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In this study, we report the synthesis of iron oxide nanoparticles (FeONPs) using micro-emulsion-hydrothermal method. By adjusting the synthesis temperature, we successfully produced FeO nanorods and nanospheres. In addition, the 2-octanol, and the surfactant cetyltrimethylammonium bromide served as a solvent in the synthesis process. Using iron nitrate hexahydrate as the salt precursor allowed for the formation of FeONPs with varied sizes, shapes and phases. The synthesized materials were extensively characterized using XRD, SEM, TEM, EDS, FTIR, and XPS techniques. However, the structural analysis revealed rhombohedral (hematite), and (magnetite) crystal structures in the materials synthesized at different temperature and durations, with particles ranged in size from 12 to 97 nm. More importantly, the magnetic characterization, performed with a vibrating sample magnetometer and SQUID magnetometer, indicated that the NPs showed not clear superparamagnetic behavior. In conclusion, this work demonstrates the synthesis of FeONPs with controlled size, shape and phase using microemulsion hydrothermal technique, with detailed characterization offering valuable insights into their magnetic and structural properties. [ABSTRACT FROM AUTHOR]

Cold tumors lack antitumor immunity and are resistant to therapy, representing a major challenge in cancer medicine. Because of the immunosuppressive spirit of the tumor microenvironment (TME), this form of tumor has a low response to immunotherapy, radiotherapy, and also chemotherapy. Cold tumors have low infiltration of immune cells and a high expression of co-inhibitory molecules, such as immune checkpoints and immunosuppressive molecules. Therefore, targeting TME and remodeling immunity in cold tumors can improve the chance of tumor repression after therapy. However, tumor stroma prevents the infiltration of inflammatory cells and hinders the penetration of diverse molecules and drugs. Nanoparticles are an intriguing tool for the delivery of immune modulatory agents and shifting cold to hot tumors. In this review article, we discuss the mechanisms underlying the ability of nanoparticles loaded with different drugs and products to modulate TME and enhance immune cell infiltration. We also focus on newest progresses in the design and development of nanoparticle-based strategies for changing cold to hot tumors. These include the use of nanoparticles for targeted delivery of immunomodulatory agents, such as cytokines, small molecules, and checkpoint inhibitors, and for co-delivery of chemotherapy drugs and immunomodulatory agents. Furthermore, we discuss the potential of nanoparticles for enhancing the efficacy of cancer vaccines and cell therapy for overcoming resistance to treatment. [ABSTRACT FROM AUTHOR]

The high incidence and mortality rates of colorectal cancer (CRC) in Alabama African Americans (AAs) and Oklahoma American Indians (AIs) are recognized as cancer disparities, yet the underlying causes have been poorly demonstrated. By evaluating CRC whole-exome sequencing and mutational profiles, here we report sets of mutated genes whose frequencies differed significantly (p < 0.05) in a race-specific manner. Secondary screening with cancer database identified "survival-critical genes (SCGs)" (i.e., genes whose mutations/alterations are associated with significant differences in the patients' survival rates) among the differentially mutated genes. Notable SCGs with race-pronounced variants were different from DEGs and their involved pathways included nucleotide catabolism and cell cycle checkpoints for AAs, and extracellular matrix organization for AIs. The inclusion of these SCGs with race-pronounced variants in the clinical CRC next-generation sequencing panels and the development of targeting drugs will serve as refinements for precision medicine to overcome racial disparities in health outcomes of CRC. [ABSTRACT FROM AUTHOR]

Invasive weeds are responsible for one-third of the world's crop losses, weeds are a constant nuisance that drastically reduces agricultural productivity and quality. India, like many other Asian countries, is dealing with aggressive weeds, however these weeds are also a good source of antimicrobial and antioxidant phytochemicals. Antimicrobial and antioxidant phytochemicals have enormous potential to control illnesses in both plants and people. Both weeds Parthenium hysterophorus Linnaeus and Lantana camara Linnaeus have garnered significant attention due to their diverse chemical compositions and potential medicinal properties. Different modes of action have been used by phytochemicals to combat both susceptible and resistant diseases potentially. By utilizing weeds as raw material for plant-derived biocides, individuals could effectively manage diseases while simultaneously creating economic opportunities from these otherwise unwanted plants This covers the existing research on the phytochemical constituents and antimicrobial activity of P. hysterophorus and L. camara against various pathogens. [ABSTRACT FROM AUTHOR]

Purpose of Review: Despite growing evidence supporting the diagnostic utility of coronary computed tomographic angiography (CCTA) for anatomical assessment of coronary artery disease (CAD), its is underutilized in peri-procedural planning especially in the acute setting. Recent Findings: Incorporation of flow reserve measurement techniques into CCTA has expanded its sensitivity and specificity for obstructive disease, and continued improvement in CCTA technology permits more accurate cross-sectional plaque characterization. Summary: CCTA has the potential to constitute the mainstay of pre-procedural planning for patients with CAD, who are being considered for percutaneous coronary intervention , reducing their ad hoc nature while facilitating equipment selection and improving catheterization lab safety and throughput. Future studies are needed to compare the cost and benefits of more frequent use of routine pre-procedural CCTA prior to coronary angiography and intervention. [ABSTRACT FROM AUTHOR]

First principles investigations have been carried out for half-Heusler alloys MnCrX (X = P, As, Sb). The electronic band structures and density of states are computed for under study alloys. Reststrahlen bands are explained through the phonon dispersion curve calculated using Quantum ESPRESSO. The positive phonon frequencies in full zone ascertain the dynamic stability of all these under-study alloys. MnCrP exhibits a larger value of the Reststrahlen band than the other two and is an important candidate in Far Infra-Red spectroscopy applications. Moreover, the thermoelectric coefficients as a function of temperature have been studied using the Boltzmann transport equation. Half Heusler alloys have not yet explored for these properties. The power factor of MnCrP is the highest amongst these studied materials. [ABSTRACT FROM AUTHOR]

