Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model for Association in Bivariate Survival Data

SUMMARY A reparameterization of a model introduced by D. G. Clayton for association in bivariate life-tables is discussed. Inference for the parameter governing the association is con- sidered when the marginal distributions are specified up to Lehmann alternatives. The information matrix is derived explicitly and it is shown that the parameterization is moderately successful in introducing orthogonality between the association parameter and the two scale parameters. The likelihood proposed by Clayton for the case that the marginal distributions are completely unknown is criticized. An alternative non- parametric estimator based on Kendall's coefficient of concordance is proposed and its asymptotic variance evaluated. IF G(s) = Pr(S>s) and H(t) = Pr(T>t) are continuous univariate survivor functions with G(O) = H(O) = 1 and 0 > 1, then the function F(s, t) = Pr (S > s, T > t) given by F(s, t) = {G )} +{H()} -1) (1) is a bivariate survivor function with G and H as marginals. This paper considers inference about the parameter 0 governing the association between S and T, given a sample of size n from their bivariate distribution. We suppose first that the marginals G(s) and H(t) are known up to Lehmann alternatives, and secondly that they are completely unknown. The model (1) is a reparameterization of a model introduced by Clayton (1978) for association in bivariate life-tables. Section 2 describes some properties of the family, including the limits as 0 -+1 and 0 > oo, a simple interpretation of 0 in terms of hazard functions and a random-effects derivation of the model. This section leans heavily on Clayton's work. Section 3 discusses parametric inference for 0, including tests of the hypothesis 0 = 1 of independence between S and T. We assume that the marginal survivor functions G(s) and H(t) are specified up to Lehmann alternatives. Asymptotic variance formulae are derived in Section 4, which also explores the extent to which the parameterization achieves orthogonality. Clayton (1978) proposed an ingenious method for estimating 0 in the absence of knowledge of the marginal distributions. Unfortunately there are difficulties with his proposed con- ditional likelihood function; in particular the estimated variance from the second derivative appears to be incorrect. Clayton's derivation is described and criticized in our Section 5. In principle, rank invariance properties of (1) when G(s) and H(t) are completely unknown yield a marginal likelihood for 0. Although this likelihood appears to be intractable in general, in Section 6 it is evaluated when n = 3 and shown to differ from Clayton's likelihood. A simpler non-parametric estimator of 0, based on the coefficient of concordance, is proposed in Section
A Finite-Volume Method for Predicting a Radiant Heat Transfer in Enclosures With Participating Media

A new finite-volume method is proposed to predict radiant heat transfer in enclosures with participating media. The method can conceptually be applied with the same nonorthogonal computational grids used to compute fluid flow and convective heat transfer. A fairly general version of the method is derived, and details are illustrated by applying it to several simple benchmark problems. Test results indicate that good accuracy is obtained on coarse computational grids, and that solution errors diminish rapidly as the grid is refined.
Reactive Case Detection Strategy for Malaria Control and Elimination: A 12 Year Systematic Review and Meta-Analysis from 25 Malaria-Endemic Countries

Reactive case detection (RACD) is the screening of household members and neighbors of index cases reported in passive surveillance. This strategy seeks asymptomatic infections and provides treatment to break transmission without testing or treating the entire population. This review discusses and highlights RACD as a recommended strategy for the detection and elimination of asymptomatic malaria as it pertains in different countries. Relevant studies published between January 2010 and September 2022 were identified mainly through PubMed and Google Scholar. Search terms included “malaria and reactive case detection”, “contact tracing”, “focal screening”, “case investigation”, “focal screen and treat”. MedCalc Software was used for data analysis, and the findings from the pooled studies were analyzed using a fixed-effect model. Summary outcomes were then presented using forest plots and tables. Fifty-four (54) studies were systematically reviewed. Of these studies, 7 met the eligibility criteria based on risk of malaria infection in individuals living with an index case < 5 years old, 13 met the eligibility criteria based on risk of malaria infection in an index case household member compared with a neighbor of an index case, and 29 met the eligibility criteria based on risk of malaria infection in individuals living with index cases, and were included in the meta-analysis. Individuals living in index case households with an average risk of 2.576 (2.540–2.612) were more at risk of malaria infection and showed pooled results of high variation heterogeneity chi-square = 235.600, (p < 0.0001) I2 = 98.88 [97.87–99.89]. The pooled results showed that neighbors of index cases were 0.352 [0.301–0.412] times more likely to have a malaria infection relative to index case household members, and this result was statistically significant (p < 0.001). The identification and treatment of infectious reservoirs is critical to successful malaria elimination. Evidence to support the clustering of infections in neighborhoods, which necessitates the inclusion of neighboring households as part of the RACD strategy, was presented in this review.
Genome-wide identification and expression analysis of diacylglycerol acyltransferase genes in soybean (Glycine max)

