Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusing the spatial structure of electroencephalogram channels can increase the individualization of the functional connectivity network

An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive to the integrity of channels and crucially relies on signal preprocessing; therefore, finding a new presentation for FC networks may help increase the performance of the identification algorithms. EEG signals are smooth across space owing to the volume conduction effect. Considering such spatial relationships among channels can provide a more accurate representation of FC networks. In this study, we propose an EEG FC network with virtual nodes that combines the spatial relationships and functional connectivity of channels. The comparison results for individual identification show that the novel EEG network is more individualized and achieves an accuracy of 98.64% for data without preprocessing. Furthermore, our algorithm is more robust in reducing the number of channels and can perform well even when a large area of channels is removed.
RYTHME NYCTHÉMÉRAL DE DÉRIVE DES INSECTES ET DES POISSONS DANS LES RIVIÈRES DE CÔTE D'IVOIRE

RÉSUMÉ Un rythme nycthéméral de dérive de l’entomofaune et de l’ichiyofaune a t% mis en évidence dans les rivières de Côte d’ivoire. Ce phénomène déjà @tudié en zone tempérée, pour l’enfomofaune, se produit donc également en zone tropicale. La dérive nocturne est beaucoup plus importante que la dérive diurne, le maximum se situanf entre 22 et 24 h pour l’enfomofaune. La dérive des poissons esf vraisemblablement liée à leur acfivité trophique.
Inequality and redistribution: evidence from Scandinavian and Mediterranean countries

Purpose The purpose of this paper is to investigate the adjustment of government redistributive policies in Scandinavian and Mediterranean countries following changes in income inequality over the period 1980–2021. Design/methodology/approach The authors first modelled the time-varying dynamics between income inequality and redistribution and then used a non-linear framework to test for the existence of asymmetries and cointegration in their long-run relationship. The authors used two complementary measures of inequality – the share of total income accruing to top percentile income holders and the ratio of the share of total income accruing to top decile income holders divided by that accumulated by the bottom 50% – and computed redistribution as the difference between the two inequality indicators before and after taxes and transfers. Findings The authors found that the sign of the relationship between income inequality and redistribution is mostly positive and time-varying. Overall, the authors also found evidence that the impact of increases in inequality on redistributive measures is higher than that of decreases. Finally, the authors obtained a significant long-run relationship between both variables in all countries except Denmark and Spain. These results hold for both Scandinavian and Mediterranean countries. Originality/value To the best of the authors’ knowledge, this is the first paper to account for the potential existence of non-linearities and to examine the asymmetries in the adjustment of redistributive policies to increases in income inequality using alternative income inequality metrics.
Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images

Cardiac substructure segmentation is a prerequisite for cardiac diagnosis and treatment, providing a basis for accurate calculation, modeling, and analysis of the entire cardiac structure. CT (computed tomography) imaging can be used for a noninvasive qualitative and quantitative evaluation of the cardiac anatomy and function. Cardiac substructures have diverse grayscales, fuzzy boundaries, irregular shapes, and variable locations. We designed a deep learning-based framework to improve the accuracy of the automatic segmentation of cardiac substructures. This framework integrates cardiac anatomical knowledge; it uses prior knowledge of the location, shape, and scale of cardiac substructures and separately processes the structures of different scales. Through two successive segmentation steps with a coarse-to-fine cascaded network, the more easily segmented substructures were coarsely segmented first; then, the more difficult substructures were finely segmented. The coarse segmentation result was used as prior information and combined with the original image as the input for the model. Anatomical knowledge of the large-scale substructures was embedded into the fine segmentation network to guide and train the small-scale substructures, achieving efficient and accurate segmentation of ten cardiac substructures. Sixty cardiac CT images and ten substructures manually delineated by experienced radiologists were retrospectively collected; the model was evaluated using the DSC (Dice similarity coefficient), Recall, Precision, and the Hausdorff distance. Compared with current mainstream segmentation models, our approach demonstrated significantly higher segmentation accuracy, with accurate segmentation of ten substructures of different shapes and sizes, indicating that the segmentation framework fused with prior anatomical knowledge has superior segmentation performance and can better segment small targets in multi-target segmentation tasks.
Pengaruh Fundamental Skills Dalam Membangun Kesiapan Kerja Mahasiswa Tinjauan Pendekatan Situational Judgement Test

