Search Results (17,208)

The extended application of pesticides has intensified the resistance problem in Liriomyza trifolii within Hainan Province. This study aimed to elucidate the underlying mechanisms contributing to the elevated resistance observed in this pest by employing Whole-Genome Re-sequencing (WGR) technology. Through the analysis and comparison of WGR data focusing on voltage-gated sodium channel (VGSC) from diverse regions and LT-S of L. trifolii in Hainan Province, we identified a total of six nonsynonymous single nucleotide polymorphisms (nsSNPs) and thirty-one synonymous single nucleotide polymorphisms (sSNPs) in five wild populations MY, TS, DA, TY, and JY. Among the six nsSNPs, three (PyR1: M918T, L1014F, and PyR2: T933I) have been confirmed as linked to pyrethroid resistance, while one (D IVS6: V1845I) was associated with resistance to indoxacarb. Moreover, the frequency of these four mutations generally increases with decreasing latitude. Additionally, under sustained pesticide selection pressure, L. trifolii exhibits an evolutionary pattern characterized by a dN/dS ratio (nsSNP/sSNP = 6/31 ≈ 0.19) of less than 1. Among the 31 sSNPs that held an absolute quantitative advantage, the highest occurrence frequency reached 94.44% (G2033: JY), and this sSNP occurred in all populations. In contrast, among a limited number of 6 nsSNPs, the highest occurrence frequency attained 100% (L1014F: all populations). This study substantiates that the elevated resistance observed in L. trifolii within Hainan Province can be ascribed to the presence of four nsSNPs-M922T, T933I, L1018F, and V1845I in their VGSC. Furthermore, the emergence of cross-resistance between pyrethroids and indoxacarb has been identified. This research offers a novel theoretical foundation for future investigations into the resistance mechanisms of L. trifolii. Full article

Currently, in influenza vaccine production via the chicken embryo splitting method, embryo viability detection is a pivotal quality control step—non-viable embryos are prone to microbial contamination, directly endangering the vaccine batch quality. However, the predominant manual candling method suffers from unstable accuracy and occupational visual health risks. To address this challenge, we developed a novel real-time embryo viability detection system based on photoplethysmography (PPG) technology, comprising a hardware circuit for chicken embryo PPG signal collection and customized software for real-time signal filtering and time–frequency-domain analysis. Based on this system, we conducted three pivotal experiments: (1) impact of the source–detector spatial arrangement on PPG signal acquisition, (2) viable/non-viable embryo discrimination, and (3) embryo PPG signal detection performance for days 10–14. The experimental results show that within the sample size (15 viable, 5 non-viable embryos), the system achieved a 100% discrimination accuracy; meanwhile, it realized 100% successful multi-day (days 10–14) PPG signal capture for the 15 viable embryos, with consistent performance across the developmental stages. This PPG-based system overcomes limitations of traditional and existing automated methods, provides a non-invasive alternative for embryo viability detection, and presents significant implications for standardizing vaccine production quality control and advancing optical biosensing for biological viability detection. Full article

The craft beer market is continually expanding, driven by the consumers’ demand for product diversification, which leads to innovation in the brewing industry. While traditional brewing focuses on consistency and high-volume efficiency using standard yeasts, craft brewing prioritizes small-batch experimentation and flavor complexity. Traditionally, Saccharomyces cerevisiae (Ale beer) and Saccharomyces pastorianus (Lager beer) yeast are used in brewing. The craft brewing revolution introduced the use of non-conventional yeast. These yeasts possess distinct technological characteristics compared to commercial starters, such as a richer enzyme profile. This biological diversity produces beers with novel, complex aroma profiles, and opens exciting avenues for flavor creation. Recently, non-alcoholic beer and low-alcoholic beer (NABLAB), and functional beer have become the new horizons for the application of non-conventional yeasts. In recent years, the brewing potential of these alternative yeasts has been extensively explored. However, some aspects relating to the interactions between yeast and raw materials precursors involved in the aroma of the final beer, and the management of yeasts in fermentation, remain unexplored. This review systematically outlines the various innovative ways in which non-conventional yeasts are applied in brewing, including healthier beer. Here, we explore how these yeasts can foster innovation in the beer sector and provide the possibility for sustainable development in contemporary brewing. Full article

