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Bacterial canker disease caused by Clavibacter michiganensis is a substantial threat to the cultivation of tomatoes, leading to considerable economic losses and global food insecurity. Infection is characterized by white raised lesions on leaves, stem, and fruits with yellow to tan patches between veins, and marginal necrosis. Several agrochemical substances have been reported in previous studies to manage this disease but these were not ecofriendly. Thus present study was designed to control the bacterial canker disease in tomato using green fabricated silver nanoparticles (AgNps). Nanosilver particles (AgNPs) were synthesized utilizing Moringa oleifera leaf extract as a reducing and stabilizing agent. Synthesized AgNPs were characterized using UV–visible spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray (EDX), and Fourier transform infrared spectrometry (FTIR). FTIR showed presence of bioactive compounds in green fabricated AgNPs and UV-visible spectroscopy confirmed the surface plasmon resonance (SPR) band in the range of 350 nm to 355 nm. SEM showed the rectangular segments fused together, and XRD confirmed the crystalline nature of the synthesized AgNPs. The presence of metallic silver ions was confirmed by an EDX detector. Different concentrations (10, 20, 30, and 40 ppm) of the green fabricated AgNPs were exogenously applied on tomato before applying an inoculum of Clavibacter michigensis to record the bacterial canker disease incidence at different day intervals. The optimal concentration of AgNPs was found to be 30 µg/mg that exhibited the most favorable impact on morphological (shoot length, root length, plant fresh and dry weights, root fresh and dry weights) and physiological parameters (chlorophyll contents, membrane stability index, and relative water content) as well as biochemical parameters (proline, total soluble sugar and catalase activity). These findings indicated a noteworthy reduction in biotic stress through the increase of both enzymatic and non-enzymatic activities by the green fabricated AgNPs. This study marks a first biocompatible approach in assessing the potential of green fabricated AgNPs in enhancing the well-being of tomato plants that affected with bacterial canker and establishing an effective management strategy against Clavibacter michiganensis. This is the first study suggests that low concentration of green fabricated nanosilvers (AgNPs) from leaf extract of Moringa oleifera against Clavibacter michiganensis is a promisingly efficient and eco-friendly alternative approach for management of bacterial canker disease in tomato crop. [ABSTRACT FROM AUTHOR]

Inter-frame tampering in surveillance videos undermines the integrity of video evidence, potentially influencing law enforcement investigations and court decisions. This type of tampering is the most common tampering method, often imperceptible to the human eye. Until now, various algorithms have been proposed to identify such tampering, based on handcrafted features. Automatic detection, localization, and determine the tampering type, while maintaining accuracy and processing speed, is still a challenge. We propose a novel method for detecting inter-frame tampering by exploiting a 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatic feature extraction, employing an autoencoder to significantly reduce the computational overhead by reducing the dimensionality of the feature's space; analyzing long-range dependencies within video frames using long short-term memory (LSTM) and gated recurrent units (GRU), which helps to detect tampering traces; and finally, adding a fully connected layer (FC), with softmax activation for classification. The structural similarity index measure (SSIM) is utilized to localize tampering. We perform extensive experiments on datasets, comprised of challenging videos with different complexity levels. The results demonstrate that the proposed method can identify and pinpoint tampering regions with more than 90% accuracy, irrespective of video frame rates, video formats, number of tampering frames, and the compression quality factor. [ABSTRACT FROM AUTHOR]

In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression. [ABSTRACT FROM AUTHOR]

Optoelectronic devices are being extensively employed nowadays that are vital to diverse range of the appliances. The exploration of apposite materials for such efficient devices appears to be challenging. So, this study recommends novel combinations of chalcogenides as high energy materials that are seldom ever described. Here, density function theory (DFT) is employed to analyze the phase stability, optoelectronic, mechanical, thermodynamic, and vibrational properties of tellurium-based ternary chalcogenides XScTe2 (X = Li, Rb) by utilizing Perdew–Burke–Ernzerhof-generalized gradient approximation (PBE-GGA) and HSE06 through CASTEP simulation code. The observed minimum magnitude of free energy for these structures is found to be well-consistent with the stability requirements of the thermodynamics that confirms their chemical stability. The indirect energy band gap declares them semiconductors. Significant absorption is noticed in the ultraviolet (UV) range of the electromagnetic spectrum. The mechanical properties determined through Voigt–Reuss–Hill approximation determine mechanical instability due to the appearance of some negative elastic constants. Pugh's ratios evaluated as larger than 1.75 while demonstrating its ductile nature. The comprehensive analysis of thermodynamic behavior declares that these are thermodynamically stable materials. The density functional perturbation theory (DFPT) delineates no imaginary frequencies signifying both these compounds as stable materials. The thorough examination suffices to identify these materials as effective ones for optoelectronic uses. [ABSTRACT FROM AUTHOR]

Vitamin D is an essential micronutrient; it is necessary for human well-being, including brain and heart health; its deficiency is widespread and can lead to fatal diseases. Daily consumed foods are enriched and fortified with vitamin D; still, adequate bioavailability and safe delivery are significant issues. In the food system, due to unfavorable conditions, vitamin D loses its nature and functionality; likewise, it is fat-soluble and can't be incorporated into juices and soft drinks. Due to the acidic nature of the gastric environment, the timely release and adequate bioavailability of vitamin D is also hampered. Therefore, the most challenging and critical issue is the application of techniques to safeguard vitamin D for its optimum functionality. Recently food researchers and nutritionists are developing innovative methods using nanotechnology to ensure the safety of vitamin D, its timely release and absorbance during digestion. This review reflects valuable data, precisely the effectiveness of recently developed nano-based methods on vitamin D's sustainability, retention, and bioavailability, including nano-encapsulation, nanoemulsion, mixed micelles, complexation, and Conjugation. This manuscript also discusses some of the important clinical trialed based health benefits of vitamin D, summarizes the most recent techniques developed, and demonstrates understanding of novel methods and future developments. [ABSTRACT FROM AUTHOR]

Thermo-mechanical and mechanical properties of Magnesia-Carbon (MgO–C) refractories are of great importance in industrial furnace applications and are greatly influenced by the amount of carbon in the MgO Matrix. In this research work, the effect of exfoliated graphite nanosheets (EGS) content on mechanical behavior and thermal shock resistance of MgO-based refractories was examined. MgO nanoparticles were synthesized by the co-precipitation method and micro-MgO particles were used as received. EGS was added in both micro and nano-MgO in variable amounts ranging from 0.5 to 2.5 wt % to study its effect on fracture strength and thermal shock resistance. The morphology of synthesized MgO nanoparticles and the fractured surfaces of the resultant composites were analyzed by field emission scanning electron microscopy (FESEM) coupled with energy dispersive x-ray (EDX) spectroscopy, transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), and compression testing. The synthesized nano-MgO and as received micro-MgO particles were found to be of spherical morphology with no impurity elements as observed by FESEM and EDX mapping. XRD analysis did not show any phase change after the sintering of the composites. The FESEM and TEM images of the fractured surfaces of MgO-EGS refractory samples displayed the fine distribution of EGS. EGS were embedded in MgO particles, enhancing the composite-refractory samples' fractured strength. Fracture strength and thermal shock resistance were increased with the increasing amount of EGS in MgO. However, the highest fracture strength and thermal shock resistance values were achieved for nano-MgO with 2.5 wt% EGS. The resulting refractory samples can be used as furnace linings having a lower carbon content and enhanced thermo-mechanical properties. [ABSTRACT FROM AUTHOR]

