Miracle-dz/UniFAR-FTUs huggingface dataset:
Intraseasonal Variability of Moisture and Rainfall over the South American Altiplano

Abstract Precipitation over the South American Altiplano (about 4000 m above sea level) is mostly concentrated during the austral summer (December–January–February) when mean easterly flow in the middle and upper troposphere favors the moisture transport from the interior of the continent toward the central Andes. Within the wet season, rainy days tend to cluster in rainy episodes of about a week long, interrupted by somewhat longer dry periods. Based on one-site, research-quality observations over the western Altiplano, it has been suggested that occurrence of deep, moist convection is largely controlled by the availability of water vapor in the local boundary layer. In this work the author evaluates the representativeness of the observations in the western Altiplano in a regional context and investigates if the hypotheses derived from these data are generally applicable to the rest of the plateau. The study is based on surface synoptic data and atmospheric reanalysis. The relationship between moisture f.
Traffic Prediction for Optical Fronthaul Network Using Self-Attention Mechanism-Based Transformer

The research on traffic patterns in advance is significant to improve the transmission performance of delay sensitive services by shortening the delay of queuing and computing in optical networks. However, it is challenging to accurately and rapidly predict changes of traffic in optical networks because of dynamicity and uncertainty of traffic patterns. This paper proposes a model for traffic analysis based on Transformer which can make sure of the accuracy of prediction as fast as possible. Based on the practical traffic data collected from optical fronthaul network, the effectiveness of the model is verified, the prediction error is 5.384%.
Application of Chitosan-Based Molecularly Imprinted Polymer in Development of Electrochemical Sensor for p-Aminophenol Determination

Molecularly Imprinted Polymers (MIPs) have specific recognition capabilities and have been widely used for electrochemical sensors with high selectivity. In this study, an electrochemical sensor was developed for the determination of p-aminophenol (p-AP) by modifying the screen-printed carbon electrode (SPCE) with chitosan-based MIP. The MIP was made from p-AP as a template, chitosan (CH) as a base polymer, and glutaraldehyde and sodium tripolyphosphate as the crosslinkers. MIP characterization was conducted based on membrane surface morphology, FT-IR spectrum, and electrochemical properties of the modified SPCE. The results showed that the MIP was able to selectively accumulate analytes on the electrode surface, in which MIP with glutaraldehyde as a crosslinker was able to increase the signal. Under optimum conditions, the anodic peak current from the sensor increased linearly in the range of 0.5–35 µM p-AP concentration, with sensitivity of (3.6 ± 0.1) µA/µM, detection limit (S/N = 3) of (2.1 ± 0.1) µM, and quantification limit of (7.5 ± 0.1) µM. In addition, the developed sensor exhibited high selectivity with an accuracy of (94.11 ± 0.01)%.
Text2EL: Exploiting Unstructured Text for Event Log Enrichment

Process mining provides a range of methods and techniques to analyse business processes through information stored in so-called event logs. The richer these event logs and the higher quality they are, the more insights we can obtain. Till now, information in the form of unstructured text, e.g. notes, comments, reviews, and posts, is not fully and systematically exploited for the purposes of log enrichment. In this paper, we introduce Text2EL, a two-phase event log enrichment approach based on unstructured text. In Phase 1, events, case attributes, and event attributes are extracted from unstructured text associated with organisational processes. In Phase 2, the extracted events and attributes are semantically and contextually validated before enriching the event log. Our approach applies techniques from natural language processing, sentence embeddings, and contextual and expression validation. We evaluated the completeness, concordance, and correctness of an enriched event log through experiments with a real-life healthcare data set. The experiments showed the feasibility and applicability of our approach.
Review of the Effects of Enclosure Complexity and Design on the Behaviour and Physiology of Zoo Animals

Simple Summary Habitat complexity is important for the maintenance of high levels of welfare for captive animals, especially at zoos. Generally, individuals who experience greater enclosure complexity express higher diversity of behaviours and show better physiological well-being. However, positive outcomes of providing habitat complexity should be species-specific, and not all species would benefit from it. Thus, it is important to provide and constantly evaluate the habitat complexity of zoo animals. Complexity can change temporally and spatially. We discuss in this paper how habitat complexity can positively influence animal welfare, and provide some ideas on how to add habitat complexity and functional structures for captive animals. Abstract The complexity of the habitat refers to its physical geometry, which includes abiotic and biotic elements. Habitat complexity is important because it allows more species to coexist and, consequently, more interactions to be established among them. The complexity of the habitat links the physical structure of the enclosure to the biological interactions, which occur within its limits. Enclosure complexity should vary temporally, to be able to influence the animals in different ways, depending on the period of the day and season and throughout the year. In the present paper, we discuss how habitat complexity is important, and how it can positively influence the physical and mental states of zoo animals. We show how habitat complexity can ultimately affect educational projects. Finally, we discuss how we can add complexity to enclosures and, thus, make the lives of animals more interesting and functional.
Metal–Organic-Framework-Mediated Fast Self-Assembly 3D Interconnected Lignin-Based Cryogels in Deep Eutectic Solvent for Supercapacitor Applications

A cost-effective and sustainable method is successfully developed to produce lignin-based cryogels with a mechanically robust 3D interconnected structure. A choline chloride–lactic acid (ChCl–LA)-based deep eutectic solvent (DES) is used as a cosolvent to promote the synthesis of lignin–resorcinol–formaldehyde (LRF) gels that can self-assemble a robust string-bead-like framework. The molar ratio of LA to ChCl in DES has a significant influence on the gelation time and properties of the ensuing gels. Moreover, it is discovered that doping the metal–organic framework (MOF) during the sol–gel process can greatly accelerate the gelation of lignin. It takes a mere 4 h to complete the LRF gelation process at a DES ratio of 1:5 combined with 5% MOF. This study yields LRF carbon cryogels doped with copper that exhibit 3D interconnected bead-like carbon spheres with a prominent micropore of 1.2 nm. A specific capacitance as high as 185 F g−1 can be obtained for the LRF carbon electrode at a current density of 0.5 A g−1, and it has an excellent long-term cycling stability. This study provides a novel method of synthesizing high-lignin-content carbon cryogels with promising potential for application in the field of energy storage devices.
Wall-Bounded Turbulence: Structure and Passive Control

The nature of wall-bounded turbulence is investigated with respect to spectral characteristics. As the Reynolds number approaches infinity, the k−1 x overlap region does not appear to be present, yet the k −5/3 x asymptotically takes shape in the logarithmic and wake regions. In terms of coherent structures, a new scaling is proposed that describes the size of the very-large-scale motions throughout the flow. The corresponding similarity to the large-scale motion scaling suggests that these two phenomena might be interrelated. These observations appear to be robust to assumptions of a wavelengthindependent convection velocity, although it is clear that Taylors hypothesis indeed leads to some spectral distortion, especially close to the wall. Passive techniques to control wall-bounded turbulence are investigated with a focus on frictional drag reduction. Surfaces coated with a thin layer of immiscible liquid can delay the transition of a flow to a turbulent state, thereby reducing the energy dissipation in the flow. It is hypothesized that interfacial tension between the two fluids acts to stabilize disturbances, helping to maintain a laminar flow. The friction reducing properties of superhydrophobic surfaces, in which micro-scale pockets of air are trapped within surface textures, are confirmed in turbulent TaylorCouette flow, with drag reductions up to 11% reported. The drag reduction on the surface decreases with increasing Reynolds number, that is, as the viscous length scale becomes smaller compared to the surface feature size. Further, turbulent drag reduction is measured over a textured surface infused with a second immiscible liquid, with values of 4% in Taylor-Couette flow and 10% in boundary layer flow.
An Immersed Boundary-Lattice Boltzmann Simulation of Particle Hydrodynamic Focusing in a Straight Microchannel

An immersed boundary (IB)-lattice Boltzmamm method (LBM) coupled model is utilized to study the particle focusing in a straight microchannel. The LBM is used to solve the incompressible fluid flow over a regular Eulerian grid, while the IB method is employed to couple the bead-spring model which represents the fluid-particle interaction. After model validation, the simulations for hydrodynamic focusing of the single and multi particles are performed. The particles can be focused into the equilibrium positions under the pressure gradient and self-rotation induced forces, and the particle radius and Reynolds number are the key parameters influencing the focusing dynamics. This work demonstrates the potential usefulness of the IB-LBM model in studying the particle hydrodynamic focusing.
FPGA Implementation Of Reconfigurable Address Generator For Various Standard Interleaver

This paper describes a reconfigurable design for a multi-standard address generator and interleaver using a simpler technique. The importance of this effort is focused on the reuse of hardware. It also focuses on minimising hardware requirements in order to support numerous standards. The suggested multimode interleaver reduces hardware complexity by 60.28%. It supports 3GPP, LTE, WiMAX (802.16e), and WLAN (802.11a/b/g/n), protocols that operate at various modulation schemes and coding rates. The design is being operated with45 nm CMOS conventional cell technology and in synthesis of ASIC As a result, the data rate reconstructable address generator created. In comparison to the current condition of the address generators, latency will be reduced by 52.23%.
FEATURE EXTRACTION USING HISTOGRAM OF RADON TRANSFORM FOR PALMPRINT MATCHING

This paper presents principle line based palm print matching model as principle lines are easily extracted in even in low resolution images. This paper proposes use of histogram of Radon transform (HRT) to extract the features of palm. However the HRT is sensitive to the rotation and scaling due to normalization process. Therefore here logarithm is employed while discarding the normalization process and its histogram is utilized as feature vector. To compare the palm prints, the calculation of correlation coefficient of this logarithm histogram is proposed in the paper. The proposed model is applied to polyU database and results are analyzed in terms of receiver operating characteristic.
A Bayesian Race Model for Recognition Memory

