Search Results (10,063)

Coastal erosion is a critical environmental challenge in the Upper Gulf of Thailand, driven by both natural processes and human activities. This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). The results show that the Random Forest algorithm, utilizing spectral bands and indices (NDVI, NDWI, MNDWI, SAVI), achieved the highest classification accuracy (98.17%) and a Kappa coefficient of 0.9432, enabling reliable delineation of land and water boundaries. The extracted annual shorelines were validated with high accuracy, yielding RMSE values of 13.59 m (2018) and 8.90 m (2023). The DSAS analysis identified significant spatial and temporal variations in shoreline erosion and accretion. Between 1988 and 2006, the most intense erosion occurred in regions 4 and 5, influenced by sea-level rise, strong monsoonal currents, and human activities. However, from 2006 to 2018, erosion rates declined significantly, attributed to coastal protection structures and mangrove restoration. The period 2018–2023 exhibited a combination of erosion and accretion, reflecting dynamic sediment transport processes and the impact of coastal management measures. Over time, erosion rates declined due to the implementation of protective structures (e.g., bamboo fences, rock revetments) and the natural expansion of mangrove forests. However, localized erosion remains persistent in low-lying, vulnerable areas, exacerbated by tidal forces, rising sea levels, and seasonal monsoons. Anthropogenic activities, including urban development, mangrove deforestation, and aquaculture expansion, continue to destabilize shorelines. The findings underscore the importance of sustainable coastal management strategies, such as mangrove restoration, soft engineering coastal protection, and integrated land-use planning. This study demonstrates the effectiveness of combining machine learning and geoinformatics for shoreline monitoring and provides valuable insights for coastal erosion mitigation and enhancing coastal resilience in the Upper Gulf of Thailand. Full article

The main goal of the present study is to explore the noise mitigation potential using an active control strategy based on rotor phase synchronization. This work is focused on the effects of the inflow velocity on the noise interference effect. The inflow velocity does not affect the phase at which the interference phenomenon is observed, as expected. On the other hand, the intensity of the pressure fluctuations is influenced by the inflow velocity for all of the rotor phase shift conditions investigated. Specifically, as the inflow velocity increases, maintaining a constant rotational speed, in the Overall Sound Pressure Level graphs, a reduction of approximately 10 dB is observed. This effect also applies to cases of destructive interference, highlighting the remarkable versatility of this noise reduction technique. Full article

Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Cancer Genome Atlas. Two classification workflows were examined: (1) whole-volume input and (2) lesion-focused region of interest. Multiple DL architectures, including ResNet, DenseNet, transformer-based UNeST, and Attention Multiple-Instance Learning (MIL), were implemented within the PyTorch-based MONAI framework. The class imbalance was mitigated using focal loss, oversampling, and dynamic class weighting. The hyperparameters were optimised with Optuna, and balanced accuracy was the primary metric. Results: For a preliminary dataset, the proposed framework demonstrated feasibility for the multi-class classification of ovarian masses. The initial experiments highlighted the potential of transformers and MIL for identifying the relevant imaging features. Conclusions: A reproducible methodological pipeline for DL-based ovarian mass classification using CT and MRI scans has been established. Future work will leverage a multi-institutional dataset to refine these models, aiming to enhance clinical workflows and improve patient outcomes. Full article

The widespread adoption of social networking sites (SNSs) has brought social-engineering victimisation (SEV) to the forefront as a significant concern in recent years. Common examples of social-engineering attacks include phishing websites, fake user accounts, fraudulent messages, impersonation of close friends, and malicious links shared through comments or posts on SNS platforms. The increasing number of SNS users is closely linked to a rise in SEV incidents. Consequently, it is essential to explore relevant theories, frameworks, and contributing factors to better understand this phenomenon. This study systematises and analyses 47 scholarly works on SEV in SNSs, examining theories, frameworks, and influencing factors. A total of 90 independent variables were identified and grouped into seven perspectives: socio-demographics, personality traits, socio-emotional factors, habitual factors, perceptual/cognitive factors, message characteristics, and sender characteristics; these were considered alongside mediating variables. The correlations between these variables and victimisation outcomes were evaluated, uncovering factors that increase vulnerability and highlighting contradictory findings in existing studies. This systematised analysis emphasises the limitations in current research and identifies future research directions in order to deepen the understanding of the factors influencing SEV. By addressing these gaps, this study aims to advance mitigation strategies and provide actionable insights to reduce SEV in SNS contexts. Full article

Background and objectives: Atrial fibrillation (AF), the most prevalent sustained arrhythmia, poses a significant public health challenge due to its links with stroke, heart failure, and mortality. Hypertension, a primary modifiable cardiovascular risk factor, is a well-established risk factor for AF that facilitates structural and electrical changes in the atria, including dilation, fibrosis, and pressure overload. Material and Methods: we conducted a literature search regarding the shared mechanisms, risks and treatments of hypertension and atrial fibrillation. Results: The renin–angiotensin–aldosterone system plays a pivotal role in this remodelling and inflammation, increasing AF susceptibility. Uncontrolled hypertension complicates AF management, diminishing the effectiveness of mainstay treatments, including antiarrhythmic drugs, catheter ablation, and cardioversion. Effective blood pressure management, particularly with therapies targeting the renin–angiotensin–aldosterone system (RAAS), can lower the risk of new-onset AF and reduce the incidence of recurrent AF, enhancing the success of rhythm control strategies. These antihypertensive therapies mitigate myocardial hypertrophy and fibrosis and attenuate both atrial pressure strain and the inflammatory response, mitigating the substrates for AF. Conclusion: This review highlights the urgent need for integrated strategies that combine BP control, AF screening, and lifestyle modifications to minimise the burden of AF and its complications. Future research should investigate the specific mechanisms of cellular-level interactions associated with a hypertensive predisposition to AF, including systematic inflammation and the role of genetics, the impact of blood pressure variations on AF risk, and individualised treatment strategies specifically targeting the shared mechanisms, simultaneously propagating hypertension and AF. Full article

Longevity risk affecting older adults can be transferred to the insurance market by purchasing a lifetime annuity. Special-rate life annuities, which are priced, among other factors, on the basis of health and lifestyle factors, go beyond traditional considerations of age and sex by using modified mortality tables. However, they are not available in many countries. In regions where life annuities are priced solely via standard mortality tables, retirees with below-average life expectancy may face unfair pricing. This study aims to quantify this actuarial unfairness and proposes an alternative annuitisation strategy for these retirees. The strategy allows them to transfer longevity risk by acquiring a life annuity on the basis of their actual mortality probabilities, thereby mitigating actuarial inequities. Additionally, the paper examines how tax incentives can exacerbate actuarial unfairness and, specifically for Spanish tax regulations, compares different alternatives under two scenarios related to the sources used for purchasing life annuities. Full article

Switched reluctance motor drives are becoming attractive for electric vehicle propulsion systems due to their simple and cheap construction. However, their operation is degraded by torque ripples due to the salient nature of the stator and rotor poles. There are several methods of mitigating torque ripples in switched reluctance motors (SRMs). Apart from changing the geometrical design of the motor, the less costly technique involves the development of an adaptive switching strategy. By selecting suitable turn-on and turn-off angles, torque ripples in SRMs can be significantly reduced. This work combines the benefits of Harris Hawks Optimization (HHO) and Radial Basis Functions (RBFs) to search and estimate optimal switching angles. An objective function is developed under constraints and the HHO is utilized to perform search stages for optimal switching angles that guarantee minimal torque ripples at every speed and current operating point. In this work, instead of storing the values in a look-up table, the values are passed on to an RBF model to learn the nonlinear relationship between the columns of data from the HHO and hence transform them into high-dimensional outputs. The values are used to train an enhanced neural network (NN) in an adaptive switching strategy to address the nonlinear magnetic characteristics of the SRM. The proposed method is implemented on a current chopping control-based SRM 8/6, 600 V model. Percentage torque ripples are used as the key performance index of the proposed method. A fuzzy logic switching angle compensation strategy is implemented in numerical simulations to validate the performance of the HHO-RBF method. Full article