Currently, synthetic and semi-synthetic polymers are widely used in the development of various drug delivery systems (DDSs), biomedical and surgical devices, and healthcare materials. However, many drawbacks and problems are associated with these polymeric materials, including toxicity, immunogenicity, non-biodegradability, nonbiocompatibility, and complicated, costly synthesis. To address such drawbacks, nowadays, naturally occurring swellable polysaccharides (NOSPs) are being evaluated for the possible replacement of synthetic polymers. NOSPs have shown remarkable stimuli-responsive properties, which made them an ideal material to develop stimuli-responsive DDSs, especially smart tablets. The present review focuses on the summarization of stimuli-responsive properties (swelling, on-off switching, and drug release) of smart/stimuli-responsive tablets that respond to various stimuli, e.g., pH, solvent, transit, time, etc. This article highlights the need to develop NOSPs-based smart tablets for intelligent and targeted drug delivery. [ABSTRACT FROM AUTHOR]

This study analyzed the human resources of public libraries in Pakistan. It was observed that LIS professionals administered most libraries; nevertheless, a sizable number of libraries were managed by personnel with no professional background. There needed to be more staff in public libraries, including LIS, IT, and nonprofessional staff. In the information age and global economy, our libraries fall short of preparing the population for the future, let alone the present. The concerned authorities should enhance the library’s workforce; professionals should administer the library to offer better services and IT experts should be hired to solve IT related issues. [ABSTRACT FROM AUTHOR]

Under paddy soil conditions, rice plants are vulnerable to arsenic (As) accumulation, thus causing potential threat to human health. Here we investigated the influence of foliar-applied phosphorus (P: 10 and 20 mg L−1), silicon (Si: 0.6 and 1.5 g L−1) and selenium (Se: 5 and 10 mg L−1) on As accumulation, morphological and physiological attributes of two contrasting rice genotypes (KSK-133 and Super Basmati) under As stress (25 mg kg−1 as arsenate). Silicon foliar dressing significantly (p < 0.05) reduced grain As uptake (up to 67%) and improved rice growth and chlorophyll content (28–66%) in both rice genotypes over their controls. Phosphorus foliar application resulted in a notable decrease (17%) in grain As uptake of coarse rice genotype (KSK-133), while it slightly increased grain As uptake in the fine one (Super Basmati; 6%) compared to controls. However, foliar-applied Se did not show significant effects on rice plants growth attributes and As uptake in both genotypes. Similarly, biochemical and enzymatic attributes (i.e., lipid peroxidation, electrolyte leakage, peroxidase and catalase) were improved with Si application in rice plants, except for P treatment that was only effective for coarse one. Foliar-applied Si also resulted in reduced cancer risk and hazard quotient (< 0.10) for both rice genotypes. This study advances our understanding on critical role of different foliar-applied nutrients and rice genotypes, which is imperative to develop effective As remediation and management strategies in coarse and fine rice genotypes and protect human health. This study provided new insights on the significance of foliar-applied phosphorus, silicon and selenium for the management and remediation of arsenic in fine (Super Basmati) and coarse (KSK-133) rice genotypes. Foliar-applied silicon was the most promising strategy to mitigate arsenic uptake and minimizing health risk in rice grain of both genotypes, while phosphorus was effective only for coarse one, thus showing a genotype dependent response. Interestingly, selenium foliar application had no significant effect on arsenic accumulation in both rice genotypes. [ABSTRACT FROM AUTHOR]

The sustainability of the concrete industry is in jeopardy because it is one of the biggest consumers of natural resources. Environmental and monetary issues are the main difficulties the concrete industry is currently dealing with. In this study, the potential substitution of sawdust ash for cement in the production of concrete is explored. In this project, the potential substitution of sawdust ash for cement in concrete production was explored, a typical carpentry waste, and then we utilize several testing techniques to examine how it impacts the mechanical characteristics of concrete. In an experiment, the compressive, tensile, and flexural strengths of concrete samples made with various ratios of sawdust ash and cement were examined. The samples were made following ASTM C-109, ASTM C-496 and ASTM C-78 for compression, tensile and flexural testing. In place of cement, saw dust ash was added to the M-15 (M indicates 'mix' and 15 indicates compressive strength of 15MPA) sample in weight percentages of 5%, 10%, 15%, 20%, and 25%. The concrete samples were tested to ascertain their compressive, tensile, and flexural strengths after 14 days. Comparisons between the results and untreated concrete were done. In this study, the behavior of concrete was investigated when sawdust ash was replaced for cement to weight-based extents of 0%, 5%, 10%, 20%, and 25%. This could address the problem of how to dispose of sawdust ash while also enhancing the properties of concrete. [ABSTRACT FROM AUTHOR]

Safety evaluation of a newly designed polymeric drug delivery system (DDS), with/without the addition of active pharmaceutical ingredients (APIs), is now mandatory for their regulatory approval for human use. Hence, Salvia spinosa seed mucilage/hydrogel (SSH) was treated with methacrylic acid (MAA) to synthesize a composite hydrogel (SSH-co-MAA). Acute oral and acute dermal toxicity studies of the SSH-co-MAA for API delivery were ascertained following OECD guidelines 420 and 402, respectively. Moreover, an ocular toxicity study was also performed and analyzed through Draize scale. Animals of two species, rodent (rat) and non-rodent (rabbit), were divided into four groups. Group A of both rats and rabbits was assigned as control and remained untreated. Meanwhile, groups B, C, and D were labelled as treated groups and received a single dose of SSH-co-MAA, i.e., 0.05, 0.3 and 2 g/kg body weight of the animal. During 14 days after the treatment, animal monitoring was done for behavioral changes, food and water intake, adverse effects, and mortality. All animals remained alive, with no statistically significant abnormality. Hematological and biochemical parameters of control and treated animals were analyzed after the completion of 14 days and found in harmonization. The vital organs of animal models were removed to determine absolute organ weights. Histopathology of the vital organs of animal models revealed normal cellular architecture, without any lesions. SSH-co-MAA was also free from dermal and ocular toxicity. The overall results of acute oral and dermal toxicity studies prove that SSH-co-MAA is safe, especially after oral administration. Hence, SSH-co-MAA can be used as a non-toxic excipient for drug delivery systems. [ABSTRACT FROM AUTHOR]

