Search Results (26)

The preservation and innovation of traditional craftsmanship under industrialization pressures constitute critical challenges for cultural sustainability. Focusing on Chengdu lacquerware—a Chinese intangible cultural heritage facing multifaceted preservation dilemmas—this study develops a digital methodology for its systematic documentation and contemporary adaptation. Through computational analysis of 307 historical artifacts spanning four craftsmanship categories (carved silver mercer, carved lacquer hidden flower, carved filling, and broach needle carving), we established a three-phase digital preservation framework: (1) image preprocessing of 280 qualified samples using adaptive binarization and Canny edge detection for ornament extraction, (2) chromatic analysis via two-stage K-means clustering to decode traditional color schemes, and (3) creation of a digital repository encompassing color profiles and ornamental elements. The resource library facilitated three practical applications: modular recombination of high-frequency motifs, cross-media design adaptations, and interactive visualization of craftsmanship processes. Technical analysis confirmed that adaptive binarization effectively mitigated image noise compared to conventional methods, while secondary clustering enhanced color scheme representativeness. These advancements demonstrate that structured digital archiving coupled with computational analysis can reconcile traditional aesthetics with modern design requirements without compromising cultural authenticity. The workflow provides a transferable model for intangible heritage preservation, emphasizing rigorous documentation alongside adaptive reuse mechanisms. Full article

Here, we report an ultrasoft extra long-lasting, reusable hydrogel-based sensor that enables high-quality electrophysiological recording with low-motion artifacts. The developed sensor can be used and stored in an ambient environment for months before being reused. The developed sensor is made of a self-adhesive electrical-conductivity-enhanced ultrasoft hydrogel mounted in an Ecoflex-based frame. The hydrogel’s conductivity was enhanced by incorporating polypyrrole (PPy), resulting in a conductivity of 0.25 S m⁻¹. Young’s modulus of the sensor is only 12.9 kPa, and it is stretchable up to 190%. The sensor was successfully used for electrocardiography (ECG) and electromyography (EMG). Our results indicate that using the developed hydrogel-based sensor, the signal-to-noise ratio of recorded electrophysiological signals was improved in comparison to that when medical-grade silver/silver chloride (Ag/AgCl) wet gel electrodes were used (33.55 dB in comparison to 22.16 dB). Due to the ultra-softness, high stretchability, and self-adhesion of the developed sensor, it can conform to the skin and, therefore, shows low susceptibility to motion. In addition, the sensor shows no sign of irritation or allergic reaction, which usually occurs after long-term wearing of medical-grade Ag/AgCl wet gel electrodes on the skin. Further, the sensor is fabricated using a low-cost and scalable fabrication process. Full article

Among the indicators of the value and power ascribed to statues in Mesopotamia, reuse is a particularly significant one. By studying some of the best-documented examples of the usurpation and reassignment of a new function to sculptures in the round from the 3rd and 2nd millennia BC, our study reveals the variety of motives and methods employed. We hereafter explore the ways in which the status of such artefacts is maintained, reactivated, or adapted in order to secure their agency. Full article

There is a limited amount of research dedicated to designing and developing computing curricula specifically tailored for students with autism spectrum disorder (ASD), and thus far, no study has examined the effectiveness of an accessible computing curriculum designed specifically for students with ASD. The goal of this study is to evaluate the effectiveness of an accessible curriculum in improving the learning of computational thinking concepts (CTCs) such as sequences, loops, parallelism, conditionals, operators, and data, as well as the development of proficiency in computational thinking practices (CTPs) including experimenting and iterating, testing and debugging, reusing and remixing, and abstracting and modularizing. The study involved two groups, each comprising twenty-four students. One group received instruction using the accessible curriculum, while the other was taught with the original curriculum. Evaluation of students’ CTCs included the analysis of pretest and posttest scores for both groups, and their CTPs were assessed through artifact-based interview scores. The results indicated improvement in both groups concerning the learning of CTCs, with no significant difference between the two curricula. However, the accessible computing curriculum demonstrated significant enhancements in students’ proficiency in debugging and testing, iterating and experimenting, modularizing and abstracting, as well as remixing and reusing. The findings suggest the effectiveness of accessible computing curricula for students with ASD. Full article

As an important forest product, particleboard can greatly save forestry resources and promote low-carbon development by reusing wood processing residues. The size of the entire particleboard is large, and there are problems with less image feature information and blurred defect outlines when obtaining the particleboard images. The super-resolution reconstruction technology can improve the quality of the particleboard surface images, making the defects clearer. In this study, the super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that the super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct the dense block, and the dense blocks were constructed via a densely skip connection. Then, the corresponding 52,400 image blocks with high resolution and low resolution were trained, verified, and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the three indexes of PSNR, SSIM, and LPIPS. It was found that compared with BICUBIC, SRGAN, and SWINIR, the PSNR index of SRDAGAN increased by 4.88 dB, 3.25 dB, and 2.68 dB, respectively; SSIM increased by 0.0507, 0.1122, and 0.0648, respectively; and LPIPS improved by 0.1948, 0.1065, and 0.0639, respectively. The reconstructed images not only had a clearer texture, but also had a more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of the particleboard images with high quality. In the future, it can also be further combined with defect detection for the actual production to improve the quality of forestry products and increase economic benefits. Full article

Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer’s disease and frontotemporal dementia, and healthy controls. The dataset was collected using a clinical EEG system with 19 scalp electrodes while participants were in a resting state with their eyes closed. The data collection process included rigorous quality control measures to ensure data accuracy and consistency. The dataset contains recordings of 36 Alzheimer’s patients, 23 frontotemporal dementia patients, and 29 healthy age-matched subjects. For each subject, the Mini-Mental State Examination score is reported. A monopolar montage was used to collect the signals. A raw and preprocessed EEG is included in the standard BIDS format. For the preprocessed signals, established methods such as artifact subspace reconstruction and an independent component analysis have been employed for denoising. The dataset has significant reuse potential since Alzheimer’s EEG Machine Learning studies are increasing in popularity and there is a lack of publicly available EEG datasets. The resting-state EEG data can be used to explore alterations in brain activity and connectivity in these conditions, and to develop new diagnostic and treatment approaches. Additionally, the dataset can be used to compare EEG characteristics between different types of dementia, which could provide insights into the underlying mechanisms of these conditions. Full article

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively. Full article

Over the years in the field of conservation of cultural heritage, a wide use of traditional products for the plastic reintegration of wooden artifacts has been seen. However, they are usually not designed for this purpose. The present study also shows, in terms of material compatibility, the material most suited for wood restoration, cellulose pulp, from the perspective of a new green approach of reusing waste. Indeed, microcellulose was obtained by simple alkaline treatment from softwood almond shells. In particular, Prunus dulcis Miller (D.A.) Webb. was considered an agro-industrial waste largely available in southern Italy. To value the possibility of using this material in a circular economy framework, a microcellulosic material was used, by adding different binders, to manufacture several stuccos to utilize as wood consolidants. Successively, in order to obtain stuccos with biocidal properties against fungal colonization or insect infestation, to which wooden artifacts are often exposed, cellulose pulp was combined with the essential oil of Thymus capitaus (L.) Hoffmanns. & Link., whose biological properties have been largely reported. The physical flexion properties of all new materials were tested. Full article

The building and construction industry is a key sector behind the ecological transition in that it is one of the main responsible factors in the consumption of natural resources. Thus, in line with circular economy, the use of waste aggregates in mortars is a possible solution to increase the sustainability of cement materials. In the present paper, polyethylene terephthalate (PET) from bottle scraps (without chemical pretreatment) was used as aggregate in cement mortars to replace conventional sand aggregate (20%, 50% and 80% by weight). The fresh and hardened properties of the innovative mixtures proposed were evaluated through a multiscale physical-mechanical investigation. The main results of this study show the feasibility of the reuse of PET waste aggregates as substitutes for natural aggregates in mortars. The mixtures with bare PET resulted in less fluid than the specimens with sand; this was ascribed to the higher volume of the recycled aggregates with respect to sand. Moreover, PET mortars showed a high tensile strength and energy absorption capacity (with Rf = 1.9 ÷ 3.3 MPa, Rc = 6 ÷ 13 MPa); instead, sand samples were characterized by a brittle rupture. The lightweight specimens showed a thermal insulation increase ranging 65–84% with respect to the reference; the best results were obtained with 800 g of PET aggregate, characterized by a decrease in conductivity of approximately 86% concerning the control. The properties of these environmentally sustainable composite materials may be suitable for non-structural insulating artifacts. Full article

Small bowel polyps exhibit variations related to color, shape, morphology, texture, and size, as well as to the presence of artifacts, irregular polyp borders, and the low illumination condition inside the gastrointestinal GI tract. Recently, researchers developed many highly accurate polyp detection models based on one-stage or two-stage object detector algorithms for wireless capsule endoscopy (WCE) and colonoscopy images. However, their implementation requires a high computational power and memory resources, thus sacrificing speed for an improvement in precision. Although the single-shot multibox detector (SSD) proves its effectiveness in many medical imaging applications, its weak detection ability for small polyp regions persists due to the lack of information complementary between features of low- and high-level layers. The aim is to consecutively reuse feature maps between layers of the original SSD network. In this paper, we propose an innovative SSD model based on a redesigned version of a dense convolutional network (DenseNet) which emphasizes multiscale pyramidal feature maps interdependence called DC-SSDNet (densely connected single-shot multibox detector). The original backbone network VGG-16 of the SSD is replaced with a modified version of DenseNet. The DenseNet-46 front stem is improved to extract highly typical characteristics and contextual information, which improves the model’s feature extraction ability. The DC-SSDNet architecture compresses unnecessary convolution layers of each dense block to reduce the CNN model complexity. Experimental results showed a remarkable improvement in the proposed DC-SSDNet to detect small polyp regions achieving an mAP of 93.96%, F1-score of 90.7%, and requiring less computational time. Full article

A large number of publicly available documents, including patent documents and journal articles, can provide designers with creative stimuli, which could facilitate product innovation and collaborative design. As an important tacit knowledge, the acquisition, sharing, and reuse of design rationale (DR) is of great value to designers, which could help designers to better understand design intentions and ideas, support design automation, and promote better collaborative design. However, due to the fragmentation of DR in documentation, this hinders designer acquisition and reuse. If the DR fragments could be automatically extracted from the technical documents to build an interconnected knowledge network system, the problem would be effectively solved, which would further promote the development and utilization of digital archives. To address this issue, this study proposes a three-dimensional design knowledge network metamodel, Design Knowledge Semantic Network (DKSN), and a DKSN-based knowledge fusion method for the construction of a Design Knowledge Network (DKN). We set up an empirical experiment to verify the feasibility and performance of the method. Patent documents and open access research articles are used as sample documents, and a product data dictionary imported from ISO/TS 23768-1 is used as the predefined artifact dictionary. The results further confirm the feasibility and good application prospects of the proposed method. Full article

When sparsely sampled data are used to accelerate magnetic resonance imaging (MRI), conventional reconstruction approaches produce significant artifacts that obscure the content of the image. To remove aliasing artifacts, we propose an advanced convolutional neural network (CNN) called fully dense attention CNN (FDA-CNN). We updated the Unet model with the fully dense connectivity and attention mechanism for MRI reconstruction. The main benefit of FDA-CNN is that an attention gate in each decoder layer increases the learning process by focusing on the relevant image features and provides a better generalization of the network by reducing irrelevant activations. Moreover, densely interconnected convolutional layers reuse the feature maps and prevent the vanishing gradient problem. Additionally, we also implement a new, proficient under-sampling pattern in the phase direction that takes low and high frequencies from the k-space both randomly and non-randomly. The performance of FDA-CNN was evaluated quantitatively and qualitatively with three different sub-sampling masks and datasets. Compared with five current deep learning-based and two compressed sensing MRI reconstruction techniques, the proposed method performed better as it reconstructed smoother and brighter images. Furthermore, FDA-CNN improved the mean PSNR by 2 dB, SSIM by 0.35, and VIFP by 0.37 compared with Unet for the acceleration factor of 5. Full article

This paper outlines the path towards a method focusing on a process model for the integrated engineering of Digital Innovation (DI) and Design Science Research (DSR). The use of the DSR methodology allows for achieving both scientific rigor and practical relevance, while integrating the concept of innovation strategies into the proposed method enables a conscious approach to classify different Information Systems (IS) artifacts, and provides a way to create, transfer, and generalize their design. The resulting approach allows for the systematic creation of innovative IS artifacts. On top of that, cumulative DSR knowledge can be systematically built up, facilitating description, comparability, and reuse of the artifacts. We evaluate this newly completed approach in a case study for an automated conversational call center interface leveraging the identification of the caller’s age and gender for dialog optimization, based on machine learning models trained on the SpeechDat spoken-language resource database. Moreover, we validate innovation strategies by analyzing additional innovative projects. Full article

Enterprise Architecture (EA) has been applied widely in industry as it brings substantial benefits to ease communication and improve business-IT alignment. However, due to its high complexity and cost, EA still plays a limited role in many organizations. Existing research recommends realizing more of the EA potential. EA can be developed for specific purposes, accumulated in a digital repository, and reused when needed later. Due to the diversity and inconsistency of the repository, it is challenging to find relevant EA data and reuse it. In the present research, we propose using slicing techniques to extract EA models for reuse. We validate the method with an official EA repository hosted by The Open Group. The result shows that the method could facilitate extracting existing EA model components for developing new EA artifacts to save cost, alleviate maintenance effort, and help keep the repository consistent for future (re)use. Full article

The rapidly growing number of software-based features in the automotive domain as well as the special requirements in this domain ask for dedicated engineering approaches, models, and processes. Nowadays, software development in the automotive sector is generally developed as product line development, in which major parts of the software are kept adaptable in order to enable reusability of the software in different vehicle variants. In addition, reuse also plays an important role in the development of new vehicle generations in order to reduce development costs. Today, a high number of methods and techniques exist to support the product line driven development of software in the automotive sector. However, these approaches generally consider only partial aspects of development. In this paper, we present an in-depth literature study based on a conceptual model of artifacts and activities for the managed evolution of automotive software product line architectures. We are interested in the coverage of the particular aspects of the conceptual model and, thus, the fields covered in current research and research gaps, respectively. Furthermore, we aim to identify the methods and techniques used to implement automotive software product lines in general, and their usage scope in particular. As a result, this in-depth review reveals that none of the studies represent a holistic approach for the managed evolution of automotive software product lines. In addition, approaches from agile software development are of growing interest in this field. Full article