The lanthanum doped cobalt ferrite (La+3-CoFe2O4) incorporated in MXene sheets bifunctional electrocatalyst was prepared and then subjected to various analyses to assess their structural, morphological, and functional group characteristics. X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR) were employed for this purpose. The electrochemical performance of La-CoFe2O4@MXene was investigated in an alkaline solution to assess the bifunctional (OER/HER) performances. The results revealed that La-CoFe2O4@MXene exhibited significantly lower overpotential and a lesser Tafel slope during OER compared to both CoFe2O4 and La-CoFe2O4 materials. Similarly, during HER, La-CoFe2O4@MXene demonstrated superior performance. The electrochemical impedance (EIS) analysis was also conducted on all samples. These results indicated that the La-CoFe2O4@MXene electrocatalyst displayed reduced charge transfer resistance (2.18 Ω) and a higher exchange current density (2.94 mA cm−2) compared to its counterparts. These results collectively demonstrate the exceptional electrocatalytic behavior of La-CoFe2O4@MXene. This enhanced performance is likely attributable to the synergistic effect of lanthanum doping, which introduces defects into the material, and the presence of MXene sheets, which facilitates faster charge transfer within the electrocatalyst. To the best of our knowledge, this is the first study to explore La-CoFe₂O₄@MXene for water splitting. Our results demonstrate that this material requires significantly lower overpotential for OER and HER in an alkaline medium, showcasing its potential as an efficient and cost-effective catalyst for water splitting. Highlights: MXene-based La+3-doped-CoFe2O4 electrocatalyst has been prepared for bifunctional electrocatalytic activity. The coprecipitation and ultrasonication methods were adopted for the synthesis of bifunctional electrocatalysts, which were responsible for enhanced electrochemical active surface area and superior OER/HER performance. The La-CoFe2O4@MXene exhibited an OER onset potential of 1.47 V versus RHE, while for HER it is 0.26 V versus RHE. The bifunctional material exhibited a 277 mV overpotential to attain 10 mA cm−2 current density during OER and a 285 mV overpotential to reach 100 mA cm−2 current density during HER performance. The excellent bifunctional performance is attributed to the presence of MXene sheets, which facilitates higher conductivity, a greater number of active sites, and superior charge transfer kinetics for the water splitting reactions. [ABSTRACT FROM AUTHOR]

Potholes are a significant hazard, causing severe damage to vehicles and potentially leading to fatal accidents. The automatic detection of potholes is crucial for timely maintenance and reducing these risks. This research evaluates the performance of three pre-trained Convolutional Neural Network models—ResNet 50, ResNet 18, and MobileNet—in classifying pavement images. Initially, the models distinguish between images containing potholes and those without (Potholes vs. Normal). Subsequently, they categorize pavement images into three classes: Small Pothole, Large Pothole, and Normal. Pavement images were captured from two different heights: 3.5 feet (waist height) and 2 feet. Among the models, MobileNet v2 demonstrated the highest accuracy of 98% for detecting potholes. For images taken at 2 feet, the classification accuracies for large potholes, small potholes, and normal pavement were 87.33%, 88.67%, and 92%, respectively. For images taken from waist height, the accuracies increased to 98.67%, 98.67%, and 100%, respectively. The study highlights the potential of these models in enhancing road safety through reliable and automated pothole detection, providing valuable insights for infrastructure maintenance. Article Highlights: Potholes are dangerous and can cause serious vehicle damage. Train CNN models for the classification of potholes These CNNs, for different sizes of potholes can be used for the rehabilitation of potholes. Among all CNN modes, MobileNet v2 achieves 98% accuracy in detecting the presence of a pothole. [ABSTRACT FROM AUTHOR]

Environmental pollution and climatic change impel economies to discover new methods for sustainable economic development. Green growth is considered an effective way of environmental sustainability. From this perspective, this study evaluates the impact of fiscal policy shocks, financial institutions, and markets development on the green growth of high-polluting economies. The study employed a CS-NARDL approach for investigating the short-run and long-run estimates. The results show that positive shocks in government spending tend to significantly increase green growth in the long-run. In contrast, the negative shock is government spending does not report any significant influence on green growth in the long-run. Financial institution's and financial market's efficiency have a positive impact on green growth in the long-run. Thus, the findings of the study suggest that government spending should be increased with a particular focus on financial markets efficiency and financial institution's efficiency to enhance green growth. [ABSTRACT FROM AUTHOR]

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions. [ABSTRACT FROM AUTHOR]

Purpose of Review: Acute pulmonary embolism (PE) is a leading cause of cardiovascular death and morbidity, and presents a major burden to healthcare systems. The field has seen rapid growth with development of innovative clot reduction technologies, as well as ongoing multicenter trials that may completely revolutionize care of PE patients. However, current paucity of robust clinical trials and guidelines often leave individual physicians managing patients with acute PE in a dilemma. Recent Findings: The pulmonary embolism response team (PERT) was developed as a platform to rapidly engage multiple specialists to deliver evidence-based, organized and efficient care and help address some of the gaps in knowledge. Several centers investigating outcomes following implementation of PERT have demonstrated shorter hospital and intensive-care unit stays, lower use of inferior vena cava filters, and in some instances improved mortality. Summary: Since the advent of PERT, early findings demonstrate promise with improved outcomes after implementation of PERT. Incorporation of artificial intelligence (AI) into PERT has also shown promise with more streamlined care and reducing response times. Further clinical trials are needed to examine the impact of PERT model on care delivery and clinical outcomes. [ABSTRACT FROM AUTHOR]

In today's interconnected world, the line that separates the network perimeter can no longer be identified. This has led to the development of Zero Trust Networking (ZTN) and Software Defined Perimeter (SDP) concepts, which aim to extend the perimeter of trust to every entity connected to the network regardless of their physical location. However, implementing complex security mechanisms and constant trust assurance for every interaction can be challenging. One solution is integrating blockchain technology into Zero Trust to provide security. Blockchain offers features such as data decentralization, anonymity, cryptography, and immutable record of transactions that can be utilized. This work proposes a mechanism for secure service session management using blockchain capabilities. Non-fungible tokens (NFT) are applied to access and provider tokens representing a policy agreement for service consumption. These tokens are mapped to the public addresses of entities registered in the blockchain. The proposal is realized through an open-source Zero Trust platform and a private Ethereum blockchain. [ABSTRACT FROM AUTHOR]