Objective: To determine the causes of normal blood flow in coronary arteries in patients presenting with STEMI and receiving pPCI. Study Design: Observational study conducted retrospectively. Setting: National Institute of Cardiovascular Disease (NICVD) Karachi, Cath Lab. Period: August 17 2020 to November 2020. Material & Methods: Data was obtained from patients presented to the emergency department with STEMI and experienced primary PCI in last two years from January 2016 to Decemebr 2018.Social Sciences (SPSS) version 19 was used analyze these data. Th in those patients who had suboptimal coronary blood flow. Young patients <40 years of age had more (14%) normal coronary blood flow as compared to elders i.e >40 years. Diabetes mellitus, Hypertension, and smoking had no significant impact on coronary blood flow in these patients. Conclusion: In this study, it was found that most of the coronary arteries had abnormal blood flow during primary PCI. Multiple factors are affecting the blood flow in coronaries. The onset of symptoms and the time presented in the hospital was the main factors. Further studies are required to find more reasons for having normal blood flow in coronaries in STEMI. [ABSTRACT FROM AUTHOR]

In this work, we demonstrate a successful synthesis of heteroatom co‐doped TiO2 decorated with Ag nanoparticles using a direct combustion approach. These materials serve as a high‐performance active layer for advanced battery‐type supercapacitors. Among all the investigated materials, N,S co‐doped TiO2 nanoparticles and Ag/N,S co‐doped TiO2 nanocomposite exhibited impressive specific capacitances of 360 and 480 F g−1 at a current density of 1 A g−1. Moreover, we employed Ag/N,S co‐doped TiO2 nanocomposite as symmetric supercapacitor in a wide potential window of −1 to 1 V. The symmetric device demonstrated a specific capacitance of 183 F g−1 at 3 A g−1 with an energy density of 105 Wh kg−1 at a power density of 6 kW kg−1. One of the most promising aspects of this study was the high cyclic stability of the device, with capacity retention of 80 % when tested for 3000 discharge cycles at 3 A g−1.The improved electrochemical performance of Ag nanoparticle decorated N,S co‐doped TiO2 structure confirms the synergistic behavior of each component in the nanocomposite. These results indicate the remarkable durability of the device. Additionally, the materials used in this work are environmentally friendly, making them attractive candidates for battery‐type supercapacitors. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) have rapidly received increasing attention and great interest as potential materials for energy storage and catalyst fields, which is due to their unique physicochemical and electrical properties. With continuous improvements in fabrication routes, CNTs have been modified with various types of materials, opening up new perspectives for research and state-of-the-art technologies. Amorphous CNTs (aCNTs) are carbon nanostructures that are distinctively different from their well-ordered counterparts, such as single-walled and multi-walled carbon nanotubes (SWCNTs and MWCNTs, respectively), while the atoms in aCNTs are grouped in a disordered, crystalline/non-crystalline manner. Owing to their unique structure and properties, aCNTs are attractive for energy storage, catalysis, and aerospace applications. In this review, we provide an overview of the synthetic routes of aCNTs, which include chemical vapor deposition, catalytic pyrolysis, and arc discharge. Detailed morphologies of aCNTs and the systematic elucidation of tunable properties are also summarized. Finally, we discuss the future perspectives as well as associated challenges of aCNTs. With this review, we aim to encourage further research for the widespread use of aCNTs in industry. [ABSTRACT FROM AUTHOR]

DFT study on the structural, optoelectronic, thermodynamic, vibrational, and mechanical properties of Ruddlesden Popper (RP) perovskites Sr2XO4 (X = Zr, Hf) is made with the help of first principle simulation in the framework of WIEN2K code. The lattice constants in bohr unit are found to be a=7.313,b=7.313,c=23.346 for Sr2ZrO4, and a=7.310,b=7.310,andc=23.368 for Sr2HfO4. The calculated band gap values are 2.65 eV for Sr2ZrO4 and 2.58 eV for Sr2HfO4. The band structure and electronic density of states reveal the semiconductor nature of these materials having an indirect band gap. Also, Kramers–Krönig relations are used for optical analysis which unveils that these compounds are suitable for applications in optoelectronic. The vibrational investigations are done while using harmonic approximation. Phonon dispersion curves are plotted to observe the vibrational modes by DFPT to confirm the dynamical stability of studied compounds. Although few soft modes have appeared, however, these compounds are found to be thermally stable. Raman modes appeared at low and high frequencies whereas IR modes are noticed at intermediate frequencies for considered compounds. Upon thermodynamical examination, the maximum value of free energy at 1000 K is noted to be −1.95 eV for Sr2ZrO4 and −2.25 eV for Sr2HfO4. The elastic constants are calculated by using the Voigt–Reuss–Hill approximation. The calculated anisotropic values for these compounds are 1.077 (A) and 0.913 (A) which indicate that Sr2ZrO4 has isotropic behavior and Sr2HfO4 has anisotropic behavior. From our calculations, Voigt Young's modulus of Sr2ZrO4 and Sr2HfO4 is 266.99 (GPa) and 279.42 (GPa) along with Poison's ratio of 0.29 and 0.26, respectively. [ABSTRACT FROM AUTHOR]

Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation. [ABSTRACT FROM AUTHOR]

Sorghum is the 4th largest cereal crop in the world with diverse flavonoid and phenolic compounds. It is resistant to biotic and abiotic stresses. Sweet sorghum is mostly grown for its grain as well as for the production of ethanol and biofuels. Sorghum is also the main food source with numerous health benefits. Plant growth and development are sustained by metabolites which include both primary and secondary metabolites. Sorghum varieties are characterized on the basis of morphological, molecular, and biochemical factors. Morphometric characterization is a simple and cost-effective method; however, it is greatly influenced by environmental factors. While metabolite profiling pinpoints the basis of plant morphological variations. Metabolomics provides a link between morphological and genetic factors. Moreover, it has applications in gene discovery via metabolomic quantitative traits loci (mQTL) identification. This study was aimed at characterizing three sweet sorghum accessions with characteristic seed colors. Phenotypic data related to grain-related traits was recorded. Flavonoid and phenolic contents of these accessions were also analyzed. Sorghum accession with dark red seed color contained higher phenolic contents as compared to the accession with white seed color. The study provides an interpretation of morphological variations in the grain-related traits of these accessions from the metabolomics perspective. [ABSTRACT FROM AUTHOR]

