Month: February 2024

Abstract

Numerous cities throughout the world are experiencing tremendous population growth in their peripheral areas, resulting in a progressive modification of landscapes and raising serious concerns about natural environments, notably forests and agricultural area. Monitoring LULC changes can assist in understanding historical trends, while simulation-based modelling shed light on possible potential future developments. Both of these tactics are indispensable and complimentary for implementing effective land use policies to mitigate the adverse ramifications of urbanization. Present area of investigation, Siliguri town one of prime trading hub of whole north-east India surrounded by ecologically sensitives zones Himalayas. To monitor land use dynamics of peri-urban spaces in Siliguri town Landsat images of 2000, 2010 and 2020 were derived from USGS and classified using Support vector machine learning algorithms. Following the quantification of the previous trend of landuse change, an integrated Artificial Neural Network (ANN) and CA-Markov chain Model was utilized to forecast LULC for the years 2030 and 2050. Eleven pertinent geographical factors, comprising topographical, socioeconomic, and connectivity information, were generated and validated using the crammer v test. The results from LULC modeling predicts as compared to 2020, the urban area is expected to increase by 48.23%, while forest areas, other vegetation cover, and agricultural areas are predicted to shrink by 9.42%, 29.83%, and 26.60% respectively, by the year 2050. The results could provide useful information about historical and potential landuse change and as well as assist local governments in formulating management strategies for the protection of ecological resource.

Abstract

On March 27, 2022, a tailings dam in Jiaokou County, Shanxi Province, China, experienced a sudden breach leading to 224,000 m³ of released tailings that flowed downstream and damaged infrastructure. The tailings flow exhibited relatively slow movement according to eyewitnesses, with the complete duration of the failure process lasting 20–30 min. In order to reconstruct the slow motion of the tailings flows, we have adapted the mass flow simulation software r.avaflow to effectively simulate the slow-flow dynamic. In addition to the numerical simulation, we conducted a comprehensive investigation using satellite-based interferometric synthetic aperture radar (InSAR), optical satellite images, field surveys, unmanned aerial vehicle (UAV) photogrammetry, borehole drilling, and laboratory tests, with the aim of identifying underlying causes of this failure incident. The numerical simulation results showed a good representation of the observed impact area and reported time span, illustrating that the maximum simulated velocities during the entire flow slightly exceeded 6 m/s. InSAR analysis indicated a phase of rapid deformation that coincided with an unprecedented autumn storm in early October 2021, followed by a prolonged period of slow movements until the collapse, with an average line-of-sight (LOS) velocity of 24 mm/year. Through remote sensing surveys and field investigations, we observed the following deficiencies in design, construction, and operation of the Jiaokou TSF: (i) the TSF underwent an extra round of excessive loading from July 2021 onwards, despite being decommissioned in December 2019. and (ii) the TSF fell short of conventional design standards by the absence of drainage and seepage control measures and inappropriate dam construction methods (loess stacking). We conclude that the stability of the Jiaokou TSF was adversely affected by the extreme storm event in conjunction with deficiencies in TSF management, in particular the overloading that likely led to pore water pressure build-up and sudden strength loss. This failure incident is of significance as it draws attention to the need for more robust risk management of the thousands of existing TSFs in China.

Abstract

The accurate and reliable simulation and prediction of runoff in the Beas River Basin are becoming more and more important due to the increased uncertainty posed by climate change, which is making it difficult to manage water resources efficiently. In order to minimize the effect of flash floods, estimating the accurate peak flow is essential. It can be challenging to comprehend and anticipate peak flow due to natural streamflow variance as well as the streamflow management offered by dams and reservoirs. Which makes it difficult to mimic hydrologic behavior on a daily scale with reliable accuracy. This study evaluated the efficacy of physics-aided machine learning (ML) based regression models for modeling streamflow in combination with process-based hydrological SWAT (soil and water assessment tool). Performance of eight machine learning (ML) models including linear regression (LR), multi-layer perceptron (MLP), light gradient-boosting machine (LGBM), extreme gradient boosting (XGBoost), kernel ridge (KR), elastic net (EN), Bayesian ridge (BR), and gradient boosting (GB) have been analyzed and compared with the calibrated-SWAT (cSWAT) model. The Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and coefficient of determination (R²) were used to assess the effectiveness of both models. Results showed that the uncalibrated SWAT in combination with ML regression models (cSWAT-ML) performed well and found comparable to calibrated SWAT (cSWAT), though, few ML regression models in combination with uncalibrated SWAT (uSWAT-MLmodels models) performed superior. cSWAT model performed well with R² values of 0.73, RMSE value of 276.92 m³/s and NSE value of 0.72. In uSWAT-ML, EN and BR have obtained better results with R² values of 0.89 and 0.89, NSE values of 0.87 and 0.87, and RMSE values of 158.31 m³/s and 159.48 m³/s. The approached uSWAT-ML models have effectively predicted the peak stream flow rates with models BR and EN have predicted with better results of R² value of 0.71 each. This study’s findings highlight the potential of all the eight ML models as promising techniques for predicting the peak flow discharge values when uncalibrated process-based models are combined.

