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From sources to Levels of Reference (LoRef) for the virtual reconstructions of the Priene Theatre: an interoperable and informative HBIM workflow
No full textVirtual reconstruction of archaeological architecture requires transparent and reproducible methods for managing heterogeneoushistorical sources within three-dimensional models. This paper presents a source-driven HBIM workflow that defines a Level ofReference (LoRef) to explicitly link reconstructed architectural elements to the documentary evidence supporting them. The methodology is applied to the virtual reconstruction of the ancient Theatre of Priene, considering multiple historical configurationsderived from archival drawings, excavation documentation, survey data, and interpretative studies. Main sources were classified using the IDOVIR Source Classification Taxonomy and mapped into the buildingSMART Data Dictionary (bSDD) to ensure semantic interoperability within an openBIM environment. LoRef information is assigned to HBIM elements via shared parameters and visual thematic labelling (source filtering), enabling explicit representation of source provenance at the component level. Instead of using predefined metrics for encoding accuracy or reliability, the proposed approach treats LoRef as a primary information layer, from which qualitative and quantitative assessments are derived in a second stage based on source typology and consistency. The resulting HBIM model functions as an interoperable, FAIR-compliant knowledge system that supports transparent documentation, reuse, and cross-platform dissemination of virtual reconstructions
Intent-driven network isolation for the cloud computing continuum
Full text linkThe computing continuum is a revolutionary cloud paradigm that integrates edge, fog, and cloud layers into a cohesive distributed system of interconnected devices, enabling seamless resource sharing across heterogeneous environments and administrative domains. Its interwoven nature introduces novel challenges, including enforcing proper network isolation between workloads by managing all possible communications. Existing solutions are inadequate as they fail to address the dynamicity and heterogeneity of the computing continuum, exposing users to security risks like cross-tenant interference or side-channel attacks. To address these security challenges, this paper proposes a security solution to automate the configuration of network isolation across the computing continuum. The solution facilitates the enforcement of advanced security patterns, such as zero trust and least privilege, across the several cloud layers involved in the continuum. It employs an intent-based approach, enabling users to specify security requirements in an intuitive, high-level language. The process relies on two core phases: smart verification and harmonization, followed by translation. Their design aims to ensure consistency in the defined intents and adaptability in addressing the evolving nature of the continuum, by simplifying the configuration of advanced security patterns and providing tenants with fine-grained control over network isolation. The approach was implemented in Kubernetes, demonstrating its effectiveness in automating the enforcement of user-defined intents via Kubernetes Network Policies, a common mechanism for network isolation in Kubernetes. The developed implementation was validated both qualitatively in a comprehensive use case, confirming its effectiveness for security management, and quantitatively to assess the performance of the different phases of the process
Horse ReIDing: Addressing Re-Identification in Horse Racing Scenarios
No full textRe-Identification (ReID) tasks, traditionally employed in person tracking across diverse camera views, face unique challenges in the domain of horse racing due to frequent occlusions, dynamic motion, and varying environmental conditions. This study addresses these complexities by developing a custom pipeline and dataset for jockey ReID, specifically collected from horse racing footage. A ResNeXt-based architecture is employed to process input data, with additional experiments exploring the inclusion of segmentation mask information for improved performance. Empirical evaluations demonstrate the model's efficacy in both closed-set and open-set scenarios, showcasing significant gains in mean Average Precision (mAP) and top-k Cumulative Matching Characteristic (CMC) rank metrics when segmentation masks are incorporated. Comparative analysis across different ResNeXt configurations underscores the robustness and scalability of the proposed approach, contributing as a pioneering framework for ReID in high-motion sports contexts and advancing the application of computer vision technologies in horse racing scenarios
An Integrated MCA–GIS Framework for Ground Mounted Solar Photovoltaic (GMPV) Site Selection: Methodological Proposal for the Italian Context
