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Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.

This study investigates the impact of FinTech adoption on traditional financial inclusion in 22 countries in sub-Saharan Africa (SSA). The study utilizes the World Bank’s World Development Indicators data and the International Monetary Fund’s Financial Access Survey data. This study employed Principal Component Analysis (PCA) to construct the dimensions of traditional financial inclusion and the overall financial inclusion index. Applying the Generalized Method of Moments estimation technique to annual data spanning from 2004 to 2022, the findings show that FinTech has a negative and statistically significant effect on the geographic and usage dimensions. However, it has a positive and statistically significant impact on the demographic dimension and the overall traditional financial inclusion index. These findings indicate that FinTech does not have a detrimental impact on traditional financial inclusion, which is contrary to the findings of other studies. Therefore, in order to enhance the degree of financial inclusion in SSA, it is important for traditional financial inclusion to effectively utilize FinTech.

Mycobacterium avium subsp. paratuberculosis (MAP) is responsible for the persistent infectious illness known as bovine paratuberculosis, which is one of the most easily overlooked diseases in China amid a lack of epidemiological data. In this study, we evaluated the agreement of milk and blood antibody tests for paratuberculosis and showed an overall agreement of 92.0%, with a 95.0% negative coincidence rate and a 78.6% positive coincidence rate. The milk test was then used to examine the prevalence and incidence of dairy cows in Hubei Province, China. We found that, at the individual level, the highest lacto-prevalence reached up to 22.9%; the farm-level prevalence was as high as 92.3% (12/13) and 84.6% (11/13) in January and April 2018, respectively. The total incidence risk of all farms was 6% per three months. We also found that large-scale farms had a significantly lower prevalence and incidence than small-scale farms. Finally, the correlation between paratuberculosis and milk quality was evaluated, and we confirmed that MAP can significantly alter milk quality and raise somatic cell counts in the milk. This study provides valuable information for assessing the prevalence and incidence risk of paratuberculosis in China. It further provides an essential basis for calling for the prevention and control of paratuberculosis in China.

Malnutrition among in-school children is a complex issue influenced by socio-economic, environmental, and health-related factors, posing significant challenges to their well-being and educational trajectories in developing countries. This review synthesized evidence on the multifaceted aspects of child malnutrition within the educational setting in developing countries. This review followed the six steps outlined by Arksey and O’Malley’s framework. Four main databases (PubMed, CENTRAL, JSTOR, and Scopus) were searched. Additional searches were conducted in WHO Library, ProQuest, HINARI, Google Scholar, and Google. Reference lists of eligible papers were checked. This review found that low family income, varying family sizes, parental employment status, and educational levels significantly impact malnutrition among in-school children and adolescents. Environmental elements, including rural/urban residence, household sanitation, and living conditions, also influence malnutrition. In addition, nutrition knowledge, dietary habits, nutrient deficiencies, physical activity, and prevalent health conditions compound the risk of malnutrition. This study underscores the extensive health impact of malnutrition on general health, specific nutrient deficiencies, fetal/maternal health concerns, and overall morbidity. Also, malnutrition affects school performance and attendance, impacting cognitive abilities, and academic achievements. Addressing these challenges requires comprehensive policy actions aligned with Sustainable Development Goals, emphasizing poverty alleviation, health literacy, and gender equity.

Mycoplasmopsis bovis is a causative agent of crucial diseases in both dairy and beef cattle leading to substantial economic losses. However, limited control measures for M. bovis-related diseases exist due to a lack of understanding about the virulence factors of this pathogen, a common challenge in mycoplasma research. Consequently, this study aimed to characterize a novel nucleomodulin as a virulence-related factor of M. bovis. Employing bioinformatic tools, we initially predicted MbovP467 to be a secreted protein with a nuclear localization signal based on SignalP scores and the cNLS (Nuclear Localization Signal) Mapper, respectively. Subsequently, the MbovP467 gene was synthesized and cloned into a pEGFP plasmid with EGFP labeling to obtain a recombinant plasmid (rpEGFP-MbovP467) and then was also cloned in pET-30a with a consideration for an Escherichia coli codon bias and expressed and purified for the production of polyclonal antibodies against the recombinant MbovP467 protein. Confocal microscopy and a Western blotting assay confirmed the nuclear location of MbovP467 in bovine macrophages (BoMacs). RNA-seq data revealed 220 up-regulated and 20 down-regulated genes in the rpEGFP-MbovP467-treated BoMac group compared to the control group (pEGFP). A GO- and KEGG-enrichment analysis identified associations with inflammatory responses, G protein-coupled receptor signaling pathways, nuclear receptor activity, sequence-specific DNA binding, the regulation of cell proliferation, IL-8, apoptotic processes, cell growth and death, the TNF signaling pathway, the NF-κB signaling pathway, pathways in cancer, and protein families of signaling and cellular processes among the differentially expressed up-regulated mRNAs. Further experiments, investigating cell viability and the inflammatory response, demonstrated that MbovP467 reduces BoMac cell viability and induces the mRNA expression of IL-1β, IL-6, IL-8, TNF-α, and apoptosis in BoMac cells. Further, MbovP467 increased the promoter activity of TNF-α. In conclusion, this study identified a new nucleomodulin, MbovP467, for M. bovis, which might have an important role in M. bovis pathogenesis.

