M.J.P. Rohilkhand University

Triticum aestivum L. (Bread wheat) is a vital global staple, necessitating innovative approaches for accurate grain yield prediction. Grain weight, a critical determinant of crop yield, is influenced by genetic and environmental factors as well as agronomic practices and seed morphometric traits. This study aimed to explore the relationship between seed morphometric traits and grain weight in wheat using digital image analysis and statistical methods. Seed traits such as length, breadth, thickness, and area were measured in 122 genotypes, and their impact on grain weight was assessed through multi-linear regression analysis and structural equation modeling. Our results indicated that seed length and thickness were significant predictors of seed weight, with mean length showing the highest standardized effect. Additionally, grain volume, width, and perimeter were essential factors influencing thousand-grain weight, accounting for over 99% of the variation in TGW. Horizontal seed area and perimeter were strong predictors of seed length, while vertical seed area and perimeter predicted seed width. Furthermore, vertical seed circularity, area, and perimeter were significant predictors of thickness. The structural equation model revealed that these factors strongly influence seed weight, with thickness and seed length being the most influential. Digital imaging proved to be an effective, non-destructive, and cost-efficient method for evaluating seed morphology, although challenges like the reliance on external features and the limitations of 2D imaging were noted. To address these issues, the study suggests integrating advanced techniques such as near-infrared spectroscopy and 3D imaging for a more comprehensive analysis. Overall, this research highlights the potential of seed morphometry in wheat breeding programs, emphasizing the importance of size and shape traits for improving grain quality and yield.

Antimony chalcogenides (Sb₂S₃ and Sb₂Se₃) have emerged as promising materials for solar energy conversion due to their exceptional optical and physicochemical properties. These materials are widely utilized as absorber layers in thin‐film solar cells, offering a cost‐effective and sustainable alternative for photovoltaic applications. In recent years, the solar cell capacitance simulator (SCAPS‐1D) has become an indispensable tool for predicting and optimizing solar cell performance, bridging the gap between theoretical modelling and experimental design. This article reviews the key insights from both experimental studies and SCAPS‐1D‐based simulations on Sb₂S₃ and Sb₂Se₃ solar cells. Despite significant progress, a notable disparity persists between theoretical predictions and experimental efficiencies, underscoring the need for further experimental advancements. This review also addresses current challenges and outlines future research directions to enhance the performance and scalability of Sb₂S₃ and Sb₂Se₃ solar cells. By offering a comprehensive overview, this work aims to benefit the researchers in advancing the development of high‐efficiency antimony chalcogenide‐based solar technologies.

This study explores the extraction and characterization of dietary fiber (DF) from carambola fruits, focusing on both peel and pulp fractions. After pretreatment with 96% ethanol to mitigate enzymatic activity, DF was extracted using both hot-air oven-drying and freeze-drying methods. Physicochemical properties, functional attributes, and nutritional composition of the extracted fractions were analyzed. Notably, DF derived from the peel exhibited superior functional, antioxidant, hydration, and physicochemical properties compared to DF from the pulp. The peel fraction of freeze-dried (FE) showed exceptional attributes, boasting the highest levels of DPPH assay (68.73%), total flavonoid content (14.98 mg QE/100 g DM), total phenolic content (156.6 mg GAE/100 g DM), ferric-reducing antioxidant power (458.3 mg AAC/g DM), vitamin C (52.12 mg ascorbic acid/100 g DM), and vitamin A (140.2 μg β-carotene/100 g DM). Additionally, it demonstrated a remarkable oil holding capacity (2.591 g oil/g DM). Furthermore, employing freeze drying as the extraction method proved advantageous, yielding DF with superior physicochemical, nutritional, antioxidant, and hydration properties compared to oven drying. Notably, freeze drying also demonstrated environmental benefits by minimizing energy consumption and CO2 emissions, aligning with green extraction practices.

