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Estimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence. [ABSTRACT FROM AUTHOR]

Carbonate reservoirs hold vast oil reserves, but their complex properties make traditional enhanced oil recovery (EOR) methods challenging. This study explores the application of low-salinity water flooding (LSWF) as a novel EOR method for India's largest offshore carbonate oil field. Conventional EOR techniques were deemed unsuitable due to reservoir heterogeneity, pressure decline, high temperature, and the offshore location. Favorable factors for LSWF included successful seawater flooding history, medium-weight crude oil, and existing infrastructure. Following core flooding experiments demonstrating a 6–16% increase in oil recovery, a multi-pronged evaluation process was implemented. Single-well chemical tracer tests (SWCTT) and reservoir simulations confirmed the potential of LSWF. A specific target area was chosen based on reservoir characteristics, production data, and available facilities. Simulations predicted a 1.5% incremental oil recovery using diluted seawater (25% salinity) at 30% pore volume injection. After a positive techno-economic analysis, the first offshore LSWF project in India was completed within 3 years. Initial monitoring results are encouraging. This study highlights the successful journey of LSWF from concept to field deployment in a challenging carbonate reservoir, showcasing its potential for revitalizing such fields. Furthermore, this work provides valuable data relevant to Indian offshore environments, where factors like salinity, mineralogy, and crude oil composition pose unique challenges compared to other LSWF applications. These detailed data fill a critical gap in the existing literature. [ABSTRACT FROM AUTHOR]

Various earth observation (EO) data and related spatial technologies are employed to monitor land degradation (LD) and its impacts on the drylands. However, a comprehensive framework that integrates multiple data sources and metrics to generate a consistent output still needs to be explored. Furthermore, the lack of consistency and the limited reliabilities of existing approaches make it challenging to identify proper countermeasures (avoid, reduce, reverse LD) and support decisions to combat LD in line with the Land Degradation Neutrality (LDN) Framework. The "Multi‐Layered Land Degradation Tracer (ML‐LDT)" is a framework that addresses the above research gaps. It is based on a comprehensive framework that integrates data extraction, LD monitoring, and forecasting capacities and consists of three functional layers, that is, a base data processor, monitoring/modeling components, and a forecasting layer employing machine learning (ML) algorithms. ML‐LDT has a state‐of‐art capacity to model wind erosion together with sand and dust storms (SDS), making it particularly suited to the arid lands most prone to these processes. It is implemented over cloud processors with open‐source components based on industry standards. Therefore, the routines are easily adaptable to user requirements in LDN planning. We evaluated the ML‐LDT framework in the Indian Thar Desert and Inner Mongolian drylands. The results showed the overall robustness of the procedures and that ML‐based models provide good insights to forecast future LD/SDS patterns that can be used to simulate LDN scenarios and targets. We validated the forecasting results against LD maps and remote sensing observations. [ABSTRACT FROM AUTHOR]

Aim and objective: To Examine the effect of addition of dexamethasone and clonidine to local anaesthetic drug in (0.5% Bupivacaine+ 2% lignocaine with adrenaline) in PNS guided Supraclavicular brachial plexus block. Methodology: 90 patients presenting for upper limb surgery were divided into three groups. Group D receive Dexamethasone 8 mg as an adjuvant to Bupivacaine+ lignocaine whereas Group C receive Clonidine 75 mcg as an adjuvant to bupivacaine+ lignocaine and Group S is control group receiving saline as an adjuvant to bupivacaine+ lignocaine with adrenaline in Supraclavicular Brachial Plexus block. Results: The mean duration of motor block in group S was 5.74±0.384 hr, whereas in grp C it was 8.86±0.846 hr, for group D it was 9.32±0.964 hr on comparing the three groups there is significant difference between three group. The mean duration of sensory block in group S was 6.86±0.38 hr, whereas in grp C it was 9.86±0.98 hr, and group D it was 10.68±0.88 hr on comparing the three groups there is significant difference between these group. Onset of sensory and motor block was also better in dexamethasone group. Conclusions: We conclude from our study that dexamethasone added to bupivacaine plus lignocaine are of better efficacy in prolonging analgesia than clonidine and control with better hemodynamic fluctuation having no side effects. [ABSTRACT FROM AUTHOR]

In last decade, pervaporation has been recognized as a promising novel process for desulfurization of FCC (Fluidized Catalytic Cracking) gasoline. This study deals with the comparison of the removal of different types of sulfur containing species by a spiral wound pervaporation module. For this, different binary systems containing 2-methyl thiophene, 1-butanethiol and diethyl sulfide as solute and n-heptane as solvent are selected as model gasoline. Results reported in our previous study on n-heptane/thiophene systems are also included in the analysis. Experiments were performed on a spiral wound module with PDMS/PI (Polydimethylsiloxane/ Polyimide) composite membrane at a variety of operating conditions. A suitable mathematical model was then used to predict the module performance theoretically. Some of the experimental results were used to predict the membrane transport parameters for different sulfur containing compounds, and the remaining experimental results were then used to validate the mathematical model. Comparison of the model predictions and experimental results validated the mathematical model, which suggests that the reported mathematical model can be applied to design the spiral wound pervaporative modules to remove higher molecular weight thiophenes as well as other sulfur containing compounds. The presented results show that the PDMS/PI membrane is selective for all three sulfur containing compounds. However, the pervaporation process is found more suitable for removal of lower molecular weight thiophenes. Results further show that the optimum operating conditions for sulfur removal may depend on the functional groups present and molecular weight of sulfur containing compounds present in the gasoline. [ABSTRACT FROM AUTHOR]