Background Soybean (Glycine max) is a major protein and vegetable oil source. In plants, diacylglycerol acyltransferase (DGAT) can exert strong flux control, which is rate-limiting for triacylglycerol biosynthesis in seed oil formation. Methods Here, we identified soybean DGAT genes via a bioinformatics method, thereby laying a solid foundation for further research on their function. Based on our bioinformatics analyses, including gene structure, protein domain characteristics, and phylogenetic analysis, 26 DGAT putative gene family members unevenly distributed on 12 of the 20 soybean chromosomes were identified and divided into the following four groups: DGAT1, DGAT2, WS/DGAT, and cytoplasmic DGAT. Results The Ka/Ks ratio of most of these genes indicated a significant positive selection pressure. DGAT genes exhibited characteristic expression patterns in soybean tissues. The differences in the structure and expression of soybean DGAT genes revealed the diversity of their functions and the complexity of soybean fatty acid metabolism. Our findings provide important information for research on the fatty acid metabolism pathway in soybean. Furthermore, our results will help identify candidate genes for potential fatty acid-profile modifications to improve soybean seed oil content. Conclusions This is the first time that in silico studies have been used to report the genomic and proteomic characteristics of DGAT in soybean and the effect of its specific expression on organs, age, and stages.
WI-SPI: Utilizing CC2500 RF Transceiver to Implement a Low-Cost Full Duplex Wireless Communication Protocol for Ultra Low Power Embedded Applications

The paradigm shift from cable-linked electronics and control systems to wireless devices, opened the pandora box to a plethora of wireless applications between embedded micro controllers for computing and automation systems, robotics, unmanned area vehicles, wireless sensor networks, and wireless consumer electronics. However, implementing a wireless embedded application comes with its own challenges for do-it-yourself hobbyists and embedded system developers as one among many is which RF module is most suitable to implement a wireless application. This paper aims to introduce and utilize the CC2500 RF transceiver to implement a short-range full duplex wireless SPI communication between two micro controllers.
MR Brain 2D image Tumor and Cyst Classification Approach: an Empirical Analogy

Brain tumors and cysts might prove to be fatal if detection is delayed. Early detection of brain tumors and cysts ensures improved survival rates and thus has become a cause of concern. Multiple models using various algorithms have been used to make the detection accurate at an early stage. A cyst might be an alarming factor and might be the early stage of a brain tumor. This paper focuses on comparing the different methods used to detect brain tumors. The proposed model is trained and tested using six different algorithms. The Kaggle dataset has been used for both training and testing. Two classes from the dataset are used. One of the classes consists of Pituitary Tumors, while the other consists of No tumors. A total of 1222 images have been used for training the proposed model. Images have been resized into 200×200 dimensions for better results. Most of the tumors detected while testing is found to be pituitary tumors. We have used the ensembler classifier having six classes, Support Vector Machine (SVM), Logistics Regression, Random Forest, K Nearest Neighbors (KNN), Decision Tree, and Naive Bayes. Among all the algorithms, Logistic Regression shows the highest accuracy of 99.6%, followed by Random Forests and SVM. Thus, the paper helps us to determine a better algorithm among multiple algorithms for detecting brain tumors and to specify whether it is a pituitary tumor or not.
An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications

Accurate environment perception can be achieved with multiple sensors with different measuring principles – such as camera, lidar and radar sensors. In automated driving applications, this diversity aims, for example, to reduce the susceptibility of sensors to inclement weather. Fusion combines data from sensors and creates a generic representation of the vehicle's surroundings – which is known as environment model. Based on this, a safe trajectory can be planned and driving functions can actuate accordingly. In this paper, a high-level fusion algorithm is implemented. Its aim is to combine different object lists generated at the sensor-level into a unique global object list. This software is implemented in Python in a ROS workspace and is published as open-source code on GitHub1. It was verified that the algorithm can successfully fuse data acquired from simulation-based test drives with sensor models, and create a global object list based on the detections at the sensor-level.
Analysis of Failure Data with Missing Labels

This paper presents a new technique for the analysis of failure data when some of the labels are missing. When multiple systems are in operation, the label associated with a failure are usually given to indicate the system type or the specific system the failure belongs to. Data records in practice often suffer from missing labels. Missing labels can be partially known or completely unknown. A statistical inference procedure based on the expectation maximization algorithm is proposed to address this problem. Give the observed data, the proposed technique derives explicitly the distribution of the missing labels. The advantage of this technique is that it is a general inference procedure and is flexible to account for different parameter settings and failure rate functions. The method is applied to real case data on lift failures. It shows that the method can well handle parameter estimation in the face of missing labels.
Direct particle acceleration by two identical crossed radially polarized laser beams

Electrons and {alpha} particles injected midway between two ultrahigh intensity crossed laser beams of radial polarization are shown to be accelerated in vacuum to several gigaelectron volts and to have average energy gradients in excess of 150 GeV/m. A unique model of the crossing beams is suggested, which maximizes the particle energy gain and minimizes the particle-beam diffraction.
Joint Optimization of Caching Placement and Power Allocation in Virtualized Satellite-Terrestrial Network

With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.
A CPW-Fed Antenna Array with MIMO and Phased Array Operations for Sub-6-GHz 5G Smartphones

A co-planar waveguide (CPW)-fed MIMO antenna design with compact resonators and suitable features is introduced for smartphone applications. Two rows of antennas are mounted on two different sides of the mainboard to arrange an 8×8 MIMO configuration. The antenna elements of the introduced MIMO design have been etched onto the same layer as the ground plane. The antenna element contains a T-ring CPW-fed resonator operating at the frequency range of 3.2-4.2 GHz of sub 6 GHz 5G cellular communications. The designed array occupies a relatively small area of the board. Its critical properties are studied in detail. Due to the arrangement of the CPW-fed resonators, the proposed design is able to operate as two sets of beam-steerable arrays with high-gain radiation. It has been determined that the optimal S-parameters, patterns, efficiency, and gain are all achievable with the planned MIMO array design and meet the needs of future 5G handheld smartphones.
What can we learn about climate feedbacks from short-term climate variations?

ABSTRACT This study examines the radiative feedbacks resulting from changes in the climate over the last 30 yr. These ‘short-term’ feedbacks correspond to both external changes in the forcing [from greenhouse gases (GHGs), aerosols etc.] and internal climate variations [mostly due to El Niño Southern Oscillation (ENSO)]. They differ from the ‘long-term’ (century scale, mainly due to GHG warming) feedbacks both in magnitude (by 24% on the global average) and geographical distribution, according to the Coupled Model Intercomparison Project phase 3 multi-model dataset. In addition, the inter-model spread of the short-term feedbacks is larger than the long-term ones even for the models with the best ENSO performance, which indicates that important aspects of the ENSO variability are still poorly understood and/or simulated. Information from observations and from advanced reanalysis systems can be very useful to improve the model short-term climate responses. However, long and accurate observational records are critical in order to obtain confident results.
Path Planning of Cellular-Connected UAV Travelling Fixed Way Points Using Reinforcement Learning

With the development of Unmanned Aerial Vehicles (UAV) and cellular networks, path planning for cellular-connected UAV has become a very popular research area. Previous researches on path planning for cellular-connected UAV focused on point-to-point path planning which can be solved by estimate approximate Shortest Path Problem (SPP). However, cellular-connected UAV has extra constraints that it must keep a good connection with Ground Base Stations (GBS) at all times to make the UAV stay in control, thus make the problem more challenging. In this paper, we studied path planning for cellular-connected UAV in a specific scenario where UAV have to visit multiple points while keeping connected with GBSs and get back to where it starts finally. In other words, we want to study multi-points path planning which can be abstracted as Travel Salesman Problem (TSP). In order to solve this kind of problem, a reinforcement learning based model has been proposed and developed and the experiment results of our model have proved that it can solve the UAV path planning problem with a good performance.
Light, Water, and Melatonin: The Synergistic Regulation of Phase Separation in Dementia

The swift rise in acceptance of molecular principles defining phase separation by a broad array of scientific disciplines is shadowed by increasing discoveries linking phase separation to pathological aggregations associated with numerous neurodegenerative disorders, including Alzheimer’s disease, that contribute to dementia. Phase separation is powered by multivalent macromolecular interactions. Importantly, the release of water molecules from protein hydration shells into bulk creates entropic gains that promote phase separation and the subsequent generation of insoluble cytotoxic aggregates that drive healthy brain cells into diseased states. Higher viscosity in interfacial waters and limited hydration in interiors of biomolecular condensates facilitate phase separation. Light, water, and melatonin constitute an ancient synergy that ensures adequate protein hydration to prevent aberrant phase separation. The 670 nm visible red wavelength found in sunlight and employed in photobiomodulation reduces interfacial and mitochondrial matrix viscosity to enhance ATP production via increasing ATP synthase motor efficiency. Melatonin is a potent antioxidant that lowers viscosity to increase ATP by scavenging excess reactive oxygen species and free radicals. Reduced viscosity by light and melatonin elevates the availability of free water molecules that allow melatonin to adopt favorable conformations that enhance intrinsic features, including binding interactions with adenosine that reinforces the adenosine moiety effect of ATP responsible for preventing water removal that causes hydrophobic collapse and aggregation in phase separation. Precise recalibration of interspecies melatonin dosages that account for differences in metabolic rates and bioavailability will ensure the efficacious reinstatement of the once-powerful ancient synergy between light, water, and melatonin in a modern world.
Joint Optimal Data Collection Scheduling and Time Allocation in Full-Duplex WP-IoT Networks

