Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Term Performance of Epicardial versus Transvenous Left Ventricular Leads for Cardiac Resynchronization Therapy

Aims: to study the technical performance of epicardial left ventricular (LV) leads placed via video assisted thoracic surgery (VATS), compared to transvenously placed leads for cardiac resynchronization therapy (CRT). Methods: From 2001 until 2013, a total of 644 lead placement procedures were performed for CRT. In the case of unsuccessful transvenous LV lead placement, the patient received an epicardial LV lead. Study groups consist of 578 patients with a transvenous LV lead and 66 with an epicardial LV lead. The primary endpoint was LV-lead failure necessitating a replacement or deactivation. The secondary endpoint was energy consumption. Results: The mean follow up was 5.9 years (epicardial: 5.5 ± 3.1, transvenous: 5.9 ± 3.5). Transvenous leads failed significantly more frequently than epicardial leads with a total of 66 (11%) in the transvenous leads group vs. 2 (3%) in the epicardial lead group (p = 0.037). Lead energy consumption was not significantly different between groups. Conclusions: Epicardial lead placement is feasible, safe and shows excellent long-term performance compared to transvenous leads. Epicardial lead placement should be considered when primary transvenous lead placement fails or as a primary lead placement strategy in challenging cases.
Network pharmacology and experimental verification reveal the mechanism of safranal against glioblastoma (GBM)

Introduction Safranal is an active component of the traditional Tibetan medicine (TTM) saffron, which has potential anticancer activity. Methods and results Here, we studied the therapeutic effect and mechanism of safranal on GBM. CCK-8, GBM-brain organoid coculture experiments and 3D tumour spheroid invasion assays showed that safranal inhibited GBM cell proliferation and invasion in vitro. Network pharmacology, RNA-seq, molecular docking analysis, western blotting, apoptosis, and cell cycle assays predicted and verified that safranal could promote GBM cell apoptosis and G2/M phase arrest and inhibit the PI3K/AKT/mTOR axis. In vivo experiments showed that safranal could inhibit GBM cell growth alone and in combination with TMZ. Conclusion This study revealed that safranal inhibits GBM cell growth in vivo and in vitro, promotes GBM cell apoptosis and G2/M phase arrest, inhibits the PI3K/AKT/mTOR axis and cooperate with TMZ.
White Counselor Education Faculty on the Topics of Racism and Privileges in the Classroom: A Personal Journey

Engaging in critical conversations about race and racism requires counselor educators to be courageous and fearless. The current study explored how White counselor educators engage in honest discussions regarding race and racism. Using semi-structured interviews, three White counselor educators from CACREP Accredited programs were interviewed to explore their experiences of fearlessly leading courageous conversations on race and racism. A thematic analysis yielded three consistent themes, including a pattern centered around creating a safe space for discussions, being okay with being imperfect, and encounters that transform. The last theme also had two sub-themes: constructing intentional relationships with truth-tellers and world-changing experiences. These findings offer insight for White counselor educators to traverse challenges associated with having provocative conversations about race and racism, both inside and outside of the classroom.
LFVB-BioSLAM: A Bionic SLAM System with a Light-Weight LiDAR Front End and a Bio-Inspired Visual Back End

Simultaneous localization and mapping (SLAM) is one of the crucial techniques applied in autonomous robot navigation. The majority of present popular SLAM algorithms are built within probabilistic optimization frameworks, achieving high accuracy performance at the expense of high power consumption and latency. In contrast to robots, animals are born with the capability to efficiently and robustly navigate in nature, and bionic SLAM algorithms have received increasing attention recently. Current bionic SLAM algorithms, including RatSLAM, with relatively low accuracy and robustness, tend to fail in certain challenging environments. In order to design a bionic SLAM system with a novel framework and relatively high practicality, and to facilitate the development of bionic SLAM research, in this paper we present LFVB-BioSLAM, a bionic SLAM system with a light-weight LiDAR-based front end and a bio-inspired vision-based back end. We adopt a range flow-based LiDAR odometry as the front end of the SLAM system, providing the odometry estimation for the back end, and we propose a biologically-inspired back end processing algorithm based on the monocular RGB camera, performing loop closure detection and path integration. Our method is verified through real-world experiments, and the results show that LFVB-BioSLAM outperforms RatSLAM, a vision-based bionic SLAM algorithm, and RF2O, a laser-based horizontal planar odometry algorithm, in terms of accuracy and robustness.
Auxiliary Assessment of Cardiovascular Health Using High-Dimensional Characteristics of Camera-Based iPPG Monitoring

Noninvasive, convenient, and reliable cardiovascular health assessment using pervasive healthcare applications can help in preventing cardiovascular diseases (CVDs). This study proposes a novel cardiovascular health state detection procedure based on the multiple imaging photoplethysmography (iPPG) characteristics collected by camera-based iPPG monitoring. In this study, we collect a dataset from 226 subjects, derive 20 iPPG characteristic variables, and construct a two-way classification to predict whether subjects are CVDs relevant patients with a history of coronary heart disease or hypertension. The cross-validation accuracy of full-feature random forest (RF) model is 0.860 in training set and weighted F1-score and area under curve (AUC) 0.900 and 0.7831 in test set, respectively. The contribution ratio of heart rate variability (HRV) and iPPG average waveform characteristics is 3:2. Sample equalization can improve the model performance and increase AUC to 0.975, with the contribution ratio of HRV and iPPG average waveform characteristics remaining consistent as 7:5. The proposed scheme emphasizes the advantages of using noncontact and high-dimensional features of camera-based iPPG healthcare approach for auxiliary assessment. The effective use of iPPG full-features and RF modeling has a strong application significance for the identification of the cardiovascular health of status and is hoped to further contribute to the prevention and intervention of CVDs.
Spatiotemporal variability in the diet of nonbreeding Brandt's Cormorant (Phalacrocorax penicillatus) in the Monterey Bay region

Brandt’s Cormorant ( Phalacrocorax penicillatus ) diet composition was investigated using pellets (n = 285) collected on 19 sampling days at 3 locations during the 2006-07 and 2007-08 nonbreeding seasons in the Monterey Bay region. The conventional diet method was used, and the efficacy of the all-structure technique was evaluated. Although 29 species were consumed, Northern Anchovy ( Engraulis mordax ) dominated the diet and Speckled Sanddab ( Citharichthys stigmaeus ) also was important. Few rockfishes ( Sebastes spp.) and Market Squid ( Doryteuthis opalescens ) were consumed compared with the number consumed in previous studies in the region during the 1970s. El Niño and La Niña conditions during the study provided a unique opportunity to examine predator response. Greatest prey number and diversity occurred at locations within Monterey Bay during cooler ocean conditions, whereas the outer coast location remained unchanged. Short-term specialization was observed, but mean prey diversity indicated a generalist feeding mode. Patterns of prey number and diversity within a nonbreeding season were not consistent among locations. This study demonstrated the importance of periodic sampling at multiple locations within a region to detect spatiotemporal variability in the diet of this opportunistic generalist.
Enhancing Structural Crack Detection through a Multiscale Multilevel Mask Deep Convolutional Neural Network and Line Similarity Index

This paper proposes a novel and practical crack-detection method for infrastructure. The proposed method exhibits three key components. First, a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) is proposed to accurately estimate crack candidates comprising linear and curvilinear features. Second, the proposed neural network is trained using only public image-sets. The main principle of this approach is that cracks have unique and distinct features, and therefore, public image-sets provide sufficient information to estimate crack candidates for a neural network. Third, a line similarity index (LSI), which is calculated using the Hough transform and coordinate transformation with principal component analysis, is incorporated to eliminate non-crack candidates from crack candidates based on two key characteristics: the variation in crack features with respect to the representative line and the number of crack features that crossed the representative line. Addressing these two crack-related characteristics improves accuracy and robustness by effectively eliminating non-crack features. Field tests performed inside a building and in an underground power tunnel demonstrated the effectiveness of the proposed method. The MSML Mask DCNN outperformed other neural networks, accurately recognizing local crack candidates characterized by linear and curvilinear features even though only public image-sets were used for training. The proposed LSI also effectively eliminated non-crack candidates estimated by the MSML Mask DCNN. The proposed method is practical for real-world applications, where several non-crack objects and noises are typically present.
EQConvMixer: A Deep Learning Approach for Earthquake Location From Single-Station Waveforms

