Search Results (37)

The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable low noise amplifier (LNA) and a single pole double throw (SPDT) switch. The HARRF architecture supports both X-band (8–12 GHz) and K/Ka-band (23–28 GHz) operations, enabling seamless adaptation between radar, satellite communication, and millimeter-wave (mmWave) 5G applications. The proposed receiver front-end employs a 0.15 μ$\mathrm{m}$ pseudomorphic high electron mobility transistor (pHEMT) process, optimised through a three-stage cascaded LNA topology. A switched-tuned matching network is utilised to achieve reconfigurability between X-band and K/Ka-band. Performance evaluations indicate that the X-band LNA achieves a gain of 23–27 dB with a noise figure below 7 dB, whereas the K/Ka-band LNA provides 23–27 dB gain with a noise figure ranging from 2.3–2.6 dB. The SPDT switch exhibits low insertion loss and high isolation, ensuring minimal signal degradation across operational bands. Network analysis and scattering parameter extractions were conducted using advanced design system (ADS) simulations, demonstrating superior return loss, power efficiency, and impedance matching. Comparative analysis with state-of-the-art designs shows that the proposed HARRF outperforms existing solutions in terms of reconfigurability, stability, and wideband operation. The results validate the feasibility of the proposed reconfigurable RF front-end in enabling efficient spectrum utilisation and energy-efficient transceiver systems for next-generation communication networks. Full article

The impacts of COVID-19 among men who have sex with men (MSM), who face limited access to HIV services due to stigma, discrimination, and violence, need to be assessed and quantified in terms of HIV treatment outcomes for future pandemic preparedness. This study aimed to evaluate the effects of the COVID-19 lockdown on the HIV treatment cascade among MSM in selected provinces of South Africa using routine programme data after the implementation of differentiated service delivery (DSD) models. An interrupted time series analysis was employed to observe the trends and patterns of HIV treatment outcomes among MSM in Gauteng, Mpumalanga, and KwaZulu-Natal from 1 January 2018 to 31 December 2022. Interrupted time series analysis was applied to quantify changes in the accessibility and utilisation of HIV treatment services using the R software version 4.4.1. The segmented regression models showed a decrease followed by an upward trend in all HIV treatment outcomes. After the implementation of the DSD model, significant increases in positive HIV tests (estimate = 0.001572; p < 0.001), linkage to HIV care (estimate = 0.001486; p < 0.001), ART initiations (estimate = 0.001003; p = 0.004), ART collection (estimate = 0.001748; p < 0.001), and taking viral load tests (estimate = 0.001109; p = 0.001) were observed. There was an overall increase in all HIV treatment outcomes during the COVID-19 lockdown in light of the DSD model. Full article

The article presents a detailed exposition of a hardware–software complex that has been developed for the purpose of enhancing the productivity of accounting for the state of the production process. This complex facilitates the automation of the identification of parts in production containers and the utilisation of supplementary markers. The complex comprises a mini computer (system unit in industrial version) with connected cameras (IP or WEB), a communication module with LED and signal lamps, and developed software. The cascade algorithm developed for the detection of labels and objects in containers employs trained convolutional neural networks (YOLO and VGG19), thereby enhancing the recognition accuracy while concurrently reducing the size of the training sample for neural networks. The efficacy of the developed system was assessed through laboratory experimentation, which yielded experimental results demonstrating 93% accuracy in detail detection using the developed algorithm, in comparison to the 72% accuracy achieved through the utilisation of the traditional approach employing a single neural network. Full article

