Strong Complementarity Research Articles

A spatio-temporal-spectral fusion framework for downscaling Sentinel-2 images using UAV images

ABSTRACT The Sentinel-2 multispectral (S2-MS) images, equipped with three red-edge (Red-E) bands, serve as an optimal data source for vegetation monitoring. However, its spatial resolution of 10–20 m restricts greatly its utility for local, precise monitoring. The widely used consumer-grade unmanned aerial vehicle (UAV) provides much finer spatial resolution images but typically only in the visible and near-infrared spectral bands. UAV and S2-MS images have strong complementarity in spatial, temporal, and spectral resolution. This paper establishes a spatio-temporal-spectral (STS) fusion framework for downscaling S2-MS images using UAV images. First, the spatio-temporal (ST) fusion method of Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is applied to the spatio-spectral (SS) fusion of UAV and S2-MS images, and it is verified to perform better than the existing SS fusion methods and exhibits robustness across spatial scales. Then, CA-STARFM is generated by coupling STARFM with Consistent Adjustment of the Climatology to Actual Observations (CACAO) and used to further optimize SS fusion results, yielding more competent performance. Moreover, the applicability of CA-STARFM to STS fusion is further verified based on the UAVlike image generated by the ST fusion of UAV and S2-MS images. The results indicate that STARFM is competent for SS fusion at large spatial scales, while CA-STARFM can not only optimize the ST fusion of UAV and satellite images but also be promising for SS fusion. Therefore, the proposed fusion framework provides a potential solution to integrate spatial, temporal, and spectral information of UAV and S2-MS images for precise monitoring.

How do sectoral elasticities affect the transmission of monetary shocks?

PurposeThe presence of heterogeneity in price stickiness has been previously shown to be the main determinant of the real effects of nominal shocks. The paper presents theoretical results that highlight the significance of labor mobility and intratemporal elasticity of substitution across sectors.Design/methodology/approachThis paper explores output dynamics in a closed economy, relying on a New Keynesian model that accounts for heterogeneity in price stickiness across sectors.FindingsThe key finding from the analytical results is that changes in labor mobility and intratemporal elasticity of substitution across sectors have an equivalent impact on sectoral output when accounting for differences in price stickiness. The model is then calibrated for the US economy using data from the manufacturing and services sectors, providing validation for the analytical results.Originality/valueConventional models often assume either immediate labor reallocation across sectors following shocks or no labor mobility at all. Additionally, these models assume either strong complementarity in consumption or some degree of substitutability. This paper demonstrates the crucial role that labor reallocation and the elasticity of substitution across goods across sectors play in generating well-known economic outcomes.

LiDAR-camera fusion: dual-scale correction for vehicle multi-object detection and trajectory extraction

The different principles of sensor technology determine their distinct performance in vehicle detection and microscopic tracking. Vision-based sensors can provide rich semantic information but lack reliable spatial information, and their reliability is reduced in complex lighting conditions. On the other hand, LiDAR can offer high-precision spatial information independent of lighting conditions, but it suffers from low resolution and effective sampling rate limitations. Considering the strong complementarity between images and point clouds, efficient object detection can be achieved by leveraging their synergy. However, existing research has not fully explored the correlation between the features of these two types of data. This paper proposes a novel dual-scale correction strategy for feature-level fusion of camera and LiDAR data. This strategy captures spatial features of point clouds and semantic features of images at both global and local scales and establishes mapping relationships separately. The global correction results are iteratively updated based on the results of local precision correction. To validate the effectiveness of the proposed method, data is collected from highway and urban expressway scenarios. The results indicate improvements in both object detection and microscopic trajectory tracking performance compared to using single sensors alone. Furthermore, the fusion approach outperforms other methods in terms of detection accuracy and processing time. This research offers a method for real-time and accurate extraction of vehicle trajectories using roadside cameras and LiDAR devices, with potential applications in real-time trajectory tracking and traffic management.