Cacao cultivation is an important economic and social activity for tropical regions worldwide. Elevated cadmium (Cd) concentrations in soil and cacao beans have become a serious concern for producers and consumers, particularly following the implementation of stricter Cd limits for cacao products in the European Union since 2019. Cadmium is a potentially toxic element that can bioaccumulate in different plant tissues, raising concerns about the future of cacao exports and posing a significant threat to the food chain through consuming products with high Cd concentrations. Therefore, understanding the origins of Cd in cacao-producing countries’ agricultural soils is essential. Equally important is the need to investigate the factors influencing its availability, uptake, translocation, and distribution within the cacao plant, in addition to strategies for mitigating its effects or reducing its concentration in agriculturally relevant tissues. This review aims to contextualize the sources of Cd in the cacao agroecosystems while highlighting recent advances and perspectives in applying essential and beneficial elements, selecting low-accumulator genotypes, and utilizing associated microbiota. These strategies seek to mitigate Cd bioaccumulation and minimize its negative impacts on the cocoa value chain. Full article

Afghanistan is located on the Eurasian tectonic plate’s edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush–Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. This study maps Afghanistan’s tectonic structures, identifying the most active geological faults and regions with heightened seismicity. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property. Full article

The human microbiota, a complex ecosystem of microorganisms, plays a pivotal role in regulating host immunity and metabolism. This review investigates the interplay between microbiota and inflammatory markers, emphasizing their impact on metabolic and autoimmune disorders. Key inflammatory biomarkers, such as C-reactive protein (CRP), interleukin-6 (IL-6), lipopolysaccharides (LPS), zonulin (ZO-1), and netrin-1 (Ntn1), are discussed in the context of intestinal barrier integrity and chronic inflammation. Dysbiosis, characterized by alterations in microbial composition and function, directly modulates the levels and activity of these biomarkers, exacerbating inflammatory responses and compromising epithelial barriers. The disruption of microbiota is further correlated with increased intestinal permeability and chronic inflammation, serving as a precursor to conditions like type 2 diabetes (T2D), obesity, and non-alcoholic fatty liver disease. Additionally, this review examines therapeutic strategies, including probiotics and prebiotics, designed to restore microbial balance, mitigate inflammation, and enhance metabolic homeostasis. Emerging evidence positions microbiota-targeted interventions as critical components in the advancement of precision medicine, offering promising avenues for diagnosing and treating inflammatory and metabolic disorders. Full article

Background/Objectives: The increased frequency of extreme weather events related to climate change, including the occurrence of extreme temperatures, severely affects crop yields, impairing global food security. Heat stress resulting from temperatures above 30 °C is associated with poor germination performance and stand establishment. The combination of climate-resilient crop genotypes and tailored seed priming treatments might represent a reliable strategy to overcome such drawbacks. This work explores the potential of hydropriming as a tool to mitigate the heat-stress-mediated impact on germination performance in orphan legumes. Methods: For each tested species (Lathyrus sativus L., Pisum sativum var. arvense and Trigonella foenum-graecum L.), two accessions were investigated. Germination tests were performed at 25 °C, 30 °C, 35 °C and 40 °C to assess the heat stress tolerance threshold. Hydropriming was then applied and germination tests were performed at 40 °C to test the impact of the treatment on the seeds’ ability to cope with heat stress. An alkaline comet assay and Quantitative Real Time-Polymerase Chain Reaction were performed on embryos excised from primed and control seeds. Results: Phenotyping at the germination and seedling development stage highlighted the accession-specific beneficial impact of hydropriming under heat stress conditions. In L. sativus seeds, the alkaline comet assay revealed the dynamics of heat stress-induced DNA damage accumulation, as well as the repair patterns promoted by hydropriming. The expression patterns of genes involved in DNA repair and antioxidant response were consistently responsive to the hydropriming and heat wave conditions in L. sativus accessions. Full article

Over the past decade, China’s air quality has improved significantly. To further mitigate the concentration levels of fine particulate matter (PM_2.5), this study analyzed the spatio-temporal evolution of PM_2.5 concentrations from 2012 to 2022. Furthermore, the study integrated the generalized additive model (GAM) and GeoDetector to investigate the main driving factors and explored the complex response relationships between these factors and PM_2.5 concentrations. The results showed the following: (1) The annual average concentration of PM_2.5 in China peaked in 2013. The annual reductions of PM_2.5 in each city ranged from 1.48 to 7.33 μg/m³. In each year, the PM_2.5 concentrations were always consistently higher in north and east China and lowest in northeast and southwest China. (2) In terms of spatial distribution, the North China Plain, the Middle and Lower Yangtze River Plain, and the Sichuan Basin exhibited the highest PM_2.5 concentration levels and showed high aggregation characteristics. (3) The GeoDetector analysis identified the concentrations of SO₂, NO₂, and CO and the meteorological conditions as important factors influencing the spatial differentiation of PM_2.5. The results of the GAM showed that the meteorological factors, such as temperature, atmospheric pressure, wind speed, and precipitation, generally had specific inflection points in their effects on the PM_2.5 concentration levels. The relationship of PM_2.5 with the gross domestic product and population density followed an inverted U shape. The PM_2.5 concentrations under the land use types of cropland, barren, impervious, and water were higher than others. The concentration of PM_2.5 decreased significantly under all land use types. Our work can be used as a strong basis for providing insights crucial for developing long-term pollution control strategies and promoting environmental sustainability. Full article

