Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Human Recognition Techniques using Deep Learning

—As a key research subject in the fields of health and human-machine interaction, human activity recognition (HAR) has emerged as a major research focus over the past few decades. Many artificial intelligence-based models are being created for activity recognition. However, these algorithms are failing to extract spatial and temporal properties, resulting in poor performance on real-world long-term HAR. A drawback in the literature is that there are only a small number of publicly available datasets for physical activity recognition that contain a small number of activities, owing to the scarcity of publicly available datasets. In this paper, a hybrid model for activity recognition that incorporates both convolutional neural networks (CNN) are developed. The CNN network is used for extracting spatial characteristics, while the LSTM network is used for learning time-related information. Using a variety of traditional and deep machine learning models, an extensive ablation investigation is carried out in order to find the best possible HAR solution. The CNN approach can achieve a precision of 90.89%, indicating that the model is suitable for HAR applications.
A Deep CNN Framework for Distress Detection Using Facial Expression

Recent medical developments have projected panic attacks as a forerunner in high-stress job environments. This medical cause has emerged to a considerable extent due to the masses’ lifestyle and food consumption habits. Assessment and prevention of such attacks have become imperative to arrest the situation. In this regard, the present study attempts to detect human distress from facial expressions. Convolutional Neural Networks (CNN) serves as one of the best feature extractors. AlexNet being one of the primitive CNN models, has been employed to study the stress content in a facial expression.AlexNet can perform multi-GPU training, which widely reduces the training time for larger models. Training other models comparatively require higher computations that result in escalated time and energy, which might cause consequent lesser efficiency. We have achieved a training accuracy of 93.4%, and validation accuracy of 92.5% —the image set comprised 35340 images generated from 593 video sequences from 123 people at 30fps. Although AlexNet being one of the primitive CNN models, the results of this study are motivating.
Real-Time Adjustment of Tracking Offsets Through a Brain-Computer Interface for Weight Perception in Virtual Reality

Provision of the perception of pseudo-weight through tracking offsets in virtual reality (VR) allows users to estimate the weight of virtual objects. However, the contribution of the user's real-time perception to such illusions is unknown. Here, we focus on this issue using a brain-computer interface (BCI), through which the user's perception of the weight of virtual objects can be detected in real-time and used to adjust the tracking offset in a closed loop. We first trained a computational model with electroencephalography (EEG) data by asking users to imagine lifting a heavy or a light ball. With this model, the user's perception of the object weight could be detected through the BCI in real-time to adjust the tracking offset, thereby enabling further generation of a more realistic visual sensation. Then, we evaluated the effects of the BCI tracking offset on the perception of the weights of three virtual objects used to simulate real objects, namely, tennis, billiard, and bowling balls. Our results showed that the BCI tracking offset could assist participants in generating perceived weights for virtual objects in VR. We further showed that our approach can provide weight perception through real-time adjustment of the tracking offset, which might be useful for new virtual objects that appear suddenly in the virtual environment. Additionally, most participants (78%) preferred this BCI tracking offset system for weight perception. This article provides the first quantification of weight perception for a virtual object with a BCI that can be used to adjust the tracking offset in real-time for pseudo-weight perception in VR.
Description of the growth curve of the biquinho-type pepper plant under water stress

The consumption of pepper is quite common in Brazil, being used not only in food, but also in the pharmaceutical and hygienic segment, providing health benefits for having antibiotic effects, anti-inflammatory action and several vitamins. The objective of this work was to characterize the growth curve of the biquinho-type pepper plant, using the nonlinear regression models logistic, Gompertz and von Bertalanffy, to the plant's height data subjected to irrigation of 20% of its water requirement. The experiment was conducted in a greenhouse, belonging to the Agricultural Engineering Academic Unit (UAEAg) of the Federal University of Campina Grande - UFCG. All the models used proved to be appropriate, but the von Bertalanffy model was the most adequate to fit the data according to the AIC statistics, estimating a maximum growth of 7.58cm for the plants in 163 days after sowing the crop. with the highest speed of growth per day obtained up to 38 days after sowing, when the height reached was 2.27cm.
Quantifying the immunological distinctiveness of emerging SARS-CoV-2 variants in the context of prior regional herd exposure