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models’ effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model’s area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing the explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Existing XAI methods aim to provide insights about the methodology used by the black-box model in making decisions by highlighting the most relevant regions within the input image that contribute to the model’s prediction. Recently, several XAI methods for image classification tasks have been introduced. In contrast, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where a higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare the performances of three XAI methods, Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol, using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images. Our code is publicly available at GitHub.

Magneto-plasmonic Ag/Ni and Ag/Fe nanoparticles (NPs) were synthesized in this work using the environmentally safe and contaminant-free dual-pulsed Q-switched Nd:YAG 1064 nm laser ablation method. The optical and magnetic characteristics of synthesized nanomaterials were investigated using a vibrating sample magnetometer and an ultraviolet-visible absorption spectrometer. According to transmission electron microscopy (TEM), the shape of Ag/Ni and Ag/Fe NPs seems to be spherical, with mean diameters of 7.3 nm and 11.5 nm, respectively. X-ray diffraction (XRD) was used in order to investigate and describe the phase structures of the synthesized nanomaterials. The synthesized NPs reached maximum temperatures such as 48.9, 60, 63.4, 70, 75, and 79 °C for Ag/Ni nanofluid and 52, 56, 60, 68, 71, and 72 °C for Ag/Fe nanofluid when these nanofluids were subjected to an NIR 808 nm laser with operating powers of 1.24, 1.76, 2.36, 2.91, 3.5, and 4 W, respectively. Because of the plasmonic hyperthermia properties of nanoparticles, nanofluids display higher temperature profiles than pure water. According to these findings, plasmonic nanoparticles based on silver might be used to treat hyperthermia.

Nickel-supported hierarchical zeolite catalysts were prepared through a desilication reassembly process under optimized conditions and applied in one-pot menthol synthesis. In this work, the hierarchical zeolite-supported metal bifunctional catalysts were prepared with the help of desilication re-assembly and wetness impregnation techniques and applied in menthol synthesis via citral hydrogenation. The prepared catalysts were characterized using PXRD, BET, FE-TEM, NH3-TPD, H2-TPR, pyridine adsorption, and ICP-OES techniques. As a result, the physicochemical and acidic properties, such as mesopore surface area, metal dispersion, acidity, catalytic activity, and strong Lewis acid sites of pure microporous ZSM-5/USY zeolites, were significantly improved. Consequently, with the occurrence of superior physicochemical and acidic properties, the Ni/HZ-0.5 M catalyst exhibited outstanding catalytic activity (100% conversion, TOF 7.12 h−1) and menthol selectivity (83%, 4 h) with uniform stability at 100 °C, 1.0 MPa hydrogen. Similarly, the cracking rate decreased with the decrease in Bronsted acid sites.

Hydrothermal liquefaction (HTL) is a thermochemical depolymerization technology, also known as hydrous pyrolysis, that transforms wet biomass into biocrude and valuable chemicals at a moderate temperature (usually 200–400 °C) and high pressure (typically 10–25 MPa). In HTL, water acts as a key reactant in HTL activities. Several properties of water are substantially altered as the reaction state gets closer to the critical point of water, which can result in quick, uniform, and effective reactions. The current review covers the HTL of various feedstocks, especially lignocellulosic and high protein-containing feeds with their in-depth information of the chemical reaction mechanisms involved in the HTL. Further, this review gives insight and knowledge about the influencing factors such as biomass pretreatment, process mode, process conditions, etc., which could affect the efficiency of the hydrothermal process and biocrude productivity. In addition, the latest trends, and emerging challenges to HTL are discussed with suitable recommendations.

