Miracle-dz/UniFAR-FTUs huggingface dataset:
The 30 May 1998 Spencer, South Dakota, Storm. Part I: The Structural Evolution and Environment of the Tornadoes

Abstract On the evening of 30 May 1998 atmospheric conditions across southeastern South Dakota led to the development of organized moist convection including several supercells. One such supercell was tracked by both a Weather Surveillance Radar-1988 Doppler (WSR-88D) from Sioux Falls, South Dakota (KFSD), and by a Doppler On Wheels (DOW) mobile radar. This supercell remained isolated for an hour and a half before being overtaken by a developing squall line. During this time period the supercell produced at least one strong and one violent tornado, the latter of which passed through Spencer, South Dakota, despite the absence of strong low-level environmental wind shear. The two tornadoes were observed both visually and with the DOW radar at ranges between 1.7 and 12.9 km. The close proximity to the tornadoes permitted the DOW radar to observe tornado-scale structures on the order of 35 to 100 m, while the nearest WSR-88D only resolved the parent mesocyclone in the supercell. The DOW observations revealed .
Evaluation of Composites Reinforced by Processed and Unprocessed Coconut Husk Powder

Engineering activities aim to satisfy the demands of society. Not only should the economic and technological aspects be considered, but also the socio-environmental impact. In this sense, the development of composites with the incorporation of waste has been highlighted, aiming not only for better and/or cheaper materials, but also optimizing the use of natural resources. To obtain better results using industrial agro waste, we need to treat this waste to incorporate engineered composites and obtain the optimal results for each application desired. The objective of this work is to compare the effect of processing coconut husk particulates on the mechanical and thermal behavior of epoxy matrix composites, since we will need a smooth composite in the near future to be applied by brushes and sprayers with a high quality surface finish. This processing was carried out in a ball mill for 24 h. The matrix was a Bisphenol A diglycidyl ether (DGEBA)/triethylenetetramine (TETA) epoxy system. The tests that were performed were resistance to impact and compression, as well as the linear expansion test. Through this work, it can be observed that the processing of coconut husk powder was beneficial, allowing not only positive improvements to the properties of the composite, but also a better workability and wettability of the particulates, which was attributed to the change in the average size and shape of particulates. That means that the composites with processed coconut husk powders have improved impact strength (46 up to 51%) and compressive strength (88 up to 334%), in comparison with unprocessed particles.
An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks

Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms.
Evaluation of the Drug Utilization Pattern among Pregnant Women in a Tertiary Care Teaching Hospital

A retrospective study was conducted in a tertiary care teaching hospital to evaluate the drug utilization pattern in pregnant women. A total of 876 pregnant women were enrolled in the study. Parameters such as demography, gravidity, comorbid illness, neonatal outcome, drugs prescribed and their utilization details were collected. The mean age of the study population was found to be 29.4±4.4 y (mean±standard deviation). Majority of them underwent caesarean section (68.2 %) compared to normal vaginal delivery (31.8 %). Most of the neonates were healthy (94.3 %) whereas 0.8 % of the neonates expired during the postnatal period. Diabetes mellitus (33.6 %) was the most common comorbidity, followed by hypertension (28.3 %) and thyroid disorders (18.3 %). Folic acid (15.7 %) and calcium (15.2 %) were the most prescribed drugs, followed by ferrous fumarate (7.1 %), vitamin B12 (5.3 %) and vitamin B6 (3.4 %). In terms of defined daily dose/100 bed d, levothyroxine (369.3) was the most frequently utilized drug, followed by digoxin (53.6), dydrogesterone (27.4) and salbutamol (20.5). Estradiol was only the food and drug administration pregnancy category X drug prescribed, as estradiol supplement as this was required to improve pregnancy rates after in vitro fertilization.
Some properties of Stepanov-like almost automorphic functions and applications to abstract evolution equations