The rapid development of technology and times has changed work dynamics in many countries, including Indonesia. One of the markers of the times is the presence of the industrial revolution 4.0. The industrial revolution 4.0 requires someone to have the appropriate skills in their field and work skills as supporting abilities. Students, as prospective workers, need to understand what kind of work skills are necessary for addition to technical skills. This study aims to determine the level of student work skills in the aspect of fundamental skills and how far the level of contribution of fundamental skills is in building students' work readiness using a situational judgment test approach. This study uses quantitative methods. The sample comprised 167 students majoring in informatics technology at Gorontalo State University. The results of this study describe the percentage of students' fundamental skill levels in the high category, with a frequency of 50,9% of students getting high scores, then the ratio of fundamental skills contribution of 12.1% to build student work readiness Pesatnya perkembangan teknologi dan zaman telah mengubah dinamika kerja di banyak negara, termasuk Indonesia. Salah satu penanda zaman adalah hadirnya revolusi industri 4.0. Revolusi industri 4.0 ini menuntut seseorang untuk memiliki keterampilan yang sesuai dengan bidangnya dan keterampilan kerja sebagai kemampuan penunjang. Mahasiswa sebagai calon tenaga kerja perlu memahami keterampilan kerja seperti apa yang diperlukan selain keterampilan teknis. Penelitian ini bertujuan untuk mengetahui tingkat keterampilan kerja siswa pada aspek keterampilan dasar dan sejauh mana kontribusi keterampilan dasar dalam membangun kesiapan kerja siswa dengan menggunakan pendekatan tes penilaian situasional. Penelitian ini menggunakan metode kuantitatif. Sampel terdiri dari 167 mahasiswa jurusan teknologi informatika Universitas Negeri Gorontalo. Hasil penelitian ini menggambarkan persentase tingkat keterampilan dasar fundamental skills mahasiswa dalam kategori tinggi, dengan frekuensi 50,9% mahasiswa mendapat nilai tinggi, kemudian rasio kontribusi keterampilan fundamental sebesar 12,1% dalam membangun kesiapan kerja mahasiswa.
The Value of Virtual Engagement: Evidence from a Running Platform

Fueled by remarkable developments in mobile and wearable technologies coupled with the power of the Internet a multibillion industry constructed on digital platforms, Connected Fitness, has emerged. Sustained engagement is a crucial prerequisite for healthy behavior, and platforms that offer connected fitness services must contend with the challenge of engaging new users. In this study, we examine whether a virtual race, an event in which runners can participate remotely in an organized activity, can serve as an effective strategy to amplify users’ long-term engagement related to healthy behavior. We study the impact of offering virtual races to runners on a digital fitness platform on their engagement with the platform and level of physical activity. Leveraging the match of the theme of the virtual races and users’ zodiac signs as the instrument to support causal inference, we find that a virtual race increases the engagement of new users by 42.8 days and total spending on related e-commerce on the platform by 48.4% during our study period. We also find that the positive impact of virtual races transcends the typical mechanism of social connections; results show that virtual races can increase the engagement with the platform even when their social connections are low. Finally, we find that existing users who participate in a virtual race outrun those in the control group by 4.41 km in a three-week period. Our study highlights the potential of new forms of digital interactions, powered by mobile and wearable technologies, on user behavior in fitness platforms. This paper was accepted by Anindya Ghose, information systems. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023. 4945 .
Vibration-Based Structural Health Monitoring via Phase-Based Motion Estimation Using Deep Residual Networks

In recent years, deep residual networks (ResNets) have been successfully used to detect structural damage. However, the performance of ResNets greatly depends on large high-quality datasets for learning, which can be difficult to obtain in actual engineering scenarios. In addition, in order to improve identification accuracy, vibration signals from multiple positions can be fused together to train the ResNets. On the one hand, vibration signals from multipositions can increase the quantity of the training data. On the other hand, hidden correlations among multivibration signals may also reflect some features improving identification accuracy. However, traditional vibration acquisition methods, such as accelerometers, cannot ensure that the vibration signals from multipositions are synchronized in the temporal domain, causing the correlations among multivibration signals not to be fully recorded. To solve the above problems, this study presents a novel structural health monitoring method by combining phase-based motion estimation (PME) with the use of ResNets. With the PME method, each pixel in a video can be regarded as a displacement sensor; thus, it is possible to obtain millions of vibration signals from a single video; in addition, all vibration signals obtained from a single video are perfectly synchronized, greatly facilitating ResNet applications. Here, an open gearbox mechanism was used for experimental validation of our approach. With the proposed method, only one video sample obtained under each structural condition was required to train a ResNet model for detecting gear misalignment (difference of only 0.2°) with 100% accuracy, indicating the outstanding performance of the proposed method.
P53‑microRNA interactions regulate the response of colorectal tumor cells to oxaliplatin under normoxic and hypoxic conditions

Oxaliplatin (OXA)-containing regimens are used as first-line chemotherapy in colorectal cancer (CRC). However, OXA resistance remains a major challenge in CRC treatment. CRC cells that adapt to hypoxia can potentially develop OXA resistance, and the underlying molecular mechanisms still need to be further investigated. In the current study, the OXA drug sensitivity of two CRC cell lines, HCT116 (TP53WT) and HT29 (TP53MT), was compared under both normoxic and hypoxic conditions. It was found that under normoxic condition, HCT116 cells showed significantly higher OXA sensitivity than HT29 cells. However, both cell lines showed remarkable OXA resistance under hypoxic conditions. It was also revealed that P53 levels were increased after OXA and hypoxia treatment in HCT116 cells but not in HT29 cells. Notably, knocking down P53WT decreased normoxic but increased hypoxic OXA sensitivity in HCT116 cells, which did not exist in HT29 cells. Molecular analysis indicated that P53WT activated microRNA (miR)-26a and miR-34a in OXA treatment and activated miR-23a in hypoxia treatment. Cell proliferation experiments indicated that a high level of miR-23a decreased OXA sensitivity and that a high level of miR-26a or miR-34a increased OXA sensitivity in HCT116 cells. Additionally, it was demonstrated that miR-26a, miR-34a and miR-23a affect cell apoptosis through regulation of MCL-1, EZH2, BCL-2, SMAD 4 and STAT3. Taken together, the present findings revealed the dual function of P53 in regulating cellular chemo-sensitivity and highlighted the role of P53-miR interactions in the response of CRC cells to OXA chemotherapy under normoxic and hypoxic conditions.
Optimization of the Flashing Processes in Mineral Metallurgy Based on CFD Simulations

In mineral metallurgy, the flashing unit is key to returning the slurry to atmospheric conditions. Its primary function is to achieve pressure letdown, and during the process, shock waves are generated to maximize energy dissipation. Investigating the location and expansion of shock waves is the focal point of this study due to their importance in flashing unit design. A CFD model coupled with a homogeneous relaxation model (HRM) was used to simulate the flashing process of mineral slurry. The flow behavior, including velocity, pressure, and the shape and location of the shock waves, were obtained using simulation under different unit design and operating parameters. These results would provide valuable insights into the design of flashing units and guidance for the safe operation and maintenance of devices.
MICROBIOMES OF HUMAN, LIVESTOCK ANIMAL GASTROINTESTINAL TRACTS AND OF FOOD PRODUCTS AND COMPOUND FEEDS: CONNECTIONS AND IMPACTS. PART 1

The physiological mechanisms of food digestion in humans and feed digestion in animals are determined by the structure of the gastrointestinal tract (GIT) and diet. Accordingly, humans are omnivores, while domestic animals are divided into ruminants, monogastric herbivores, and monogastric omnivores, and birds are divided into herbivores (geese, ducks) and omnivores (chickens, turkeys, etc.). The digestion and assimilation of food and feed depends not only on own mechanisms but also on the GIT microbiome. The location of the most important part of this microbiome and its composition depend on the species: in ruminants, it is the rumen microbiome, in horses – the cecum (it is a counterpart of the rumen), in humans and pigs – the intestine, in birds – the crop, gizzard and cecum. These microbiomes are in constant close connection with the host organism, and this connection is realized through numerous molecular mechanisms of interaction between bacterial cells and host cells and tissues. GIT microorganisms not only help to assimilate food (feed) by partially digesting it, but also secrete biologically active substances that have protective, stimulating and other beneficial effects for the host. In adult hosts, this GIT microbiota is well developed and stable, while in children and young animals it can be much more mobile and vulnerable. Food and feed contain many components that are a favorable medium for the development of microorganisms. Raw materials and components of animal origin are the most contaminated, while vegetable raw materials and components, as well as premixes, contain significantly fewer microorganisms. Among the microorganisms colonizing raw materials, food and feeds, coliforms, salmonellae and molds may be present. In young animals, the feed microbiota ingested into the GIT, even without taking into account obligate or opportunistic pathogens, can cause shifts or changes in the digestive microenvironment towards deterioration, which will have a corresponding impact on the efficiency of feed absorption and, through it, on the efficiency of feeding and animal productivity.
Improving Unsupervised Domain Adaptation via Multiple Adversarial Learning