Background: After its initial detection in Wuhan, China, in December 2019, SARS-CoV-2 has spread rapidly, causing successive epidemic waves worldwide. This study aims to provide a genomic epidemiology of SARS-CoV-2 in Burkina Faso. Methods: Three hundred and seventy-seven SARS-CoV-2 genomes obtained from PCR-positive nasopharyngeal samples (PCR cycle threshold score < 35) collected between 5 May 2020, and 31 January 2022 were analyzed. Genomic sequences were assigned to phylogenetic clades using NextClade and to Pango lineages using pangolin. Phylogenetic and phylogeographic analyses were performed to determine the geographical sources and time of virus introduction in Burkina Faso. Results: The analyzed SARS-CoV-2 genomes can be assigned to 10 phylogenetic clades and 27 Pango lineages already described worldwide. Our analyses revealed the important role of cross-border human mobility in the successive SARS-CoV-2 introductions in Burkina Faso from neighboring countries. Conclusions: This study provides additional insights into the genomic epidemiology of SARS-CoV-2 in West Africa. It highlights the importance of land travel in the spread of the virus and the need to rapidly implement preventive policies. Regional cross-border collaborations and the adherence of the general population to government policies are key to prevent new epidemic waves.

Aluminum nitride (AlN) is a semiconductor material possessing a hexagonal wurtzite crystal structure with a large band gap of 6.2 eV. AlN thin films have several potential applications and areas for study, particularly in optoelectronics. This research study focused on the preparation of Ni-doped AlN thin films by using DC and RF magnetron sputtering for optoelectronic applications. Additionally, a comparative analysis was also carried out on the as-deposited and annealed thin films. Several spectroscopy and microscopy techniques were considered for the characterization of structural (X-ray diffraction), morphological (SEM), chemical bonding (FTIR), and emission (PL spectroscopy) properties. The XRD results show that the thin films have an oriented c-axis hexagonal structure. SEM analysis validated the granular-like morphology of the deposited sample, and FTIR results confirm the presence of chemical bonding in deposited thin films. The photoluminescence (PL) emission spectra exhibit different peaks in the visible region when excited at different wavelengths. A sharp and intense photoluminescence peak was observed at 426 nm in the violet-blue region, which can be attributed to inter-band transitions due to the incorporation of Ni in AlN. Most of the peaks in the PL spectra occurred due to direct-band recombination and indirect impurity-band recombination. After annealing, the intensity of all observed peaks increases drastically due to the development of new phases, resulting in a decrease in defects and a corresponding increase in the crystallinity of the thin film. The observed structural, morphological, and photoluminescence results suggest that Ni: AlN is a promising candidate to be used in optoelectronics applications, specifically in photovoltaic devices and lasers.

In this study, we synthesized hybrid systems based on manganese oxide@zinc oxide (Mn3O4@ZnO), using sol gel and hydrothermal methods. The hybrid materials exhibited hierarchical morphologies and structures characterized by the hexagonal phase of ZnO and the tetragonal phase of Mn3O4. The hybrid materials were tested for degradation of methylene blue (MB), methyl orange (MO), and malachite green (MG) under ultraviolet (UV) light illumination. The aim of this work was to observe the effect of various amounts of Mn3O4 in enhancing the photocatalytic properties of ZnO-based hybrid structures towards the degradation of MB, MO and MG. The ZnO photocatalyst showed better performance with an increasing amount of Mn3O4, and the degradation efficiency for the hybrid material containing the maximum amount of Mn3O4 was found to be 94.59%, 89.99%, and 97.40% for MB, MO and MG, respectively. The improvement in the performance of hybrid materials can be attributed to the high charge separation rate of electron-hole pairs, the co-catalytic role, the large number of catalytic sites, and the synergy for the production of high quantities of oxidizing radicals. The performance obtained from the various Mn3O4@ZnO hybrid materials suggest that Mn3O4 can be considered an effective co-catalyst for a wide range of photocatalytic materials such as titanium dioxide, tin oxide, and carbon-based materials, in developing practical hybrid photocatalysts for the degradation of dyes and for wastewater treatment.