We propose a Fabry–Perot cavity (FPC) antenna to suppress a sidelobe level (SLL) while maintaining a reasonably high gain. Generally, conventional FPC antennas (FPCAs) produce a high SLL because waves in their FPC leak considerably through lateral openings, which is a primary reason for lowering antenna gains. We propose two design approaches to solve this problem: the reflection magnitude tapering of a partially reflective surface (PRS) and considering different incident modes for the PRS design. First, the proposed tapering can remarkably reduce an SLL by providing the PRS with more radiation opportunities. Second, the different incident modes of transverse electric (TE) and transverse magnetic (TM) can increase an antenna gain by considering a more realistic illumination environment of the PRS. We have proven that our antenna provides 19.8 dBi realized gain with high sidelobe suppression (SLS) of more than 23 dB. Consequently, the proposed FPCA can suppress sidelobes significantly while maintaining a high gain. Good agreement between simulations and experiments demonstrates the usefulness of our proposal. [ABSTRACT FROM AUTHOR]

The present study deals with the green synthesis of silver nanoparticles (Ag NPs) using a chemically modified polysaccharide as a reducing and capping agent. Dextran succinate (Dex-Suc) was synthesized by succinylation of the dextran with succinic anhydride at 80 °C for 24 h under stirring and N2. The Dex-Suc was used as a reducing and capping agent for the synthesis of Ag NPs in the presence of sunlight with high UV index. The Ag NPs were characterized using UV-Vis spectroscopy, SEM, and EDS techniques. The SEM data revealed the preparation of spherical Ag NPs having 50 nm size. The Ag NPs showed antimicrobial properties against different bacterial and fungal strains. [ABSTRACT FROM AUTHOR]

This study aims at analyzing the factors of aberrant driving behaviors and road accidents among Chinese ride-hailing drivers. Four hundred and twenty ride-hailing drivers (Male = 65%) completed a web-based questionnaire survey that assessed personal attributes, work-condition factors, aberrant driving behaviors, and self-reported road accidents in the last three years. A 10-item violations Driver Behavior Questionnaire (DBQ) scale was adopted to explore the aberrant driving behaviors of ride-hailing drivers. The ordinal regression model was used to examine the effects of personal attributes and work-condition factors on aberrant driving behaviors. A binary logistic regression model was employed to investigate the predictors of road accidents. The descriptive statistics indicate that among ride-hailing drivers, the traditional taxi drivers were found to be more involved in aberrant driving behaviors than private car drivers. The results from the Principal Component Analysis (PCA) reveal that ride-hailing drivers were involved in "risky violations." Male and young ride-hailing drivers were found to be more involved in risky violations than their counterparts. Furthermore, it is revealed that a one-unit increase in risky violations increased the probability of being involved in road accidents by 60%. Furthermore, a one-unit increase in work-condition factors increased the likelihood of being involved in road accidents by 41%. The findings in this study can help better understand the aberrant driving behaviors of ride-hailing drivers and contribute to a more effective policy for reducing the road accidents caused by ride-hailing drivers. [ABSTRACT FROM AUTHOR]

The accurate classification of seizure types using electroencephalography (EEG) signals plays a vital role in determining a precise treatment plan and therapy for epilepsy patients. Among the available deep network models, Convolutional Neural Networks (CNNs) are the most widely adopted models for learning and representing EEG signals. However, typical CNNs have high computational complexity, leading to overfitting problems. This paper proposes the design of two effective, lightweight deep network models; the 1D multiscale neural network (1D-MSCNet) model and the Long Short-term Memory (LSTM)-based compact CNN (EEG-LSTMNet) model. The 1D-MSCNet model comprises three modules: a spectral–temporal convolution module, a spatial convolution module, and a classification module. It extracts features from input EEG trials at multiple frequency/time ranges, identifying relationships between the spatial distribution of their channels. The EEG-LSTMNet model includes three convolutional layers, namely temporal, depthwise, and separable layers, a single LSTM layer, and two fully connected classification layers to extract discriminative EEG feature representations. Both models have been applied to the same EEG trials collected from the Temple University Hospital (TUH) database. Results revealed F1-score values of 96.9% and 98.4% for the 1D-MSCNet and EEG-LSTMNet, respectively. Based on the demonstrated outcomes, both models outperform related state-of-the-art methods due to their architectures' adoption of 1D modules and layers that reduce the computational effort needed, solve the overfitting problem, and enhance classification efficiency. Hence, both models could be valuable additions for neurologists to help them decide upon precise treatments and drugs for patients depending on their type of seizure. [ABSTRACT FROM AUTHOR]

This study investigated the status of library automation in the university libraries of Khyber Pakhtunkhwa, Pakistan. The survey research method was employed to collect data from libraries heads of central libraries in public sector universities. The sample comprised the entire population of 27 regional public-sector universities. The data collected through questionnaires were analyzed using descriptive statistics and interpreted through tables. The findings highlight the respondents' demographic information, revealing most male participants and a diverse range of designations and experience levels. The study reveals that most libraries were only partially automated, with a significant number in the initial stages of automation. The analysis of various library services and operations indicates a predominant reliance on manual approaches across most functions, with cataloguing being the only service showing some level of automation. Regarding software usage, the most frequently employed solution was Koha, followed by LIMS and SLIMS. The study revealed that automation saves time, improves information searching, improve efficiency and quick access to materials. However, challenges such as limited resources, power supply interruptions, and inadequate IT infrastructure were identified. The findings can inform decision-making processes and guide efforts to improve automation practices, resource allocation, and training opportunities to enhance library services and operations. [ABSTRACT FROM AUTHOR]

Exosomes are small-diameter endosomal vesicles secreted in all biological fluids and play biological/pathological roles in the cell. These pathological roles are played by exosome's cargo molecules through inter-cellular communication. Exosomal cargo molecules contain proteins and miRNAs. miRNAs are small non-coding RNA fragments involved in the reduction of final protein output by destabilizing or suppressing the translation of target messenger RNA (mRNA). This deregulation of the protein due to miRNAs ultimately accelerates the process of disease pathogenesis. The role of exosomal miRNAs has been investigated in different diseases and the limited number of studies have been published concerning exosomal miRNAs and rheumatoid arthritis (RA). The current study is designed to investigate the role of exosomal miRNAs (miRNA-103a-3p, miRNA-10a-5p, miRNA-204-3p, miRNA-330-3p, and miRNA-19b) in the pathogenesis of RA. Furthermore, the role of selected exosomal miRNAs in RA pathogenesis was further explored by estimating oxidative stress and histone deacetylation in RA patients. In the current study, 306 RA patients and equal numbers of age/gender-matched controls were used. The level of expression of above-mentioned exosomal miRNAs was assessed by performing qRT PCR. Deacetylation and oxidative stress assays were performed to estimate the 8-hydroxydeoxyguanosine (8-OHdG level) and histone deacetylation levels using the Enzyme-linked immunosorbent assay (ELISA). Statistical analysis indicated a significantly downregulated expression of miRNA-103a-3p (p<0.0001), miR-10a-5p (p<0.0001), miR-204-3p (p<0.0001), miR-330-3p (p<0.0001) and miR-19b (p<0.0001) in RA patients compared to controls. Significantly increased levels of 8-OHdG (p<0.0001) and histone deacetylation (p<0.0001) were observed among RA patients compared to controls. Spearman correlation showed a negative correlation between the deregulated exosomal miRNAs and increased oxidative stress and histone deacetylation in RA patients. Receiver operating characteristics (ROC) curve analysis showed a good diagnostic specificity/sensitivity of the above-mentioned exosomal miRNAs among RA patients. These analyses indicated the potential role of deregulated exosomal miRNAs in the initiation of RA by targeting oxidative stress and histone deacetylation processes. [ABSTRACT FROM AUTHOR]

