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Upwelling is a physical phenomenon that occurs globally along the eastern boundary of the ocean and supports pelagic fishery which is an important source of protein for the coastal population. Though upwelling and associated small pelagic fishery along the eastern Arabian Sea (EAS) is known to exist at least for the past six decades, our understanding of the factors controlling them are still elusive. Based on observation and data analysis we hypothesize that upwelling in the EAS during 2017 was modulated by freshwater-induced stratification. To validate this hypothesis, we examined 17 years of data from 2001 and show that inter-annual variability of freshwater influx indeed controls the upwelling in the EAS through stratification, a mechanism hitherto unexplored. The upper ocean stratification in turn is regulated by the fresh water influx through a combination of precipitation and river runoff. We further show that the oil sardine which is one of the dominant fish of the small pelagic fishery of the EAS varied inversely with stratification. Our study for the first time underscored the role of freshwater influx in regulating the coastal upwelling and upper ocean stratification controlling the regional pelagic fishery of the EAS.

Cloud computing is a dynamic and diverse environment across different geographical locations. In reality, it consists of a vast number of tasks and computing resources. In cloud, task scheduling algorithm is the core player which identifies the suitable virtual machine (VM) for a task. The task scheduling algorithm is responsible for reducing the makespan of the schedule. In recent years, nature-inspired algorithms are applied to task scheduling which performs better than conventional algorithms. In this paper, crow search algorithm (CSA) is proposed for task scheduling in cloud. It is inspired from the food collecting habits of crow. In reality, the crow keeps on eyeing on its other mates to find a better food source than current food source. In this way, the CSA finds a suitable VM for the task and minimizes the makespan. Experiments are carried out using cloudsim to measure the performance of the CSA along with Min–Min and ant algorithms. Simulation results reveal that CSA algorithm performs better compared to Min–Min and Ant algorithms.

The mixed layer is the most variable and dynamically active part of the marine environment that couples the underlying ocean to the atmosphere and plays an important role in determining the oceanic primary productivity. We examined the basin-scale processes controlling the seasonal variability of mixed layer depth in the Bay of Bengal and its association with chlorophyll using a suite of in situ as well as remote sensing data. A coupling between mixed layer depth and chlorophyll was seen during spring intermonsoon and summer monsoon, but for different reasons. In spring intermonsoon the temperature-dominated stratification and associated shallow mixed layer makes the upper waters of the Bay of Bengal nutrient depleted and oligotrophic. In summer, although the salinity-dominated stratification in the northern Bay of Bengal shallows the mixed layer, the nutrient input from adjoining rivers enhance the surface chlorophyll. This enhancement is confined only to the surface layer and with increase in depth, the chlorophyll biomass decreases rapidly due to reduction in sunlight by suspended sediment. In the south, advection of high salinity waters from the Arabian Sea and westward propagating Rossby waves from the eastern Bay of Bengal led to the formation of deep mixed layer. In contrast, in the Indo–Sri Lanka region, the shallow mixed layer and nutrient enrichment driven by upwelling and Ekman pumping resulted in chlorophyll enhancement. The mismatch between the nitrate and chlorophyll indicated the inadequacy of present data to fully unravel its coupling to mixed layer processes.

In the wake of changing climatic conditions, plants are frequently exposed to a wide range of biotic and abiotic stresses at various stages of their development, all of which negatively affect their growth, development, and productivity. Drought is one of the most devastating abiotic stresses for most cultivated crops, particularly in arid and semiarid environments. Conventional breeding and biotechnological approaches are used to generate drought-tolerant crop plants. However, these techniques are costly and time-consuming. Plant-colonizing microbes, notably, endophytic fungi, have received increasing attention in recent years since they can boost plant growth and yield and can strengthen plant responses to abiotic stress. In this review, we describe these microorganisms and their relationship with host plants, summarize the current knowledge on how they “reprogram” the plants to promote their growth, productivity, and drought tolerance, and explain why they are promising agents in modern agriculture.

Drug-target interaction (DTI) prediction is a critical task in computational drug discovery, enabling drug repurposing, precise medicine, and large-scale virtual screening. Traditional in-silico methods, such as molecular docking, classical machine learning, and deep learning, have made significant progress in addressing this issue. However, existing approaches are hindered by computational inefficiencies, reliance on manual feature engineering, and struggles to generalize across diverse molecular structures, limiting their molecular capabilities. Recent advancements in Quantum Machine Learning (QML) are paving the way for its practical applications, unlocking unprecedented capabilities in predictive accuracy, scalability, and efficiency by leveraging the unique powers of quantum computing, namely superposition and entanglement. This study proposes QKDTI - Quantum Kernel Drug-Target Interaction, a novel quantum-enhanced framework for DTI prediction. It used Quantum Support Vector Regression (QSVR) with quantum feature mapping that takes into account a quantum feature space for molecular descriptors and allows encoding molecular and protein features, improved predictions of binding affinities. To enhance the model to be more computationally feasible, integration of the Nystrom approximation into the model allows providing an efficient kernel approximation while reducing overhead expenses. QKDTI was evaluated on benchmark datasets - Davis and KIBA, and validated independently on BindingDB. This model achieves 94.21% accuracy on DAVIS, 99.99% on KIBA, and 89.26% on BindingDB, significantly outperforming classical and other quantum models. Further, the statistical tests have been conducted on the compared models to provide the reliability of the results. This indicates that introducing quantum computing into DTI pipeline can revolutionize computational drug discovery by improving predictive accuracy and providing a better generalization over multiple datasets.