Under the China–Pakistan Economic Corridor, renewable energy projects gradually receive due attention, among which the photovoltaic power stations in Quaid-e-Azam Solar Park represents the most typical power stations in Pakistan. The construction and development processes of the photovoltaic power stations are divided into three stages, with enterprises involved including TBEA Xinjiang Sunoasis Co., Ltd., Urumqi, China and Zonergy Solar Technology Co., Ltd., Zigong, China. The development model of the photovoltaic power stations changed from engineering, procurement, and construction (EPC) plus operation and maintenance (O&M) mode to the Build–Own–Operate (BOO) mode. Through solar power generation and marginal emission factors of photovoltaic power stations, the cumulative electricity generation during the operation period can reach nearly 40.09 billion kWh, and the cumulative emission reduction potential of photovoltaic power stations can reach 23.82 Mt CO2-eq. Based on the multi-stage construction of photovoltaic power stations in the solar park, the land, infrastructure, and transmission facilities could be arranged to simplify the procedures and attract overseas investment. The construction and development model of Quaid-e-Azam Solar Park could provide a reference for the promotion of low-carbon transition, the adjustment of traditional energy structures, the fulfillment of carbon reduction commitments, and the mitigation of climate change, which could provide policy implications for renewable energy development and low-carbon transition in Pakistan.

Wastewater from the textile industry is chronic and hazardous for the human body due to the presence of a variety of organic dyes; therefore, its complete treatment requires efficient, simple, and low cost technology. For this purpose, we grew ZnO microstructures in the presence of psyllium husk, and the role of psyllium husk was to modify the surface of the ZnO microstructures, create defects in the semiconducting crystal structures, and to alter the morphology of the nanostructured material. The growth process involved a hydrothermal method followed by calcination in air. Additionally, the psyllium husk, after thermal combustion, added a certain value of carbon into the ZnO nanomaterial, consequently enhancing the photocatalytic activity towards the degradation of methylene blue. We also investigated the effect of varying doses of photocatalyst on the photocatalytic properties towards the photodegradation of methylene blue in aqueous solution under the illumination of ultraviolet light. The structure and morphology of the prepared ZnO microstructures were explored by scanning electron microscopy (SEM) and powder X-ray diffraction (XRD) techniques. The degradation of methylene blue was monitored under the irradiation of ultraviolet light and in the dark. Also, the degradation of methylene blue was measured with and without photocatalyst. The photodegradation of methylene blue is highly increased using the ZnO sample prepared with psyllium husk. The photodegradation efficiency is found to be approximately 99.35% for this sample. The outperforming functionality of psyllium-husk-assisted ZnO sample is attributed to large surface area of carbon material from the psyllium husk and the synergetic effect between the incorporated carbon and ZnO itself. Based on the performance of the hybrid material, it is safe to say that psyllium husk has high potential for use where surface roughness, morphology alteration, and defects in the crystal structure are vital for the enhancing the functionality of a nanostructured material. The observed performance of ZnO in the presence of psyllium husk provides evidence for the fabrication of a low cost and efficient photocatalyst for the wastewater treatment problems.

This study primarily addresses the development of dynamic, selective and economical metal–acid (bifunctional) catalysts for one-pot menthol production by citral hydrogenation. Specifically, various metals such as Pd, Pt, Ni, Cs and Sn were doped over alumina support. Additionally, bifunctional composite catalysts were also prepared with the impregnation of heteropoly acids and Pd precursors over alumina support. Analytical techniques (e.g., BET, PXRD, FT-IR, pyridine adsorption and amine titration methods) were applied for characterization of the most efficient and selective catalysts (e.g., Al2O3 and PTA-Cat-I). Similarly, most of the essential operational variables (e.g., loading rate of metal precursor, type of heteropoly acid, temperature, gas pressure and reaction time) were examined during this study. The experimental data shows that the bifunctional catalyst (PTA-Cat-I) produced 45% menthol at full citral substrate conversion (r = 0.038 mmoles.min−1) in liquid-phase citral hydrogenation (at optimized operating conditions: 70 °C, 0.5 MPa and 8 h). However, the heteropoly acid-supported bifunctional catalysts (e.g., PTA-Cat-I, PMA-Cat-I, SMA-Cat-I and STA-Cat-I) resulted in cracking and the dehydration of isopulegol/menthol by the generation of side products (e.g., 4-isopropyl-1-methyl, cyclohex-1-ane/ene); therefore, menthol yield was extensively diminished. On the other hand, non-acidic catalysts (e.g., Cat-I, Cat-II, Cat-III, Cat-IV and Cat-V) readily promoted hydrogenation reactions. The optimum menthol yield occurred due to the presence of strong Lewis and weak Bronsted acid sites. Mass transfer and reaction rate were substantially diminished due to acidity strength, heteropoly acid type and blockage of pores by the applied bifunctional catalysts.

