Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical Validation of Ultrasound Assessment of the Thyroid Gland in Cattle

Simple Summary Cattle are not routinely screened for thyroid anomalies. Scientific data regarding thyroid diseases are also scarce. This may be due to the lack of easily accessible tools to assess the thyroid function of these animals. Thyroid ultrasound examination is a cheap, easy and non-invasive technique that can be performed without sedation. Apart from morphological information, it may also provide functional data about this gland. In this study, we attempted to validate the use of thyroid ultrasound in cattle by measuring intra- and inter-observer variability. Thyroid estimated measurements were repeated in five calves and five cows, by three different operators with different trainings. Results show that this examination has good repeatability and may be used more routinely in veterinary practice. Abstract Little is known about thyroid diseases in ruminants, probably due to the lack of diagnosis techniques developed in this species. However, thyroid ultrasound (TU) is widely used in human and in companion animal’s medicine. It is a cheap and non-invasive examination, which allows for the identification of thyroid structures or diffuse diseases. The aim of this study was to evaluate the accuracy of TU in five calves and five cows through inter- and intra-observer repeatability. The size of the thyroid gland was measured from three views: left sagittal, right sagittal and transverse; nine measurements per view. The intra-observer coefficient was calculated for each observer. For the inter-observer, the first observer was a board-certified imagist (European College of Veterinary Diagnostic Imaging diplomate), the second was a board-certified specialist in bovine and herd management (European College of Bovine Health Managementdiplomate) and the third was an in-trained veterinarian for the TU. They each scanned the thyroid gland successively, following the same method. The intra-observer variabilities for observers 1, 2 and 3 were 8.22%, 5.53%, 5.38%, and 7.18%, 8.65% and 6.36%, respectively, for calves and cows. The inter-observer variability for calves was 10.4% and for cows, 11.8%. This study confirms the feasibility of repeatable intra- and inter-observer TU-estimated measurements in cattle.
Spatial Distribution of Inhibitory Innervations of Excitatory Pyramidal Cells by Major Interneuron Subtypes in the Auditory Cortex

Mental disorders, characterized by the National Institute of Mental Health as disruptions in neural circuitry, currently account for 13% of the global incidence of such disorders. An increasing number of studies suggest that imbalances between excitatory and inhibitory neurons in neural networks may be a crucial mechanism underlying mental disorders. However, the spatial distribution of inhibitory interneurons in the auditory cortex (ACx) and their relationship with excitatory pyramidal cells (PCs) remain elusive. In this study, we employed a combination of optogenetics, transgenic mice, and patch-clamp recording on brain slices to investigate the microcircuit characteristics of different interneurons (PV, SOM, and VIP) and the spatial pattern of inhibitory inhibition across layers 2/3 to 6 in the ACx. Our findings revealed that PV interneurons provide the strongest and most localized inhibition with no cross-layer innervation or layer specificity. Conversely, SOM and VIP interneurons weakly regulate PC activity over a broader range, exhibiting distinct spatial inhibitory preferences. Specifically, SOM inhibitions are preferentially found in deep infragranular layers, while VIP inhibitions predominantly occur in upper supragranular layers. PV inhibitions are evenly distributed across all layers. These results suggest that the input from inhibitory interneurons to PCs manifests in unique ways, ensuring that both strong and weak inhibitory inputs are evenly dispersed throughout the ACx, thereby maintaining a dynamic excitation–inhibition balance. Our findings contribute to understanding the spatial inhibitory characteristics of PCs and inhibitory interneurons in the ACx at the circuit level, which holds significant clinical implications for identifying and targeting abnormal circuits in auditory system diseases.
Historical Audio Search and Preservation: Finding Waldo Within the Fearless Steps Apollo 11 Naturalistic Audio Corpus [Applications Corner]

Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal. Manual annotation is costly, so this makes it necessary to determine robust speaker identification and tracking methods. In this study, a subset of 100 h derived from the collective 9,000 h of the Fearless Steps (FSteps) Apollo 11 audio data were investigated, corresponding to three critical mission phases: liftoff, lunar landing, and lunar walk. A speaker recognition assessment is performed on 140 speakers from a collective set of 183 NASA mission specialists who participated, based on sufficient training data obtained from 5 (out of 30) mission channels. We observe that SincNet performs the best in terms of accuracy and F score achieving 78.6% accuracy. Speaker models trained on specific phases are also compared with each other to determine if stress, g-force/atmospheric pressure, acoustic environments, etc., impact the robustness of the models. Higher performance was obtained using i-vector and x-vector systems for phases with limited data, such as liftoff and lunar walk. When provided with a sufficient amount of data (lunar landing phase), SincNet was shown to perform the best. This represents one of the first investigations on speaker recognition for massively large team-based communications involving naturalistic communication data. In addition, we use the concept of “Where’s Waldo? ” to identify key speakers of interest (SOIs) and track them over the complete FSteps audio corpus. This additional task provides an opportunity for the research community to transition the FSteps collection as an educational resource while also serving as a tribute to the “heroes behind the heroes of Apollo. ”
Network-Compute Co-Optimization for Service Chaining in Cloud-Edge-Radio 5G Networks