Abstract

Purpose

The incidence of cutaneous melanoma is rising, and Melanoma related deaths are highest among people aged 65–74. Herein, we aim to understand the impact of novel and established melanoma treatment methods on CM related mortality and all-cause mortality. We further compared these effects among Hispanic and non-Hispanic Whites (NHW).

Methods

The data was extracted from the Texas Cancer Registry from 2007 to 2017. A Cox Proportional Hazard regression analysis was performed to assess treatment effect on melanoma mortality and all-cause mortality, with race-ethnicity as an effect modifier.

Results

A higher percentage of Hispanic patients presented with CM-related mortality (22.11%) compared to NHW patients (14.39%). In both the Hispanic and NHW, post-diagnosis radiation (HR = 1.610, 95% CI 0.984–2.634, HR = 2.348, 95% CI 2.082–2.648, respectively), post-diagnosis chemotherapy (HR = 1.899, 95% CI 1.085–3.322, HR = 2.035, 95% CI 1.664–2.489, respectively), and post-diagnosis immunotherapy (HR = 2.100, 95% CI 1.338–3.296, HR = 2.402, 95% CI 2.100–2.748) are each associated with an increased risk in CM-related mortality. Similar results were seen with post-diagnosis radiation (Hispanic HR = 1.640, 95% CI 1.121–2.400, NHW HR = 1.800, 95% CI 1.644–1.971), post-diagnostic chemotherapy (Hispanic HR = 1.457, 95% CI 0.898–2.364, NHW HR = 1.592, 95% CI 1.356–1.869), and post-diagnosis immunotherapy (Hispanic HR = 2.140, 95% CI 1.494–3.065, NHW HR = 2.190, 95% CI 1.969–2.435) with respect to all-cause mortality. Post-diagnosis surgery (HR = 0.581, 95% CI 0.395–0.856, HR = 0.622, 95% CI 0.571–0.678) had the opposite effect in CM-related mortality for Hispanics and NHWs respectively.

Conclusion

Our results propose differences in all-cause and CM-only related mortality with separate treatment modalities, particularly with chemotherapy, radiation therapy and immunotherapy. In addition, this retrospective cohort study showed that health disparities exist in the Hispanic Medicare population of Texas with CM.

Abstract

In recent years, especially as a result of war in Ukraine, enormous movements of migration to Poland from eastern European countries have been reported, including people living with Human Immunodeficiency Virus (HIV). We have conducted multi-center, prospective study, which aimed to establish HIV-1 subtype and assess the presence of primary drug resistance mutations to nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors and protease inhibitors in antiretroviral treatment naïve patients. The clinical trial recruited 117 individuals during 2 years period (2020–2022). The prevalence of HIV-1 subtype A was statistically significantly more frequent in Ukrainian, and HIV-1 subtype B in Polish patients (p < 0.05). Drug resistance mutations were detected in 44% of all cases and the comparison of presence of mutations in the analyzed groups, as well as in the subgroups of subtype A and B HIV-1 has not revealed any significant differences (p > 0.05), nevertheless Polish patients had multidrug resistance mutations more frequent (p < 0.05). The results from our trial show no increased risk of transmission of multidrug resistant HIV strains in our cohort of Ukrainian migrants.

Clinical trials. Gov number NCT04636736; date of registration: November 19, 2020.

Abstract

Conclusive evidence on the relationship between corruption and migration has remained scant in the literature to date. Using 2008–2018 data on bilateral migration flows across EU28 and EFTA countries and four measures of corruption, we show that corruption acts as both push and pull factors on migration patterns. Based on a gravity model, a 1-unit increase in the corruption level in the origin country is associated with an 11% increase in out-migration. The same 1-unit increase in corruption in the destination country is associated with a 10% decline in in-migration.

Abstract

Lung cancer is a prevalent malignancy, despite the great breakthroughs in detection and prevention, and it remains the important cause of death. In recent days, artificial intelligence has exploded in all fields of science. The use of deep learning in medical science has improved in accuracy and precision of predicting this infestation in the initial stages. In the work, a novel molecular imaging-based Res-U-Net is proposed for classifying two different types of lung cancer. The PET/CT (positron emission tomography/computed tomography) employing an injection ¹⁸F-FDG has developed as a useful tool in therapeutic oncologic imaging for both metabolic and anatomic analysis. The proposed model uses Res-U-Net to classify small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from normal by using ¹⁸F-FDG PET/CT images from the radiogenomics dataset. This dataset images are pre-processed by Gaussian smoothing to reduce the noise from the PET/CT images. Finally, the classification result is obtained through the support vector machine (SVM) classifier which proves the efficiency of the proposed technique. The outcome of the proposed technique yields the best and most accurate results, and it yields the classification accuracy rate of 96.45%for lung cancer into NSCLC and SCLC.

