Search Results (5,168)

The European industries are transitioning from natural gas usage to renewable gases to enhance climate neutrality and energy security—therefore, hydrogen and ammonia gases could be great alternatives to natural gas. Hydrogen can be produced via electrolysis powered by renewable energy or from natural gas with carbon capture. Moreover, ammonia, composed of hydrogen and nitrogen, could also act as an energy carrier and storage medium. This study investigates the combustion process and efficiency of the hydrogen-enriched NH₃ and CH₄ blends using nonthermal plasma assistance. The experiments were performed with a gas burner with a thermal power of 1.30 kW using fully premixed gas blends. The nonthermal plasma was created with a high-voltage and high-frequency generator at 120 kHz and 8.33 kV. Time-resolved chemiluminescence data for OH* and NH₂* were captured using an ICCD camera, an MIR emission spectrometer and a thermal irradiance flux meter. The results indicated that nonthermal plasma enhances the flame stability and increases the infrared radiation intensity. The MIR spectroscopy showed an intensity increase of 13% for ammonia-hydrogen blends under plasma assistance and heat flux measurements showed a 15% increase for the 70% ammonia and 20% hydrogen mixture. These results demonstrate that plasma-assisted combustion can enhance the efficiency and stability of low-carbon fuel blends, facilitating their integration into current infrastructure while reducing greenhouse gas emissions. Full article

Deep Neural Networks (DNNs) are the primary approach for enhancing the real-time performance and accuracy of Keyword Spotting (KWS) systems in speech processing. However, the exceptional performance of DNN-KWS faces significant challenges related to computational intensity and storage requirements, severely limiting its deployment on resource-constrained Internet of Things (IoT) edge devices. Researchers have sought to mitigate these demands by employing Binary Neural Networks (BNNs) through single-bit quantization, albeit at the cost of reduced recognition accuracy. From an information-theoretic perspective, binarization, as a form of lossy compression, increases the uncertainty (Shannon entropy) in the model’s output, contributing to the accuracy degradation. Unfortunately, even a slight accuracy degradation can trigger frequent false wake-ups in the KWS module, leading to substantial energy consumption in IoT devices. To address this issue, this paper proposes a novel Probability Smoothing Enhanced Binarized Neural Network (PSE-BNN) model that achieves a balance between computational complexity and accuracy, enabling efficient deployment on an FPGA platform. The PSE-BNN comprises two components: a preliminary recognition extraction module for extracting initial KWS features, and a result recognition module that leverages temporal correlation to denoise and enhance the quantized model’s features, thereby improving overall recognition accuracy by reducing the conditional entropy of the output distribution. Experimental results demonstrate that the PSE-BNN achieves a recognition accuracy of 97.29% on the Google Speech Commands Dataset (GSCD). Furthermore, deployed on the Xilinx VC707 hardware platform, the PSE-BNN utilizes only 1939 Look-Up Tables (LUTs), 832 Flip-Flops (FFs), and 234 Kb of storage. Compared to state-of-the-art BNN-KWS designs, the proposed method improves accuracy by 1.93% while reducing hardware resource usage by nearly 65%. The smoothing filter effectively suppresses noise-induced entropy, enhancing the signal-to-noise ratio (SNR) in the information transmission path. This demonstrates the significant potential of the PSE-BNN-FPGA design for resource-constrained edge IoT devices. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

Background/Objectives: Vaccine usage rates (VURs) for COVID-19 vaccines (C19V) in KwaZulu-Natal (KZN), South Africa, remain insufficiently documented. This study assessed VUR for Pfizer–BioNTech (Pfizer) and Janssen Biotech Inc. (J&J) and reviewed monitoring systems in public (PUBS) and private (PRIVS) sectors. Methods: A dual-phase, multicentre study was conducted in PUBS and PRIVS facilities. Phase 1 comprised a retrospective, cross-sectional analysis of VUR for Pfizer and J&J (May 2021–July 2022). Phase 2 involved qualitative interviews with public (PUBSR) and private (PRIVSR) sector respondents (January–March 2024). Results: Pfizer VURs were 78.7% (PUBS) and 104.4% (PRIVS), while J&J recorded 64.7% (PUBS) and 40.2% (PRIVS). Stock reconciliations were complete across PUBSR and PRIVSR, but challenges persisted in stock on hand, reporting systems, and operational indicators. Conclusions: Pfizer achieved higher VURs than J&J, with PRIVS exceeding 100% due to under-reporting of issued doses. Integrated, real-time monitoring of VURs is urgently required to strengthen evidence-based policymaking, optimise supply chain management, reduce wastage, and improve vaccine uptake. Standardised monitoring frameworks across PUBS and PRIVS are essential to align with national objectives. Full article