As a developing nation, Pakistan faces financial obstacles in road construction, which is essential for economic growth but costly. A sustainable approach to address environmental concerns involves using recycled materials in road construction. The construction industry has largely overlooked recycling despite the significant amount of debris generated during construction and demolition. Repurposing materials from building and demolition activities, such as rubber from tires and drywall waste, can serve as viable materials for subbase layers in roads. These recycled materials can improve road quality and performance while mitigating environmental and health risks associated with stockpiling and disposal. This research evaluates the physical and mechanical properties of recycled concrete aggregates (RCA) combined with drywall waste (DW), waste tire rubber (WTR), and quarry dust (QD) for flexible pavement subbases. The study investigated the effect of adding 0.5%, 1%, 1.5%, and 2% crumb rubber with 5%, 10%, 15%, and 20% DW as a percentage of fine aggregate with QD in RCA. By subjecting RCA to a series of tests, their strength, durability, and stability are thoroughly assessed to determine the optimal ratios of WCR and DW. As a result of these evaluations, a notable improvement in compressive strength is observed. Cost analysis demonstrates the economic feasibility of using recycled materials in pavement subbase construction, offering a cost-effective, sustainable, and environmentally safe solution that reduces reliance on fresh aggregates (FA). [ABSTRACT FROM AUTHOR]

The elevated occurrence rate of wind shear (WS) events near airport runways presents one of the major hazards to the safe and efficient operation of landing and takeoff procedures. As a consequence of this, aircraft are more likely to experience the possibility of losing control or encountering hindrances. Hence, it is crucial to assess the factors influencing wind shear occurrence. Previous studies extensively reported the susceptibility of the runways at Hong Kong International Airport (HKIA) to significant wind shear events. Therefore, in order to estimate WS magnitude near runways at HKIA and assess various contributing factors, this study presents a novel Local Cascade Ensemble (LCE) model with its hyperparameters optimized via a Tree-Structured Parzen Estimator (TPE) to estimate the wind shear magnitude. The pilot report data obtained from HKIA between 2017 and 2021 was employed for the training and evaluation of the TPE-tuned LCE model. The outcomes of the TPE-tuned LCE model were also compared to those of other contemporary machine learning (ML) models. The findings indicated that the TPE-tuned LCE model exhibited better predictive performance in comparison to other models, as assessed by a mean absolute error (MAE) of 4.38 knots, a mean squared error (MSE) of 70.28 knots, a root mean squared error (RMSE) of 8.38 knots, and coefficient of determination (R2) value of 0.79. Subsequently, model interpretation via SHapley Additive exPlanations (SHAP) technique was performed on the outcomes of TPE-tuned LCE. It indicated that that certain runways at HKIA, such as runway 07 C, 07 L, 25 C, and 25R, had a higher likelihood of experiencing elevated wind shear conditions within 1000 ft above the runway level. [ABSTRACT FROM AUTHOR]

The dynamic nature of coronavirus-19 (Covid-19) has caused a wreaked havoc globally, with millions of confirmed cases and deaths. Therefore, it is important to understand the psychological impact of the Covid-19 on the patients. In the present study, we examine whether intolerance of uncertainty was related to the severity of symptoms and whether this relationship is mediated by perception of illness and covid-19 fear. The study sample comprised of 98 Covid-19 patients (Mean = 35.17 SD = 12.89). Mediation analysis was conducted using the PROCESS macro for SPSS. Results of mediation analysis showed that the direct effect of intolerance of uncertainty on symptom severity was insignificant. However, the indirect effect via illness perception was significant, reflecting full mediation. The findings add knowledge to our understanding of the psychological consequences of Covid-19. The present study has implications for mental health services for patients with Covid-19, which will play a vital role in recovery from the illness. [ABSTRACT FROM AUTHOR]

Accumulating evidence has implicated impaired extracellular matrix (ECM) clearance as a key factor in fibrotic disease. Despite decades of research elucidating the effectors of ECM clearance, relatively little is understood regarding the upstream regulation of this process. Collagen is the most abundant constituent of normal and fibrotic ECM in mammalian tissues. Its catabolism occurs through extracellular proteolysis and cell-mediated uptake of collagen fragments for intracellular degradation. Given the paucity of information regarding the regulation of this latter process, here we execute unbiased genome-wide screens to understand the molecular underpinnings of cell-mediated collagen clearance. Using this approach, we discover a mechanism through which collagen biosynthesis is sensed by cells internally and directly regulates clearance of extracellular collagen. The sensing mechanism appears to be dependent on endoplasmic reticulum-resident protein SEL1L and occurs via a noncanonical function of this protein. This pathway functions as a homeostatic negative feedback loop that limits collagen accumulation in tissues. In human fibrotic lung disease, the induction of this collagen clearance pathway by collagen synthesis is impaired, thereby contributing to the pathological accumulation of collagen in lung tissue. Thus, we describe cell-autonomous, rheostatic collagen clearance as an important pathway of tissue homeostasis. Mechanisms regulating collagen clearance may be useful for treating fibrosis. Here, the authors conducted functional genome-wide screens and found that collagen biosynthesis directly regulates collagen clearance via a noncanonical function of SEL1L. [ABSTRACT FROM AUTHOR]

Zinc-solubilizing bacteria (ZSB) can convert insoluble zinc to an accessible form and increase Zn bioavailability in soil, which helps mitigate Zn deficiency in crops. In this study, different bacterial strains were screened for different Zn solubilization and plant growth promotion traits. Two bacterial strains, Acinetobacter pittii DJ55 and Stenotrophomonas maltophilia DJ24, were tested for their Zn-solubilizing potential on plate media, and both showed variable levels of Zn solubilization. The results showed that the bacterial strains applied to the plants in the pot experiment caused improvements in growth parameters compared to control conditions. DJ55, when applied with an insoluble source, enhanced plant height, leaf number, and leaf area compared to DJ24 and control conditions, while the maximum fruit weight was noticed in plants treated with ZnSO4. An increase in chlorophyll contents was noted in plants treated with ZnSO4, while maximum carotenoid contents were observed in plants treated with DJ55 + ZnO when compared with their controls. Plants supplemented with ZnO and DJ55 showed higher zinc content and iron content as compared to their respective controls. The expression patterns of the SLZIP5 and SLZIP4 genes were changed in the root and shoot. Application of ZnO stimulates both gene expression and protein synthesis in tomato roots and shoots. Inoculation of tomato plants with ZSB and insoluble ZnO reduced the expression of the SLZIP5 and SLZIP4 genes in the root and shoot. In conclusion, both strains can be considered as potential zinc-solubilizing bioinoculants to promote the growth and production yield of tomato. [ABSTRACT FROM AUTHOR]