The integration of automated tools in engineering education has the potential to improve student assessments, ensuring consistency and reducing instructor workload. This study introduces a Python-based automation tool designed to evaluate student Computer-Aided Design (CAD) submissions. The tool utilises software API and Open Cascade library to calculate model parameters. These parameters are compared against expected values from a solution file and marks are assigned based on deviations relative to the solution file. As a use case, seventy-five Solid Edge CAD files were assessed for geometric properties such as volume, surface area, and centre of gravity location to evaluate inter- and intra-marker reliability. The results showed perfect agreement, with a Cohen kappa of 1.0 for both metrics. Furthermore, the automated tool reduced grading time by 89.7% compared to manual evaluation. The potential of automation in improving marking efficiency, consistency, and objectivity in engineering education has been shown, providing a foundation for further integration of software. The python-based automation script is openly available on GitHub. Full article

In the context of locomotive traction systems, the power electronic traction transformer (PETT) represents a pivotal component, fulfilling essential functions pertaining to electrical isolation and power control. The majority of existing PETT prototypes employ a combination of a cascade structure and an ISOP structure. The aforementioned scheme effectively reduces the voltage withstand level of the power devices on the input side, yet it also necessitates the utilisation of a greater number of power devices and passive components within the PETT. In order to further reduce the number of power devices used, this paper proposes a new cascaded PETT design. The proposed PETT will adopt a scheme combining a cascade structure and a hardware circuit multiplexing. This paper first analyses the operation principle of the new circuit under non-single frequency PWM control. It then derives the design equations for some hardware parameters and control circuits. Finally, it verifies the effectiveness of the proposed PETT through simulation analysis. Full article

This study investigates the profound and multifaceted impacts of Artificial Intelligence (AI) and geopolitical developments on global dynamics by 2040. Utilising a Delphi process coupled with probabilistic modelling, the research constructs detailed scenarios that reveal the cascading effects of these emerging forces across economic, societal, and security domains. The findings underscore the transformative potential of AI, predicting significant shifts in employment patterns, regulatory challenges, and societal structures. Specifically, the study forecasts a high probability of AI-induced unemployment reaching 40–50%, alongside the rapid evolution of AI technologies, outpacing existing governance frameworks, which could exacerbate economic inequalities and societal fragmentation. Simultaneously, the study examines the critical role of geopolitical developments, identifying increased nationalisation, the expansion of conflicts such as the Russia–Ukraine war, and the strategic manoeuvres of major powers like China and Israel as key factors that will shape the future global landscape. The research highlights a worrying lack of preparedness among governments and societies, with a 10% probability of their being equipped to manage the complex risks posed by these developments. This low level of readiness is further complicated by the short-term orientation prevalent in Western businesses, which prioritise immediate returns over long-term strategic planning, thereby undermining the capacity to respond effectively to these global challenges. The study calls for urgent, forward-looking policies and international cooperation to address the risks and opportunities associated with AI and geopolitical shifts. It emphasises the need for proactive governance, cross-sector collaboration, and robust regulatory frameworks to ensure that the benefits of technological and geopolitical advancements are harnessed without compromising global stability or societal well-being. As the world stands on the brink of unprecedented change, the findings of this study provide a crucial roadmap for navigating the uncertainties of the future. Full article

Plants reprogramme their proteome to alter cellular metabolism for effective stress adaptation. Intracellular proteomic responses have been extensively studied, and the extracellular matrix stands as a key hub where peptide signals are generated/processed to trigger critical adaptive signal transduction cascades inaugurated at the cell surface. Therefore, it is important to study the plant extracellular proteome to understand its role in plant development and stress response. This study examined changes in the soluble extracellular sub-proteome of sorghum cell cultures exposed to a combination of sorbitol-induced osmotic stress and heat at 40 °C. The combined stress significantly reduced metabolic activity and altered protein secretion. While cells treated with osmotic stress alone had elevated proline content, the osmoprotectant in the combined treatment remained unchanged, confirming that sorghum cells exposed to combined stress utilise adaptive processes distinct from those invoked by the single stresses applied separately. Reactive oxygen species (ROS)-metabolising proteins and proteases dominated differentially expressed proteins identified in cells subjected to combined stress. ROS-generating peroxidases were suppressed, while ROS-degrading proteins were upregulated for protection from oxidative damage. Overall, our study provides protein candidates that could be used to develop crops better suited for an increasingly hot and dry climate. Full article

