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DMD-based Multi-Object Spectrograph (D-MOS): AIV and first light results
A Digital Micromirror Device (DMD)-based Multi-Object Spectrograph (D-MOS) with an integrated imager has been developed. The optical performance of the MOS is evaluated through comprehensive laboratory calibration and on-sky observations using the 1.3-meter J.C. Bhattacharya (JCB) Telescope at the Vainu Bappu Observatory (VBO). The system is designed to assess the viability of using a DMD as a programmable slit mechanism for future ultraviolet-optical space missions. A complete imager-cum-spectrograph assembly was constructed using off-the-shelf optical components and configured for operation in the optical band, employing a DLP9500 DMD with a 19201080 micromirror array. Calibration experiments established the DMD-to-detector coordinate mapping and validated the strategies for object selection and slit placement. On-sky tests in crowded stellar fields confirmed successful slit targeting, precise object alignment, and multiplexed spectral acquisition. The spectrograph achieved a peak efficiency of 32%, a spectral resolving power of R1000 at 6000 a multiplexing capability of up to 46 slits (extendable to 85), and a contrast ratio of 6000. These results demonstrate the robustness and effectiveness of the DMD MOS system under real observational conditions and raise its TRL level for use in next-generation spectroscopic space missions. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Digital micromirror device characterization in optical band for astronomical multi-object spectrograph
The Digital Micromirror Device (DMD), a micro-electro-mechanical system (MEMS) consisting of individually controllable micromirrors, has emerged as a versatile tool for astronomical instrumentation, particularly in multi-object spectroscopy (MOS). Unlike traditional slit masks or fiber-based systems, DMDs offer dynamic reconfigurability, enabling efficient light modulation and enhanced spectral acquisition. Their adaptability has led to widespread adoption in ground-based spectrographs (e.g., RITMOS, BATMAN, SAMOS, IRMOS) and feasibility studies for space missions (e.g., EUCLID, CASTOR, SUMO, SIRMOS). DMDs have demonstrated robustness in space qualification tests, including radiation exposure, thermal cycling, and mechanical stress, making them viable for space-based applications. Recent advancements, such as UV-transparent windows and enhanced coatings, further expand their potential for ultraviolet astronomy. In India, the success of AstroSats Ultra Violet Imaging Telescope (UVIT) has motivated the development of the next-generation INdian Spectroscopic and Imaging Space Telescope (INSIST), which includes a DMD-based MOS for UV/optical observations. To advance its Technology Readiness Level (TRL), we evaluated the Texas Instruments DLP9500 DMD (1920 1080 micromirrors, 10 m pitch) in the optical band, assessing key parameters such as diffraction efficiency, reflectivity, contrast, micromirror repeatability, and Point Spread Function (PSF) alignment. This study establishes a foundation for future UV-optimized DMD applications in INSIST and other astronomical missions. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Adapting Employee Engagement Strategies Amid Crisis: Insights from the COVID-19 Pandemic
Crises are unpredictable events that have the potential to strike at any moment, causing significant disruptions to work, daily routines, and the normal course of life. The COVID-19 Pandemic served as a facilitator for transformative changes in the way we work, shifting to an era of remote and flexible work arrangements across industries. This crisis underlined the importance of employee engagement and organizational culture-building in navigating unforeseen situations. As organizations prepare for the future, it becomes crucial to anticipate and adapt to potential crises that may arise. The effect of the pandemic varied from industry to industry. When the technology industry worked towards creating a virtual workspace, the production industry strived to continue production without disruption. However, irrespective of the industry, HR teams across the board were dedicated to identifying and addressing the challenges posed by the crisis. They have worked tirelessly to ensure employee engagement remains a priority. This qualitative study explores the challenges encountered by HR teams during the pandemic and explores the strategies and policies they adopted to foster employee engagement. The data was collected through an in-depth interview with 39 HR Practitioners from different industries. The significant challenges included the need to cultivate a sense of community, navigate muddled up HR processes, sustain productivity amid disruptions, and prioritize employee wellness. To provide a comprehensive analysis, this study examined industry-specific approaches, employing within-case analysis to understand key strategies in communication, rewards and recognition, employee benefits, wellness initiatives, and fostering an enjoyable virtual workplace. This study offers a forward-looking perspective and serves as a guide for organizations aiming to thrive in times of uncertainty, ensuring that employee engagement remains a strategic priority regardless of the crisis at hand. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Integrated biogasification and carbon capture pathways: a system-level review of technologies, storage options, and deployment challenges