Flexible resource management can be facilitated by recent drastic advances in software-defined networking (SDN) and network function virtualization (NFV) technologies. Most of recent studies on resource management have mainly focused on the independent optimization of network function and computing function on top of a single-layer SDN architecture. In this paper, we focus on networking and processing resource orchestration to enhance the performance of network/computing functions in a hierarchical cloud-edge-radio 5G network architecture. Specifically, we propose a dynamic resource management algorithm, namely RACER, to process network/computing functions by opportunistically exploiting a tradeoff between the characteristics of the cloud and edge with respect to the amount of available resources and transmission delay. Herein, we decompose an original long-term problem including complex decisions in all network layers (that is, cloud, edge, and radio) into spatio-temporal subproblems without loss of optimality by leveraging Lyapunov optimization. Then, we derive the RACER algorithm to find the decisions in each layer and each time slot. This online-fashioned algorithm is pragmatic because it not only exploits information obtained from instantaneous time slots, but also operates in a distributed manner with small message exchanges between the different layers. Moreover, we demonstrate the excellent performance improvement of the proposed dynamic and holistic RACER algorithm compared to static and independent policies in the 5G network environment.
Short-time asymptotics of hydrodynamic dispersion in porous media

Abstract We consider convection–diffusion transport of a passive scalar within porous media having a piecewise-smooth and reflecting pore–grain interface. The corresponding short-time expansion of molecular displacement time-correlation functions is determined for the generalized steady convection field. By interpreting the generalized short-time expansion of dispersion dynamics in the context of low-Reynolds-number flow through macroscopically homogeneous porous media, we demonstrate the connection between hydrodynamic permeability and short-time dynamics. The analytical short-time expansion is compared with numerical simulation data for steady low-Reynolds-number flow through a random close-pack array of mono-disperse spheres. The quadratic short-time expansion term of the dispersion coefficient closely predicts the numerical data for a mean displacement of at least 10 % of the sphere diameter for a Péclet number of 54.49.
Graphene-Based Hyperbolic Metamaterial Acting as Tunable THz Power Limiter

In this paper, we investigate the possibility of employing a graphene-based hyperbolic metamaterial as a tunable optical power limiter and/or controllable laser noise suppressor for terahertz frequency range. This is the first time that the mentioned functionalities are obtained with a voltage-controlled nonlinear hyperbolic metamaterial utilizing graphene. We believe that such structures, feasible by means of planar deposition techniques, will find practical applications in THz systems requiring optical power limiting.
Design and Implementation of Solar Powered Battery and Diesel Generator of Electric Vehicle Charging Station Using Hybrid Intelligent Controller

Power converters are necessary between a power supply and a load. Thus, the use of renewable energysources and battery power has been on the rise. Thefundamental purpose of this project is to use a BES and DG Generator Set to charge an electric car. SolarPV panels and a battery energy storage system (BES) make up the charging stations means of fueling the EV. A diesel generator set or the grid is used when the solar power stations batteries are depleted or cloudy weather prevents solar collection(DG set). Furthermore, the charging station can facilitate active/reactive power transfers between cars and the grid, as well as between vehicles and the home or between vehicles. In the daytime, the PV array produces enough electricity to recharge the E$\mathrm{V}^{\prime}$s battery. During peak sunshine hours, the PV array not only recharges the EVs battery but also feeds any extra power back into the standard single-phase electrical system. Electric vehicle batteries may be charged by an inverter when the sun isnt shining or the sky is overcast. The ANN controller used in the closed-loop operation of this project was developed using MATLAB simulation.
Compact Multipole GaN-on-Si SPDT Switch Using Inductive Parasitic Effects of Hybrid HEMT Devices