No full textThe increasing demand for renewable energy has led to a growing interest in ground-mounted photovoltaic (GMPV) installations. However, the selection of suitable locations for these installations requires a multi-dimensional evaluation that integrates regulatory, environmental, and technical constraints. This study addresses the challenge of locating suitable land for GMPV systems in Italy. We develop a spatial decision support model that integrates Multi-Criteria Analysis (MCA) with Geographic Information Systems (GIS) to incorporate multiple factors, including regulatory requirements, hydrogeological and geotechnical hazards, land use attributes, solar irradiation potential, proximity to infrastructures, and terrain morphology. Each criterion is assigned a weighted value reflecting its relative significance, and the combined analysis yields a priority index that supports decision-makers in identifying optimal sites for photovoltaic deployment. The methodology aims to reconcile the need for increased renewable energy production with broader environmental, social, and economic objectives, ensuring minimal conflicts with other land uses. By systematically evaluating risks such as flood vulnerability and slope instability, the framework facilitates the avoidance of high-risk zones. Furthermore, by considering variables like irradiation levels, land productivity, and ease of grid connection, it maximizes energy yields while limiting environmental impacts and infrastructural costs. This structured and transparent approach can guide regional authorities, urban planners, and private investors in implementing sustainable energy projects. The stakeholder perspective is integral to building consensus, facilitating more inclusive decision-making, and ensuring long-term acceptance and viability at the local level. This ultimately fosters more collaborative planning, aligning energy goals with societal and environmental demands
ShortNeXt: A novel method for accurate classification of colorectal cancer histopathology images
Full text linkCancer is a chaotic disease known as the plague of our age and there are many subtypes of the cancer. Cancer is commonly seen disorder and its mortality rate is very high. Therefore, many researchers have worked/studied on the cancer detection and treatment. To contribute cancer studies according to machine learning, we have presented a new generation convolutional neural network (CNN) termed ShortNeXt in this research. The presented ShortNeXt has inspired by ResNet, ConvNeXt and MobileNet architectures to use the advantages these CNNs together. This model, which aims to extract robust feature map using convolution-based residual blocks, is named ShortNeXt because it incorporates more than one shortcut. The ShortNeXt architecture has four main stages and these stages are: (i) an input/stem, (ii) ShortNeXt, (iii) downsampling, and (iv) output. In this CNN architecture, convolution, batch normalization and the Gaussian Error Linear Unit (GELU) activation functions have been utilized. In this aspect, the implementation of the recommended ShortNeXt is simple. The stem stage uses a 4 × 4 sized convolution with stride 4 like ConvNeXt and Swin Transformer and this operation is named patchify operation. Additionally, a 2 × 2 patchify block has been used in the downsampling block. In the ShortNeXt block, an inverted bottleneck has been used, and both 1 × 1 and 3 × 3 convolution blocks are employed in the expansion phase. The output layer has increased the number of filters from 768 to 1280 by using pixel-wise convolution, drawing inspiration from MobileNetV2 and a final feature map with a length of 1280 has been obtained by deploying global average pooling (GAP). In the classification phase, fully connected and softmax operators have been used.
To get comparative results about to the recommended ShortNeXt, a publicly available histopathological image dataset has been used and this dataset contains nine classes, and the proposed ShortNeXt has achieved 97.82% and 97.86% validation and test accuracy, respectively. The obtained results and findings openly showcases that ShortNeXt is an effective deep learning method for histopathological image classification for cancer detection/classification
Enhancing electric vehicle battery performance and safety through simulation and testing of key electrical components
Full text linkscenarios like fast charging and acceleration. The effects of these incidents are excessive heating, mechanical
stress and failure of electrical connections. This study presents a prototype of a multi-physics Digital Twin that
is a model of electromagnetic, thermal, and structural environments that evaluates the functionality of copper
busbars operating at EV operating current levels (100–500 A). The framework will consist of a combination
of cross-domain corroboration as simulations with the use of finite-elements and experimental testing. The
measurements indicate steady operation up to 200 A, small voltage drops (0.009–0.047 V), contact resistances
(12.4–18.7 μΩ), and magnetic emissions (8–40 mT), all within the IEC 60269-1, IEC 61439-1 and CISPR
25 limits. Localized heating (273 ◦C) and deformation (>1 mm) at 500 A transient loads are characteristic
of critical conditions, leading to design changes in the form of increased cross-sectional thickness, integral
cooling, and laminated structures. The paper presents an experimentally evaluated prototype of a Digital Twin
which is a bridge between simulation and physical experiment and can be used as a predictive instrument in
safer and more dependable busbar design in future EV battery systems
Environmental Impacts and Sustainability of Tannery: A Case Study
Full text linkLeather has been a commodity since ancient times, when primitive men hunted animals for food and used their hides and skins for clothes and tents. Nowadays, the tanning process is highly industrialised. The chromium tanning is the most widely used because it produces high-quality leather despite its serious environmental impacts. The purpose of this study is to analyse the environmental impact of an Indian company that carries out post-tanning operations on bovine hides, that is to say, from the so-called wet-blue to finished crust.