Widespread of insecticide resistance amongst the species of the Anopheles gambiae complex continues to threaten vector control in Senegal. In this study, we investigated the presence and evolution of the Ace-1 and Gste2 resistance genes in natural populations of Anopheles gambiae s.l., the main malaria vector in Senegal. Using historical samples collected from ten sentinel health districts, this study focused on three different years (2013, 2017, and 2018) marking the periods of shift between the main public health insecticides families (pyrethroids, carbamates, organophosphates) used in IRS to track back the evolutionary history of the resistance mutations on the Ace-1 and Gste2 loci. The results revealed the presence of four members of the Anopheles gambiae complex, with the predominance of An. arabiensis followed by An. gambiae, An. coluzzii, and An. gambiae–coluzzii hybrids. The Ace-1 mutation was only detected in An. gambiae and An. gambiae–coluzzii hybrids at low frequencies varying between 0.006 and 0.02, while the Gste2 mutation was found in all the species with a frequency ranging between 0.02 and 0.25. The Ace-1 and Gste2 genes were highly diversified with twenty-two and thirty-one different haplotypes, respectively. The neutrality tests on each gene indicated a negative Tajima’s D, suggesting the abundance of rare alleles. The presence and spread of the Ace-1 and Gste2 resistance mutations represent a serious threat to of the effectiveness and the sustainability of IRS-based interventions using carbamates or organophosphates to manage the widespread pyrethroids resistance in Senegal. These data are of the highest importance to support the NMCP for evidence-based vector control interventions selection and targeting.

Vehicular ad hoc network (VANET) is a specialized form of wireless network that is solely intended for collaboration between vehicles. Several studies have shown that standard routing protocols cannot be implemented in VANETs because of their unique characteristics such as their significant count of vehicles on the network and the rapid evolution of the network’s design. Because VANET communication links are broken very frequently, it is necessary to address the routing consistency of these highly dynamic networks. The transmission of VANET data may result in a substantial amount of overhead in the routing process; thus, it is vital to address the issue of overhead to enhance the overall network performance. The proposed protocol named compacted area with effective links (CAEL) is designed to focus on decreasing overhead to achieve an enhancement in PDR performance inside the network. The communication between selected nodes that have been judged to be dependable in terms of geographical location and appropriate existing links between vehicles is focused on achieving this goal. With the inclusion of the reliability factor, it is possible to complete the important step of removing extraneous nodes, with the selection of the trustworthy nodes being made based on the link expiration time during the whole routing procedure. When compared to our previously published protocols, i.e., Dynamic Trilateral Enrollment (DyTE) and Reliable Group of Vehicles (RGoV), the results of the simulations demonstrate that CAEL has achieved an overall improvement in the performance of the network.

The annual number of cancer deaths continues increasing every day; thus, it is urgent to search for and find active, selective, and efficient anticancer drugs as soon as possible. Among the available anticancer drugs, almost all of them contain heterocyclic moiety in their chemical structure. Xanthone is a heterocyclic compound with a dibenzo-γ-pyrone framework and well-known to have “privileged structures” for anticancer activities against several cancer cell lines. The wide anticancer activity of xanthones is produced by caspase activation, RNA binding, DNA cross-linking, as well as P-gp, kinase, aromatase, and topoisomerase inhibition. This anticancer activity depends on the type, number, and position of the attached functional groups in the xanthone skeleton. This review discusses the recent advances in the anticancer activity of xanthone derivatives, both from natural products isolation and synthesis methods, as the anticancer agent through in vitro, in vivo, and clinical assays.

Nonlinear phenomena occur in various fields of science, business, and engineering. Research in the area of computational science is constantly growing, with the development of new numerical schemes or with the modification of existing ones. However, such numerical schemes, objectively need to be computationally inexpensive with a higher order of convergence. Taking into account these demanding features, this article attempted to develop a new three-step numerical scheme to solve nonlinear scalar and vector equations. The scheme was shown to have ninth order convergence and requires six function evaluations per iteration. The efficiency index is approximately 1.4422, which is higher than the Newton’s scheme and several other known optimal schemes. Its dependence on the initial estimates was studied by using real multidimensional dynamical schemes, showing its stable behavior when tested upon some nonlinear models. Based on absolute errors, the number of iterations, the number of function evaluations, preassigned tolerance, convergence speed, and CPU time (sec), comparisons with well-known optimal schemes available in the literature showed a better performance of the proposed scheme. Practical models under consideration include open-channel flow in civil engineering, Planck’s radiation law in physics, the van der Waals equation in chemistry, and the steady-state of the Lorenz system in meteorology.