The development of cross-border hydrogen energy value chains involves complex interactions between technological, regulatory, and logistical subsystems. Static assessment models often fail to capture the dynamic response of these coupled systems to external perturbations. This study addresses this gap by proposing the Dual Carbon Cooperation Index (DCCI), a data-driven framework designed to quantify the synergy efficiency of the China–Korea hydrogen ecosystem. We construct a dynamic state estimation model integrating three coupled dimensions—Technology Synergy, Regulatory Alignment, and Supply Chain Resilience—utilizing an adaptive weighting algorithm (Triple Dynamic Response). Based on multi-source heterogeneous data (2020–2024), the model employs Natural Language Processing (NLP) for vectorizing unstructured regulatory texts and incorporates an exogenous signal detection mechanism (GPR). Empirical results reveal that the ecosystem’s composite synergy score recovered from 0.38 to 0.50, driven by robust supply chain resilience but constrained by high impedance in technological transfer protocols. Crucially, the novel dynamic weighting algorithm significantly reduces state estimation error during high-volatility periods compared to static linear models, as validated by bootstrapping analysis (1000 resamples). The study provides a quantitative engineering tool for monitoring ecosystem coupling stability and proposes a technical roadmap for reducing system constraints through secure IP data architectures and synchronized standard protocols. Full article

The dramatic growth in the emerging optical applications, including Lidar, short-range optical communication, and optical integrated sensing and communication (ISAC) calls for high-bandwidth transimpedance amplifiers (TIA) with low noise and low power in advanced CMOS technology nodes. To address the issues of existing TIA design, including the conventional RGC structure and the dual-feedback regulated cascode (RGC) TIA, design with complex feedback paths, i.e., limited bandwidth, extra noise, and high power consumption for enough bandwidth, this paper presents a novel TIA with the following key contributions. A novel RGC structure with cascode-feedback is proposed to increase feedback gain, thereby extending bandwidth and reducing noise. Design strategy of the proposed RGC TIA in a low-power advanced CMOS process is carried out to exploit weak inversion operation to achieve better power efficiency. Frequency response and noise analysis are also conducted to achieve target bandwidth and noise performance. The proposed TIA is designed and simulated in 40 nm CMOS with a target PD capacitance of 0.15 pF, achieving a −3 dB bandwidth of 9.2 GHz and a transimpedance gain of 71 dBΩ. The average input-referred noise current spectral density is 18.3 pA/$\sqrt{\mathrm{H}\mathrm{z}}$. Operating at 1.2 V, the core circuits consume only 6.6 mW, excluding the output buffer. Compared with prior RGC TIA designs, the proposed TIA achieves a 7.4×~243× enhancement in figure of merit. Full article

Data prefetching is essential for modern file storage systems operating in large-scale cloud and data-intensive environments, where high performance increasingly depends on intelligent, adaptive mechanisms. Traditional rule-based methods and recently proposed machine learning-based techniques often struggle to cope with the complex and rapidly evolving data access patterns characteristic of big-data workloads. In this paper, we introduce an online, streaming machine learning (SML) approach for predictive data prefetching that retrieves useful data into the cache ahead of time. We present a novel online training framework that extracts features in real time and continuously updates streaming ML models to learn and adapt from large and dynamic access streams. Building on this framework, we design new SML-driven prefetching algorithms that decide when, how, and what data to prefetch into the cache with minimal overhead. Extensive experiments using production traces from Huawei Technologies Inc. and Google workloads from the SNIA IOTTA repository demonstrate that our intelligent policies consistently deliver the highest byte hits among competing approaches, achieving 97% prefetch byte precision and reducing data access latency by up to 2.8 times. These results show that streaming ML can deliver immediate performance gains and offers a scalable foundation for future adaptive storage systems. Full article

