Search Results (8,917)

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems. Full article

The rising prevalence of food intolerances and increased health and environmental consciousness has driven the demand for free-from (FF) and organic products. This study aims to analyze consumer needs, motivations, and challenges related to these products in Portugal through an online survey with 2268 eligible responses, with a median age of the participants of 41 years. The sample was predominantly female (76.9%), with male participants comprising 23.1% of the sample. The results show that 97.4% of the respondents were aware of FF products, with 60.0% being regular consumers, particularly of lactose-free milk and gluten-free bread and biscuits. Significant market gaps were identified in the variety of FF bread (62.8%), pastries (49.0%), and cookies (38.4%). The consumers identified high prices (84.9%) and excessive sugar/fat content (52.1%) as the main drawbacks of FF products currently on the market. The women showed greater knowledge and purchase levels compared to the men. The trust in organic certification was low (21.4%), with skepticism higher among the lower-income and male participants. Despite this, 78.1% believed in the health benefits of organic products, and 72.2% agreed that increased demand could lower prices. Overall, this study highlights the need for greater product variety, improved transparency, and consumer education to enhance market trust and accessibility. Full article

This study aims to develop biodegradable films by combining hemicellulose B (HB) with methylcellulose (MC) and carboxymethyl cellulose (CMC) at two mass ratios, HB/MC 90/10 and HB/CMC 60/40. The effect of plasticizers, glycerol (GLY) and polyethylene glycol (PEG), on these films’ mechanical and physicochemical properties was also investigated. Results showed that the film thickness increased with the addition of GLY and PEG. Moisture content was lower in plasticized films, possibly contributing to better storage. Plasticizers also induced more pronounced color changes, intensifying the lightness and yellowness. Physical attributes such as peel ability, foldability, and transparency were also noticeably improved, particularly in films with higher GLY and PEG concentrations. Additionally, plasticizers enhanced the mechanical properties more significantly in the HB/CMC films, as evidenced by improved tensile stress, elongation at break, elastic modulus, and toughness. However, oxygen and water vapor permeabilities, two of the most critical factors in food packaging, were reduced in the HB/MC films with plasticizers compared to the HB/CMC counterparts. The findings of this study bear significant implications for developing sustainable packaging solutions using hemicellulose B isolated from agricultural material processing waste. These biopolymer-based films, in conjunction with biobased plasticizers, such as glycerol biopolymer, can help curtail our reliance on conventional plastics and alleviate the environmental impact of plastic waste. Full article

As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, focusing on the interpretation of ML models. SIBILA simplifies model development by allowing users to set objectives and preferences before automating the search for optimal ML pipelines. Unlike traditional AutoML frameworks, SIBILA is specifically designed to exploit the computational capabilities of HPC platforms, thereby accelerating the model search and evaluation phases. The emphasis on interpretability is particularly crucial when model transparency is mandated by regulations or desired for stakeholder understanding. SIBILA has been validated in different tasks with public datasets. The results demonstrate that SIBILA consistently produces models with competitive accuracy while significantly reducing computational overhead. This makes it an ideal choice for practitioners seeking efficient and transparent ML solutions on HPC infrastructures. SIBILA is a major advancement in AutoML, addressing the rising demand for explainable ML models on HPC platforms. Its integration of interpretability constraints alongside automated model development processes marks a substantial step forward in bridging the gap between computational efficiency and model transparency in ML applications. The tool is available as a web service at no charge. Full article

Polystyrene (PS) is widely used because of its transparency, mechanical strength, and ease of production. With rising health concerns, antibacterial PS is increasingly sought after, but few polymer-based antibacterial agents have been prepared to date. In this study, polystyrene was synthesized using a cationic radical initiator, 2,2′-azobis-[2-(1,3-dimethyl-4,5-dihydro-1H-imidazol-3-ium-2-yl)] propane triflate (ADIP), and evaluated as an antibacterial additive. The PS polymerized with ADIP (ADIP-PS) was prepared with number-average molecular weights (M_n) from 15,000 to 40,000. Further, blending 5–10% ADIP-PS with an M_n of 23,000 into general-purpose polystyrene (GPPS) provided antibacterial activity against Staphylococcus aureus while maintaining the transparency and strength of GPPS. Surface analysis revealed hydrophilic properties and exposed cationic groups, as confirmed by contact angle measurement and anionic dye titration, respectively. In addition, the antibacterial activity increased with higher cationic group concentrations, particularly at lower molecular weights. This method presents a promising approach to introducing antibacterial properties to PS products. Full article