Organic photovoltaics (OPVs) die due to their interactions with environmental gases, i.e., moisture and oxygen, the latter being the most dangerous, especially under illumination, due to the fact that most of the active layers used in OPVs are extremely sensitive to oxygen. In this work we demonstrate solution-based effective barrier coatings based on composite of poly(vinyl butyral) (PVB) and mica flakes for the protection of poly (3-hexylthiophene) (P3HT)-based organic solar cells (OSCs) against photobleaching under illumination conditions. In the first step we developed a protective layer with cost effective and environmentally friendly methods and optimized its properties in terms of transparency, barrier improvement factor, and bendability. The developed protective layer maintained a high transparency in the visible region and improved oxygen and moisture barrier quality by the factor of ~7. The resultant protective layers showed ultra-flexibility, as no significant degradation in protective characteristics were observed after 10 K bending cycles. In the second step, a PVB/mica composite layer was applied on top of the P3HT film and subjected to photo-degradation. The P3HT films coated with PVB/mica composite showed improved stability under constant light irradiation and exhibited a loss of

Fomes fomentarius, usually referred to as tinder conk, is a common wood-based fungus rich in many interesting phytochemicals and with an unique porous structure. Dry or wet ball milling of this sponge on a planetary mill results in small particles with sizes in the range of 10 µm or below. Suspended in water and without preservatives or other stabilizers, the resulting micro-suspensions are sterile for around six days, probably due to the increased temperatures of around 80 °C especially during the wet milling process. The suspensions also exhibit excellent antioxidant activities as determined in the DPPH, ferric reducing antioxidant potential (FRAP) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays. In the DPPH assay, IC50 values of 0.02–0.04% w/v and 0.04% w/v were observed for dry and wet milled samples, respectively. In the FRAP assay, IC50 values of w/v and 0.04% w/v were observed for dry and wet milled samples, respectively. In contrast, the ABTS assay provided IC50 values of 0.04% w/v and 0.005% w/v, respectively. Notably, this activity is mostly—albeit not exclusively—associated with the highly porous particles and their large surfaces, although some active ingredients also diffuse into the surrounding aqueous medium. Such suspensions of natural particles carrying otherwise insoluble antioxidants on their surfaces provide an interesting avenue to unleash the antioxidant potential of materials such as sponges and barks. As dry milling also enables longer storage and transport, applications in the fields of medicine, nutrition, agriculture, materials and cosmetics are feasible.

One of the most critical issues encountered by polymeric membranes for the gas separation process is the trade-off effect between gas permeability and selectivity. The aim of this work is to develop a simple yet effective coating technique to modify the surface properties of commonly used polysulfone (PSF) hollow fiber membranes to address the trade-off effect for CO2/CH4 and O2/N2 separation. In this study, multilayer coated PSF hollow fibers were fabricated by incorporating a graphene oxide (GO) nanosheet into the selective coating layer made of polyether block amide (Pebax). In order to prevent the penetration of Pebax coating solution into the membrane substrate, a gutter layer of polydimethylsiloxane (PDMS) was formed between the substrate and Pebax layer. The impacts of GO loadings (0.0–1.0 wt%) on the Pebax layer properties and the membrane performances were then investigated. XPS data clearly showed the existence of GO in the membrane selective layer, and the higher the amount of GO incorporated the greater the sp2 hybridization state of carbon detected. In terms of coating layer morphology, increasing the GO amount only affected the membrane surface roughness without altering the entire coating layer thickness. Our findings indicated that the addition of 0.8 wt% GO into the Pebax coating layer could produce the best performing multilayer coated membrane, showing 56.1% and 20.9% enhancements in the CO2/CH4 and O2/N2 gas pair selectivities, respectively, in comparison to the membrane without GO incorporation. The improvement is due to the increased tortuous path in the selective layer, which created a higher resistance to the larger gas molecules (CH4 and N2) compared to the smaller gas molecules (CO2 and O2). The best performing membrane also demonstrated a lower degree of plasticization and a very stable performance over the entire 50-h operation, recording CO2/CH4 and O2/N2 gas pair selectivities of 52.57 (CO2 permeance: 28.08 GPU) and 8.05 (O2 permeance: 5.32 GPU), respectively.