Poultry and forestry waste residues, despite their environmental concerns, offer nutrient-rich content and wider availability. Utilising them in cascading approaches can create high-value products and establish new value chains in bioeconomy. This study aims to evaluate the environmental consequences of coupling forestry residue pyrolysis and poultry litter anaerobic digestion processes in a waste-to-energy cascading system. Moreover, a scenario analysis was conducted considering six scenarios with varying total solids loading with biochar (8%, 15%, and 28%) and final energy products (bioelectricity and upgraded biomethane). Life cycle assessment (LCA) results demonstrated a net reduction in selected potential impact categories across all scenarios, though with considerable variation in mitigation levels among them. Analysis revealed a major influence of selection of biogas utilisation pathway (electricity/biomethane) on overall impacts. The displaced processes such as natural gas contributed majorly towards the reduction in climate change and fossil depletion, whereas electricity grid mix contributed to terrestrial acidification and freshwater eutrophication. This study suggests that integrating pyrolysis and anaerobic digestion processes effectively valorises poultry and forestry residue waste, presenting a promising opportunity for promoting new value chains within Ireland’s bioeconomy. This approach enhances bioresource utilisation, resulting in the production of value-added products with reduced environmental costs. Full article

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss among the elderly in the developed world. Whilst AMD is a multifactorial disease, the involvement of the complement system in its pathology is well documented, with single-nucleotide polymorphisms (SNPs) in different complement genes representing an increased risk factor. With several complement inhibitors explored in clinical trials showing limited success, patients with AMD are still without a reliable treatment option. This indicates that there is still a gap of knowledge in the functional implications and manipulation of the complement system in AMD, hindering the progress towards translational treatments. Since the discovery of the CRISPR/Cas system and its development into a powerful genome engineering tool, the field of molecular biology has been revolutionised. Genetic variants in the complement system have long been associated with an increased risk of AMD, and a variety of haplotypes have been identified to be predisposing/protective, with variation in complement genes believed to be the trigger for dysregulation of the cascade leading to inflammation. AMD-haplotypes (SNPs) alter specific aspects of the activation and regulation of the complement cascade, providing valuable insights into the pathogenic mechanisms of AMD with important diagnostic and therapeutic implications. The effect of targeting these AMD-related SNPs on the regulation of the complement cascade has been poorly explored, and the CRISPR/Cas system provides an ideal tool with which to explore this avenue. Current research concentrates on the association events of specific AMD-related SNPs in complement genes without looking into the effect of targeting these SNPs and therefore influencing the complement system in AMD pathogenesis. This review will explore the current understanding of manipulating the complement system in AMD pathogenesis utilising the genomic manipulation powers of the CRISPR/Cas systems. A number of AMD-related SNPs in different complement factor genes will be explored, with a particular emphasis on factor H (CFH), factor B (CFB), and complement C3 (C3). Full article

There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network’s feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper feature layer, and it does not give enough consideration to the little items in the loss function, which result in serious missing object detection and low object detection accuracy. To tackle these issues, a new algorithm called RSAD (Resnet Self-Attention Detector) that takes advantage of the self-attention mechanism has been proposed. The proposed RSAD algorithm utilises the residual structure of the ResNet-50 backbone network, which is more capable of feature extraction, in order to extract deeper features from UAV image information. It then utilises the SAFM (Self-Attention Fusion Module) to reshape and concatenate the shallow and deep features of the backbone network, selectively weighted by attention units, ensuring the efficient fusion of features to provide rich semantic features for small object detection. Lastly, it introduces the Focal Loss loss function, which adjusts the corresponding parameters to enhance the contribution of small objects to the detection model. The ablation experiments show that the mAP of RSAD is 10.6% higher than that of the SSD model, with SAFM providing the highest mAP enhancement of 7.4% and ResNet-50 and Focal Loss providing 1.3% and 1.9% enhancements, respectively. The detection speed is only reduced by 3FPS, but it meets the real-time requirement. Comparison experiments show that in terms of mAP, it is far ahead of Faster R-CNN, Cascade R-CNN, RetinaNet, CenterNet, YOLOv5s, and YOLOv8n, which are the mainstream object detection models; In terms of FPS, it slightly inferior to YOLOv5s and YOLOv8n. Thus, RSAD has a good balance between detection speed and accuracy, and it can facilitate the advancement of the UAV to complete object detection tasks in different scenarios. Full article