Objectives: Measles is one of the most familiar contagious diseases in children associated with numerous systemic complications. The aim of this study is to find the frequency of Myocarditis in children suffering from measles. Materials and Methods: This cross-sectional study was conducted in the department of pediatrics, MTI Lady Reading Hospital Peshawar from 6th December 2019 to 5th June 2020. Ninety-six patients of both genders and ages ranging from 1-12 years having measles for more than one week were enrolled in the study after informed consent. Ethical approval for the study was obtained from the Ethical Review Board. Patient demographic data were collected on a structured proforma and the frequency of children having clinical presentation of myocarditis was recorded. Data were entered and analyzed using SPSS 22. Results: The frequency of Myocarditis was 7.3% in children presented with Measles. The mean age was 6.6± 3.06 years. There were 47.9% males and 52.1% females. Most patients belonged to middle socioeconomic status i.e. 61.5%. Maternal education was 49% in the 6-10 age group. In sampled population, 46.9% belonged to urban areas and 5.1% belonged to rural areas. There were 18.8% vaccinated children and 81.3% were not vaccinated for measles. Myocarditis was significantly associated with the younger age group (p-value 0.028), however, there was no difference in gender (p-value 0.781). Children with low socioeconomic status had significantly higher frequency (p-value 0.05) of myocarditis. Conclusion: Myocarditis is commonly presented in children having measles especially patients in younger age groups and belonging to poor socioeconomic status. [ABSTRACT FROM AUTHOR]

The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom 'lightweight' convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%. [ABSTRACT FROM AUTHOR]

Power augmentation in a small-scale horizontal axis wind turbine, with its rotor encased in a flanged diffuser is explored. The power output of the wind turbine varies with changes in the diffuser design and the resulting back pressure. Reduction in this back pressure also results in early flow separation at the diffuser surface, which hinders turbine performance. The main aim of this study is to numerically investigate the local configuration of the wind turbine location inside the diffuser by varying diffuser angles and wind speeds. Therefore, shroud and flange were modeled and analyzed using the computational fluid dynamic (CFD) analyses and experiments were performed at two wind speeds 6 m/s and 8 m/s with and without the diffuser for model validation. The divergence angle of 4° was found to have no flow separation, thus maximizing flow rate. The proposed design shows wind speed improvement of up to 1.68 times compared to the baseline configuration. The corresponding optimum flange height was found to be 250 mm. However, increasing the divergence angle had a similar output. The dimensionless location of wind turbine was found to be between 0.45 and 0.5 for 2° and 4° divergence angle respectively. Furthermore, the maximum augmentation location varies with wind speed and diffuser's divergence angle as described by dimensionless location of wind turbine, thus presenting a noteworthy contribution to the horizontal axis wind turbine area with the flanged diffuser. [ABSTRACT FROM AUTHOR]

Nutraceuticals are defined as food or food components with therapeutic capabilities that have few side effects and are regarded as a natural therapy for preventing the onset of several life-threatening illnesses. The use of microbial cell factories to produce nutraceuticals is considered to be sustainable and promising for meeting market demand. Among the diverse strategies for optimizing microbial cell factories, the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) system has emerged as a valuable tool for gene integration, deletion, activation, and downregulation. With the advent of multiplexed and precise CRISPR strategies, optimized microbial cell factories are revolutionizing the yield of nutraceuticals. This review focuses on the development of highly adaptable CRISPR strategies to optimize the production in microbial cell factories of some important nutraceuticals (belonging to the class of carotenoids, flavonoids, stilbenoids, polysaccharides, and nonprotein amino acids). Further, we highlighted current challenges related to the efficiency of CRISPR strategies and addressed potential future directions to fully harness CRISPR strategies to make nutraceutical synthesis in microbial cell factories an industrially favorable method. [ABSTRACT FROM AUTHOR]

Abstract With the introduction of the metaverse, augmented reality (AR) is expected to become the next interface between people and computers. Research is conducted to improve the usability of AR applications and devices. Color transparency and background effect are the two critical factors to consider while developing AR applications. Ten user interface conditions (5 color transparency levels × 2 background conditions) were evaluated in an AR environment. The task completion time and mean error were noted along with the subjective scores of button visibility, background visibility, and motion sickness. The results revealed a significant difference in task completion time, button visibility, and background visibility between transparency conditions, but not in the number of errors and total motion sickness. The transparency level of T0.5 and T0.75 is recommended for the interface of AR applications. This study could be helpful when creating augmented reality applications for the metaverse and other platforms. [ABSTRACT FROM AUTHOR]

Accurately identifying the seizure type is vital in the treatment plan and drug prescription for epileptic patients. The most commonly adopted test for identifying epileptic seizures is electroencephalography (EEG). EEG signals include important information about the brain's electrical activities and are widely used for epilepsy analysis. Among various deep network architectures, convolutional neural networks (CNNs) have been widely used for EEG signal representation learning for epilepsy analysis. However, most of the existing CNN-based methods suffer from the overfitting problem due to a small number of EEG trials and the huge number of learnable parameters. This paper introduces the design of an efficient, lightweight, and expressive deep network model based on ResNet theory and long short-term memory (LSTM) for classifying seizure types from EEG trials. A 1D ResNet module is adopted to train a deeper network without encountering vanishing gradient problems and to avoid the overfitting problem of CNN models. The LSTM module encodes and learns long-term dependencies over time. The synthetic minority oversampling technique (SMOTE) is applied to balance the data by increasing the trials of minority classes. The proposed method was evaluated using the public domain benchmark TUH database. Experimental results revealed the superior performance of the proposed model over other state-of-the-art models with an F1-score of 97.4%. The proposed deep learning model will help neurologists precisely interpret and classify epileptic seizure types and enhance the patient's life. [ABSTRACT FROM AUTHOR]

The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends. [ABSTRACT FROM AUTHOR]

Social entrepreneurship (SE) is an all-encompassing concept in comparison to a typical non-government organization (NGO). It is a topic that has captured the interest of academics investigating nonprofit, charitable, and nongovernmental organizations. Despite the interest, few studies have examined the overlap and convergence of entrepreneurship and non-governmental organizations (NGOs), in congruence with the new phase of globalization. The study gathered and evaluated 73 peer-reviewed papers using a systematic literature review methodology, mainly from Web of Science but also from Scopus, JSTOR, and Science Direct, and supplemented by a search of existing databases and bibliographies. Based on the findings, 71 percent of studies suggest that organizations must reconsider the concept of social work, which has evolved rapidly, aided by globalization. The concept has changed from the NGO model to a more sustainable one, such as that proposed by SE. However, it is difficult to draw broad generalizations regarding the convergence of context-dependent complex variables such as SE, NGOs, and globalization. The results of the study will significantly contribute to a better understanding of the convergence of SE and NGOs, as well as the recognition that many aspects of NGOs, SE, and post-COVID globalization remain unexamined. [ABSTRACT FROM AUTHOR]

