Search Results (16,848)

This article presents the design of a novel, compact, planar-array antenna that provides quad-beam radiation in the broadside direction, and it enhances coverage and serviceability for millimeter-wave applications. The antenna utilizes a corporate (parallel) feed network to deliver equal power and phase to all elements. Non-uniform element spacing in the two orthogonal planes, exceeding ( being the wavelength at 30 GHz), results in a quad-beam radiation pattern. Two beams are formed in the xz-plane and two in the yz-plane, oriented at angles of . However, this spacing leads to null radiation at the center and splits the radiation energy, reducing the overall gain. The measured half-power beamwidth (HPBW) is 30° in the xz-plane and 35° in the yz-plane, with X-polarization levels of −20.5 dB and −26 dB, respectively. The antenna achieves a bandwidth of 28.5–31.1 GHz and a peak gain of 10.6 dBi. Furthermore, increasing the aperture size enhances the gain and narrows the beamwidth by replicating the structure and tuning the feed network. These features make the proposed antenna suitable for 5G wireless communication systems. Full article

Residential areas are primary functional spaces of urban built-up areas, representing urban social structure externally and influencing urban spatial fabric (SF). Chinese cities have increasingly experienced urban renewal following significant population growth and urban expansion in the last four decades. We selected built-up urban areas of Shanghai as the research scope, considering 6731 residential quarters as research objects, which were identified and classified into six types. Based on complex network theory and analysis methods, an urban residential spatial network (URSN) was constructed in central Shanghai implementing through code. The degree of distribution and network robustness of the URSN was examined, and network “communities” were identified. The findings indicate that URSN stability, like robustness, implies harmonious and smooth social interactions and information transfer, consistent with the SDG 11, where the large-degree node residential quarters play an important role and must be prioritized in urban renewal. Meanwhile, the identification results of the URSN “communities” help us understand territory identity in built-up urban areas. This research provides new concepts and methods for examining SF in urban residential areas that integrate “physical” and “social” spaces, compares this approach to the traditional point-axis structure, and pioneers the study of urban SF from the perspective of complex networks by providing a new way of visualizing the spatial relationship between residential quarters as a network-like structure. Full article

Deep temperate lakes are increasingly reported to suffer consequences of climate warming, affecting thermal stratification and plankton seasonality. Long-term studies offer a unique opportunity for detecting changes in the zooplankton taxa composition related to climate change. Sampling zooplankton organisms alongside abiotic variables, such as water column temperature, enables the linking of changes in plankton communities with variations in the stratification regime. This provides evidence of direct and indirect warming effects. Our study in Lake Maggiore investigated variations in the population density and persistence of Diaphanosoma brachyurum, a thermophile species that is a member of the original zooplankton community of the lake, along with the increase in water temperature and the duration of the thermal stratification during the period 2013–2023. Our results highlighted an increase in the abundance and persistence of Diaphanosoma during the study period, along with an increase in the mean water temperature of the layer 0–10 m and of the length of the thermal stratification. Such changes, affecting the population dynamics of predators and competitors and their food sources (i.e., small algae), might lead to a rearrangement of the structure of the pelagic food chain. Full article

As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe Lighthouse project called ENFIELD: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) zero-knowledge proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) privacy-preserving machine learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization. Full article

This research analyzes the technical feasibility and implementation of an appropriate technology for the production of briquettes from Pinus spp. waste (sawdust and shavings) in a rural community in Michoacán, Mexico. The results indicate that local small-scale briquette production in the Pichátaro community has the potential to boost a local economy based on the manufacturing and marketing of densified solid biofuels. The design of the manual briquetting machine was developed through a participatory approach with community users. Structural simplicity and locally accessible maintenance were prioritized, the aspects that were addressed little in previous studies. The machine allows for the production of briquettes using a low-cost mixture composed of sawdust and Pinus spp. shavings, corn starch, and water. Based on local conditions and production needs, parameters such as reduced processing times and simplified manufacturing methods were identified as essential to establishing an efficient regional production and supply chain. Furthermore, the valorization of solid waste through the production of alternative biofuels contributes to the diversification of the energy matrix in rural residential sectors and small industries in communities in Mexico. The estimated cost of the machine is USD 75.44, and most of its components are easily replaceable, which favors its sustainability and prolonged use. This study demonstrates that the implementation of a low-pressure briquette system based on appropriate rural technologies represents a viable strategy for the use of wood waste and the promotion of sustainable energy solutions in rural communities. Full article

This interdisciplinary study examined the migration, settlement, and identity formation of Afro-Caribbean communities in Canada, with a focus on Haitian and Jamaican diasporas. It explored the historical transitions from racially exclusionary immigration policies to contemporary multicultural frameworks and assessed their impact on the socioeconomic integration and cultural identity of these communities. Drawing from archival records, government reports, and sociological literature, this research highlights how Afro-Caribbean populations have navigated systemic barriers while contributing significantly to Canadian society. Using racial capitalism as a theoretical lens, the study critiqued the structural inequalities embedded within Canadian immigration and labour policies, underscoring the persistent challenges of economic marginalization and racial discrimination. Additionally, it engaged with cultural capital theories to examine the resilience and agency of Afro-Caribbean communities in forging new opportunities through cultural knowledge and community networks. Ultimately, this research advocates for policy reforms that foster a more equitable and inclusive society by addressing the systemic exclusions that continue to shape the experiences of Afro-Caribbean Canadians. Full article

