Search Results (974)

In recent years, Ammonia has emerged as a promising carbon-free fuel alternative, offering considerable potential to reduce CO₂ emissions and contribute to the decarbonization of the transportation industry. This study focuses on the economic feasibility and market price of ammonia now and in the future, highlighting the necessary infrastructure for emission-free transport operation. The project compares various production pathways for alternative fuels including hydrogen, ammonia, methanol, LNG, and diesel, considering both “green” and “gray” production methods. A key output of this research is the development of a flexible cost calculation tool, which allows users to simulate various scenarios by adjusting variables to ensure the continuity of the project. This tool enables dynamic analysis of future fuel prices and operational costs, accounting for the fluctuating electricity prices for green ammonia production and the long-term rise in CO₂ prices. Moreover, the study provides detailed cost modeling, infrastructure requirements, and refueling options for ammonia in comparison to other fuels. The findings indicate that ammonia is a promising long-term option for the maritime sector. While the adaptation to ammonia-based engines remains in the research phase, the long-term benefits of lower emissions and operating costs justify the investment in the necessary research and infrastructure, such as storage and refueling facilities. Full article

Protecting aquatic biodiversity while ensuring reliable hydropower production and water supply remains a core challenge for both water security and biosecurity. In this PRISMA-based systematic review, we synthesize evidence from 96 studies on fish guidance and deterrence at hazardous water intakes. We examine non-physical barriers, including acoustic and light cues, electric fields, bubble curtains, and chemical stimuli, as well as physical barriers such as racks, guidance structures, and nets or screens that aim to divert fish away from intakes and toward selective passage routes. Overall, guidance and deterrence performance is strongly species- and site-specific. Multimodal systems that combine multiple cues show the highest mean guidance efficiency (~80%), followed by light-based deterrents (~77%). Acoustic, electric, and bubble barriers generally achieve intermediate efficiencies (~55–58%), whereas structural devices alone exhibit lower mean performance (~46%), with substantial variability among sites and designs. Physical screens remain effective for larger size classes but can increase head loss and debris accumulation. By contrast, non-physical systems offer more flexible, low-footprint options whose success depends critically on local hydraulics, the sensory ecology of target species, and ambient environmental conditions. We identify major knowledge gaps relating to underlying sensory and behavioral mechanisms, hydraulics-based design rules, and standardized performance metrics. We also highlight opportunities to integrate advanced monitoring and AI-based analytics into adaptive, site-specific guidance systems. Taken together, our findings show that carefully selected and tuned barrier technologies can provide practical pathways to enhance water security and biosecurity, while supporting sustainable fish passage, improving invasive-species control, and reducing ecological impacts at water infrastructure. Full article

A novel fluorinated diamine monomer, 4,4′-((bicyclo[2.2.1]hept- 5-ene-2,3-diylbis (methylene)) bis(oxy))bis(3- (trifluoromethyl) aniline) (NFDA), featuring a tailored alicyclic norbornane core, flexible ether linkages, and pendant trifluoromethyl groups, was successfully synthesized. This monomer was polymerized with six commercial dianhydrides to produce a series of poly(amic acid) precursors, which were subsequently converted into high-performance polyimide (PI) films via a thermal imidization process. The strategic integration of the alicyclic, ether, and fluorinated motifs within the polymer backbone resulted in materials with an exceptional combination of properties. These PI films display outstanding solubility in a wide range of organic solvents, including low-boiling options like chloroform and tetrahydrofuran, highlighting their superior solution processability. The films are amorphous and exhibit remarkable hydrophobicity, evidenced by high water contact angles (up to 109.4°) and minimal water absorption (as low as 0.26%). Furthermore, they possess excellent optical transparency, with a maximum transmittance of 86.7% in the visible region. The materials also maintain robust thermal stability, with 5% mass loss temperatures exceeding 416 °C, and offer a desirable balance of mechanical strength and flexibility. This unique set of attributes, stemming from a rational molecular design, positions these polyimides as highly promising candidates for next-generation flexible electronics and advanced photovoltaics. Full article

Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article

Pacific Island communities have long navigated the challenges of climate change. Supporting adaptation options is critical for protecting livelihoods, especially given that these countries will continue to unfairly bear the brunt of global climate change impacts. Understanding and strengthening the capacity of individuals and communities to adapt is vital to ensure effective options are available. However, adaptive capacity is highly context-specific and explicit examples, particularly from the Pacific, remain limited. This study focuses on the experiences of women market vendors, for whom marketplaces are integral to food security, income generation, and cultural and social life. Building on existing global and regional frameworks, we assess the adaptive capacity of market vendors across Vanuatu through interviews with women market vendors (n = 69) and key informants (n = 18). The findings informed the development of a new, tailored adaptive capacity framework that identifies six key drivers: access to tangible resources, human assets, social assets, livelihood diversity and flexibility, systems of influence and mindsets, and decision-making capacity. This study presents a context-specific framework grounded in empirical evidence, offering insights for development and adaptation initiatives that aim to strengthen adaptive capacity. We encourage further research to apply and refine this framework across diverse Pacific contexts and sectors to deepen understanding of adaptive capacity and inform effective adaptation strategies. Full article

Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) remains a major challenge in healthcare settings due to its persistence on inanimate surfaces and resistance to conventional cleaning methods. Bacteriophages (phages) represent a promising biocontrol option owing to their high specificity and lytic activity. Methods: This study evaluated the use of a personal hand-held vibrating mesh nebulizer (VMN) as a rapid and localized delivery platform for phage aerosols. Using two lytic phages (ϕ2, Podovirus; ϕ11, Myovirus), we assessed phage stability under different storage conditions, viability during VMN operation, and surface decontamination efficacy under varying spray parameters. Results: In saline, both phages showed optimal long-term stability at 4 °C, whereas storage at −20 °C resulted in a progressive reduction in infectivity exceeding 3 logs over the storage period. VMN aerosolization did not compromise viability. A 3 min spray achieved >99.9% surface reduction: ϕ2 was effective at 1 × 10⁷ PFU/mL, whereas ϕ11 required 1 × 10⁸ PFU/mL. Importantly, residual ϕ2 activity persisted for at least 24 h, preventing detectable recolonization under the assay conditions, while ϕ11 protection was limited to 6 h. Conclusions: These findings establish the hand-held sprayer as a practical, low-cost, and flexible approach to deliver viable phage aerosols, providing an effective complement to large-scale disinfection systems and offering a targeted strategy to enhance infection control in healthcare environments. Full article

The transition to environmentally friendly mobility inevitably requires users to use sustainable modes of transport. Rapid urbanization, along with the growing demand for efficient, inclusive, and ecological transport systems, has highlighted the urgent need for research and analysis into the acceptability and experiences of transitioning to sustainable modes of transport. This article proposes a six-step procedure to support the selection of vehicles for car-sharing fleets in cities. The analysis utilizes the Analytic Hierarchy Process method, which allows for the comparison and evaluation of five vehicle variants with different powertrains, taking into account various evaluation criteria: ecological and economic. To refine the research, criterion weights were determined based on original surveys among six car-sharing operators and eighty-seven experts in the field of decarbonization of urban transport. The results indicated that plug-in hybrid vehicles are the most advantageous option for car-sharing fleets, providing a balance between emissions, cost-effectiveness and operational flexibility. The solution obtained is in line with expectations, confirming that the proposed analytical approach is a reliable decision support tool that reduces the risk of making the wrong decision regarding the choice of powertrains. Full article

The Paris Agreement and related international climate frameworks aim to reduce global carbon intensity; however, carbon dioxide emissions from electricity generation remain high, motivating the development of coal–renewable coupling technologies to lower the carbon intensity of power production. Coal–renewable coupling refers to the technical integration of conventional coal-fired power systems with renewable energy sources such as wind and solar to form a synergistic and complementary energy supply system. At present, systematic reviews and comprehensive analyses of coal–renewable coupling technologies are still limited. Accordingly, this paper categorizes existing approaches into two pathways—deep flexible load regulation and co-firing-based emission reduction—and systematically reviews the current state of technological development, identifies key challenges, and discusses potential future directions. Deep flexible load regulation includes flexibility retrofitting of coal-fired units and the integration of energy storage modules, whereas co-firing-based emission reduction mainly involves the co-combustion of coal with zero-carbon fuels. The analysis focuses on large-scale coal-fired units, covering low-load stable combustion technologies, steam turbine retrofitting, and rapid start-up and shut-down strategies. For energy storage-assisted load regulation, both conventional options and emerging technologies such as molten salt and high-temperature solid particle thermal energy storage are examined. Zero-carbon fuels considered include biomass, ammonia, and hydrogen. Furthermore, the economic feasibility of the various technologies is evaluated, providing reference value for deep flexibility retrofitting and substantial emission reduction in large-scale coal-fired power plants. Full article