Abstract The COVID-19 pandemic has seen the persistent emergence of immune-evasive SARS-CoV-2 variants under the selection pressure of natural and vaccination-acquired immunity. However, it is currently challenging to quantify how immunologically distinct a new variant is compared to all the prior variants to which a population has been exposed. Here, we define “Distinctiveness” of SARS-CoV-2 sequences based on a proteome-wide comparison with all prior sequences from the same geographical region. We observe a correlation between Distinctiveness relative to contemporary sequences and future change in prevalence of a newly circulating lineage (Pearson r = 0.75), suggesting that the Distinctiveness of emergent SARS-CoV-2 lineages is associated with their epidemiological fitness. We further show that the average Distinctiveness of sequences belonging to a lineage, relative to the Distinctiveness of other sequences that occur at the same place and time (n = 944 location/time data points), is predictive of future increases in prevalence (Area Under the Curve, AUC = 0.88 [95% confidence interval 0.86 to 0.90]). By assessing the Delta variant in India versus Brazil, we show that the same lineage can have different Distinctiveness-contributing positions in different geographical regions depending on the other variants that previously circulated in those regions. Finally, we find that positions that constitute epitopes contribute disproportionately (20-fold higher than the average position) to Distinctiveness. Overall, this study suggests that real-time assessment of new SARS-CoV-2 variants in the context of prior regional herd exposure via Distinctiveness can augment genomic surveillance efforts.
Study on performance traits of laying hens with crossing the White Leghorn and the Rhode Island breeds

The current steady increase in the global population is accompanied by a dramatic growth in demand for food, whose satisfaction relies heavily on animal protein sources. Poultry farming is already mankind’s key source of protein. Changes in consumer habits combined with the negative impacts of climate change are posing new challenges to poultry breeding companies today. Certain traits properties relating to egg production among offspring groups resulting from the crossing of the White Leghorn and the Rhode Island breeds show improvements relative to the corresponding properties of pure-bred offspring. This literature review is aimed at summing up experiments focusing on White Leghorn and Rhode Island crosses in terms of the main traits properties relating to egg production. Experiments involving the crossing of the White Leghorn and Rhode Island breeds tend to occur mainly during pure-bred poultry breeding. Studies discussing the performance of groups of offspring of White Leghorn (WL) and Rhode Island (RI) breeds, without analysing physiological types of experiments, have been few and far between in the past 20 years. Most of the few such studies originate from countries of the third world focusing in most cases on matters of feeding. This is explained, for the most part, by the fact that in the current period of modern poultry farming, only the world’s top breeding companies are in possession of high performance Leghorn and Rhode lines and they seldom ever publish scientific papers allowing glimpse into breeders’ development or strategic activities of breeders.
Bloom filter based acceleration scheme for flow table lookup in SDN switches

Flow table lookup is a well-known bottleneck in software-defined network switches. Associative lookup is the fastest but most costly method. On the other hand, an approximate flow classification based on Bloom filters has an outstanding cost-benefit ratio but comes with a downside of false-positive results. Therefore, we propose a new flow table lookup scheme based on Bloom filters and RAM, which offers a good compromise between cost and performance. We solve the problem of false positives of primary Bloom filters by verifying the results and, if necessary, by linearly searching the contents of secondary RAM. Also, we provide a practical implementation in the FPGA-based SDN switch and experimentally show that the proposed solution can achieve better performance than the classic linear search at the low cost typical of Bloom filters.
Linguistic and Acoustic Resources of the Computer-Based System for Analysis and Interpretation of Speech Intonation

This paper considers suprasegmental parameters such as intonation, stress and speech rhythm. Correct articulation of sounds and correct pronunciation in the target language depend on prosodic structures presented by standard intonation patterns. Linguistic and acoustic resources provide the basis for computer-aided intonation training in and outside the classroom. There is a great need for new and innovative computer-aided programs. But there is no necessary software available for such teaching system and, therefore, such system appears to be of great relevance. Presented work describes as well the algorithm of designing of the computer-based system for analysis and interpretation of speech intonation.
Business Strategy Analysis of Cocoa Agribusiness Commodity Export Company at Agro Tama

Cocoa, which is known as one of the people's plantations, is one of the commodities that has a positive contribution during the Covid-19 pandemic. The positive results of macro data related to the contribution of agriculture, especially cocoa to Indonesia's income during the pandemic, in fact experienced problems in raw materials which experienced a gradual decline in productivity in recent years, and also affected the productivity of several domestic cocoa companies. The decline in raw materials and the COVID-19 pandemic that has occurred since the end of 2019 also added to other problems faced by agricultural commodity export companies, including Agro Tama. Agro Tama is a domestic export company located in Makassar City which has been established for more than 20 years and contributes to the Indonesian economy through the export of cocoa beans. This study aims to analyze the company's internal and external factors, namely Strength, Weakness, Opportunity and Threat to produce an alternative strategy that is right for the company's condition. The results of the SWOT analysis show that the company's condition is in quadrant III strategy, namely WO (Weakness Opportunity) and produces 4 alternative strategies which are then analyzed again using the Analytical Hierarchy Process model. The results of the AHP analysis resulted in a proposed priority strategy, namely optimizing the company's export commodity diversification, with the main objective of the analysis model being the company's business development during the pandemic with a weight of 0.303.
Rickettsia felis and Other Rickettsia Species in Chigger Mites Collected from Wild Rodents in North Carolina, USA

Chiggers are vectors of rickettsial pathogenic bacteria, Orientia spp. , that cause the human disease, scrub typhus, in the Asian–Pacific area and northern Australia (known as the Tsutsugamushi Triangle). More recently, reports of scrub typhus in Africa, southern Chile, and the Middle East have reshaped our understanding of the epidemiology of this disease, indicating it has a broad geographical distribution. Despite the growing number of studies and discoveries of chigger-borne human disease outside of the Tsutsugamushi Triangle, rickettsial pathogens in chigger mites in the US are still undetermined. The aim of our study was to investigate possible Rickettsia DNA in chiggers collected from rodents in North Carolina, USA. Of 46 chiggers tested, 47.8% tested positive for amplicons of the 23S-5S gene, 36.9% tested positive for 17 kDa, and 15.2% tested positive for gltA. Nucleotide sequence analyses of the Rickettsia-specific 23S-5S intergenic spacer (IGS), 17 kDa, and gltA gene fragments indicated that the amplicons from these chiggers were closely related to those in R. felis, R. conorii, R. typhi, and unidentified Rickettsia species. In this study, we provide the first evidence of Rickettsia infection in chiggers collected from rodents within the continental USA. In North Carolina, a US state with the highest annual cases of spotted fever rickettsioses, these results suggest chigger bites could pose a risk to public health, warranting further study.
Deep Neural Network As a Tool for Appraising Housing Prices: A Case Study of Busan, South Korea

This study examines whether the number of hidden layers in a deep neural network significantly influences the model accuracy and efficiency for appraising housing prices. We provide empirical evidence that the deep neural network can achieve high accuracy with a small number of hidden layers on our dataset, which contains various hedonic variables. Furthermore, we show that adding layers does not necessarily guarantee the model’s accuracy and effectiveness of the computing time.
HATR-I: Hierarchical Adaptive Temporal Relational Interaction for Stock Trend Prediction

Stock trend prediction is a hot issue in the Fintech field. Effective stock profiling is challenging due to highly non-stationary dynamics and complex interplays. Existing methods usually regard each stock independently or detect simplistic homogeneous structures. Practically, stock correlation originates from diverse aspects and underlying relationship signals are implicit in comprehensive graphs. Besides, RNNs are extensively used to simulate stock volatility while inadequate in capturing fine-granular patterns across local time snippets. To this end, in this paper we propose HATR-I, a Hierarchical Adaptive Temporal-Relational Interaction model to characterize and predict stock evolutions. Specifically, we grasp short- and long-term transition regularities of stock dynamics based on cascaded dilated convolutions and gating paths. By formulating different views of domain adjacency graphs into a unified multiplex network with edge attributes, we inject node- and semantic-level dual attention to refine the propagation of inter-stock collaborative information. Particularly, the stock pair matching is proceeding along each time-stage rather than until final compressed representations, meanwhile significant feature points and scales are identified considering the effect of time attenuation. Finally, we deduce latent shared clusters as global regularization to optimize the stock representations. Experiments on three real-world stock market datasets demonstrate the effectiveness of our proposed model.
Method of calculating the optical fiber geometric parameters with multiple cores

The paper considers a simplified method for calculating the geometric parameters of an optical fiber with many cores. Such a fiber allows for an increase in the throughput of fiber-optic transmission systems by the method of spatial multiplexing. The proposed technique is based on the concept of light energy distribution in the mode spot and the allowable level of mutual influence between different cores. Algorithmic and software that implements the proposed technique has been developed. A comparative analysis of the triangulation and rectangular structure of fiber with many cores was performed. The technique considers the influence options from the nearest cores and the far layers cores. The analysis of the stability of the received results to minor deviations of actual parameters from the accepted ones in the model is executed. The technique is resistant to minor deviations in the shape of the cores from the circular and to small bends of the optical fiber. The method’s theoretical basis is used representations of wave optics and relatively simple representations of geometric optics. The obtained formulas are quite simple to analyze. Therefore, the obtained results can be offered for fast analysis of complex geometric structures of an optical fiber with many cores. Direct measurements or more complex theories compared the obtained results with the known estimates. It is shown that more than 100 cores can be concentrated in a fiber with a diameter of 200-400 μm, taking into account the typical values ​​of noise in real fiber-optic transmission systems. That is, with a slight increase in the size of the optical cable, you can increase the total transmission rate by more than 100 times.
Prospective Role of Bioactive Molecules and Exosomes in the Therapeutic Potential of Camel Milk against Human Diseases: An Updated Perspective

Camel milk (CM) constitutes an important dietary source in the hot and arid regions of the world. CM is a colloidal mixture of nutritional components (proteins, carbohydrates, lipids, vitamins, and minerals) and non-nutritional components (hormones, growth factors, cytokines, immunoglobulins, and exosomes). Although the majority of previous research has been focused on the nutritional components of CM; there has been immense interest in the non-nutritional components in the recent past. Reckoning with these, in this review, we have provided a glimpse of the recent trends in CM research endeavors and attempted to provide our perspective on the therapeutic efficacy of the nutritional and non-nutritional components of CM. Interestingly, with concerted efforts from the research fraternities, convincing evidence for the better understanding of the claimed traditional health benefits of CM can be foreseen with great enthusiasm and is indeed eagerly anticipated.
Gene Expression Profiling of Markers of Inflammation, Angiogenesis, Coagulation and Fibrinolysis in Patients with Coronary Artery Disease with Very High Lipoprotein(a) Levels Treated with PCSK9 Inhibitors

Besides lipids, inflammation, angiogenesis, coagulation and fibrinolysis play very important roles in coronary artery disease (CAD). We measured gene expression of the inflammatory markers interleukin (IL)-1β (IL1B) and interferon (IFN)-γ (IFNG), vascular endothelial growth factor-A (VEGF-A) (VEGFA), and coagulation and fibrinolysis markers tissue factor (TF) (F3) and plasminogen activator inhibitor-1 (PAI-1) (SERPINE) in healthy controls and CAD patients with high lipoprotein(a) (Lp(a)). The aim of our study was to identify, first, if there is a difference in these markers between controls and patients; secondly, if these markers are associated with lipids; and third, what the influence of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors is on these markers. We included 124 subjects, 27 controls and 97 patients with CAD (30 in placebo and 67 in the PCSK9 group). Blood samples were collected for lipid and gene measurement. The results showed higher expression of IL1B (p < 0.0001), VEGFA (p < 0.0001), and F3 (p = 0.018) in controls in comparison with patients. Significant correlations were observed between IL1B and lipids. Treatment with PCSK9 inhibitors increased VEGFA (p < 0.0001) and F3 (p = 0.001), and decreased SERPINE (p = 0.043). The results of our study underpin the importance of IL-1β, VEGF-A and TF in CAD as well as the effect of PCSK9 treatment on these markers.
After the Killing of Atatiana Jefferson: Black Stakeholder Experiences Within a Municipal Listening Structure

Starting with a public relations pioneer’s maxim to “listen to stakeholders,” many contemporary scholars have emphasized listening as the key to the inclusion of marginalized communities. Based on 25 in-depth interviews including 19 Black residents of Fort Worth, this study amplifies the voices of Black community members after the killing of Atatiana Jefferson in her home by a White police officer. Their perceptions of systemic racism within the city’s listening processes and their recognition of pseudo-listening and non-action provide an understanding of and suggestions for how the city government can facilitate equitable engagement.
Human Umbilical Cord Mesenchymal Stem Cells Alleviate Nasal Mucosa Radiation Damage in a Guinea Pig Model

Nasal complications after radiotherapy severely affect the quality of life of nasopharyngeal carcinoma patients, and there is a compelling need to find novel therapies for nasal epithelial cell radiation damage. Therefore, we investigated the therapeutic effect of human umbilical cord mesenchymal stem cells (hUC‐MSCs) in guinea pig model of nasal mucosa radiation damage and explored its therapeutic mechanism. Cultured hUC‐MSCs were injected intravenously immediately after radiation in the nasal mucosa‐radiation‐damage guinea pig model. Migration of hUC‐MSCs into the nasal mucosa and the potential for differentiation into nasal epithelial cells were evaluated by immunofluorescence. The therapeutic effects of hUC‐MSCs were evaluated by mucus clearance time (MCT), degree of nasal mucosa edema, and the nasal mucosa cilia form and coverage ratio. Results indicate that the hUC‐MSCs migrated to the nasal mucosa lamina propria and did not differentiate into nasal epithelial cells in this model. The MCT and degree of mucosal edema were improved at 1 week and 1 month after radiation, respectively, but no difference was found at 3 months and 6 months after radiation. The nasal mucosa cilia form and coverage ratio was not improved 6 months after radiation. Thus, hUC‐MSCs can migrate to the nasal mucosa lamina propria and improve MCT and mucosa edema within a short time period, but these cells are unable to differentiate into nasal epithelial cells and improve nasal epithelial regeneration in the nasal mucosa radiation damage guinea pig model. J. Cell. Biochem. 116: 331–338, 2015. © 2014 Wiley Periodicals, Inc.