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field. Full article

Lanthanum oxide (La₂O₃) layers are widely used in electronics, optics, and optoelectronics due to their properties. Lanthanum oxide is also used as a dopant, modifying and improving the properties of other materials in the form of layers, as well as having a large volume. In this work, lanthanum oxide layers were obtained using MOCVD (Metalorganic Chemical Vapor Deposition) on the inner walls of tubular substrates at 600–750 °C. The basic reactant was La(tmhd)₃ (tris(2,2,6,6-tetramethyl-3,5-heptanedionato)lanthanum(III)). The evaporation temperature of La(tmhd)₃ amounted to 170–200 °C. Pure argon (99.9999%) and air were used as the carrier gases. The air was also intended to remove the carbon from the synthesized layers. Tubes of quartz glass were used as the substrates. La₂O₃ layers were found to be growing on their inner surfaces. The value of the extended Gr_x/Re_x² criterion, where Gr—Grashof’s number, Re—Reynolds’ number, x—the distance from the gas inflow point, was below 0.01. The microstructure of the deposited layers of lanthanum oxide was investigated using an electron scanning microscope (SEM). Their chemical composition was analyzed via energy-dispersive X-ray (EDS) analysis. Their phase composition was tested via X-ray diffraction. The transmittance of the layers of lanthanum oxide was determined with the use of UV-Vis spectroscopy. The obtained layers of lanthanum oxide were characterized by a nanocrystalline microstructure and stable cubic structure. They also exhibited good transparency in both ultraviolet (UV) and visible (Vis) light. Full article

Femtosecond laser processing combines highly accurate structuring with low residual heating of materials, low thermal damage, and nonlinear absorption processes, making it suitable for the machining of transparent brittle materials. However, with high average powers and laser pulse repetition rates, residual heating becomes relevant. Here, we present a study of the femtosecond laser pulse-on-demand operation regime, combined with regular scanners, aiming to improve throughput and quality of processing regardless of the scanner’s capabilities. We developed two methods to define the needed pulse-on-demand trigger sequences that compensate for the initial accelerating scanner movements. The effects of pulse-on-demand operation were studied in detail using direct process monitoring with a fast thermal camera and indirect process monitoring with optical and topographical surface imaging of final structures, both showing clear advantages of pulse-on-demand operation in precision, thermal effects, and structure shape control. The ability to compensate for irregular scanner movement is the basis for simplified, cheaper, and faster femtosecond laser processing of brittle and heat-susceptible materials. Full article

In this paper, a depth-sensing method employing active irradiation of a semi-annular beam is proposed for observing the multi-layered reflective surfaces of transparent samples with higher resolutions and lower interference. To obtain the focusing resolution of the semi-annular aperture diaphragm system, a model for computing the diffracted optical energy distribution of an asymmetric aperture diaphragm is constructed, and mathematical formulas are deduced for determining the system resolution based on the position of the first dark ring of the amplitude distribution. Optical simulations were performed under specific conditions; the lateral resolution ${\delta }_r$ of the depth-sensing system was determined to be 0.68 μm, and the focusing accuracy ${\delta }_z$ was determined to be 0.60 μm. An experimental platform was established under the same conditions, and the results were in accord with those of the simulation results, which validated the correctness of the formula for calculating the amplitude distribution of the diffracted light from the asymmetric aperture diaphragm. Full article

In this study, we introduce a novel electrochemical sensor combining reduced graphene oxide (rGO) sheets with a bismuth–mercury (Bi/Hg) film, electroplated onto pencil graphite electrodes (PGEs) for the high-sensitivity detection of trace amounts of gallium (Ga³⁺) and indium (In³⁺) in water samples using square wave anodic stripping voltammetry (SWASV). The electrochemical modification of PGEs with rGO and bimetallic Bi/Hg films (ERGO-Bi/HgF-PGE) exhibited synergistic effects, enhancing the oxidation signals of Ga and In. Graphene oxide (GO) was accumulated onto PGEs and reduced through cyclic reduction. Key parameters influencing the electroanalytical performance, such as deposition potential, deposition time, and pH, were systematically optimized. The improved adsorption of Ga³⁺ and In³⁺ ions at the Bi/Hg films on the graphene-functionalized electrodes during the preconcentration step significantly enhanced sensitivity, achieving detection limits of 2.53 nmol L⁻¹ for Ga³⁺ and 7.27 nmol L⁻¹ for In³⁺. The preferential accumulation of each post-transition metal, used in transparent displays, to form fused alloys at Bi and Hg films, respectively, is highlighted. The sensor demonstrated effective quantification of Ga³⁺ and In³⁺ in tap water, with detection capabilities well below the USEPA guidelines. This study pioneers the use of bimetallic films to selectively and simultaneously detect the post-transition metals In³⁺ and Ga³⁺, highlighting the role of graphene functionalization in augmenting metal film accumulation on cost-effective graphite rods. Additionally, the combined synergistic effects of Bi/Hg and graphene functionalization have been explored for the first time, offering promising implications for environmental analysis and water quality monitoring. Full article

