Search Results (302)

The Nile, Indus, and Yellow River deltas are historically significant and have experienced extensive shoreline changes over the past 50 years, yet the roles of human interventions and natural events remain unclear. In this study, the Net Shoreline Movement and End Point Rate (EPR) were calculated to quantify the erosion and accretion of the shoreline, respectively. Subsequently, linear trend analysis was employed to identify potential directional shifts in shoreline behavior. These measures are combined with segment-scale cumulative area and the EPR trend to reveal where erosion or accretion intensifies, weakens, or reverses through time. Results show distinct, system-specific trajectories, the Nile lost ~27 km² from 1972 to1997 as a result of the dam construction and sediment reduction, and lost only ~3 km² more from 1997 to 2022, with local stabilization. The Indus switched from intermittent gains before 1990s to sustained loss after that, totaling ~300 km² of cumulative land loss mainly due to upstream dam constructions and storm events. The Yellow River gained ~500 km² from 1973 to 1996 then lost ~200 km² after main-channel relocation and reduced sediment supply despite active-mouth management. These outcomes indicate that deltas are very vulnerable to system wide human activities and natural events. Combined, satellite-derived metrics can help prioritize locations, guide feasible interventions, establish annual monitoring and trigger action. A major caveat of this study is that yearly shoreline rates and 5–10-yearaverages can mask short-lived or very local shifts. Targeted field surveys and finer-scale modeling (hydrodynamics, subsidence monitoring, bathymetry) are therefore needed to refine the design and inform better policy choices. Full article

Chronic kidney disease is a systemic disease of multiple factors and slow progression, and is now becoming a rapidly changing global pathological problem affecting healthcare systems. Anyone who can go through diagnosis before getting to stage 5 Chronic Kidney Disease (CKD) or end stage renal failure has a better shot at the result. This work involves 1659 patient records and dependent variables include demographics, lifestyle, and clinical biochemistry of CKD. Based on the supervised techniques of machine learning which are Random Forest, K Nearest Neighbors (KNN), Logistic Regression, and Naïve Bayes, it was agreed that the performance of the model metrics such as accuracy, precision and recall would need to be used. These models were applied, evaluated by means of more or less simple effectiveness parameters including, for instance, accuracy, precision, or recall. Out of these [best algorithm] achieved [accuracy value] % of predictive accuracy in CKD, and so can be used for diagnosis of CKD in its early stage. This work offers the Framework and results in the development of data-integrated approaches in healthcare and improves the disease control and management. Full article

Heart disease is the leading cause of death across the world. However, such an early prediction of heart attacks can save lives if clinical data are used to predict it accurately. For this, we use four machine learning models: Naive Bayes, Decision Tree, Random Forest and K-Nearest Neighbors (KNN) to predict heart attacks from the data of the patients. Models developed achieved an average accuracy of 65.08%; however, this paper explores the performance of these models in real world healthcare applications. Our focus is on improving model performance by improving the quality of the data, the features and hyperparameter tuning. Future directions indicate combining deep learning techniques and larger dataset for more accurate prediction. Full article

The quality of mangoes is a crucial factor in both domestic and commercial markets that directly influences consumer satisfaction and economic value. Traditional methods of checking mango quality often involve destructive techniques, which lead to the loss of the fruit in the testing process. This study presents an advanced approach that could predict the quality of mangoes using advance non-destructive methods leveraging machine learning algorithms to predict quality parameters such as ripeness, sweetness and overall freshness without damaging the fruit. In this research, a dataset consisting of various mango samples was collected, with attributes including color, texture, size, weight and acidity levels. Sensors, such as pH sensors (for acidity) and e-nose sensors (for aroma and sweetness detection), were used to gather data, while a combination of machine learning models such as Decision Tree, K-Nearest Neighbors (KNN), and Automated Machine Learning (AutoMLP), Naive Bayes were applied to predict the mangoes’ quality. The accuracy of each model was measured based on its ability to classify mangoes as fresh, ripe, or rotten. The results determine that the AutoMLP model performs the best out of the traditional models, achieving an accuracy of 98.46%, making it the most suitable model for mango quality prediction. The research explains the significance of feature extraction methods, model optimization, and sensor data pretreatment in reaching a high prediction accuracy. Full article

With increasingly severe global climate change, reducing carbon emissions has become an important way to promote sustainable development. However, few scholars have researched carbon emissions and carbon reduction in the Huairou district, Beijing, China. Based on the Long-range Energy Alternatives Planning System (LEAP) model, this study sets four scenarios, including a baseline scenario (BAS), an industrial structure upgrading scenario (Indus), a technological progress scenario (Tech), and a comprehensive transformation scenario (COM), to simulate the long-term annual carbon emissions of Huairou district from 2021 to 2060. The results indicate that all four scenarios could realize the target of carbon peaking by 2030. Among them, the peak carbon emissions under the Indus scenario are the highest (2608.26 kilotons), while the peak under the COM scenario is the lowest (2126.58 kilotons). Moreover, by distinguishing the carbon emissions of sectors, it can be found that the commercial sector is the largest source of carbon emissions. The proportion of carbon emissions from the industrial sector will decline, while that from the urban household sector will increase. Furthermore, the analysis of the carbon emission reduction potential of sectors reveals that the commercial and industrial sectors have the greatest potential for carbon emission reduction in the medium term. However, the focus of carbon emission reduction needs to shift towards the commercial and urban household sectors in the long term. This study could provide references for formulating carbon emission reduction pathways and realizing sustainable development. Full article

Millions of people worldwide suffer from asthma disease, and frequently, early diagnosis and efficient treatment are needed to enhance patient outcomes. Through an analysis of clinical and environmental characteristics, this study investigates a machine learning algorithm for predicting asthma using decision trees, K-Nearest Neighbors, random forests, and the naïve Bayes method. A dataset related to asthma disease is divided into two parts, with the first part for training consisting of around 70% and the second part for testing comprising 30%. Before dividing the subset, SMOTE is applied to balance the dataset because the dataset is unbalanced. Regarding the four algorithms, the decision tree attained better accuracy than the other algorithms. K-NN (K Nearest Neighbor) attained 97.50% accuracy, random forest attained 97.35% accuracy, naïve Bayes attained 69.99% accuracy, and the decision tree attained 67.65% accuracy. In all algorithms, the decision tree performed with high accuracy, as its prediction is 97.65% correct in detection. These algorithms can be applied to related predictive healthcare tasks. Full article

Background: The diagnosis of tuberculosis (TB) in children and adolescents is often delayed. We conducted a study to understand the barriers and facilitators to the diagnosis of TB in children and adolescents in a not-for-profit private hospital in Karachi, Pakistan. Methods: We conducted a convergent mixed-methods study comprising quantitative surveys with caregivers of 100 TB patients < 18 years old and 40 semi-structured interviews with caregivers and healthcare providers. Results: Among TB patients whose caregivers were surveyed, 82% were adolescents 10–17 years old. Caregivers reported a median of 91 days (IQR 58–160) between symptom onset and treatment initiation. Time was divided relatively evenly between symptom onset and the first visit to a healthcare provider (median 73, IQR 42–130 days), and between this visit and TB diagnosis (median 65 days, IQR 30–114). While 69% of caregivers initially visited general physicians, many felt that these general physicians did not provide satisfactory healthcare. Caregivers mentioned financial constraints as a major barrier affecting all stages of the journey to diagnosis and treatment. Conclusions: Interventions that overcome financial barriers and strategies that enhance the capacity of private sector general physicians are necessary to reduce delays in TB diagnosis and treatment initiation for children and adolescents. Full article

Migratory birds globally face escalating anthropogenic threats, with crane species being particularly vulnerable. This study assessed human-perceived threats to cranes (Grus virgo & Grus grus) along Pakistan’s vital Indus Flyway using 400 stakeholder questionnaires across eight districts (2021–2024). We quantified perceived threat based on frequency (1 = Very Rare; 5 = Very Frequent) and severity (1 = Not Severe; 5 = Extremely Severe), revealing poaching (illegal killing) as the dominant threat (frequency = 4.9; severity = 4.8), followed by illegal wildlife trade (4.7; 4.5) and taming (4.6; 4.3). Spatial analysis showed strikingly higher perceived threats in southern Pakistan (Rajanpur: frequency = 4.88, severity = 4.82) versus central regions (Khushab: 3.76, 4.02; p < 0.001), with riverbanks identified as high-risk poaching zones (42 incidents). Cluster analysis also confirmed Rajanpur as a critical hotspot within three distinct threat tiers. Critically, analysis of socio-demographic drivers revealed threat type (frequency: F = 104.92, p < 0.001; severity: F = 153.64, p < 0.001) and poaching method (frequency: F = 10.14, p < 0.001; severity: F = 15.43, p < 0.001) as significant perception-shapers, while education, occupation, and crane species preference (frequency: F = 1.17, p = 0.310) exerted a non-significant influence. These results highlight that individual backgrounds minimally modulate threat perceptions. The study aligns with global evidence of uniform crane threats demanding the following urgent conservation action: region-specific enforcement (especially southern hotspots), community-led anti-poaching initiatives, and targeted awareness programs to shift high-threat communities toward crane-friendly coexistence practices. Full article