Despite advancements, Smart Cities encounter hazards. Smart Cities’ higher reliance on interconnected systems and networks makes them susceptible to risks beyond conventional ones, leading to cascading effects. Hence, the effective use of technological innovations is vital. This effective use involves understanding the existing use of technology innovations for resilience making in Smart Cities and the wise utilisation of them as suitable for different contexts. However, there is a research gap for a fundamental study that synthesises the emerging and disruptive technologies that are being used to improve the disaster resilience in Smart Cities and how they can be classified. Therefore, this research aimed to address that need, so that a Smart City evaluating the technologies/tools for disaster resilience could wisely utilise the available resources and prioritise the most suitable for their context-specific needs. Following a comprehensive literature review, the study identified 24 technologies and/or tools for creating, sustaining, and enhancing the resilience within Smart Cities. In doing so, they should collect and manage citywide geodata and foster public participation. While the wise utilisation of the most suitable and feasible tools and technologies is a measure of smartness in a Smart City, the findings suggested four key factors with which these technologies could be assessed. These four factors included impact on society, the adoption speed by Smart Cities, the maturity of the technology, and the capabilities offered to the community. Full article

Background: Familial Hypercholesterolemia (FH) is a hereditary condition that causes a rise in blood cholesterol throughout a person’s life. FH can result in myocardial infarction and even sudden death if not treated. FH is thought to be caused mainly by variants in the gene for the low-density lipoprotein receptor (LDLR). This study aimed to investigate the genetic variants in FH patients, verify their pathogenicity, and comprehend the relationships between genotype and phenotype. Also, review studies assessed the relationship between the LDLR null variants and the reaction to lipid-lowering therapy. Methods: The study utilised high-throughput next-generation sequencing for genetic screening of FH-associated genes and capillary sequencing for cascade screening. Furthermore, bioinformatic analysis was employed to describe the pathogenic effects of the revealed novel variant on the structural features of the corresponding RNA molecule. Results: We studied the clinical signs of hypercholesterolemia in a Saudi family with three generations of FH. We discovered a novel frameshift variant (c.666_670dup, p.(Asp224Alafs*43) in the LDLR and a known single nucleotide variant (c.9835A > G, p.(Ser3279Gly) in the APOB gene. It is thought that the LDLR variant causes a protein to be prematurely truncated, likely through nonsense-mediated protein decay. The LDLR variant is strongly predicted to be pathogenic in accordance with ACMG guidelines and co-segregated with the FH clinical characteristics of the family. This LDLR variant exhibited severe clinical FH phenotypes and was restricted to the LDLR protein’s ligand-binding domain. According to computational functional characterization, this LDLR variant was predicted to change the free energy dynamics of the RNA molecule, thereby affecting its stability. This frameshift variant is thought to eliminate important functional domains in LDLR that are required for receptor recycling and LDL particle binding. We provide insight into how FH patients with a null variant in the LDLR gene respond to lipid-lowering therapy. Conclusions: The findings expand the range of FH variants and assist coronary artery disease preventive efforts by improving diagnosis, understanding the genotype-phenotype relationship, prognosis, and personalised therapy for patients with FH. Full article

Goat’s milk is an excellent source of nutrients, with greater benefits compared to cow’s milk. Limited information is available on autochthon goat breeds, which are important for biodiversity preservation. In this study, the aim of using label-free quantification was to investigate the milk proteome of two goat breeds, the autochthon Teramana and Saanen breeds, which are commonly used by the industry. Utilising label-free proteomic analysis, 749 and 666 proteins, respectively were identified and quantified from the Teramana and Saanen goat milk. Moreover, utilising statistical analysis, 29 proteins were able to discriminate the two goat breeds, with many of the identified proteins involved in complement and coagulation cascades. This work enhances our understanding of the goat milk proteome and shows differences between the two breeds, leading to an important contribution toward a more detailed molecular-view of this unique substrate. Additionally, charactersation of the milk proteins can help in guiding genetic improvements in the goat herds, and thus increasing its use in human nutrition. Full article

Renewable-based sources can be interconnected through power electronic converters and connected with local loads and energy storage devices to form a microgrid. Nowadays, DC microgrids are gaining more popularity due to their higher efficiency and reliability as compared to AC microgrid systems. The DC Microgrid has power electronics converters between the DC loads and renewable-based energy sources. The power converters controlled with an efficient control algorithm for maintaining stable DC bus voltage in DC microgrids under various operating modes is a challenging task for researchers. With an aim to address the above-mentioned issues, this study focuses on the DC link voltage enhancement of a DC Microgrid system consisting of PV, DFIG-based wind energy conversion system (WECS), and battery Energy Storage System (ESS). To elevate PV output voltage and minimize the oscillations in DC link voltage, a high-gain Luo converter with Cascaded Fuzzy Logic Controller (CFLC) is proposed. Droop control with virtual inertia and damping control is proposed for DFIG-based WECS to provide inertia support. Artificial Neural Network (ANN) based droop control is utilised to regulate the ESS’s State of Charge (SOC). The effectiveness of the proposed converter and its control algorithms for maintaining stable DC bus link voltage has been analysed using MATLAB/Simulink and experimentally validated using a prototype model and FPGA Spartan 6E controllers. Full article

Despite mitigation and adaptation efforts, the residual risks of climate change will continue to impact the most vulnerable communities globally. Highly exposed regions, such as the Pacific Islands, will continue to experience profound negative loss and damage as a result of climate change, which will challenge current ways of life. Knowledge on the extent to which regional and national climate change polices can identify and respond to non-economic loss and damage (NELD) is limited. From the perspectives of stakeholders in the Pacific Islands region, this research aims to gain insights into how regional and national policies are responding to NELD, as the well as the barriers, shortcomings, and requirements for future responses. Utilising a mixed qualitative–quantitative approach, this research explores the perspectives of expert informants, including those from the government, donors and development partners, civil society, intergovernmental organisations, and other relevant bodies, such as universities. The key findings of this study indicate that current policy responses include a regional policy that integrates disaster and climate change losses, national efforts to preserve traditional and local knowledge, national adaptation and resilience planning, community-based projects, and relocation and resettlement. Additionally, NELD is a relatively new concept for policymakers, practitioners, and researchers, and it is difficult to conceptualise the diversity of issues related to NELD in the region. Owing to this poor understanding, a key gap relates to the dominance of the economic lens when characterising climate-induced impacts in the region. As such, there is a limited holistic consideration of climate change impacts, and thus a limited appreciation of the interrelated factors of NELD within policy responses that then cascade towards communities. Finally, the paper outlines key policy insights as follows: policies on integration, adaptation, resilience planning, relocation and resettlement have advanced; the economic lens dominates when characterising climate-induced impacts on the region; there is a limited appreciation of the interrelated factors of NELD; and there exists a need to account for residual and intangible losses to land, culture, traditional knowledge, biodiversity, ecosystem services, and human agency. The insights gained from this research can provide a practical basis for guiding local to regional action and help support and design comprehensive risk management solutions in order to address NELD associated with climate change. Full article