This article considers a full-duplex wireless powered Internet of Things (WP-IoT) network, supporting simultaneous wireless power transfer of a hybrid access point in the downlink and collected data transfer of devices in the uplink. Compared to existing works where the data collection process is mostly ignored, the problem of joint data collection scheduling and time allocation is investigated under two objectives, i.e., total throughput maximization subject to a total time constraint and total time minimization subject to minimum collected data constraints. For both objectives, the optimal solution is derived in closed-form. It is revealed that the proposed algorithms outperform various baselinealgorithms in existing literature.
Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques

Acid–base disorders occur when the body’s normal pH is out of balance. They can be caused by problems with kidney or respiratory function or by an excess of acids or bases that the body cannot properly eliminate. Acid–base and potassium imbalances are mechanistically linked because acid–base imbalances can alter the transport of potassium. Both acid–base and potassium imbalances are common in critically ill patients. This study investigated machine learning models for predicting the occurrence of acid–base and potassium imbalances in intensive care patients. We used an institutional dataset of 1089 patients with 87 variables, including vital signs, general appearance, and laboratory results. Gradient boosting (GB) was able to predict nine clinical conditions related to acid–base and potassium imbalances: mortality (AUROC = 0.9822), hypocapnia (AUROC = 0.7524), hypercapnia (AUROC = 0.8228), hypokalemia (AUROC = 0.9191), hyperkalemia (AUROC = 0.9565), respiratory acidosis (AUROC = 0.8125), respiratory alkalosis (AUROC = 0.7685), metabolic acidosis (AUROC = 0.8682), and metabolic alkalosis (AUROC = 0.8284). Some predictions remained relatively robust even when the prediction window was increased. Additionally, the decision-making process was made more interpretable and transparent through the use of SHAP analysis. Overall, the results suggest that machine learning could be a useful tool to gain insight into the condition of intensive care patients and assist in the management of acid–base and potassium imbalances.
ADMM-Based Throughput Maximization Precoding Design for Multibeam Satellite Systems

In this paper, a fast linear precoding algorithm is proposed to improve the total throughput of the multibeam satellite communication systems. The precoding design problem is constructed into the throughput maximization problem. By analyzing the relationship between the satellite beam throughput and the mean square error (MSE) of the received signal, we convert the sum-throughput maximization problem into a weighted mean square error minimization problem. Furthermore, the alternating direction method of multipliers (ADMM) is used to decompose the problem into multiple parallel sub-problems to handle and closed-form solutions can be obtained in parallel. The simulation results show that the proposed algorithm can realize the comparable throughput performance with the optimal SR algorithm based on semidefinite relaxation while reducing the complexity of the algorithm significantly.
Stability Analysis of the Synchronous Solution in Arrays of Memristive Chua’s Circuits

This brief studies synchronization in an array of diffusively-coupled identical memristor Chua’s circuits (MCCs) with Dirichlet’s boundary conditions. We start from the description of MCCs in the flux-charge domain and the state space decomposition in invariant manifolds and then we apply three different techniques, of numerical and analytic nature, for finding the synchronization condition in terms of a circuit parameter. Two of these techniques provide conditions to guarantee synchronization on any manifold and for any attractor on a manifold, while a third technique allows analyzing synchronization for each specific attractor on a fixed manifold through more precise conditions. The pros and cons of the three techniques are discussed and an application to a more realistic memristor model is provided.
NaCl stress mediated lipid and carotenoid production in freshwater microalga Kirchneriella obesa by optimization of medium composition using response surface methodology

Abstract Optimal nitrate, phosphate and salinity for maximal biomass, lipid and carotenoid content of the freshwater microalga Kirchneriella obesa were investigated by using Box-Behnken Design. In an optimised condition, these three factors enhanced biomass production by 0.64 g/L more than the control (0.25 g/L). At 2.5 g/L nitrate, 0.04 g/L phosphate, and 25 mM NaCl, the maximum lipid accumulation (46.02%) and carotenoid content (2.58 mg/L) were achieved. The fit of the model yielded R2 values of up to 0.9955 for biomass, 0.9984 for lipids and 0.9999 for carotenoid, respectively. For the highest carbohydrate (418.28 mg/L) content, the medium was supplemented with 1.5 g/L nitrate, 0.04 g/L phosphate, and 40 mM NaCl. While the highest protein content (320.53 mg/L) was attributed to the medium with 3 g/L nitrate, 0.02 g/L phosphate and 25 mM NaCl. The major fatty acid compositions found in this algae were C18:1trans (16.36 ± 1.4), C18:1cis (6.001 ± 0.44), C18:2 (8.32 ± 1.1) and C18:3 (34.39 ± 2.3%), demonstrating their importance in biofuel production and human health.