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from −59 to 17 mm/yr. The results show that subsidence is −28.48 mm/yr in 2018, the subsidence reached −49.02 mm/yr in 2019, while in 2020, the subsidence reached −49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence. [ABSTRACT FROM AUTHOR]

The elimination of hazardous industrial pollutants from aqueous solutions is an emerging area of scientific research and a worldwide problem. An efficient catalyst, Ag–CuO was synthesized for the degradation of methylene blue, the chemical sensing of ammonia. A simple novel synthetic method was reported in which new plant material Capparis decidua was used for the reduction and stabilization of the synthesized nanocatalyst. A Varying amount of Ag was doped into CuO to optimize the best catalyst that met the required objectives. Through this, the Ag–CuO nanocomposite was characterized by XRD, SEM, HR-TEM, EDX, and FTIR techniques. The mechanism of increased catalytic activity with Ag doping involves the formation of charge sink and suppression of drop back probability of charge from conduction to valance band. Herein, 2.7 mol % Ag–CuO exhibited better catalytic activities and it was used through subsequent catalytic experiments. The experimental conditions such as pH, catalyst dose, analyte initial concentration, and contact time were optimized. The as-synthesized nanocomposite demonstrates an excellent degradation efficacy of MB which is 97% at pH 9. More interestingly, the as-synthesized catalyst was successfully applied for the chemical sensing of ammonia even at very low concentrations. The lower limit of detection (LLOD) also called analytic sensitivity was calculated for ammonia sensing and found to be 1.37 ppm. [ABSTRACT FROM AUTHOR]

Biomolecules such as L-aspartic acid can be well-reducing and capping agents to produce metal nanoparticles with unique bioapplications. Silver nanoparticles (AgNPs) were synthesized using L-aspartic acid and evaluated as antibacterial and anticancer agents. The L-aspartic acid-AgNPs were characterized by utilizing spectroscopic instruments. Ultraviolet–visible (UV–Vis) spectroscopy gave peaks at 416 nm. Also, based on Fourier transform infrared spectroscopy (FTIR) analyses, it was determined that there are different functional groups of L-aspartic acid, namely, amino groups (-NH2), carboxylic acids (-COOH), and R-groups (side chains), that are responsible for bioreduction of Ag salt and the production of AgNPs. According to transmission electron microscopy (TEM), AgNPs are almost spherical in shape and have a nanoscale size of 11.3 nm. X-ray diffraction (XRD) study revealed a distinctive diffraction peak indicating crystalline nanoparticle formation, which agrees with AgNPs' spherical structure. Field emission scanning electron microscopy (FE-SEM) was carried out with the energy-dispersive X-ray spectroscopy (EDX) analysis to determine the elemental composition of biosynthesized AgNPs, which included silver, oxygen, and carbon. Dynamic light scattering (DLS) was designated with a hydrodynamic radius of 109 nm, polydispersity (PdI) is good at 0.266, and a Z-potential of − 14.5 mV indicates good stability. AgNPs showed antibacterial activity against gram-positive (Staphylococcus aureus) and gram-negative (Escherichia coli). AgNPs have cytotoxicity 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay against breast cancer MCF-7 cell line, half-maximal inhibitory concentration (IC50) of 1159.6 µg/mL, and genotoxicity by comet assay, emphasizing apoptosis cells through cell cycle flow cytometry. Biogenic nano-formulations of L-aspartic acid-AgNPs possess antibacterial and anticancer therapeutic applications because they are safe, cost-effective, and scalable. [ABSTRACT FROM AUTHOR]

The opto-electronic properties of Ru 2 FeZ (Z = Si , Ge , Sn) full Heusler alloys are explored by using first-principles calculations. The wien2k package with Full potential-linearized augmented plane wave (FP + LAPW) method is used and the exchange–correlation functional is dealt within the framework of Generalized Gradient Approximation amended by Perdew–Burke–Ernzerhof. Electronic band structure and density of states in both spin channels show the overlapping of some valence and conduction bands, which confirms their metallic nature. Optical properties over a wide range of incident photon energy revealed their effectiveness in photo sensors. [ABSTRACT FROM AUTHOR]

The go-around is a safety-critical procedure in civil aviation that is rarely executed but is essential to avoid risky landings. Analyzing the factors that trigger go-around events can aid in identifying measures that could lower their frequency. This involves circumstances that could be deemed abnormal and intrinsically harmful. The study employed the Explainable Boosting Machine (EBM), a contemporary transparent model, to predict aircraft go-arounds and interpret different influential factors. The model proposed exhibits comparable accuracy to black-box models. The study utilized pilot reports and applied SMOTE-ENN to address the imbalance problem. The EBM model was trained with treated data in conjunction with Bayesian optimization. The EBM model's performance was evaluated using holdout data and compared to binary logistic regression and decision tree models, as well as black-box models such as adaptive boosting, random forest, and extreme gradient boosting. The EBM model exhibited superior performance compared to other models in terms of precision (83.15%), recall (79.77%), geometric mean (77.29%), and Matthews's correlation coefficient (0.453). The EBM algorithm enables the comprehensive interpretation of individual and pairwise factor interactions in predicting aircraft go-around outcomes from both global and local perspectives. This facilitates the assessment of the impact of different factors on go-around outcomes. [ABSTRACT FROM AUTHOR]

Diversity inventories are critical to creating accurate species range maps and estimating population sizes, which in turn lead to better informed landscape and wildlife management decisions. Metabarcoding has facilitated large-scale environmental diversity surveys. However, the use of a metabarcoding approach with bird feces to survey arthropod diversity is still relatively undeveloped. The aim of this study was to see if and how a metabarcoding approach with bird feces could contribute to a saproxylic Coleoptera survey of traditional insect traps. We compared two methods of surveying saproxylic Coleoptera diversity (metabarcoding birds feces and deploying traditional traps) over two elevations in a mountain system. The two methods caught different species and different levels of functional guild richness. The metabarcoding method successfully recorded both distinct and overlapping portions of diversity from traditional collections, and the approach was also effective in signaling the presence of both rare species and nine country records. Our results show that metabarcoding Passerine bird feces can be successful when used alongside traditional collection methods to capture a broad diversity of saproxylic Coleoptera. This method, however, has quantitative and qualitative limitations, including the inability to produce species abundance data as well as the generation of false positives and negatives due to biases within the metabarcoding pipeline. Implications for insect conservation: As many terrestrial ecosystems lose insect diversity, insect diversity surveys are essential to understand the scope of the loss. Despite metabarcoding approach shortcomings, the declining costs and shorter survey and processing time required for this approach compared to traditional survey methods indicate that it can be a valuable addition to the toolkit for saproxylic Coleoptera diversity surveys. [ABSTRACT FROM AUTHOR]

Monitoring a specific area to analyze a continuous change has become more accessible by using optical images in remote sensing technology. However, several natural and artificial aspects such as fog and air pollution make it difficult to extract correct geometric information. To overcome the limitation of optical images, Synthetic Aperture Radar (SAR) images can be used to access more accurate information with respect to the targeted area. In this manner, optical and SAR images can be utilized together to detect the scale of change even in bad weather conditions. To process optical and SAR images, an image translation process-oriented Deep Adaptation-based Change Detection Technique (DACDT) is proposed. An optimized U-Net++ model is proposed that helps to improve the global and regional impacts of the images. Moreover, a multi-scale loss function is utilized to access the features of different dimensions. In this manner, the final change maps are generated by transferring the features of optical images to the SAR images for better change analysis. The prediction performance of the proposed approach is evaluated on four different datasets such as Gloucester I, Shuguang Village, Gloucester-II, and California. The calculated outcomes define the prediction performance of the proposed solution by registering the accuracy of 98.67%, 99.77%, 97.68%, and 98.87%, respectively. [ABSTRACT FROM AUTHOR]

Aircraft landings are especially perilous when the wind is gusty near airport runways. For this reason, an aircraft may deviate from its glide slope, miss its approach, or even crash in the worst cases. In the study, we used the state-of-the-art glass-box model, the Explainable Boosting Machine (EBM), to estimate the variation in headwind speed and turbulence intensity along the airport runway glide slope and to interpret the various contributing factors. To begin, the wind field characteristics were examined by developing a scaled-down model of Hong Kong International Airport (HKIA) runway as well as and the surrounding buildings and complex terrain in the TJ-3 atmospheric boundary layer wind tunnel. The placement of probes along the glide slope of the model runway aided in the measurement of wind field characteristics at different locations in the presence and absence of surrounding buildings. Next, the experimental data was used to train the EBM model in conjunction with Bayesian optimization approach. The counterpart black box models (extreme gradient boosting, random forest, extra tree and adaptive boosting) as well as other glass box models (linear regression and decision tree) were compared with the outcomes of the EBM model. Based on the holdout testing data, the EBM model revealed superior performance for both variation in headwind speed and turbulence intensity in terms of mean absolute error, mean squared error, root mean squared error and R-square values. To further evaluate the impact of different factors on the wind field characteristics along the airport runway glide slope, the EBM model allows for a full interpretation of the contribution of individual and pairwise interactions of factors to the prediction results from both a global and a local perspective. [ABSTRACT FROM AUTHOR]

High-elevation insectivorous birds are currently confronted with the reality of a changing climate, land use shifts, and the decline of many prey groups. The diet dynamics among many imperiled animals in this group are still unresolved. Examining the diets of tree-line Passerine birds to the species level of the prey allows for stronger population predictions. This study uses DNA metabarcoding to identify prey arthropods from adult Passerine bird feces at and slightly below tree-line in a Pyrenean forest. Our objective was to quantify the intra-and inter-species richness and overlap of Passerine bird diet over time and space. The results showed that adult Passerine diets have high inter- and intra-species dietary variability and low inter- and intra-species dietary overlap. The lack of association between dietary richness and open space, season, and elevation and lack of differences between dietary overlap and open space and elevation suggest high-elevation Passerine birds have very high dietary flexibility. The results also showed that aphids known to be pests to conifers, and other conifer pests, were prevalent in the birds' diets. The Passerine diets and high rate of rare dietary items are mainly in line with other recent DNA metabarcoding studies. Implications for the long-term projections relative to tree-line Passerine populations are discussed. [ABSTRACT FROM AUTHOR]

Glass, carbon, and hybrid/glass/carbon-reinforced polymer composites are the most important materials used in defense, aerospace, automobile, and sports industry because of their excellent mechanical properties. End-milling process is the final and important machining process of composites to achieve the desired shape and dimensions. The main concern of this research is to minimize delamination and surface roughness in carbon/glass/epoxy hybrid composites after end milling. The effects of tool material, spindle speed, feed rate, and cut depth on delamination and surface quality were investigated. Taguchi design of experiment method was used to vary the machining parameters. Surface quality was examined by a surface roughness tester, and the delamination of milled surfaces was inspected by non-destructive technique using ultrasonic testing. The surface morphology of milled surfaces was studied by scanning electron microscopy. Results were verified by signal-to-noise ratio and analysis of variance technique. Carbon/glass/epoxy hybrid composite exhibited better surface finish with diamond flute end mill cutter, spindle speed of 1500 rpm, feed rate of 0.15 mm/min, and cut depth of 0.05 mm. After nondestructive testing, minimum delamination in carbon/glass/epoxy hybrid composite was found using diamond flute end mill cutter at a spindle speed of 1500 rpm, feed rate of 0.10 mm/min, and cut depth of 0.10 mm. [ABSTRACT FROM AUTHOR]

The proposed paper provides a detailed, critical and comprehensive review of the widely used and recently developed global maximum power point tracking (GMPPT) algorithms for photovoltaic (PV) systems. For the ease of comparison, the algorithms are categorized into four major groups, (1) optimization algorithms, (2) hybrid techniques of two different optimization algorithms, (3) hybrid techniques of optimization algorithm with the conventional algorithm, and (4) other GMPPT algorithms. A comprehensive table is also presented at the end of the discussion of each category to evaluate the performance of GMPPT algorithms in the context of algorithm complexity, converter's type, tracking speed, efficiency, sensor types and quantities, and partial shading detection scheme. The analysis in the proposed paper will help the designers to select the appropriate GMPPT algorithm for their photovoltaic system. [ABSTRACT FROM AUTHOR]

As water is considered one of the essential assets of nature, the recognition of the availability of water at a specific location can help government bodies to take necessary action toward water conservation. Monitoring water from satellite images is considered one of the most difficult areas of pattern recognition. In this manner, a novel multi-level feature fusion approach is proposed to predict the pattern of water concerning a specific location to analyze the scale and availability. The proposed framework can access the spatial features from sentinel-2 images by utilizing the concept of structural learning. For evaluating the prediction performance, the calculated outcomes are compared with the traditional and modern pattern recognition approaches. It has been observed that the proposed approach is more robust in terms of pattern analysis as compared to the state-of-the-art approaches. Moreover, the performance of the proposed approach is evaluated on different training and testing ratios such as 70:30, 75:25, and 80:20. In this manner, the calculated outcomes define the pattern recognition efficiency of the proposed approach over the state-of-the-art approaches by achieving 94.51% of accuracy. [ABSTRACT FROM AUTHOR]

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. [ABSTRACT FROM AUTHOR]

Aggregate demand in every country is one of the main components of macroeconomics, and Pakistan is no exception. This paper uses a fresh econometric framework to analyze the nonlinear impact of aggregate demand drivers on CO2 emissions in Pakistan by using a nonlinear ARDL approach over the data period from 1975 to 2019. The linear findings reveal that consumption and government expenditure indicates deteriorating effects on carbon emissions in long run in Pakistan. However, nonlinear findings revealed that positive change in government expenditure and trade has positive effects on carbon emissions. The positive change in investment has a negative significant and meaningful effect on the environment in Pakistan and asymmetric findings are also country-specific. Therefore, this study offers a few important policy implications for theorists, academics, and policymakers of Pakistan as well developing economies. [ABSTRACT FROM AUTHOR]

Structural, electronic, and magnetic properties of MoFe2O4 have been investigated using DFT with the Perdew-Burke-Ernzerhof generalized-gradient approximation as the exchange-correlation functional. The properties of molybdenum ferrite were ascertained via self consistent field calculations using this optimized parameter as an input. The optimized lattice parameter closely matches the experimental value, with only a 1.6548% difference. The mechanical stability is proved by the elastic stability criteria for this material. This materials is further investigated for stability purpose from different thermodynamical parameters at high pressure and temperature range. The results show that molybdenum ferrite has a high magnetic moment of (12.00 \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mu _{B}$$\end{document}), and the magneto crystalline anisotropy energy calculations confirm its potential use in manufacturing of spintronics devices such as spin valve magnetic memory and sensors. The thermoelectric parameters like Seebeck coefficient, electrical conductivity, electronic part of the thermal conductivity and power factor against temperature and potential have also been investigated for energy applications. [ABSTRACT FROM AUTHOR]

In this paper, various thermal management designs are explored for robotic operation in extreme temperature environments, which includes options for component and chip level electronics heating/cooling like TIM’s, heat sinks, heat spreader, insulation material, and PCM’s. Furthermore, other active/passive thermal management systems are investigated, such as Heat pipes, Loop Heat Pipes (LHP), and Capillary Pump Loop (CPL) developed by NASA, Fuzzy Logic and PID temperature control methods. Some of the thermal management design challenges faced by the heat pipes, LHP, and CPL devices are decrease in the heating/cooling efficiency with abrupt drop in temperatures, costly equipment, high power consumption, and increase in the overall weight of the robot due to several components. With the limited space available inside the robot design, it requires the use of minimal hardware. A Fuzzy Logic temperature controller is considered for the mobile robot to overcome all the aforementioned thermal challenges. A Fuzzy Logic control system is designed and tested in a simulation environment using a flow simulation software; Ansys Fluent, to authenticate its ability to minimize power consumption by controlling the amount of power supplied to the fan/heater based on the current temperature reading from the sensor, a set-point temperature, and if–then based logical rule-base. The heating/cooling simulation setup consists of four heating units located at different locations inside the robot body and two miniature fans. Moreover, three outlets are included on the wall opposite to the fans for the cooling setup. The opening/closing of the outlets is contingent to the temperature inside the robot and will allow the excess heat to escape the robot's body. Based on the internal flow simulation results, the Fuzzy Logic controller setup was able to increase the extreme low temperature of – 40° C to the desired temperature of 8° C in a total time of 80 s and with a maximum power of 10 W for each heating unit and fans rotating at 1800 rpm for air circulation. However, the power output was minimized by the Fuzzy Logic controller as the internal temperature approached closer to the desired temperature. Moreover, an extreme high temperature of 50° C was decreased rapidly to 8° C in a total time of 12 s with the pressure outlet and inner wall temperatures lower than 15° C. However, in a situation where the initial temperature of the pressure outlet and inner wall are the same as the internal air temperature, the electronics can be cooled by the fans through the forced convection method. [ABSTRACT FROM AUTHOR]

Doping is among the fundamental approach to enhance the attributes of functional materials according to the desired application. In this work, we present physical attributes of novel lanthanum-based perovskite functional materials LaFe(1−x)CrxO3 for (x = 0, 0.25, 0.50, 0.75 and 1) by using state-of-the-art density functional theory. The detailed analysis includes structural, mechanical, optical, thermodynamic and electromagnetic properties. All studied compounds have shown thermodynamic stability and can be formed experimentally. The substitution of Cr in LFO is performed on the Fe site which has shown remarkable modifications in pristine LFO. The structural stability is justified by following the standard approach in calculating formation energies, tolerance factors, elastic stability criteria and optimizations. In electronic properties, large spin polarization effects with majority spin-up as metallic and minority spin-down as semiconducting configurations are observed by the Moss–Burstein effect. While magnetic properties suggest a fluctuation in the magnetic moment values in which the highest ferromagnetism is observed for Cr-50% doping. The suggested ferromagnetic nature makes the doped LFO suitable for application in a magnetic tunnel junction. In optical analysis, studied materials are highly transparent under incident light. We expect that these compounds are suitable functional materials for novel magnetic or spintronic devices and as transparent conducting materials. [ABSTRACT FROM AUTHOR]