Substitution effect of Asian economies on China’s industrial and supply chains: from the perspective of global production network

This paper views China Production Network (CPN) as an integral component of the Global Production Network (GPN) and constructs null model (GIVCBN-I model) and the counterfactual model (GIVCBN-II or GIVCBN-III model) to identify and measure the potential industrial relocation risk exposure of CPN based on the Multi-Regional Input-Output (MRIO) table. The main findings are as follows. Firstly, Altasia will induce more of China’s industrial and supply chains to break compared with ASEAN, which also means that strong industrial complementarity exists between Japan, South Korea, India, Bangladesh and the ASEAN member countries, enabling Altasia to have a more significant substitution effect on China. Secondly, the counterfactual models’ network-level characteristic indicators are worse than those of the null model in economic terms, suggesting that removing trade barriers for intermediate goods within Altasia could lead to the decoupling of industrial sectors in the CPN, thereby accelerating the trend of China’s industrial and supply chains relocating offshore. Thirdly, according to the comparison results of node-level characteristic indicators, Altasia has weakened China’s influence scope, profitability, and robustness of risk within the global industrial and supply chains, but mainly concentrating on its resource-intensive and labor-intensive sectors rather than capital-intensive and technology-intensive sectors, which indicates that some China’s industrial sectors still maintain substantial competitive advantages in the GPN. In sum, this paper provides theoretical guidance for identifying and analyzing the trends of industrial relocation in the Asia-Pacific region and helps industrial policymakers deepen the understanding of regional economic integration and its impact.

A Co-production system of cement and methanol: Unveiling its advancements and potential

Both the cement and methanol sectors face increasing pressure to reduce their carbon emissions to combat climate change. Coupling the production process can provide economic and environmental benefits when implementing breakthrough technologies in these industries. The advantages and application potential of the co-production system need to be assessed quantitatively in long-term decarbonization pathways. This study proposes a co-production system of cement and methanol that capitalizes on strong complementarity through bidirectional material flow. Multi-regional planning models are established to evaluate the spatiotemporal deployment of low-carbon technologies, including coupling technologies. The results demonstrate that the co-production system can achieve a 6.6% higher emissions reduction and a 21.2 % lower production cost of clinker by 2060 compared to implementing breakthrough technologies separately. The co-production routes may be widely distributed across China by 2060, accounting for 24.1 % of clinker output and 54.7 % of methanol output. Introducing coupling technologies can influence the industrial layout and trade patterns of China's cement and methanol sectors, carrying policy implications for the deployment of low-carbon technologies.

Analysis of the competitiveness and complementarity of China-Vietnam bilateral agricultural commodity trade.

Vietnam's agricultural exports to China have remained strong, with the country maintaining its position as the top destination for Agri-products. This article primarily utilizes the Revealed Comparative Advantage (RCA) Index, and Trade Complementarity (TC) index to examine the trade comparative advantage, and the complementary of twenty major agricultural products between China and Vietnam from 2012 to 2021. The study results showed that Vietnam and China frequently exchange agricultural products. Vietnam has more stronger competitiveness than China in terms of agricultural products. China's exports to Vietnam were highly complementary to Vietnam's imports in category 0 whiles Vietnam's exports to China showed strong complementarity with China's imports in category 2. This paper analyzes the complementarity and comparative advantages of agricultural trade between China and Vietnam, and proposes informed suggestions for policy-making to promote agricultural trade between the countries. The proposed suggestions aim to expand agricultural trade between the two countries, reduce the trade imbalance, and achieve mutual benefit and win-win results.

