Miracle-dz/UniFAR-FTUs huggingface dataset:
The MAP Kinase PvMK1 Regulates Hyphal Development, Autophagy, and Pathogenesis in the Bayberry Twig Blight Fungus Pestalotiopsis versicolor

Bayberry twig blight caused by the ascomycete fungus Pestalotiopsis versicolor is a devastating disease threatening worldwide bayberry production. However, the molecular basis underlying the pathogenesis of P. versicolor is largely unknown. Here, we identified and functionally characterized the MAP kinase PvMk1 in P. versicolor through genetic and cellular biochemical approaches. Our analysis reveals a central role of PvMk1 in regulating P. versicolor virulence on bayberry. We demonstrate that PvMk1 is involved in hyphal development, conidiation, melanin biosynthesis, and cell wall stress responses. Notably, PvMk1 regulates P. versicolor autophagy and is essential for hyphal growth under nitrogen-depleting conditions. These findings suggest the multifaceted role of PvMk1 in regulating P. versicolor development and virulence. More remarkably, this evidence of virulence-involved cellular processes regulated by PvMk1 has paved a fundamental way for further understanding the impact of P. versicolor pathogenesis on bayberry.
North Atlantic Oscillation: Concept and an Application

Abstract An outline of the concept of the North Atlantic Oscillation (NAO), along with some of its history is presented. This is followed by a brief presentation of the results and implications of an encouraging new application of the NAO to a regional climate problem—the interannual variation of Moroccan winter-semester precipitation. That precipitation is shown to be inversely related to the concurrent state of the NAO, and the relationship is relatively strong by the standards of recent research into the mechanisms of tropical and subtropical precipitation fluctuations. It is suggested that the NAO is of particular significance for the important issue of the long-range prediction of Moroccan (and probably also Spanish, Portuguese, and Algerian) winter precipitation, and that further research on this subject is warranted. Several specific recommendations in the latter regard are made.
Research on Anomaly Network Detection Based on Self-Attention Mechanism

Network traffic anomaly detection is a key step in identifying and preventing network security threats. This study aims to construct a new deep-learning-based traffic anomaly detection model through in-depth research on new feature-engineering methods, significantly improving the efficiency and accuracy of network traffic anomaly detection. The specific research work mainly includes the following two aspects: 1. In order to construct a more comprehensive dataset, this article first starts from the raw data of the classic traffic anomaly detection dataset UNSW-NB15 and combines the feature extraction standards and feature calculation methods of other classic detection datasets to re-extract and design a feature description set for the original traffic data in order to accurately and completely describe the network traffic status. We reconstructed the dataset DNTAD using the feature-processing method designed in this article and conducted evaluation experiments on it. Experiments have shown that by verifying classic machine learning algorithms, such as XGBoost, this method not only does not reduce the training performance of the algorithm but also improves its operational efficiency. 2. This article proposes a detection algorithm model based on LSTM and the recurrent neural network self-attention mechanism for important time-series information contained in the abnormal traffic datasets. With this model, through the memory mechanism of the LSTM, the time dependence of traffic features can be learned. On the basis of LSTM, a self-attention mechanism is introduced, which can weight the features at different positions in the sequence, enabling the model to better learn the direct relationship between traffic features. A series of ablation experiments were also used to demonstrate the effectiveness of each component of the model. The experimental results show that, compared to other comparative models, the model proposed in this article achieves better experimental results on the constructed dataset.
Turbulence, Condensation, and Liquid Water Transport in Numerically Simulated Nonprecipitating Stratocumulus Clouds.