This article is concerned with some properties of Stepanov-like almost automorphic (S p -a.a.) functions. We establish a composition theorem about S p -a.a. functions, and with its help, study the existence and uniqueness of almost automorphic solutions for semilinear evolution equations in Banach spaces. Moreover, integration and differentiation of S p -a.a. functions are discussed. Some theorems extend earlier results.
Ergodic and mixing sequences of transformations

Abstract The notions of ergodicity, strong mixing and weak mixing are defined and studied for arbitrary sequences of measure-preserving transformations of a probability space. Several results, notably ones connected with mean ergodic theorems, are generalized from the case of the sequence of all powers of a single transformation to this case. The conditions for ergodicity, strong mixing and weak mixing of sequences of affine transformations of compact groups are investigated.
The Congressional Leadership Dilemma

Previous theories assume that congressional party leaders internalize the welfare of the parties they lead. Accordingly, existing work deemphasizes the role of agency problems in explaining the conditions under which parties grant more political resources to their leaders. To show how agency problems can still arise even when the party leader wants only to maximize collective goods and stay in office, I offer a model that borrows two ideas from models of political accountability: leaders vary in quality and giving the leader more resources makes it more difficult to remove her. The model implies that the party faces a tradeoff between maximizing the leader’s capacity to produce collective goods and preserving its ability to remove low-quality leaders from office. This theory offers novel predictions, integrates existing results as implications of a single theory, and explains why the leader’s resources sometimes change even as the political context remains the same (JEL D72: Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior).
Changes in multimorbidity burden over a 3–5 year period among people with HIV

Introduction: As people living with HIV age, the increasing burden of multimorbidity poses a significant health challenge. The aims of this study were to identify common patterns of multimorbidity and examine changes in their burden, as well as their associations with risk factors, over a 3–5 year period in people with HIV, enrolled in the Pharmacokinetic and clinical Observations in PeoPle over fiftY (POPPY) study. Methods: Common multimorbidity patterns were identified in POPPY participants with HIV using principal component analysis, based on Somers’ D statistic. Multimorbidity burden scores were calculated for each participant/pattern at study entry/follow-up and were standardised relative to the mean in the sample at baseline (scores >0 thus reflect a greater number of comorbidities relative to the mean). Two multivariable linear regression models were fitted to examine the associations between risk factors and burden z-scores at baseline and change in z-scores over a 3–5 year period. Results: Five patterns were identified among the 1073 POPPY participants with HIV {median age [interquartile range (IQR)], 52 (47–59) years; 85% male and 84% white}: Cardiovascular diseases (CVDs), Sexually transmitted diseases (STDs), Neurometabolic, Cancer and Mental-gastro-joint. The multivariable linear regression showed that older age, behavioural factors (i.e., body mass index (BMI), history of injection drug use, current recreational drug use and sex between men), and HIV-specific factors (i.e., duration since HIV diagnosis and a prior AIDS diagnosis) were associated with higher multimorbidity burden at baseline. However, only three of the factors (age, BMI and duration since HIV diagnosis) were significantly associated with an increase in burden across specific patterns over time. Discussion: Key modifiable and non-modifiable factors contributing to an increase in burden of multimorbidity were identified. Our findings may inform the development of more targeted interventions and guidelines to effectively prevent and manage the rising burden of multimorbidity in people with HIV.
TRANSITION AND CHAOS IN TWO-DIMENSIONAL FLOW PAST A SQUARE CYLINDER

The unsteady wake of a long square cylinder has been numerically analyzed in the present study. Velocity signals at selected locations in the near-wake and the instantaneous forces on the cylinder have been recorded from the numerical model at various Reynolds numbers. These form the basis of investigating the dynamic behavior of the flow system. Results of the present work show the following. Flow past a square cylinder undergoes a sequence of transitions from a steady pattern up to a Reynolds number of 40 to a chaotic one around a Reynolds number of 600. The transition to chaos is manifested through a quasi-periodic route that includes the frequency-locking phenomenon. The quasi-periodicity is seen to set in with two or more Hopf bifurcations. The transition to chaos in the wake of a bluff object is related to the three-dimensionality of the flow. In a 2D simulation, this appears in the form of new harmonics in the velocity traces. The quasi-periodic route to chaos has been established through different characterization tools, such as the spectra, autocorrelation function, time-delay reconstruction, and the Poincare section. Chaotic behavior is quantified through the calculation of Lyapunov exponent and fractal dimension.
Heavy Atom-Free Triplet Photosensitizers: Molecular Structure Design, Photophysical Properties and Application in Photodynamic Therapy

