Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimized Algorithm for Continuous Spectrum Extraction of Ship Noise

The continuous spectrum future is one of the important distinguishing criterions for underwater target detection and identification.The corresponding target feature extraction requires timeliness for completing the sentence in short time when the target appears.Because of the problem that it has a long computing cycle and has the bur on the cohesion department for the traditional algorithm,the paper presents a fast and effective algorithm.First,it uses segmentation combination of the fixed-fitting frequency,and then uses a straight line instead of curves on the cohesion department based on the segment overlap junction points,which can effectively reduce the run time and solve the glitch problem.The simulation of measured data can get the desired result.Compared with the traditional method,the algorithm is more time-sensitive and has practical value in the practical project.
Is Cashless Going Right in Japan?: An Observation Report

In this research, we investigated the recent trend of the diffusion of cashless payment in Japan by using the survey data of students in our institute during 2018 and 2021. Cashless payment has achieved remarkable development with the wide usage of smartphones and the prevalence of consequent smartphone-based system. Before the bar code, either two- and single-dimensional, was introduced in the cashless system, the major technology that was used is a smart card or integrated circuit card-based system. We investigated the behavior of technology adoptions to cashless payment, which we called “Smart Payment System” in 2018[5]. Since 2018, we conducted surveys and found that the effect of COVID-19 resulted in the increase adoption to the cashless payment system among students and the increase usage of credit cards. The survey showed that the use of cashless payment system increased after 2020 in comparison to 2018 and 2019. Since the COVID-19 pandemic in spring of 2020, the usage of online shopping increased, and the usage of real stores decreased. Although we did not use big data, such as the log data of mobile phone usages, the results indicated that our observation of the use of cashless payment system among young users is consistent with the common sense.
Resource Allocation Strategy during COVID-19 Period and Linear Programming Model Based on Three Meta-Heuristic Algorithms

English Teachers resource allocation problem (TRAP) which is a highly complex multi-level system is a talent scheduling problem (TSP) with limited human, material and financial resources. It is of great significance to study the allocation of teacher resource in a century-long plan based on education. In this paper, under the effective control of COVID-19, taking the Bayannur City of Inner Mongolia as an example, teaching sites are set up to study the TRAP for the resumption of classes in the graduating grade. In order to minimize the total cost of the whole distribution system, a multi-objective linear hybrid model (MOLHM) is proposed based on the fact about different demands on the number of teachers in each site, the different daily salary of teachers with different teaching experience and degree level, and the different cost of transporting teachers to respective destination. And three heuristic algorithms, ant colony optimization algorithm (ACOA), tabu search algorithm (TSA) and particle swarm optimization algorithm (PSOA) are used to solve the model. Through numerical experiments, the feasibility of them is verified, and the performances of them is compared in terms of optimization results and running time. In the system of the paper, the optimization result of ACOA is optimal, and TSA has better performance of running time. Under the condition that the equal number of ants and particles, the running time of PSOA is better than that of ACOA.
MUFNet: Toward Semantic Segmentation of Multi-spectral Remote Sensing Images

In this paper, a new convolutional neural network called multi-U fusion networks (MUFNet) is proposed for accurate semantic segmentation of multi-spectral remote sensing. Essentially, MUFNet is inspired by UNet, MFNet and CAM and fully combines their advantages. First, MUFNet introduces the skip connections into a multi-encoder-to-mono-decoder architecture, thereby facilitating the fusion of multi-scale and multi-channel spectral information. Second, the shortcut module in the decoder is revised by concatenating multiple spectral features from different encoders and then feeding the concatenated data into a CAM unit. Thus, the multi-spectral context semantics are fused and also the redundant feature maps are attention-compressed. Extensive simulations were conducted by testing UNet, UNet-4ch, MFNet and MUFNet on the 8400 RGB-NIR multi-spectral images with five categories from the GID image dataset. The visual results clearly showed that the proposed MUFNet can achieve more smoothing and complete segmentation performance than the other networks. Moreover, the measure values of mIoU, FWIoU and PA indicate that the proposed MUFNet can outperform the other networks in average semantic segmentation accuracy.
Deep-Learning-Based Diagnosis of Cassava Leaf Diseases Using Vision Transformer