Abstract Condensation and turbulent liquid water transport in stratocumulus clouds involve complicated interactions between turbulence dynamics and cloud microphysical processes, and play essential roles in defining the cloud structure. This work aims at understanding this dynamical–microphysical interaction and providing information necessary for parameterizations of the ensemble mean condensation rate and turbulent fluxes of liquid water variables in a coupled turbulence–microphysics model. The approach is to simulate nonprecipitating stratocumulus clouds with a coupled large eddy simulation and an explicit bin-microphysical model, and then perform a budget analysis for four liquid water variables: mean liquid water content, turbulent liquid water flux, mean cloud droplet number concentration, and the number density flux. The results show that the turbulence contribution to the mean condensation rate comes from covariance of the integral cloud droplet radius and supersaturation, which enhances condensat.
An Improved Conditional Privacy Protection Scheme Based on Ring Signcryption for VANETs

Vehicular ad hoc networks (VANETs) can effectively provide vehicle driving safety, high-speed data communication, intelligent traffic management, and vehicle entertainment. However, compared with traditional networks, VANETs are more vulnerable to attacks from adversaries, such as eavesdropping, tampering, tracking users’ privacy, etc. In order to provide security and privacy protection for VANET communication, many conditional privacy protection (CPP) authentication schemes have been reported. In 2021, Cai et al. designed a novel CPP scheme based on ring signcryption suitable for VANETs. They proved that their scheme enjoys confidentiality, unforgeability, anonymity of sender’s identity, and traceability of malicious vehicle users. But we demonstrate their scheme has some defects in construction and security. First, there is a small flaw in the ring signcryption algorithm in the scheme, which makes a legitimate receiver unable to get the original message sent by the sender from the received valid ciphertext. Second, Cai et al.’s scheme cannot provide anonymous protection of an honest sender’s identity. At the same time, it is unable to reveal the identity of a malicious vehicle user. Finally, we present an improved scheme of Cai et al.’s scheme and supply its security proofs in the random oracle model, and analyze its performance. Ours is superior to the original scheme in security and efficiency. It is very suitable for providing security and privacy protection for vehicular users in VANETs.
Zn2+ and Cu2+ Interaction with the Recognition Interface of ACE2 for SARS-CoV-2 Spike Protein

The spike protein (S) of SARS-CoV-2 is able to bind to the human angiotensin-converting enzyme 2 (ACE2) receptor with a much higher affinity compared to other coronaviruses. The binding interface between the ACE2 receptor and the spike protein plays a critical role in the entry mechanism of the SARS-CoV-2 virus. There are specific amino acids involved in the interaction between the S protein and the ACE2 receptor. This specificity is critical for the virus to establish a systemic infection and cause COVID-19 disease. In the ACE2 receptor, the largest number of amino acids playing a crucial role in the mechanism of interaction and recognition with the S protein is located in the C-terminal part, which represents the main binding region between ACE2 and S. This fragment is abundant in coordination residues such as aspartates, glutamates, and histidine that could be targeted by metal ions. Zn2+ ions bind to the ACE2 receptor in its catalytic site and modulate its activity, but it could also contribute to the structural stability of the entire protein. The ability of the human ACE2 receptor to coordinate metal ions, such as Zn2+, in the same region where it binds to the S protein could have a crucial impact on the mechanism of recognition and interaction of ACE2–S, with consequences on their binding affinity that deserve to be investigated. To test this possibility, this study aims to characterize the coordination ability of Zn2+, and also Cu2+ for comparison, with selected peptide models of the ACE2 binding interface using spectroscopic and potentiometric techniques.
Hyperspectral Image Classification Based on Multiscale Hybrid Networks and Attention Mechanisms

Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing processing. The attention mechanism is preferable to a convolutional neural network (CNN), due to its superior ability to express information during HSI processing. Recently, numerous methods combining CNNs and attention mechanisms have been applied in HSI classification. However, it remains a challenge to achieve high-accuracy classification by fully extracting effective features from HSIs under the conditions of limited labeled samples. In this paper, we design a novel HSI classification network based on multiscale hybrid networks and attention mechanisms. The network consists of three subnetworks: a spectral-spatial feature extraction network, a spatial inverted pyramid network, and a classification network, which are employed to extract spectral-spatial features, to extract spatial features, and to obtain classification results, respectively. The multiscale fusion network and attention mechanisms complement each other by capturing local and global features separately. In the spatial pyramid network, multiscale spaces are formed through down-sampling, which can reduce redundant information while retaining important information. The structure helps the network better capture spatial features at different scales, and to improve classification accuracy. Experimental results on various public HSI datasets demonstrate that the designed network is extremely competitive compared to current advanced approaches, under the condition of insufficient samples.
Functional discriminant analysis for gene expression data via radial basis expansion

