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Several studies have attributed the rapid drying during flash droughts to vapor pressure deficit (VPD) by using VPD anomaly based metrics. In this study, we use an analytical approach to investigate the role of VPD in the depletion of soil moisture through evapotranspiration (ET) and evaluate if the anomalies reflect its actual contribution toward drying. We model the energy and water balance of the land and lower atmosphere using a simplified slab model and use the energy and water balance equations of the model to decompose the daily changes in soil moisture during flash droughts into those caused by external forcings such as precipitation, wind and radiation on a day and the system response driven by the initial conditions of ground temperature, air temperature, soil moisture and specific humidity on the day. We apply this approach to investigate the role of VPD in ET‐driven soil moisture depletion in 10 major watersheds of India. Contrary to previous studies, we find negligible contribution of VPD toward soil moisture depletion in humid regions and high contribution in arid regions. In arid regions, even low anomalies of VPD can contribute significantly to soil moisture depletion due to the large magnitude of VPD, whereas in humid regions even large VPD anomalies do not lead to significant impact on soil moisture depletion. Our results show that the influence of VPD is underestimated in arid regions and overestimated in humid regions in previous studies which have used VPD anomaly based metrics for quantifying its effect on flash droughts. Key Points An analytical framework is used to quantify the role of vapor pressure deficit (VPD) in soil moisture depletion in flash droughts VPD increases during flash droughts through two pathways: sensible heating and dry heat advection VPD anomaly based metrics used in previous studies underestimate its contribution in arid regions and overestimate it in humid regions

With the growing availability of global glacier thickness data sets, there is a need for fast and adaptable approach to assess how glacier thickness changes over time. This study demonstrates how machine learning (ML) models can be trained using existing thickness data sets and later applied with updated inputs to quantify the changes in glacier thickness. This study introduces an innovative skeletonization distance approach, which converts glacier outlines into thin central structure and computes each pixel's distance from this structure to represent glacier geometry more effectively. This new input variable, combined with elevation and slope, was used to train four tree‐based ML models: Random Forest, XGBoost (XGB), AdaBoost‐RF, and AdaBoost‐DT. Among them, XGB performed best, achieving R2 values of 0.82–0.88 (training) and 0.73–0.82 (testing). The best‐performing XGB model was then used to analyze glacier thickness changes between 2000 and 2015 for the Bara Shigri, Gangotri, and Zemu glaciers. The analysis revealed heterogeneous thinning concentrated in debris‐covered ablation zones of the study glaciers. Incorporating skeletonization improved test accuracy by up to 13%. To test transferability, the approach was applied to Aletsch (Switzerland), Koxkar (China), and Saskatchewan (Canada) glaciers, effectively capturing regional thinning patterns consistent with previous studies. The trained models can be directly applied with Digital Elevation Models and glacier boundaries of different years, allowing researchers to estimate glacier thickness changes.

Recent studies show variations in precipitation‐gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global‐scale effectiveness, but applying them to regional studies, particularly in small to medium‐sized sub‐basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we present a newly developed Clusters Based‐Minimum Error approach to assimilate different open‐source gridded precipitation data sets for forming an accurate precipitation product over small to medium‐sized hilly terrain basins, with limited precipitation gauges. This methodology generates the New Gridded Precipitation Data Set (NGPD) from 1991 to 2022 for the Upper Ganga Basin in the western Himalaya, covering approximately 22,292 km2. The study utilizes nine open‐source gridded precipitation data sets and 11 observed precipitation gauges, NGPD is evaluated through station‐wise, grid‐wise, and elevation‐wise analyses using statistical parameters, quantile‐quantile plots, daily coefficient of determination, Rainfall Anomaly Index, and seasonality/precipitation pattern analyses. Results demonstrate the superior performance of NGPD compared to other gridded precipitation sources across various evaluation metrics. Nash‐Sutcliffe Efficiency (NSE), Coefficient of determination (R2), and Root mean squared error (RMSE) range from 0.67 to 0.90, 0.73–0.93, and 4.4–10.69 mm/day, respectively, w.r.t 11 observed precipitation gauges. NGPD outperforms the widely used IMD data set in India, exhibiting a monthly scale improvement of 18.47% and 17.7% in average NSE and R2 values, respectively. Additionally, the methodology is also successfully applied to the Tamor Basin in Nepal, proving its reliability for various Himalayan regions. This approach reliably creates accurate gridded precipitation data sets for hilly sub‐basins, especially in Himalayan regions with limited station data. Key Points A cluster‐based data assimilation approach to develop accurate gridded precipitation data in the Himalayan basins Consideration of topographic and climatic parameters to identify homogenous rainfall clusters to incorporate precipitation change Multi‐level evaluation of the newly developed gridded precipitation w.r.t. observed and open sources global gridded precipitation data sets