Laser space networks are an important development direction for inter-satellite communication. Detecting the angle of arrival (AOA) of multiple satellites in a wide field of view (FOV) is the key to realize inter-satellite laser communication networking. The traditional AOA detection method based on the lens system has a limited FOV. In this paper, we demonstrate a system that uses a spatially distributed sensor array to detect the AOA in a wide FOV. The basic concept is to detect AOA using the signal strength of each sensor at different spatial angles. An AOA detection model was developed, and the relationship of key structural parameters of the spatially distributed sensor array on the FOV and angular resolution was analyzed. Furthermore, a spatially distributed sensor array prototype consisting of 5 InGaAs PIN photodiodes distributed on a 3D-printed structure with an inclination angle of 30° was developed. In order to improve the angle calculation accuracy, a multi-sensor data fusion algorithm is proposed. The experimental results show that the prototype’s maximum FOV is 110°. The root mean square error (RMSE) for azimuth is 0.6° within a 60° FOV, whereas the RMSE for elevation is 0.67°. The RMSE increases to 1.1° for azimuth and 1.7° for elevation when the FOV expands to 110°. The designed spatially distributed sensor array has the advantages of a wide FOV and low size, weight, and power (SWaP), presenting great potential for multi-satellite laser communication applications. Full article

We demonstrate a thermally controlled ultra-wideband wide incident angle metamaterial absorber with switchable transmission at the THz band in this paper. The underlying hybrid structure of FSS-VO₂ thin films make them switchable between absorption mode and transmission mode by controlling the temperature. It can achieve ultra-wideband absorption with above 90% absorption from 1 THz to 10 THz and exhibits excellent absorption performance under a wide range of incident and polarization angles at a high temperature (80 °C). At room temperature (27 °C), it acts in transmission mode with a transmission coefficient of up to 60% at 3.1278 THz. The transmission region is inside the absorption band, which is very important for practical applications. The metamaterial absorber has the advantage of easy fabrication, an ultra-wideband, a wide incident angle, switchable multi-functions, and passivity with wide application prospects on terahertz communication and radar devices. Full article

Composting is an environmentally friendly treatment technology that recycles and sanitizes organic solid waste. This study aimed to assess the evolution of nutrients, maturity, and microbial communities during the composting of different plant-derived wastes. The composting process was conducted over 49 days using three types of plant-derived waste: wheat bran (WB), peanut straw (PS), and poplar leaf litter (PL). This process was examined through physical, chemical, and biological parameters. The results revealed that after 49 days of composting, the three groups experienced significant changes. They were odorless, were insect-free, exhibited a dark brown color, had an alkaline pH value, and had an electrical conductivity (EC) value of less than 4 mS/cm. These characteristics indicated that they had reached maturity. Nutrient content was the most significant factor influencing the degree of humification of the different composting materials, while changes in microbial community diversity were the key driving factors. Significantly, the compost PS, derived from peanut straw, entered the thermophilic phase first, and by the end of composting, it had the lowest organic matter (OM) loss rate (17.4%), with increases in total nitrogen (TN), total phosphorus (TP), and total potassium (TK) in the order of PS > PL > WB. The increase in humus carbon (HSC) content and the humic acid/fulvic acid (HA/FA) ratio followed the order PS > WB > PL. FTIR spectra indicated that PS had greater aromatic characteristics compared to the other samples. The abundance and diversity of bacterial and fungal communities in the compost increased significantly, accompanied by more complex community structures. Crucially, there were no phytotoxic effects in any of the three composting treatments, and the compost PS boasted a high germination index (GI) of 94.79%, with the lowest heavy metal contents. The findings indicate that the compost PS has the highest potential for resource utilization and is suitable for agricultural applications. Our results demonstrate that composting technology for plant-derived waste has the potential to enhance soil fertility and provide a reference for the composting treatment and resource utilization of other plant-derived waste. Full article

Trust and reputation relationships among objects represent key aspects of smart IoT object communities with social characteristics. In this context, several trustworthiness models have been presented in the literature that could be applied to IoT scenarios; however, most of these approaches use scalar measures to represent different dimensions of trust, which are then integrated into a single global trustworthiness value. Nevertheless, this scalar approach within the IoT context holds a few limitations that emphasize the need for models that can capture complex trust relationships beyond vector-based representations. To overcome these limitations, we already proposed a novel trust model where the trust perceived by one object with respect to another is represented by a directed, weighted graph. In this model, called T-pattern, the vertices represent individual trust dimensions, and the arcs capture the relationships between these dimensions. This model allows the IoT community to represent scenarios where an object may lack direct knowledge of a particular trust dimension, such as reliability, but can infer it from another dimension, like honesty. The proposed model can represent trust structures of the type described, where multiple trust dimensions are interdependent. This work represents a further contribution by presenting the first real implementation of the T-pattern model, where a neural-symbolic approach has been adopted as inference engine. We performed experiments that demonstrate the capability in inferring trust of both the T-pattern and this specific implementation. Full article