Monetary policy and central banks faced significant challenges in recent decades, like the Great Recession and the 2008–2009 financial crisis, and the Global Inflation Surge of 2021–2022. The introduction of blockchain technology triggered major financial innovations. Nevertheless, the adoption of digital currencies and stablecoins in particular has been limited and does not have wide and everyday use, like national currencies. To understand non-national currency usage better, we examine free banking in Scotland and the U.S., and specifically note issuance. Lessons from these periods suggest the importance of reserves and coordination mechanisms. Based on these free banking cases, we propose that banks and corporations should have the freedom to issue their own stablecoins. More specifically, we examine the freedom for regulated banks to issue their own stablecoins in a competitive environment, learning from historical precedents how to manage such a system. Free banking stablecoins could provide significant benefits, especially in countries with unstable monetary systems, like emerging economies. Such benefits can range from better monetary policy, inflation targeting, and stability, to a broader range of innovative financial markets and services that can contribute towards entrepreneurship, investments, and economic development. Citizens, entrepreneurs, and domestic and foreign investors can gain from these benefits. At the same time, the banking sector and financial institutions can maintain an important role and further expand and develop by offering innovative financial services in an evolving and challenging environment due to financial technology and disintermediation. Finally, governments and central banks could also benefit from increased financial inclusion, higher economic growth and development, but also from more competition and financial stability, and from financial innovation and technology services. Full article

Climate change is increasingly recognized as a critical factor of food contamination risks, particularly through its influence on pesticide behavior and usage. Rising temperatures, altered precipitation patterns, and the proliferation of crop pests are leading to intensified and extended pesticide application across agricultural systems. These shifts increase the likelihood of elevated pesticide residues in food and water and affect their environmental persistence, mobility, and accumulation within the food chain. At the same time, current regulatory frameworks and risk assessment models often fail to account for the synergistic effects of chronic low-dose exposure to multiple residues under climate-stressed conditions. This review provides a multidisciplinary overview of how climate change intensifies the pesticide residue burden in food, emphasizing emerging toxicological concerns and identifying critical gaps in current mitigation strategies. In particular, it examines sustainable adsorbent technologies, primarily carbon-based materials derived from agro-industrial waste, which offer promising potential for removing pesticide residues from water and food matrices, aligning with a circular economy approach. Beyond their technical performance, the real question is whether such materials and the thinking behind them can be meaningfully integrated into next-generation food safety systems that are capable of responding to a rapidly changing world. Full article

ToM (ToM) and empathy, integral components of children’s social cognitive development, are shaped by multifaceted factors. The developmental trajectories of ToM and empathy in kindergarten children have long been focal points of inquiry for researchers and educators. Among these determinants, environmental factors emerge as significant predictors of children’s ToM and empathetic abilities. In contemporary society, digital screens have transformed into a ubiquitous medium for kindergarten children, deeply embedded in their daily life, learning, and recreational activities. Consequently, screen exposure has become a novel and distinctive environmental context for childhood development, diverging from traditional settings. This shift raises critical questions that have become focal in recent developmental media research: Does screen exposure correlate with children’s ToM and empathy? And how do key dimensions of screen use (e.g., duration, content) influence the development of these social cognitive skills? To address these queries, this study employed a two-phase experimental approach. Initially, a total of 642 parental questionnaires were collected to comprehensively investigate the current status of digital screen usage among Chinese kindergarten children. Subsequently, the ToM and empathy levels of 126 children were systematically evaluated. The findings revealed that the average daily duration of children’s screen time exhibited a significant negative predictive effect on their ToM level, consistent with prior longitudinal studies that linked early excessive screen exposure to poorer later ToM performance. Conversely, engagement with child-friendly content (e.g., prosocial narratives) and parent–child discussions regarding character emotions during screen exposure (e.g., dialogic questioning while co-viewing) emerged as positive predictors of ToM. Notably, no significant predictive relationships were identified between various dimensions of screen exposure and children’s empathy. This research elucidates the impact of screen exposure on crucial aspects of children’s social cognition, offering practical implications for optimizing screen device utilization to foster children’s holistic development. Full article

