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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 fusion of Silicon (Si) with MXene nanostructures is a promising avenue to boost the performance of lithium-ion batteries (LIBs). This comprehensive review delves into recent progress and future prospects in the realm of Si-based MXene composites for LIB applications. Starting with an in-depth analysis of the structure and characteristics of Si-based MXene nanostructures, the paper elucidates their synthesis and characterization methodologies. The subsequent discussion delves into the mechanism and properties of LIBs, pinpointing the hurdles and possibilities for enhancement. The spotlight then shifts to the utilization of MXene materials in LIBs, emphasizing their pivotal role in ameliorating battery performance. Closing off, the review explores ongoing research trends and forthcoming directions in the field, offering insights into the potential applications and obstacles of Si-based MXene composites for the evolution of next-generation LIBs. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore the role of blockchain (BCT) and Artificial Intelligence (AI) technologies in enhancing and incentivizing environmentally responsible, socially inclusive and economically viable tourism practices within the hospitality and tourism (H&T) industry. Design/methodology/approach: This study is based on a critical reflection research approach that enables a synthesis of information derived from existing literature's insights and the authors' experiences and observations. By examining frameworks and theories in the literature, critical reflection also helps develop a more comprehensive understanding of the topic. Findings: This study portrays how BCT could be used to track the sustainability credentials of tourism providers and how AI can optimize energy usage in hotels. This study depicts how adopting technology-driven sustainable practices in the H&T industry can increase profitability, improve reputation, compliance with regulations, efficiency and a better guest experience. Practical implications: The study suggests targeted actions and policy frameworks that can be tailored to different stakeholder groups (hospitality businesses, policymakers and tourists) to overcome barriers and maximize the positive societal and environmental impacts of adopting BCT and AI for sustainable tourism. Originality/value: The originality of this study lies in its ability to offer new perspectives and novel recommendations on the diverse uses of AI and BCT in the context of sustainable tourism. Furthermore, the study provides strategic and policy elements (Targeted actions and policy frameworks) for stakeholders to integrate sustainable tourism practices using BCT and AI successfully. This study differs from earlier review studies that primarily focused on adopting emerging technologies and ignoring the sustainability angle in the use of technology. [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]

In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC. [ABSTRACT FROM AUTHOR]

Severe wind shear events near airport runways pose serious safety risks and are a growing concern in civil aviation. Identification of severe wind shear risk factors may enhance aviation safety. Although rare, severe wind shear impacts safety by affecting the airspeed, lift, and maneuverability of aircraft. This study presents TabNet, a novel deep learning technique coupled with Bayesian optimization (BO) to predict wind shear severity in the runway vicinity using Doppler LiDAR data from Hong Kong International Airport. To address imbalanced wind shear data, it was first processed by resampling techniques and then used as input to TabNet. The analysis demonstrated that Bayesian-tuned TabNet (BO-TabNet) with SVM-SMOTE-processed data led to better performance compared to other strategies. The TabNet architecture employs the attention mechanism to enable model-specific interpretability. Analysis showed that the most important contributing factor was the summer season, followed by the wind shear encounter location (1 nautical miles from the runway at the departure end). Additionally, a more comprehensive model-agnostic LIME method was used to elucidate the model from a local perspective. By predicting severe wind shear and assessing contributing factors, aviation stakeholders can proactively manage and mitigate the associated risks, leading to safer and more efficient operations. [ABSTRACT FROM AUTHOR]

Background & objective: It's no secret that medical students have access to a plethora of medical literature, which can make it challenging to determine the right sources and how to cover them effectively using various study habits and strategies. Our objective was to determine the association of study habits (time and location), study duration, study strategies, and study sources with academic performance. Methods: It was a cross-sectional study conducted in six Medical Colleges in Peshawar, Pakistan between 13th April 2023 to 13th Jun 2023. Total 138 students were selected for the study. A well-structured questionnaire was used for data collection. Participants were from both genders and in both public and private sector medical colleges. First-year students and students from medical colleges outside of Peshawar were excluded from the study. SPSS (Version 20.0) was used for data analysis. Results: The study revealed that daily study hours (during normal days), residence status, library study, morning study timings, active recall, MCQS solving, and watching online videos made a significant association with academic performance (P < 0.05). Pearson's correlation showed a positive and statistically significant association between daily study hours (during normal days), study strategies, and academic performance (p < 0.05). The multiple regression model explained 30.5% of the variance in academic performance. AConclusion: This study established a noteworthy link between academic performance and various factors, including daily study hours, strategies, and specific study sources. Notably, morning study sessions, active recall, MCQs-based strategies, and online video resources showed significant associations with enhanced academic performance. [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]