To do this, the Life Cycle Assessment (LCA) is implemented using the primary data provided by the company. The analysis has been carried out by the OpenLCA software, and 16 environmental impact categories have been evaluated. The results show that the processes for producing fuel (coal and diesel oil) and chromium(III) salts are the main contributors to the environmental impact for nearly all categories. These types of impacts are upstream, whereas the operations carried out by the company have impacts on the climate change category, due to the use of fossil fuels in the production process. Therefore, the direct action that the company could take is the substitution of fuel to produce energy with a renewable energy source. The comparison of these results with the whole tanning process present in the software confirms the limited impact of the post-tanning. At last, the results also evidence the methodological value of Life Cycle Assessment, which can be used to show what can be improved in one installation to reduce its environmental impact
Experimental investigation on the monotonic and cyclic behaviour of a structured clay at high confinement
Full text linkIn this paper, the monotonic and cyclic behaviour of a structured clay is experimentally
evaluated by means of anisotropically consolidated undrained triaxial tests. The material
behaviour under high confinement pressure is analysed with a view to underground gas storage
applications by imposing one-way fatigue loads characterised by large periods. The experimental results highlight the role of the material structure and the consequences of its progressive
degradation, which implies a fragile stress–strain response. The results also show the relevance
of time-dependent effects. Under monotonic strain-controlled conditions, such effects induce a
strong strain rate dependence of the peak strength. Under cyclic stress-controlled loading, the
fatigue life is shown to be influenced by the loading period and the characteristics of the sinusoidal history. Longer loading periods result in a lower number of cycles to failure. Similarly, larger
maximum imposed deviatoric stresses also result in a reduction in fatigue life. Conversely, a
somewhat counter-intuitive response is observed in tests carried out with the same maximum
imposed deviatoric stress, since an increase in loading amplitude implies an increase in the number of cycles to failure. The reasons for the observed results are discussed in detail in the light of
the peculiarities of the material response
A Symplectic Numerical Power Flow Framework Based on Wave Finite-Element Method for Assembled Structural Systems
Full text linkIdentifying the propagation paths of dominant wave modes in complex assembled structure is critical for implementing wave-based vibration and noise control strategies, such as phononic band gaps. This paper presents a symplectic numerical framework to compute the wave-mode power flow in engineering assembled structures based on wave finite element method (WFEM). The power orthogonality among wave modes is explicitly formulated through the symplectic orthogonality (SO) and its adjoint form (SAO), and this formulation is further extended to the Zhong-Williams and lambda(phi) symplectic schemes. The generalized symplectic adjoint orthogonality (GSAO) and phi_SAO are subsequently proposed, providing a physically consistent basis for modal diagonalization and coherent wave propagation within the generalized symplectic eigenspace. These developments enable direct computation of the forced response and power flow entirely within the symplectic space, without reverting to the wave space. Six power-flow formulations are systematically compared and shown to yield consistent results on both beam and cylindrical shell structures. An electric motor housing is used as a case study, in which the proposed approach establishes a wave-mode power flow network. It is noted that the power-flow formulation relies on symplectic orthogonality defined for conservative WFEM systems and therefore cannot be directly applied to non-Hermitian systems
CFD and Machine Learning Approaches for Predicting Air Permeability in Technical Textiles
Full text linkPredicting the thermo-physiological comfort of technical clothing requires an understanding of how microscopic textile structures influence macroscopic properties such as air,
heat, and moisture permeability. This work represents the first step towards a multi-scale
predictive tool capable of estimating key comfort-related properties from the geometrical
features of woven fabrics. Focusing on air permeability, the effect of structural and design parameters was investigated while keeping the fibre material (cotton) constant. A
computational framework that combines validated Computational Fluid Dynamics (CFD)
simulations with a Fully Connected Neural Network (FCNN) was developed, enabling
fast and accurate predictions before production. The CFD model accounts for both intraand inter-yarn porosity, ensuring reliability across a wide range of fabric configurations.
The FCNN, trained on simulation and literature data, achieved a mean absolute relative
error of 2.01% and a maximum error of 7.72%, demonstrating excellent agreement with
experimental results. The analysis highlights how weave type and yarn density govern
airflow resistance, offering an efficient tool for the design and optimisation of breathable
technical textiles