A Class of Second Order Difference Approximation for Solving Space Fractional Diffusion Equations

A class of second order approximations, called the weighted and shifted Gr\"{u}nwald difference operators, are proposed for Riemann-Liouville fractional derivatives, with their effective applications to numerically solving space fractional diffusion equations in one and two dimensions. The stability and convergence of our difference schemes for space fractional diffusion equations with constant coefficients in one and two dimensions are theoretically established. Several numerical examples are implemented to testify the efficiency of the numerical schemes and confirm the convergence order, and the numerical results for variable coefficients problem are also presented.Comment: 24 Page

SWAKK: a web server for detecting positive selection in proteins using a sliding window substitution rate analysis

We present a bioinformatic web server (SWAKK) for detecting amino acid sites or regions of a protein under positive selection. It estimates the ratio of non-synonymous to synonymous substitution rates (K(A)/K(S)) between a pair of protein-coding DNA sequences, by sliding a 3D window, or sphere, across one reference structure. The program displays the results on the 3D protein structure. In addition, for comparison or when a reference structure is unavailable, the server can also perform a sliding window analysis on the primary sequence. The SWAKK web server is available at

Modeling the yield of winter maize using biomass distribution index in the tropical region of Yunnan, China

O objetivo deste trabalho foi estabelecer e validar um modelo de previsão de distribuição de massa de matéria seca e de rendimento, com base no tempo de desenvolvimento fisiológico, para comparar as diferenças entre o modelo de índice de distribuição de matéria seca e o modelo de coeficiente de distribuição de matéria seca, para a simulação da massa de matéria seca da espiga e para melhorar a precisão de modelos de crescimento do milho para a previsão de rendimento. Os experimentos foram realizados em três locais (Longchuan, Mangshi e Ruili), na região tropical da província de Yunnan, China. O NRMS da massa de matéria seca e o rendimento da espiga foram geralmente menores que 10. O método do índice de distribuição da massa de matéria seca (NRMS = 5,44% e RMSE = 807,22 kg ha-1 para massa de matéria seca da espiga; e o NRMS = 7,32% e RMSE = 707,67 kg ha-1 para rendimento de grãos) é melhor do que o método do coeficiente de distribuição de massa de matéria seca (NRMS = 7,52% e RMSE = 1115,31 kg ha-1 para massa de matéria seca de espiga; NRMS = 8,6% e RMSE = 830,76 kg ha-1 para rendimento de grãos) para a simulação da massa de matéria seca de espiga e o rendimento de grãos de milho. O modelo do índice de distribuição melhora a precisão do modelo, o que é valioso para o futura produção de milho e seu manejo em Yunnan.The objective of this work was to establish and validate the dry matter distribution and yield prediction models based on physiological developmental timing, to compare the differences between the dry mass distribution index model and the dry mass distribution coefficient model, for the simulation of ear dry mass and to improve the accuracy of maize growth models for predicting yield. The experiments were conducted in three tropical sites (Longchuan, Mangshi, and Ruili) in the tropical region of Yunnan Province, China. The NRMS of ear dry mass and yield were generally less than 10. The dry mass distribution index method (NRMS = 5.44% and RMSE = 807.22 kg ha-1 for ear dry mass; and NRMS = 7.32% and RMSE = 707.67 kg ha-1 for grain yield) is better than the dry mass distribution coefficient method (NRMS = 7.52% and RMSE = 1115.31 kg ha-1 for ear dry mass; NRMS = 8.6% and RMSE = 830.76 kgha-1 for grain yield) to simulate maize ear dry mass and grain yield. The distribution index model improves the accuracy of the model, which is valuable for future maize production and management in Yunnan

Study on Multi-step Forming Paths for Double Curved Parts of 1561 Aluminium Alloy