Most machine learning methods assume the training and test sets to be independent and have identical distributions. However, this assumption does not always hold true in practical applications. Direct training usually induces poor performance if the training and test data have distribution shifts. To address this issue, a three-part model based on using a feature extractor, a classifier, and several domain discriminators is adopted herein. This unsupervised domain adaptation model is based on multiple adversarial learning with samples of different importance. A deep neural network is used for supervised classification learning of the source domain. Numerous adversarial networks are used to constitute the domain discriminators to align each category in the source and target domains and effectively transfer knowledge from the source domain to the target domain. Triplet loss functions—classification loss, label credibility loss, and discrimination loss—are presented to further optimize the model parameters. First, the label similarity metric is designed between the target and source domains data. Second, a credibility loss function is proposed to obtain an accurate label for the unlabeled data of the target domain under training iterations. Finally, a discrimination loss is designed for multiple adversarial domain discriminators to fully utilize the unlabeled data in the target domain during training. The discrimination loss function uses predicted label probabilities as dynamic weights for the train data. The proposed method is compared with mainstream domain adaptation approaches on four public datasets: Office-31, MNIST, USPS, and SVHN. Experimental results show that the proposed method can perform well in the target domain and improve generalization performance of the model.
Overview of the herpetofauna of the unexplored Cordillera del Cóndor of Ecuador

The Cordillera del Condor is an area rich in unique vegetation assemblages and endemic faunal elements; the herpetofauna is especially diverse, particularly the anurans. The montane forest and sandstone tepuis, located atop large andesite and quartz formations, provide a variety of habitats and microhabitats in which the herpetofauna finds food, shelter, and reproductive sites, such as terrestrial and arboreal bromeliads and a soil type termed "bamba" that is covered with mosses and roots. Information compiled from publications and recent studies has revealed the presence of 120 species of amphibians and 59 species of reptiles, including 41 probable new species (36 amphibians and five reptiles) in the genera Centrolene, Dendrobates, Pristimantis, Lynchius, Chiasmocleis, Bolitoglossa, Anolis, Erythrolamprus, Tantilla, and Dipsas. Resumen. —La Cordillera del Condor es un area rica en formaciones vegetales unicas y elementos faunisticos endemicos; presenta una singular diversidad herpetofaunistica, particularmente de la anurofauna. Los bosques montanos y los de "tepuy," asentados sobre piedras grandes de andesita y cuarzo crean variedad de habitats y microhabitats, en donde la herpetofauna encuentra alimento, refugio y lugares para la reproduccion, como por ejemplo las bromelias terrestres y arboreas y un suelo denominado "bamba" que esta cubierto de musgos y raices. La informacion recopilada de material publicado y de los estudios realizados en los ultimos anos revela la presencia de 120 especies de anfibios y 59 especies de reptiles. Los resultados incluyen 41 especies posiblemente nuevas (36 anfibios y cinco reptiles) de los generos: Centrolene, Dendrobates, Pristimantis, Lynchius, Chismocleis, Bolitoglossa, Anolis, Erythrolamprus, Tantilla, y Dipsas.
Tunable Optical Frequency Comb Generated Using Periodic Windows in a Laser and Its Application for Distance Measurement

A novel method for the generation of an optical frequency comb (OFC) is presented. The proposed approach uses a laser diode with optical feedback and operating at a specific nonlinear dynamic state named periodic window. In this case, the laser spectrum exhibits a feature with a series of discrete, equally spaced frequency components, and the repetition rate can be flexibly adjusted by varying the system parameters (e.g., external cavity length), which can provide many potential applications. As an application example, a dual-OFC system for distance measurement is presented. The results demonstrate the system’s ability to achieve target distance detection, underscoring its potential for real-world applications in this field.
Chitu: Accelerating Serverless Workflows with Asynchronous State Replication Pipelines

Serverless workflows are characterized as multi-stage computing, while downstream functions require accessing intermediate states or the output of upstream functions for running. The workflow's performance can be easily affected due to the inefficiency of data access. Studies accelerate data access with various policies, such as direct and indirect methods. However, these methods may fail due to various limitations such as resource availability. In this paper, we propose asynchronous state replication pipelines (ASRP) to speed up workflows for general applications, replacing the sequential computing pattern of current workflows. Chitu is built based on the insight with three main points. First, differentiable data types (DDT) are provided at the programming model level to support incremental state sharing and computation. Second, ASRP works by continuously delivering changes of DDT objects in real-time so that downstream functions can consume the objects without waiting for the ending of upstream functions. Third, we make a systematic design to support DDT and ASRP in Chitu framework, including direct communication and change propagation. We implement Chitu atop OpenFaaS, compare it with popular serverless workflow frameworks, and evaluate it with three commonly seen cases. The results show that Chitu accelerates data transmission in general serverless workflows up to 1.7×, and speeds up end-to-end applications by up to 57%.