Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of A Miniaturized Wideband 16-Way Power Divider Using Loaded Spurlines

A New wideband Wilkinson 16-way power divider on microstrip line operating at X-band(8-12GHz) using loaded slow-wave structure of spurline to accomplish miniaturization is presented in this paper.The equations used for the 2-way power divider unit loaded with symmetrical single-spurline structures are based on the concept of spurline and odd-even modes using transmission line analysis.Then the 2-way power divider in X-band is cascaded to design 16-way power divider, while every cascade transmission line is loaded with symmetrical-spurline structure.The designed 16-way power divider has compact size of 197 mm × 55.1 mm × 6.808 mm including cavity and provides good impedence matching at all ports.Its return loss is better than 12.98dB, insertion loss is less than 15.1dB and isolation is better than 15dB across the 8 to 12GHz range, which is in good agreement with the simulation results.
Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class “head” we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.
Improved Valve-Point Optimization Algorithm for Economic Load Dispatch Problem with Non-convex Fuel Cost Function

There is no polynomial-time algorithm that can be obtain the optimal solution for economic load dispatch problem with non-convex fuel cost functions. Therefore, electrical field uses quadratic fuel cost function unavoidably. This paper proposes a valve-point optimization (VPO) algorithm for economic load dispatch problem with non-convex fuel cost functions. This algorithm sets the initial values to maximum powers for each generator. It then reduces the generation power of generator i with an average power cost of to a valve point power . The proposed algorithm has been found to perform better than the extant heuristic methods when applied to 13 and 40-generator benchmark data. This paper consequently proves that the optimal solution to economic load dispatch problem with non-convex fuel cost functions converges to the valve-point power of each generator.
Remission of Type 2 Diabetes Mellitus (T2DM) after Sleeve Gastrectomy (SG), One-Anastomosis Gastric Bypass (OAGB), and Roux-en-Y Gastric Bypass (RYGB): A Systematic Review

Background and Objectives: The three most widely performed bariatric surgeries are Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), and one-anastomosis gastric bypass (OAGB). Aside from the benefits of weight loss, current findings suggest that these procedures can also induce remission of T2DM (type 2 diabetes mellitus). There are limited data that directly compare these three procedures. This study aims to compare the short-term and long-term remission of T2DM after RYGB, SG, and OAGB. Materials and Methods: Three databases (Embase, PubMed, and Cochrane) were searched for randomised controlled trials, prospective studies, and retrospective studies that compared the effects of RYGB, SG, and OAGB on T2DM remission. Studies published between 2001 and 2022 were analysed. Only patients with T2DM and who had primary bariatric surgery were included. Results: After applying the inclusion and exclusion criteria, seven articles were included in the review. It was found that all three procedures had comparable T2DM remission. RYGB was noted to have the highest complication rate when compared to SG and OAGB. Importantly, it was noted that other predictive factors such as age, duration of diabetes, baseline HbA1c, BMI, and use of antidiabetic medication play a crucial role in T2DM remission. Conclusions: This systematic literature review confirms the existing data that all three bariatric surgeries induce remission of T2DM. Increasing in popularity, OAGB had comparable outcomes to RYGB and SG in inducing T2DM remission. In addition to the choice of bariatric surgery, there are other independent predictive factors that have an impact on T2DM remission. Further studies with larger sample sizes, longer follow-up periods, and studies that control confounding factors are required in this field.
Twenty Questions for Localizing Multiple Objects by Counting: Bayes Optimal Policies for Entropy Loss

We consider the problem of twenty questions with noiseless answers, in which we aim to locate multiple objects by querying the number of objects in each of a sequence of chosen sets. We assume a joint Bayesian prior density on the locations of the objects and seek to choose the sets queried to minimize the expected entropy of the Bayesian posterior distribution after a xed number of questions. An optimal policy for accomplishing this task is characterized by the dynamic programming equations, but the curse of dimensionality prevents its tractable computation. We rst derive a lower bound on the performance achievable by an optimal policy. We then provide explicit performance bounds relative to optimal for two computationally tractable policies: greedy, which maximizes the one-step expected reduction in entropy; and dyadic, which splits the search domain in successively ner partitions. We also show that greedy performs at least as well as the dyadic policy. This can help when choosing the policy most appropriate for a given application: the dyadic policy is easier to compute and nonadaptive, allowing its use in parallel settings or when questions are inexpensive relative to computation; while the greedy policy is more computationally intensive but also uses questions more eciently, making it the better choice when robust sequential computation is possible. Numerical experiments demonstrate that both procedures outperform a divide-and-conquer benchmark policy from the literature, called sequential bifurcation. Finally, we further characterize performance under the dyadic policy by showing that the entropy of the posterior distribution is asymptotically normal.
Towards topological methods for complex scalar data

Recent years have witnessed an unprecedented growth in the generation and availability of large scale and complex data. Such growth has fostered the development of new qualitative methods of data analysis. Among the most prominent are the set of methods falling under the rubric of topological methods. From their inception, topological methods have been gaining in prominence mainly in the studies of scalar data, in particular the topology of scalar functions over manifolds. With the increasing complexity of the available data, there is an interest in extending the topological methods to deal with more complex types of data such as multiple scalar fields and vector fields over manifolds, scalar data over point clouds, as well as scalar fields over high dimensional stratified spaces. In this dissertation we study the extension of topological methods to the treatment of multiple scalar fields, and scalar fields over a variety of domains ranging from the planar to the stratified. We first showcase the use of scalar field topological methods to study 2D vector fields over planar domains. We achieve this by transforming the vector field into multiple scalar fields through the use of Hodge decomposition. Hodge decomposition in 2D allows us to transform the vector field into two scalar fields where we can apply scalar field based topological methods. We take advantage of this decomposition to generate topologically-aware visualization of the vector field. A particular case of multiple scalar functions is a time varying function, where time is treated as a second scalar function. We study two topological constructs in the context of time varying functions: persistent homology and contour trees. We present an algorithm to incrementally update the persistence pairing of a time-varying function defined on a triangular mesh in O(log n), and contour trees of time-varying functions in O(log n + |lnk( p)∩lnk(q)|) where | lnk(p)| is the size of the link of a vertex p in the tetrahedral mesh. We then examine the relation between multiple scalar functions through the use of the Jacobi set. The Jacobi set attempts to quantify the correlation between two or more scalar fields defined on a common domain. We investigate the computation and simplification of the Jacobi set for point cloud data through the computation of the gradient in eigen space. Finally we look at recovering spaces of intrinsic dimensions of one (1-strata) from a point cloud embedded in high dimensional space. We make use of the well known Reeb graph to recover the space, and the result is a simple and efficient algorithm that avoids the prohibitive cost of a full space reconstruction using traditional methods. In summary, we present a variety of methods for exploring the use of topological methods in the study of complex settings involving scalar fields. We hope in the future to adapt current methods to point clouds sampled from probability distributions with the presence of outliers and background noise.
High Sensitivity SIW-CSRR Temperature Sensor Based on Microwave Scattering

Aiming at the real-time monitoring requirements of equipment temperature in harsh environments such as ultrahigh temperature, high-spin and multi-metal, a wireless passive high-sensitivity temperature sensor with semicircular complementary split resonant ring (CSRR) structure, based on the principle of microwave scattering, was designed by using substrate integrated waveguide (SIW) and high frequency structure simulator (HFSS) electromagnetic simulation software, and prepared by laser cutting, laser drilling, screen printing, and high-temperature sintering, which was made of alumina ceramic sensitive element. The high-temperature test platform was built to test the sensor. The test results showed that the sensitivity of the sensor is 106.67 kHz/°C in the low-temperature range of 25 °C–400 °C, and the sensitivity is 437.5 kHz/°C in the high-temperature range of 400 °C–1200 °C, indicating that the sensitivity of the sensor in the high-temperature range is much higher than that in the low-temperature range. The morphology of platinum film on the surface of the sensor at different stages was characterized by scanning electron microscopy (SEM), which can explain that long-time high-temperature use is the main reason for sensor failure.
A Simulation Method for Millimeter-Wave Radar Sensing in Traffic Intersection Based on Bidirectional Analytical Ray-Tracing Algorithm

