Search Results (243)

Intermediate-band photovoltaics promise single-junction efficiencies that exceed the Shockley and Queisser limit, yet viable material platforms and device geometries remain under debate. Here, we perform comprehensive two-dimensional device-scale simulations using Silvaco Atlas TCAD to analyze p-i-n In_0.20Ga_0.80N solar cells in which the intermediate band is supplied by In_0.35Ga_0.65N quantum dots located inside the intrinsic layer. Quantum-dot diameters from 1 nm to 10 nm and areal densities up to 116 dots per period are evaluated under AM 1.5G, one-sun illumination at 300 K. The baseline pn junction achieves a simulated power-conversion efficiency of 33.9%. The incorporation of a single 1 nm quantum-dot layer dramatically increases efficiency to 48.1%, driven by a 35% enhancement in short-circuit current density while maintaining open-circuit voltage stability. Further increases in dot density continue to boost current but with diminishing benefit; the highest efficiency recorded, 49.4% at 116 dots, is only 1.4 percentage points above the 40-dot configuration. The improvements originate from two-step sub-band-gap absorption mediated by the quantum dots and from enhanced carrier collection in a widened depletion region. These results define a practical design window centred on approximately 1 nm dots and about 40 dots per period, balancing substantial efficiency gains with manageable structural complexity and providing concrete targets for epitaxial implementation. Full article

This study explores the potential of craft-based activities in the context of special education, focusing on a papier mâché sculpting workshop implemented at the Special Kindergarten of Komotini, Greece, as part of the Horizon Europe Craeft project. The initiative aimed to assess how such creative activities could enhance the learning experience of children with intellectual and motor impairments, foster socialization, and develop fine motor skills. With reference to literature in art therapy, craft education, and inclusive pedagogy, the study applied a mixed-methods approach combining observation, visual analysis, and a survey. The findings indicate that, despite varied levels of participation based on individual needs, all students engaged meaningfully with the materials and activities. School professionals observed increased student engagement, emotional comfort, and communication, while also identifying the activity as well adapted and replicable in similar contexts. The results highlight the value of crafts in special education, not only as a sensory and cognitive stimulus but also as a means of fostering inclusion and self-expression. The study concludes with a call for further research into the role of tactile materials and hand gestures in relation to specific impairments. Full article

Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic audio, potentially enabling unauthorized access through ASV systems. To counter these threats, forensic detectors have been developed to distinguish between real and fake speech. Although these models achieve strong performance, their deep learning nature makes them susceptible to adversarial attacks, i.e., carefully crafted, imperceptible perturbations in the audio signal that make the model unable to classify correctly. In this paper, we explore adversarial attacks targeting speech deepfake detectors. Specifically, we analyze the effectiveness of Basic Iterative Method (BIM) attacks applied in both time and frequency domains under white- and black-box conditions. Additionally, we propose an ensemble-based attack strategy designed to simultaneously target multiple detection models. This approach generates adversarial examples with balanced effectiveness across the ensemble, enhancing transferability to unseen models. Our experimental results show that, although crafting universally transferable attacks remains challenging, it is possible to fool state-of-the-art detectors using minimal, imperceptible perturbations, highlighting the need for more robust defenses in speech deepfake detection. Full article

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

Because neural networks pervade many industrial domains and are increasingly complex and accurate, the trained models themselves have become valuable intellectual properties. Developing highly accurate models demands increasingly higher investments of time, capital, and expertise. Many of these models are commonly deployed in cloud services and on resource-constrained edge devices. Consequently, safeguarding them is critically important. Neural network watermarking offers a practical solution to address this need by embedding a unique signature, either as a hidden bit-string or as a distinctive response to specially crafted “trigger” inputs. This allows owners to subsequently prove model ownership even if an adversary attempts to remove the watermark through attacks. In this manuscript, we adapt three state-of-the-art watermarking methods to “tiny” neural networks deployed on edge platforms by exploiting symmetry-related properties that ensure robustness and efficiency. In the context of machine learning, “tiny” is broadly used as a term referring to artificial intelligence techniques deployed in low-energy systems in the mW range and below, e.g., sensors and microcontrollers. We evaluate the robustness of the selected techniques by simulating attacks aimed at erasing the watermark while preserving the model’s original performances. The results before and after attacks demonstrate the effectiveness of these watermarking schemes in protecting neural network intellectual property without degrading the original accuracy. Full article

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies. Full article

Understanding information extracted from receipts is a critical task for real-world applications such as financial tracking, auditing, and enterprise resource management. In this paper, we introduce ReceiptQA, a novel large-scale dataset designed for receipt understanding through question-answering (QA). ReceiptQA contains 171,000 question–answer pairs derived from 3500 receipt images, constructed via two complementary methodologies: (1) LLM-Generated Dataset: 70,000 synthetically generated QA pairs, where each receipt is paired with 20 unique, context-specific questions. These questions are produced using a state-of-the-art large language model (LLM) and validated through human annotation to ensure accuracy, relevance, and diversity. (2) Human-Created Dataset: 101,000 manually crafted questions spanning answerable and unanswerable queries. This subset includes carefully designed templates of varying difficulty (easy/hard) to comprehensively evaluate QA systems across diverse receipt domains. To benchmark performance, we evaluate leading vision–language models (VLMs) and language models (LMs), including GPT-4o, Phi-3B, Phi-3.5B, LLaVA-7B, InternVL2 (4B/8B), LLaMA-3.2, and Gemini. We further fine-tune a LLaMA-3.2 11B model on ReceiptQA, achieving significant improvements over baseline models on validation and test sets. Our analysis uncovers critical strengths and limitations of existing models in handling receipt-based QA tasks, establishing a robust benchmark for future research. Full article