One-day examination of triple nuclear medicine imaging and application in evaluating transarterial embolization

A diagnosis based on multiple nuclear medicine imaging (NMI) was more comprehensive in approaching the nature of pathological changes. In this research, a method to realize triple NMIs within one day was developed based on the reasonable arrangements of 68Ga-RGD PET/CT specialized on neovascularization, 99mTc-HL-91 SPECT/CT specialized on hypoxia and 18F-FDG PET/CT specialized on tumor metabolism. Feasibility was verified in evaluating the therapeutic effects of transarterial embolization (TAE) performed on rabbit models with VX2 tumor. Radiation dosimetry was carried out to record the radiation exposure from multiple injections of radiopharmaceuticals. In results, the one-day examination of triple NMIs manifested the diversity of the postoperative histological changes, including the local neovascularization induced by embolization, hypoxic state of embolized tissues, and suppression of tumor metabolism. More importantly, radiation dosage from radiopharmaceuticals was limited below 5.70 ± 0.90 mSv. In conclusion, the strong timeliness and complementarity of one-day examination of triple nuclear medicine imaging made it clinically operative and worthy of popularizing. There was flexibility in combining distinct NMIs according to the clinical demands, so as to provide comprehensive information for diagnosis.

Maize cultivar mixtures increase aboveground biomass and grain quality via trait dissimilarity and plasticity

Despite being visually cryptic, conspecific cultivar mixtures increase diversity and can increase productivity in agricultural systems. However, the mechanisms underlying this increase are unclear. In natural systems, differences in traits among species and among ecotypes correspond with diversity-increased productivity, but whether these attributes are important in cultivar mixtures is still unclear. To explore how cultivar mixtures affect agroecosystem productivity and the roles of trait dissimilarity, we conducted a four-year (2017–2020) field experiment in Northeast China with maize. The experimental treatments included eight single cultivar monocultures, four two-cultivar mixtures, one four-cultivar mixture, one six-cultivar mixture and one eight-cultivar mixture. Experiments were arranged in a randomized complete block design with three replications. Across the four years, all cultivar mixture combinations increased aboveground biomass and crude protein in grain by 7.0% and 4.1%, respectively, compared to the average of the eight monocultures. Grain yield, aboveground biomass, and grain crude protein in overyielding cultivar combinations increased by 2.0–9.0%, 13.0–20.0% and 4.8–10.7%, respectively, compared to the average of the component monocultures of each mixture over four years. We calculated positive complementarity effects for grain yield, aboveground biomass and grain crude protein. All selection effects of cultivar mixtures were negative or neutral. There were strong positive correlations between complementarity effect and functional trait dissimilarity in cultivar mixtures, based on grain yields (P< 0.010), aboveground biomass (P< 0.001) and grain crude protein (P< 0.050), whereas there were no such positive relationships between selection effects and functional trait dissimilarity. Moreover, we found higher levels of trait dissimilarity for shoot diameter, leaf area and specific leaf area in cultivar mixtures than in monocultures. Also, the shoot diameter, leaf area and specific leaf area of specific cultivars were higher in mixtures than in monocultures. Our results indicated that niche complementarity derives from functional trait dissimilarity, and strong complementarity effects correlate with greater functional trait dissimilarity in cultivar mixtures. These findings provide evidence that plasticity in traits, rather than only inherent traits, contributed to the diversity effects, and the greater functional trait dissimilarity among cultivars in mixtures can increase crop productivity via increasing cryptic diversity in agroecosystems. Our results link changes to plant aboveground traits in cultivar mixtures, which shed light on yield advantages in diverse agroecosystems.

Neutrino window to scalar leptoquarks: From low energy to colliders

Leptoquarks are theorized particles of either scalar or vector nature that couple simultaneously to quarks and leptons. Motivated by recent measurements of coherent elastic neutrino-nucleus scattering, we consider the impact of scalar leptoquarks coupling to neutrinos on a few complementary processes, from low energy to colliders. In particular, we set competitive constraints on the typical mass and coupling of scalar leptoquarks by analyzing recent COHERENT data. We compare these constraints with bounds from atomic parity violation experiments, deep inelastic neutrino-nucleon scattering and collider data. Our results highlight a strong complementarity between different facilities and demonstrate the power of coherent elastic neutrino-nucleus scattering experiments to probe leptoquark masses in the sub-TeV range. Finally, we also present prospects for improving current bounds with future upgrades of the COHERENT detectors and the planned European Spallation Source. Published by the American Physical Society 2024

