Miracle-dz/UniFAR-FTUs huggingface dataset:
Context Encoder Network with Channel-Wise Attention Mechanism for Nerve Fibers Detection in Corneal Confocal Microscopy Images

Diabetic peripheral neuropathy (DPN), one of the common long-term complications of diabetes, may affect the physical condition and quality of life of patients. Corneal confocal microscopy (CCM) is a rapid non-invasive ophthalmic imaging technique that can be used to observe the form of nerve fibers in sub-basal corneal nerve plexus directly. Analysis of the nerve fibers in CCM images quantifies features of nerve fibers, can apply to clinical diagnosis of DPN. This paper presents an attention deep learning model for detecting nerve fibers from CCM images, which combine context encoder network and squeeze-and-excitation networks. The algorithm with attention mechanism can solve the problem of the segmentation result is easily influenced by high level noise in CCM images and imbalance of nerve fiber pixels and background pixels to a certain degree. The proposed algorithm shows the best performance among common image segmentation deep learning model.
CBAM-SLAM: A Semantic SLAM Based on Attention Module in Dynamic Environment

Traditional visual SLAM systems are stable in static environments, but have low accuracy and unstable in dynamic condition, which affects positioning accuracy and mapping effects. To solve this problem, CBAM-SLAM is proposed in this paper which is a semantic SLAM system based on attention module in dynamic environment. First, we integrate the CBAM (Convolutional Block Attention Module) into the feature extraction network of Mask R-CNN to extract key features such as dynamic objects better and enhance the segmentation accuracy of dynamic objects. Subsequently, the masks generated by the improved Mask R-CNN network are input into the SLAM system as prior information. Next, the dynamic feature points are eliminated according to the prior information, and the static feature points are kept to tracking in VSLAM system. Finally, we conduct comparative experiment on the public dataset. The result shows that CBAM-SLAM effectively reduces the error of the real trajectory and the estimated trajectory, improves the accuracy and keep VSLAM system robust especially in high dynamic condition.
TransIDS: A Transformer-based approach for intrusion detection in Internet of Things using Label Smoothing

As Internet of Things (IoT) is rapidly developing and popularizing, IoT devices generate a large amount of network traffic information, which requires reliable IoT traffic intrusion detection techniques to continuously improve network security mechanisms. Most existing intrusion detection methods based on machine learning and deep learning in IoT rely on complex feature reduction and feature selection techniques, and the models cannot focus on important features adaptively and have poor global modeling capability for high-dimensional sequential features. To overcome the dependence on feature preprocessing, realize the model adaptively focus on important features, and further enhance the ability to extract global features, this paper proposes a Transformer-based IoT intrusion detection method called TransIDS. TransIDS has remarkable global modeling capability by introducing a multi-headed self-attention mechanism, which can extract multiple global temporal features, adaptively adjusting the attention to high-dimensional features. To overcome the undesirable effects of unbalanced datasets, we adopt Label Smoothing to add noise to sample labels to avoid over-reliance on training samples, which can enhance the generalization ability of the model. Finally, the performance of the proposed method is verified on a TON-IoT standard dataset in a real environment, and the experimental results show that the proposed method achieves the superior recognition performance compared to other advanced methods. In addition, we also investigate the effect of hyperparameters on the detection performance.
Multiple Attribute Group Decision Making Method Based on Fuzzy Influence Diagram and D-S Evidence Theory

For comprehensive evaluation problems,a multiple attributes group decision making method is proposed.This method constructs multilayer decision making model using fuzzy influence diagram,and transfers the opinions of experts using fuzzy relation matrix,and aggregates the opinions of experts using D-S evidence theory.In method,the objective fuzzy relation between decision attributes is used to make decisions.So the decision making method does not need to calculate the weights of decision attributes.An illustrative example shows that the decision making method this paper proposed is valid,and can reduce the uncertainty degree of decision making information.
Efficient Method of RSA Key-Pair Generation

RSA is a very important public-key encryption algorithm.The bottleneck problems of RSA efficiency are big prime number finding and exponential modular computation.RSA key-pair generation depends on the two problems mentioned just now.This paper carefully analyse the main algorithms used by the key-pair generation process,and improves the preselection algorithm by modifying the method of random number generation.
A Note on Aerosol Processing by Droplet Collision‐Coalescence