Recent innovations in poultry feed technology have emphasized the role of postbiotics and phytogenics as promising strategies to strengthen gut health and improve overall performance in broilers. Within this context, the current study evaluated the effectiveness of Biostrong™ Dual (Cargill Inc., Cedar Rapids, IA, USA), a novel product that integrates Saccharomyces cerevisiae fermentation-derived postbiotic products (SCFPs) with a proprietary blend of essential oil compounds (EOCs). The objective was to determine whether this dual formulation could consistently enhance growth, feed efficiency, and carcass quality across multiple production phases. To test this, three independent trials were conducted using commercial broiler strains. Birds were allocated to either a control group (CON) receiving a basal diet or a treatment group (DUAL) receiving the same diet supplemented with 0.4 kg/MT of Biostrong™ Dual. Each trial employed a randomized block design with 24 replicates per treatment and 16–25 birds per replicate. Results consistently demonstrated that DUAL improved (p < 0.05) body weight and the cumulative feed conversion ratio (cFCR) at 42 days. Pooled analysis revealed body weight gains of 5.5%, a cFCR improvement of 5 points, increased feed intake, and a 0.86% rise in breast meat yield. Additionally, one trial showed reduced footpad lesion scores. Collectively, these findings highlight Biostrong™ Dual as a valuable nutritional intervention to optimize productivity and carcass quality in poultry production and further research is needed to understand the mode of action of the product. Full article

The adoption of circular economy approaches in agri-food value chains in developing countries remains underexplored, particularly in contexts dominated by smallholder farmers. This paper aims to analyze existing circular practices and identify key barriers to circular transformation in developing country agri-food value chains, with a specific focus on Indonesia. Using a qualitative research design, the study draws on semi-structured interviews, with different value chain players, to empirically examine circularity within the cashew value chain in Indonesia. The findings reveal that while a range of circular practices are undertaken by individual actors across the value chain, these activities remain largely fragmented and weakly coordinated. Key barriers to further circular transformation include limited awareness, economic imperatives, constrained access to appropriate technologies, and insufficient institutional support. Notably, access to finance was not perceived as a major constraint. This study contributes to the literature by providing a multi-actor, value chain perspective on circularity in smallholder-based agri-food systems in developing countries. It offers novel empirical evidence that existing informal circular practices play an important role and should be preserved as value chains modernize. The findings further highlight the importance of stronger vertical and horizontal coordination to scale and integrate circular activities and support a more holistic sustainable transition. Full article

This study examines the behavioral intention of Muslims in Bosnia and Herzegovina to adopt blockchain-based zakat systems. It offers novel insights from a European Islamic context. Prior studies have primarily focused on Southeast Asia and the Middle East. The research develops an SEM model on an extended Unified Theory of Acceptance and Use of Technology framework. Data were collected through a survey of Bosnian Muslims. The aim was to investigate how these factors influence the acceptance of digital zakat platforms. The findings highlight the potential of blockchain to address inefficiencies and trust issues in traditional zakat management. The study shows that performance expectancy, intrinsic religiosity, and trust are significant determinants of adopting blockchain-based zakat platforms. These results emphasize both technological and faith-based drivers of acceptance. Technology can enhance the trustworthiness and inclusivity of zakat administration for a broader group of Muslims in Bosnia and Herzegovina and similar contexts. This work contributes to broader international discussions on the intersection of technology, religion and finance. The article is laying a foundation for the future development of blockchain-based zakat platforms in Muslim societies. Full article

To enhance the climate adaptability and diagnostic precision of university sustainability frameworks, this study presents a critical advancement to the PICSOU (Performance Indicators for Core Sustainability Objectives of Universities) framework’s Indoor Environmental Quality (IEQ) module. The research employs a comparative approach across two distinct climate zones: the campus of Chengdu Jincheng College in a humid subtropical climate (CDJCC; Köppen Cwa) with natural ventilation, and the campus of Tallinn University of Technology in a temperate climate (TalTech; Köppen Dfb) with mechanical ventilation. A key innovation at CDJCC was the deployment of a novel, integrated sensor that combines a Frequency-Modulated Continuous Wave (FMCW) radar module for real-time occupancy detection with standard IEQ sensor suite (CO₂, PM_2.5, temperature, humidity), enabling unprecedented analysis of occupant-IEQ dynamics. At TalTech, comprehensive IEQ monitoring was conducted using standard sensors. Results demonstrated that mechanical ventilation (TalTech) effectively decouples indoor conditions from external fluctuations. In contrast, natural ventilation (CDJCC) exhibits strong seasonal coupling, reflected by a Seasonal Ventilation Efficacy Coefficient (${\lambda }_{\mathit{season}}$), indicating that seasonal differences in effective ventilation are present but vary by indoor space type under occupied conditions. Consistent with this stronger indoor–outdoor linkage, PM_2.5 infiltration was also pronounced in naturally ventilated spaces, as evidenced by a high infiltration factor ($I/O$ ratio) that remained consistently elevated. This work conclusively validates a conditional, climate-resilient workflow for PICSOU’s IEQ category, integrating these empirical coefficients to transform its IEQ assessment into a dynamic and actionable tool for optimizing campus sustainability strategies globally. Full article