Purpose The purpose of this paper is to explore the relationship between quality management (QM) and performance, specifically how the infrastructure and core QM practices affect quality and business performance, in Indian manufacturing organizations. Design/methodology/approach In this study, the empirical data were drawn from 262 manufacturing organizations in India. The research model was tested using the structural equation modeling technique. Findings The findings of the empirical study revealed that infrastructure QM practices have a positive effect on core QM practices and indirectly on quality performance, whereas, core QM practices have a positive effect on quality performance. Also, quality performance has a positive effect on business performance. Research limitations/implications This study considered QM from two dimensions (infrastructure and core quality practices), the study further contributes to the understanding of the different roles played by diverse QM dimensions in determining business performance in terms of increased return on investment, shareholder and stakeholder value. Practical implications The study showed that infrastructure quality practices support the application of core quality practices. Therefore, managers must develop and maintain their organization’s quality system and sufficient resources need to be allocated to both types of practices in order to achieve the superior business performance. Originality/value This study considers both total quality management and Six Sigma practices for defining a new set of infrastructure and core QM practices in Indian manufacturing organizations.

PurposeThe study aims to map the links between Industry 4.0 (I-4.0) technologies and circular economy (CE) for sustainable operations and their role to achieving the selected number of sustainable development goals (SDGs).Design/methodology/approachThe study adopts a systematic literature review method to identify 76 primary studies that were published between January 2010 and December 2020. The authors synthesized the existing literature using Scopus database to investigate I-4.0 technologies and CE to select SDGs.FindingsThe findings of the study bridge the gap in the literature at the intersection between I-4.0 and sustainable operations in line with the regenerate, share, optimize, loop, virtualize and exchange (ReSOLVE) framework leading to CE practices. Further, the study also depicts the CE practices leading to the select SDGs (“SDG 6: Clean Water and Sanitation,” “SDG 7: Affordable and Clean Energy,” “SDG 9: Industry, Innovation and Infrastructure,” “SDG 12: Responsible Consumption and Production” and “SDG 13: Climate Action”). The study proposes a conceptual framework based on the linkages above, which can help organizations to realign their management practices, thereby achieving specific SDGs.Originality/valueThe originality of the study is substantiated by a unique I-4.0-sustainable operations-CE-SDGs (ISOCES) framework that integrates I-4.0 and CE for sustainable development. The framework is unique, as it is based on an in-depth and systematic review of the literature that maps the links between I-4.0, CE and sustainability.

Carbon fiber (CF) reinforced polypropylene composites with a different weight percentage of CF were synthesized using a viable industrial dispersion process utilizing a twin-screw extruder. Their mechanical, thermal, electrical, and fracture morphology properties were inspected for electromagnetic interference (EMI) shielding applications. An increase in the tensile strength of the composite noted at the maximum loading (20 wt%) of CF. Improvement in thermal stability with degradation temperature of (480 °C) measured at 20 wt% CF. A noteworthy increase in electrical conductivity (3.73 × 10 −3 S cm −1 ) of these composites seen, which yielded the EMI shielding effectiveness of − 32.92 dB at 20 wt% CF in the X-band recommending their probability as a thermally steady EMI shielding material.

Accurate orientation and position estimation are critical elements in optimizing real-time object tracking performance when leveraging smartphone sensors such as accelerometers and gyroscopes. The primary challenges encountered in smartphone-based object tracking are attributed to the GPS signal, canyon effect, and orientation errors, accumulation error in sensor. To address these limitations, a novel approach is proposed wherein a smartphone application is developed based on IMU Multi -sensor fusion using Kalman filter and Rotation vector. The proposed approach integrates Kalman filtering to fuse sensor data and leverages the rotation vector for precise orientation estimation. Additionally, geohash filtering is employed to efficiently proficiency in quantifying intricate spatial interdependencies and display track paths on maps within the application. A detailed mathematical analysis and thorough comparison with existing algorithms in the field proves the dexterity of the proposed object tracking scheme. The comprehensive evaluation showcases the algorithm’s capability and advancement compared to state-of-the-art approaches.

Purpose Studies have so far focused on learning in organizations, factors affecting learning, learning effectiveness and so on but the concept of learning in a hybrid work arrangement is yet unexplored. The purpose of this study is to measure the perception of faculty members in higher education institutions towards learning in a hybrid work arrangement and also to measure the differences of perception towards hybrid work arrangement based on employees’ gender and organization type. Design/methodology/approach The data was collected from a sample of 390 faculty members composing of Assistant Professors, Associate Professors and Professors, purposely chosen from two of the premier higher education institutions (one private and one public) located in Punjab, India. A self-structured questionnaire was administered to the faculty members who are working on a regular basis and have minimum of two years of work experience with the chosen university. For analysing the collected data exploratory factor analysis and other descriptive statistics have been applied. Findings The findings of the survey show that in terms of gender differences, it is the female employees who are more satisfied with different aspects of hybrid/remote work arrangement as compared to male employees. In regard to organizational differences in the perception towards learning in a hybrid work arrangement it is found that public university employees have a more positive attitude so far as individual factors are concerned, but in terms of organizational factors, it is the private university that is scoring better than the public university. Research limitations/implications The study is limited to only two higher education institutions, and its findings to be applicable in all higher education institutions, further studies may be required on a larger canvas. Future studies may be undertaken using advanced statistical tools like structural equation modelling to explore various variables associated with learning in a hybrid work arrangement. Originality/value Applicability of hybrid work arrangement is very high in higher education institutions and to the best of the authors’ knowledge, this is the first study which adds to the literature on perception of employees towards organizational learning in a hybrid work arrangement.

Continuous demand for an increase in food production due to climate change and a steady rise in world population requires stress-resilient, sustainable agriculture. Overuse of chemical fertilizers and monoculture farming to achieve this goal deteriorated soil health and negatively affected its microbiome. The rhizosphere microbiome of a plant plays a significant role in its growth and development and promotes the plant’s overall health through nutrient uptake/availability, stress tolerance, and biocontrol activity. The Intermountain West (IW) region of the US is rich in native plants recommended for low water use landscaping because of their drought tolerance. The rhizosphere microbiome of these native plants is an excellent resource for plant growth-promoting rhizobacteria (PGPR) to use these microbes as biofertilizers and biostimulants to enhance food production, mitigate environmental stresses and an alternative for chemical fertilizer, and improve soil health. Here, we isolated, purified, identified, and characterized 64 bacterial isolates from a native plant, Ceanothus velutinus , commonly known as snowbrush ceanothus, from the natural habitat and the greenhouse-grown native soil-treated snowbrush ceanothus plants. We also conducted a microbial diversity analysis of the rhizosphere of greenhouse-grown native soil-treated and untreated plants (control). Twenty-seven of the 64 isolates were from the rhizosphere of the native region, and 36 were from the greenhouse-grown native soil-treated plants. These isolates were also tested for plant growth-promoting (PGP) traits such as their ability to produce catalase, siderophore, and indole acetic acid, fix atmospheric nitrogen and solubilize phosphate. Thirteen bacterial isolates tested positive for all five plant growth-promoting abilities and belonged to the genera Pantoea , Pseudomonas , Bacillus , and Ancylobacter . Besides, there are isolates belonging to the genus Streptomyces , Bacillus , Peribacillus , Variovorax , Xenophilus , Brevundimonas , and Priestia , which exhibit at least one of the plant growth-promoting activities. This initial screen provided a list of potential PGPR to test for plant health improvement on model and crop plants. Most of the bacterial isolates in this study have a great potential to become biofertilizers and bio-stimulants.

PurposeThe aim of this paper is to deploy Six Sigma (SS) methodology for addressing the customer complaints pertaining to Chemical-X in an Indian chemical company.Design/methodology/approachThe study followed a structured Define, Measure, Analyze, Improve, Control (DMAIC) approach to address the customer complaints. The complaints have been classified into different categories along with a project charter in the define phase. In the measure phase, measurement system analysis (MSA) and supplier, input, process, output and control (SIPOC) have been applied. In the analyze and improve phase, why–why analysis, process capability study, how–how analysis, Gage repeatability and reproducibility and Taguchi design have been applied to optimize the manufacturing process parameters for Chemical-X. Lastly, in the control phase, validation of 20 batches has been piloted to validate the optimized parameters.FindingsThe findings of this study highlight the optimization and prioritization of the process parameters. It shows that humidity has the least impact on the manufacturing of Chemical-X, whereas shift type has the maximum impact. The experimental output indicates that the 1st Shift, the holding time after grinding should be twenty-four hours, and the temperature after grinding should be 40 °C to reduce the customer complaints concerning lumps formation in Chemical-X.Research limitations/implicationsThe study is performed for a single product (Chemical-X). It has focused only from the manufacturing process view and not from the transportation, suppliers and downstream supply chain view.Originality/valueA systematic and data-driven approach of the SS methodology ensured that the customer complaints due to lumps formation reduced from 5% (approx.) to 1% (approx.) which resulted in the cost saving of INR 4 million (approx.) annually.