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius rc, unconfined concrete strength fco, thickness nt, the elastic modulus of fiber Ef, the elastic modulus of mortar Em. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available. [ABSTRACT FROM AUTHOR]

Diarrhea is one of the most common infectious diseases that affect people of all ages and is a serious public health concern around the world. The main causes of diarrhea include food quality, water, indoor meteorological, and outdoor meteorological conditions. In this study, a dew computing-assisted smart monitoring framework is developed to evaluate the relationship among the health, indoor meteorological, and food factors of an individual to predict the cause of diarrhea with the scale of severity. Smart sensors are utilized at the physical layer to collect the targeted parameters of health, indoor meteorological, and food of the individual. The captured events are classified at the cyber layer by utilizing the Probabilistic Weighted-Naïve Bayes (PW-NB) classification approach for quantifying abnormal health events. Furthermore, a Multi-scale Gated Recurrent Unit (M-GRU) is suggested to obtain the scale of severity by analyzing the correlation between irregular health, food, and environmental events. In this manner, the proposed model M-GRU has achieved a high precision value of ( 93.26 % ), whereas, LSTM, RNN, SVM achieved the precision value of ( 89.13 % ), ( 90.43 % ), ( 88.23 % ), respectively. In addition, the precision value of the PW-NB is ( 97.15 % ), which is also higher as compared to KNN ( 93.25 % ) and DT ( 96.91 % ). The outcome of the proposed solutions is shown the higher Precision values on dew computing and cloud computing. Moreover, a comparative analysis defines the prediction effectiveness of the proposed solution over several other decision-making solutions with regards to event classification, severity determination, monitoring stability, and prediction efficiency. [ABSTRACT FROM AUTHOR]

The current study utilizes first principles calculations based on density functional theory to investigate structural, elastic, mechanical, electronic and optical properties of Lead free Thallium based halide perovskites TlGeX3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeX}_{3}}$$\end{document}  (X = Cl, Br, I) for their potential application in photovoltaic technology. The optimized lattice constants of TlGeCl3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeCl}}_{3}$$\end{document}, TlGeBr3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeBr}}_{3}$$\end{document} and TlGeI3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeI}}_{3}$$\end{document} are 5.272 Å, 5.527 Å  and 5.900 Å. These optimized lattice constants are used to compute the elastic constants, C11\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{C}}_{11}$$\end{document}, C12\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{C}}_{12}$$\end{document} and C44\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{C}}_{44}$$\end{document}. In addition to elastic moduli like shear, bulk, and Young’s modulus, we have calculated the different mechanical parameters, Poisson’s ratio, Pugh’s ratio and Cauchy pressure. These materials proved to be mechanically stable. The electronic properties including electronic band profile, and density of states were obtained using Perdew Burke Ernzerhof, GGA-PBE, Tran-Blaha modified Becke–Johnson, TB-mBJ, and meta GGA, Strongly Constrained and Appropriately Normed, SCAN exchange and correlation functionals. The electronic band gaps of TlGeCl3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeCl}}_{3}$$\end{document}, TlGeBr3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeBr}}_{3}$$\end{document} and TlGeI3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeI}}_{3}$$\end{document} are 1.41 eV, 1.01 eV and 0.74 eV using TB-mBJ and these perovskites have direct band gap. The optical response of TlGeX3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeX}}_{3}$$\end{document} (X = Cl, Br, I) against incident electromagnetic radiation upto 30 eV is calculated by exploring absorption coefficient, optical conductivity, dielectric constants, refraction and energy loss with variety of methods. The electronic and optical properties revealed that TlGeX3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{TlGeX}}_{3}$$\end{document} (X = Cl, Br, I) can be utilized in the optoelectronic device fabrications being a narrow band gap with high absorption and optical conductivity. [ABSTRACT FROM AUTHOR]

Previously, several studies have tried to analyze the various determinants of energy intensity for different countries and regions. However, none of the studies have focused on the asymmetric determinants of energy intensity in the context of China. Therefore, the primary aim of this study is to investigate the asymmetric determinants of energy intensity in China. We have gathered data for China over the period 1985–2019 and applied linear and non-linear ARDL models. In the linear model, only energy prices reduce the energy intensity in the long run. Moreover, the long-run effects of both energy prices and economic development are asymmetric; however, positive changes in both variables help to reduce energy intensity. Clearly, the results are asymmetric in nature; hence, the policymakers should consider both positive and negative shocks in energy prices and economic development while making any policy with regard to lowering energy intensity. [ABSTRACT FROM AUTHOR]

The present study was carried out to monitor the ambient concentrations of particulate matter and carbon dioxide caused by vehicular pollution in Srinagar City of Jammu and Kashmir, India, for a period of 12 months from June 2019 to May 2020 as the major contributions in these areas are due to vehicular movement. Out of five, four locations (viz. Dalgate, Jehangir Chowk, Parimpora and Pantha Chowk) had highest traffic density in the city and the fifth location (Shalimar) had low traffic volume. The sampling was done on every fortnight using AEROCET 831—aerosol mass monitor and CDM 901—CO2 monitor with each sampling being carried out three times a day, i.e. morning (9:00 am–10:30 am), afternoon (1:00 pm–2:30 pm) and evening (4:30 pm–6:00 pm) with three replications at each site based on the peak traffic hours. The results show that during the whole period, average PM1 concentrations ranged from 15.10 to 108.9 µg/m3, PM2.5 (28.70–577.50 µg/m3), PM4 (44.50–780.87 µg/m3), PM10 (57.13–1225.53 µg/m3), total suspended particulates (77.77–1410.27 µg/m3) and CO2 (332.4–655.0 ppm). The average concentrations of these parameters showed that the maximum PM1 concentration was found at Dalgate (53.77 µg/m3) and PM2.5 had its maximum average concentration at Jehangir Chowk (140.13 µg/m3). Other parameters like PM4, PM10, TSP and CO2 had a maximum average values at Jehangir Chowk (240.23 µg/m3, 633.40 µg/m3, 853.50 µg/m3 and 533.20 ppm, respectively). The pollution load was observed to be maximum during winter season followed by autumn, summer and spring. The lowest concentration of all pollutants except CO2 was observed in April 2020 and this might be due to COVID-19 lockdown observed in the country during the same period. [ABSTRACT FROM AUTHOR]