In this paper we introduce functional discriminant analysis which is an extension of the classical method of logistic discriminant analysis to the data where predictor variables are functions or curves. The functional discriminant analysis approach can classify curves belong to two distinct classes eﬀectively by imposing smoothness constraint on the predictor functions and coeﬃcient function via regularized radial basis expansion. In order to select the number of basis functions to be expanded and the value of smoothing parameter which are essential in regularization, we derive an information criterion which enables us to evaluate model estimated by regularization. The proposed method is illustrated with the example in the analysis of yeast cell cycle microarray data. It is shown that functional discriminant analysis performs well especially in the sense of prediction accuracy.
Development and Validation of a Method for Quantitative Determination of Innovative Antitumor Peptide Drugs in Order to Study their Pharmacokinetics

According to the World Health Organization (WHO), oncological diseases hold second place in the world in terms of mortality after cardiovascular diseases; and in the next few decades the number of cases will continue to increase [1]. Development of medicines based on modified endogenous peptides is one of the most relevant areas of modern medicine. The most interesting among them are antitumor peptide drugs, which are no less effective than chemotherapy with its pronounced side effects, or surgical interventions that often take a toll on patients [2]. When conducting studies of peptide preparations in biological fluids, the structure of the peptide molecule and its physicochemical properties are taken into account.
Insights into the Mechanisms Involved in Lead (Pb) Tolerance in Invasive Plants—The Current Status of Understanding

Invasive plant species possess remarkable abilities to establish themselves in new environments and to displace native species. Their success can be attributed to various physiological and biochemical mechanisms, allowing them to tolerate adverse environmental conditions, including high lead (Pb) toxicity. Comprehension of the mechanisms responsible for Pb tolerance in invasive plants is still limited, but it is rapidly evolving. Researchers have identified several strategies in invasive plants to tolerate high levels of Pb. This review provides an overview of the current understanding of the ability of invasive species to tolerate or even accumulate Pb in plant tissues, including vacuoles and cell walls, as well as how rhizosphere biota (bacteria and mycorrhizal fungi) help them to enhance Pb tolerance in polluted soils. Furthermore, the article highlights the physiological and molecular mechanisms regulating plant responses to Pb stress. The potential applications of these mechanisms in developing strategies for remediating Pb-contaminated soils are also discussed. Specifically, this review article provides a comprehensive understanding of the current status of research on the mechanisms involved in Pb tolerance in invasive plants. The information presented in this article may be useful in developing effective strategies for managing Pb-contaminated soils, as well as for developing more resilient crops in the face of environmental stressors.
Insights from Spitzer on massive star formation in the LMC

Abstract Spitzer's sensitive mid-IR photometric surveys of the Magellanic Clouds provide a relatively extinction-free census of star formation activity, and sub-parsec resolution permits the study of individual massive protostars and small clusters. Using the SAGE survey of the LMC, we identify over 1000 massive YSO candidates by their MIR colors. Analysis of their spectral energy distributions (SEDs) constrains the stellar content and evolutionary state, beginning to realize for the first time the unique potential of the Clouds to study an entire galaxy's population of individual protostars. We probe the physics underlying the Schmidt-Kennicutt scaling law by analyzing how it begins to break down at 10–100 pc spatial scales. MIR spectroscopic surveys currently underway like SAGE-SPEC will enable us to couple the circumprotostellar dust distribution (the evolutionary state reflected in the SED) with the physical state of the gas, dust and ice.
Unambiguous 2-D Angle-of-Arrival Estimation Receiver Based on a 16-Port Interferometer

Multiport interferometer (MPI) techniques are getting more and more attention, especially for passive receiver (Rx) beyond 24 GHz. However, MPIs with more than six ports are rarely been studied. And the existing MPI techniques only can achieve 2-D angle-of-arrival (AOA) estimation in a narrow range of angle, which means they have an ambiguous problem. In this article, an unambiguous 2-D AOA estimation Rx based on a 16-port interferometer is proposed and demonstrated. It has three main components: eight antennas, a 16-port interferometer, and a signal processor. The 16-port interferometer with eight input ports and eight output ports is the key part and the main point of this article. The eight input signals are superposed on eight output port signals in the interferometer. First, a 16-port interferometer and the corresponding AOA estimation algorithm are proposed. The algorithm transposes the phase differences between the antenna elements into the power relationship of the output signals. Then, the Rx error models are established to obtain the systematic errors. After that, a method for reducing angle ambiguity is introduced. The method allows for the distance between the antenna elements to be larger than $\lambda $ /2, and the angle detection range is theoretically–90° to 90°. An Rx operating at 31 GHz was fabricated. The measurements demonstrate that the system operates as expected, with AOAs ranging from–40° to 40° and a mean squared error (MSE) of approximately 1°.
REMUNERASI EKSEKUTIF DAN NILAI PERUSAHAAN: DAMPAK PANDEMI COVID-19

ABSTRACTThis study aims to examine the effect of executive remuneration and the covid-19 pandemic on firm value. The population of this study are companies listed on the Indonesia Stock Exchange in 2018 - 2020. This research is included in quantitative research and the data used are secondary data. The sampling method of this study uses the purposive sampling method. This study uses panel data regression and is processed using the EViews 10 program. The estimation method used in this study is the Fixed Effect Model (FEM. The results show that remuneration has no significant effect on firm value. However, the Covid-19 pandemic has a significant negative effect on firm value). In addition, the average executive remuneration received during the covid-19 pandemic is greater than the average remuneration received before the Pandemic. However, this average difference is not statistically significant. The implication of this research is that the Covid-19 pandemic resulted in the decline in the firm value in Indonesia, so that investors are more careful in investing in the capital market during the pandemic, and shareholders are more concerned about the remuneration scheme given to executives because there are no significant results that executive remuneration can increase the firm value.Keywords: Covid -19; Firm Value; Remuneration;ABSTRAKPenelitian ini bertujuan untuk menguji pengaruh remunerasi eksekutif dan pandemi covid-19 terhadap nilai perusahaan. Populasi penelitian ini adalah perusahaan yang terdaftar di Bursa Efek Indonesia tahun 2018 – 2020. Penelitian ini termasuk ke dalam penelitian kuantitatif dan data yang digunakan adalah data sekunder. Metode sampling penelitian ini menggunakan metode purposive sampling. Penelitian ini menggunakan regresi data panel dan diolah menggunakan program EViews 10. Metode estimasi yang digunakan penelitian ini adalah Fixed Effect Model (FEM). Hasil penelitian menunjukkan bahwa Remunerasi tidak berpengaruh signifikan terhadap nilai perusahaan. Akan tetapi, Pandemi Covid-19 berpengaruh negatif signifikan terhadap nilai perusahaan. Selain itu, rata – rata remunerasi eksekutif yang diterima saat pandemi covid-19 lebih besar daripada rata-rata remunerasi yang diterima sebelum Pandemi. Akan tetapi selisih rata – rata ini secara statistis tidak signifikan. Implikasi penelitian ini adalah Pandemi Covid-19 mengakibatkan penurunan pada nilai perusahaan di Indonesia, sehingga investor lebih berhati -hati dalam investasi di pasar modal saat pandemi. Serta, shareholder lebih memperhatikan terkait skema remunerasi yang diberikan kepada eksekutif karena tidak ditemukan hasil signifikan bahwa Remunerasi eksekutif dapat meningkatkan nilai perusahaan.Kata Kunci: Covid-19; Nilai Perusahaan; Remunerasi;
Refund of Consumption Tax to Low-Income People: Impact Assessment Using Difference-in-Differences

