Multiple Environments Research Articles

Factors Affecting the Quality of Work Life for MBA Faculty In Kerala

Objectives: The primary objective of this study is to identify and examine the factors influencing the quality of work life (QWL) for MBA faculty members in Kerala, India. The study aims to assess elements such as workload, physical workspace, compensation, opportunities for advancement, flexibility in scheduling, recognition, decision-making participation, student interactions, organizational culture, technology access, ethnic compliance, and adherence to legal requirements. By identifying these influencing factors, the study seeks to offer insights that can help improve job satisfaction and work-life balance for MBA faculty, thereby supporting their professional well-being and academic effectiveness. Methods: To achieve these objectives, the study utilizes a factor analysis methodology, a statistical technique designed to identify underlying relationships among various variables. This method helps determine which specific factors significantly impact the quality of work life for MBA faculty members. Data for the analysis was collected through surveys administered to faculty members across several educational institutions in Kerala. Factor analysis was then applied to the collected data to categorize the factors and understand their impact on work-life quality for faculty. Results: The results of the factor analysis reveal several critical factors that substantially impact the quality of work life for MBA faculty members in Kerala. Key findings include the importance of manageable workloads, supportive organizational culture, adequate physical workspace, and a fair compensation structure. Additionally, factors such as flexible scheduling options, opportunities for professional growth, and recognition for achievements were shown to contribute positively to faculty satisfaction. The findings suggest that these elements collectively influence faculty job satisfaction and their overall quality of work life. Conclusion: The study concludes that enhancing the quality of work life for MBA faculty in Kerala requires a comprehensive approach addressing multiple work environment factors. Educational institutions should focus on optimizing workloads, improving workplace facilities, offering competitive compensation, and fostering an inclusive and supportive organizational culture. Implementing such improvements is anticipated to result in higher levels of job satisfaction, which could positively impact faculty performance and academic success in Kerala’s educational institutions. The insights gained from this study are expected to be valuable for policymakers and administrators aiming to create a supportive and productive work environment for MBA faculty.

DESENVOLVIMENTO DE UM SISTEMA DE GESTÃO PARA CLOUD (INFRAESTRUTURA MULTI-CLOUD)

This work aims to develop a multi-cloud resource management system to simplify the administration of infrastructures distributed across different cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI). The system was built using Node.js for the backend and Vue.js for the frontend, leveraging generic SDKs for integration with cloud services. The platform enables users to centrally view and manage resources such as computing instances, virtual networks, and object storage efficiently. The project concluded that the system fully met the established functional requirements, highlighting the importance of technological solutions in the modern context of resource management in multi-cloud environments. The developed solution stands out for its robustness and ability to optimize the administration of complex infrastructures, making it essential for companies operating across multiple cloud environments in an integrated manner.

The twin deficits hypothesis in Tanzania: Exploring the effect of budget deficits on trade deficits

The Twin Deficit Theory has been researched extensively across multiple economic environments. Still, its relevance and real-world consequences can vary dramatically based on the particular economic condition of a country. Therefore, this study empirically analyzes the impact of the budget deficit on the trade deficit in Tanzania. Quarterly time series data is collected from 2005 to 2023. Autoregressive Distributed Lag (ARDL) Model estimation with a bound cointegration test establishes the short- and long-term relationship. The results show that the budget deficit increases the trade deficit in the long run. This implies that the theory exists in Tanzania. The results highlight the critical need for the country to reduce its budget deficit by improving revenue collection, expanding the tax base, fighting tax evasion, and improving expenditure efficiency for better public resource management.

Integrative multi-environmental genomic prediction in apple

Abstract Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, non-additive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard G-BLUP. Including non-additive and enviromic-based effects resulted in a predictive ability very similar to the benchmark model. The deep learning approach achieved the highest predictive ability for three traits with oligogenic genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

Off-Policy Temporal Difference Learning with Bellman Residuals

In reinforcement learning, off-policy temporal difference learning methods have gained significant attention due to their flexibility in utilizing existing data. However, traditional off-policy temporal difference methods often suffer from poor convergence and stability when handling complex problems. To address these issues, this paper proposes an off-policy temporal difference algorithm with Bellman residuals (TDBR). By incorporating Bellman residuals, the proposed algorithm effectively improves the convergence and stability of the off-policy learning process. This paper first introduces the basic concepts of reinforcement learning and value function approximation, highlighting the importance of Bellman residuals in off-policy learning. Then, the theoretical foundation and implementation details of the TDBR algorithm are described in detail. Experimental results in multiple benchmark environments demonstrate that the TDBR algorithm significantly outperforms traditional methods in terms of both convergence speed and solution quality. Overall, the TDBR algorithm provides an effective and stable solution for off-policy reinforcement learning with broad application prospects. Future research can further optimize the algorithm parameters and extend its application to continuous state and action spaces to enhance its applicability and performance in real-world problems.

Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

Transforming Software Development Through Generative AI : A Systematic Analysis of Automated Development Practices

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.

Bioinspired cooperation in a heterogeneous robot swarm using ferrofluid artificial pheromones for uncontrolled environments

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.

Spectral density modulation and universal Markovian closure of fermionic environments.

The combination of chain-mapping and tensor-network techniques provides a powerful tool for the numerically exact simulation of open quantum systems interacting with structured environments. However, these methods suffer from a quadratic scaling with the physical simulation time, and therefore, they become challenging in the presence of multiple environments. This is particularly true when fermionic environments, well-known to be highly correlated, are considered. In this work, we first illustrate how a thermo-chemical modulation of the spectral density allows replacing the original fermionic environments with equivalent, but simpler, ones. Moreover, we show how this procedure reduces the number of chains needed to model multiple environments. We then provide a derivation of the fermionic Markovian closure construction, consisting of a small collection of damped fermionic modes undergoing a Lindblad-type dynamics and mimicking a continuum of bath modes. We describe, in particular, how the use of the Markovian closure allows for a polynomial reduction of the time complexity of chain-mapping based algorithms when long-time dynamics are needed.

Optimizing the Allocation of Trials to Sub-regions in Crop Variety Testing with Multiple Years and Locations

AbstractField experiments in crop variety testing are conducted in multiple environments. When the targeted growing area can be stratified into sub-regions (zones), a design problem arises regarding the number of trials to be performed in each sub-region. We propose an analytical solution and a computational approach for optimal allocation of trials to sub-regions in multi-environment crop variety testing for multi-year experiments. For our purpose, we use a linear mixed model and we determine allocations that are optimal for the prediction of pairwise linear contrasts of genotype effects. For computations, we use the OptimalDesign package. The analytical results are illustrated by a real data example.

N-doped carbon dots/SnS2 heterostructures for ultra-fast and sub-ppb-level detection of NO2 in multiple environments

Nitrogen dioxide (NO2) is one of the most common toxic gases, which not only impairs human health but also produces secondary pollutants induced by sunlight via associated photochemical reactions. Herein, we design a high-performance NO2 sensor based on the N-doped carbon dots/SnS2 (N-CDs/SnS2) heterostructure using density functional theory (DFT) calculations. The DFT results approve that the decoration of N-doped carbon dots (N-CDs) onto the SnS2 nanosheet enhances adsorption capacity and electron transportation compared with those of pure SnS2, significantly enhancing the sensing responses. In addition, these in-situ synthesized N-CDs onto SnS2 nanosheet leads to band alignment, thereby significantly improves conductivity and enhances transportation and storage properties of carriers via a synergistic interaction. Furthermore, employment of UV illumination significantly increases active adsorption sites and enhances response and recovery kinetics. As a result, the developed NO2 sensor achieves fast responses of a few seconds and a limit of detection down to sub-ppb-level with the help of UV illumination. It also shows good linearly, operated in both air and oxygen-free environments, at both room or low temperature (238 K) conditions. The developed multi-framework offers an efficient and precision detection of NO2 with ultrafast responses and ultralow concentrations in multiple environments.

