Following the establishment of the National Vaccine Institute, Ghana should be in the position to start the production of established vaccines such as those covered under EPI, whereas collaborative research is needed to discover new vaccines. Finally, it is critical to network childhood immunization records across the nation to ensure enhanced data for planning.

Abstract is missing (Short communication)

Cellular lightweight concrete (CLC) block masonry is an eco-friendly building material gaining popularity in both developed and developing countries. Despite its increasing use, the material's behavior under seismic loading remains insufficiently understood. This research involved quasi-static testing of two full-scale CLC blocks masonry walls strengthened with ferrocement overlay. One of the strengthened walls was subjected to confinement, while the other was unconfined. The aim was to assess their seismic performance by comparing force deformation curves, damping, stiffness degradation, and structural performance levels with non-strengthened walls. The findings indicate that the strengthened masonry outperformed the unreinforced version, suggesting that CLC block masonry exhibits potential for effective performance in low to moderate seismic zones.

Aims and objectivesTo determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function.BackgroundPost-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.DesignDescriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection.MethodsParticipants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.ResultsParticipants reported experiencing a mean of 21 symptoms (range 1-93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and "brain pressure" among the most distressing and impacting daily life.ConclusionsPost-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being.Relevance to clinical practiceThis study advances our understanding of the symptoms of PASC and their health impacts.

Olive mill wastewater (OMW) has serious environmental implications due to its high organic matter content, particularly its phenolic compounds. Using OMW in crop protection has been suggested as an environmentally friendly alternative to reduce the impact of chemical pesticides on human health and the environment. This study aimed to investigate the antifungal activity of fresh and stored OMW, as well as its ethyl acetate extract, against several phytopathogenic fungi: Syncephalastrum racemosum, Paramyrothecium roridum, Fusarium oxysporum, and Verticillium dahliae. OMW was stored at 25 °C and 45 °C for three months, and both fresh and stored OMW were used in non-sterile, sterile, and centrifuged conditions. Phenolic and flavonoid compounds were extracted and identified by high-performance liquid chromatography (HPLC) analysis. Results showed that fresh OMW and its derivative compounds significantly inhibited the studied fungi. In contrast, OMW storage, sterilization, and centrifugation increased the mycelium growth of the fungi, particularly S. racemosum, which demonstrated relative resistance to stored OMW and its ethyl acetate extract. During storage, some phenolic and flavonoid compounds disappeared (resorcinol and vanillic acid), while the concentration of others increased (gallic acid, chlorogenic acid, and quercetin). This work highlights the potential use of fresh OMW as a bio-agent to protect plants from fungal diseases.

Abstract Background Candidatus Ornithobacterium hominis (O. hominis), which was identified in nasopharyngeal swabs from Egypt, has been associated with respiratory disorders in humans. O. hominis, a recently identified member of the Flavobacteriaceae family, belongs to the largest family within the Bacteroidetes phylum. This family includes hundreds of species and 90 genera, including major human pathogens such as Capnocytophaga canimorsus and Elizabethkingia meningoseptica. Herein, we presented two draft genome assemblies of O. hominis that were extracted from metagenomic data using the Illumina sequencing method. The alignment of reads against the O. hominis genome was accomplished using BLASTN, and the reads with significant hits were extracted using Seqtk and assembled using SPAdes. The primary goal of this study was to obtain a more profound understanding of the genomic landscape of O. hominis, with an emphasis on identifying the associated virulence, antimicrobial genes, and distinct defense mechanisms to shed light on the potential role of O. hominis in human respiratory infections. Results The genome size was estimated to be 1.84 Mb, including 1,931,660 base pairs (bp), with 1,837 predicted coding regions and a G+C content of 35.62%. Genes encoding gliding motility, antibiotic resistance (20 genes), and the toxA gene were all included in the genome assembly. Gliding motility lipoproteins (GldD, GldJ, GldN, and GldH) and the gliding motility-associated ABC transporter substrate-binding protein, which acts as a crucial virulence mechanism in Flavobacterium species, were identified. The genome contained unique genes encoding proteins, such as the ParE1 toxin that defend against the actions of quinolone and other antibiotics. The cobalt-zinc-cadmium resistance gene encoding the protein CzcB, which is necessary for metal resistance, urease regulation, and colonization, was also detected. Several multidrug resistance genes encoding proteins were identified, such as MexB, MdtK, YheI, and VanC. Conclusion Our study focused on identifying virulence factors, and antimicrobial resistance genes present in the core genome of O. hominis. These findings provide valuable insights into the potential pathogenicity and antibiotic susceptibility of O. hominis.

Abstract Background Patients with chronic kidney disease (CKD) are at risk of a many of central nervous system complications such as stroke and cognitive impairment. Studies based on diffusion tensor imaging (DTI) confirmed that the integrity of white matter in CKD patients has changed and these alterations can be observed before tissue morphological changes occurring on conventional MRI. Early use of DTI in CKD patients can distinguish early individuals with normal cognitive system function and will develop cognitive impairment in later stages. Results As compared with controls, patients with CKD showed increased MD and decreased FA values in different brain regions with frontal, parietal regions and genu of corpus callosum more affected more than the occipital regions. For combined FA and MD in differentiating cases with CKD, there was statistically significant difference in both thalami, internal capsule, CC, cingulum, ILF and CST as p value