The processing of aerosol by droplet collision‐coalescence is analyzed in three‐dimensional simulations of drizzling stratocumulus using a Lagrangian cloud model for the representation of aerosol and cloud microphysics. Collision‐coalescence processing is shown to create a characteristic bimodality in the aerosol size distribution. We show that the large‐scale dynamics of the stratocumulus‐topped boundary layer are key to understanding the amount of time available for collision‐coalescence processing. The large‐scale dynamics enable aerosol particles to repeat a cycle of droplet condensation, collision‐coalescence, and evaporation, which causes a steady increase in aerosol size. This process is continued until the aerosol grows so large that droplet growth is substantially accelerated and multiple collisions occur within one cycle, forming precipitation‐sized droplets that are lost to the surface, including the aerosol. An analytical relationship is derived that captures the fundamental shape of the processed aerosol size distribution.
Design of a novel above-knee prosthetic leg with a passive energy-saving mechanism

The push-off phase is a critical part of initiating movement during walking, and it requires a significant amount of energy. Recent research has shown that the passive use of springs in parallel with the leg can harvest the push-off energy and reduce the total metabolic energy of walking for healthy subjects. In this study, we present the design of a prosthetic leg with a passive-based mechanism to reduce walking energy consumption for above-knee amputees. The mechanism stores energy during the stance phase of the gait cycle and releases it to support the prosthetic leg during locomotion. The known polycentric knee joint 3R36 and the ankle-foot joint ESAR were chosen and adopted for this study. We also utilized a ratchet clutch that connects with a spring and rope from the pylon to the foot which regulates movement and saves energy. Our simulations demonstrate that the spring stores elastic energy from approximately 22% of the gait cycle and reaches its maximum energy storage at approximately 50% of the walking cycle. The energy is then released at approximately 58% of the stride cycle during the push-off phase. The motion of the proposed prosthetic leg for individuals with transfemoral amputations mimics the normal walking pattern of healthy individuals well.
Targeting Mitochondrial DNA Transcription by POLRMT Inhibition or Depletion as a Potential Strategy for Cancer Treatment

Transcription of the mitochondrial genome is essential for the maintenance of oxidative phosphorylation (OXPHOS) and other functions directly related to this unique genome. Considerable evidence suggests that mitochondrial transcription is dysregulated in cancer and cancer metastasis and contributes significantly to cancer cell metabolism. Recently, inhibitors of the mitochondrial DNA-dependent RNA polymerase (POLRMT) were identified as potentially attractive new anti-cancer compounds. These molecules (IMT1, IMT1B) inactivate cancer cell metabolism through reduced transcription of mitochondrially-encoded OXPHOS subunits such as ND1-5 (Complex I) and COI-IV (Complex IV). Studies from our lab have discovered small molecule regulators of the mitochondrial matrix caseinolytic protease (ClpP) as probable inhibitors of mitochondrial transcription. These compounds activate ClpP proteolysis and lead to the rapid depletion of POLRMT and other matrix proteins, resulting in inhibition of mitochondrial transcription and growth arrest. Herein we present a comparison of POLRMT inhibition and ClpP activation, both conceptually and experimentally, and evaluate the results of these treatments on mitochondrial transcription, inhibition of OXPHOS, and ultimately cancer cell growth. We discuss the potential for targeting mitochondrial transcription as a cancer cell vulnerability.
Toward the Observability of Cloud-Native Applications: The Overview of the State-of-the-Art

The Cloud-native model, established to enhance the Twelve-Factor patterns, is an approach to developing and deploying applications according to DevOps concepts, Continuous Integration/Continuous Delivery, containers, and microservices. The notion of observability can help us cope with the complexity of such applications. We present a Systematic Mapping Study (SMS) in the observability of Cloud-native applications. We have chosen 56 studies published between 2018 and 2022. The selected studies were thoroughly analyzed, compared, and classified according to the chosen comparative criteria. The presented SMS assesses engineering approaches, maturity, and efficiency of observability by deliberating around four research questions: 1) What provides the motivations for equipping Cloud-native applications with observability capabilities? 2) Which research areas are addressed in the related literature? 3) How are observability approaches implemented? 4) What are the future trends in the Cloud-native applications observability research?
Comparative experimental study of bridge detection methods based on deep learning

As a kind of transportation hub, bridge has important social and military value. This paper constructs bridge data sets of multi-source satellite remote sensing images, and realize the automatic bridge detection based on deep learning method to solve the problem that traditional method has bad results and poor stability. The effect of Faster RCNN and YOLO-v3 in remote sensing image detection of different types of bridges are mainly studied, and the advantages and disadvantages of two typical deep learning methods in bridge detection are analyzed through experiments. Through the experiment, the two methods can achieve the automatic bridge detection and the accuracy is all over 90%. In terms of detection accuracy, YOLO-v3 is higher than Faster RCNN(AP50), but its detection stability is lower than Faster RCNN. In addition, the training time and single image detection time of YOLO-v3 are both better than that of Faster RCNN.
Nexus between corporate governance and earnings management in family and non-family firms

