Hasil untuk "Technology (General)"

R. Buyya, Shin Chee, Yeo et al.

R. Want

F. V. Perevoshchikov, V. G. Bukreev

Currently, the development of new control approaches for asynchronous electric drives with stringent requirements for vibration-acoustic performance and spectral composition of autonomous inverter output currents represents a highly relevant research challenge. The key challenges in designing this class of electric drives stem from the relatively low effectiveness of existing technical solutions. This limitation arises either from constraints in current controller synthesis methods or from rigorous demands regarding power-to-weight and dimensional parameters. This paper presents an original method for generating control signals in an alternating current electric drive autonomous inverter. The proposed approach utilizes regulation based on the deviation of the generalized output voltage vector amplitude in the autonomous inverter. The synthesis procedure for such a controller begins with defining the desired closed-loop system transfer function. The system dynamic processes are determined by a characteristic polynomial that can be of arbitrary type. For comparative analysis, two controller types are examined: one based on a Butterworth filter and another utilizing a Newton polynomial. The study proposes employing bilinear transformation to implement the derived continuous functions in discrete form, enabling software implementation in Simulink and subsequent microprocessor-based execution. The developed model, which accounts for discrete control signal generation, has yielded the spectral composition of the drive converter output currents and voltage-frequency characteristics under parametric disturbances introduced by the control object. Results demonstrate that the Butterworth filter-based controller shows superior efficiency compared to both open-loop systems and closed-loop systems with Newton polynomial-based controllers. The obtained results can be effectively applied in the development of low-noise electric drives for specialized applications.

Xiaofang Li, Chunli Lei, Xiang Bai et al.

Condition monitoring of rotating machinery is critical for ensuring industrial safety and operational reliability. As a core component of intelligent diagnostic systems, domain adaptation methods have achieved notable progress in mechanical fault diagnosis. However, most existing approaches presume a fully shared label space between source and target domains, limiting their effectiveness under partial domain adaptation scenarios commonly encountered in industrial practice. In addition, they often struggle with classification uncertainty near decision boundaries. To address these challenges, this paper proposes a Selective Adversarial Augmentation Network (SAAN) for cross-domain rolling bearing fault diagnosis with partial label space alignment. The proposed framework designs a multi-level feature extraction module to enhance transferable feature representation and a Balanced Augmentation Selective Adversarial Module (BASAM) to dynamically balance class distributions and selectively filter irrelevant source classes, thereby mitigating negative transfer and achieving fine-grained class alignment. Furthermore, an uncertainty suppression mechanism is put forth to reinforce classifier boundaries by minimizing the impact of ambiguous samples. Comprehensive experiments conducted on public and proprietary bearing datasets demonstrate that SAAN consistently surpasses state-of-the-art benchmarks in diagnostic accuracy and robustness, providing an effective solution for practical applications under class-imbalanced and variable operating conditions.

Yao Zou, Mian Zhong, Shichen Li et al.

Flexible wearable strain sensors based on laser-induced graphene (LIG) have attracted significant interest due to their simple preparation process, three-dimensional porous structure, excellent electromechanical characteristics, and remarkable mechanical robustness. In this study, we demonstrated that LIG with various defects could be prepared on the surface of polyimide (PI) film, patterned in a single step by adjusting the scanning speed while maintaining a constant laser power of 12.4 W, and subjected to two repeated scans under ambient air conditions. The results indicated that LIG produced at a scanning speed of 70 mm/s exhibited an obvious stacked honeycomb micropore structure, and the flexible strain sensor fabricated with this material demonstrated stable resistance. The sensor exhibited high sensitivity within a low strain range of 0.4–8.0%, with the gauge factor (GF) reaching 107.8. The sensor demonstrated excellent stability and repeatable response at a strain of 2% after approximately 1000 repetitions. The flexible wearable LIG-based sensor with a serpentine bending structure could be used to detect various physiological signals, including pulse, finger bending, back of the hand relaxation and gripping, blinking eyes, smiling, drinking water, and speaking. The results of this study may serve as a reference for future applications in health monitoring, medical rehabilitation, and human–computer interactions.

Xiaoyuan Huang, Silvia Mirri, Su-Kit Tang

Artificial intelligence (AI) currently exhibits considerable potential within the realm of biodiversity conservation. However, high-quality regionally customized datasets remain scarce, particularly within urban environments. The existing large-scale bird image datasets often lack a dedicated focus on endangered species endemic to specific geographic regions, as well as a nuanced consideration of the complex interplay between urban and natural environmental contexts. Therefore, this paper introduces Macao-ebird, a novel dataset designed to advance AI-driven recognition and conservation of endangered bird species in Macao. The dataset comprises two subsets: (1) Macao-ebird-cls, a classification dataset with 7341 images covering 24 bird species, emphasizing endangered and vulnerable species native to Macao; and (2) Macao-ebird-det, an object detection dataset generated through AI-agent-assisted labeling using grounding DETR with improved denoising anchor boxes (DINO), significantly reducing manual annotation effort while maintaining high-quality bounding-box annotations. We validate the dataset’s utility through baseline experiments with the You Only Look Once (YOLO) v8–v12 series, achieving a mean average precision (mAP50) of up to 0.984. Macao-ebird addresses critical gaps in the existing datasets by focusing on region-specific endangered species and complex urban–natural environments, providing a benchmark for AI applications in avian conservation.

Cristhian Roman-Vicharra, Yiran Chen, Jiang Hu

In heterogeneous integration, where different dies may utilize distinct technologies, floorplanning across multiple dies inherently requires simultaneous technology selection. This work presents the first systematic study of multi-die and multi-technology floorplanning. Unlike many conventional approaches, which are primarily driven by area and wirelength, this study additionally considers performance, power, and cost, highlighting the impact of technology selection. A simulated annealing method and a reinforcement learning techniques are developed. Experimental results show that the proposed techniques significantly outperform a naive baseline approach.

Jinming Liu, Junyan Lin, Yuntao Wei et al.

Classical visual coding and Multimodal Large Language Model (MLLM) token technology share the core objective - maximizing information fidelity while minimizing computational cost. Therefore, this paper reexamines MLLM token technology, including tokenization, token compression, and token reasoning, through the established principles of long-developed visual coding area. From this perspective, we (1) establish a unified formulation bridging token technology and visual coding, enabling a systematic, module-by-module comparative analysis; (2) synthesize bidirectional insights, exploring how visual coding principles can enhance MLLM token techniques' efficiency and robustness, and conversely, how token technology paradigms can inform the design of next-generation semantic visual codecs; (3) prospect for promising future research directions and critical unsolved challenges. In summary, this study presents the first comprehensive and structured technology comparison of MLLM token and visual coding, paving the way for more efficient multimodal models and more powerful visual codecs simultaneously.

Antonios Saravanos

As technology increasingly aligns with users' personal values, traditional models of usability, focused on functionality and specifically effectiveness, efficiency, and satisfaction, may not fully capture how people perceive and evaluate it. This study investigates how the warm-glow phenomenon, the positive feeling associated with doing good, shapes perceived usability. An experimental approach was taken in which participants evaluated a hypothetical technology under conditions designed to evoke either the intrinsic (i.e., personal fulfillment) or extrinsic (i.e., social recognition) dimensions of warm-glow. A Multivariate Analysis of Variance as well as subsequent follow-up analyses revealed that intrinsic warm-glow significantly enhances all dimensions of perceived usability, while extrinsic warm-glow selectively influences perceived effectiveness and satisfaction. These findings suggest that perceptions of usability extend beyond functionality and are shaped by how technology resonates with users' broader sense of purpose. We conclude by proposing that designers consider incorporating warm-glow into technology as a strategic design decision.

Amir Mirzadeh Phirouzabadi

This research unravels the stationary or transitionary dilemma of hybrid technologies in transitions processes. A system dynamics technology interaction framework is built and simulated based on Technological Innovation System and Lotka-Volterra to investigate the inter-technology relationship impacts and modes that hybrid technologies establish with incumbent and emerging technologies. This is conducted for the case of conventional, hybrid and battery electric vehicles under various scenarios . Results reveal that, by acting as an exploration-hybrid solution, hybrid technologies maintain a transitionary role by supporting mainly the technological development side of emerging technology. On the contrary, by acting as an exploitation-hybrid solution, they hardly (or never) sustain an inhibitive role against both the technological and market development sides of incumbent technology. While hybrid technologies may play a stationary role on the market development side in transitions processes, simulation results show that maintaining all inter-technology relationship modes as business-as-usual (i.e., baseline scenario) but instead simultaneously strengthening the various socio-technical dimensions of emerging technology and destabilising the various socio-technical dimensions of incumbent technology (i.e., sociotechnical scenario) is a more promising pathway in both short term (e.g., an accelerated uptake of emerging technology and decline of incumbent technology) and long term (e.g., highest emission reduction). Findings, additionally, reinforce the existence of both spillover and try-harder versions of 'sailing-ship effect', which are either seriously doubted in the literature or partially validated using raw bibliometric and patents data.

Giuseppe Altieri, Sabina Laveglia, Mahdi Rashvand et al.

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R²P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R²P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R² = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling.

Hameed Byju, Hegde Maitreyi, Raveendran Natarajan et al.

Background Wetlands, globally, face significant threats from human activities, and waterbirds, as key indicators of wetland health, are essential to maintaining ecological balance. Any long-term conservation measures should prioritize coordinated habitat preservation, wetland restoration, and sustainable management practices involving local communities. Monitoring and analyzing waterbird population trends are critical for understanding restoration, conservation, and management practices. Methods The present study was carried out in five bird sanctuaries Chitrangudi, Kanjirankulam (Ramsar sites), Therthangal, Sakkarakottai, and Mel-Kel Selvanoor of Tamil Nadu, Southeast coast of India, over one year (April 2022 to March 2023). Monthly surveys using direct and block methods, with additional fortnightly visits during the breeding season, were conducted from vantage points to record species diversity, nesting activity, and conservation threats. Assessments of the residential status, national status (SOIB), and Convention for Migratory species (CMS) status were done along with the alpha and beta biodiversity profiles, principal component analysis, Pearson correlation and other statistical methods performed to assess breeding waterbirds community structure. Threats to the breeding waterbirds were categorised into high, medium, and low impacts based on degree of severity and irreversibility. Results The avifaunal checklist revealed a diversity of waterbird species utilizing the sanctuaries for breeding. Notable findings include two Near-Threatened species like, Asian Woolly-necked Stork Ciconia episcopus, and Spot-billed Pelican Pelecanus philippensis, where Asian Woolly-necked Stork recorded only in Therthangal Bird Sanctuary. Avifauna of each sanctuary with breeding waterbirds in parenthesis is as follows: Chitragundi 122 (13); Mel-Kel Selvanoor 117 (19); Therthangal 96 (23); Sakkarakottai 116 (17) and Kanjirankulam 123 (14). The breeding activity (incubation in nests) was from November to February except for Glossy Ibis and Oriental Darter whose breeding started in December; Spot-billed Duck and Knob-billed Duck breed only during January and February. Among the 131 species recorded from all the sanctuaries, 78% were resident birds; 27% were breeding waterbirds, and 21% were Winter visitors. The SOIB and CMS statuses underscore the necessity of implementing effective conservation measures to protect breeding habitats amid anthropogenic pressures. Water unavailability and nest tree unavailability in the sanctuaries are found to be the high degree threats to breeding waterbirds than others. This research provides critical baseline data for the forest department’s future wetland management plans.

Pratyusha Kiran

Streamlined production and innovative retail strategies have enabled the fashion industry to experience rapid growth in recent decades, with its complex global supply chain posing serious environmental and social sustainability challenges. Policymakers and advocacy groups have been demanding a transition toward a sustainable fashion system as awareness about the impact of this industry continues to rise. Although multiple initiatives and alternative business models have emerged in the sociotechnical system of fashion, a sustainable transition in this sector has not yet been realized. Conversely, the fashion system exhibits indications of being locked into unsustainable practices. This article aims to understand the barriers preventing the transition of the fashion industry to a sustainable system from the perspective of the actors within the supply chain. Ethnographic interviews with manufacturers and industry experts in India are leveraged to understand the challenges within the supply chain that are reinforcing the unsustainable practices in this industry. This article highlights the perspective of a developing country in the sustainability discourse. The interview analysis demonstrates that in fashion, brands implement various methods to attain sustainability through certifications or compliance standards in the manufacturing regions while lacking an understanding of local circumstances and contexts. This article argues that the disconnect between certifications and their implementation reinforces unsustainable behaviors in the supply chain instead of addressing them. Furthermore, it asserts that compliance efforts should steer away from the Western definition of sustainability and pivot toward sustainability strategies grounded in the local context of the manufacturing country.

McLuret, S. Joe Patrick Gnanaraj, Vanthana Jeyasingh

This study focuses on optimizing IoT-enabled stepped basin solar stills by integrating the Taguchi method, Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The objective was to enhance distillate yield, thermal efficiency, and system performance by optimizing key parameters—water depth, basin material, phase change material (PCM) type, and reflector angle. The Taguchi orthogonal array minimized experimental runs, while PSO and ABC algorithms refined parameter selection. Experimental results showed that a combination of 5 mm water depth, black copper basin, salt hydrate PCM, and a 45° internal reflector angle achieved a distillate yield of 3200 ml/day with 78.05 % efficiency, nearing the theoretical maximum of 4100 ml/day. Real-time IoT monitoring enabled dynamic adjustments, further improving efficiency. The findings highlight the effectiveness of combining smart monitoring and advanced optimization techniques to create scalable and sustainable solar desalination solutions for water-scarce regions.

George Tsamis, Georgios Evangelos, Aris Papakostas et al.

One significant digital initiative that is changing Greece’s tax environment is the myDATA platform. The platform, which is a component of the wider digital governance agenda, provides significant added value to enterprises and the tax administration, despite the challenges of adaption. Despite the positive response, we find that the development of the platform could have been carried out quickly and at a significantly lower cost and could have been able to cope much faster with the rapid and necessary changes that the platform will have to comply with. For these reasons, development in WordPress would be considered essential as this CMS platform guarantees a fast and developer-friendly environment. In this publication, as a contribution, we provide all the necessary information to develop a myDATA-like platform in a fast, economical and functional way using the WordPress CMS. Our contribution also contains the analysis of the minimum necessary amount of services of the myDATA platform in order to perform its basic functionalities, the description of the according database relational model, which must be implemented in order to provide the same functionality with the myDATA platform, and the analysis of available methods to quickly create the necessary forms and services. In addition, we study how to develop Artificial Intelligence mechanisms with a success rate reaching up to 90% for automatic tax violation detection algorithms.

Josh Andres, Chris Danta, Andrea Bianchi et al.

Generative AI capabilities are rapidly transforming how we perceive, interact with, and relate to machines. This one-day workshop invites HCI researchers, designers, and practitioners to imaginatively inhabit and explore the possible futures that might emerge from humans combining generative AI capabilities into everyday technologies at massive scale. Workshop participants will craft stories, visualisations, and prototypes through scenario-based design to investigate these possible futures, resulting in the production of an open-annotated scenario library and a journal or interactions article to disseminate the findings. We aim to gather the DIS community knowledge to explore, understand and shape the relations this new interaction paradigm is forging between humans, their technologies and the environment in safe, sustainable, enriching, and responsible ways.

Adriana Simancas, Justus Braach, Eric Buschmann et al.

Monolithic active pixel sensors (MAPS) produced in a 65 nm CMOS imaging technology are being investigated for applications in particle physics. The MAPS design has a small collection electrode characterized by an input capacitance of ~fF, granting a high signal-to-noise ratio and low power consumption. Additionally, the 65 nm CMOS imaging technology brings a reduction in material budget and improved logic density of the readout circuitry, compared to previously studied technologies. Given these features, this technology was chosen by the TANGERINE project to develop the next generation of silicon pixel sensors. The sensor design targets temporal and spatial resolutions compatible with the requirements for a vertex detector at future lepton colliders. Simulations and test-beam characterization of technology demonstrators have been carried out in close collaboration with the CERN EP R&D program and the ALICE ITS3 upgrade. TCAD device simulations using generic doping profiles and Monte Carlo simulations have been used to build an understanding of the technology and predict the performance parameters of the sensor. Technology demonstrators of a 65 nm CMOS MAPS with a small collection electrode have been characterized in laboratory and test-beam facilities by studying performance parameters such as cluster size, charge collection, and efficiency. This work compares simulation results to test-beam data. The experimental results establish this technology as a promising candidate for a vertex detector at future lepton colliders and give valuable information for improving the simulation approach.

Jun Cui

This study investigates the impact of integrating DevSecOps and Generative Artificial Intelligence (GAI) on software delivery performance within technology firms. Utilizing a qualitative research methodology, the research involved semi-structured interviews with industry practitioners and analysis of case studies from organizations that have successfully implemented these methodologies. The findings reveal significant enhancements in research and development (R&D) efficiency, improved source code management, and heightened software quality and security. The integration of GAI facilitated automation of coding tasks and predictive analytics, while DevSecOps ensured that security measures were embedded throughout the development lifecycle. Despite the promising results, the study identifies gaps related to the generalizability of the findings due to the limited sample size and the qualitative nature of the research. This paper contributes valuable insights into the practical implementation of DevSecOps and GAI, highlighting their potential to transform software delivery processes in technology firms. Future research directions include quantitative assessments of the impact on specific business outcomes and comparative studies across different industries.

S. Peil, W. Tobias, J. Whalen et al.

While optical clock technology has advanced rapidly in recent years, incorporating the technology into operational timescales has progressed more slowly. The highest accuracy frequency standards for groundbreaking measurements do not easily translate to critical timing where continuous, uninterrupted operation over many months and years is required. For example, intermittent steering of a hydrogen maser with an optical standard fails to harness all of the dramatic improvements possible with optical technology. Here we present progress on development and integration of optical-clock technology for operational timescales. An optical oscillator steered to an atomic fountain comprises a hybrid clock with optical-level stability at short times and a reliable long-term reference, and obviates the need for a steered maser. Atomic beam optical clocks are being developed to support 24/7 operations at a level that improves upon the performance of the U.S. Naval Observatory's rubidium fountains. An optical lattice is being developed as a gold-standard frequency reference, complementing the role of the atomic beam clocks.

Jose Manuel PINILLOS RUBIO, Minerva VIGUERA MORENO

This project develops a cloud-based solution for securely managing clinical data and patient-reported outcomes (PROMs) for multiple sclerosis (MS) patients. Utilizing REDCap for data collection, we incorporated clinical outcomes and PROMs from 300 MS patients over 18 months, supporting a machine learning (ML) based clinical decision support system. Our cloud architecture, featuring segregated data handling and enhanced security protocols using AWS, ensures robust data integrity and confidentiality. Key improvements include streamlined data ETL processes and an interactive online-based dashboard that facilitates the visualization of clinical data and PROMs, crucial for effective clinical decision-making. Initial results indicate a successful implementation in enhancing data management, with implications for personalized and predictive medicine. This framework not only elevates clinical data handling efficiency but also integrates PROMs into clinical practice effectively.