Carbon-negative energy systems that integrate bioenergy production with permanent carbon dioxide (CO2) sequestration are increasingly recognized as essential for achieving global net-zero and beyond-zero climate targets. While extensive research exists on individual components such as biogasification, carbon capture technologies, and geological storage, a coherent system-level synthesis linking these pathways remains fragmented. This review addresses this gap by providing an integrated assessment of biogasification-based carbon capture and storage (CCS) systems, with particular emphasis on techno-economic performance, capture efficiency, subsurface storage options, and deployment challenges. Following the PRISMA 2020 guidelines, 112 studies were systematically selected from an initial pool of 780 publications and analyzed to compare advanced gasification routes, emerging capture technologies, and storage strategies. The results indicate that hybrid gasificationsolid oxide fuel cell systems can achieve efficiencies of up to 55%, while cryogenic carbon capture consistently delivers CO? purities above 95% with reduced energy penalties. Supercritical water gasification and hydrothermal pathways demonstrate strong potential for wet biomass conversion, achieving hydrogen yields exceeding 1150 mmol/L and carbon efficiencies above 80%. Despite these technical advances, large-scale deployment remains constrained by high costs (USD 8001350 per tonne CO2), infrastructure limitations, and policy uncertainty. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
A bibliometric analysis of sustainability and organizations performance
The incorporation of sustainability into an organizations performance is becoming an emerging topic to work upon. Moreover, conventional economic systems have had significant negative consequences for sustainable management, as well as imbalanced wealth distribution, which has resulted in natural catastrophes and population disparity. Sustainability practices in the current environment represent better quality performances and affect organizations performance. This research highlights the key areas and current evolution in the notion of sustainable development and organizational performance, as well as recommendations for further studies. Using the bibliometric analysis we examine a sample of 1442 articles published in Scopus between 1994 till 2021. The researcher identifies prominent authors, publications, and journals by employing a variety of network analysis techniques such as term co-occurrence, co-citation, and bibliography coupling with the help of VOS viewer. To the best of the authors knowledge, no other study has examined bibliographic data on sustainability and organizations performance; hence, this research is a one-of-a-kind addition to the literature. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Role of energy sources in promotion of sustainable development: moderating implications of globalisation
This study empirically investigates the impact of renewable and non-renewable energy generation on sustainable development for a balancedpanel of 68 developed and developing economies from 1990 to 2019. This is done to scrutinise the intricate interplay between energy sources and sustainable development outcomes at the global level. The estimated models also control for the effects of globalisation, urbanisation, and government expenditure. The Westerlund cointegration establishes a significant long-run relationship between the variables under consideration. In this regard, the two-step dynamic system-generalised moment method (system GMM) demonstrates a positive impact of renewable energy, globalisation, and government expenditure on sustainable development. In contrast, non-renewable energy and urbanisation exert detrimental influences on it. However, both the energy sources demonstrate an amplified positive impact on sustainable development under the moderating influence of globalisation. The Feasible Generalised Least Squares estimation also confirms the long-run reliability of these baseline findings. Furthermore, Granger based non-causality test establishes a significant causal relationship between the variables under consideration. Potential policy suggestions for promoting the sustainable development are also discussed. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Investigating and analyzing the causality amid tourism, environment, economy, energy consumption, and carbon emissions using TodaYamamoto approach for Himachal Pradesh, India
Himachal Pradesh is a preferred tourist destination with a Compound Annual Growth Rate (CAGR) of 10.76% between 201112 and 202021. The increasing trend of CAGR has boosted the tourism economy in the state while impacting the local environment. The negative impacts have recently increased due to changes in climatic patterns and increased tourism influx during the post-pandemic period. In this context, the present study analyzed the impact of tourism on the environment, economy, and energy consumption using the Environmental Kuznets Curve (EKC) hypothesis. The novelty of this study is to the existing literature on sustainable tourism development through investigating the interrelationship between tourism, environment, economy, energy consumption, and carbon emissions by employing the TodaYamamoto (TY) technique. This study will be a pioneering scientific investigation with quantitative results in the western Himalayan states of India, encompassing Jammu & Kashmir, Uttarakhand, and Himachal Pradesh. The annual data for each variable, such as per capita carbon emission (CEP), per capita Gross State Domestic Product (GSDP), per capita GSDP square, per capita energy consumption (ECP), and per capita tourism receipts (TRP), was collected from 2010 to 2021. This study exhibited an inverted-U EKC in the state, signifying the initial stage of economic development and extensive exploitation of natural resources for tourism. The TY results indicated an inter-causal relationship and feedback association among the variables in the study area. Thus, increased TRP would lead to an upsurge in energy consumption affecting the environmental quality due to increased carbon emissions. Such environmental degradation in the state would negatively impact the tourism sector in the long run. The research findings would guide planners and policymakers in promoting sustainable tourism. The Author(s), under exclusive licence to Springer Nature B.V. 2023. -
Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-II-based optimisation method
The demand for alternative fuels is growing due to the depletion of fossil fuel resources. Non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like Non-dominated Sorting Genetic Algorithm-II. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26mg/kg concentration. The Author(s), under exclusive licence to Springer Nature B.V. 2023. -
Semi-analytical framework for dynamic stress concentration in semi-elliptical notches of thin walled piezoelectric media under SH-wave excitation and KNN
This study develops a semi-analytical framework to investigate the dynamic response of semi-elliptical notches in piezoelectric half-spaces subjected to shear-horizontal (SH) wave excitation. By employing wave function expansions in elliptical coordinates and Mathieu functions, the model efficiently solves boundary value problems in electromechanically coupled media and demonstrates greater versatility compared to conventional techniques. The analysis highlights how notch depth, wave incidence angle, and excitation frequency govern surface displacement and stress amplification. In particular, deeper notches under high-frequency excitation yield pronounced dynamic stress concentration, which raises concerns regarding the structural integrity of piezoelectric devices. Comparative results further reveal that materials with stronger piezoelectric coupling, such as PZT-5H, exhibit more severe stress localization than PZT-6B or BaTiO?. The study also examines the role of weak interfaces and nanoscale surface effects. Weak interfaces are shown to reduce stiffness in phonon and phason fields while increasing stiffness in the electric field for Rayleigh waves, with such effects becoming most prominent under strongly dispersive conditions. At the nanoscale, surface and interface influences effectively mitigate dynamic stress concentration, with diffraction stress concentration factor (DSCF) decreasing monotonically as the nano-influence factor increases, eventually tending to vanish in the limit of diminishing defect size. To complement the analytical formulation, a K-Nearest Neighbors (KNN) machine learning (ML) model was implemented using the analytical DSCF dataset. The classifier achieved nearly 90% accuracy in distinguishing between low and high stress concentration regimes. Decision maps highlighted frequencygeometry combinations most prone to defect amplification, while the confusion matrix confirmed reliable detection of critical hot-spots. This integration of ML provides a rapid surrogate framework that complements the semi-analytical method, enabling efficient prediction, defect screening, and design optimization in advanced piezoelectric systems. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
The development and validation of the digital intelligence scale for students
Digital intelligence is increasingly recognized as a vital skill in navigating both academic and personal digital environments, yet existing tools often use multidimensional or adult-oriented frameworks. This study aims to develop and validate the Digital Intelligence Scale for Students (DISS), a unidimensional self-report instrument designed to assess the general digital intelligence of school, college, and university students. The study was conducted in two phases. In the first phase, data was collected from 786 students in India to examine the factor structure of the model. The analysis supported a unidimensional model, indicating that all items measured a single underlying construct. In the second phase, data was collected from 611 students in India to confirm the unidimensional model. Results supported a robust unidimensional structure, with excellent internal consistency (? = 0.954). The DISS was found to be significantly correlated with Internet Skills Scale and Digital Literacy Scale, providing evidence for convergent validity. Divergent validity was assessed using State-Trait Anxiety Inventory and Big Five Personality Inventory. This scale provides a practical framework for evaluating digital readiness in educational settings and guiding interventions. Subsequent studies could validate its relevance across cultural contexts and examine developmental trajectories in digital intelligence. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network