Dynamic Multiobjective Optimization Based on Multi-Environment Knowledge Selection and Transfer

Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment as soon as possible. In particular, the introduction of transfer learning in DMOAs has led to good results in solving DMOPs. However, the selection of valuable historical knowledge and the mitigation of negative transfer remain important problems in existing transfer learning-based DMOAs. Method: A DMOA based on multi-environment knowledge selection and transfer (MST-DMOA) is proposed in this article. First, by clustering historical Pareto optima, some representative solutions that can reflect the main evolutionary information are selected as knowledge of the environment. Second, the similarity between the historical and current environments is evaluated, and then the knowledge of multiple similar environments is selected as valuable historical knowledge to construct the source domain. Third, solutions with high quality in the new environment are obtained to form the target domain, which can better help historical knowledge to adapt to the current environment, thus effectively alleviating negative transfer. Conclusions: We compare the proposed MST-DMOA with five state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results verify the excellent performance of MST-DMOA in solving DMOPs.

Migración de serverbox hacia un sistema de microservicios aplicando arquitectura vertical

Efficient data management is essential in public administration for the effective operation of municipal cadastral systems. In 2010, the Multifinality Cadastral Modernization Program was implemented, a partnership between the Puebla municipality government and Sistemas de Información Geográfica S.A. de C.V. (SIGSA), which included the ServerBox platform. Over time, the monolithic architecture of ServerBox showed limitations in maintenance, updates, and change deployment. This project hypothesizes that migrating ServerBox to a distributed microservices architecture, using a vertical approach, can overcome these challenges and improve operational areas of the system. The proposed design establishes a modular and scalable structure that facilitates system maintenance and evolution. The implementation includes the development of TagHelpers in ASP.NET Core to simplify HTML element creation and the use of snippets to streamline coding. Additionally, a JSON configuration file is defined to manage multiple deployment environments. This strategic approach to migrating ServerBox to a distributed and vertical architecture offers significant advantages in terms of scalability, maintenance, and software development efficiency, and can serve as a guide for similar projects in public administration.

SERAV Deep-MAD: Deep Learning-Based Security–Reliability–Availability Aware Multiple D2D Environment

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively.

Connect, care, create: practical framework for physical environment design for person-centred palliative care.

This article aims to explore architectural-rich insights derived from users' experiences within everyday practice in palliative environments and provides a practical framework for healthcare organisations, architects and researchers involved in (re)designing palliative environments for person-centred care. An ethnographic study involving participatory observation was undertaken to gain insight into the influence of palliative environments on the diverse users' subjective experiences. This immersive research took place across multiple palliative environments, encompassing a palliative care unit, a day care centre for palliative care and a hospice located in Belgium. Also, informal conversations and photo-elicitation interviews were conducted with residents, family members, healthcare professionals, volunteers and maintenance staff. Analysing the subjective experiences yielded a practical framework of four architectural atmospheres (proximity, support, engagement, comfort) intended to serve as guiding principles for designing palliative environments. To shape these atmospheres, this article elaborates on 17 spatial aspects and aligns them with real-life experiences of users within palliative environments, thereby enriching and contextualising these insights. This study presents a practical framework encompassing atmospheres, spatial aspects and overarching insights. It is augmented by supplemental material featuring real-life user experiences, all directed towards guiding the design of palliative environments in pursuit of person-centred palliative care. This article advocates for a collaborative, interdisciplinary, holistic design approach that acknowledges these considerations' interconnectedness and considers the users' multiple perspectives. Ultimately, this approach serves as a means of bridging design intentions and actual experiences encountered by users in their real-life contexts to assist healthcare organisations and architects in creating environments for person-centred palliative care.

Governing the Slovenian National Cancer Control Programme - Key Lessons Learnt

Abstract Issue Strategic documents addressing major public health concerns should extend beyond health sector. Cancer control is commonly perceived only as a health issue, thus involving diverse stakeholders is challenging. Description of the problem The inclusion of stakeholders within and beyond the health sector is crucial in the preparation, and through implementation of National Cancer Control Programmes (NCCP). Lessons learned on how to involve and maintain fruitful collaboration with diverse stakeholders in Slovenian NCCP are presented. Results Over the past 14 years Slovenia’s NCCP has built a reliable and reputable partnership with diverse stakeholders, a collaboration that continues to evolve. Efforts extend across multiple sectors, encompassing health, education, employment, research, environment, technology, and non-governmental organizations. NCCP has set up multistakeholder governance structures: an advisory board, board of experts, specialised expert groups, and a group of representatives of cancer hospitals. These groups engage a diverse range of experts and institutions nationwide. Whenever feasible, representatives of patient organizations are included. All these structures hold regular meetings, complemented by an annual event that convenes all stakeholders and also provides an invaluable platform for informal networking. To ensure seamless coordination of all activities, a dedicated team is essential. Our approach has evolved to embrace a dual leadership, comprising both a public health specialist and a clinical expert. Additionally, we benefit from the expertise of communication experts, who offer valuable insights into effectively reaching diverse audiences. Lessons Governing Slovenia’s NCCP by involving diverse stakeholders in planning and implementation activities has demonstrated its effectiveness. Furthermore, we are witnessing an increasing number of new stakeholders actively seeking out to participate in the NCCP. Key messages • Slovenia’s NCCP is recognized for its credibility and strategic approach. • Involving diverse stakeholders in cancer control endeavours has proven effective, fostering trust and partnerships.

Genetic loci associated with sorghum drought tolerance in multiple environments and their sensitivity to environmental covariables.

Climate change can limit yields of naturally resilient crops, like sorghum, challenging global food security. Agriculture under an erratic climate requires tapping into a reservoir of flexible adaptive loci that can lead to lasting yield stability under multiple abiotic stress conditions. Domesticated in the hot and dry regions of Africa, sorghum is considered a harsh crop, which is adapted to important stress factors closely related to climate change. To investigate the genetic basis of drought stress adaptation in sorghum, we used a multi-environment multi-locus genome-wide association study (MEML-GWAS) in a subset of a diverse sorghum association panel (SAP) phenotyped for performance both under well-watered and water stress conditions. We selected environments in Brazil that foreshadow agriculture where both drought and temperature stresses coincide as in many tropical agricultural frontiers. Drought reduced average grain yield (Gy) by up to 50% and also affected flowering time (Ft) and plant height (Ph). We found 15 markers associated with Gy on all sorghum chromosomes except for chromosomes 7 and 9, in addition to loci associated with phenology traits. Loci associated with Gy strongly interacted with the environment in a complex way, while loci associated with phenology traits were less affected by G × E. Studying environmental covariables potentially underpinning G × E, increases in relative humidity and evapotranspiration favored and disfavored grain yield, respectively. High temperatures influenced G × E and reduced sorghum yields, with a ~ 100kgha-1 average decrease in grain yield for each unit increase in maximum temperature between 29 and 38°C. Extreme G × E for sorghum stress resilience poses an additional challenge to breed crops for moving, erratic weather conditions.

Punjab Tar-1: A New Short Duration, High Yielding and Disease Resistance Long Melon Variety (Cucumis melo) Released in Pakistan.

The development of new crop varieties with favorable features is a continuing effort. With the passage of time, a cultivated variety becomes subjected to various biotic and abiotic plant stresses. This article describes development of an innovative and imaginative long melon cultivar called "Punjab Tar-1."This article discusses creation of a novel and inventive long melon variety known as "Punjab Tar-1." Current long melon varieties in Pakistan are sensitive to climatic conditions and susceptible to pest and disease attack. A New Short Duration, High Yielding and Disease Resistance Long Melon Variety (Cucumis melo) is released in Pakistan to address these current issues of long melon varieties A high yielding and early maturing variety of long melon “Punjab Tar-1” has been developed by Vegetable Research Institute, Faisalabad, Pakistan through mass selection. Plants were selected from an open pollinated indigenous source population with main emphasis on earliness, fruit shape, color and taste and yield. Selection intensity was 15% in 1st three selection cycles and 30% in subsequent four selection cycles whereas yield trials were started from 8th selection cycle. The fruit of this variety has a strong longitudinal rib that makes it difficult for eating by caterpillars. This variety has wide adaptability and exhibits stable yield across multiple environments. Notably, "Punjab Tar-1" contains 10.59 % crude protein and 1.08% crude fat content. Genetic uniformity and distinctiveness of this variety was confirmed through SSR polymorphism. Punjab Tar-1 variety has been approved by Punjab Seed Council in its meeting held on 2021 and was recommended for general cultivation in Punjab, Pakistan. Its certified seed is available for farmers at Vegetable Research Institute, Faisalabad. This newly approved variety will aid to save the precious foreign reserves by reducing vegetable seed import bill. In Pakistan, seed market of long melon is occupied by improved varieties. In multi-location evaluation, one said variety has exhibited high yield and stability than exotic germplasm. This is the first variety developed in Pakistan having good taste, early maturity, long shape, and high yield potential. DNA fingerprinting has further confirmed that it is distinct from local material, having novelty and diversity.

Semi-Supervised Online Continual Learning for 3D Object Detection in Mobile Robotics

Continual learning addresses the challenge of acquiring and retaining knowledge over time across multiple tasks and environments. Previous research primarily focuses on offline settings where models learn through increasing tasks from samples paired with ground truth annotations. In this work, we focus on an unsolved, challenging, yet practical scenario, specifically, the semi-supervised online continual learning in autonomous driving and mobile robotics. In our settings, models are tasked with learning new distributions from streaming unlabeled samples and performing 3D object detection as soon as the LiDAR point cloud arrives. Additionally, we conducted experiments on both the KITTI dataset, our newly built IUSL dataset and Canadian Adverse Driving Conditions (CADC) Dataset. The results indicate that our method achieves a balance between rapid adaptation and knowledge retention, showcasing its effectiveness in the dynamic and complex environment of autonomous driving and mobile robotics. The developed ROS packages and IUSL dataset will be publicly available at: https://github.com/npu-ius-lab/OCL3D.

Considerations on time resolution of neutron irradited single pixel 3D structures at fuences up to 1017 neq/cm2 using 120 GeV SPS pion beams

The proven radiation hardness of silicon 3D devices up to fluences of 1 × 1017 neq/cm2 makes them an excellent choice for next generation trackers, providing <10 μm position resolution at a high multiplicity environment. The anticipated pile-up increase at HL-LHC conditions and beyond, requires the addition of <50 ps per hit timing information to successfully resolve displaced and primary vertices. In this study, the timing performance, uniformity and efficiency of neutron and proton irradiated single pixel 3D devices is discussed. Fluences up to 1 × 1017 neq/cm2 in three different geometrical implementations are evaluated using 120 GeV SPS pion beams. A MIMOSA-26 type telescope is used to provide detailed tracking information with a ∼5 μm position resolution. Productions with single- and double-sided processes, yielding active thicknesses of 130 and 230 μm respectively, are examined with varied pixel sizes from 55 × 55 μm2 to 25 × 100 μm2 and a comparative study of field uniformity is presented with respect to electrode geometry. The question of electronics bandwidth is extensively addressed with respect to achievable time resolution, efficiency and collected charge, forming a 3D phase space to which an appropriate operating point can be selected depending on the application requirements.