Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Optimizing Lamb Wave Acoustic Mass Sensors’ Performance through Adjustment of the Transduction Electrode Metallization Ratio

This paper presents the design and simulation of a mass sensitive Lamb wave microsensor with CMOS technology provided by SilTerra. In this work, the effects of the metalization ratio variation on the transmission gain, total harmonic distortion (THD), and two different resonant modes (around 66 MHz and 86 MHz) are shown. It has been found that the metalization ratio can be adjusted in order to obtain a compromise between transmission gain and sensitivity, depending on the design criteria. By adding a Si3N4 layer on top of the device, a five-fold improvement in transmission gain is reached. It was also shown that the transmission of the input differential IDT configuration is 20% more efficient than a single terminal. With this combination, the mass sensitivity is about 114 [cm2/gr].
Optimized Deep-Learning-Based Method for Cattle Udder Traits Classification

We propose optimized deep learning (DL) models for automatic analysis of udder conformation traits of cattle. One of the traits is represented by supernumerary teats that is in excess of the normal number of teats. Supernumerary teats are the most common congenital heritable in cattle. Therefore, the major advantage of our proposed method is its capability to automatically select the relevant images and thereafter perform supernumerary teat classification when limited data are available. For this purpose, we perform experimental analysis on the image dataset that we collected using a handheld device consisting of a combined depth and RGB camera. To disclose the underlying characteristics of our data, we consider the uniform manifold approximation and projection (UMAP) technique. Furthermore, for comprehensive evaluation, we explore the impact of different data augmentation techniques on the performances of DL models. We also explore the impact of only RGB data and the combination of RGB and depth data on the performances of the DL models. For this purpose, we integrate the three channels of RGB data with the depth channel to generate four channels of data. We present the results of all the models in terms of four performance metrics, namely accuracy, F-score, precision, and sensitivity. The experimental results reveal that a higher level of data augmentation techniques improves the performances of the DL models by approximately 10%. Our proposed method also outperforms the reference methods recently introduced in the literature.
Perioperative pain management based on enhanced recovery after surgery in children undergoing adenotonsillectomy: A prospective, randomized controlled trial

Pain management, as a key component of enhanced recovery after surgery (ERAS), can effectively relieve perioperative pain and anxiety. However, there are few studies on the application of pain management based on ERAS in pediatric surgery patients. We aimed to examine the effect of ERAS‐based perioperative pain management in children with obstructive sleep apnea (OSA) undergoing adenotonsillectomy.
Techniques for blocking the propagation of two simultaneous contagions over networks using a graph dynamical systems framework

Abstract We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NP-hard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many real-world networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILP-based approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm.
Determining Change Agent using Social Network Analysis

The studies on organizational change had been one of the prominent studies in history. For an organization to stay relevant, they had to “ change ” to fit with the demands and supply in the market. However, despite knowing that change is compulsory for an organization to evolve, the successful rate of organizational change does not improve significantly. Organizations do not really understand what major factors contribute to their change failure and repetitive mistakes were done continuously. Finding a change agent can be a major help to focus on executing change. However, the classical method of selecting change agent is based on hierarchical level, seniority, and random selection. Therefore, this paper suggests an empirical method to select change agent through the method of Social Network Analysis (SNA). This paper proposes the concept of centrality as an important value to determine the right change agent to drive a successful change.
Patterns, facilitators and barriers to physical activity among Nigerian pregnant women

Introduction pregnancy is associated with sedentary behaviors and/or low levels of physical activity (PA). This study aimed to assess patterns, barriers, and facilitators of PA among pregnant women. Methods a convergent parallel mixed method design study involving a concurrent collection of quantitative (n=198) and qualitative (n=36) data was carried out. Respondents were drawn from five selected health care facilities in Ile-Ife, Osun state, South-West, Nigeria. Physical activity was assessed using the pregnancy physical activity questionnaire. Focus group discussions were used to qualitatively explore barriers and facilitators of PA. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data were analyzed using thematic content analysis. Results the mean total PA score for the population was 118.663±81.522 mets-min/wk. While it was 118.743±92.062 mets-min/wk, 113.861±72.854 mets-min/wk, and 25.429±87.766 mets-min/wk for the first, second, and third trimester respectively. The respondents engaged more in moderate (44.27±37.07) than vigorous (13.89±18.87) intensity PA. Respondents in the third trimester had the highest and the least scores for household-related PA (45.7±33.0) and vigorous-intensity PA (10.0±14.0) respectively. Major themes that emerged on enablers and barriers of PA engagement during pregnancy were related to intrapersonal, interpersonal, availability of specialized health personnel and policy for PA, good built environment/neighborhood factors, and pervading cultural beliefs and myths about pregnancy. Conclusion moderate intensity and household-related PA were most common among Nigerian pregnant women. Contextual facilitators and barriers to PA during pregnancy were largely related to intrapersonal, interpersonal, environmental or organizational, policy, and cultural factors.
Regional microbiome differentiation of the invasive Sargassum muticum (Fucales, Phaeophyceae) follows the generalist host hypothesis across the North East Atlantic

ABSTRACT Over 90% of introduced marine species are seaweeds. Seaweeds rely on their microbiome for host settlement, nutrition, development and health. As such, it is likely that microbiomes are involved in seaweed invasions. Sargassum muticum, indigenous to Southeast Asia, inhabits the North-east Atlantic from Norway to Morocco. This is the only known successful case of a non-clonal marine invader with almost no genetic variation over its large NE Atlantic introduced range. This makes it a very interesting model to study an invasive seaweeds microbiome, as it practically uncouples host genetic variation from microbiome variation. Associated bacteria potentially contribute to the plasticity and acclimation of S. muticum leading to its success over the last 50 years. Dispersing host organisms can either bring their acquired microbes along or obtain new ones locally, following the mutualist and generalist host hypothesis, respectively. We used partial 16S rRNA gene amplicon sequencing to characterize the total and core microbiome across S. muticum structures/tissues from five NE Atlantic regions, from Norway to Morocco, covering over 30° of latitude. In contrast to host genotypes, highly diverse, regional, total and core microbiomes, with differentiation levels depending on tissue, bacterial community structure were detected. Atlantic S. muticum follows the generalist host hypothesis, possibly recruiting a new microbiome in each new region. This host promiscuity may promote the invasiveness of S. muticum. Diversity was lower in the young/annual tissues compared with the older tissues for the total bacterial community, suggesting that these are mostly transitory bacteria accumulating over time in the older parts. The total core microbiome included 10 OTUs, representing dominant community members commonly found in other seaweeds’ cores. The core Granulosicoccus OTU followed a clear regional pattern where northern and southern regions clustered differentially, suggesting a regional signature even at an OTU level. HIGHLIGHTS North-east (NE) Atlantic Sargassum muticum microbiome differentiation does not match host genetic background. NE Atlantic S. muticum shows a geographic region and tissue-specific total and core bacterial community matching bestthe generalist host hypothesis. Core OTU – the bacterium Granulosicoccus – shows a strong regional specialization.
Integrated Analysis of Transcriptome and Metabolome Reveals New Insights into the Formation of Purple Leaf Veins and Leaf Edge Cracks in Brassica juncea

Purple leaf veins and leaf edge cracks comprise the typical leaf phenotype of Brassica juncea; however, the molecular mechanisms and metabolic pathways of the formation of purple leaf veins and leaf edge cracks remain unclear. In this study, transcriptome and metabolome analyses were conducted to explore the regulation pathway of purple leaf vein and leaf edge crack formation based on four mustard samples that showed different leaf colors and degrees of cracking. The results showed genes with higher expression in purple leaf veins were mainly enriched in the flavonoid biosynthesis pathway. Integrating related genes and metabolites showed that the highly expressed genes of ANS (BjuA004031, BjuB014115, BjuB044852, and BjuO009605) and the excessive accumulation of dihydrokaempferol and dihydroquercetin contributed to the purple leaf veins by activating the synthetic pathways of pelargonidin-based anthocyanins and delphinidin-based anthocyanins. Meanwhile, “alpha-farnesene synthase activity” and “glucan endo-1, 3-beta-D-glucosidase activity” related to the adversity were mainly enriched in the serrated and lobed leaves, indicating that the environmental pressure was the dominant factor controlling the change in leaf shape. Overall, these results provided new insights into the regulation pathways for formation of purple leaf veins and leaf edge cracks, which could better accelerate the theoretical research on purple leaf vein color and leaf edge cracks in mustard.
An Asymptotic Model for Solving Mixed Integral Equation in Position and Time

In this paper, we considered a mixed integral equation (MIE) of the second kind in the space L 2 − b , b × C 0 , T , T < 1. The kernel of position has a singularity and takes some different famous forms, while the kernels of time are positive and continuous. Using an asymptotic method of separating the variables, we have a Fredholm integral equation (FIE) in position with variable parameters in time. Then, using the Toeplitz matrix method (TMM), we obtain a linear algebraic system (LAS) that can be solved numerically. Some applications with the aid of the maple 18 program are discussed when the kernel takes Coleman function, Cauchy kernel, Hilbert kernel, and a generalized logarithmic function. Also the error estimate, in each case, is computed.
Investigating the Effect of Alcohol Dehydrogenase Gene Knockout on Lipid Accumulation in Mucor circinelloides WJ11

Mucor circinelloides is an oleaginous, dimorphic zygomycete fungus species that produces appreciable levels of ethanol when grown under aerobic conditions in the presence of high glucose, indicating the fungus is a Crabtree-positive microorganism. Engineering efforts to redirect carbon flux from ethanol to lipid biosynthesis may shed light on the critical role of ethanol biosynthesis during aerobic fermentation in M. circinelloides. Therefore, in this study, the alcohol dehydrogenase gene (ADH1) of M. circinelloides WJ11 was deleted, and its effects on growth, lipid production, and fatty acid content were analyzed. Our results showed that knocking out of adh1∆ reduced the ethanol concentration by 85–90% in fermented broth, indicating that this gene is the major source of ethanol production. Parallel to these findings, the lipid and fatty acid content of the mutant was decreased, while less change in the growth of WJ11 was observed. Furthermore, a fermentation study showed the lipid and fatty acid content was restored in the mutant strain when the fermentation media was supplemented with 0.5% external ethanol, indicating the importance of alcohol dehydrogenase and its product on growth and lipid biosynthesis in M. circinelloides. To our knowledge, this is the first study to show a link between alcohol dehydrogenase and lipid production in M. circinelloides.
Retraction Notice Recognizing Student’s Learning-Centered Affective States in Conversation with Intelligent Multimodal Analytics

Coach-led conversations are the most common way for teachers to monitor students’ learning progress and provide just-in-time interventions. Students will experience a complex mixture of learning-centered affective states during the conversations, including concentration, confusion, frustration, and boredom, which have been widely acknowledged as crucial components for inferring a student’s learning states. Effectively recognizing students’ learning-centered affective states, especially negative ones such as confusion and frustration, could indicate that a student has a need for assistance, and help a human teacher improve their perceptual and real-time decision-making capability in providing personalized and adaptive support in coaching activities. Many lines of research have explored the automatic measurement of students’ single emotional states in pre-designed student vs. computer-teacher tasks. It still remains a challenge to detect the learning-centered affective states of students in face-to-face teacher-student coach-led conversations. Meanwhile, “in-the-wild” contexts with real operational environments and real teacher-student conversations pose unique challenges in collecting, validating, and interpreting data. In this study, we attempted to first develop an advanced multi-sensor-based system and applied it in small-scale meetings to collect multi-modal teach-er-student conversation data. Then, we demonstrate a multimodal analysis framework to characterize students’ learning-centered affective states from multiple perspectives: facial, audio, and physiological cues. A series of interpretable proxy features were derived from these modalities and used to train a set of supervised learning classifiers with various multimodal fusion approaches, signal-channel-level, feature-fusion-level, and decision-fusion- level, to recognize students’ learning-centered affective states. We achieved a mean AUC of 0.76 for the facial and audio feature-level fusion classifier. Our results provide evidence of the potential practical value of fusing multi-modal data to explore students’ “in-the-wild” learning-centered affective states in teach-er-student coach-led conversations.
Containerized Architecture Performance Analysis for IoT Framework Based on Enhanced Fire Prevention Case Study: Rwanda

Nowadays, building infrastructures are pushed to become smarter in response to desires for the environmental comforts of living. Enhanced safety upgrades have begun taking advantage of new, evolving technologies. Normally, buildings are configured to respond to the safety concerns of the occupants. However, advanced Internet of Things (IoT) techniques, in combination with edge computing with lightweight virtualization technology, is being used to improve users’ comfort in their homes. It improves resource management and service isolation without affecting the deployment of heterogeneous hardware. In this research, a containerized architectural framework for support of multiple concurrent deployed IoT applications for smart buildings was proposed. The prototype developed used sensor networks as well as containerized microservices, centrally featuring the DevOps paradigm. The research proposed an occupant counting algorithm used to check occupants in and out. The proposed framework was tested in different academic buildings for data acquisition over three months. Different deployment architectures were tested to ensure the best cases based on efficiency and resource utilization. The acquired data was used for prediction purposes to aid occupant prediction for safety measures as considered by policymakers.
Accuracy Assessment of Low-Cost Lidar Scanners: An Analysis of the Velodyne HDL-32E and Livox Mid-40's Temporal Stability

Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a temporally correlated instability in the lidar scanner where the measured distance between target and sensor changes over time while both are held stationary. This research presents an assessment of two low-cost lidar scanners, the Velodyne® HDL–32E and Livox® Mid–40, in which their temporal stability is analyzed and methods to mitigate systematic error are implemented. By immobilizing each scanner as it observes a stationary target surface over the course of multiple hours, trends in scanner precision are identified. Scanner accuracy is then determined using a terrestrial lidar scanner, the Riegl® VZ-400, to observe both subject scanner and target, and extracting the distances between scanner origin and observed surface. Patterns identified in each scanner’s distance measurements indicate temporal autocorrelation, and, by exploiting the high linear correlation between scanner internal temperature and measured distance in the HDL–32E, it is possible to mitigate the resulting error. Application of the proposed solution lowers the Velodyne® scanner’s measurement RMSE by over 60%, providing levels of measurement accuracy comparable to more expensive lidar systems.
Cross-Language Speech Emotion Recognition Using Bag-of-Word Representations, Domain Adaptation, and Data Augmentation

To date, several methods have been explored for the challenging task of cross-language speech emotion recognition, including the bag-of-words (BoW) methodology for feature processing, domain adaptation for feature distribution “normalization”, and data augmentation to make machine learning algorithms more robust across testing conditions. Their combined use, however, has yet to be explored. In this paper, we aim to fill this gap and compare the benefits achieved by combining different domain adaptation strategies with the BoW method, as well as with data augmentation. Moreover, while domain adaptation strategies, such as the correlation alignment (CORAL) method, require knowledge of the test data language, we propose a variant that we term N-CORAL, in which test languages (in our case, Chinese) are mapped to a common distribution in an unsupervised manner. Experiments with German, French, and Hungarian language datasets were performed, and the proposed N-CORAL method, combined with BoW and data augmentation, was shown to achieve the best arousal and valence prediction accuracy, highlighting the usefulness of the proposed method for “in the wild” speech emotion recognition. In fact, N-CORAL combined with BoW was shown to provide robustness across languages, whereas data augmentation provided additional robustness against cross-corpus nuance factors.
Geometry of PR-warped products in para-Kaehler manifolds

In this paper, we initiate the study of $\p R$-warped products in para-K\"ahler manifolds and prove some fundamental results on such submanifolds. In particular, we establish a general optimal inequality for $\p R$-warped products in para-K\"ahler manifolds involving only the warping function and the second fundamental form. Moreover, we completely classify $\p R$-warped products in the flat para-K\"ahler manifold with least codimension which satisfy the equality case of the inequality.
Lattice gas models for the study of intercollisional interference

A partially discrete stochastic model and a fully discrete deterministic lattice gas are introduced for the study of intercollisional interference phenomena. These models are useful in evaluating and extending the kinetic theory of vector and tensor intercollisional interference as developed by Lewis and van Kranendonk [1], and the theory of scalar intercollisional interference as put forward by Herman et al. in 1979 (see [2] for details). The models are also of use in improving data analysis methods for simulation studies of intercollisional interference phenomena. Simulation results obtained with both models are presented.
A Genome-Wide Association Study of Genetic Variants of Apolipoprotein A1 Levels and Their Association with Vitamin D in Korean Cohorts

Dyslipidemia is an important independent risk factor for cardiovascular disease (CVD). Specifically, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and the ApoB/A1 ratio have been linked to CVD. We conducted a genome-wide association study meta-analysis of two Korean cohorts containing a total of 12,924 patients to identify novel single nucleotide polymorphisms (SNPs) associated with ApoA1 and ApoB levels and the ApoB/A1 ratio. Additionally, an expression quantitative trait locus (eQTL) and differentially expressed genes (DEGs) analysis were performed. The statistically significant eQTL, DEG, and Gene Ontology (GO) results were used to explore the predicted interaction networks and retrieve the interacting genes and proteins. We identified three novel SNPs (rs11066280, p = 3.46 × 10−21; rs1227162, p = 2.98 × 10−15; rs73216931, p = 5.62 × 10−9) associated with ApoA1. SNP rs73216931 was an eQTL for KMT5A in the pancreas and whole blood. The network analysis revealed that HECTD4 and MYL2:LINC1405 are associated with AKT1. Our in silico analysis of ApoA1 genetic variants revealed heart muscle-related signals. ApoA1 also correlated positively with vitamin D, and genes associated with ApoA1 and vitamin D were found. Our data imply that more research into ApoA1 is needed to understand the links between dyslipidemia and CVD and vitamin D and CVD.