This case study tackles a real-world problem of a transportation company that is modeled as a scheduling optimization problem. The main goal of the considered problem is to schedule the maximum number of jobs that must be performed by vehicles over a specific planning horizon in order to minimize the total operational costs. Here, each customer request corresponds to a job composed of multiple operations, such as loading, unloading, and mandatory jobs, each associated with a specific location and time window. Once a job is allocated to a vehicle, all its operations must be executed by that same vehicle within their designated time constraints. Due to the imposed limitations, not every job can feasibly be scheduled. To address this challenge, two distinct methodologies are proposed. The first, a Holistic approach, solves the entire problem formulation using a black-box optimizer, serving as a comprehensive benchmark. The second, a Divide-and-Conquer approach, combines a heuristic greedy algorithm with a binary linear programming, decomposing the problem into sequential subproblems. Both approaches are implemented using the solver Hexaly. A comparative analysis is conducted under different scenarios and problem settings to highlight the advantages and drawbacks of each approach. The results show that the Divide-and-Conquer approach significantly improves computational efficiency, reducing time by up to 99% and vehicle usage by around 15–20% compared to the Holistic method. On the other hand, the Holistic method better ensures that mandatory jobs are completed, although at the cost of more resources. Full article

Grapes (Vitis spp.) are a globally significant fruit crop with a long history of cultivation and substantial cultivar diversity. Their high genetic differentiation and complex evolutionary history make them a valuable system for studying plant evolution. The chloroplast genome, known for its structural conservation and uniparental inheritance, offers a reliable molecular marker for phylogenetic reconstruction. In this study, we sequenced and assembled the complete chloroplast genomes of nine representative grape cultivars, analyzed their phylogenetic relationships, and compared structural variations. All chloroplast genomes displayed a typical quadripartite structure, with high conservation in genomic architecture, gene order and content, codon usage, and simple sequence repeats (SSRs). However, additional sequence comparisons revealed seven regions with high variation, including the genes rbcL and ndhF, and the intergenic regions rps16-trnQ, ndhC-trnV, accD-psaI, ndhF-rpl32, and trnL-ccsA. At the same time, seven natural variation sites were identified in the amino acid sequences of rbcL and ndhF. Additionally, the study’s maximum likelihood (ML) phylogenetic trees and photosynthetic index measurements suggest that developmental characteristics of grape photosynthesis may be related to the evolutionary origins of different populations. This phylogenetic classification not only elucidates the evolutionary origins of these germplasm resources but also provides a foundation for molecular-assisted breeding by identifying distinct genetic groups. Full article

The echelon utilization of electric vehicle batteries is regarded as an effective method for treating waste batteries, enabling the recycling and reuse of retired electric vehicle batteries. However, the efficiency of battery disassembly is a crucial factor that impacts the potential for battery recycling. When manufacturers take disassembly efficiency into account during the design phase of new electric vehicle batteries, they can significantly reduce disassembly costs at the time of decommissioning. This, in turn, incentivizes recycling and echelon utilization of waste batteries. Our research aims to promote the echelon use of waste batteries and analyze how market competition intensity and profits from battery echelon utilization influence decision-making within the battery recycling supply chain. This paper explores the effect of market competition on battery recycling and echelon utilization, while developing a supply chain model that includes a battery manufacturer responsible for determining the level of battery disassembly design and recycling waste batteries from the market, as well as a new energy vehicle manufacturer that focuses solely on recycling waste batteries. The findings indicate that as market competition increases, the battery manufacturer tends to lower both the level of battery disassembly design and the recycling price for waste batteries. Additionally, the recycling price for waste batteries offered by new energy vehicle manufacturers is also influenced by the intensity of market competition. In scenarios with low competition intensity, the recycling price tends to rise as competition intensifies. Conversely, in highly competitive markets, the recycling price decreases with increased competition. Furthermore, the overall volume of battery recycling is impacted by the intensity of market competition; in highly competitive markets, waste battery recycling is hindered. To enhance the echelon utilization of battery recycling, relevant government agencies should strive to maintain market competition at lower levels while also encouraging the recycling of batteries that do not meet usage standards. This dual approach will improve the benefits associated with the echelon utilization of waste batteries, thereby fostering greater enthusiasm for recycling among the involved enterprises. Full article

Elderly populations often face significant challenges when it comes to dietary intake tracking, often exacerbated by health complications. Unfortunately, conventional diet assessment techniques such as food frequency questionnaires, food diaries, and 24 h recall are subject to substantial bias. Recent advancements in machine learning and computer vision show promise of automated nutrition tracking methods of food, but require a large, high-quality dataset in order to accurately identify the nutrients from the food on the plate. However, manual creation of large-scale datasets with such diversity is time-consuming and hard to scale. On the other hand, synthesized 3D food models enable view augmentation to generate countless photorealistic 2D renderings from any viewpoint, reducing imbalance across camera angles. In this paper, we present a process to collect a large image dataset of food scenes that span diverse viewpoints and highlight its usage in dietary intake estimation. We first collect quality 3D objects of food items (NV-3D) that are used to generate photorealistic synthetic 2D food images (NV-Synth) and then manually collect a validation 2D food image dataset (NV-Real). We benchmark various intake estimation approaches on these datasets and present NutritionVerse3D2D, a collection of datasets that contain 3D objects and 2D images, along with models that estimate intake from the 2D food images. We release all the datasets along with the developed models to accelerate machine learning research on dietary sensing. Full article