Diabetes, a chronic metabolic disease marked by elevated blood glucose levels, affects millions of people globally. A lower quality of life and a markedly higher chance of potentially deadly consequences, such as heart disease, renal failure, and other organ dysfunctions, are closely linked to it. In order to effectively manage diabetes and avoid serious consequences, early detection and ongoing monitoring are essential. Remote health monitoring has emerged as a viable and promising option for proactive healthcare due to the development of contemporary technology, particularly in the areas of wearables and mobile computing. In this work, we suggest a thorough and sophisticated framework for remote monitoring that is intended to automatically predict, identify, and manage diabetes risks. To facilitate real-time data collection analysis and tailored feedback, the system makes use of the integration of smartphones, wearable sensors, and specialized medical equipment. In addition to enhancing patient engagement and lowering the strain on conventional healthcare infrastructures, our suggested model aims to assist patients and healthcare providers in maintaining improved glycemic control. We employed a tenfold stratified cross-validation approach to assess the efficacy of our framework and the results showed remarkable performance metrics. A score of 79.00 percent for clarity (specificity) 87.20 percent for sensitivity, and 83.20 percent for accuracy were all attained by the system. The outcomes show how our framework can be a dependable and scalable remote diabetes management solution, opening the door to more intelligent and easily accessible healthcare systems around the world. Full article

Predicting vehicle maintenance is an important task to reduce downtime and cost. Traditional methods based on mileage and manufacturer direction can lead to maintenance at an early stage or too late. By leveraging machine learning, we can predict maintenance in a better way that can save time and cost effectively. In our paper, we have used machine learning models to predict the maintenance needs based on vehicle features. We have an imbalanced dataset, which contains information about 50,000 vehicles; first, we balanced it using SMOTE (generated new sample points and increased the size up to 80,000). After addressing the imbalance dataset challenge, we have applied several algorithms, including Random Forest, Decision Tree, gradient booster, naïve Bayes and KNN. Decision tree perform maximum performs well on both imbalanced and balanced data samples and achieves an accuracy of 99.97%. These finding highlights the importance of machine learning in predicting vehicle maintenance to save cost and downtime. Full article

In the race for industrialization and urbanization, the concentration of greenhouse gases like CO₂ and CH₄ is growing rapidly and ultimately resulting in global warming. An Ni-based catalyst over MgO support (Ni/MgO) offers a catalytic method for the conversion of these gases into hydrogen and carbon monoxide through the dry reforming of methane (DRM) reaction. In the current research work, 1–4 wt% strontium is investigated as a cheap promoter over a 5Ni/MgO catalyst to modify the reducibility and basicity for the goal of excelling the H₂ yield and H₂/CO ratio through the DRM reaction. The fine catalytic activities’ correlations with characterization results (like X-ray diffraction, surface area porosity, photoelectron–Raman–infrared spectroscopy, and temperature-programmed reduction/desorption (TPR/TPD)) are established. The 5Ni/MgO catalyst with a 3 wt.% Sr loading attained the highest concentration of stable active sites and the maximum population of very strong basic sites. 5Ni3Sr/MgO surpassed 53% H₂ yield (H₂/CO ~0.8) at 700 °C and 85% H₂ yield (H₂/CO ratio ~0.9) at 800 °C. These outcomes demonstrate the catalyst’s effectiveness and affordability. Higher Sr loading (>3 wt%) resulted in a weaker metal–support contact, the production of free NiO, and a lower level of catalytic activity for the DRM reaction. The practical and cheap 5Ni3Sr/MgO catalyst is scalable in industries to achieve hydrogen energy goals while mitigating greenhouse gas concentrations. Full article

