Hong Kong Virtual University

Diamond, composed of sp³ covalent carbon bonds, is renowned for its exceptional hardness, thermal conductivity, and wide bandgap, yet its intrinsic brittleness severely limits deformation and processing. Here, we report an amorphization-mediated ultralarge plasticity in nanodiamonds using a custom-designed in-situ transmission electron microscopy mechanical holder. Unlike conventional mechanisms such as dislocation motion or crystalline phase transformation, the deformation is governed by the formation of an interconnected amorphous carbon network that accommodates stress and enables cooperative grain rotation and sliding. This amorphization-mediated plasticity allows nanodiamonds to sustain compressive strains exceeding 90% without fracture. A distinct size-dependent transition is identified: ultralarge plasticity occurs only below ~13 nm, while larger diamonds deform in a brittle manner. This work provides critical insights into nanoscale mechanics and offering exciting opportunities for diamond-based nano-manufacturing, strain engineering, and advanced quantum or electronic device applications.

Compute-in-memory technology offers promising solutions for neural network acceleration but its potential is severely limited by inflexible and resource-intensive analog-to-digital converters. Here, we present a memristor-based analog-to-digital converter featuring adaptive quantization for diverse output distributions. Our design employs analog content-addressable memory cells with programmable overlapped boundaries to establish optimized quantization thresholds, demonstrating excellent integral and differential non-linearities. Extensive experiments validate the robustness of our approach by achieving 89.55% accuracy on CIFAR-10 (VGG8) at 5-bit adaptive quantized precision and maintaining competitive performance on ImageNet (ResNet18) through a proposed super-resolution strategy under experimental memristor variations. Compared to state-of-the-art designs, our converter achieves a 15.1× improvement in energy efficiency and a 12.9× reduction in area. Furthermore, integrating our converter into CIM systems reduces the energy and area overhead by up to 57.2% and 30.7%, respectively. This work establishes a paradigm for efficient and accurate signal quantization in practical compute-in-memory systems.

Renewable-fuelled plant factories (RFPFs) offer great promise for resilient food production, yet assessing their supply potential and environmental impacts is crucial for wider adoption. Here we conduct a multidimensional geospatial analysis to devise RFPF deployment schemes that aim to meet the population's dietary vegetable demand in China's 369 city-level regions. Results indicate that RFPFs provide multifaceted benefits, particularly in a cross-city scenario that ensures a sufficient supply for all regions, saves 51,390 km2 of cropland and maintains an affordable cost at 5.88 Chinese Yuan kg-1. Nevertheless, compared with conventional methods, RFPFs increase greenhouse gas emissions by 1.99-2.55-fold, with the majority being embodied in the manufacturing of power modules and facilities. Adopting a low-carbon transition pathway mitigates these emissions by approximately 70%, enabling RFPFs to achieve environmental gains. These results show the potential of RFPFs to innovate food production systems while underscoring low-carbon transition as a condition for their large-scale implementation.

Abstract Do real assets protect against inflation? Stocks’ core inflation betas are negative, while their energy betas are positive. Currencies, commodities, and real estate mostly hedge against energy inflation, but not core inflation. These hedging properties are reflected in the prices of inflation risks: only core inflation carries a negative risk premium, and its magnitude is consistent within and across asset classes, uniquely among macroeconomic risk factors. Energy inflation has become more procyclical and volatile since the 1990s, which helps explain the time-varying correlation between stock and bond returns. A two-sector New Keynesian asset pricing model accounts for these facts quantitatively.

IntroductionCanine infectious respiratory diseases (CIRDs) are prevalent causes of respiratory illnesses in dogs. Clinical signs are non-specific, including coughing, rhinorrhea, and fever, making it challenging for veterinarians, especially at the onset of symptoms, to identify the causative pathogens based on clinical presentation alone. On the other hand, early and accurate diagnosis is crucial for preventing progression to severe complications such as pneumonia and widespread outbreaks. The ability to differentiate between viral and bacterial etiologies can guide appropriate treatment and medication directions, such as avoiding misuse of antibiotics. Therefore, this study aimed to develop a novel multiple molecular assay suitable for an automated detection system using a nested polymerase chain reaction (PCR) method. The assay covers 14 common canine respiratory pathogens using 15 gene targets, including canine influenza virus (H3N2, H3N8, and H1N1), canine distemper virus, canine parainfluenza virus, canine herpesvirus, pseudorabies virus, rabies virus, canine adenovirus (types 1 and 2), canine coronavirus, Mycoplasma canis, Bordetella bronchiseptica, and Streptococcus equi subsp. zooepidemicus.MethodsTheir primers and probes in the assay were first developed according to the nested PCR protocol and designed parameters required in the automated system. This developed assay has then been rigorously validated.Results and discussionThe assay developed has demonstrated high analytical sensitivity and specificity. The results obtained from the automated system are comparable to those from conventional laboratory procedures. The assay has shown possibility in rapidly detecting multiple pathogens in canines, and its utility can potentially be extended beyond companion animals to other mammalian species as well. Its application can enhance infection surveillance in animal populations and potentially mitigate zoonotic transmission risks.