The pollination of kiwiberry flowers is closely related to fruit growth, development, and yield. Rapid and precise identification of flowers under natural field conditions plays a key role in enhancing pollination efficiency and improving overall fruit quality. Flowers and buds are densely distributed, varying in size, and exhibiting similar colors. Complex backgrounds, lighting variations, and occlusion further challenge detection. To address these issues, the YOLO11s-RFBS model was proposed. The P5 detection head was replaced with P2 to improve the detection of densely distributed small flowers and buds. RFAConv was incorporated into the backbone to strengthen feature discrimination across multiple receptive field scales and to mitigate issues caused by parameter sharing. The C3k2-Faster module was designed to reduce redundant computation and improve feature extraction efficiency. A weighted bidirectional feature pyramid slim neck network was constructed with a compact architecture to achieve superior multi-scale feature fusion with minimal parameter usage. Experimental evaluations indicated that YOLO11s-RFBS reached a mAP@0.5 of 91.7%, outperforming YOLO11s by 2.7%, while simultaneously reducing the parameter count and model footprint by 33.3% and 31.8%, respectively. Compared with other mainstream models, it demonstrated superior comprehensive performance. Its detection speed exceeded 21 FPS in deployment, satisfying real-time requirements. In conclusion, YOLO11s-RFBS enables accurate and efficient detection of kiwiberry flowers and buds, supporting intelligent pollination robots. Full article

Due to the diverse needs of users, such as the requirement for rapid charging in time-sensitive situations and the need to minimize battery power consumption to extend battery life when the device is idle, a wireless charging system that combines fast and slow charging capabilities is crucial for adapting to various usage scenarios. This paper proposes a multi-mode wireless charging system based on a reconfigurable transmitter, which can simultaneously charge different types of batteries with both fast and slow charging capabilities. By applying different control logic to the power devices in the reconfigurable inverter, the system can achieve four operating modes: two different constant current (CC) modes and two different constant voltage (CV) modes. Furthermore, the system can switch between these modes by configuring the MOSFETs operating states: two three-coil configurations are used for the two CC modes, while two two-coil configurations are used for the two CV modes. Therefore, the system exhibits high versatility. To verify the theoretical analysis of the proposed system, an experimental prototype with an output specification of 3 A/2.2 A/78 V/65 V is built. Full article

Large Area Additive Manufacturing (LAAM) enables the rapid production of thermoplastic polymer structures but suffers from significant anisotropy and 3D printability limitations. These limitations often require additional material and time in order to incorporate supporting structures. This research explores the integration of continuous fiber reinforced thermoplastics (CFRTP) with LAAM structures. A series of experimental trials were performed, which demonstrate the feasibility and benefits of CFRTP integration, as it can improve structural strength, lightweighting, and manufacturing flexibility. The findings suggest that CFRTP integration can significantly enhance LAAM by reducing material usage, improving mechanical properties, and expanding design possibilities. While further research is needed to optimize the process for specific applications, this process of Hybrid Advanced Additive Manufacturing (HAAM) presents a promising approach for advancing large-scale additive manufacturing. Full article

Active exosuits provide various assistive force profiles but are limited by battery life, weight, and complex maintenance requirements. Passive exosuits, by contrast, are economical and lightweight while also offering unlimited usage times; however, due to their fixed stiffness levels, they can provide only a limited set of optimized assistive force profiles for different movements. To address these issues, this paper proposes a new variable elastic band for semi-passive exosuits. It comprises rubber bands and webbings connected in parallel, with the elongation of the rubber bands restricted according to the webbing length. By connecting these segments in series, a range of elasticities can be generated. Experimental results confirmed that the band could generate different stiffness levels, which were accurately predicted with an average coefficient of determination (R²) of 0.9985 and an average root mean square error of 0.8993. Additionally, based on tests involving participants wearing the device, the variable elastic band effectively modulated the assistive force profile. These findings overcome the previous limitations of passive components, opening the door to future research on enhancing the efficiency of passive systems and enabling further customization. Full article