The China–Pakistan Karakoram Highway links China to South Asia and the Middle East through Pakistan. Rockfall and debris flows are dangerous geological risks on the main route, and they often disrupt traffic and result in fatalities. As a result, the landslide susceptibility map (LSM) evolution along the highway could make it safer to drive. In this article, remote sensing data are combined with machine learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and k-Nearest Neighbors (KNN) to develop the LSM. Initially, 274 landslide locations we determined and mapped in ArcGIS software and randomly divided into a ratio of 8/2. Secondly, ten landslide susceptibility factors were developed using satellite imagery and different topographical and geological maps. Finally, the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, was used to estimate the model's effectiveness. Our consequences showed that, for three models, the RF, XGBoost, and KNN models, as well as slope, elevation, and distance from the river parameters, had the maximum influence upon landslide sensitivity. Accordingly, the prediction rates are 83.5%, 82.7%, and 80.7% for RF, XGBoost, and KNN. Furthermore, the RF method has better efficiency as compared to other models on the base of AUC. Our findings show that all three machine learning algorithms positively impact, and the results may assist the highway's safe operations. [ABSTRACT FROM AUTHOR]

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields, such as computer vision and healthcare. Particularly, DL is experiencing an increasing development in advanced medical image analysis applications in terms of segmentation, classification, detection, and other tasks. On the one hand, tremendous needs that leverage DL's power for medical image analysis arise from the research community of a medical, clinical, and informatics background to share their knowledge, skills, and experience jointly. On the other hand, barriers between disciplines are on the road for them, often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i.e., MEDAS–the MEDical open-source platform As Service. To the best of our knowledge, MEDAS is the first open-source platform providing collaborative and interactive services for researchers from a medical background using DL-related toolkits easily and for scientists or engineers from informatics modeling faster. Based on tools and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed platform implements tools in pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks, concerning lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realizable by using MEDAS. MEDAS is available at http://medas.bnc.org.cn/. [ABSTRACT FROM AUTHOR]

In this study, researchers examined the effect of replacing a high-volume of cement with sugarcane bagasse ash (BA) and silica fume (SF). In addition to the control, three binary and three ternary blends of concrete containing different percentages of cement/BA and cement/BA/SF were tested to determine the various mechanical and microstructural properties of concrete. For each mix, eighteen cylindrical concrete specimens were cast followed by standard curing (moist at 20 °C) to test the compressive and tensile strengths of three identical specimens at 7, 28, and 91 days. The test results indicated that the binary mix with 20% BA and ternary mix with 33% BA and 7% SF exhibited higher strengths than all the other mixes, including the control. The higher strengths of these mixes are also validated by their lower water absorption and apparent porosity than the other mixes. Following mechanical testing, the micro and pore structures of all mixes were investigated by performing scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM–EDS), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and nitrogen (N2) adsorption isotherm analysis. In SEM–EDS analysis, a dense and compact microstructure was observed for the BA20 and BA33SF7 mixtures due to the formation of high-density C–S–H and C–H phases. The formation of a large amount of C–S–H phases was observed through FTIR, where a prominent shift in peaks from 955 to 970 cm−1 was observed in the spectra of these mixes. Moreover, in N2 adsorption isotherm analysis, a decrease in the intruded pore volume and an increase in the BET surface area of the paste matrix indicate the densification of the pore structure of these mixes. As observed through TGA, a reduction in the amount of the portlandite phase in these mixes leads to the formation of their more densified micro and pore structures. The current findings indicate that BA (20%) and its blend with SF (40%) represents a potential revenue stream for the development of sustainable and high-performance concretes in the future. [ABSTRACT FROM AUTHOR]

Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct measurement of the subsidence rate and its relation to city submergence danger has been made. SAR (Synthetic Aperture Radar) interferometry is a powerful method for obtaining millimeter-accurate surface displacement measurements. The Sentinel-1 satellite data provide extensive geographical coverage, regular acquisitions, and open access. This research used the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technology with Sentinel-1 SAR images to monitor ground subsidence in Karachi, Pakistan. The SARPROZ software was used to analyze a series of Sentinel-1A images taken from November 2019 to December 2020 along ascending and descending orbit paths to assess land subsidence in Karachi. The cumulative deformation in Line of Sight (LOS) ranged from − 68.91 to 76.06 mm/year, whereas the vertical deformation in LOS ranged from − 67.66 to 74.68 mm/year. The data reveal a considerable rise in subsidence from 2019 to 2020. The general pattern of subsidence indicated very high values in the city center, whereas locations outside the city center saw minimal subsidence. Overall, the proposed technique effectively maps, identifies, and monitors land areas susceptible to subsidence. This will allow for more efficient planning, construction of surface infrastructure, and control of subsidence-induced risks. [ABSTRACT FROM AUTHOR]

Various old nexuses are getting new empirical attention in advanced econometric. Therefore, we examine the asymmetric influence of remittances and FDI on CO2 emissions by using the NARDL approach for China from 1981 to 2019. Based on NARDL empirical findings, a negative change in remittances has also positive effects on CO2 emissions in the short and long run. We found that positive and negative change in FDI has also a positive effect on CO2 emissions, while a positive change in FDI is relatively more effective on CO2 emissions than a negative change in FDI in long run. Asymmetry is observed in the only magnitude but not in direction. Our study implies that the China government should redesign the environmentally friendly policies and enforces the foreign investors to role play in environmental quality. [ABSTRACT FROM AUTHOR]