This study explores the relationship between internal corporate governance mechanism and accruals and real earnings management (EM), following Pakistan’s 2012 amended Code of Corporate Governance. It also examines the moderating role of family firms on the relationship among corporate governance mechanisms and accruals and real earnings management. For this purpose, we used a sample of 172 firms listed on the Pakistan Stock Exchange (PSX) for 2012–2019. The dependent variables were estimated by utilizing the generalized method of moments (GMM), fixed random effects and moderation analysis were estimated through Stata. Hausman specification test was used to choose among random and fixed effects. Results report that board size showed a significant positive impact on abnormal discretionary expenses and overall real earnings management. Board independence led to a substantial and negative impact with accruals EM. Managers of familyowned firms are also opportunistically altering the reported earnings. Results illustrate to the users of financial statements that the reliability of financial information depends on the effectiveness of corporate governance. Also, highlight the efficiency of monitory mechanisms and suggest that board independence is one of the practical approaches for an emerging market to restrain the EM practices. Findings are helpful for regulators, policymakers, and investors to regulate and make policies to improve the financial reporting quality in Pakistan. As per the authors’ knowledge, this study adds the novelty by focusing on two critical internal monitoring mechanisms (board independence and board size) in mitigating earnings management, following the 2012 amended Code of Corporate Governance in Pakistan. It will add uniqueness by studying the moderating role of family ownership, as the literature is scarce. The findings of this study highlighted the impact of the internal monitoring mechanisms on earning management practices in Pakistan.
Effects of heavy metal ions on white clover (Trifolium repens L.) growth in Cd, Pb and Zn contaminated soils using zeolite

According to the Greek Ministry of Environment, there are 2,000 contaminated sites in Greece. The agricultural production in these sites should be evaluated to provide an assessment and recommendations on the necessary actions required for crop sustainability. This study investigated the effects of heavy metals on White clover ( Trifolium repens L.) growth in an above-referenced multi-metal contaminated site in the absence/presence of zeolite as an agent empowering the rehabilitation of pollution and immobilization of heavy metals. The addition of 1% zeolite to the polluted soils significantly contributed to plant growth by limiting the availability of Cd, Pb and Zn. However, the dry biomass of plants grown in the presence of zeolite was evaluated to be unsuitable as a raw material (feed) in livestock production, due to the high Cd toxicity. In the context of sustainable production, this study aims to holistically approach and evaluate mechanisms of phytoremediation, bioaccumulation and the disposal of the bioaccumulator as a high value-added product (feed).
Design and modelling of operational amplifier for energy systems

In the present study the single channel operational amplifier IS-OU1 with 15 V supply voltage (open loop gain ~100-110 dB) design is based on n-p-n type bipolar transistors has been developed. It is shown that the proposed macromodel sufficiently describes behaviour of operational amplifier. It is established that obtained electrical parameters characteristics of operational amplifier macromodel have good agreement with design requirements. Consequently the proposed design operational amplifier can be applied in industry production for further use in electrical integrated analogue circuits and energy systems.
Drug-selective pressure effect on HIV integrase mutations in antiretroviral naïve and experienced patients

Introduction: Resistance to antiretroviral drugs is a serious problem often related to selective drug-induced pressure and sub-optimal drug dosing. This study aimed to investigate drug resistance-associated mutations in human immunodeficiency virus type 1 (HIV-1) integrase gene caused by the drug pressure of reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs). Material and methods: For this purpose, RNA of 50 HIV-infected patients (25 drug-naïve patients and 25 patients under antiretroviral therapy [INI naive]) was extracted and one step RT-nested PCR was carried out on HIV integrase (IN). Then, gene sequences were analyzed to determine sub-types and antiretroviral resistance-associated mutations (RAMs). Results: Phylogenetic analysis revealed that recombinant sub-type CRF35-AD was the most prevalent in all patients (87.2%), followed by A1 sub-type (12.8%). Among the 25 ART-experienced patients, two mutations (N155I, G163R) associated with resistance to integrase inhibitors (INI) were found. Among the 25 naïve patients, several polymorphisms were observed, which was also lower in this group than in the ART group. Conclusions: The results of this study indicated that the integrase mutations can be caused by the effect of selective pressure induced by antiviral agents, such as RTIs and PIs. Therefore, examination of the integrase drug resistance mutations is recommended before starting treatment in Iran.
Study of the Influence of Process Parameters on the Morphology of ZnO Nanostructures