Viral diseases are major causes leading to the poor yields of cassava, which is the second-largest source of food carbohydrates in Africa. As symptoms of these diseases can usually be identified by inspecting cassava leafs, visual diagnosis of cassava leaf diseases is of significant importance in food security and agriculture development. Considering the shortage of qualified agricultural experts, automatic approaches for the image-based detection of cassava leaf diseases are in great demand. In this paper, on the basis of Vision Transformer, we propose a deep learning method to identify the type of viral disease in a cassava leaf image. The image dataset of cassava leaves is provided by the Makerere Artificial Intelligence Lab in a Kaggle competition, consisting of 4 subtypes of diseases and healthy cassava leaves. Our results show that Vision-Transformer-based model can effectively achieve an excellent performance regarding the classification of cassava leaf diseases. After applying the K-Fold cross validation technique, our model reaches a categorization accuracy 0.9002 on the private test set. This score ranks top 3% in the leaderboard, and can get a silver medal prize in the Kaggle competition. Our method can be applied for the identification of diseased plants, and potentially prevent the irreparable damage of crops.
Modulation Recognition of Underwater Acoustic Communication Bandpass Signals Based on Deep Learning∗

This paper proposes a modulation recognition method of under-water acoustic (UWA) bandpass signal based on deep learning. By building a simulated UWA communication system, the data set of UWA communication signal can be obtained by simulation. The excellent model of image recognition is applied to the modulation recognition of UWA signal by deep learning method
RICHTER: hybrid P2P-CDN architecture for low latency live video streaming

Content Distribution Networks (CDN) and HTTP Adaptive Streaming (HAS) are considered the principal video delivery technologies over the Internet. Despite the wide usage of these technologies, designing cost-effective, scalable, and flexible architectures that support low latency and high quality live video streaming is still a challenge. To address this issue, we leverage existing works that have combined the characteristics of Peer-to-Peer (P2P) networks and CDN-based systems and introduce a hybrid CDN-P2P live streaming architecture. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams, such hybrid systems can provide low latency and high quality streams by enabling the delivery architecture to switch between the CDN and the P2P modes. However, modern networking paradigms such as Edge Computing, Network Function Virtualization (NFV), and distributed video transcoding have not been extensively employed to design hybrid P2P-CDN streaming systems. To bridge the aforementioned gaps, we introduce a hybRId P2P-CDN arcHiTecture for low LatEncy live video stReaming (RICHTER), discuss the details of its design, and finally give a few directions of the future work.
The Impact of Perceived Success on Consumer Forgiveness: The Mediating Role of Power Distance

The purpose of this study is to examine the impact of perceived team success on consumer forgiveness and the mediating role of power distance. It also investigates whether the type of apologizer (individual vs. team) has an impact on the benefits of team success. Study 1 used one-factor (success contribution: individual or team) between-subjects design to investigate whether the different power distance of the apologizers will affect the consumer forgiveness. Participants in study 1 were set to see the apology on social media. Study2 used 2 (success contribution: individual or team) × 2 (apologizer: individual or team) between-subjects design to investigate whether success contribution and number of apologizer have an impact on consumer forgiveness. The study also found that the power distance between consumption and successful individuals or groups may be the underlying mechanism. Participants in study 2 were set to see the apology on social media app and in an email apology letter. The results of this study suggest that when companies are perceived by consumers to owe their success to the team, they receive less power distance than companies that are perceived to owe their success to individuals, which in turn leads to higher levels of forgiveness from consumers when service failures occur. In addition, when crisis communications require an apology letter to be sent, it is more effective to send it in the name of the successful team rather than the successful individual.
A Scalable and Fair Power Allocation Scheme Based on Deep Multi-Agent Reinforcement Learning in Underwater Wireless Sensor Networks

Providing qualified communications and optimizing network performance for Underwater Wireless Sensor Networks (UWSNs) is difficult due to limited battery power and storage, unpredictable channel conditions, and significant communication interference (including ambient noise and inter-nodes interferences). Power allocation is an important technology for UWSNs. In this paper, we analyzed the constraints of UWSNs and proposed a distributed power allocation scheme based on deep multi-agent reinforcement learning, which dynamically tunes the independent transmit power according to changing environments. We improve the number of concurrent communications and optimizes network capacity by fully leveraging the spatial separation of wireless networks. We compared the proposed approach with baseline methods in network capacity and communication fairness in different communication scenarios when the number of underwater nodes increases. Experiments confirmed that our solution achieves a significantly better trade-off between network capacity and fairness, while still satisfying the lifetime criteria.
Applied AI matters: AI4Code

The marriage of Artificial Intelligence (AI) techniques to problems surrounding the generation, maintenance, and use of source code has come to the fore in recent years as an important AI application area1. A large chunk of this recent attention can be attributed to contemporaneous advancements in Natural Language Processing (NLP) techniques and sub-fields. The naturalness hypothesis, which states that "software is a form of human communication" and that code exhibits patterns that are similar to (human) natural languages (Devanbu, 2015; Hindle, Barr, Gabel, Su, & Devanbu, 2016), has allowed for the application of many of these NLP advances to code-centric usecases. This development has contributed to a spate of work in the community --- much of it captured in a survey by Allamanis, Barr, Devanbu, and Sutton (2018) that focuses on classifying these approaches by the type of probabilistic model applied to source code. This increase in the variety of AI techniques applied to source code has found various manifestations in the industry at large. Code and software form the backbone that underpins almost all modern technical advancements: it is thus natural that breakthroughs in this area should reflect in the emergence of real world deployments.
Chronic Alcohol Exposure Among People Living with HIV Is Associated with Innate Immune Activation and Alterations in Monocyte Phenotype and Plasma Cytokine Profile

Despite advances in antiretroviral therapy, chronic immune activation continues to be observed among individuals with well-controlled HIV viral loads, and is associated with non-AIDS defining morbidities among people living with HIV. Alcohol use disorder impacts a significant proportion of individuals living with HIV, and alcohol exposure is known to damage the intestinal epithelium which may increase translocation of pathogens and their molecular products, driving systemic immune activation and dysregulation. The aim of this study was to determine if adults living with HIV with well-controlled viral loads, who also suffer from alcohol use disorder with and without hepatitis C virus co-infection (n=23), exhibit evidence of advanced systemic immune activation, intestinal damage, and microbial translocation, as compared to adults living with HIV who are not exposed to chronic alcohol or other substances of abuse (n=29). The impact of a 1-month intervention to treat alcohol-use disorder was also examined. Alcohol-use disorder was associated with evidence of advanced innate immune activation, alterations in monocyte phenotype including increased expression of Toll-like receptor 4, increased burden of stimulatory ligands for Toll-like receptor 4, and alterations in plasma cytokine signature, most notably elevations in soluble CD40 ligand and transforming growth factor beta. Alcohol-associated immune activation was more pronounced among individuals with hepatitis C virus co-infection. Although the 1-month intervention to treat alcohol use disorder did not result in significant reductions in the interrogated indicators of immune activation, our findings suggest that chronic alcohol exposure is a major modifiable risk factor for chronic immune activation and dysregulation among people-living with HIV.
Genome-Wide Characterization and Expression Analysis of HD-ZIP Gene Family in Dendrobium officinale

The homeodomain-leucine zipper (HD-ZIP) gene family, as one of the plant-specific transcription factor families, plays an important role in regulating plant growth and development as well as in response to diverse stresses. Although it has been extensively characterized in many plants, the HD-ZIP family is not well-studied in Dendrobium officinale, a valuable ornamental and traditional Chinese medicinal herb. In this study, 37 HD-ZIP genes were identified in Dendrobium officinale (Dohdzs) through the in silico genome search method, and they were classified into four subfamilies based on phylogenetic analysis. Exon–intron structure and conserved protein domain analyses further supported the prediction with the same group sharing similar gene and protein structures. Furthermore, their expression patterns were investigated in nine various tissues and under cold stress based on RNA-seq datasets to obtain the tissue-specific and cold-responsive candidates. Finally, Dohdz5, Dohdz9, and Dohdz12 were selected to validate their expression through qRT-PCR analysis, and they displayed significantly differential expression under sudden chilling stress, suggesting they might be the key candidates underlying cold stress response. These findings will contribute to better understanding of the regulatory roles of the HD-ZIP family playing in cold stress and also will provide the vital targets for further functional studies of HD-ZIP genes in D. officinale.
Rap1 Activity Is Essential for Focal Adhesion and Slit Diaphragm Integrity

Glomerular podocytes build, with their intercellular junctions, part of the kidney filter. The podocyte cell adhesion protein, nephrin, is essential for developing and maintaining slit diaphragms as functional loss in humans results in heavy proteinuria. Nephrin expression and function are also altered in many adult-onset glomerulopathies. Nephrin signals from the slit diaphragm to the actin cytoskeleton and integrin β1 at focal adhesions by recruiting Crk family proteins, which can interact with the Rap guanine nucleotide exchange factor 1 C3G. As Rap1 activity affects focal adhesion formation, we hypothesize that nephrin signals via Rap1 to integrin β. To address this issue, we combined Drosophila in vivo and mammalian cell culture experiments. We find that Rap1 is necessary for correct targeting of integrin β to focal adhesions in Drosophila nephrocytes, which also form slit diaphragm-like structures. In the fly, the Rap1 activity is important for signaling of the nephrin ortholog to integrin β, as well as for nephrin-dependent slit diaphragm integrity. We show by genetic interaction experiments that Rap1 functions downstream of nephrin signaling to integrin β and downstream of nephrin signaling necessary for slit diaphragm integrity. Similarly, in human podocyte culture, nephrin activation results in increased activation of Rap1. Thus, Rap1 is necessary for downstream signal transduction of nephrin to integrin β.
Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI

Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automatically identify anomalies in fetal organs. This architecture can identify normal fetal anatomy, as well as detect anomalies present in the (1) brain, (2) spinal cord, and (3) heart. In this retrospective study, we included fetal 3-dimensional (3D) SSFP sequences of 36 participants. We classified the images on a slice-by-slice basis. FOAC-Net achieved a classification accuracy of 85.06, 85.27, 89.29, and 82.20% when predicting brain anomalies, no anomalies (normal), spinal cord anomalies, and heart anomalies, respectively. In a comparison study, FOAC-Net outperformed other state-of-the-art classification architectures in terms of class-average F1 and accuracy. This work aims to develop a novel classification architecture identifying the affected organs in fetal MRI.
Bioprospecting Indigenous Marine Microalgae for Polyunsaturated Fatty Acids Under Different Media Conditions

Graphical Abstract Marine microalgae produce a number of valuable compounds that have significant roles in the pharmaceutical, biomedical, nutraceutical, and food industries. Although there are numerous microalgal germplasms available in the marine ecosystem, only a small number of strains have been recognized for their commercial potential. In this study, several indigenous microalgal strains were isolated from the coast of the Arabian Sea for exploring the presence and production of high-value compounds such as polyunsaturated fatty acids (PUFAs). PUFAs are essential fatty acids with multiple health benefits. Based on their high PUFA content, two isolated strains were identified by ITS sequencing and selected for further studies to enhance PUFAs. From molecular analysis, it was found both the strains were green microalgae: one of them was a Chlorella sp. , while the other was a Planophila sp. The two isolated strains, together with a control strain known for yielding high levels of PUFAs, Nannochloropsis oculata, were grown in three different nutrient media for PUFA augmentation. The relative content of α-linolenic acid (ALA) as a percentage of total fatty acids reached a maximum of 50, 36, and 50%, respectively, in Chlorella sp. , Planophila sp. , and N. oculata. To the best of our knowledge, this is the first study in exploring fatty acids in Planophila sp. The obtained results showed a higher PUFA content, particularly α-linolenic acid at low nutrients in media.
The Association of New-Onset Acute Kidney Injury and Mortality in Critically Ill Patients With COVID-19 With Less Severe Clinical Conditions at Admission: A Moderation Analysis

Acute kidney injury (AKI), electrolyte, and acid–base disorders complicate the clinical course of critically ill patients with coronavirus-associated disease (COVID-19) and are associated with poor outcomes. It is not known whether the severity of clinical conditions at admission in the intensive care unit (ICU) changes the clinical significance of AKI and/or electrolyte or acid–base disorders developing during ICU stay. We conducted a retrospective study in critically ill patients with COVID-19 to evaluate whether the severity of clinical conditions at admission in the ICU affects the impact of AKI and of serum electrolytes or acid–base status on mortality. We carried out a 28-day retrospective follow-up study on 115 critically ill patients consecutively admitted to ICU for severe COVID-19 at a tertiary care university hospital and surviving longer than 24 h. We collected baseline demographic and clinical characteristics, and longitudinal data on kidney function, kidney replacement therapy, serum electrolytes, and acid–base status. We used Cox proportional hazards multiple regression models to test the interaction between the time-varying variates new-onset AKI or electrolyte or acid–base disorders and Sequential Organ Failure Assessment (SOFA) or Acute Physiology and Chronic Health Evaluation II (APACHE II) score at admission. After adjusting for age, sex, Charlson’s comorbidity index, and AKI present at ICU admission, new-onset AKI was significantly associated with 28-day mortality only in the patients in the lowest and middle SOFA score tertiles [lowest SOFA tertile, hazard ratio (HR) 4.27 (95% CI: 1.27–14.44; P = 0.019), middle SOFA tertile, HR 3.17 (95% CI: 1.11–9.04, P = 0.031), highest SOFA tertile, HR 0.77 (95% CI: 0.24–2.50; P = 0.66); P = 0.026 for interaction with SOFA as a continuous variable]. After stratifying for APACHE II tertile, results were similar [adjusted HR (aHR) in the lowest tertile 6.24 (95% CI: 1.85–21.03, P = 0.003)]. SOFA or APACHE II at admission did not affect the relationship of serum electrolytes and acid–base status with mortality, except for new-onset acidosis which was associated with increased mortality, with the HR of death increasing with SOFA or APACHE II score (P < 0.001 and P = 0.013, respectively). Thus, unlike in the most severe critically ill patients admitted to the ICU for COVID-19, in patients with the less severe conditions at admission the development of AKI during the stay is a strong indicator of increased hazard of death.