With the blooming of oil and gas industries, oily saline wastewater treatment becomes a viable option to resolve the oily water disposal issue and to provide a source of water for beneficial use. Reverse osmosis (RO) has been touted as a promising technology for oily saline wastewater treatment. However, one great challenge of RO membrane is fouling phenomena, which is caused by the presence of hydrocarbon contents in the oily saline wastewater. This study focuses on the fabrication of antifouling RO membrane for accomplishing simultaneous separation of salt and oil. Thin film nanocomposite (TFN) RO membrane was formed by the layer by layer (LbL) assembly of positively charged TNS (pTNS) and negatively charged TNS (nTNS) on the surface of thin film composite (TFC) membrane. The unique features, rendered by hydrophilic TNS bilayer assembled on TFC membrane in the formation of a hydration layer to enhance the fouling resistance by high concentration oily saline water while maintaining the salt rejection, were discussed in this study. The characterization findings revealed that the surface properties of membrane were improved in terms of surface hydrophilicity, surface roughness, and polyamide(PA) cross-linking. The TFC RO membrane coated with 2-bilayer of TNS achieved >99% and >98% for oil and salt rejection, respectively. During the long-term study, the 2TNS-PA TFN membrane outperformed the pristine TFC membrane by exhibiting high permeability and much lower fouling propensity for low to high concentration of oily saline water concentration (1000 ppm, 5000 ppm and 10,000 ppm) over a 960 min operation. Meanwhile, the average permeability of uncoated TFC membrane could only be recovered by 95.7%, 89.1% and 82.9% for 1000 ppm, 5000 ppm and 10,000 ppm of the oily saline feedwater, respectively. The 2TNS-PA TFN membrane achieved almost 100% flux recovery for three cycles by hydraulic washing.

Android-based applications are widely used by almost everyone around the globe. Due to the availability of the Internet almost everywhere at no charge, almost half of the globe is engaged with social networking, social media surfing, messaging, browsing and plugins. In the Google Play Store, which is one of the most popular Internet application stores, users are encouraged to download thousands of applications and various types of software. In this research study, we have scraped thousands of user reviews and the ratings of different applications. We scraped 148 application reviews from 14 different categories. A total of 506,259 reviews were accumulated and assessed. Based on the semantics of reviews of the applications, the results of the reviews were classified negative, positive or neutral. In this research, different machine-learning algorithms such as logistic regression, random forest and naïve Bayes were tuned and tested. We also evaluated the outcome of term frequency (TF) and inverse document frequency (IDF), measured different parameters such as accuracy, precision, recall and F1 score (F1) and present the results in the form of a bar graph. In conclusion, we compared the outcome of each algorithm and found that logistic regression is one of the best algorithms for the review-analysis of the Google Play Store from an accuracy perspective. Furthermore, we were able to prove and demonstrate that logistic regression is better in terms of speed, rate of accuracy, recall and F1 perspective. This conclusion was achieved after preprocessing a number of data values from these data sets.

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance.

L-glutamaic acid is the principal excitatory neurotransmitter in the brain and an important intermediate in metabolism. In the present study, lactic acid bacteria (218) were isolated from six different fermented foods as potent sources of glutamic acid producers. The presumptive bacteria were tested for their ability to synthesize glutamic acid. Out of the 35 strains showing this capability, strain MNZ was determined as the highest glutamic-acid producer. Identification tests including 16S rRNA gene sequencing and sugar assimilation ability identified the strain MNZ as Lactobacillus plantarum. The characteristics of this microorganism related to its glutamic acid-producing ability, growth rate, glucose consumption and pH profile were studied. Results revealed that glutamic acid was formed inside the cell and excreted into the extracellular medium. Glutamic acid production was found to be growth-associated and glucose significantly enhanced glutamic acid production (1.032 mmol/L) compared to other carbon sources. A concentration of 0.7% ammonium nitrate as a nitrogen source effectively enhanced glutamic acid production. To the best of our knowledge this is the first report of glutamic acid production by lactic acid bacteria. The results of this study can be further applied for developing functional foods enriched in glutamic acid and subsequently γ-amino butyric acid (GABA) as a bioactive compound.