The increasing need for effective antiviral strategies has led to the development of innovative surface coatings to combat the transmission of viruses via fomites. The aim of this review is to critically assess the efficacy of antiviral coatings in mitigating virus transmission, particularly those activated by visible light. The alarm created by the COVID-19 pandemic, including the initial uncertainty about the mechanisms of its spread, attracted attention to fomites as a possible source of virus transmission. However, later research has shown that surface-dependent infection mechanisms need to be carefully evaluated experimentally. By briefly analyzing virus–surface interactions and their implications, this review highlights the importance of shifting to innovative solutions. In particular, visible-light-activated antiviral coatings that use reactive oxygen species such as singlet oxygen to disrupt viral components have emerged as promising options. These coatings can allow for obtaining safe, continuous, and long-term active biocidal surfaces suitable for various applications, including healthcare environments and public spaces. This review indicates that while the significance of fomite transmission is context-dependent, advances in material science provide actionable pathways for designing multifunctional, visible-light-activated antiviral coatings. These innovations align with the lessons learned from the COVID-19 pandemic and pave the way for sustainable, broad-spectrum antiviral solutions capable of addressing future public health challenges. Full article

This study examines the potential constraints that firms may face when occupying central positions within supply chain networks, particularly in terms of innovation. While prior research highlights the benefits of centrality for resource acquisition and knowledge flow, our findings suggest that such positioning can, under certain conditions, hinder innovation. Using unbalanced panel data from Chinese A-share listed firms in Shanghai and Shenzhen (2009–2021), we conduct an empirical investigation into this effect, incorporating the mediating role of R&D investment and the moderating influence of ownership structure. The analysis reveals that supply chain network centrality has a significantly negative impact on total innovation output, invention patents, and low-end patents, with all effects statistically significant at the 0.001 level. This adverse impact is particularly pronounced in state-owned enterprises, where dependence on established networks further restrains innovation. These results suggest that supply chain centrality may hinder firms’ long-term innovation capacity, which could, in turn, weaken their sustainability by limiting their ability to adapt to technological change and evolving industrial environments. These findings suggest that policymakers could implement targeted incentives, such as R&D subsidies, to mitigate the innovation constraints faced by central firms. Meanwhile, corporate managers should adopt strategies like open innovation and supply chain diversification to sustain long-term innovation. Full article

Seasonal and industrial fluctuations in natural gas demands require optimized gas storage operations, especially in depleted reservoirs, to ensure a stable supply. This study proposes a novel optimization model for injection–production processes using the simulated annealing–particle swarm optimization (SA-PSO) algorithm. The model focuses on minimizing pressure variances across different reservoir blocks during injection–production cycles. The approach is applied to the W23 underground gas storage facility, where a high-precision 3D geological model and numerical simulations were developed. The SA-PSO algorithm effectively reduced pressure differentials during the sixth injection–production cycle, improving reservoir efficiency. During the gas injection period, the optimized pressure difference between blocks was reduced to one-eighth of that in the initial plan. The average formation pressures in Phase I and Phase II decreased by 0.35 MPa and 0.76 MPa, respectively. During the gas production period, the optimized pressure difference between blocks was reduced to one-tenth of that in the initial plan. The average formation pressures in Phase I and Phase II increased by 0.4 MPa and 1.21 MPa, respectively. The optimized injection–production strategy enhances working gas capacity, maintains balanced formation pressures, and mitigates risks such as high pressure and salt precipitation. The findings demonstrate the potential of SA-PSO optimization to improve the operational efficiency and safety of gas storage reservoirs. Full article