PurposeThe main objective of this paper is to present a holistic approach for measuring overall bank efficiency and its decomposition in intermediation and profitability efficiencies.Design/methodology/approachTwo-stage network data envelopment analysis (NDEA) model has been used for obtaining intermediation and profitability efficiencies along with overall bank efficiency. Additionally, bootstrap truncated regression has also been adopted to explore the influential predictors of two stages.FindingsA comparative analysis between Indian private-sector and public-sector banks showed that the former is efficient than the latter in profitability efficiency stage. Another interesting finding is that none of the banks is efficient in overall study tenure. Finally, outcomes of bootstrap truncated regression show that differences in intermediation efficiency are explained by firm size, return on asset, market share and ownership while profitability stage is determined by diverse, gross domestic product and ownership.Research limitations/implicationsThis study will guide the Indian banking sector to act on which they are lagging, for the betterment of their overall performances. Finally, parameters like loan waives and disposal income of non-performing assets (NPAs) are not considered because of the unavailability of information in the output measures of NDEA model.Originality/valueThis paper not only provides a detailed performance assessment of Indian banks but also examines banks’ internal efficiency by deposits as an intermediary measure.

Background Stroboscopy is an endoscopy that is performed with intermittent light at a frequency that approximates the frequency of a moving object so that it appears in slow motion or motionless. It is used to analyze the structure and motion of the vocal fold. Aim and objective To compare the stroboscopic findings of various vocal parameters such as symmetry, amplitude, periodicity, mucosal wave of vocal folds, and glottis closure before and after elective endotracheal intubation and to compare acoustic analysis of voice using fundamental frequency, intensity, maximum phonation time, and dysphonia severity index in patients before and after elective intubation, who were undergoing otological surgeries. It also assesses the correlation between changes in these vocal and acoustic parameters and the size of the endotracheal tube, duration of intubation, and number of attempts made during intubation. This study creates awareness and provides insights to avoid intubation-related vocal fold injury. Methods This was a prospective cohort study involving 31 patients done in a Tertiary Care Centre. All patients who underwent otological surgeries by elective endotracheal intubation were included. All of them underwent stroboscopic and acoustic evaluation preoperatively, 24 h, and 7 days postoperatively. Results Statistically significant changes in mucosal wave pattern score were observed in the 1st postoperative day that reverted to normal by the end of 7th postoperative day and in GRBAS which was significant at the end of both 1st and 7th POD. Other parameters like fundamental frequency, intensity, DSI, MPT, amplitude, symmetry, periodicity, and glottis closure remain unaltered. There was a moderate positive correlation between the duration of intubation (minutes) and mucosal wave 1st POD ( P -value: 0.003). Conclusion The majority of the patients (61%) had normal laryngeal structures. A total of 39% had evidence of injury, the most common being right vocal fold hemorrhage improved by the end of 7th postoperative day and became normal.

A cross-equatorial low-level wind, known as Findlater Jet (FJ), modulates the thermocline in the Arabian Sea (AS) during summer monsoon (June to September). By analysing ocean and atmospheric data, we show that the FJ signal gets ‘trapped’ in the AS in the form of upper ocean heat content till the following winter months (December to February). This memory is the consequence of the combined effect of FJ-induced wind stress curl and the annual downwelling Rossby waves in the AS. During the summer monsoon months, the strong low-level westerly winds cause a negative wind stress curl in the south of the FJ axis over the central AS, resulting in a deep thermocline and high magnitude of heat being trapped. In winter monsoon months, though the wind stress curl is positive over large parts of the AS and could potentially shoal the thermocline and reduce the upper ocean heat content in the central AS, this does not happen due to two reasons. Firstly, winds are weaker, and spread over a larger area over the AS making the magnitude of the wind stress curl low. Secondly, westward propagating downwelling Rossby wave radiated from the eastern AS deepens the thermocline and prevents ventilation of the trapped heat. During the following spring, the collapse of the Rossby waves leads to the shoaling and mixing of underlying waters with surface waters thereby resurfacing of the trapped heat. The resurfacing of the trapped heat makes the AS a memory bank of the FJ induced signal.

Population explosion in recent years has driven the environment to overuse nondegradable substances. Microbial polyesters known as polyhydroxyalkanoates (PHAs) are generated and retained as cytoplasmic granules in microorganisms with restricted nutritional availability and can be used to manufacture bioplastics. The current study attempts to screen soil isolates for PHA production and optimize their media parameters. Among all the isolates, 17 were identified and confirmed by Sudan black staining, as they are screening for PHA production and are identified by their colony characteristics. The isolation of the most promising strain, GS-14, was achieved through the sodium hypochlorite method, and subsequent quantification involved establishing a standard curve of crotonic acid. Notably, isolate GS-14 presented the highest yield, which was determined by extrapolating its data onto the standard curve. Characterization of the PHA polymer was subsequently performed, and the results were used to discern its properties. FTIR confirmed characteristic PHA absorption bands, with a prominent C = O stretching peak at 1732 cm⁻¹. LC-MS detected a molecular mass of 641.6 g/mol, indicative of an oligomeric species, while the actual polymer molecular weight is estimated between 5,000 and 20,000 Da. DSC revealed an exothermic peak at 174 °C, allowing the calculation of crystallinity, a key determinant of mechanical properties. Furthermore, the PHA-producing organism was identified as Bacillus australimaris through the sequencing of 16 S ribosomal RNA. The media optimization was performed via Minitab software, with statistical analyses employed to interpret the resulting data comprehensively.