Tocopherol (AT) exhibits poor aqueous dispersibility owing to its lipophilic nature and has constraint of low bioavailability. Phospholipid-α-tocopherol complexes (PL-ATs) were developed to improve its nanodispersibility, stability in lipolytic milieu, and proapoptotic potential. PL-ATs were prepared using phospholipid (PL) and AT in the 1.25:1, 1:1, and 1:1.25 weight ratios using solvent evaporation method. 1H-NMR (p-nuclear magnetic resonance), FTIR (Fourier transform infra-red), DSC (differential scanning calorimetry), and conductometric characterization showed that PL-ATs complexation resulted from hydrophobic interaction between their carbons chains, as well weak interaction between polar moieties of PL and AT. Ternary plots were constructed using Tween 20/ethanol taken as surfactant/co-surfactant mix (Smix) at 1:1, 2:1, and 3:1 weight ratios taking PL-AT complex (1:1) as oil phase. PL-ATs exhibit marked variations in the ternary phase behavior over non-complexed AT. Fully dilutable pattern could be ascribed to formation of one phase region among ternary components (PL-AT, Smix, and water) producing self-nanoemulsification of PL-AT. However, its non-complexed counterpart had two-phase region in ternary plots with no distinct dilution lines, eventually become coarse dispersion. Optimized formulation (F2) had Smix 1:1 (65% w/w), containing PL-AT 13.0% (w/w) produced self-nanoemulsification of PL-AT (1:1) upon aqueous phase dilution with droplet size of 12 ± 1.5 nm. F2 showed an extended release over a non-complexed AT (F7) in the phosphate buffer at pH 6.8 and in HCl buffer at pH 1.2. PL-AT (1:1) formulations were stable in pancreatic lipase milieu and remained nanoemulsified up to 2 h longer than non-complexed AT, without releasing free fatty acids (FFA) in the digest medium. MTT assays showed that the optimized nanoemulsion formulation (F2) had proapoptotic activity in MDA-MB-231 (breast cancer) cell lines. PL-AT interaction confers physical and lipolysis stability to AT through phospholipid complexation and facilitated development of nanoemulsion systems. PL-AT complexation–based self nanoemulsifying systems could be exploited as SNEDDS to improve aqueous solubilization of AT and their stabilization. Graphical Abstract

This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel‐2 imagery, we observed an increase in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high‐confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R² of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data‐driven afforestation efforts and inform sustainable environmental management practices.

Cadmium stress significantly affects plant growth by disrupting essential physiological and biochemical functions. It slows nutrient intake, causing slowed development and decreased biomass. Cd also produces reactive oxygen species, causing oxidative stress, which harms cell components like lipids, proteins, and DNA. This lowers chlorophyll levels, making photosynthesis difficult and stunting development. Cd’s toxicity affects hormone balance, enzyme activity, and cell structural integrity, leading to poor plant growth and decreased agricultural output. Acidified biochar (BC) can effectively overcome this problem. Biochar features high cation exchange capacity (CEC) and oxygen-containing functional groups may aid in the immobilization of heavy metals in soil via surface complexation and precipitation. Cd immobilization can be increased by treating biochar with acid, which exposes additional adsorption sites. It can significantly enhance plant growth by improving soil structure, encouraging water retention, and improving microbial activity as a slow-release nutrient. This study investigates the effects of combining BC as amendments to spinach, both with Cd and without stress. Four treatments (control, 0.45BC, 0.90BC, and 1.20BC) were applied using a completely randomized design in four replications. Results showed that 1.20BC treatment showed a significant increase in shoot fresh weight (86.21%), root fresh weight (96.20%), shoot dry weight (223.24%), root dry weight (42.38%), total soluble sugar (16.05%), total soluble protein (54.70%), compared to the 0BC under 20 mg Cd/kg soil contamination. Additionally, there were notable improvements in chlorophyll a (121.26%), chlorophyll b (10.91%), and total chlorophyll (32.12%) above the control in Cd stress, also showing the potential of 1.20BC. A significant increase in N, P, and K concentrations of shoot and root of spinach was also noted, which validated the effectiveness of 1.20BC over 0BC under cadmium stress. It is concluded that applying 1.20BC can potentially alleviate the Cd-induced stress in spinach.

The elemental concentrations/profile in plants is mainly influenced by various factors including genotype, species, and the environment. The extent of toxic elements and the soil and rice plant mineral composition in the rice growing region of Haryana, India is not known. To discern rice genotypes for nutritional and toxic element profiles, we gathered 58 indica rice genotypes cultivated across soils with pH levels ranging from 6.29 to 7.92. Sampling from 11 diverse sites in Haryana during the kharif seasons of 2020–2021 and 2021–2022, we analysed the concentration of 29 elements using Inductively Coupled Plasma Mass Spectrometry. Our investigation unveiled substantial disparities in elemental concentrations among genotypes from distinct locations, underscoring the influence of genetic, physiological, and environmental factors on ionomic variations. Notably, total Arsenic (As) and Cadmium (Cd) concentrations in grains spanned from 0.017 to 1.17 mg kg-1 and 0.03 to 0.66 mg kg-1dry weight, respectively. Alarmingly, several genotypes surpassed the Codex international standard’s proposed limit of inorganic As at 0.20 mg kg-1. Correlation analysis revealed significant disparities among elements across samples, illuminating diverse degrees of positive and negative interactions. Principal component analysis further indicated that ionomic alterations across genotypes predominantly stemmed from variations in soil-to-plant transport pathways. The study underscores that even within plants of identical genotypes, shifts in soil conditions trigger ionomic variations. Moreover, the associations primarily revolve around genotype screening for multi-element accumulation effects, offering insights for breeders to develop biofortified rice varieties with safe levels of hazardous elements like As and Cd.

This study aimed to evaluate the effects of the water-soluble fraction of crude oil (WSFO) on Indian carp (Labeo rohita) with and without treatment with zinc oxide nanoparticles (Nano-ZnO). A total of 225 fish were randomly assigned to five groups in triplicate for 21 days. Group I served as the control group. Groups II and III were exposed to 0.5% and 1% untreated WSFO, respectively. Groups IV and V received 5% and 10% WSFO treated with Nano-ZnO, while Groups VI and VII received 5% and 10% WSFO treated without Nano-ZnO. No blood samples were obtained from fish exposed to untreated WSFO, due to increased hemolysis. Exposure to treated WSFO increased creatine phosphokinase, alkaline phosphatase, aspartate aminotransferase, lactate dehydrogenase, and gamma-glutamyl transferase activities, while alanine aminotransferase activity decreased. Although a significant decrease was observed in total protein, globulin, and triglyceride levels, albumin and cholesterol increased. Thiol groups and glutathione peroxidase activity significantly decreased, while superoxide dismutase, catalase, total antioxidant capacity, and malondialdehyde levels increased. The findings showed that exposure to WSFO, whether treated or untreated, induces significant biochemical and oxidative stress responses in Labeo rohita. Although WSFO treated with Nano-ZnO mitigated hemolysis, it was unable to prevent enzyme and antioxidant imbalances, indicating persistent physiological stress.

The present study aimed to optimize a mouth-dissolving film (MDF) made from Pongamia pinnata stem bark extract to increase patient compliance and accelerate oral disease therapy. Several stem bark extracts were prepared, and karanjin was used as an herbal marker for the extracts. The ethanolic extract showed the maximum yield (12.10% ± 0.09%) and cytotoxic activity against human oral cancer (KB 3-1) and embryonic kidney cell lines. The MDF formulation was focused on incorporating a fixed amount of the extract and varying concentrations of HPMC E5 polymer, along with evaluating the performance of plasticizers like PEG 400 and propylene glycol (PG). An optimized formulation was determined based on disintegration time, wetting time, and folding endurance. The formulation consisted of HPMC E5 as a film-forming polymer, PG as a superior plasticizer, ascorbic acid as an antioxidant, and other ingredients contributing to solubility, dispersion, sweetening, and appearance. High-performance thin-layer chromatography-mass spectrometry analysis confirmed higher levels of karanjin in the optimized formulation, ensuring its successful incorporation and stability. Taste masking evaluations indicate a favorable taste profile and a high potential for patient compliance. The stability study displayed no significant changes in the physical characteristics of the film, affirming its stability and quality. In conclusion, the developed herbal-based optimized MDF presents a promising drug delivery system, offering enhanced patient compliance, taste masking, and stability. The MDF holds great potential for effective treatment and management of oral diseases, providing convenience and improved therapeutic outcomes.

The increasing electricity demand coupled with concerns over environmental degradation has propelled the quest for sustainable energy sources. Solar energy stands out as a favorable solution in terms of abundant availability, scalability, and minimal environmental effect. It explores the advancements in solar energy technologies and their role in achieving sustainable electricity generation. The abstract begins by elucidating the principles of solar energy conversion through solar photovoltaic cells and concentrated solar power (CSP) systems. It discusses the efficiency improvements and cost reductions achieved through technological innovations, such as multi-junction PV cells, thin-film technologies, and next-generation CSP designs. It highlights the importance of energy storage solutions with lithium-ion batteries and molten salt thermal storage, in mitigating intermittency issues and enabling the continuous incorporation of solar power into the grid. Moreover, it examines the socioeconomic consequences of widespread solar adoption which further facilitate different employment creation, energy access in remote areas, and economic growth. It underscores the significance of policy support, incentives, and regulatory frameworks in fostering the deployment of solar energy technologies on a large scale. In conclusion, this abstract emphasizes the pivotal role of solar energy technologies in realizing sustainable electricity generation. Through continued research, innovation, and strategic deployment, solar energy holds the promise of transforming the international energy model toward a cleaner, resilient, and sustainable future.

To explore and analyse the linkages between talent management strategies (TMS) and succession planning interventions in corporate organizations. A descriptive research design has been utilized to conduct the study based on cross-section panel data collected from senior-level human resources professionals associated with listed firms. Their insights have been collected through a structured questionnaire in Google Form. The study offers that as a part of TMS, the formal system for career aspiration does not necessarily indicate that there is a readymade/proactive succession plan exists in the listed firm. Further, it reveals from top management executives that if offered a development program, then it creates a greater chance for effective succession. Further, it occurs that standard talent reviews don’t necessarily influence the succession planning process in corporate firms. The present study will be beneficial for corporate companies and startups to plan their agenda for having the right talent and good successors on key management profiles, like appointing candidates to board-level positions. Even, a sound business continuity plan can be portrayed with having a proactive leadership succession in place. This study offers fresh perspectives on the board of director’s perspectives on managing talent and succession planning. The study is a first-of-its-kind attempt to investigate succession planning and TMS in listed corporate entities in an empirically designed methodology. The study is based on a small respondent size of 130. Findings might differ when a larger sample is analysed. Executive reluctance is another limitation while collecting data in a structured form. Keywords: Succession Planning, Talent Management Strategies, Career Aspirations, Top Management, Business Continuity

Mobile manipulators are time-varying, strongly coupled, nonlinear dynamical systems that are extremely susceptible to outside interference and system uncertainties. The position/force control problem of constrained mobile manipulators with time-varying disturbances and uncertainties in the system is addressed in this study. The design of the position/force control scheme first introduces a fractional-order sliding manifold, which guarantees a faster and fixed-time convergence of tracking errors with a precise assessment of the stopping time for the closed-loop dynamical system. Based on the fractional-order sliding manifold, a robust fixed-time fractional-order hybrid control scheme is proposed for position/force control of constrained mobile manipulators. The radial basis function neural network is employed in the proposed controller to approximate the nonlinear coupled states of the dynamics of the mobile manipulator system. A fast terminal sliding mode type reaching law is adopted for the faster response of system states toward their equilibrium points. The Lyapunov method and fractional-order Barbalat’s lemma are applied to examine the stability of the closed-loop system, and the weight matrices that make up the neural network’s structure are updated online. A numerical simulation investigation is carried out to demonstrate the superiority of the proposed control strategy over comparable ones that exist in the literature. Consequently, the robust position/force tracking control of the system’s states has been achieved and the tracking performance of the mobile manipulator is improved by the proposed control approach.

Tuta absoluta is one of the most destructive pests of tomatoes. Chemical insecticides used to control this leafminer harm all organisms, increasing the risk to public health and the environment. Developing natural alternatives, such as bioinsecticides formulated from essential plant oils, is a key strategy to address this problem. These volatile compounds, derived from the secondary metabolic pathways of plants, exhibit targeted activity against specific pest species. Their use is consistent with an environmentally responsible framework that reduces adverse impacts on ecosystems, protects non-target organisms, safeguards human health, and enhances the efficacy of integrated crop management systems. This study aims to determine the chemical composition of the essential oils (EOs) of from round leaf mint (Mentha rotundifolia) and crown chrysanthemum (Chrysanthemum coronarium) and to evaluate their toxicity to T. absoluta larvae in-vitro. The chemical composition of EOs obtained by steam distillation from the leaves of the plants was analyzed by gas chromatography coupled with mass spectrometry (GC-MS). Indeed, 77 volatile compounds representing 98.19% of the total oil of M. rotundifolia, including cyclobutane acetonitrile, 1-methyl-2-(1-methyl ethenyl)-, terpinene-4-ol, p-menthane, germacrene D, caryophyllene and myrcene, were the main compounds. However, farnesene, myrcene, eugenol, germacrene D, phytol, and pinene were significant components among 69 compounds representing 95.39% of the total oil of C. coronarium. Results showed that the EOs were toxic to the different larval stages. According to the Finney method, concentrations 2.88 and 1.07% are the LC50 of M. rotundifolia and C. coronarium oils, which induce 50% mortality of T. absoluta within 7 days of exposure. Statistical analysis of in-vitro tests showed that both EOs had a similar level of insecticidal efficacy by contact. The overall results showed that the oils used have been shown to have an important insecticidal effect and can be used as a source of biological and natural treatment against tomato leafminer (TLM).

This work examines the development of Automatic Speech Recognition (ASR) systems for low-resource languages, focusing on Hindi and Marathi, particularly in multilingual and code-switching environments. ASR systems, which convert spoken language into text, face significant challenges when applied to low-resource languages with limited data for training models. These challenges are exacerbated in multilingual settings, particularly during code-switching, where speakers alternate between languages within a conversation. This paper underscores the current state of ASR for Indic languages, highlighting linguistic complexities such as diverse sentence structures, phonetic variety, and frequent code-switching. Code-switching introduces additional challenges, as ASR systems must rapidly identify language boundaries and adapt to linguistic shifts. Present systems struggle to perform adequately with code-switched data due to the complexity of phonetic structures and the lack of comprehensive, annotated speech corpora. This work critically evaluates current methods and proposes improvements using modern deep-learning techniques to address the primary challenges in developing efficient ASR models for Hindi and Marathi. Moreover, performance comparisons of monolingual, bilingual, and multilingual ASR systems indicate that multilingual approaches are more effective in managing linguistic diversity. The efficacy of these systems can be evaluated using performance metrics such as the Phoneme Error Rate (PER) and the Word Error Rate (WER), which assess word recognition accuracy.

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.

Two marine-derived bacteria, Bacillus paralicheniformis (HR-1) and Bacillus haynesii (HR-5), were isolated from sediments and identified using 16S ribosomal RNA gene amplification and sequencing as well as biochemical analysis. The development of a bacterial consortium (HR-1 & HR-5) from these two bacteria was used to increase the production of the protease enzyme under various conditions, including fermentation media, carbon and nitrogen sources (1% w/v), different pH levels, incubation time, and the obtained enzyme, were detected using SDS-PAGE followed by purification. Bacterial consortium HR-1 & HR-5 exhibited maximum protease production (330.42 ± 4.47 U/mL) than the individual isolates HR-1 (156.32 ± 2.14 U/mL) and HR-5 (185.73 ± 5.14 U/mL) on supplementing peptone (1% w/v), 2.8% skim milk + N-broth, pH 9, and dextrose (1% w/v) after 48 h of incubation time. The purified enzyme showed increased activity at alkaline pH 9.0 and also in the presence of ions such as Ca +2 , Fe +3 , Mg +2 , and Mn +2. The purified protease obtained from the consortium HR-1 and HR-5 shows improved efficiency for stain removal from cloth as well as high keratinolytic efficiency for poultry feather degradation, making this enzyme suitable for industrial use, particularly in the textile and tannery sectors.

Autonomous underwater vehicles (AUVs) are highly nonlinear, coupled, uncertain, and time‐varying mechatronic systems that inevitably suffer from uncertainties and environmental disturbances. This study presents an intelligent hybrid fractional‐order fast terminal sliding mode controller that utilizes the positive aspects of a model‐free control approach, designed to enhance the tracking control of AUVs. Using a nonlinear fractional‐order fast terminal sliding manifold, the proposed control approach integrates intelligent hybrid sliding mode control with fractional calculus to guarantee finite‐time convergence of system states and provide explicit settling time estimates. The nonlinear dynamics of the AUVs is modeled using radial basis function neural networks, while bound on uncertainties, external disturbances, and the reconstruction errors are accommodated by the adaptive compensator. By using a fast terminal‐type sliding mode reaching law, the controller exhibits enhanced transient response, resulting in robustness and finite‐time convergence of tracking errors. Using fractional‐order Barbalat's lemma and the Lyapunov technique, the stability of the control scheme is validated. The effectiveness of the proposed control scheme is validated by a numerical simulation study, which also shows enhanced trajectory tracking performance for AUVs over existing control schemes. This hybrid technique addresses the complicated nature of AUV dynamics in unpredictable circumstances by utilizing the advantages of model‐free intelligent control and fractional calculus.