Perovskite, a star material with extraordinary opto‐electronic properties has shown promising results in both perovskite solar cells (PSCs) and perovskite light‐emitting diodes (PeLEDs). Taking advantage of the similar configuration of PSCs and PeLEDs, next generation devices with dual functionality of light‐harvesting and light‐emission can be realized. Such devices hold enormous application prospects. However, the necessary tradeoff resulted from the opposite working principles required for each mode of operation and challenges such as non‐radiative recombination loss resulted from bulk and surface defects in perovskite films and mismatched energy levels have hindered mass production. To provide a roadmap for rationally designing efficient light emitting perovskite solar cells (LEPSCs), a comprehensive review focusing on operating principle, device architecture, recent developments and limitations is required. We begin with a brief overview of the basic principles underlying the working mechanism of LEPSCs such as photon to electricity conversion and viceversa. The focus of this review then shift towards deligently combining and overviewing the important breakthroughs reported in this newly developed field such as morphology optimization, defect passivation, interface engineering, energy level alignment and dimensional control. Finally, this work concludes with discussing future challenges and providing a roadmap for rational design of efficient and stable LEPSCs. [ABSTRACT FROM AUTHOR]

The power conversion efficiency (PCE) of perovskite solar cells (PSCs) has reached an impressive value of 26.1%. While several initiatives such as structural modification and fabrication techniques helped steadily increase the PCE and stability of PSCs in recent years, the incorporation of metal–organic frameworks (MOFs) in PSCs stands out among other innovations and has emerged as a promising path forward to make this technology the front‐runner for realizing next‐generation low‐cost photovoltaic technologies. Owing to their unique physiochemical properties and extraordinary advantages such as large specific surface area and tunable pore structures, incorporating them as/in different functional layers of PSCs endows the devices with extraordinary optoelectronic properties. This article reviews the latest research practices adapted in integrating MOFs and derivative materials into the constituent blocks of PSCs such as photoactive perovskite absorber, electron‐transport layer, hole‐transport layer, and interfacial layer. Notably, a special emphasis is placed on the aspect of stability improvement in PSCs by incorporating MOFs and derivative materials. Also, the potential of MOFs as lead absorbents in PSCs is highlighted. Finally, an outlook on the critical challenges faced and future perspectives for employing MOFs in PSCs in light of the commercialization of PSCs is provided. [ABSTRACT FROM AUTHOR]

We investigated the effectiveness of interventions provided by traditional healers for common mental disorders (CMDs) together with associated barriers and facilitators. Electronic databases including MEDLINE, APA Psych Info, Allied and Complementary Medicine, Embase, CINAHL, Social Science Citation Index, and Scopus were searched from inception until October 2021. Randomised controlled trials (RCTs) assessing interventions by traditional healers for CMDs and qualitative and mixed-methods studies examining traditional healers and their attendees' views about the treatment of CMDs by traditional healers were included. Cochrane Risk of Bias Assessment tool (RoB-1) and Critical Appraisal Skills Programme (CASP) were used for the quality assessment of studies. A meta-analysis and thematic synthesis were conducted. Sixteen RCTs (1,132 participants) and 17 qualitative or mixed-methods studies (380 participants) were included. Improvement in symptoms was greater for interventions by traditional healers compared to control groups for both depression and anxiety. Subgroup analyses indicated that only "spiritual passe" interventions showed improvement in depression and anxiety, and participants with co-morbid anxiety and physical conditions showed improvement in anxiety. Facilitators to engaging with interventions by traditional healers were shared faith-based worldview exhibited by traditional healers and their attendees and perceived effectiveness of traditional healing. Stigma and concealing mental illness were found to be barriers not only to formal healthcare but also to traditional healers' services. Interventions by traditional healers such as "spiritual passe" are effective in improving CMDs. However, evidence is still limited due to the low quality of studies and lack of long-term evidence. [ABSTRACT FROM AUTHOR]

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model's reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework's broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide. [ABSTRACT FROM AUTHOR]

The accurate prediction of residual compressive strength (RCS) of concrete plays a critical role in assessing concrete constructions' safety and structural integrity following exposure to elevated temperatures. Existing ensemble models exhibit RCS prediction capabilities, yet they are constrained by their opaque nature. This research endeavors to develop an intelligible model for RCS by employing five ensemble machine-learning models, namely, random forest (RF), adaptive boosting (AdaBoost), gradient boosting (GBoost), light gradient boosting (LGBoost), and extreme gradient boosting (XGBoost), and integrating Shapley additive explanations (SHAP) to ascertain the precise importance of each input variable in forecasting the RCS of concrete under elevated temperature conditions. The input variables encompass concrete type, compressive strength, aggregate type, water-cement ratio, heating type, heating rate, maximum core temperature, and cooling type. Model performance is appraised using established performance metrics such as mean absolute error (MAE), mean squared error (MSE), root-mean squared error (RMSE), and coefficient of determination (R2). The analytical results exhibit the efficacy of employing machine-learning models in accurately predicting the RCS of concrete under elevated temperature conditions. Among the implemented models, XGBoost demonstrated the highest performance, yielding an R2 value of 0.876, closely trailed by the LGBoost model at 0.871. The SHAP analysis elucidates the crucial role of core temperature, water-cement ratio, heating rate, and compressive strength in determining the RCS of concrete. [ABSTRACT FROM AUTHOR]

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R2 of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM2.5 concentrations, followed by humidity and temperature. Elevated PM2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management. [ABSTRACT FROM AUTHOR]

In today's digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. Traditional machine learning techniques have been extensively employed for this purpose due to their high accuracy and low false alarm rates. However, these methods often fall short in detecting sophisticated and evolving threats, particularly those involving subtle variations or mutations of known attack patterns. To address this challenge, our study presents the "Optimized Random Forest (Opt-Forest)," an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. The genetic algorithms based decision forest construction offers notable benefits by traversing a wider exploration space and mitigating the risk of becoming stuck in local optima, resulting in the discovery of more accurate and compact decision trees. Leveraging advanced feature selection techniques, including Best-First Search, Particle Swarm Optimization (PSO), Evolutionary Search, and Genetic Search (GS), along with contemporary dataset, this research aims to enhance the adaptability and resilience of NIDS against modern cyber threats. We conducted a comprehensive evaluation of the proposed approach against several well-known machine learning models, including AdaBoostM1 (AbM1), K-nearest neighbor (KNN), J48-Decision Tree (J48), multilayer perceptron (MLP), stochastic gradient descent (SGD), naïve Bayes (NB), and logistic model tree (LMT). The comparative analysis demonstrates the effectiveness and superiority of our method across various performance metrics, highlighting its potential to significantly enhance the capabilities of network intrusion detection systems. [ABSTRACT FROM AUTHOR]

Since energy and its sustainability are key global issues, extensive research is currently underway to advance the emerging energy sector. Hydrogen is anticipated to assume a substantial role as a worldwide energy carrier in forthcoming energy frameworks. Oman has set an ambitious target to annually produce one million tons of green hydrogen by 2040, aspiring to become one of the leading global producers and exporters of green hydrogen. These hydrogen scenarios are integral components of the broader global energy scenarios, primarily focused on achieving decarbonization. Green hydrogen serves as a pivotal element in Oman’s pursuit of de-carbonization, as well as its economic and energy security objectives. This paper aims to accentuate the challenges associated with hydrogen development, particularly in the context of green hydrogen production, by examining both present and future advancements. It provides a comprehensive overview of the consumption, applications, and economic considerations of green hydrogen based on expert organizations’ projections. Additionally, the paper addresses key considerations pertaining to the utilization and transportation of hydrogen as compared to electricity.

Recently, climate change, reduction of species diversity, and food problems are among the problems that worry the whole world. Plants play an important role in solving these problems. For this purpose, we introduce various food, medical, and industrial plants. One of the plants with different fields of application is the species belonging to the genus Berberis L. For this purpose, the bioecology of Berberis L. species was studied to regulate the ecological balance. To study the introduction and adaptation of Berberis L. species to the Absheron conditions on a scientific basis, it is important to evaluate the biological and morphological features of these species at individual stages of development.During the introduction of barberry species, it is a very important issue to study the biology of individual development of each species. Ontogeny - the individual development of the species - covers the period from the zygote to the natural death of the species. Information about the morphological characteristics of the species is obtained in the developmental stages of ontogenesis.

The Dubai Municipality has launched the ‘Green Roofs Awareness Initiative’ to achieve a green area per capita of 23.4 m2 in the city. Given the numerous symptoms caused by the unprecedented urban expansion, green roofs and facades can provide various benefits to the city. This study intends to develop an evaluation method for the performance of green roofs (artificial ground reforestation) by quantifying the thermal environment functionality of a low-maintenance lightweight green roof. As a methodology, this study analyzed the change in the thermal environment of the surface, including sensible heat and latent heat, using four types of plants, namely Polytrichum Commune (PC), Thuidium Kanedae (TK), Anomobryum Filiforme (AF), and Kentucky Bluegrass (KB). The results showed that PC reduced the ground surface temperature by 2.58°C to 3.81°C while planting PC increased relative humidity (RH) from 48.0% to 93.3%. KB had the highest effect on humidity control, resulting in a 49.31% increase in RH. Regarding the temperature change by ground surface type, the surface temperature was 0.4 ~ 0.9°C lower for PC, AF, and TK at 18 ~ 24°C, while KB showed a 0.5 ± 0.2°C lower than the set temperature. KB did not show any significant change in temperature up to 24°C, but as the set temperature increased from 30 to 36°C, the surface temperature decreased remarkably. This research provides primary data that will serve to raise awareness, reduce installation costs, and systemize government support for green roofs in Dubai.

Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews’ correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways.

This paper focuses on the seismic vulnerabilities of multi-storey buildings in hilly regions like Sikkim and Uttarakhand, where rapid construction is driven by population growth and tourism. The study particularly evaluates step-back buildings on hilly slopes, comparing their vulnerability to standard buildings on flat terrain. Using non-linear analysis to assess structural aspects like displacement and storey drift ratio, the research examines the performance of these buildings in both uphill and downhill orientations against typical three-storey and six-storey structures, respectively. The findings indicate that step-back buildings, especially those without infill walls, are more susceptible to seismic damage. For instance, on the uphill side, a step-back building shows a mean drift ratio 15.11% greater in the X direction and 4.57% greater in the Y direction compared to a three-storey regular building (3SR). This vulnerability is exacerbated when infill walls are absent, with mean drift ratios in step-back buildings being 74.75% and 33% higher in the X and Y directions, respectively. Moreover, at a seismic acceleration of 0.36 g, the mean displacement of a step-back building is 83% greater in the X direction and 51% greater in the Y direction than those with infill walls (SBIN), underscoring the significant role of infill walls in enhancing earthquake resilience. The study also highlights that short columns in step-back buildings are particularly prone to severe damage, especially just above the uppermost foundation level. While infill walls offer substantial mitigation of damage at the Design Basis Earthquake (DBE) level, at the Maximum Considered Earthquake (MCE) level, step-back buildings still endure severe damage compared to regular buildings with infill walls. Consequently, the research establishes that step-back buildings demonstrate greater vulnerability at DBE levels without infill walls and are more susceptible to damage than flat terrain buildings at MCE levels, emphasizing the need for careful design and reinforcement strategies in earthquake-prone hilly areas.

Surface coatings are widely used for preventive maintenance, as they prolong the durability of concrete structures. Partial short cover depth may result due to poor construction quality and uneven formwork. In this study, lithium nitrite-based gel was used as an anodic corrosion inhibitor coating material to investigate its influence on the corrosion of rebar embedded in a partial short cover depth in the presence of chloride. The specimens were cured at a temperature of 20 °C and a relative humidity of higher than 95%. (a) Macrocell corrosion current density, (b) microcell corrosion current, and (c) polarization curves were measured, and the results were compared with those of uncoated specimens after aging for 28 days. The results revealed that the corrosion rate was reduced considerably due to the application of the coating with the gel type.

Niche overlap between sympatric species can indicate the extent of interspecific competition. Sympatric competing species can exhibit spatial, temporal, and dietary adjustments to reduce competition. We investigated spatial, temporal, and dietary niche overlap of sympatric Asian palm civet (Paradoxurus hermaphroditus) and small Indian civet (Viverricula indica), in and around Pir Lasura National Park, Pakistan. We used remote cameras to determine the frequency and timing of detections to estimate spatial and temporal overlap, and prey remains from scats to estimate dietary overlap. We collected scat samples of Asian palm civet (n = 108) and small Indian civet (n = 44) for dietary analysis. We found low spatial (Oij = 0.32) and temporal (Δ = 0.39) overlap, but high dietary niche overlap (0.9) between these two civet species. Both civet species were detected at only 11 camera sites and small Indian civets were detected most frequently during 2:00–5:00 h and 8:00–10:00 h, whereas Asian palm civets detections were greatest during 20:00–2:00 h. The overall niche breadth of Asian palm civet was slightly narrower (L = 9.69, Lst = 0.31) than that of the small Indian civet (L = 10, Lst = 0.52). We identified 27 dietary items (15 plant, 12 animal) from scats of Asian palm civet including Himalayan pear (Pyrus pashia; 27%), Indian gerbil (Tatera indica; 10%), Rhesus monkey (Macaca mulatta; 4%), and insects (5%). Scat analysis of small Indian civets revealed 17 prey items (eight plant, nine animal) including Himalayan pear (24%), domestic poultry (15%), Indian gerbil (11%), and house mouse (Mus musculus; 5%). Both civet species consumed fruits of cultivated orchard species. Spatial and temporal partitioning of landscapes containing diverse foods appears to facilitate coexistence between Asian palm civets and small Indian civets.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P