The United Arab Emirates (UAE) faces obstacles in guaranteeing food security because of its desert climate, restricted arable land, and significant reliance on food imports. Establishing a robust and transparent food supply chain is crucial. This study investigates the crucial functions of blockchain technology in protecting and improving food security in the UAE. Using bibliographic and co-citation network analyses, this study examines 143 research articles that provide a thorough review of the current status of blockchain technology in relation to food security. We examine the interrelationships among studies, highlighting significant themes and identifying three emerging food security patterns in the incorporation of blockchain into the food security domain. This study enhances the understanding of how blockchain technology can transform the food security dimensions of availability, accessibility, utilization, and stability in the UAE and worldwide. Full article

The COVID-19 pandemic has revealed weaknesses in traditional supply chain finance systems, highlighting the need for digital change. Blockchain technology, with its ability to create secure and transparent records of transactions, offers a potential solution. This study uses bibliometric analysis and a literature review to examine research on blockchain-enabled supply chain finance, drawing on a database of 446 articles from ScienceDirect and Scopus. The findings show a growing interest in how blockchain can improve transparency, efficiency, and security in supply chain finance, addressing challenges like information asymmetry. This study suggests future research should focus on real-world applications of blockchain, how it can be used with other technologies, regulations and governance, and the social and environmental impacts of blockchain-based supply chain finance. This research also highlights the different priorities of the Global North and South in blockchain-enabled supply chain finance. The North focuses on efficiency and traceability, while the South emphasizes adding value and transparency. A lack of research on fair pricing, especially in the Global South, points to a critical gap that future research needs to address to ensure fairness in global trade. Full article

This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards. Full article

Governments are embracing an open data philosophy and making their data freely available to the public to encourage innovation and increase transparency. However, the number of available datasets is still limited. Finding relationships between related datasets on different data portals enables users to search the relevant datasets. These datasets are generated from the training data, which need to be curated by the user query. However, relevant dataset retrieval is an expensive operation due to the preparation procedure for each dataset. Moreover, it requires a significant amount of space and time. In this study, we propose a novel framework to identify the relationships between datasets using structural information and semantic information for finding similar datasets. We propose an algorithm to generate the Concept Matrix (CM) and the Dataset Matrix (DM) from the concepts and the datasets, which is then used to curate semantically related datasets in response to the users’ submitted queries. Moreover, we employ the proposed compression, indexing, and caching algorithms in our proposed scheme to reduce the required storage and time while searching the related ranked list of the datasets. Through extensive evaluation, we conclude that the proposed scheme outperforms the existing schemes. Full article

The development of new encapsulating coatings for flexible solar cells (SCs) can help address the complex problem of the short lifespan of these devices, as well as optimize the technological process of their production. In this study, new laminate-type protective composite coatings were prepared using a silicon oxynitride thin-film matrix obtained by curing the pre-ceramic polymer perhydropolysilazane (PHPS) through two low-temperature methods: (i) thermal annealing at 180 °C and (ii) exposure to UV radiation at wavelengths of 185 and 254 nm. Single-walled carbon nanotubes (SWCNTs) were used as fillers via dry transfer, facilitating their horizontal orientation within the matrix. The optical, adhesive, and structural properties of the matrix films and SiO_xN_y/SWCNT composite coatings, along with their long-term stability, were studied using Fourier transform infrared spectroscopy (FTIR), UV-Vis spectroscopy, HR-SEM, spectral ellipsometry, and a progressive-load scratch test. In this work, the optical constants of PHPS-derived films were systematically studied for the first time. An antireflection effect was observed in the composites revealing their two-component nature associated with (i) the refractive index of the SiO_xN_y matrix film and (ii) the embedding of a SWCNT filler into the SiO_xN_y matrix. The curing method of PHPS was shown to significantly affect the resulting properties of the films. In addition to being used as protective multifunctional coatings for SCs, both SiO_xN_y/SWCNT composites and SiO_xN_y matrix films also function as broadband optical antireflective coatings. Furthermore, due to the very low friction coefficients observed in the mechanical tests, they show potential as scratch resistant coatings for mechanical applications. Full article