To address the lack of inertia in full-power converter wind turbines and the inability of existing mechanical speed regulation technologies to achieve power smoothing without converters, this paper proposes a novel hybrid wind energy storage system integrating a Continuously Variable Transmission (CVT) and an electromechanical flywheel. This system establishes a cascaded topology featuring “CVT-based source-side speed regulation and electromechanical flywheel-based terminal power stabilization.” By utilizing the CVT for speed decoupling and introducing the flywheel via a planetary differential branch, the system retains physical inertia by eliminating large-capacity converters and overcomes the bottleneck of traditional mechanical transmissions, which struggle to balance constant frequency with stable power output. Simulation results demonstrate that the proposed system reduces the active power fluctuation range by 47.60% compared to the raw wind power capture. Moreover, the required capacity of the auxiliary motor is only about 15% of the rated power, reducing the reliance on power electronic converters by approximately 85% compared to full-power converter systems. Furthermore, during a grid voltage dip of 0.6 p.u., the system restricts rotor speed fluctuations to within 0.5%, significantly enhancing Low Voltage Ride-Through (LVRT) capability. Full article

Background: The coordinated improvement of yield, quality and resistance is a primary goal in rice breeding. Gene editing technology is a novel method for precise multiplex gene improvement. Methods: In this study, we constructed a multiplex CRISPR/Cas9 vector targeting yield-related genes (GS3, OsPIL15, Gn1a), fragrance gene (OsBADH2) and rice blast resistance gene (Pi21) to pyramid traits for enhanced yield, quality, and disease resistance in rice. A tRNA-assisted CRISPR/Cas9 multiplex gene editing vector, M601-OsPIL15/GS3/Gn1a/OsBADH2/Pi21-gRNA, was constructed. Genetic transformation was performed using the Agrobacterium-mediated method with the japonica rice variety Xin Dao 53 as the recipient. Mutation editing efficiency was detected in T₀ transgenic plants. Grain length, grain number per panicle, thousand-grain weight, 2-acetyl-1-pyrroline (2-AP) content, and rice blast resistance of homozygous lines were measured in the T₃ generations. Results: Effectively edited plants were obtained in the T₀ generation. The simultaneous editing efficiency for all five genes reached 9.38%. The individual gene editing efficiencies for Pi21, GS3, OsBADH2, Gn1a, and OsPIL15 were 78%, 63%, 56%, 54%, and 13%, respectively. Five five-gene homozygous edited lines with two genotypes were selected in the T₂ generation. In the T₃ generation, compared with the wild-type (WT), the edited homozygous lines showed increased grain number per panicle (14.60–25.61%), increased grain length (7.39–11.16%), increased grain length–width ratio (8.37–13.02%), increased thousand-grain weight (3.79–9.15%), a 42–64 folds increase in the fragrant substance 2-AP content, and significantly enhanced rice blast resistance. Meanwhile, there were no significant changes in other agronomic traits. Conclusions: CRISPR/Cas9-mediated multiplex gene editing technology enabled the simultaneous editing of genes related to rice yield, quality, and disease resistance. This provides an effective approach for obtaining new japonica rice germplasm with blast resistance, long grains, and fragrance. Full article

Freshwater scarcity is increasing day by day and has already reached a threatening level, especially in remotely populated areas. One of the technological solutions to this rising concern could be the use of the solar-based humidification–dehumidification (SHDH) method for water desalination. This technology is a promising solution but has challenges such as solar intermittency. This challenge can be solved by integrating SHDH with the phase change material as a solar energy storage medium. Therefore, a novel nano-enhanced binary eutectic phase change material (NEPCM) was developed in this project. PCM consisting of 70 wt.% stearic acid (ST) and 30 wt.% suberic acid (SBU) with a varying concentration of silicon carbide (SiC) nanoparticles (NPs) (0.1 to 3 wt.%) was synthesized specifically considering the need of SHDH application. The systematic thermophysical characterization was conducted to investigate their energy storage capacity, thermal durability, and performance consistency over repeated cycles. DSC analysis revealed that the addition of SiC NPs preserved the thermal stability of the NEPCM, while the phase transition temperature remained nearly unchanged with a variation of less than 0.74%. The value of latent heat is inversely related to the nanoparticle concentration, i.e., from 142.75 kJ/kg for the base PCM to 131.24 kJ/kg at 3 wt.% loading. This corresponds to reductions in latent heat ranging between 0.98% and 8.06%. The FTIR measurement confirms that no chemical reactions or no new functional groups were formed. All original functional groups of ST and SBU remained intact, showing that incorporating the SiC NP to the PCM lead to physical interactions (e.g., hydrogen bonding or surface adsorption). The TGA analysis showed that the SiC NPs in the NEPCM act as supporting material, and its nano-doping enhanced the final degradation temperature and thermal stability. There was negligible change in thermal conductivity for nanoparticle loadings of 0.1% and 0.4%; however, it increased progressively by 5.2%, 10.8%, 23.12%, and 25.8% at nanoparticle loadings of 0.7%, 1%, 2%, and 3%, respectively, at 25 °C. Thermal reliability was analyzed through a DSC thermal cycling test which confirmed the suitability of the material for the desired applications. Full article

From a configuration perspective, by using 99 prefecture-level cities in the Yellow River basin as a sample, this paper reveals a variety of pathways through which digital and intelligent technologies, in synergy with multiple factors, strengthen the resilience of agricultural industrial chains. The research findings are as follows: First, none of the antecedent conditions are essential for strengthening the resilience of the high agricultural industrial chain in the Yellow River Basin. Nevertheless, digital and intelligent technologies and digital infrastructure are central conditions in all four configurations that enhance the resilience of the agricultural industrial chain. Second, the four configurations that produce high agricultural industrial chain resilience are enabled by technology, driven by information, facilitated through multi-stakeholder collaboration, and guided by policy, and there are certain complementary and substitutive relationships among these conditions. Third, the configuration which is empowered by technology fits regions with well-developed digital infrastructure and established goose-formation agricultural entities; the configuration that is driven by information fits areas with limited fiscal support but robust digital infrastructure; the multi-stakeholder collaborative configuration fits regions with strong economic foundations, robust fiscal support, and advanced digital infrastructure; and the configuration which is guided by policy fits areas with weaker economic foundations but advanced digital infrastructure and diverse agricultural entities. The above conclusions, by revealing the diverse pathways by means of which digital technologies strengthen the resilience of the agricultural industrial chain in the Yellow River Basin, demonstrate that regional development must adopt tailored methods which are suited to local conditions. They also provide novel solutions for sustainable agricultural development. Full article

Point cloud semantic segmentation is a key technology for applications such as autonomous driving, robotics, and virtual reality. Current approaches are heavily reliant on local relative coordinates and simplistic attention mechanisms to aggregate neighborhood information. This often leads to an ineffective joint representation of geometric perturbations and feature variations, coupled with a lack of adaptive selection for salient features during context fusion. On this basis, we propose LSSCC-Net, a novel segmentation framework based on LACV-Net. First, the spatial-feature dynamic aggregation module is designed to fuse offset information by symmetric interaction between spatial positions and feature channels, thus supplementing local structural information. Second, a dual-dimensional attention mechanism (spatial and channel) is introduced to symmetrically deploy attention modules in both the encoder and decoder, prioritizing salient information extraction. Finally, Lovász-Softmax Loss is used as an auxiliary loss to optimize the training objective. The proposed method is evaluated on two public benchmark datasets. The mIoU on the Toronto3D and S3DIS datasets is 83.6% and 65.2%, respectively. Compared with the baseline LACV-Net, LSSCC-Net showed notable improvements in challenging categories: the IoU for “road mark” and “fence” on Toronto3D increased by 3.6% and 8.1%, respectively. These results indicate that LSSCC-Net more accurately characterizes complex boundaries and fine-grained structures, enhancing segmentation capabilities for small-scale targets and category boundaries. Full article