CH₄ is a powerful greenhouse gas that is thought to be one of the main causes of global warming. The catalytic conversion of methane in the presence of oxygen into hydrogen-rich syngas, known as the partial oxidation of methane (POM), is highly appealing for environmental and synthetic concerns. In search of a cheap catalytic system, the Ni-supported MgO-based (5Ni/MgO) catalyst and the promotional supplement of 1–3 wt.% Sr over 5Ni/MgO are investigated for the POM reaction. Catalysts are characterized by N₂ sorption isotherm analysis, X-ray diffraction spectroscopy, Raman spectroscopy, temperature-programmed desorption techniques, and thermogravimetry. Increasing the loading of strontium over Ni/MgO induced a strong interaction of NiO with the support, pronouncedly. In the presence of oxygen during the POM, the moderate-level interaction of NiO with the support grows markedly. Overall, at a 600 °C reaction temperature, the 5Ni2Sr/MgO catalyst shows 72% CH₄ conversion (~67% H₂ yield) at 14,400 mL/h/g_cat GHSV and ~86% CH₄ conversion (84% H₂ yield) at 3600 mL/h/g_cat GHSV. Achieving a higher activity towards the POM over cheap Ni, Sr, and MgO-based catalysts might draw the attention of environmentalists and industrialists as a low-cost and high-yield system. Full article

This study introduces a cost-effective solution and sensor arrays for the multipoint liquid-level measuring sensor based on an intensity modulation technique. The sensor structure is based on the twisting of two fibers and creates cascading to achieve a multipoint detection. Three sensors are fabricated on a single illuminated polymer optical fiber. The twisting creates side-coupling between two fibers, and the coupled power is attenuated when liquid emerges in the coupled region. Each sensor has its own output source, which is connected to the power meter. When the liquid-level increases, the coupled power is continuously decreased. The multipoint liquid-level sensor is theoretical and experimentally tested. The experimental results show that sensors have a good response and linearity. The sensors are able to measure the liquid-level up to 12 cm and have a sensitivity of about 0.2726 μW/cm, 0.1715 μW/cm, and 0.1281 μW/cm, respectively. The different flow rate (50 mL/min–300 mL/min) is also analyzed to validate the dynamic response of the sensor. The sensor demonstrates a high sensitivity and resolution in the liquid-level detection. Meanwhile, the liquid-level variation is individually and simultaneously measured. The system does not require any decoupling technique as the system relies on a single LED source, and the coupled power is individually measured from each power meter. The system represents a significant advancement in precise liquid-level sensing technology, as the system has advantages of a flexible, durable, cost-effective, and active response with respect to changes in the liquid-level. Full article

Fin analysis is crucial to improve the rate of heat transfer. The main objective of this research is to investigate various fin designs in order to enhance the heat transfer efficiency of cooling fins through modifications in the geometry of the cylinder fins. The investigation of thermal analysis of the cylinder through variation in material, geometry, number, and size of the fins is carried out. Different materials are considered to design the fins, including cast iron, aluminum alloy 6061, and copper. The design of the engine, featuring various fins, is modeled with CATIA, and analysis is performed with ANSYS 2023 R2. The findings indicate that for the modified design-2, the total heat flux is more for aluminum alloy 6061 compared to the other two materials. Additionally, the use of aluminum alloy 6061 results in lower weight, making it a better choice compared to cast iron and copper. Full article

The present study provides a detailed compilation and analysis of the bulk density and magnetic susceptibility of the rocks from the northwest Himalayas, Pakistan. The area is tectonically extremely complex and comprises sedimentary, metamorphic, and igneous rocks. These rocks range in age from Early Proterozoic to Recent. During the fieldwork, 476 rock samples were collected for density measurements and 410 for magnetic susceptibility measurements from the major rock units exposed in the study area. The measured physical parameters reveal a significant difference in the density and susceptibility of the rocks present in the investigated area. The sedimentary rock units belonging to the Indian Plate show the lowest mean values for bulk density, followed by metasedimentary rocks, Early Proterozoic rocks, igneous and metaigneous rock units of the Indian Plate, Indus Suture Melange Zone, and Kohistan Island Arc rocks, respectively. The magnetic susceptibility of sedimentary rock units of the Indian Plate has the lowest mean values, followed by metasedimentary rocks of the Indian Plate, igneous and metaigneous rock units of the Indian Plate, Early Proterozoic rocks of the Indian Plate, Kohistan Island Arc rocks, and Indus Suture Melange Zone. In brief, the sedimentary rocks of the Indian Plate have the lowest bulk density and magnetic susceptibility values, whereas the Kohistan Island Arc rocks have the highest values. Overall, the bulk density and magnetic susceptibility of rock units in the study area follow those predicted for different types of rocks. These measurements can be used to develop possible potential field models of the northwest Himalayas to better understand the tectonics of the ongoing continental-to-continental collision, as well as for many other geological analyses. Full article