Digitalisation has evolved as a multidimensional phenomenon and impacts the business world. SMEs heavily invest in digital platform capabilities to keep track of digital transformation, enabling them to perform business model experimentation to generate and develop innovation. This paper explores the role of these two crucial growth-promoting variables in the performance of art and craft-based firm’s performance. Through this paper, the researchers contest the argument that, although digital platform capabilities accelerate business model experimentation for firm performance, competitive advantage plays a significant mediating role. Along with these arguments, this study also explores the role of digital platform capability in business model experimentation. It examines the mediating role of business model experimentation in the forming of a competitive advantage. The research model under examination belongs to the explorative school of research; hence, the researchers have used partial least square–structural equation modelling (PLS-SEM) on a sample of 211 Indian firms belonging to the category of art and craft-based businesses. The hypothesis testing results facilitate exciting insights about the direct and indirect effects of digital platform capabilities, business model experimentation, and competitive advantage on firm performance. In light of the research findings, policymakers, SME consultants, and managers may obtain practical insights in order to develop an intervention mechanism. Researchers working in this area will glean a fresh look at the antecedents of SME performance as this model is explorative; future research may explore the testing of the model in different geographic locations and industry contexts. Full article

This study explores spiritual resilience as a mechanism for sustaining cultural landscapes, focusing on the UNESCO World Heritage Site of the Studenica Monastery (Serbia). By analysing the monastery’s sacred network, which includes monasteries, hermitages, and churches, the study demonstrates how material heritage (architecture, art), intangible practices (monastic life, liturgy, traditional crafts), and the natural environment (UNESCO MaB Golija–Studenica Biosphere Reserve) form a cohesive system of resilience. The concept of spiritual resilience is examined as a dynamic process that links sacred architectural structures and enduring religious practices with authentic land use preserved over centuries. We have utilised a methodological framework combining historical mapping, GIS viewshed analysis in spatial planning, and multidisciplinary data synthesis (historical, architectural, artistic, ecological, ethnographic) with resilience indicators aligned with the UNESCO’s Cultural Landscape approach. The findings reveal that Studenica’s sacred network operates as a coupled socio-ecological system. Spiritual practices, including annual processions and land stewardship rituals, have been identified as key factors in enhancing biodiversity conservation while mitigating land-use conflicts. Historical mapping has been used to highlight the overlap between sacred sites and protected ecological zones, reflecting traditional stewardship practices. By reframing heritage as an adaptive process where spirituality serves as a conduit between tradition and innovation, the study proposes replicable strategies for UNESCO sites worldwide. The concept of sacred landscapes as resilience hubs is furthered by alignment with SDG 11 (Sustainable Cities and Communities). Full article

This study investigates the effects of post-curing temperatures on the tensile properties of hybrid basalt-jute-glass-carbon fiber-reinforced polymers (FRPs). Composite specimens were post-cured at 60 °C and 100 °C for 60 min, and their tensile behavior was assessed using a servo-hydraulic testing machine. Numerical simulations using the Abaqus software V6.14 were also conducted to compare experimental and computational results. The findings indicate that post-curing heat treatment enhances ductility due to increased polymer cross-linking, but excessive heat treatment at 100 °C negatively impacts elongation at fracture. The results revealed that specimens post-cured at 60 °C exhibited the optimal balance between strength and ductility, with increased elongation and moderate tensile strength. However, at 100 °C, while tensile strength improved in some cases, a significant decrease in elasticity and an increased risk of brittleness were observed, suggesting that extreme heat treatment may degrade polymer integrity. Natural fiber composites, particularly jute-based samples, outperformed synthetic composites in terms of elongation and overall mechanical stability. The numerical simulations provided further insights but showed discrepancies with experimental results, mainly due to fiber property variations and fabric waviness, underscoring the challenges of accurately modeling woven composites. The study highlights the importance of controlled post-curing temperatures in optimizing the mechanical performance of FRP composites, with 60 °C identified as the most effective condition for achieving a favorable balance between tensile strength, flexibility, and material durability. These findings offer valuable insights for material scientists and engineers working on the development of high-performance composite materials for structural and industrial applications. Full article

