11370 research outputs found
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SecureMed: Federated learning empowered approach for securing medical data
No full textIn today’s era, the world is suffering from an infectious disease COVID-19 which proved to be health predicament. The COVID-19 testing data is being produced by the healthcare center at preeminent scale. The efficient handling of such data by World Health Organization (WHO) team and medical practitioners is the considerable challenge. The researchers have introduced many deep learning approaches to detect COVID-19, but security remained challenging. In this paper, we present federated learning based approach ‘SecureMed,’ which allows to securely process the data. In this approach, two parameters, ‘client selection’ and ‘aggregation method’ are considered. The proposed scheme inherits Markov model for designing the these two parameters. The approach is been validated by using publicly available COVID-19 dataset of size 771, 634 Genomes (source: NCBI, U.S. National Library of Medicine). Taking into account the probability computations, the proposed approach succeeds in client local training by 50
Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm
Full text linkAgriculture is an essential component of human sustenance in this world. These days, with a growing population, we must significantly increase agricultural productivity to meet demand. Agriculture moved toward technologies as a result of the demand for higher yields with less resources. Increasing awareness of the significance and influence of agricultural practices in global climate change has made the use of energy-efficient innovations a vital aspect of the agriculture sector. The use of greenhouses to provide controlled environments that encourage effective plant growth is one of the current associated approaches. If not properly maintained, the energy used to run the greenhouses’ chillers, heaters, humidifiers, carbon dioxide (CO₂) generators, and carbon emissions becomes expensive. The goal of this research is to create a sustainable greenhouse model while achieving the best plant growth requirements with minimal use of energy. In order to achieve the lowest possible amount of energy consumption, the optimization model considered temperature, humidity, CO₂ levels, and sunlight as essential parameters in the environment. The Artificial Bee Colony (ABC) optimization technique was utilized for setting the environmental parameters for plant growth, considered for the suggested system. The system’s inputs were plant-preferred factors, and plant comfort was achieved by applying ABC to boost the parameters’ efficiency. A fuzzy controller was utilized to regulate different devices, including humidifiers, heaters, chillers, and CO₂ generators, by entering the introduced values. The overall efficacy of the fuzzy controllers that switch On/Off the actuators was obtained by minimizing the error between the best estimates of environmental factors and the ABC optimized values. Additionally, the suggested method was contrasted with other effective algorithms, such as Genetic Algorithm (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO). Based on the results of the comparison analysis between the ABC algorithm and current practices, present procedures do not minimize the fluctuations in the inaccuracy between the target and actual environmental parameters, which is a necessary step towards increasing energy efficiency. The suggested method used 162.19 kWh for temperature control, 84.65405 kWh for Humidity, 131.2013 kWh for Sunlight, and 603.55208 kWh for CO₂ management, indicating the maximum energy efficiency. ACO needed 172.2621 kWh, 88.269 kWh, 175.7127 kWh, and 713.2125 kWh, in contrast to FA 169.7983 kWh, 86.04496 kWh, 155.8442 kWh, and 743.7986 kWh. Temperature, Humidity, Sunlight, and CO₂ were measured by GA at 164.1609 kWh, 86.19566 kWh, 174.6429 kWh, and 734.9514 kWh, respectively. In terms of Plant comfort, the suggested approach also outperformed 0.986770848 ACO (0.944043), FA (0.949832), and GA (0.946076). It is important to note that the research being done has the potential to minimize operating costs and maximize the amount of energy needed for plant growth, thereby creating a model for sustainable greenhouse agriculture
Dr Who? Identity Crucibles and the DBA Doctoral Degree
Full text linkThis paper explores the triggers and identity crucibles facing professional doctorate students pursuing a Doctorate in Business Administration (DBA). Unlike prior research, which centres on full-time PhD students, our study examines the identity work of DBA students, many of whom do
not foresee a transition to academia. Through 35 semi-structured interviews, we explore the identity work involved in 'becoming a doctor' and the identity crucibles that DBA students encounter. We identify the internal and external triggers that lead to identity work and identity
crucibles, with accompanying emotions, and examine how these crucibles are resolved. This research develops current thinking in critical management education, revealing how DBA students do not uniformly aspire to become practitioner-scholars or academics. Instead, they start and end
their doctoral studies from various and different identity positions, highlighting the complexity of these long, intellectually challenging programmes. By exploring the triggers, identity crucibles, and resolutions, this study offers a nuanced understanding of the DBA student experience and provides valuable insights into how these individuals can be supported throughout their doctoral studies. This support enhances retention and completion rates for those tasked with managing these programmes and reduces the likelihood of losing exceptional scholars/practitioners
A finite element analysis framework for assessing the structural integrity of aero-engine ceramic matrix composite component coatings
Full text linkCeramic Matrix Composites (CMCs), and, in particular, SiC/BN/SiC, are currently being investigated to replace Nickel alloys in the manufacturing of aero-engine high-pressure turbine system components. Although superior to traditional metallic solutions in terms of resistance to high temperatures, CMCs are prone to oxidation and environmental degradation. For this reason, a multi-layer coating system is used to protect the CMC substrate. The aim of this paper is to define a Finite Element (FE) thermo-mechanical procedure to assess the integrity of the multi-layer coating. Among the four main failure mechanisms, vertical transverse cracking (denoted as “mud cracking”) and the thermally grown oxide (TGO) formation were numerically investigated. The FE (Finite Elements) procedure described in this paper, fully automated with the auxilium of MATLAB and Abaqus, is holistic and offers a simplified tool for the preliminary lifing of coating systems. TGO growth in the bond layer leads to the failure of the coating after 15,200 h, when its thickness reaches 0.02 mm, circa 20% of the bond layer (BND), and the stiffness and the strength of the BND drop to zero. The procedures and outcomes from the work are relevant for aero-engine designers and system engineers
Verification of MPI programs via compilation into Petri nets
No full textWe study parallel Java programs that use an implementation of the MPI (Message-Passing Interface) standard MPJ with the goal of proving their liveness that can be formulated in a simplified form as absence of deadlocks. In the process of parsing a given program, we create the program MPI communication model in the form of a Petri net. A static code analyzer has been developed; among its current version limitations are: point-to-point MPI communication functions Send and Recv are considered; models are obtained for a given number of processes; unfolding of loops is implemented for loops with the number of repetition known on the compilation stage. The obtained Petri net model of the program MPI communication is analyzed by Tina modeling system using the state space and symbolic state space techniques; application of the symbolic state space tool tedd for finding deadlocks yields manifold speed-up that makes feasible analyzing big software systems. The results are illustrated with a case study that includes both sets of program examples: deadlock free and containing deadlocks. The verification kit is supplied with generators of MPJ programs for testing and benchmarking the analyzer on big programs with pre-defined patterns of communications such as a chain and a loop of a given size, etc
Introduction: The nexus between taxation, human rights and sustainable development in the Global South
No full textAssessing compassionate abilities: Translation and psychometric properties of the Italian version of the compassionate engagement and action scales (CEAS)
Full text linkThis study aimed to develop the Italian version of the Compassionate Engagement and Action Scales (CEAS) and examine its validity and reliability among Italian-speaking adults. A total of 374 (mean age = 23.11) Italian speaking participants took part in the study. All of them completed a questionnaire comprising the CEAS, together with measures of self-compassion, self-criticism, social support, empathy, well-being and general distress, used to estimate the scale’s convergent and criterion-related validity. Confirmatory Factor Analysis (CFA) revealed a satisfactory fit for a model in which three second-order factors (Self-compassion, Compassion for others and Compassion from others) were further articulated in two first-order factors (Engagement and Action). All the scales presented good reliability in terms of internal consistency. Correlations with measures of social support, empathy, self-compassion, self-criticism, well-being, and general distress indicated good convergent and criterion-related validity of the Italian version of the CEAS. Taken together, these results suggest that the CEAS can be properly used with Italian-speaking individuals in order to assess the three compassion flows in terms of both engagement and action
Possession, witness or victim: A linguistic analysis of how children are positioned in discourses about family violence
Full text linkPurpose
Although the impacts of family violence on children are profound and lifelong, children are often invisible victim/survivors of family violence. This marginalization occurs both through the actions of adults around them and through the language used to discuss or occlude their suffering. A widely recognized example of this pattern is treating children as witnesses rather than victim/survivors. In this paper we explore how linguistic structures and word choices position children in submissions to two Australian inquiries that have addressed issues of child safety in the context of family violence from distinct perspectives, as set out in their terms of reference. The project was designed to understand the current positioning of children, any effects of the different foci and to consider ways of using language that are best for highlighting children’s experiences that align with their rights and realities.
Methods
The analysis addresses how children are positioned in discourse by focusing on the linguistic details of the submissions. We compiled comparable submissions into two corpora of their texts, each over two million words in size. We then used corpus linguistic tools to examine child and children and grammatical and lexical (word) choices surrounding their use in the organizational writing.
Results
With a particular focus on children as subjects of clauses, objects of clauses, the modifier of nouns and as possessors, the results reveal the forms that most meaningfully and frequently cooccur (measured by logDice Score) with child/ren and important ways these differ across the two corpora. Differences in both grammatical structures and word choices highlight key considerations relating to the position of children in professional discourse surrounding family violence, including foregrounding of their experiences and agency.
Conclusions
The inquiry that had children’s needs central to its aims was associated with word choices that amplify children’s individual capacities and experiences. This corpus of writing has a greater diversity of verbs and nouns associated with children with submission writers taking up the opportunities to consider children in more holistic ways that were offered in the terms of reference for the inquiry concerned
Unveiling circular economy pathways among small and medium hotels through a hybrid analytical framework
Full text linkSmall and medium-sized hotels (SME hotels), which constitute the majority of the global hospitality sector, face mounting pressure to align with sustainability goals. However, their transition to circular economy (CE) practices remains under-theorised and poorly implemented, particularly in emerging economies where institutional fragility and resource constraints prevail. Existing literature predominantly emphasises large firms and linear modelling approaches, overlooking the configurational, capability-driven processes that enable SMEs to adopt CE under complexity. This study addresses this gap by developing a tri-theoretical framework—grounded in the Resource-Based View, Dynamic Capabilities Theory, and Configurational Theory—to examine how internal resources and capabilities interact to shape CE adoption in SME hotels. Empirical insights were drawn from 300 executive respondents across Indian SME hotels. The study employs a hybrid analytical design integrating Partial Least Squares Structural Equation Modelling (PLS-SEM) to capture net effects, and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to explore causal asymmetries and equifinal pathways to CE implementation. Findings reveal Green Absorptive Capacity (GAC) as a consistent enabler and Green Dynamic Capabilities (GDC) as essential for adaptive CE strategies. While Green Human Capital (GHC) does not appear in dominant configurations, its role is found to be context-dependent rather than absent. The study contributes theoretically by advancing an integrated model that explains CE adoption beyond linear assumptions and practically by offering strategic guidance for SME hoteliers navigating environmental transformation. It advocates for investment in organisational learning, dynamic capability-building, and targeted policy engagement to support sustainable transitions under constraint
Enhancing GPS/IMU localization accuracy in autonomous vehicles through deep learning-based error correction models
Full text linkImproving the precision of GPS/IMU localisation in autonomous cars is crucial for ensuring safe and efficient navigation. Several research studies have concentrated on enhancing the precision of localisation systems by employing sensor fusion and sophisticated algorithms. This study proposes a novel approach to enhance GPS/IMU localization accuracy in autonomous vehicles using deep learning-based error correction models. Leveraging Long Short-Term Memory (LSTM) networks, the method captures and corrects inherent errors in GPS/IMU data, leading to significantly improved positional accuracy. The LSTM model was meticulously designed to process sequential data, incorporating an LSTM layer with 50 units to capture temporal relationships and a Dense output layer to predict corrected longitude and latitude values. The model was trained using the Adam optimizer and mean squared error (MSE) loss function, achieving notable reductions in prediction error across 50 epochs. Comparative analyses between actual and predicted coordinates demonstrated the model's high precision. The model was further validated through deployment in a Flask application for continuous testing and a web application for real-time tracking of autonomous vehicles. Results underscore the potential of deep learning models to substantially improve localization accuracy, thereby enhancing the reliability and performance of autonomous navigation systems