Recently, corrosion-resistant 1561 aluminium alloy has been widely applied to the production of curved parts. However, the sheets of this material will generate a high amount of springback during multi-point forming, which means that a large amount of springback compensation is required. In this paper, four multi-step forming paths are designed to study the effect of forming paths on the multi-point forming results of double curved parts for 1561 aluminium alloy. Numerical simulation of the multi-step forming of curved sheets is carried out by ABAQUS finite element simulation software. The simulation results indicate that the 1561 aluminium alloy double curved parts produce poor situations such as wrinkling and low forming accuracy in single-step forming, while the accuracy improves significantly and the forming quality increases after four-step forming. Therefore, a four-step forming path was adopted for stamping tests on double curved parts. The results of the accuracy inspection of the formed parts by Gom-inspect demonstrate that the quality of the curved parts can be effectively improved by four-step forming, which has a certain significance in guiding the forming preparation of parts for engineering applications

A Water-Soluble Polysaccharide from the Fruit Bodies of Bulgaria inquinans (Fries) and Its Anti-Malarial Activity

A water-soluble polysaccharide (BIWS-4b) was purified from the fruit bodies of Bulgaria inquinans (Fries). It is composed of mannose (27.2%), glucose (15.5%) and galactose (57.3%). Its molecular weight was estimated to be 7.4 kDa (polydispersity index, Mw/Mn: 1.35). Structural analyses indicated that BIWS-4b mainly contains (1 → 6)-linked, (1 → 5)-linked and (1 → 5,6)-linked β-Galf units; (1 → 4)-linked and non-reducing terminal β-Glcp units; and (1 → 2)-linked, (1 → 6)-linked, (1 → 2,6)-linked and non-reducing terminal α-Manp units. When examined by the 4-day method and in a prophylactic assay in mice, BIWS-4b exhibited markedly suppressive activity against malaria while enhancing the activity of artesunate. Immunological tests indicated that BIWS-4b significantly enhanced macrophage phagocytosis and splenic lymphocyte proliferation in malaria-bearing mice and normal mice. The anti-malarial activity of BIWS-4b might be intermediated by enhancing immune competence and restoring artesunate-suppressed immune function. Thus, BIWS-4b is a potential adjuvant of anti-malaria drugs

RIDERS: Radar-Infrared Depth Estimation for Robust Sensing

Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning. Adverse weather conditions, including haze, dust, rain, snow, and darkness, introduce significant challenges to accurate dense depth estimation, thereby posing substantial safety risks in autonomous driving. These challenges are particularly pronounced for traditional depth estimation methods that rely on short electromagnetic wave sensors, such as visible spectrum cameras and near-infrared LiDAR, due to their susceptibility to diffraction noise and occlusion in such environments. To fundamentally overcome this issue, we present a novel approach for robust metric depth estimation by fusing a millimeter-wave Radar and a monocular infrared thermal camera, which are capable of penetrating atmospheric particles and unaffected by lighting conditions. Our proposed Radar-Infrared fusion method achieves highly accurate and finely detailed dense depth estimation through three stages, including monocular depth prediction with global scale alignment, quasi-dense Radar augmentation by learning Radar-pixels correspondences, and local scale refinement of dense depth using a scale map learner. Our method achieves exceptional visual quality and accurate metric estimation by addressing the challenges of ambiguity and misalignment that arise from directly fusing multi-modal long-wave features. We evaluate the performance of our approach on the NTU4DRadLM dataset and our self-collected challenging ZJU-Multispectrum dataset. Especially noteworthy is the unprecedented robustness demonstrated by our proposed method in smoky scenarios. Our code will be released at \url{https://github.com/MMOCKING/RIDERS}.Comment: 13 pages, 13 figure

MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods.Comment: 9 pages, 8 figures, 2 table

Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response

Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as data collection, in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the task completion rate. We propose MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that incorporates several efficient designs, including global-local dual information processing and a tailored model structure for heterogeneous multi-agent systems. Global-local dual information processing encompasses the extraction and dissemination of spatial features from global information, as well as the partitioning and filtering of local information from individual agents. Regarding the construction of the model structure for heterogeneous multi-agent, we perform the following work. We design the same data structure to represent the states of different agents, prove the Markovian property of the decision-making process of agents to simplify the model structure, and also design a reasonable reward function to train the model. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant improvement in terms of task completion rate