Let σ = { σ i | i ∈ I } be some partition of the set of all primes ℙ , G a finite group and σ (G) = { σ i | σ i ∩ π (G) ≠ ∅ }. A set ℋ of subgroups of G is said to be a complete Hall σ -set of G if every non-identity member of ℋ is a Hall σ i -subgroup of G for some σ i ∈ σ and ℋ contains exactly one Hall σ i -subgroup of G for every σ i ∈ σ (G). A subgroup H of G is said to be σ -primary if it is a finite σ i -group for some i ; σ -abnormal in G if K / L K is not σ -primary whenever H ≤ L < K ≤ G and L is a maximal subgroup of K. A subgroup H of G is called -subnormal if there is a chain of subgroups H = H 0 < ⋅ H 1 < ⋅ ... < ⋅ H t = G such that H i / (H i − 1) H i ∈ for all i. This is equivalent to H i ≤ H i − 1 . In this paper, we study the structure of a finite group G in which every σ -primary cyclic subgroups are σ -quasinormal or σ -abnormal. We also describe the structure of a finite group G with self-normalizing or -subnormal σ -primary cyclic subgroups for a subgroup-closed saturated lattice formation containing all σ -nilpotent groups. [ABSTRACT FROM AUTHOR]

This work explores the heterogeneous effect of urbanization and nonrenewable energy consumption on the environment in 54 African nations. Panel data were used from 1996 to 2019. For estimation, panel quantile regression analysis, augmented mean group, panel threshold regression, and the environment Kuznets curve hypothesis were applied to check the relationship between income and carbon emissions. The study's outcome demonstrates that urbanization and nonrenewable energy consumption degrade the environment in Africa. Furthermore, an inverted U‐shape relationship exists between economic growth and CO2 emissions, confirming the environmental Kuznets curve hypothesis. The findings indicate that urbanization should be planned; otherwise, urbanization can cause environmental degradation. African countries must adopt green urbanization and use renewable energy and clean manufacturing technologies. The institutions are encouraged to execute the standard, regulatory environment, and policies to reduce carbon emissions. Countries throughout the African continent should actively respond to the issues by charting a separate and diverse route for urban development. [ABSTRACT FROM AUTHOR]

The photocatalytic performance has remained challenging due to the rapid recombination of photoexcited electron-hole (e-h) pairs. To overcome this problem, creating oxygen vacancies on the surface of semiconductors has been an effective strategy. Herein, we report the effects of oxygen vacancies (Ov) on photocatalytic HER performance of Pt nanoparticles (NPs) anchored on UiO-66-NH 2. In contrast, under the same amount of Pt NPs, UiO-66-NH 2 with high oxygen vacancies (denoted as Pt/UN-Ovh) exhibit superior photocatalytic H 2 generation than the catalyst with low oxygen vacancies (denoted as Pt/UN-Ovl) under visible-light irradiation. Based on the experimental characterization and theoretical calculations, the high oxygen vacancies not only stabilize the Pt NPs on the substrate (UiO-66-NH 2), but also develop the strong interaction between Pt NPs and support thereby Pt NPs traps more electrons from substrate and provides protons for H 2 production inhibiting the electron-hole recombination. This work provides novel strategy for enhancing the photocatalysts performance of MOF based materials. [Display omitted] • Effects of oxygen vacancies (Ov) of MOFs on photocatalytic HER. • Pt/UN-Ovh exhibited 3 times higher performance than Pt/UN-Ovl catalyst. • Vital role of Oxygen vacancies (Ov) to develop strong metal-support interaction. [ABSTRACT FROM AUTHOR]

In recent years, human–computer interaction (HCI) systems have become increasingly popular. Some of these systems demand particular approaches for discriminating actual emotions through the use of better multimodal methods. In this work, a deep canonical correlation analysis (DCCA) based multimodal emotion recognition method is presented through the fusion of electroencephalography (EEG) and facial video clips. A two-stage framework is implemented, where the first stage extracts relevant features for emotion recognition using a single modality, while the second stage merges the highly correlated features from the two modalities and performs classification. Convolutional neural network (CNN) based Resnet50 and 1D-CNN (1-Dimensional CNN) have been utilized to extract features from facial video clips and EEG modalities, respectively. A DCCA-based approach was used to fuse highly correlated features, and three basic human emotion categories (happy, neutral, and sad) were classified using the SoftMax classifier. The proposed approach was investigated based on the publicly available datasets called MAHNOB-HCI and DEAP. Experimental results revealed an average accuracy of 93.86% and 91.54% on the MAHNOB-HCI and DEAP datasets, respectively. The competitiveness of the proposed framework and the justification for exclusivity in achieving this accuracy were evaluated by comparison with existing work. [ABSTRACT FROM AUTHOR]

Mitochondrial sirtuins have diverse role specifically in aging, metabolism and cancer. In cancer, these sirtuins play dichotomous role as tumor suppressor and promoter. Previous studies have reported the involvement of sirtuins in different cancers. However, till now no study has been published with respect to mitochondrial sirtuins and glioma risks. Present study was purposed to figure out the expression level of mitochondrial sirtuins (SIRT3, SIRT4, SIRT5) and related genes (GDH, OGG1-2α, SOD1, SOD2, HIF1α and PARP1) in 153 glioma tissue samples and 200 brain tissue samples from epilepsy patients (taken as controls). To understand the role of selected situins in gliomagenesis, DNA damage was measured using the comet assay and oncometabolic role (oxidative stress level, ATP level and NAD level) was measured using the ELISA and quantitative PCR. Results analysis showed significant down-regulation of SIRT4 (p = 0.0337), SIRT5 (p<0.0001), GDH (p = 0.0305), OGG1-2α (p = 0.0001), SOD1 (p<0.0001) and SOD2 (p<0.0001) in glioma patients compared to controls. In case of SIRT3 (p = 0.0322), HIF1α (p = 0.0385) and PARP1 (p = 0.0203), significant up-regulation was observed. ROC curve analysis and cox regression analysis showed the good diagnostic and prognostic value of mitochondrial sirtuins in glioma patients. Oncometabolic rate assessment analysis showed significant increased ATP level (p<0.0001), NAD+ level [(NMNAT1 (p<0.0001), NMNAT3 (p<0.0001) and NAMPT (p<0.04)] and glutathione level (p<0.0001) in glioma patients compared to controls. Significant increased level of damage ((p<0.04) and decrease level of antioxidant enzymes include superoxide dismutase (SOD, p<0.0001), catalase (CAT, p<0.0001) and glutathione peroxidase (GPx, p<0.0001) was observed in patients compared to controls. Present study data suggest that variation in expression pattern of mitochondrial sirtuins and increased metabolic rate may have diagnostic and prognostic significance in glioma patients. [ABSTRACT FROM AUTHOR]