We present a novel deep-learning method using the ConvMixer network for automatic earthquake location. The proposed ConvMixer network utilizes three-component waveform recordings of single stations for estimating the hypocenter location. The ConvMixer network is a patch-based architecture that combines depthwise and pointwise convolutions to extract the global and local information of the earthquake waveforms. We train and test the proposed method using the Italian seismic dataset (INSTANCE). The ConvMixer network estimates the earthquake hypocenter locations with high accuracy, reaching a mean absolute error (MAE) of 2.71 km for the epicenter distance, and 1.15 km for the depth. In addition, we use the global STanford EArthquake Dataset (STEAD) to further evaluate the performance of the ConvMixer. As a result, the ConvMixer network achieves MAEs of 2.27 and 1.19 km for the distance and the depth, respectively. The proposed ConvMixer network is compared to the benchmark methods, i.e., ResNet, AlexNet, MobileNet, and Xception, and outperforms all of them.
A second-order absorbing boundary condition for two-dimensional peridynamics

The aim of this paper is to develop numerical analysis for the two-dimensional peridynamics which depicts nonlocal phenomena with artificial boundary conditions (ABCs). To this end, the artificial boundary conditions for the fully discretized peridynamcis are proposed. Then, the numerical analysis of the fully discretized scheme is developed such that the artificial boundary conditions solve the corner reflection problem with second-order accuracy. Finally numerical examples are given to verify theoretical results.
Kalman Filter Empirical Fitting on Monthly Rainfall-Runoff Responses

The majority of the methods for fitting rainfall-runoff Kalman filters use the statistical characteristics of the input (rainfall) and the output (runoff) series according to the systems analysis and control theory methodology. The problem arising with these techniques is essentially related with their inabilityto describe variations of the basin runoff response when the relevant information concerning the causes of such variations is not contained into the input to the filter. The presented empirical method of fitting solves this problem by taking into account additional input information and improving the knowledge about the hydrological system, but without changing the simple two-variable structure of the filter. This information consists of the percentage of snowfall into the monthly basin rainfall, the magnitude of basin rainfall at month t in comparison to the rainfall at month t -1 (increasing, constant, or decreasing) and the amount of basin rainfall associated with basin's moisture and runoff characteristics. The Kalman filter empirically fitted on monthly rainfall-runoff responses of the Aliakmon and Acheloos river basins in Greece is shown to be more accurate and adequate than the automatically fitted filter by using conventional statistical methods. The filter has been used in hydroelectric energy studies of the area, where accurate runoff estimation from rainfall was necessary.
AUTOMATIC CLASSIFICATION OF SELECTED CORINE CLASSES USING DEEP LEARNING BASED SEMANTIC SEGMENTATION

Abstract. In this study, deep learning-based semantic segmentation is used to automatically generate CORINE land cover (CLC) Level 2 classes for a test region in Türkiye. This is accomplished by utilizing new datasets and models created from a pilot region in Italy, which exhibits similar land use/land cover (LU/LC) characteristics to the test region in Canakkale/Türkiye. The training and validation datasets for Italy were generated by employing Sentinel-2 images from various months and different band combinations, along with CLC 2018 vector data for labelling. Different datasets were created to investigate the impact of patch sizes (128 and 256 pixels) and seasonal changes in LU/LC. For the semantic segmentation task, the U-Net architecture was selected as the primary deep learning model. Furthermore, the U-Net architecture was used in conjunction with ResNet50 and ResNet101 for transfer learning, enabling the replacement of the encoder section of the U-Net. These models were tested in the Italy region, and the best-performing ones were subsequently applied to the Canakkale test region to automatically generate CLC 2018. The results were compared with published CLC 2018 Level 2 data for the same region, and the accuracy was assessed using the Intersection over Union (IoU) metric. The findings were presented both visually and statistically.
Matrix Poincaré, Φ-Sobolev inequalities, and quantum ensembles

Sobolev-type inequalities have been extensively studied in the frameworks of real-valued functions and non-commutative Lp spaces, and have proven useful in bounding the time evolution of classical/quantum Markov processes, among many other applications. In this paper, we consider yet another fundamental setting—matrix-valued functions—and prove new Sobolev-type inequalities for them. Our technical contributions are two-fold: (i) we establish a series of matrix Poincare inequalities for separably convex functions and general functions with Gaussian unitary ensembles inputs; and (ii) we derive Φ-Sobolev inequalities for matrix-valued functions defined on Boolean hypercubes and for those with Gaussian distributions. Our results recover the corresponding classical inequalities (i.e., real-valued functions) when the matrix has one dimension. Finally, as an application of our technical outcomes, we derive the upper bounds for a fundamental entropic quantity—the Holevo quantity—in quantum information science since classical-quantum channels are a special instance of matrix-valued functions. This is obtained through the equivalence between the constants in the strong data processing inequality and the Φ-Sobolev inequality.Sobolev-type inequalities have been extensively studied in the frameworks of real-valued functions and non-commutative Lp spaces, and have proven useful in bounding the time evolution of classical/quantum Markov processes, among many other applications. In this paper, we consider yet another fundamental setting—matrix-valued functions—and prove new Sobolev-type inequalities for them. Our technical contributions are two-fold: (i) we establish a series of matrix Poincare inequalities for separably convex functions and general functions with Gaussian unitary ensembles inputs; and (ii) we derive Φ-Sobolev inequalities for matrix-valued functions defined on Boolean hypercubes and for those with Gaussian distributions. Our results recover the corresponding classical inequalities (i.e., real-valued functions) when the matrix has one dimension. Finally, as an application of our technical outcomes, we derive the upper bounds for a fundamental entropic quantity—the Holevo quantity—in quantum information science sin.
A lightweight authentication protocol for Internet of Things

The Internet of Things (IoT) refers to uniquely identifiable objects (things) which can interact with other objects through the global infrastructure of wireless/wired Internet. The communication technique among a large number of resource-constrained devices that generate large volumes of data has an impact on the security and privacy of the involved objects. In this paper, we propose an encryption method based on XOR manipulation, instead of complex encryption such as using the hash function, for anti-counterfeiting and privacy protection. The enhancement of the security is described and hardware design methodology is also demonstrated.
Revisit the Use of Index Returns as the Proxy of Market Return in CAPM

This study compares the performance of the capital asset pricing model (CAPM), which uses a mean-variance optimal portfolio, and the Jakarta Composite Index (JCI) as the market return proxy. The data samples are monthly stock returns of Kompas 100, LQ45, and IDX30 index from September 2012-August 2022. The expected returns of assets are calculated using the CAPM with rolling regression methodology based on each index's 2-5 years period. The mean-squared errors for each sample group are calculated to determine the CAPM performance. Although the market indexes have a sub-optimal risk and return profile according to their position on the efficient frontier diagram, this study finds that the JCI as the market proxy results in better CAPM performance than the optimal portfolio. However, the beta results from using JCI as the market proxy are also consistently higher than those using an optimal portfolio, leading to the overestimation bias of the asset risk. Since most studies in CAPM testing use the market index as the market return proxy, particularly JCI in Indonesia, this study provides new insight into the challenges of using the optimal portfolio as the market return proxy.
Modulation of blood redox status by the progression of induced apical periodontitis in rats

This study aimed to investigate if apical periodontitis in different periods changes systemic levels of the antioxidant and pro-oxidant parameters in Wistar rats. Twenty-four rats were randomly allocated into healthy animals, apical periodontitis at 14 days (AP14) and apical periodontitis at 28 days (AP28). The first mandibular molars were accessed in the AP groups, and the pulp chamber was exposed to the oral environment, inducing the apical lesion. After 14 and 28 days, the animals were anesthetized, euthanized, and hemimandibles were collected for micro-computed tomography (micro-CT) analysis to measure lesion volume, bone volume (BV), percent of bone to total tissue volume (BV/TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), and trabecular space (Tb.Sp). A histological examination of the remaining bone was also performed. Finally, blood samples were collected for oxidative biochemistry analysis, investigating glutathione (GSH), Trolox equivalent antioxidant capacity (TEAC), and lipid peroxidation (TBARS). The lesion volume was greater at 28 than at 14 days, as shown by micro-CT. AP14 and AP28 had decreased BV and Tb.Th, but only AP28 showed a reduction in BV/TV. Tb.N and Tb. Sp were increased in apical periodontitis at 28 days. In the histopathological analysis, AP14 had focal regions of moderate mononuclear inflammatory infiltrate, and AP28 had an intense inflammatory infiltrate with bacterial colonies. In the biochemical evaluation, GSH, TEAC, and TBARS were increased after 14 days. However, GSH returned to control levels, TEAC was similar to AP14, and TBARS increased significantly after 28 days. Therefore, the oxidative biochemistry response was modulated according to the progression of periapical damage. After 14 days, the organism could still react to the injury. However, at 28 days, the antioxidant response decreased, associated with an increase in TBARS.
Instrumentation in Remote and Dangerous Settings; Examples Using Data from GPS Spider Deployments During the 2004-2005 Eruption of Mount St. Helens, Washington

Self-contained, single-frequency GPS instruments fitted on lightweight stations suitable for helicopter-sling payloads became a critical part of volcano monitoring during the September 2004 unrest and subsequent eruption of Mount St. Helens. Known as "spiders" because of their spindly frames, the stations were slung into the crater 29 times from September 2004 to December 2005 when conditions at the volcano were too dangerous for crews to install conventional equipment. Data were transmitted in near-real time to the Cascades Volcano Observatory in Vancouver, Washington. Each fully equipped unit cost about $2,500 in materials and, if not destroyed by natural events, was retrieved and redeployed as needed. The GPS spiders have been used to track the growth and decay of extruding dacite lava (meters per day), thickening and accelerated flow of Crater Glacier (meters per month), and movement of the 1980-86 dome from pressure and relaxation of the newly extruding lava dome (centimeters per day).
Phenotypic assay for cytotoxicity assessment of Balamuthia mandrillaris against human neurospheroids

Introduction The phenotypic screening of drugs against Balamuthia mandrillaris, a neuropathogenic amoeba, involves two simultaneous phases: an initial step to test amoebicidal activity followed by an assay for cytotoxicity to host cells. The emergence of three-dimensional (3D) cell cultures has provided a more physiologically relevant model than traditional 2D cell culture for studying the pathogenicity of B. mandrillaris. However, the measurement of ATP, a critical indicator of cell viability, is complicated by the overgrowth of B. mandrillaris in coculture with host cells during drug screening, making it challenging to differentiate between amoebicidal activity and drug toxicity to human cells. Methods To address this limitation, we introduce a novel assay that utilizes three-dimensional hanging spheroid plates (3DHSPs) to evaluate both activities simultaneously on a single platform. Results and discussion Our study showed that the incubation of neurospheroids with clinically isolated B. mandrillaris trophozoites resulted in a loss of neurospheroid integrity, while the ATP levels in the neurospheroids decreased over time, indicating decreased host cell viability. Conversely, ATP levels in isolated trophozoites increased, indicating active parasite metabolism. Our findings suggest that the 3DHSP-based assay can serve as an endpoint for the phenotypic screening of drugs against B. mandrillaris, providing a more efficient and accurate approach for evaluating both parasite cytotoxicity and viability.