Sentiment analysis is one of the most widely used methods for forecasting stock market action from consumer feedback. Most of the methods associated with sentiment analysis are limited due to low accuracy and enhanced error rate. This is addressed by proposing a synergic squeeze deep info convolutional neural network-advanced variable capsule equilibrium auto encoder (SSDCNN-AVCEAE) for sentiment analysis and accurate multiple data-based stock prediction. Stock market data from NSE Nifty 50 (Mar 2, 2020May 10, 2021) and real-time twitter sentiment analysis are pre-processed through data cleaning and sentiment analyzer lexicon processes. Merging features using SSDCNN, optimized with random search algorithm, mitigates overfitting. SSDCNN eliminates redundant features. Selected features undergo classification by AVCEAE, a fusion of advanced capsule auto encoder (ACAE) and variable equilibrium optimization algorithm, enhancing prediction accuracy for rising or falling stock market movements while minimizing errors. Variable equilibrium optimization refines ACAE parameters. The proposed framework demonstrates exceptional performance with F1-Score, accuracy, false alarm rate, sensitivity, precision, specificity, and error rate reaching 98%, 99%, 0.1%, 99%, 99%, and 0.2%, respectively. The measurements highlight the model's ability to handle a variety of issues, making it a reliable option for precise stock prediction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Extended Slash Modified Lindley Distribution to Model Economic Variables Showing Asymmetry
This article introduces a novel probability distribution to model economic variables with high kurtosis and heavy tails showing a decreasing trend. From a mathematical viewpoint, it corresponds to the distribution of the ratio of two independent random variables, one with the modified Lindley distribution and another with the beta distribution. In some sense, it can be described as an extended three-parameter version of the Lindley distribution that has the ability to model data with high kurtosis. After presenting this distribution in more in-depth details, a comprehensive analysis is given, including its associated functions, moments, skewness, and kurtosis characteristics. Furthermore, a parametric estimation work is carried out. A simulation approach is used to validate the performance of the obtained estimates. The applicability of the proposed distribution is demonstrated by fitting real-world data into various socioeconomic scenarios. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
From Dharma to Dialogue: A Scoping Review of Couple Interventions Based on Buddhist Wisdom
There has been a surge of interest in interventions based on Buddhist traditions in the domain of relational therapy research. Our scoping review aimed to present a comprehensive overview of the current research landscape on this topic. Through systematic selection criteria, we identified 16 studies. We discovered that these interventions predominantly focused on mindfulness or compassiontwo pillars taken from the Buddhist tradition. Although the findings are varied, the collated evidence indicates that Buddhism-based interventions are promising in improving physical, mental, and relational health for individuals and dyads. However, the sustainability of these benefits needs to be examined. A point of concern is the possible dilution of the practices effectiveness when stripped of their comprehensive, traditional Buddhist context. We conclude from this review that while interventions such as mindfulness- and compassion-based programs can positively affect well-being, their efficacy might be constrained when these practices are detached from their broader, original Buddhist context. Therefore, future research should expand the field to develop intervention programs that maintain the integrity of holistic Buddhist wisdom to enhance relationship health and well-being. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Resource allocation in cloud auction-based market by hybrid optimization algorithm
Effective resource allocation is essential in the rapidly changing cloud computing landscape to maximize provider revenue and user satisfaction. Through competitive bidding procedures, the auction-based market model has become a potent tool for allocating cloud resources among users. In this paper, a new method for cloud computing environments is presented: Double Auction-based Resource Allocation (DARA). The auction model and optimal resource allocation are the two main parts of the DARA methodology. The Double Auction mechanism is used as the auction model in the suggested DARA framework. In this model, resource prices and allocations are decided through a competitive auction process that involves both buyers and sellers.The highest price that buyers are willing to pay for resources is expressed in bids, and the lowest price that sellers are willing to accept is expressed in asks. There are many intricate tasks involved in this two-way auction process, including matching bids and asks, determining market prices, and handling transactions. Finding the equilibrium price requires the method to solve complex optimization problems in order to balance supply and demand. In order to overcome these obstacles, the study suggests the Hippopotamus Updated Pufferfish Optimization (HUPO) algorithm for the best possible resource distribution. The HUPO algorithm is made to handle limitations like truthfulness, resource density, execution time, and operating expenses. In order to ensure that users pay fair prices and service providers make the most money, it is crucial to implement effective resource allocation strategies that balance the cost of resources with their availability. According to the mean statistical metric, the resource density for the HUPO model is 17.862, which is greater than the values of all other traditional approaches, including BES at 14.960, AOA at 12.546, ACO at 14.274, COA at 13.693, SMO at 13.452, HOA at 13.686, and POA at 13.907. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Blockchain-based node authentication algorithm for securing electronic health record data transmission
The advent of Internet of Things (IoT) technologies in healthcare has heightened risks to Electronic Health Records (EHRs), including authentication vulnerabilities and data privacy concerns. This study proposes a novel blockchain-based node authentication algorithm for IoT healthcare, integrating Hyperledger Fabric, Homomorphic Encryption, and Recurrent Neural Networks (RNN). Employing a dual-layer security approach, the methodology utilizes a challenge-response mechanism and dynamic key exchange to ensure tamper-proof data transmission. Encrypted processing preserves confidentiality, while machine learning enhances anomaly detection accuracy to 99.01%, achieving a security rate of 99%. Comprehensive evaluations demonstrate significant improvements in efficiency, scalability, and robustness, addressing latency and computational overhead challenges. By fusing blockchains immutability with intelligent encryption and authentication, this solution revolutionizes EHR protection in IoT environments and scalable healthcare data management. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Multi-dimensional changes in drought patterns across India
Indias hydroclimatic systems are undergoing unprecedented transitions in a warming climate, marked by shifts in temperature extremes, altered precipitation patterns, and increasing drought risk. This study presents a comprehensive assessment of drought trends and hydroclimatic variability across six major geographical zonesWestern, Central, Himalayan, Indo-Gangetic Plain (IGP), Peninsular, and Northeast Indiaduring the period 1971 to 2020. Using a set of advanced climate change metricsStandardized Local Anomalies (SLA), Novel Climate Scores (NCS), and changes in probability of local climate extremes alongside the Standardized Precipitation Evapotranspiration Index (SPEI), we quantify changes in drought conditions and the emergence of non-analogue climates. Changes in climatic extreme are computed using high-resolution daily gridded temperature and rainfall datasets, comparing recent decades against a 19511980 baseline. SLA quantifies deviations from historical variability, highlighting intensified warming over the Indo-Gangetic Plain, western India, and the southern peninsula. NCS reveales the emergence of novel climatescombinations of temperature and precipitation conditions not previously observed, particularly in Southeast India and the Himalayan region. The probability of local climate extremes shows a substantial increase in extreme events across India indicating enhanced climate volatility. These metrics are then integrated with drought analysis using SPEI to incorporate both precipitation and temperature-driven evaporative demand. SPEI trends indicate increasing dryness in Northeast India, the Himalayas, and the Indo-Gangetic Plain, linked to declining monsoonal rainfall and rising temperatures. Meanwhile, Western and Peninsular regions show wetting trends, driven by increased rainfall and convective precipitation events. The rainfall is the dominant drought driver during the monsoon, while high maximum temperatures intensify drought conditions in pre- and post-monsoon seasons by enhancing evaporative demand. Minimum temperature exhibits regional effects, showing a drying influence in the IGP and Himalayas, but a slight moistening signal in Peninsular India. By combining drought indices with climatic extremes metrics, this study offers a comprehensive framework to monitor hydroclimatic shifts and their regional impacts. The findings underscore the need for region-specific adaptation strategies that incorporate early warning systems, sustainable water management, and climate-resilient agriculture to address Indias evolving drought risks. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Gravitational wave distance estimation using intrinsic signal properties: dark sirens as distance indicators
Gravitational Waves (GWs) provide a powerful means for cosmological distance estimation, circumventing the systematic uncertainties associated with traditional electromagnetic (EM) indicators. This work presents a model for estimating distances to binary black hole (BBH) mergers using only GW data, independent of EM counterparts or galaxy catalogs. By utilizing the intrinsic properties of the GW signal, specifically the strain amplitude and merger frequency, our model offers a computationally efficient preliminary distance estimation approach that could complements existing Bayesian parameter estimation pipelines. In this work, we examine a simplified analytical expression for the GW luminosity distance derived from General Relativity (GR), based on the leading-order quadrupole approximation. Without incorporating post-Newtonian (PN) or numerical relativity (NR) corrections, or modeling spin, eccentricity, or inclination, we test how closely this expression can reproduce distances reported by full Bayesian inference pipelines. We apply our model to 87 events from the LIGO-Virgo-Kagra (LVK) Gravitational Wave Transient Catalogues (GWTC), computing distances for these sources. Our results demonstrate consistent agreement with GWTC-reported distances, further supported by graphical comparisons that highlight the models performance across multiple events. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
The potential of hydrolyzed chicken feather meal as a partial replacement for fish meal and its effects on the growth and health status of African catfish (Clarias gariepinus) fingerlings
The application of agricultural by-products as alternative feed has received tremendous interest from the aquaculture industry. The current study explored the potential of hydrolyzed chicken feather meal (CFM) at different percentages as fish meal (FM) replacement and the impacts on growth, feed stability, apparent protein digestibility, digestive enzyme, body amino acid profiling, body proximate analysis, hematology, and morphology of African catfish (Clarias gariepinus) fingerlings. Five isonitrogenous (32% crude protein) CFM diets were prepared [0% CFM (T1), 5% CFM (T2), 15% CFM (T3), and 30% CFM (T4)] and applied in a 70-day feeding trial. At the end of the experiment, fingerlings fed with the T2 diet exhibited the best final length, final weight, net weight gain, weight gain, specific growth rate, intraperitoneal fat, and condition factor than other treatment groups. Furthermore, the highest digestive enzyme activity and apparent protein digestibility (APD) were highest in the T2 diet. There were significant differences between the groups in the liver, muscle, and intestine amino acid profiles and proximate analysis. Moreover, the T2 group recorded the best villus length, width, and crypt depth in the anterior and posterior regions. The highest white blood cells, lymphocytosis, monocytes, red blood cells, hemoglobin, and hematocrit were also found in the T2 diet group. Meanwhile, albumin, globulin, and creatine levels were the lowest in the T4 diet group. Notably, fingerlings supplemented with the highest CFM percentage demonstrated the highest morphological deterioration in the liver and intestine. In conclusion, 5% CFM is a promising FM replacement to improve the growth, apparent protein digestibility, digestive enzyme, liver and intestine histology, and blood indices of African catfish fingerlings. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
The Role of Regular Meditation Practice, Trait Mindfulness, and Psychological Characteristics in Affective Startle Modulation: A Psychophysiological Study
Meditation practices, including mindfulness, are linked with adaptive emotional processing and regulation. Although startle response modulation among meditators has been studied using habituation and prepulse-induced startle inhibition paradigms, affective startle modulation, which refers to potentiation by negative stimuli and attenuation by positive stimuli (both relative to neutral stimuli), remains unexplored. This study examined how regular meditation practice, dispositional mindfulness, and affective difficulties influence affective modulation of the acoustic startle reflex. Seventeen meditators and thirty non-meditators were exposed to pleasant, neutral, and unpleasant images while their eye-blink startle responses were recorded. Participants also completed self-report measures of dispositional mindfulness, alexithymia, emotion regulation difficulties, depression, anxiety, and stress. Meditators, compared to non-meditators, reported higher dispositional mindfulness, particularly in the Observing and Non-reactivity domains, lower stress, and fewer difficulties in goal-oriented behaviour during negative emotions; they also had longer startle onset latencies, potentially indicating lower state anxiety, across the entire experiment regardless of the valence of visual images. Higher dispositional mindfulness correlated with lower scores on alexithymia, emotion regulation difficulties, depression, anxiety, and stress across the pooled sample. These findings suggest that mindfulness, whether cultivated through meditation or as a trait, reduces negative emotionality, highlighting its potential for emotional regulation and stress reduction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Deciphering the plant growth-promoting traits of bacteria capable of sodium dodecyl sulfate removal from graywater: a sustainable approach for water reuse for irrigation
Sodium dodecyl sulfate (SDS), an anionic detergent found in cleaning products and cosmetics, is one of the chemical pollutants in waterways. SDS-utilizing bacteria were isolated from soil and water samples using 0.05% SDS basal medium. Three bacterial isolates were selected for 16S rRNA sequencing based on their ability to solubilize phosphate, potassium, and zinc, and they were identified as Pseudomonas putida MSK86 OR192890, Klebsiella pneumoniae NET12 OR345422, and Enterobacter sp. MSK86 OR398804. Enterobacter sp. MSK86 and K. pneumoniae NET12 lowered the SDS concentration in the sample 84.78% and 75.65%, respectively, while P. putida MSK86 reduced it 33.43% on the sixth day of incubation. A phosphate-potassium-zinc co-inoculum was prepared using Enterobacter and Pseudomonas species. Laundry wash water was added with the bacteria, individually and co-inoculum, and the fortified water was used to irrigate the Capsicum annuum L. seedlings. On the 45th day, the plants were harvested, and total glucose, protein, chlorophyll, and proline were checked by comparing control plants. Enterobacter sp. MSK86 increased carbohydrate and proline levels by 37.22mg/g ( 0.54 SE) and 2.44mg/g ( 0.1 SE), while K. pneumoniae NET12-treated plants showed an increase in chlorophyll by 1.95mg/g ( 0.02 SE) and total protein by 1.94mg/g ( 0.03 SE). The bacteria in this study showed they could lower SDS levels in graywater and improve farming by adding nutrients to the soil and plants, offering a sustainable way to tackle detergent pollution, fertilizer use, and water scarcity. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.