Abstract In the current report, we have successfully synthesized nanocomposites of PMMA incorporating different doping of CeO2 through a chemical approach. XRD results reflects decent matching for CeO2 nanoparticles with 29 nm crystallite size. FTIR spectroscopy demonstrates the characteristic functional groups validating the successful formation of the composite. The optical study of PMMA and the nanocomposites has proven that the optical properties such as band gap, refractive index, optical permittivity, and loss tangent factor are affected by adding CeO2 to the PMMA matrix.The peak residing around 420 nm by UV measurements is allocated to occurring electrons photoexcitation from the valence to conduction band inherent in CeO2. The dielectric measurements were achieved using broadband dielectric spectroscopy upon a wide span of frequencies (10–1–107 Hz) and within temperatures from − 10 to 80 °C with a step of 10 °C. The permittivity decreases by adding CeO2 and the dielectric parameters are thermally enhanced, however, the temperature influence is based on CeO2 content, the higher the CeO2 amount, the higher the influence of temperature. The results of the nanocomposites revealed antibacterial activity counter to gram-positive bacteria strain (S. aureus, and B. subtilis), and gram-negative bacteria (E. coli, and K. pneumoniae), yeast (C. albicans, as well as fungi (A. niger). Inherently, the change in CeO2 concentration from 0.01 to 0.1 wt% delivers maximum influence against gram-negative bacteria. These PMMA CeO2-doped composites are beneficial for optoelectronic areas and devices.

The transportation industry is one of the greatest contributors to a growing carbon footprint. As the world increasingly prioritizes a greener and cleaner future, the transition from ICEs to EVS offers significant benefits as EVs are zero-emission vehicles. However, considering the production and disposal stages of EVs, they cannot be considered entirely emission-free. But in comparison to ICEs, they still have a much smaller carbon footprint. Moreover, studying EV technology is essential to bringing this to the lowest possible extent. However, these advancements come with challenges. In this review article, we have taken an in-depth look into the various challenges, namely the economic challenges, technological challenges, and environmental challenges, faced by EV technology today and the various solutions proposed by researchers, their limitations, and future potential in EV technology advancement. Within this context, we discuss the influence of governmental policies, accessibility and affordability of EVs to the masses, natural and man-made disasters, economic instability, technological limitations posed by the vehicle technology, and limitations posed by battery technology with batteries being the central element for EVs, limitations associated with the existing infrastructure and its ability to support widescale use of EVs, and environmental factors like carbon footprint, among other factors, on EVs technology and the opportunities this technology can make use of in the future. This paper discusses these concerns in detail and gives extensive insight into the challenges, solutions, and future trends pertaining to economic impacts, vehicle technology, battery technology, power grids and charging infrastructure, and environmental impact, of EVs and EV technology.

Abstract As transportation electrification increases globally, new technologies emerged in the past few years to meet the growth of the electricity demand. Mobile Energy Storage Systems (MESS) offer versatile solutions, aiding distribution systems with reactive power, renewables integration, and peak shaving. An MESS can be utilized to serve electric vehicles (EVs) in different parking lots (PLs), in addition to supplying power to the grid during overloads. The task of multiple stationary units can be achieved using MESS at a relatively lower cost. This paper proposes an MESS owned by multiple PLs sharing the same geographical area and sharing its capital and operational cost. The main objective of the proposed approach is to dispatch the MESS in conjunction with optimal EVs’ charging coordination to minimize operational costs and address the extra demand of PLs. A mixed‐integer nonlinear programming (MINLP) problem is formulated and solved. Considering electricity price variations and EVs uncertainties, three different case studies are performed to highlight the efficiency and success of the proposed approach. The simulation results in a huge reduction in the total operation cost and the savings reach up to 27.51% in comparison with the base case.

Abstract Foodborne pathogens can be found in various foods, and it is important to detect foodborne pathogens to provide a safe food supply and to prevent foodborne diseases. The nucleic acid base detection method is one of the most rapid and widely used methods in the detection of foodborne pathogens; it depends on hybridizing the target nucleic acid sequence to a synthetic oligonucleotide (probes or primers) that is complementary to the target sequence. Designing primers and probes for this method is a preliminary and critical step. However, new bioinformatics tools are needed to automate, specific and improve the design sets to be used in the nucleic acid‒base method. Thus, we developed foodborne pathogen primer probe design (FBPP), an open-source, user-friendly graphical interface Python-based application supported by the SQL database for foodborne pathogen virulence factors, for (i) designing primers/probes for detection purposes, (ii) PCR and gel electrophoresis photo simulation, and (iii) checking the specificity of primers/probes.

Photovoltaic (PV) systems are prone to partial shading (PS) due to the environmental factors that they function in such as vegetation, nearby structures, and clouds. All types of PS scenarios can lead to power loss and hot spots in the PV system due to module mismatch and heating of shaded cells. To mitigate the power loss that occurs due to PS, it is imperative to detect PS and its characteristics, such as the number of shaded modules and the associated shading factor (SF), in a reliable manner. This paper proposes a three-step framework to detect and locate PS, the number of shaded modules, and the SF in the PV system using a random forest (RF)-based approach. The proposed approach utilizes independent string current and voltage measurements to distinguish different PS scenarios. This approach allows for a scalable data acquisition through an uncoupled modeling scheme. PS, the number of shaded modules and the SF are deduced with accuracies of 99.5%, 92.3%, 90.2%, respectively. Further, the proposed approach is validated through two testing tiers, and its ability to detect multiple PS scenarios in a PV system has been highlighted. The results observed through different PS scenarios confirm the high reliability and demonstrate the effectiveness and scalability of the proposed RF-based approach.

In this paper, a collocation approach for the fractional Newell-Whitehead-Segel equation is described. The finite difference method is used to discretized the time-fractional derivative, and Chebyshev functions are used for interpolating the spatial derivatives. An a priori error estimate is derived. The stability of the suggested approach is presented. Several numerical examples are provided to show the viability and effectiveness of the suggested approach.