Abstract

Background

Advancements in multiagent chemotherapy have expanded the surgical indications for pancreatic cancer. Although pancreaticoduodenectomy (PD) with portal vein resection (PVR) has become widely adopted, distal pancreatectomy (DP) with PVR remains rarely performed because of its technical complexity. This study was designed to assess the feasibility of DP-PVR compared with PD-PVR for pancreatic body cancers, with a focus on PV complications and providing optimal reconstruction techniques when DP-PVR is necessary.

Methods

A retrospective review was conducted on consecutive pancreatic body cancer patients who underwent pancreatectomy with PVR between 2005 and 2020. An algorithm based on the anatomical relationship between the arteries and PV was used for optimal surgical selection.

Results

Among 119 patients, 32 underwent DP-PVR and 87 underwent PD-PVR. Various reconstruction techniques were employed in DP-PVR cases, including patch reconstruction, graft interposition, and wedge resection. The majority of PD-PVR cases involved end-to-end anastomosis. The length of PVR was shorter in DP-PVR (25 vs. 40 mm; p < 0.001). Although Clavien-Dindo ≥3a was higher in DP-PVR (p = 0.002), inpatient mortality and R0 status were similar. Complete PV occlusion occurred more frequently in DP-PVR than in PD-PVR (21.9% vs. 1.1%; p < 0.001). A cutoff value of 30 mm for PVR length was determined to be predictive of nonrecurrence-related PV occlusion after DP-PVR. The two groups did not differ significantly in recurrence or overall survival.

Conclusions

DP-PVR had higher occlusion and postoperative complication rates than PD-PVR. These findings support the proposed algorithm and emphasize the importance of meticulous surgical manipulation when DP-PVR is deemed necessary.

Abstract

A mechanism is proposed for dynamic grain growth (DGG) by subgrain boundaries driving grain-boundary migration. This mechanism is evaluated against data from an interstitial-free steel tested in tension at 850 °C and a true-strain rate of \(10^{-4}\)  s \(^{-1}\) and rapidly quenched to preserve microstructures evolved during deformation. Tensile tests produced steady-state flow, distinct subgrains, and rapid DGG. Static annealing alone produced static grain growth (SGG) that was much slower than DGG. Electron backscatter diffraction (EBSD) provided grain size and orientation measurements. High-resolution electron backscatter diffraction (HR-EBSD) was used to accurately measure subgrain sizes and subgrain boundary misorientations. The average grain size increased linearly with strain during DGG, but the average subgrain size remained constant during straining. The average subgrain boundary misorientation increased with strain, initially rapidly and then slowly. The dihedral angle imposed in grain boundaries by intersecting subgrain boundaries decreased with increasing subgrain boundary misorientation, which supports the proposed mechanism for DGG. The driving pressure for grain-boundary migration from subgrain boundaries is estimated to be approximately one order in magnitude greater than that from dislocation density reduction under the conditions examined.

Abstract

Erosion status resulting from the denudation process in data-limited basins can be achieved through analysis of hydro-morphological parameters. This study aims to the identification of erosion-prone areas using morphometric, hypsometric, and compound factor approaches in the Ruvu River Basin (RRB), Tanzania under a geoinformatics environment. The morphometric and hypsometric parameters were successfully computed. The hypsometric integral (HI) and Compound Factor (CF) ranking techniques were used to assess the erosional vulnerability of the basin. It was found that RRB is a 5th stream order basin having 475 total stream segments and an overall area of 5512.54 km² with a dendritic-type drainage pattern. The bifurcation ratio for the sub-basins from 2.64 to 12 reflects the structural stability of the basin. Drainage density values of 0.121–0.967 km/km² indicate the coarser soil formation, covered by dense vegetation, with low to moderate soil erosion. Results from shape parameters viz.; form factor (0.18–0.441); circularity ratio (0.019–0.424); elongation ratio (0.479–0.749) and compactness ratio (1.536–7.225) indicates sub-basins are elongated in shape, taking moderate lag times for peak runoff to occur, and have low to moderate soil erosion. Ruggedness number (1.625–3.110) confirms that SB1, SB2, SB4, and SB15 are situated in the highly elevated regions of the RRB, and exhibit moderate to steep slopes. These localities have a sturdy influence on intrinsically erosional susceptibility. The hypsometric curve shows the maturity stages of sub-basins and the hypsometric integral exhibits its erosivity in terms of numeric magnitude. Based on both HI and CF prioritization, SB15 has the highest susceptibility to soil erosion followed by SB1 and SB7, ranked 2nd and 3rd respectively. The study proposes that remedial measures, including engineering and non-engineering means, should be considered for SB15 to mitigate soil erosion in the study area.