Photodynamic therapy (PDT) is a promising method for the treatment of cancer, because of its advantages including a low toxicity, non-drug-resistant character, and targeting capability. From a photochemical aspect, a critical property of triplet photosensitizers (PSs) used for PDT reagents is the intersystem crossing (ISC) efficiency. Conventional PDT reagents are limited to porphyrin compounds. However, these compounds are difficult to prepare, purify, and derivatize. Thus, new molecular structure paradigms are desired to develop novel, efficient, and versatile PDT reagents, especially those contain no heavy atoms, such as Pt or I, etc. Unfortunately, the ISC ability of heavy atom-free organic compounds is usually elusive, and it is difficult to predict the ISC capability of these compounds and design novel heavy atom-free PDT reagents. Herein, from a photophysical perspective, we summarize the recent developments of heavy atom-free triplet PSs, including methods based on radical-enhanced ISC (REISC, facilitated by electron spin–spin interaction), twisted π-conjugation system-induced ISC, the use of fullerene C60 as an electron spin converter in antenna-C60 dyads, energetically matched S1/Tn states-enhanced ISC, etc. The application of these compounds in PDT is also briefly introduced. Most of the presented examples are the works of our research group.
Normative Basis for Climate-Related Civic Engagement by Residents of Lake Superior’s North Shore region

Abstract This study examines the determinants of resident engagement in climate change using a survey of a sample of residents in Lake Superior’s North Shore region in Minnesota. We examine the influence of climate concern, responsibility, subjective norms and personal norms on civic engagement. Study findings indicate that climate concern, ascription of local responsibility, and subjective norms influence residents’ personal norms, or sense of personal obligation, which in turn positively predicts civic engagement. Study findings highlight the importance of norms as a motivator of climate-related engagement. Results point to the potential for engaging community members in climate-related activities by tapping feelings of personal obligation for climate change adaptation or mitigation.
Molecular dynamics simulations of the equilibrium dynamics of non-ideal plasmas

Molecular dynamics (MD) simulations are used to compute the equilibrium dynamics of a single component fluid with Yukawa interaction potential v(r) = (Ze)^2 exp(−r/λs )/4π eps_0 r. This system, which is known as the Yukawa one-component plasma (YOCP), represents a simplified description of a non-ideal plasma consisting of ions, charge Ze, and electrons. For finite screening lengths λs, the MD results are used to investigate the domain of validity of the hydrodynamic description, i.e., the description given by the Navier-Stokes equations. The way in which this domain depends on the thermodynamic conditions of the YOCP, as well as the strength and range of the interactions, is determined. Remarkably, it is found that the domain of validity is completely determined by the range of the interactions (i.e., λs); this alone determines the maximum wave number k_max at which the hydrodynamic description is applicable. The dynamics of the YOCP at wavevectors beyond k_max are then investigated; these are shown to be in striking agreement with a simple and well known generalisation of the Navier-Stokes equations. In the extreme case of the Coulomb interaction potential (λs = ∞), the very existence of a hydrodynamic description is a known but unsolved problem [Baus & Hansen, 1980]. For this important special case, known as the one-component plasma (OCP), it is shown that the ordinary hydrodynamic description is never valid. Since the OCP is the prototypical system representing a non-ideal plasma, a number of different approaches for modelling its dynamics have been formulated previously. By computing the relevant quantities with MD, the applicability of a number of models proposed in the literature is examined for the first time.
Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.
CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification

Pathology images contain rich information of cell appearance, microenvironment, and topology features for cancer analysis and diagnosis. Among such features, topology becomes increasingly important in analysis for cancer immunotherapy. By analyzing geometric and hierarchically structured cell distribution topology, oncologists can identify densely-packed and cancer-relevant cell communities (CCs) for making decisions. Compared to commonly-used pixel-level Convolution Neural Network (CNN) features and cell-instance-level Graph Neural Network (GNN) features, CC topology features are at a higher level of granularity and geometry. However, topological features have not been well exploited by recent deep learning (DL) methods for pathology image classification due to lack of effective topological descriptors for cell distribution and gathering patterns. In this paper, inspired by clinical practice, we analyze and classify pathology images by comprehensively learning cell appearance, microenvironment, and topology in a fine-to-coarse manner. To describe and exploit topology, we design Cell Community Forest (CCF), a novel graph that represents the hierarchical formulation process of big-sparse CCs from small-dense CCs. Using CCF as a new geometric topological descriptor of tumor cells in pathology images, we propose CCF-GNN, a GNN model that successively aggregates heterogeneous features (e.g., appearance, microenvironment) from cell-instance-level, cell-community-level, into image-level for pathology image classification. Extensive cross-validation experiments show that our method significantly outperforms alternative methods on H&E-stained and immunofluorescence images for disease grading tasks with multiple cancer types. Our proposed CCF-GNN establishes a new topological data analysis (TDA) based method, which facilitates integrating multi-level heterogeneous features of point clouds (e.g., for cells) into a unified DL framework.
The mean Green's dyadic for a half-space random medium: A nonlinear approximation

The vector problem of a source embedded in a halfspace random medium is considered, and a zeroth-order solution for the mean Green's dyadic in the nonlinear approximation is derived. This is done by applying a two-variable expansion method to obtain a perturbation solution of the Dyson equation for the mean Green's dyadic. The final results of the dyadic are given in closed form as a corrected effective propagation constant, including terms of the order k_{a}^{2}\sigma^{2}l^{2} where k_{a} is the wavenumber in the average medium, l is the correlation length, and \sigma^{2} is the variance of the permittivity fluctuations. These results show a significant difference from those of the one-dimensional problem considered by Tsang and Kong [4]. Whereas the scalar solution gives different effective propagation constants for the component waves in the Green's function, the vector solution derived contains only a single propagation constant for all of the components in the Green's dyadic.
Interactions pathways for information exchange among avocado value chain stakeholders

Multi-stakeholder platforms facilitate interaction between stakeholders in the agricultural innovation system. A multi-stakeholder platform is essential to enhance the innovation of avocado value chain stakeholders and avocado planting as a climate-smart practice. Avocados can make a significant contribution to food security and nutrition. Research has shown that research on climate-smart agriculture mainly focuses on scientific and technological solutions, rather than attempting to comprehend the economic and social dimensions of this study. The study's goal was to identify the interactions between stakeholders in the avocado value chain. The data for this study were gathered using a semi-structured questionnaire and focus group discussions. The study employs the concept of an innovation systems perspective and investigates social network analysis in the context of stakeholders. Information sharing, roles and functions of stakeholders were used as proxies to denote communication. In this study, the multi-stakeholder platform's role in fostering a supportive forum was interpreted as access to information, knowledge, and resources. Findings indicate that smallholder farmers, support service providers and market stakeholders need to be strengthened and empowered to adopt and implement inclusive innovations. This was essential to ensure that stakeholders throughout the value chain had access to tools and resources to promote avocado yield for food, industry, and climate-smart practices. The study concludes that there are distinctions in the roles and responsibilities of stakeholders in the avocado innovation system. Farmers have been discovered to innovate using relevant expertise, even though they are primarily considered users of innovation. The study's recommendation is that smallholder farmers be empowered through capacity building to influence agricultural value chain development and environmental sustainability through information technology.