Millimeter-wave (mmw) radar is indispensable for intelligent transportation systems (ITSs), which can monitor traffic conditions in all weathers. An end-to-end simulation method for mmw radar monitoring and identification at traffic intersections is proposed in this article. In this method, a virtual intersection scenario model is constructed, and the scattering coefficient of the target is calculated using the bidirectional analytical ray-tracing (BART) algorithm. Combined with the generation of time-domain waveforms, the operation of frequency-domain convolution is simplified by inverse Fourier transform, and the echo signals received by the sparse array are simulated. After raw signal processing, point cloud images containing target position information and range-Doppler map (RDM) containing target state feature are obtained. The performance of mmw radar in detecting the specific location information of the target is evaluated by analyzing point cloud images. In addition, a self-defined convolutional neural network is introduced to evaluate the object recognition performance of the RDM. After the training of the neural network, the classification accuracy of this method for four types of vehicle targets can reach 92%.
ON A FUNCTIONAL EQUATION ARISING FROM PROTH IDENTITY

Abstract. We determine the general solutions f : R 2 → R of the func-tional equation f(ux−vy,uy+v(x+y)) = f(x,y)f(u,v) for all x,y,u,v ∈R. We also investigate both bounded and unbounded solutions of thefunctional inequality |f(ux−vy,uy+v(x+y))−f(x,y)f(u,v)| ≤ φ(u,v)for all x,y,u,v ∈ R, where φ : R 2 → R + is a given function. 1. IntroductionThe simple identity(1.1) x 4 +y 4 +(x +y) 4 = 2x 2 +xy +y 22 is known as the Proth identity and was ﬁrst published in 1878 (see [2]). It canbe easily established by expanding the right hand side and using the binomialtheorem. If we denote the quadratic form on the right hand side of (1.1) by(1.2) f(x,y) = x 2 +xy +y 2 ,then it can be easily veriﬁed that(1.3) f(ux −vy,uy +v(x +y)) = f(x,y)f(u,v)for all x,y,u,v ∈ R. However, it is not so obvious that the function f given in(1.2) is the only solution of (1.3). The Proth identity means that if we represent a given positive integer n asf(x,y), where x and y are integers, then we have a representation of 2n
Distributed measurement of modal birefringence in a few-mode fiber based on stimulated Brillouin scattering

This paper presents a novel technique for the distributed measurement of the modal birefringence in a few-mode fiber (FMF). The method exploits two different phenomena observed in distributed Brillouin measurements: the dependence of the Brillouin frequency shift (BFS) on the effective refractive index (ERI) of the interacting optical beams, and the spatial oscillations of the Brillouin gain deriving from multimodal interference. Using both phenomena, a wide range of ERI separations can be measured, from ≈ 10-7 to 10-2 or more. The measurements have been carried out over a two-mode graded-index FMF, using two photonic lanterns to selectively excite the desired spatial modes. We use the BFS measurements to derive the ERI difference between the LP01 and LP11 mode groups, while the spatial oscillations of the Brillouin gain reveal the birefringence between the vector components (TE01, TM01 and HE21) of the LP11 mode group. The experimental measurements are partly supported by full-vector finite-element-method (FEM) simulations. The reported method may also find application in the field of distributed sensing, by taking advantage of the dependence of modal birefringence from physical parameters such as strain and temperature.
Adolescent girls’ menstrual hygiene management: evidence from rural northern Ghana

Background: Menstrual Hygiene Management (MHM) among adolescents has emerged as global public health concern in low and middle-income countries. Although research on MHM among adolescents is vast, the peculiar cultural and economic barriers faced by Junior high schoolgirls in menstrual hygiene management in the deprived settings of northern Ghana are seldom explored, even though sanitation coverage in this part of the country does not exceed 20 percent. We thus explored menstrual hygiene management barriers among adolescent schoolgirls in Junior High Schools in the Upper West region. Methods: A case study of adolescent girls’ menstrual practices and management in selected schools in the Jirapa Municipality was conducted. We employed a qualitative approach in the collection and analysis of the data. A mix of purposive and convenience sampling procedures was used to select 44 diverse participants, comprising: basic schoolgirls (n=32), and mothers (n=12), who participated in semi-structured interviews. The data collected were analysed using the thematic analytical framework. Results: Underpinned by Gusfield’s cultural and structural analysis of public problems as well as Bronfenbrenner’s socioecological theory, we found that limited knowledge of menstruation and its management, cultural beliefs, and high costs of menstrual care products to be main barriers to effective MHM among adolescent girls in basic schools in Ghana. This has often resulted in school absenteeism and dropout, girls engaging in commercial sex to raise funds to buy menstrual hygiene products, menstruation-related stigma and seclusion and exclusion during menstruation. Conclusion: Our findings have implications for MHM policy planning and implementation. The Ministries of Education, Health and Water Resources need to collaborate and develop an MHM Policy as part of the National Sanitation and Hygiene Strategy, and a strategy to provide direction on how to implement MHM activities in basic schools in the country.
Stereo Matching using Belief Propagation with Line Grouping

In the Markov network which models disparity map with the Markov Random Fields(MRF), the belief propagation algorithm is operated by message passing between nodes corresponding to each pixel. The initial message value is converged by iterations of the algorithm and the algorithm requires many iterations to get converged messages. In this paper, we simplify the algorithm by regarding the objects in the disparity map as combinations of lines with same message valued nodes to reduce iterations of the algorithm.
Managing emotions in ongoing service relationships: new intrinsic and extrinsic emotion regulation strategies from aged care

Abstract Managing emotions at work can be challenging for employees in service roles. Most emotional labor research has been conducted in settings of brief service interactions with strangers. Yet, many traditional family roles, such as aged and child care are now outsourced to personal service roles that have ongoing relationships with clients. The continuing nature of those relationships may result in employees using previously unknown strategies to regulate emotions. To develop an understanding of intrinsic and extrinsic emotion regulation (ER) strategies in service relationships, we interviewed 42 aged care employees in China. Using template analysis we found three new employee-client relationship-based intrinsic ER strategies (i.e. family appreciation, filial reappraisal and deliberate distancing), a self-based strategy (i.e. self-deterioration anticipation) and three client-based strategies (i.e. client deterioration anticipation, vulnerability justification and meaning identification) in managing one’s own emotions in service relationships. When managing others’ emotions in ongoing service settings, a new relationship-based extrinsic ER strategy (i.e. communal bonding) along with situation-based and client-based extrinsic ER strategies were identified. Employees can regulate their own and others’ emotions by crafting their relationships with clients. This study suggests that service relationship is a critical dimension of client interactions and should be considered in workplace ER research.
Optimizing THP-1 Macrophage Culture for an Immune-Responsive Human Intestinal Model

Previously established immune-responsive co-culture models with macrophages have limitations due to the dedifferentiation of macrophages in long-term cultures. This study is the first report of a long-term (21-day) triple co-culture of THP-1 macrophages (THP-1m) with Caco-2 intestinal epithelial cells and HT-29-methotrexate (MTX) goblet cells. We demonstrated that high-density seeded THP-1 cells treated with 100 ng/mL phorbol 12-myristate 13-acetate for 48 h differentiated stably and could be cultured for up to 21 days. THP-1m were identified by their adherent morphology and lysosome expansion. In the triple co-culture immune-responsive model, cytokine secretions during lipopolysaccharide-induced inflammation were confirmed. Tumor necrosis factor-alpha and interleukin 6 levels were elevated in the inflamed state, reaching 824.7 ± 130.0 pg/mL and 609.7 ± 139.5 pg/mL, respectively. Intestinal membrane integrity was maintained with a transepithelial electrical resistance value of 336.4 ± 18.0 Ω·cm2. Overall, our findings suggest that THP-1m can be effectively employed in models of long-term immune responses in both normal and chronic inflammatory states of the intestinal epithelium, making them a valuable tool for future research on the association between the immune system and gut health.
Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier

(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.