Let σ = { σ i | i ∈ I } be some partition of the set of all primes ℙ , G be a finite group and σ (G) = { σ i | σ i ∩ π (G) ≠ ∅ }. A set ℋ of subgroups of G is said to be a complete Hall σ -set of G if every non-identity member of ℋ is a Hall σ i -subgroup of G for some i and ℋ contains exactly one Hall σ i -subgroup of G for every σ i ∈ σ (G). Let ℋ be a complete Hall σ -set of G. A subgroup A of G is said to be σ -semipermutable with respect to ℋ if A H x = H x A for all x ∈ G and all H ∈ ℋ such that (| H | , | A |) = 1 ; σ -semipermutable in G if A is σ -semipermutable in G with respect to some complete Hall σ -set of G. We say that a subgroup H of G is weakly σ -semipermutable in G if there exists a σ -permutable subgroup T of G such that HT is σ -permutable in G and H ∩ T ≤ H σ s G , where H σ s G is the subgroup of H generated by all those subgroups of H which are σ -semipermutable in G. In this paper, we study the structure of G under the condition that some subgroups of G are weakly σ -semipermutable in G. [ABSTRACT FROM AUTHOR]

Heavy metals present in surroundings tend to accumulate into the bodies of animals vide the food chains. This bioaccumulation of toxic heavy metals cause several pathological conditions, thus, imposing serious health hazards to humans and other animals. It has become extremely important to monitor levels of heavy metals for well being of humans. The present study was carried to evaluate the extent of bioaccumulation of two heavy metals in rabbit by measuring their levels in various tissues. The rabbits were divided into control (C) and two experimental groups i.e. T1 (Lead treated) and T2 (Arsenic treated). Experimental groups were orally administered lead and arsenic at concentration of 0.02 mg/L of glucose solution for a period of 28 days. Further, the concentration of above heavy metals was determined in liver, kidney and muscle using atomic absorption spectrometry. Concentration of lead in liver and kidney, while concentration of arsenic in kidney was found to be significantly higher (P≤0.05) in treatment groups as compared to the control. Higher mean concentration of lead (35.68±7.36) and arsenic (18.70±3.456) was detected in kidney in treatment groups. Lower mean concentration of lead (12.43±4.70) and arsenic (7.07±2.45) was determined in muscles in treatment groups. The lead accumulated at significantly higher rate (P<0.05) compared to arsenic in all three tissues in treatment groups. It is concluded that heavy metals tend to bioaccumulate at relatively higher concentration in tissues involved in metabolic activities i.e. kidney and liver in rabbit. [ABSTRACT FROM AUTHOR]

Objective: To assess the bacterial profile for catheter associated UTI and the antimicrobial sensitive to most commonly used antibiotics in Sheikh Zayed Hospital, Rahim Yar Khan. Study Design: Cross-sectional study. Setting: Urology Ward of Sheikh Zayed Medical College/Hospital Rahim Yar Khan. Period: 01 February, 2021 to 31 July, 2021. Material & Methods: The urine samples were collected from 100 patients. Clinical and microbiological data were analyzed from patients with CAUTI. Results: A total of 100 patients were included in the study in which 82 samples were positive for bacterial growth and 18 samples did not show growth. Out of 82 strains, E. coli (46) was the most frequently isolated pathogen followed by Klebsiella pneumonia (17), Pseudomonas (10), Proteus (4), Candida (3) and Staphylococci (2). Conclusion: Study concluded that most common cause of UTI was E. coli in catheter associated patients while the meropenem and colistin were the most sensitive drugs of choice. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model's depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google's AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment. [ABSTRACT FROM AUTHOR]

Infant formula is an instant replacer to breastfeeding; it delivers a substantial amount of energy and nutrients for the infants growth and other metabolic activities. In recent years infant formula is extensively exploited throughout the globe where the provision of mother milk is insufficient, and such formulae are proven to be associated with some short and long-term neonatal health issues. To cope with these challenges, nutritionists and researchers are using different methods and approaches to formulate a risk-free infant formula. In this review, we summarized the risks related to infant formula and current methods and knowledge on the formulation of a risk-free formula, especially the techniques and incorporation of those components that can reduce neonatal health problems. This manuscript will help disseminate current knowledge to food scientists and nutritionists and help design a risk-free formula. [ABSTRACT FROM AUTHOR]

Simple Summary: The GRAS gene family plays a critical role in regulation of growth, defense, light and hormone responses. We identified 45 GRAS genes in the pitaya genome and categorized them into nine respective subfamilies: PAT1, SHR, LISCL, HAM, SCR, RGL, LAS, DELLA and SCL-3. Among these 45 HuGRAS family members, we reported nine candidate genes that played key roles in the growth and development of the pitaya plant. The GRAS gene family is one of the most important families of transcriptional factors that have diverse functions in plant growth and developmental processes including axillary meristem patterning, signal-transduction, cell maintenance, phytohormone and light signaling. Despite their importance, the function of GRAS genes in pitaya fruit (Selenicereus undatus L.) remains unknown. Here, 45 members of the HuGRAS gene family were identified in the pitaya genome, which was distributed on 11 chromosomes. All 45 members of HuGRAS were grouped into nine subfamilies using phylogenetic analysis with six other species: maize, rice, soybeans, tomatoes, Medicago truncatula and Arabidopsis. Among the 45 genes, 12 genes were selected from RNA-Seq data due to their higher expression in different plant tissues of pitaya. In order to verify the RNA-Seq data, these 12 HuGRAS genes were subjected for qRT-PCR validation. Nine HuGRAS genes exhibited higher relative expression in different tissues of the plant. These nine genes which were categorized into six subfamilies inlcuding DELLA (HuGRAS-1), SCL-3 (HuGRAS-7), PAT1 (HuGRAS-34, HuGRAS-35, HuGRAS-41), HAM (HuGRAS-37), SCR (HuGRAS-12) and LISCL (HuGRAS-18, HuGRAS-25) might regulate growth and development in the pitaya plant. The results of the present study provide valuable information to improve tropical pitaya through a molecular and conventional breeding program. [ABSTRACT FROM AUTHOR]