Effort complementarity and role assignments in group contests

AbstractThis study characterizes role assignments in maximizing a group's winning probability under the influence of the complementarity of group members' efforts in a group contest, in contrast to prize and multiple resource allocations. We use a constant elasticity of substitution effort aggregator function to parameterize the complementarity. While the prize and resource allocation rules depend on the complementarity, the assignment rule does not when multiple roles are assignable to a single group member: All roles are assigned only to the most productive group member. However, when only a single role per group member is assignable, the assignment rule depends on the complementarity: Roles from greater to less importance are assigned to group members in descending order of their productivity under strong complementarity; only the most important role is assigned to the most productive group member and the others have no effect under weak complementarity.

Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy

Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vehicles (EVs) have received significant attention in recent times as emerging biomarkers and subjects of transformational studies. The three main branches of liquid biopsy have evolved from the three primary tumor liquid biopsy detection targets—CTC, ctDNA, and EVs—each with distinct benefits. CTCs are derived from circulating cancer cells from the original tumor or metastases and may display global features of the tumor. ctDNA has been extensively analyzed and has been used to aid in the diagnosis, treatment, and prognosis of neoplastic diseases. EVs contain tumor-derived material such as DNA, RNA, proteins, lipids, sugar structures, and metabolites. The three provide different detection contents but have strong complementarity to a certain extent. Even though they have already been employed in several clinical trials, the clinical utility of three biomarkers is still being studied, with promising initial findings. This review thoroughly overviews established and emerging technologies for the isolation, characterization, and content detection of CTC, ctDNA, and EVs. Also discussed were the most recent developments in the study of potential liquid biopsy biomarkers for cancer diagnosis, therapeutic monitoring, and prognosis prediction. These included CTC, ctDNA, and EVs. Finally, the potential and challenges of employing liquid biopsy based on CTC, ctDNA, and EVs for precision medicine were evaluated.

Hyperspectral image classification based on a novel Lush multi-layer feature fusion bias network

Convolutional neural networks (CNNs) exhibit excellent performance in hyperspectral image classification (HSIC) and have attracted significant interest. Nevertheless, the common CNN-based classification techniques still suffer from the following drawbacks. 1) Although deep CNNs can effectively extract features from hyperspectral images, a network that is too deep often leads to negative effects such as overfitting, vanishing gradients, and decreased accuracy. 2) Most of these models for HSIC do not fully consider the strong complementarity and correlation between features at different levels. To address these issues, firstly, a self-regularization parameter correction non-monotonic activation function, called Lush, was first proposed. Then, this study proposes a novel bias update method that can update network bias parameters more efficiently. On the basis of the above, a Lush multi-layer feature fusion polarization network (LMFFBNet) for hyperspectral image classification was proposed in this study. It designs a new after-melting technology to fuse deep and shallow features in multiple stages and layers, which can obtain more discriminative features, thereby improving the classification performance of hyperspectral images. Extensive experiments on five challenging datasets (Indian Pines, Pavia University, Kennedy Space Center, Salinas Valley, and Houston) have demonstrated that compared to some state-of-the-art methods, the proposed LMFFBNet method can provide better classification performance for hyperspectral images and has strong robustness. This fully proves the effectiveness of the LMFFBNet method.

Revisiting Protein-Copolymer Binding Mechanisms: Insights beyond the "Lock-and-Key" Model.