Human gesture image recognition is the process of identifying, deciphering, and classifying human gestures in images or video frames using computer vision algorithms. These gestures can vary from the simplest hand motions, body positions, and facial emotions to complicated gestures. Two significant problems affecting the performance of human gesture picture recognition methods are ambiguity and invariance. Ambiguity occurs when gestures have the same shape but different orientations, while invariance guarantees that gestures are correctly classified even when scale, lighting, or orientation varies. To overcome this issue, hand-crafted features can be combined with deep learning to greatly improve the performance of hand gesture image recognition models. This combination improves the model’s overall accuracy and dependability in identifying a variety of hand movements by enhancing its capacity to record both shape and texture properties. Thus, in this study, we propose a hand gesture recognition method that combines Reset50 model feature extraction with the Tamura texture descriptor and uses the adaptability of GAM to represent intricate interactions between the features. Experiments were carried out on publicly available datasets containing images of American Sign Language (ASL) gestures. As Tamura-ResNet50-OptimizedGAM achieved the highest accuracy rate in the ASL datasets, it is believed to be the best option for human gesture image recognition. According to the experimental results, the accuracy rate was 96%, which is higher than the total accuracy of the state-of-the-art techniques currently in use. Full article

Background: The hospital supply chain (HSC) is one of the main levers for improving the performance of any healthcare organization. HSC stakeholders evolve in a dynamic environment marked by great complexity. This observation led us to conduct research, through which we examined several factors enabling operational performance to be achieved within the HSC. Methods: For the empirical verification, we opted for a survey of a relevant sample composed of health professionals operating in different Moroccan hospitals, particularly in the logistics departments. Afterwards, the data were analyzed using a Partial Least Squares-Structural Equation Modeling (PLS-SEM) method to test the hypothesized relationships in this study. Results: The results show that the adoption of Industry 4.0 technologies improve collaborative aspects between logistics processes and flows, and thus ensure better integration of HSC. The research also highlights the moderating effect of HSC complexity in the relationship between HSC integration and HSC operational performance, i.e., HSC integration increases HSC operational performance in a context marked by high complexity. Conclusions: This paper explores the impact of Industry 4.0 technologies on HSC operational performance. The study provides hospital managers and practitioners with insights to improve HSC operational performance through integration initiatives, ultimately better meeting the needs of healthcare professionals and contributing to improve the quality of care. Full article

Different academic disciplines approach the concept of materials from varying perspectives, and exploring these diverse interpretations can reveal new avenues for creative expression. While scientific analysis offers important insights into the transformation of materials, artistic and humanistic frameworks provide complementary lenses that enrich our understanding. In the context of artistic creation, the intentional and thoughtful use of materials is crucial, often producing unexpected and profound effects that engage audiences on multiple levels. The deep connection between artists and their materials is fundamental for preserving and transmitting cultural values. As the exploration of material transformation within art continues to evolve, it opens up a wealth of creative opportunities. However, significant challenges remain, particularly in evaluating whether transformed artworks truly resonate with audiences and in developing robust criteria for assessing the process of material transformation. This study tackles these challenges by employing a two-phase evaluation framework, focusing on four pairs of paintings reinterpreted as ceramic artworks, with feedback gathered from 389 online participants. The results demonstrate that artworks crafted from diverse materials are accessible to a wide range of viewers, providing valuable insights into the artistic process and broadening perspectives on material usage in art. While artistic creation remains a subjective endeavor, future research should aim to identify consistent patterns in audience reception and further explore the expressive potential of various materials in artistic practice. Full article

It has always been understood that Pablo Picasso imbued his arts with a rich symbolism. Those representations could be understood readily, at times only with some effort, or utterly inaccessible at others. A part of that symbolism is yet to be understood, with numerous points of information and cross-reference “hiding” in plain sight. He was fond of newsprint as a substrate and medium for painting, not only during wartime, but especially so in the deprivations of World War II. The relationship between some paintings typical during the period and the newsprint on which they were done was intense, such that the substrate inhabits the medium, sharing equal part with the composition. Around the same time or after, Picasso was crafting poems of an often cryptic nature. An in-depth look at two poems reveals a multitude of references to paintings on newsprint and to the contents of that newsprint. With new understandings of those symbols, evidence emerges that Picasso’s “palette of words” was more than just metaphor, but also descriptive of a theory and a method which the artist put into practice in at least two instances of WWII-era newsprint paintings and famously cryptic poems, detailed here. Full article

In this paper, I argue for a ‘social art history’ that embraces all protagonists of ancient Egyptian artistic production and integrates them into the global process of creating prestige through art. The raison d’être of artists is to translate their skills into material and immaterial media using culturally embedded codes and ideological trends of their time. In the process, artists—or at least top artists who accessed restricted knowledge—created value and prestige as a means of competition between rival elites (and the sub-elite emulating them). This paper aims to address the question of defining social value embedded in material artifacts, especially when owned by intermediary social categories such as the New Kingdom metalworkers. It will touch upon what was seen as valuable and prestigious from the Egyptians’ perspective by looking at the iconography of New Kingdom metalworkers. The paper will examine 18th–20th dynasty goldsmiths’ self-depictions as they were in charge of creating artifacts in gold, a metal connected with solar symbolism and intertwined with the divine, kingship, and membership in the high elite. Ultimately, the paper aims to tackle the question of self-presentation for people who were not part of the elite per se, i.e., the sub-elite illustrated here by the metalworkers. In so doing, it uses, in a preliminary attempt, some concepts inherited from the Chicago School of Sociology. Full article