A reflective all-fiber optical current transformer based on a spatial non-reciprocal phase modulation technique is investigated by theoretical analysis and experimental measurement. The modulation unit, composed of a phase delay wave plate (LiNbO₃) and two Faraday rotators, achieves flexible frequency adjustment by converting modulation from the time domain to the spatial domain. Therefore, the avoidance of the impact caused by delay coils is achieved in principle. The absence of intrinsic frequency limitations eliminates the demand for precise timing control in demodulation, thereby simplifying the demodulation circuit and reducing the cost and size of the transformer. In previous studies, redundancies were identified in the optical path coupling devices. The half-wave voltage of the modulator is excessively high, and its size is considerable due to constraints inherent in the manufacturing process. The measurement range is within 1800 A. The scheme simplifies some optical path components. By optimizing the phase delay wave plate, the half-wave voltage of the modulator is significantly reduced by a factor of 150. Experimental results demonstrate that the current transformer exhibits excellent detection consistency within the rated current range of 30–3600 A (1–120%), the response time is within 3 ms, and the measurement error and peak error reach 0.052% and 0.127%. This configuration provides a novel option for the design and practical application of all-fiber optical current transformers. Full article

Unlike high-dose scans, low-dose cardiac CT perfusion imaging reduces patient radiation exposure and thereby the risk of potential health effects. However, it introduces significant image noise, degrading diagnostic quality and limiting clinical assessment. Denoising is thus a critical preprocessing step to enhance image quality without compromising anatomical or perfusion details. Traditionally used reconstruction-domain methods, such as Iterative Reconstruction and Compressed Sensing, are often limited by algorithmic complexity, dependence on raw sinogram data, and restricted adaptability. Conversely, image-domain methods offer more adaptable denoising options. Recently, learning-based approaches have further expanded this flexibility and demonstrated state-of-the-art performance across various denoising tasks. In this work, we present a deep learning-based denoising method specifically tuned for low-dose cardiac CT perfusion imaging. Our model is trained to reduce noise while preserving structural integrity and temporal contrast dynamics, which are critical for downstream analysis. Unlike many existing methods, our approach is optimized for perfusion data, where temporal consistency is essential. Residual cardiac motion remains a separate challenge, which we aim to address in our future work. Experimental results show significant improvements in quantitative image quality, using both reference-based and no-reference metrics, such as MSE/PSNR/SSIM and NIQE/FID/KID, as well as improved accuracy of perfusion measurements. Full article

Receiver-initiated MAC protocols, such as the IEEE 802.15.4e RIT scheme, are promising for energy-efficient communication in multi-hop wireless sensor networks. However, their practical use requires a better understanding of how multiple contention-avoidance mechanisms interact under realistic network conditions. This study develops an ns-3 implementation of an RIT-compliant receiver-initiated MAC protocol together with a flexible evaluation framework that enables selective activation of representative enhancement strategies, including carrier-sensing options for data and beacon transmissions and randomization of beacon intervals. Four realistic network scenarios were designed to simulate practical deployment settings. Simulation results revealed that the effectiveness of these enhancement strategies varied significantly depending on network load and topology. In particular, beacon interval randomization, although often assumed to improve robustness, was found to degrade performance under low-load conditions, indicating that even widely adopted mechanisms may behave differently depending on operational environments. Conversely, CSMA-based approaches provided consistent improvements in transmission reliability. These observations highlight the importance of considering environmental factors and parameter configurations when enabling enhancement mechanisms. Overall, the proposed platform provides a reproducible and unified environment for fair comparison of receiver-initiated MAC protocols and their optional mechanisms, offering practical insights for selecting appropriate configurations in real sensor network deployments. Full article

Storage-oriented reservoir schemes are effective for large-scale hydrological modeling, yet two important limitations remain. First, although some reservoirs seasonally adjust flood storage capacity (FSC), no global study has examined whether constant or seasonally varying FSC performs better. Second, these schemes rely on empirical operational-zone parameterization, but its impact on simulation accuracy has never been systematically assessed. This study presents an open-source Python module integrating three leading storage-oriented schemes (S25, Z17, H22) with both constant and seasonally varying FSC options. Evaluated using daily observations from 289 global reservoirs via Nash-Sutcliffe Efficiency (NSE), constant FSC significantly outperforms seasonal variation, increasing median outflow NSE by 0.18–0.47 and reducing storage error magnitude by 38–61%, and is selected as optimal for 84% of reservoirs. Sensitivity analysis across eight alternative zoning schemes shows that, under constant FSC, outflow remains stable, whereas seasonal FSC sharply increases sensitivity. Storage simulation is more sensitive overall, yet constant FSC consistently yields the smallest errors. This work provides the first global comparison of FSC strategies and the first systematic assessment of operational zone parameter uncertainty. It strongly recommends constant FSC with H22 or S25 as the default for large-scale modeling. The released module offers a flexible, reproducible platform for the community. Future extensions may incorporate demand-driven rules and hybrid calibration to further improve performance in data-rich regions. Full article

Post-quantum cryptography (PQC) is rapidly being standardized, with key primitives such as Key Encapsulation Mechanisms (KEMs) and Digital Signature Algorithms (DSAs) moving into practical applications. While initial research focused on pure software and hardware implementations, the focus is shifting toward flexible, high-efficiency solutions suitable for widespread deployment. A system-on-chip is a viable option with the ability to coordinate between hardware and software flexibly. However, the main drawback of this system is the latency in exchanging data during computation. Currently, most SoCs are implemented on FPGAs, and there is a lack of SoCs realized on ASICs. This paper introduces a complete RISC-V SoC design in an ASIC for Module Lattice-based KEM. Our system features a RISC-V processor tightly integrated with a high-efficiency Number Theoretic Transform (NTT) accelerator. This accelerator leverages custom instructions to accelerate cryptographic operations. Our research has achieved the following results: (1) The accelerator provides a speedup of up to 14.51× for NTT and 16.75× for inverse NTT operations compared to other RISC-V platforms; (2) This leads to end-to-end performance improvements for ML-KEM of up to 56.5% for security level I, 50.9% for level III, and 45.4% for level V; (3) The ASIC design is fabricated using a 180 nm CMOS process at a maximum operating frequency of 118 MHz with an area overhead of 8.7%. The chip achieved a minimum power consumption of 5.913 μW at 10 kHz and 0.9 V of supply voltage. Full article

Paratuberculosis (PTB), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic intestinal disease in ruminants. PTB is difficult to diagnose, control, and eradicate, leading to substantial economic losses. Thus, sensitive and specific detection methods are urgently required. crRNA and primers targeting the MAP ATPase FtsK gene were designed for recombinase polymerase amplification (RPA) and nested PCR. Fecal DNA was amplified using RPA or nested PCR, purified with Tris-saturated phenol-chloroform-isoamyl alcohol, and detected via CRISPR-Cas12a. Moreover, signals were read using a qPCR instrument, fluorescence reader, or lateral flow strips. RPA–CRISPR-Cas12a and nested PCR–CRISPR-Cas12a assays were optimized and validated on 50 clinical samples and 7 MAP cultures. The limits of detection were 1 × 10⁻¹⁰ μg/μL for RPA–CRISPR-Cas12a and 1 × 10⁻¹⁴ μg/μL for nested PCR–CRISPR-Cas12a. Efficient cleavage of the ssDNA reporter occurred at DNA concentrations of ≥1 × 10⁻⁴ μg/μL, producing a strong fluorescent signal. All three detection methods showed perfect agreement with reference assays across both sample sets. This study presents the first integration of RPA or nested PCR with CRISPR-Cas12a for MAP detection, enabling rapid, specific, and highly sensitive diagnosis. Flexible detection options allow adaptation to available resources and bacterial loads, supporting practical use in PTB control. Full article

The decarbonisation of district heating (DH) is an important component of the European strategy to cut greenhouse-gas emissions, yet its feasibility depends as much on social and economic conditions as on technological innovation. The objective of this study is to assess how public perceptions and socio-economic drivers shape acceptance of renewable energy sources (RES) in DH in Italy. Drawing on a survey of 1200 residents in Turin, we examine how public attitudes towards decarbonised heating options, the integration of renewables and demand-side flexibility are influenced by socio-economic characteristics. These characteristics include income, education, age, housing tenure, eco-awareness, trust in institutions, and technological affinity. Results show widespread support for the efficiency and comfort benefits of DH. However, the results also show a limited willingness to pay more for renewable heat, particularly among economically vulnerable groups. The study has important implications for policy strategies facilitating climate change mitigation and the transition towards adopting RES in DH. In particular, it contributes novel evidence on the social constraints that may limit the effective deployment of renewable DH and clarifies which levers—economic incentives, institutional trust, and clarity about benefits, as well as community engagement—can increase public acceptance. By identifying these conditions, the study shows how renewable DH can realistically support the EU’s decarbonisation agenda. Full article