: Zinc oxide nanostructures have real potential in different scientiﬁc ﬁelds. In the case of these nanostructures, it was found that the morphology of ZnO plays an essential role in developing further applications, but it is necessary a rigorous control of the main factors which inﬂuence the size, shape, agglomeration tendency, uniformity, and orientation of the nanostructures. In the present paper, our efforts are oriented to synthesize different types of ZnO nanostructures by chemical method, and optimization was achieved with varying parameters, such as the concentration of precursors, types of solvents, pH, time, or temperature, as well as the parameters required for thermal treatment. To obtain the characteristic structural and morphological information, ZnO nanostructures were investigated using Fourier transform infrared spectrometry (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). SEM analysis conﬁrms that the morphology and size of the ZnO nanostructures depend on the process parameters. The XRD results reveal that the synthesized samples have a wurtzite crystalline structure, and FTIR spectra show the presence of Zn-O bonding. The wetting capacity of continuous ZnO surfaces with different morphologies was studied by measuring the contact angle, indicating that the wetting and percolation capacity, depend on the orientation of the synthesized nanostructures.
The impacts of assimilating Aeolus horizontal line-of-sight winds on numerical predictions of Hurricane Ida (2021) and a mesoscale convective system over the Atlantic Ocean

Abstract. The Aeolus satellite, the first spaceborne wind lidar launched by the European Space Agency (ESA) on 22 August 2018, provides global measurements of horizontal line-of-sight (HLOS) winds. The assimilation of Aeolus HLOS winds has been proven to improve numerical weather predictions (NWPs). Still, its influences on forecasts of tropical cyclones (TCs) and tropical convective systems have yet to be examined in detail. This study investigates the impacts of assimilating Aeolus HLOS winds on the analysis and forecasts of Hurricane Ida (2021) and a mesoscale convective system (MCS) embedded in an African easterly wave (AEW) over the Atlantic Ocean (AO) with the mesoscale community Weather Research and Forecasting (WRF) model and the NCEP Gridpoint Statistical Interpolation (GSI)-based three-dimensional ensemble-variational (3DEnVAR) hybrid data assimilation (DA) system. Mie-cloudy and Rayleigh-clear winds are assimilated. The results for Ida (2021) show that assimilating Aeolus HLOS winds leads to better track predictions. The intensity forecasts are improved in some cases, even with limited coverage of Aeolus HLOS winds within the inner core region of Ida (2021). In addition, the structure of heavy precipitation associated with Ida (2021) is refined after the assimilation of Aeolus HLOS winds. Further diagnosis demonstrates that the improved intensity and precipitation forecasts result from enhanced divergence in the upper level of the troposphere after the assimilation of Aeolus HLOS winds. Additional results from the MCS associated with an AEW indicate that assimilating Aeolus HLOS winds enhances forecasts of its precipitation structure and the associated low-level divergence. Findings from this study suggest that the assimilation of Aeolus HLOS winds has the potential to improve forecasts for TCs and tropical convective systems.
Group velocities computation of surface waves based on the fast generalized R/T coefficient method

Group velocities of surface waves are more significative than phase velocities when we inverse the structure of the earth's interior by seismic data.In this study we calculate the group velocity of Rayleigh waves based on the Fast Generalized R/T coefficient Method (FGRT)developed by Pei (2008),for the homogeneous horizontal multilayered model and present an analytic solution for the inverse of the 4 × 4 layer matrix E in the traditional generalized R/T coefficient method of Chen(1993). Dispersion curves of two typical models are computed by numerical experiments,whose results are compared with the compound matrix method.It demonstrates that our method is feasible.Compared with the Generalized R/T coefficient method(Chen 1993),our research enhances the calculation efficiency greatly,especially in the cases of the high-frequency,thicker layers and more layers conditions.
Pixelated Photonic Crystals

Photonic crystals can prevent or allow light of certain frequencies to propagate in distinct directions in anomalous and useful ways for use as waveguides, laser cavities, and topological light propagation. However, there exist limited approaches for fundamental reconfiguration of photonic crystals, such as changing the unit cell to various and on‐demand geometries and symmetries. This work introduces the concept of pixelated 2D photonic crystals where the variability of the dielectric profile is achieved by a pixelated matrix of the material. Specifically, the cross sections of dielectric cylindrical pillars distributed in a photonic crystal lattice are replaced with pixelated circles using different resolutions and the corresponding band diagrams are calculated. The comparison to the band diagrams of the original structure shows that the original—and today typically used—cylindrical design can be well approximated by as few as 5×5$5 \times 5$ square switchable pixels while retaining less than 1%$$ change in the photonic band structure. Experimental realization of switchable pixelation is proposed based on the liquid crystal display (LCD) technology with high birefringence materials. More generally, the demonstrated approach to reconfigurable 2D photonic crystal based on switchable pixels can enable realization of diverse fundamentally reconfigurable advanced optical materials.
A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor.