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The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes, particularly landslides. This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley, Kumaun Himalaya. A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio (Vf), stream length gradient index (SL), and hypsometric integral (HI) have been used to infer tectonic regime. Morphometric analysis, including basic, linear, aerial, and relief aspects, of 59 fourth-order sub-basins, has been carried out to estimate erosion potential in the study area. The result demonstrates that 46.77% of the landslides lie in very high, 20.32% in high, 21.29% in medium, and 11.61% in low erosion potential zones respectively. In order to determine the key parameters controlling erosion potential, two multivariate statistical methods namely Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) were utilized. PCA reveals that the Higher Himalayan Zone (HHZ) has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief. The clusters created through AHC exhibit positive PCA values, indicating a robust correlation between PCA and AHC. Furthermore, the landslide density map shows two major landslide hotspots. One of these hotspots lies in the vicinity of highly active Munsiyari Thrust (MT), while the other is in the Pandukeshwar formation within the MT’s hanging wall, characterized by a high exhumation rate. High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity. This study, by identifying erosion-prone areas and elucidating the implications of tectonic activity on landslide distribution, empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation, consequently safeguarding lives and communities.

Oceans cover more than 75% of the planet’s land surface, making it the most water-rich place on the Earth. We know very little about oceans because of the extraordinary activities that take place in the depths. Underwater wireless sensors are devices that are able to monitor and record the physical and environmental parameters of their surroundings, as well as transmit these data in a continuous manner to one of the source sensors. The network that is formed by the collection of these underwater wireless sensors is referred to as an underwater wireless sensor network (UWSN). The analysis of performance parameters is thought to be most effectively done with this particular technology. In this paper, we will investigate various performance parameters in a random waypoint mobility model by shifting the maximum speed of a node and altering the number of nodes in the model. These parameters include average transmission delay, average jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive, and idle modes. The QualNet 7.1 simulator is utilized in order to conduct analyses and performance studies.

India produces around 19.0 million tonnes of tomatoes annually, which is insufficient to meet the ever-increasing demand. A big gap of tomato productivity (72.14 t ha –1 ) between India (24.66 t ha –1 ) and the USA (96.8 t ha –1 ) exist, which can be bridged by integrating trellis system of shoot training, shoot pruning, liquid fertilizers, farmyard manure, and mulching technologies. Therefore, the present experiment was conducted on tomato (cv. Himsona) during 2019–2020 at farmers' fields to improve tomato productivity and quality. There were five treatments laid in a randomized block design (RBD) with three replications; T 1 [Farmer practice on the flatbed with RDF @ N 120 :P 60 :K 60  + FYM @6.0 t ha −1 without mulch], T 2 [T 1  + Polythene mulch (50 microns)], T 3 [Tomato plants grown on the raised bed with polythene mulch + FYM @ 8.0 t ha −1  + Single shoot trellis system + Side shoot pruning + Liquid Fertilizer (LF 1 —N 19 :P 19 :K 19 ) @ 2.0 g l –1 for vegetative growth + Liquid Fertilizer (LF 2 —N 0 : P 52 : K 34 ) @ 1.5 g l –1 for improving fruit quality], T 4 [Tomato plants grown on the raised bed with polythene mulch + FYM @ 8.0 t ha −1  + Single shoot trellis system + Side shoot pruning + LF 1 @ 4.0 g l –1  + LF 2 @ 3.0 g l –1 ], and T 5 [Tomato plants grown on the raised bed with polythene mulch + FYM @ 10.0 t ha −1  + Single shoot trellis system + Side shoot pruning + LF 1 @ 6.0 g l –1  + LF 2 @ 4.5 g l –1 ]. The results revealed that tomato plant grown on the raised beds with polythene mulch, shoot pruning, trellising, liquid fertilizers, and farmyard manure (i.e., T 5 ) recorded higher shoot length, dry matter content, and tomato productivity by 20.75–141.21, 18.79–169.4, and 18.89–160.87% as compared to T 4 –T 1 treatments, respectively. The T 5 treatment also recorded the highest water productivity (28.39 kg m –3 ), improved fruit qualities, net return (10,751 USD ha –1 ), benefit–cost ratio (3.08), microbial population, and enzymatic activities as compared to other treatments. The ranking and hierarchical clustering of treatments confirmed the superiority of the T 5 treatment over all other treatments.

COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

The 20-year study investigated the effects of conservation practices (CPs) and farmers' practices (FPs) on various soil quality parameters, yield, and economics of horticultural land use systems. CPs demonstrated significant improvements in soil organic carbon (SOC), available nitrogen (N), phosphorus (P), and potassium (K), compared to FPs. Horticultural systems exhibited higher SOC and available N and P contents than FPs, with substantial variations among different fruit species. CPs also enhanced soil quality index, functional diversity, culturable microbial populations, enzyme activity, and soil microbial biomass carbon (SMBC) compared to FPs. It was observed that the SMBC values were 25.0–36.6% and 4.12–25.7% higher in 0–15 cm and 15–30 cm, respectively, under CPs compared to FPs for all the land use systems. In CPs, dehydrogenase activities (DHAs) in surface soils were 9.30 and 7.50 times higher under mango- and citrus-based horticultural systems compared to FPs. The CPs adopted in aonla, guava, mango, litchi, and citrus-based horticultural systems increased SOC by ~27.6, 32.6, 24.4, 26.8, and 22.0%, respectively, over FPs. Canopy spread, fruit yield, litter yield, and soil moisture were significantly higher in fruit-based horticultural systems under CPs. Economic viability analysis indicated higher net present values (NPVs), benefit-cost ratio (BCR), and shorter payback periods (PBPs) for horticultural land use systems under CPs. Principal component analysis (PCA) revealed that CPs had a more positive influence on soil parameters, particularly DHA, acid and alkali phosphatase activity, available N, P, and K contents, soil microbial load, and organic carbon. The maximum ecosystem services were contributed through mango-based land uses among all land uses. Mango-based horticultural systems exhibited the least impact from both CPs and FPs, while peach-based systems were most affected by CPs. Overall, the findings highlight the benefits of conservation practices in improving soil quality, microbial populations, enzyme activity, and crop productivity in horticultural systems.

India produces approximately 19.84 thousand metric tons of strawberries from 3.03 thousand hectares, but this amount needs to be increased to meet the growing demand. The USA (65.0 t ha −1 ) and India (6.55 t ha −1 ) exhibit very different levels of strawberry productivity. The average global productivity, however, is 23.37 t ha −1 , which can be improved through the integration of multiple technologies, such as mulching, farmyard manures, and liquid fertilizers containing macro- and micronutrients. Therefore, the present study was conducted on strawberries (cv. Chandler, Camarosa, and Winter Dawn) with varying mulch and liquid fertilizer levels during 2019–2022 to improve strawberry productivity and quality. The experiment included 36 treatments (fertilizers with four levels, mulches with three levels, and cultivars with three levels), replicated three times and arranged in a split–split plot design (SSPD). The results demonstrated positive correlations among most traits under investigation, except for acidity, which was negatively correlated with fruit yield. Principal component analysis revealed a total variability of 85.5% among genotypes, contributed by PC-1 (77.2%) and PC-2 (8.3%). The Chandler variety with polythene + paddy straw mulch and liquid fertilizer level 1, exhibited the highest levels of vegetative growth, fruit output, fruit quality, and dry biomass. Therefore, to maximize net yield in strawberries, the use of liquid fertilizers supplying macro- and micronutrients in combination with polythene + paddy straw mulch on raised beds proved both effective and profitable.

The healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect in their early stages, and their spread and mortality are uncontrollable. The reverse transcription polymerase chain reaction (RT-PCR) is still the first and foremost diagnostical methodology accepted worldwide; hence, it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared with its predecessor. Innovational through current studies that complement the existence of the novel coronavirus (COVID-19) to findings in the thorax (chest) X-ray imaging, the projected research’s method makes use of present deep learning (DL) models with the integration of various frameworks such as GoogleNet, U-Net, and ResNet50 to novel method those X-ray images and categorize patients as the corona positive (COVID + ve) or the corona negative (COVID -ve). The anticipated technique entails the pretreatment phase through dissection of the lung, getting rid of the environment which does now no longer provide applicable facts and can provide influenced consequences; then after this, the preliminary degree comes up with the category version educated below the switch mastering system; and in conclusion, consequences are evaluated and interpreted through warmth maps visualization. The proposed research method completed a detection accuracy of COVID-19 at around 99%.

Several inventory models were proposed for manufacturer and retailer which included competition and cooperation between manufacturer and retailer to maximize their profits. Esmaeili M et al. (2009) developed the relationship between manufacturer and Retailer for non co-operative and cooperative games. But the model did not involve any shortage cost as no shortage was allowed. In this paper researcher consider market demand is affected by marketing expenditure and price charged by retailer. This research, presented in this paper, allows shortages for the infinite planning horizon and investigates 1.The non co-operative game for manufacturer-Stackelberg model allowing shortage when manufacturer is the leader and would like to maximize his profit.2.retailer-stackelberg model when retailer is the leader and would like to maximize his profit.3.The co-operative game approach to obtain Pareto Efficient solution. Model is verified through some numerical examples.

In this study, we investigated the effect of irrigation with heavy metal loaded tannery effluent on Ocimum basilicum L., an important aromatic crop for utilization of tannery wastewater and remediation of chromium rich tannery affected soils. The soil was irrigated with three different dilutions of tannery wastewater viz. 50%, 75% and 100% of tannery effluents (T, UD and U) including control. The maximum herbal biomass (105.17 g plant−1) was found in UD100. Although oil quality was not affected by application of tannery effluents, the oil yield was decreased significantly, as compared to control, in all treatments except in UD75. The chromium concentration in different plant parts was in order of root > shoot > leaf. This indicates O. basilicum is an effective aromatic crop for remediation of tannery affected sites without any effect on crop growth, oil yield per unit area and oil quality and increase in soil fertility.

Iron complexed with dimethylacetamide (DMAc) was incorporated in variable ultra low concentration in polyimide (PI) at it precursor stage to obtain nanoscopically manipulated metallopolyimide films. The in situ generation of these nanophase structures, their stability within PI matrix, and interaction with its chains was studied using UV–vis spectroscopy. AFM topographic image shows the presence of rock‐shaped nano particles, which is produced by the formation of polyamic acid salt of Fe‐DMAc complex. Unique fluorescence and improved hydrolytic stability of these metallopolyimide films were due to the formation of nanophase structures. Different photo‐physical properties of developed films are attributed to size quantization effect. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers