Search Results (11)

Pulse-width modulation (PWM) and pulse-density modulation (PDM) are widely used in applications where electrical energy is delivered in a pulsed manner. Typical examples include LED (light-emitting diode) control, DC motor control, switched-mode power supplies (SMPS), and electric heating control. However, the pulsed operation of power switches is often associated with significant electromagnetic interference (EMI). As an alternative, stochastic pulse-density modulation (SPDM), also referred to as stochastic signal density modulation (SSDM), can be considered. This technique distributes the energy of generated harmonics over a broader frequency spectrum, thereby reducing the amplitude of individual frequency components. As a result, unwanted frequencies become easier to filter out, mitigating EMI more effectively. Full article

In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the burst state was quickly detected through the limited data analysis of pressure monitoring points. The HLR method was introduced to enhance data features. DTL was introduced to improve the accuracy of small sample burst detection. The simulated data and real data were enhanced by HLR. Then, the model was trained and obtained through the DTL. The performance of the model was evaluated in both simulated and real scenarios. The results indicate that the leaked features can be improved by 350% by the HLR. The accuracy of SSDM reaches 99.56%. The SSDM has been successfully applied to the detection of real WDNs. The proposed method provides potential application value for detecting pipe bursts. Full article

Rapid infrastructure growth in developing countries has intensified environmental challenges due to cost-prioritizing practices over sustainability. This study evaluates 21 identified sustainable-driving tools to improve the management of infrastructure throughout its life cycle, by interacting with 20 out of 36 key infrastructure system management variables (ISMVs). Using a systems thinking approach, a Sustainable Systems Dynamic Model (SSDM) is developed, comprising a nucleus representing the interconnected stages of the life cycle: planning and design (S1), procurement (S2), construction (S3), operation and maintenance (S4), and renewal and disposal (S5). The model incorporates a total of 12 balance (B) and 25 reinforcement (R) loops, enabling the visualization of critical interdependencies that influence the sustainability of the system. In addition, its analysis shows the interdependencies between variables and stages, demonstrating, for example, how the implementation of tools such as LCA, BIM, and Circular Economy principles in S1, or IoT and SHM in S4, significantly improve sustainability. A gap between theory and practice in the adoption of sustainable practices is identified, which is aggravated by the lack of knowledge in specific developing countries’ context. Hence, this study contributes to its closure by offering a model that facilitates the understanding of key interactions in infrastructure systems. Full article

In this article, the resveratrol content and antioxidant activity of extracts obtained from Japanese knotweed (Polygonum cuspidatum Siebold & Zucc.) were evaluated. The extracts were prepared by pressurized liquid extraction (PLE), maceration, ultrasound-assisted solvent extraction (UASE), and sea sand disruption method (SSDM) using different extractants (methanol, methanol–water mixture, and water). The following methods were used to study antioxidant properties: ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), FRAP (ferric reducing antioxidant power), DPPH (2,2′-diphenyl-1-picrylhydrazyl), and CUPRAC (cupric ion reducing antioxidant capacity). It was proven that the resveratrol content depends not only on the extraction technique used but also on the solvent and extraction temperature. High resveratrol content was obtained by maceration and PLE using a mixture of methanol and water as the extraction solvent. Among the extracts tested, these were the ones showed the greatest antioxidant properties. However, it was confirmed that not only resveratrol but also other components of the extracts are responsible for the antioxidant properties. It was therefore shown that not only resveratrol, most commonly associated with Japanese knotweed, but also other ingredients affect the biological activity of this valuable-for-health plant. Full article

Currently, there is a significant demand for natural biologically active compounds. Emphasis is placed on improving the quality and safety of processed natural products, which is understandable in light of the frequently observed instability of natural compounds and their degradation, among others, to compounds of unknown biological activity. In this paper, the influence of typical conditions of currently used assisted extraction techniques on the stability of 5-O-caffeoylquinic acid and 1,3-di-O-caffeoylquinic acid during their simulated and real extraction from plants was investigated. In the experiments, extraction assisted by microwave radiation, ultrasound and pressure in procedures known as MASE, UASE and PLE techniques, respectively, was used. By comparing the amounts of native plant components, i.e., compounds present in the extract obtained, as shown, by the non-destructive SSDM technique with the amounts of these compounds estimated in extracts obtained by the above-mentioned techniques, it was proven that their content is variable. These differences are a consequence of two opposing processes, i.e., the success of the isolation process (its efficiency) and the degree of degradation/transformation of the main components. The results of the studies presented here can reduce the share of the second of the above, and consequently contribute to more effective obtaining of phenolic compounds from plants. Full article

In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model’s acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance. Full article

This study was conducted as an effort to examine the association between mammalian species richness and environmental, anthropogenic, and bioclimate factors in the Province of Chungnam, Korea, using a stacked species distribution model (SSDM) approach. An SSDM model was constructed using an extensive dataset collected from 1357 mammal sampling points and their corresponding forest, geographical, anthropogenic, and bioclimatic information. Distance to forest edge, elevation, slope, population density, and distance to water channels were identified as important variables for determining species richness, whereas the impact of bioclimate variables was less important. The endemism map showed a strong correlation with species richness, suggesting the important role of endemic species. Overestimation was observed in areas with lower species richness. However, the findings of the study still demonstrated that valuable insights can be obtained through the use of the SSDM, which may be helpful to land managers, aiding in the effective management of wildlife habitats, particularly in regions with an abundance of species richness and endemism. Full article

Crop-distribution information constitutes the premise of precise management for crop cultivation. Euclidean distance and spectral angle mapper algorithms (ED and SAM) mostly use the spectral similarity and difference metric (SSDM) to determine the spectral variance associated with the spatial location for crop distribution acquisition. These methods are relatively insensitive to spectral shape or amplitude variation and must reconstruct a reference curve representing the entire class, possibly resulting in notable indeterminacy in the ultimate results. Few studies utilize these methods to compute the spectral variance associated with time and to define a new index for crop identification—namely, the spectral variance at key stages (SVKS)—even though this temporal spectral characteristic could be helpful for crop identification. To integrate the advantages of sensibility and avoid reconstructing the reference curve, an object self-reference combined algorithm comprising ED and SAM (CES) was proposed to compute SVKS. To objectively validate the crop-identification capability of SVKS-CES (SVKS computed via CES), SVKS-ED (SVKS computed via ED), SVKS-SAM (SVKS computed via SAM), and five spectral index (SI) types were selected for comparison in an example of maize identification. The results indicated that SVKS-CES ranges can characterize greater interclass spectral separability and attained better identification accuracy compared to other identification indexes. In particular, SVKS-CES2 provided the greatest interclass spectral separability and the best PA (92.73%), UA (100.00%), and OA (98.30%) in maize identification. Compared to the performance of the SI, SVKS attained greater interclass spectral separability, but more non-maize fields were incorrectly identified as maize fields via SVKS usage. Owning to the accuracy-improvement capability of SVKS-CES, the omission and commission errors were obviously reduced via the combined utilization of SVKS-CES and SI. The findings suggest that SVKS-CES application is expected to further spread in crop identification. Full article

Mullet, a coastal fish species, is commonly used as a salted dried fish in many countries, including Korea, Japan, and the southeastern United States. The purpose of this investigation was to develop high-quality products of salted semi-dried mullet (SSDM) using natural salt and Salicornia herbacea L. (SAL). The antioxidant activity of SAL was investigated by in vitro studies. The physicochemical and nutritional characteristics of fresh mullet (FM), salted control (SSDM-CON), and SAL-treated (SSDM-SAL) mullet groups were analyzed. The moisture, ash, and crude protein contents were significantly increased in the SSDM-SAL group, whereas the salinity was decreased when compared with the SSDM-CON group. Lipid oxidation occurred in the FM and SSDM groups, as indicated by the increase in peroxide (PV), acid (AV), and thiobarbituric acid reactive substance (TBARS) values during the storage period. The protein pattern on the sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed similarities between the groups, while the amino acid and fatty acid contents also varied in the FM and SSDM groups depending on their processing methods. Initially, the total bacterial count was significantly higher in the SSDM groups than in the FM group. However, the SSDM-SAL group had a markedly lower total bacteria count than the FM and SSDM-CON groups during 21 days of refrigerated storage. This result indicates that SAL treatment can improve mullet’s safety from microorganisms, includes beneficial biochemical parameters, and can extend their shelf-life through refrigerated storage. Full article

The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obstacles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) products and modeled potential species richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial patterns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized difference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually developed models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data. Full article

Switched mode power converters are nonlinear systems, and it is a constant challenge to improve their modeling accuracy and control performance. In this paper, a State Switched Discrete-time Model (SSDM) is proposed, which achieves a higher accuracy at a high frequency than that of conventional state averaged models. Instead of averaging the converter states for approximation, the states within each switching cycle are considered in the modeling. Based on total differential equations of switching-ON and switching-OFF durations, the inductor current and output voltage within a cycle are accurately calculated, which derives the SSDM. Furthermore, a Digital Predictive Voltage Programmed (DPVP) control strategy is derived through the SSDM. Through voltage prediction, a suitable duty ratio is calculated that regulates the output voltage to its reference value in the minimum switching cycles. In this way, the converter achieves a very fast load/line transient response and reference tracking speed, and it exhibits a high stability under deviated inductance. Finally, the accuracy of SSDM and the system stability are proved by frequency response analyses and experiments. Full article