A wideband dual-polarized dielectric resonator antenna (DRA) with gain-filtering response was proposed in this paper. First, a differentially fed, low-profile crossed-DRA was used to obtain orthogonal polarizations with two resonant modes. A radiation null at upper band edge was also generalized. Second, with the introduction of four parasitic patches at the top of the crossed DRA, another resonant mode at lower band was excited, and the bandwidth was greatly expanded. Moreover, the introduction of parasitic patches could also help improve the selectivity of realized gain with another radiation null at the upper band edge. Furthermore, four sequentially rotated shorting coupling structures (SRSCSs) were proposed for the first time to generalize two additional radiation nulls. Finally, a wideband bandpass filtering response of the realized gain with four radiation nulls was obtained. Prototypes of the proposed antennas were fabricated, and the testing results showed that the antenna had a wide operation band of 57.1% from 2.75 GHz to 4.95 GHz with sharp roll-off at the band edge. This technique could also be used in wireless communication devices at millimeter/optical front ends and other multi-wavelength fiber lasers with micro structures. Full article

In recent decades, the concept of innovation has gained prominence in the public sector as governments, facing economic constraints, seek solutions to increasingly complex problems. Innovation has emerged as a potential strategy to address these challenges, despite the significant obstacles it poses to traditional bureaucratic structures. Nevertheless, the notion of innovation has become widespread globally. This paper aims to explore the sources of innovation and the potential pathways for its effective consolidation. It advocates for a “human-centered” approach to ensure successful integration and demonstrates that innovation is the result of a multifaceted creative process. The consolidation of innovation, as argued, hinges on the credibility of its source—specifically, public administration. Consequently, political and administrative leadership must be persuaded to invest in innovative communication channels, both internal and external, to foster organizational flexibility and adaptability. Full article

The rapid advancement of digital technologies in education is revolutionizing learning environments and influencing the future of educational methodologies. This study seeks to determine the parameters affecting students’ satisfaction with e-learning platforms utilized during the COVID-19 pandemic, within a multimodal educational framework. A Structural Equation Modeling (SEM) approach was used to examine the contributions of different components to students’ views of e-learning tools and the inter-relationships between them. Data were gathered from 314 students via a questionnaire, with the dependent variable being student satisfaction with e-learning platforms and the independent variables comprising the perceived benefits and disadvantages, ease of use, prior experience, perceptions of the platforms, perceived risks, and communication efficiency between students and professors. The results indicated that 78% of the variance in student satisfaction was explained by these parameters (R-squared = 0.78), underscoring the substantial impact of these features on the digital learning experience. This study enhances the comprehension of the influence of e-learning platforms within a multimodal educational framework on students’ learning experiences, especially with the challenges presented by the pandemic. The collected insights can guide the development of more effective, accessible, and user-focused educational tools. Full article

Background: Increasing evidence shows that the COVID-19 pandemic has disproportionately impacted certain populations, particularly those facing structural marginalization, such as immigrants and refugees. Additionally, research highlights that structurally marginalized populations living with chronic conditions, such as cancer and/or mental health and addiction (MH&A) disorders, are more vulnerable to the adverse effects of COVID-19. These individuals face higher susceptibility to infection and worse health outcomes, including increased rates of hospitalization, severe illness, and death. To better understand the challenges faced by people living at the intersection of social and clinical disadvantages, we organized a series of Think Tank sessions to engage stakeholders in exploring barriers and identifying community-based solutions for immigrants and refugees living with cancer and/or MH&A disorders during the current and future pandemics. Objectives: Our main objectives were to gauge how earlier findings resonated with stakeholders, to identify any gaps in the work, and to co-develop actionable solutions to safeguard health and well-being during COVID-19 and future crises. Methods: Two virtual Think Tank sessions were held in September 2023 as integrative knowledge exchange forums. The Cancer Think Tank was attended by 40 participants, while the MH&A disorders Think Tank included 41 participants. Each group comprised immigrants and refugees living with or affected by cancer (in the Cancer Think Tank) or MH&A disorders (in the MH&A disorders Think Tank), alongside service providers, policymakers, and researchers from Ontario. This paper presents the key discussions and outcomes of these sessions. Results: Participants identified and prioritized actionable strategies during the Think Tank sessions. In the Cancer Think Tank, participants emphasized the importance of leveraging foreign-trained healthcare providers to address workforce shortages, creating clinical health ambassadors to bridge gaps in care, and connecting immigrants with healthcare providers immediately upon their arrival in Canada. In the MH&A disorders Think Tank, participants highlighted the need to remove silos by fostering intersectoral collaboration, empowering communities and building capacity to support mental health, and moving away from one-size-fits-all approaches to develop tailored interventions that better address diverse needs. Conclusions: The Think Tank sessions enhanced our understanding of how the COVID-19 pandemic has impacted immigrants and refugees living with cancer and/or MH&A disorders. The insights gained informed a series of actionable recommendations to address the unique needs of these populations during the current pandemic and in future public health crises. Full article

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology. Full article