Temperate mixed forest ecosystems consist of diverse plant functional types (PFTs) that exhibit variations in phenology and physiological responses to climate change. Consequently, the traditional big-leaf assumptions in carbon modeling have been criticized for oversimplifying these ecosystems, for they overlook the variability in PFT composition and their sensitivity to climate within these ecosystems. However, incorporating PFT composition into carbon and climate sensitivity simulations in heterogeneous mixed forest ecosystems presents two major challenges: (1) accurate fine-scale PFT composition mapping across large forest landscapes remains lacking, which further leads to (2) incomplete assessments of these fine-scale PFT contributions in interpreting ecosystem-scale carbon dynamics and climate sensitivity response. The recent increase in high-resolution satellite and ground observation data offers an unprecedented opportunity to resolve these challenges.To address the first challenge, we developed a novel approach integrating Fisher-transformation-based unmixing analysis with time-series spectral and radar data. We examined this approach in three representative temperate mixed landscapes in the northeastern United States, using time-series Sentinel-1 and -2 data for calibration and local airborne-derived PFT fraction maps for validation. Our results demonstrate that (1) the synergy of spectral and radar time-series features significantly improves accuracy compared to spectral time-series models; (2) optimized features based on the Fisher-transformation approach minimize within-PFT variability and maximize between-PFT variability, enhancing model generalizability across landscapes. Integration of this approach with Google Earth Engine enables accurate ecoregion-wise PFT fractional mapping. To address the second challenge, we integrate different levels of PFT-related characteristics (e.g., PFT fraction map, PFT-specific physiology approximated by satellite vegetation index) with a machine learning-based carbon modeling scheme, examining how these PFT-related characteristics and climate variables separately and jointly determined the net ecosystem carbon exchange (NEE) in real mixed forest ecosystems. Specifically, we used the CHEESEHEAD19 dataset, which includes the world's most densely distributed eddy-covariance (EC) flux towers (13+ towers) within a 10 km × 10 km domain in the mixed forest ecoregion of Wisconsin, US, providing half-hourly flux records. Daily, 3-meter resolution, gap-free maps of vegetation index (NIRv) were calculated using the PlanetScope surface reflectance product. Our results demonstrated that PFT-related characteristics play a significant role (˜50%) in interpreting half-hourly NEE dynamics, with PFT-specific NIRv playing a dominant role (~30%), followed by PFT-fraction (~20%). Furthermore, by partitioning PFT-related effects, our results reveal distinct NEE sensitivity responses to specific environmental variability within and between PFTs in the 10 km × 10 km forest landscapes. Collectively this work advances the mapping of PFT composition and highlights the importance of integrating these fine-scale forest compositions into carbon modelling and climate sensitivity assessments, particularly in heterogeneous temperate mixed ecosystems.

Korea has continued steady growth in its official development assistance volume. Korea provided USD 2.2 billion, representing 0.14 percent of its gross national income (GNI) in 2020. Korea’s ODA budget has grown steadily. The Korean ODA project is expected to continue to increase in the field of TVET (technical vocational education and training) for developing countries. In early stage, the ODA project has been centered on Asian developing countries, but it is gradually expanding to Latin America and Africa. This paper analyzes the economy and ODA support status of Bolivia, Colombia, Paraguay, and Peru which are Korea's key ODA partners. Among the four countries, Paraguay's TVET and ODA current status are intensively focused. Through this, a plan for improving ODA performance and establishing a vocational education and training system is suggested. Suggestions for the improvement policies are as establishment of goal, vision and strategy for national TVET policy, establishment of comprehensive coordination mechanism between the government and the private sector, and establishment, promotion of TVET system and program.

Abstract This paper examines the impact of China's “Zero‐Covid” policy on subnational cross‐region export performance during 2019–2021. Using monthly export data at the product‐destination level for China's 31 provinces and municipality cities, we find that new infections in a region have a significantly negative effect on the region's export growth, particularly for non‐processing exports and industries with low dependence on remote working and products that are more substitutable and used mostly in the downstream of supply chains. However, restrictions on people inflows have no significant effect. The severity of the pandemic in export destinations is negatively correlated with China's export growth, suggesting an important demand‐side reason for China's export downturn during the pandemic.

The purpose of this study is to ensure the efficiency of urban regeneration by clarifying what factors are the key to the vitalization of smart information technology in the promotion of urban regeneration in the era of the Fourth Industrial Revolution. To this end, a research model that extends the Unified Theory of Acceptance and Use of Technology (UTAUT), which is applied to derive key factors in accepting new technologies, is developed, and multiple regression analyses are conducted by using SPSS to derive factors that have positive (+) effects on intention to use smart technologies for urban regeneration. As a result, the factors of facilitating conditions, performance expectancy, social influence, and personal innovativeness were derived. The results of this study show that for the vitalization of smart information technology in urban regeneration, the innovative thinking of persons in charge and the securing and organization of human resources, as well as education and public relations designed to change various participants