One way to reduce inequality and poverty is to promote tax justice. In 2021, the government of the state of Rio Grande do Sul, Brazil, implemented a program (the Devolve-ICMS Program) that refunds consumption tax to low-income citizens (cashback). This study aims to evaluate the impacts of this Program using a differences-in-differences model and having, as response variables, the monthly sum of electronic invoices issued to the Program’s beneficiaries, as well as their value. The database used includes all invoices issued to the target population during the 12 months before the Program’s implementation and the 14 months after its implementation, resulting in 7.7 million records. To receive the tax refund, the eligible population must pick up a Citizen Card, made available by the state government, which was done by a significant part of this population. The treatment group is composed of eligible citizens who have the Card, whereas the control group comprises eligible citizens who do not have it. The results show that the Program is effective, as it has reduced tax pressure on poor people and increased both the number of invoices issued and their value.
A Forward-in-Time Anelastic Nonhydrostatic Model in a Terrain-Following Coordinate

A three-dimensional (3D) forward-in-time anelastic nonhydrostatic model in a terrain-following coordinate is developed to investigate mesoscale circulations over topography. The anelastic nonhydrostatic model utilizes the deep-continuity equation, rather than the vertical-momentum equation commonly adopted, to compute vertical velocity. Hence, the anelastic nonhydrostatic model is identical to the hydrostatic model, except that the former perturbation pressure must be solved from an elliptic equation to coincide with nondivergent wind. To enhance the efficiency of iterations for convergence, the elliptic pressure equation is transformed to an artificial parabolic form that allows semi-implicit time integration as adopted in most elastic nonhydrostatic models. Numerical experiments using the anelastic model, the same anelastic model but employing the vertical momentum equation, and the compressible model were conducted for comparisons. The presented anelastic model is reasonably accurate as compared to linear analytical solution, and the three nonhydrostatic models show similar performances for 2D, less hydrostatic flow over a bell-shaped mountain. The anelastic approach was found to be reasonably efficient for nonlinear flow over terrain slopes somewhat less than 1, above which ill convergence for the pressure equation was observed. Simulations of 3D mountain-induced wake circulation with/without the boundary layer effects indicate that there is little difference between the downstream wake forms for the hydrostatic model and the anelastic nonhydrostatic model, a substantiation of the feasibility of the presented anelastic formulation.
Dual LIDAR online calibration and mapping and perception system

Making autonomous mobile devices capable of autonomous positioning and map building in a GPS-denied environment as well as being able to circumvent people in the operating area simultaneously, is essential for many UGV appliances. In this paper, a mapping and positioning system using dual LIDAR is proposed for positioning and building maps. The system may also detect people in the operating space of the equipment using infrared heat maps. The localization and mapping information from each LIDAR is unified in a loosely coupled approach after simultaneous extrinsic calibration. We propose a method for constructing factor graph using LIDAR point cloud geometric features to optimally solve the dual LIDAR extrinsic in real time. We tested the localization accuracy with a publicly available dataset and three real-world scenarios and compared it with three existing methods. The test results in open-source datasets and real scenarios show that our proposed method improves the position and attitude estimation by up to 50% compared to the three existing methods, and the proposed dual LIDAR extrinsic calibration method can achieve an estimation accuracy of 0.05 m for the translational extrinsic and 1deg for the rotational extrinsic. In addition, our proposed method achieves well position and attitude estimation when other existing methods show severe drift in trajectory estimation in real scenarios. Thus, our proposed method is suitable for obtaining high accuracy measurements of LIDAR extrinsic parameters and for dense mapping and accurate localization in the environment of GNSS-denied and human mobility.