Using biometric modalities for person recognition is crucial to guard against impostor attacks. Commonly used biometric modalities, such as fingerprint scanners and facial recognition, are effective but can easily be tampered with and deceived. These drawbacks have recently motivated the use of electroencephalography (EEG) as a biometric modality for developing a recognition system with a high level of security. The majority of existing EEG-based recognition methods leverage EEG signals measured either from many channels or over a long temporal window. Both set limits on their usability as part of real-life security systems. Moreover, nearly all available methods use hand-engineered techniques and do not generalize well to unknown data. The few EEG-based recognition methods based on deep learning suffer from an overfitting problem, and a large number of model parameters must be learned from only a small amount of available EEG data. Leveraging recent developments in deep learning, this study addresses these issues and introduces a lightweight convolutional neural network (CNN) model consisting of a small number of learnable parameters that enable the training and evaluation of the CNN model on a small amount of available EEG data. We present a robust and efficient EEG-based recognition system using this CNN model. The system was validated on a public domain benchmark dataset and achieved a rank-1 identification result of 99% and an equal error rate of authentication performance of 0.187%. The system requires only two EEG channels and a signal measured over a short temporal window of 5 s. Consequently, this method can be used in real-life settings to identify or authenticate biometric security systems. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis has long been treated with acetylsalicylic acid, despite many side effects, including gastric ulcers. These side effects can be curtailed by preparing the to metal complexes of acetylsalicylic acid acid, such as copper (II)-acetylsalicylate (CAS). Present study evaluates the pharmacokinetics parameters of CAS and the level of copper at extended dose levels using rabbit model. The concentrations of CAS and copper in plasma samples were determined by validated HPLC and atomic absorption spectroscopic (AAS) methods, respectively. Three doses, 1-3 mg kg-1 were orally administered to six rabbits with two wash out periods. The blood samples were collected at different time intervals for 24 hours. The peak drug concentration (Cmax) for these doses at a time to peak drug concentration (tmax) 0.5 h was determined to be 0.38, 0.76 and 1.14 µg mL-1. The half-life of drug (t1/2) was 8.67, 8.73 and 8.81 h, which are perfect results for once a day dosing. The values of volume of distribution (Vd) and clearance (Cl) for CAS were 829, 833 and 837 L kg-1 and 66.30, 66.74 and 66.95 L h-1, respectively. The AAS results showed that copper levels in rabbit blood plasma were increased with increasing the dosage of CAS, but still remains under the safer limit, which was twofold higher than the reported safe limit. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technologies are becoming increasingly popular. However, studies regarding the usability of AR interfaces are scarce. This study aims to derive the optimal usability design of the AR interface by analyzing the performance of 12 interface conditions (2 button sizes × 3 distances × 2 interaction types). An experiment was conducted using Microsoft HoloLens, in which participants performed button selection tasks with an array of 3 × 3 virtual buttons. The task completion time and the number of errors were measured along with a subjective satisfaction score. The results showed that a clicker-based interaction was more effective in task completion time, the number of errors and user satisfaction than a hand gesture. Furthermore, large buttons required a significantly shorter task completion time than small buttons. There was no significant difference in the number of errors between the two sizes. A distance of at least 80 cm from the eye to the virtual object is most favorable for a good performance. There was a significant difference in the task completion times between 40 cm and 80 cm and between 40 cm and 120 cm. These results can aid the interface designs for AR applications and devices. [ABSTRACT FROM AUTHOR]

To preserve the Hadith of the Holy Prophet (SAW) on the most strict possible standards a variety of branches of Hadith Science were developed. And the compilers and editors of the Hadith have remarkably provided Hadith literature. Imām Abu dāwoood was of those compilers who compiled the famous six books of Hadith that are called Sihah Sittah(The Six Books of Hadith). His book Al-Sunan has gained great acceptability in the Muslim world for containing the authentic Hadith. The distinction of Imām Abu dāwoood is that he explained the terms he used in his book in a separate short book named: Risālah Abī dāwoood Ilā Ahli Makkah (A Magazine of Abi Dawood to people of Makkah). This article presents explanations of the terms which Imam has mentioned in this magazine and their application to his Al-Sunan. In this regard, plenty of examples have been added to the script which helps in understanding the application of these terms. The writers have concluded that this magazine needs to be taught along with Al-Sunan and that no discrepancy is reported while applying the terms of Risālah to Al-Sunan. [ABSTRACT FROM AUTHOR]

The Karakoram Highway (KKH) is a pivotal gateway within the framework of the China-Pakistan Economic Corridor. Nevertheless, its distinct and intricate geographical characteristics make it susceptible to recurrent landslides. As an essential tool, landslide susceptibility mapping (LSM) is significant in managing and alleviating landslides near the KKH. Moreover, landslide conditioning factors (LCFs) are crucial determinants influencing the outcomes of LSM. However, existing methods primarily rely on machine learning algorithms, which do not adequately account for the intricate spatial characteristics and patterns between LCFs and landslide occurrences. In response, this study introduces the feature fusion transformer (FFTR) module, constructed based on the foundations of the transformer framework, to fuse the spatial information features of all LCFs. Subsequently, the abstract high-level spatial features obtained are fed into diverse machine learning classifiers, including random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision trees (GBDT), categorical boosting (CatBoost), and extremely randomized trees (ET), to generate landslide susceptibility maps. Displacement velocity calculated by Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) is incorporated in the LSM. The results demonstrate that FFTR-RF achieves premium performance in the area under the curve (AUC) (94%), accuracy (87.31%), precision (87.21%), recall (88.02%), and F1-score (87.61%). Incorporating displacement velocity into LSM results predicted by models enhances the comprehensiveness of LSM. These methods will furnish early warning systems for landslide disasters along the KKH, thus aiding recommendations for mitigating landslides' social and economic losses. [ABSTRACT FROM AUTHOR]

Orange peel waste (OPW), a discarded part of orange fruit, is a rich source of essential constituents that can be transformed into highly value-added bioproducts. OPW is being generated in million tonnes globally and returns to the environment without complete benefit. Thus, a high volume of annually produced OPW in the industry requires effective valorization. In this regard, limited data is available that summarizes the broader spectrum for the sustainable fate of OPW to produce value-added bioproducts. The main objective of this treatise is to explore the sustainable production of bioproducts from OPW. Therefore, this review covers all the aspects of OPW, from its production to complete valorization. The review encompasses the extraction technologies employed for extracting different valuable bioactive compounds, such as: essential oil (EO), pectin, and carotenoids, from OPW. Furthermore, the suitability of bioconversion technologies (digestion/fermentation) in transforming OPW to other useful bioproducts, such as: biochemicals (lactic acid and succinic acid), biopolysaccharides (xanthan and curdlan gum), and bioenergy (biomethane and bioethanol) is discussed. Also, it includes the concept of OPW-based biorefineries and their development that shall play a definite role in future to cover demands for: food, chemicals, materials, fuels, power, and heat. Lastly, this review focuses on OPW-supplemented functional food products such as: beverages, yogurts, and extruded products. In conclusion, insights provided in this review maximize the potential of OPW for commercial purposes, leading to a safe, and waste-free environment. [ABSTRACT FROM AUTHOR]

Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care. [ABSTRACT FROM AUTHOR]