The "lock-and-key" model that emphasizes the concept of chemical-structural complementary is the key mechanism for explaining the selectivity between small ligands and a larger adsorbent molecule. In this work, concerning the copolymer chain using only the combination of N-isopropylacrylamide (NIPAm) and hydrophobic N-tert-butylacrylamide (TBAm) monomers and by large-scale atomistic molecular dynamics simulations, our results show that the flexible copolymer chain may exhibit strong binding affinity for the biomarker protein epithelial cell adhesion molecule, in the absence of hydrophobic matching and strong structural complementarity. This surprising binding behavior, which cannot be anticipated by the "lock-and-key" model, can be attributed to the preferential interactions established by the copolymer with the protein's hydrophilic exterior. We observe that increasing the fraction of incorporated TBAm monomers leads to a prevalence of interactions with asparagine and glutamine amino acids due to the emerging hydrogen bonding with both NIPAm and TBAm monomers. Our findings suggest the appearance of highly specific and high-affinity binding sites on the protein created by engineering the copolymer composition, which motivates the applications of copolymers as protein affinity reagents.

Modeling transthyretin (TTR) amyloid diseases, from monomer to amyloid fibrils.

ATTR amyloidosis is caused by deposition of large, insoluble aggregates (amyloid fibrils) of cross-β-sheet TTR protein molecules on the intercellular surfaces of tissues. The process of amyloid formation from monomeric TTR protein molecules to amyloid deposits has not been fully characterized and is therefore modeled in this paper. Two models are considered: 1) TTR monomers in the blood spontaneously fold into a β-sheet conformation, aggregate into short proto-fibrils that then circulate in the blood until they find a complementary tissue where the proto-fibrils accumulate to form the large, insoluble amyloid fibrils found in affected tissues. 2) TTR monomers in the native or β-sheet conformation circulate in the blood until they find a tissue binding site and deposit in the tissue or tissues forming amyloid deposits in situ. These models only differ on where the selection for β-sheet complementarity occurs, in the blood where wt-wt, wt-v, and v-v interactions determine selectivity, or on the tissue surface where tissue-wt and tissure-v interactions also determine selectivity. Statistical modeling in both cases thus involves selectivity in fibril aggregation and tissue binding. Because binding of protein molecules into fibrils and binding of fibrils to tissues occurs through multiple weak non-covalent bonds, strong complementarity between β-sheet molecules and between fibrils and tissues is required to explain the insolubility and tissue selectivity of ATTR amyloidosis. Observation of differing tissue selectivity and thence disease phenotypes from either pure wildtype TTR protein or a mix of wildtype and variant molecules in amyloid fibrils evidences the requirement for fibril-tissue complementarity. Understanding the process that forms fibrils and binds fibrils to tissues may lead to new possibilities for interrupting the process and preventing or curing ATTR amyloidosis.

Indirect constraints on top quark operators from a global SMEFT analysis

We perform a model-independent analysis of top-philic New Physics scenarios, under the assumption that only effective operators involving top quarks are generated at tree level. Within the SMEFT framework, we derive indirect constraints on Wilson Coefficients by combining a large set of low-energy observables: B-meson and kaon decays, meson mixing observables, precision electroweak and Higgs measurements, anomalous magnetic moments, lepton flavour violating processes, lepton flavour universality tests, and measurements of the Cabibbo angle. We consider the renormalization group evolution of the operators and use the one-loop matching of the SMEFT onto the LEFT. The global analysis is then used to perform one-parameter, two-parameter, and global fits, as well as applications to explicit ultraviolet models. We find that the inclusion of measurements from different physics sectors reveals a strong interplay and complementarity among the observables. The resulting constraints are also compared to direct bounds provided by top quark productions at the LHC.

China’s Image in the GCC Mainstream English Newspapers: A Corpus-Based Critical Discourse Analysis

ABSTRACT This paper used the corpus-based critical discourse analysis to explore China’s image represented in the Gulf Cooperation Council (GCC) mainstream English newspapers between 2010 and 2020. Findings show that China is usually reported in a very positive light. Cooperation is the main tone of diplomatic relations between China and the GCC. They have signed a series of strategic cooperation agreements and memorandums of understanding to strengthen a comprehensive and long-term bilateral strategic partnership. Moreover, China is depicted as the GCC’s important trading partner, which is very strong in the economic power and plays an important role in the world economic system. A strong economic complementarity and the historical friendly relations between the two sides are the main contributing factors for the positive representations of China in their mainstream English newspapers. However, the stereotyped “China as a Demon” image in the Western media is occasionally constructed in the GCC English newspapers because of the Western news agencies’ domination of the flow of international news through the Arab media and private ownership of the daily newspapers in the GCC states.