This article proposes a compact multipole single-pole double-throw (SPDT) switch using hybrid HEMT devices in the GaN-on-Si process. As investigated, the inductive effect of HEMTs is unwanted and usually alleviated in most reported switches for good impedance matching and high isolation. A different idea is proposed in this work, and the inductive effects of HEMTs are utilized to realize two extra transmission poles (TPs). For quantitative analysis, the circuit model and parasitic parameter extraction of two types of GaN HEMTs are investigated. The first type of HEMT has large ON-state self-inductance and small OFF-state capacitance, which is used in series for multiple TPs and high isolation. For low transmission loss and high isolation, the second type of HEMT is placed in parallel due to its low ON-state resistance and ON-state inductance. For verification, an SPDT switch with good in-band return loss and flatness is designed. Three TPs can be realized using a simple series-shunt topology, and the core chip size is only 0.39 mm2. The 1-dB bandwidth is from dc to 30 GHz with a minimum insertion loss of 0.5 dB, and the power capability is over 31 dBm.
Predicting Motion Direction and Person re- identification across surveillance Cameras Network using (LSTM)

cameras surveillance systems are develop and spread in our life because it used of various fields as monitoring the interested places, buildings and traffic. Prediction of moving object in scene still challenge as the object when change his appearance or may be occluded. This paper presents an efficient proposed system for tracking and predicting the motion direction of a person by combining deep learning and topology of a camera network in non-overlapping multi-camera surveillance. The proposed system is divided into three stages: firstly, extracted suitable features of person in single camera using YOLOv3 algorithm for target detection; secondly, tracking the target based on deep sort and Kalman filter to association observations data for unique person across cameras; finally, person re-identification when a person moving across adj acent cameras by using interested information for target with Long Short-Term Memory (LSTM) network and spatial information from cameras network topology to esti-mate the direction of target when move between cameras that will improve the tracking process and reduce the processing and traffic on bandwidth.
Parental Phubbing and Adolescent Depressive Symptoms during COVID-19: A Serial Meditating Model

Background: During the COVID-19 pandemic, depressive symptoms, a common emotional problem among adolescents, have become more prominent. Regarding the influencing factors of adolescent depressive symptoms, it is widely accepted that parents’ problematic cellphone use around the family (specifically parental phubbing) is a strong predictive factor for the development of depressive symptoms among adolescents. Notably, the COVID-19 pandemic caused a sharp increase in the number of individuals with depressive symptoms, and the negative consequences of parental phubbing and depressive symptoms might have been exacerbated. Accordingly, this study aimed to examine the association between parental phubbing and adolescent depressive symptoms as well as their underlying mechanism. Method: To test our hypotheses, we conducted an offline/online survey with 614 adolescents in Central China from May to June 2022, which corresponded to a period of strict lockdowns in some areas due to the outbreak of the Omicron variant. The participants completed a set of measures, including a technology interference questionnaire, a parent–child relationship scale, a self-concept clarity scale, and the depressive symptoms scale. Results: Parental phubbing was positively associated with adolescent depressive symptoms; the parent–child relationship and self-concept clarity could independently mediate this relationship; and the parent–child relationship and self-concept clarity were also serial mediators in this association. These findings extend previous research by highlighting the impact of parental technology use on their children and the underlying mechanism explaining adolescent depressive symptoms. They provide practical recommendations for parents to prioritize fostering a positive family environment and minimizing phubbing behaviors to enhance adolescent development, particularly in the context of the COVID-19 pandemic.
Engineering Adenoviral Vectors with Improved GBM Selectivity

Glioblastoma (GBM) is the most common and aggressive adult brain cancer with an average survival rate of around 15 months in patients receiving standard treatment. Oncolytic adenovirus expressing therapeutic transgenes represent a promising alternative treatment for GBM. Of the many human adenoviral serotypes described to date, adenovirus 5 (HAdV-C5) has been the most utilised clinically and experimentally. However, the use of Ad5 as an anti-cancer agent may be hampered by naturally high seroprevalence rates to HAdV-C5 coupled with the infection of healthy cells via native receptors. To explore whether alternative natural adenoviral tropisms are better suited to GBM therapeutics, we pseudotyped an HAdV-C5-based platform using the fibre knob protein from alternative serotypes. We demonstrate that the adenoviral entry receptor coxsackie, adenovirus receptor (CAR) and CD46 are highly expressed by both GBM and healthy brain tissue, whereas Desmoglein 2 (DSG2) is expressed at a low level in GBM. We demonstrate that adenoviral pseudotypes, engaging CAR, CD46 and DSG2, effectively transduce GBM cells. However, the presence of these receptors on non-transformed cells presents the possibility of off-target effects and therapeutic transgene expression in healthy cells. To enhance the specificity of transgene expression to GBM, we assessed the potential for tumour-specific promoters hTERT and survivin to drive reporter gene expression selectively in GBM cell lines. We demonstrate tight GBM-specific transgene expression using these constructs, indicating that the combination of pseudotyping and tumour-specific promoter approaches may enable the development of efficacious therapies better suited to GBM.
A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Brain tumor is the 10th major cause of death among humans. The detection of brain tumor is a significant process in the medical field. Therefore, the objective of this research work is to propose a fully automated deep learning framework for multistage classification. Besides, this study focuses on to develop an efficient and reliable system using a convolutional neural network (CNN). In this study, the fast bounding box technique is used for segmentation. Moreover, the CNN layers‐based three models are developed for multistage classification through magnetic resonance images on three publicly available datasets. The first dataset is obtained from Kaggle Repository (Dataset‐1), the second dataset is known as Figshare (Dataset‐2), and the third dataset is called REMBRANDT (Dataset‐3) to classify the MR images into different grades. Different augmentation techniques are applied to increase the data size of MR images. In pre‐processing, the proposed models achieved higher Peak Signal‐to‐Noise ratio to remove noise. The first proposed deep CNN framework mentioned as Classification‐1 has obtained 99.40% accuracy, which classified MR images into two classes, that is (i) normal and (ii) abnormal, while the second proposed CNN framework mentioned as Classification‐2 has obtained 97.78% accuracy, which classified brain tumor into three types, which are meningioma, glioma, and pituitary. Similarly, the third developed CNN framework mentioned as Classification‐3 has obtained 98.91% accuracy that further classified MR images of tumors into four different classes as: Grade I, Grade II, Grade III, and Grade IV. The results demonstrate that the proposed models achieved better performance on three large and diverse datasets. The comparison of obtained outcomes shows that the developed models are more efficient and effective than state‐of‐the‐art methods.
Random and Fixed Effects Selection for Weighted Ridge

Using penalized profiled log-likelihood and penalized limited profiled log-likelihood, respectively, together with the weighted ridge penalized term, we offer a method in this study for choosing the fixed and random effects in linear mixed models. Then, we use the penalized restricted profiled log-likelihood to perform in the random effects depending on the chosen tuning parameter. Second, we use the penalized profiled log-likelihood to choose the fixed effect parameters. There is no closed-form solution for the choice of the fixed and random effects, hence the Newton-Raphson technique is employed to iteratively estimate the parameters. We use a simulation study to show how well the suggested strategy works. Lastly, we use two separate datasets to use the methods to further evaluate the newly proposed model.
Teachers’ Challenges on Self-directed Modular Distance Learning (SML): Basis for Extension Program Development

The covid-19 pandemic brought a remarkable impact on mankind. Like the economy, education suffers a tremendous trial in the system. The shift of the delivery of instruction to self-directed modular learning had challenged both the students and teachers. Hence, the study aimed to determine the challenges that teachers and students faced in the self-directed modular learning modalities in secondary schools of the Municipality of Katipunan Zamboanga del Norte. The study employed the sequential explanatory mixed methods design and thematic analysis technique. The study revealed that teachers encountered challenges in dealing with the Self-directed Modular Learning Modality. However, the challenges met do not affect the student’s academic performance. The study strongly recommends that the implementation of the Self-Modular Learning Modality will be reviewed carefully. Coping Strategies applied in overcoming the challenges will be examined thoroughly to devise constructive inputs to improve DepEd school's existing programs and guidelines. As partner institution, JRMSU-Katipunan Campus through the College of Education with the collaboration of the Guidance office will conduct intervention programs for students and teachers. Orientation for parents on home-based learning will be conducted. Budget allocation for internet connectivity, bending machines and computer sets will be requested from the government through the barangay, and training programs in line with technological skills development for teachers will be implemented.
Approximate functional dependency discovery and one-dimensional selectivity estimation

Tables are the fundamental data representation in modern database systems and one of the most fundamental representations in data mining and machine learning. Several researchers in the database community shown that Shannon entropy can be used naturally to quantify statistical properties of tables. One interesting property highlighted is that of an approximate functional dependency (AFD) which quantifies one-way a dependencies between columns. Entropy can also be used to quantify the “skewness” of the data in a column. Motivated by this work, we have examined two problems: (1) AFD discovery and (2) single dimensional selectivity estimation. Problem (1) is motivated by the fact that such discovered dependencies can provide valuable knowledge about the data. Quite a bit of research has gone into the discovery of functional dependencies (FDs), but not their a approximate counterparts. We take up the issue first by considering how to measure the degree to which an FD is approximate. We developed a set of axioms from intuition and prove that a unique measure satisfies them. Next we address the algorithmic problem of discovering all minimal AFDs that hold in a table. We develop and compare a level-wise and depth-first approach. Finally, we introduce an idea for applying discovered AFDs in relational query evaluation based on table decompositions. Problem (2) is motivated by the fact that modern relational database systems (e.g. Oracle, Access) estimate the result size (selectivity) of queries to aid in the search for efficient execution plans. A common approach is to build compact summary data structures and use these to estimate selectivity. A commonly used structure are histograms. We examine the use of “skewness quantification” with entropy in the construction of histograms. In particular we address the selectivity estimation problem for one commonly studied subclass of queries: single dimensional selection queries.