SiRENE: A new generation of engineering simulator for real-time simulators at EDF

For Safety Assisted Engineering works, real-time simulators have emerged as a mandatory tool among all the key actors involved in the nuclear industry (utilities, designers and safety authorities). EDF, Electricité de France, as the leading worldwide nuclear power plant operator, has a crucial need for efficient and updated simulation tools for training, operating and safety analysis support.This paper will present the work performed at EDF/DT to develop a new generation of engineering simulator to fulfil these tasks. The project is called SiRENE, which is the acronym of Re-hosted Engineering Simulator in French. The project has been economically challenging. Therefore, to benefit from existing tools and experience, the SiRENE project combines:- A part of the process issued from the operating fleet training full-scope simulator.- An improvement of the simulator prediction reliability with the integration of High-Fidelity models, used in Safety Analysis. These High-Fidelity models address Nuclear Steam Supply System code, with CATHARE thermal-hydraulics system code and neutronics, with COCCINELLE code.- And taking advantage of the last generation and improvements of instructor station.The intensive and challenging uses of the new SiRENE engineering simulator are also discussed. The SiRENE simulator has to address different topics such as verification and validation of operating procedures, identification of safety paths, tests of I&C developments or modifications, tests on hydraulics system components (pump, valve etc.), support studies for Probabilistic Safety Analysis (PSA). etc. It also emerges that SiRENE simulator is a valuable tool for self-training of the newcomers in EDF nuclear engineering centers.As a modifiable tool and thanks to a skillful team managing the SiRENE project, specific and adapted modifications can be taken into account very quickly, in order to provide the best answers for our users' specific issues.Finally, the SiRENE simulator, and the associated configurations, has been distributed among the different engineering centers at EDF (DT in Lyon, DIPDE in Marseille and CNEPE in Tours). This distribution highlights a strong synergy and complementarity of the different engineering institutes at EDF, working together for a safer and a more profitable operating fleet.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

The dynamics of AI capability and its influence on public value creation of AI within public administration

Artificial Intelligence (AI) technologies in public administration are gaining increasing attention due to the potential benefits they can provide in improving governmental operations. However, translating technological opportunities into concrete public value for public administrations is still limited. One of the factors hindering this progress is the lack of AI capability within public organisations. The research found that various components of AI capability are essential for successfully developing and using AI technologies, including tangible, intangible, and human-related factors. There is a distinction between the AI capability to develop and the AI capability to implement AI technologies, with more administrations capable of the former but finding difficulties in the latter. A lack of in-house technical expertise to maintain and update the AI systems, legal challenges in deploying developed AI systems, and the capability to introduce changes in the organisation to ensure the system remains operational and used by relevant end-users are among the most critical limiting factors for long-term use of AI by public administrations. The research underlines the strong complementarity between historical eGovernment developments and the capability to deploy AI technologies. The study suggests that funding alone may not be enough to acquire AI capability, and public administrations need to focus on both the capability to develop and implement AI technologies. The research emphasizes that human skillsets, both technical and non-technical, are essential for the successful implementation of AI in public administration.

BUSINESS CYCLES WITH CYCLICAL RETURNS TO SCALE

AbstractWe study business cycles with cyclical returns to scale. Contrary to tightly parameterized conventional production functions, we empirically identify strong input complementarity that leads to procyclical returns to scale. We, therefore, propose a flexible translog production function that allows complementarity‐induced procyclical returns to scale. We integrate this function into a standard medium‐scale dynamic stochastic general equilibrium model. Our estimated model with input complementarity (i) features procyclical returns to scale and acyclical price markups, (ii) better matches the cyclicality of factor shares, and (iii) significantly decreases the contribution of markup shocks to output fluctuations relative to those of the standard model.