ABSTRACT Many psychological models use the idea of a trace, which represents a change in a person’s cognitive state that arises as a result of processing a given stimulus. These models assume that a trace is always laid down when a stimulus is processed. In addition, some of these models explain how response times (RTs) and response accuracies arise from a process in which the different traces race against each other. In this article, we present a Bayesian hierarchical model of RT and accuracy in a difficult recognition memory experiment. The model includes a stochastic component that probabilistically determines whether a trace is laid down. The RTs and accuracies are modeled using a minimum gamma race model, with extra model components that allow for the effects of stimulus, sequential dependencies, and trend. Subject-specific effects, as well as ancillary effects due to processes such as perceptual encoding and guessing, are also captured in the hierarchy. Predictive checks show that our model fits the data well. Marginal likelihood evaluations show better predictive performance of our model compared to an approximate Weibull model. Supplementary materials for this article are available online.
Transfer learning for bearing fault diagnosis based on improved residual network

Up to now, the machine learning and deep learning algorithms discussed are usually designed to work alone. In the actual industrial operating environment, the working conditions are complex and changeable, so the model once trained in the source domain is used in the target domain, the diagnostic accuracy cannot be guaranteed, and it takes a lot of time to train the model. In view of the above two problems, this paper proposes a bearing fault diagnosis method based on the Transfer Learning Convolutional Neural Network (CRNN). Experimental results show that compared with other existing methods, the CRNN model proposed in this paper reduces the training time of the model while ensuring high accuracy, and the generalization ability of the model is improved, which has a good application value.
The Role of Selected Adipocytokines in Ovarian Cancer and Endometrial Cancer

Due to their multidirectional influence, adipocytokines are currently the subject of numerous intensive studies. Significant impact applies to many processes, both physiological and pathological. Moreover, the role of adipocytokines in carcinogenesis seems particularly interesting and not fully understood. For this reason, ongoing research focuses on the role of these compounds in the network of interactions in the tumor microenvironment. Particular attention should be drawn to cancers that remain challenging for modern gynecological oncology—ovarian and endometrial cancer. This paper presents the role of selected adipocytokines, including leptin, adiponectin, visfatin, resistin, apelin, chemerin, omentin and vaspin in cancer, with a particular focus on ovarian and endometrial cancer, and their potential clinical relevance.
An Interference Aware User-Group Pilot Assignment for Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (MIMO) system is introduced to fulfill the requirement of higher data rates by serving a user through multiple access points (APs). However, due to a large number of users and a limited number of pilot sequences, the CF network suffers from severe inter-user interference, which deteriorates the achievable throughput. In this letter, we propose an interference aware user-group (IA-UG) pilot assignment scheme that addresses the issue of inter-user interference while supporting a large number of users. In the proposed IA-UG scheme, we count the number of common APs between two user pairs and use it as a likely measure of the number of interferers. Subsequently, we focuses on creating the user-groups by minimizing the number of interferers in the group having the maximum count. Numerical results show that the proposed pilot assignment scheme outperforms the existing schemes and yields a superior quality of channel estimates as well as the throughput.
Audiovestibular Findings in a Cohort of Patients with Chiari Malformation Type I and Dizziness

Chiari Malformation Type I (CM1) is a neurological condition in which the cerebellar tonsils extend past the foramen magnum. While many studies have reported dizziness symptoms in patients with CM1, the prevalence of peripheral labyrinthine lesions is largely unknown. This study aimed to comprehensively describe the audiovestibular phenotype in a cohort of patients with CM1 expressly referred for dizziness. Twenty-four patients with CM1 and a complaint of dizziness/vertigo were evaluated. Hearing and auditory brainstem tract function were essentially normal. While vestibular abnormalities were most prevalent during rotational testing (33%), abnormal functional balance was the most common finding (40%). Patients with CM1 had a greater likelihood of exhibiting an abnormal sensory organization test (SOT) postural stability score for fixed platform conditions, and for the somatosensory analysis score. While no significant associations were identified between tonsillar ectopia extent and any vestibular/balance outcome measure, a significant negative association was identified between neck pain and the somatosensory sensory analysis score. Abnormal functional balance in the somatosensory domain was remarkable, with poorer scores associated with neck pain. An isolated peripheral vestibulopathy was present in only 8% of patients. Despite the low prevalence of vestibulopathy, vestibular/balance assessment is warranted to identify patients who may benefit from referral to specialized medical disciplines.
Entropy-based Reward Design for Deep Reinforcement Learning-enabled Routing, Modulation and Spectrum Assignment of Elastic Optical Networks

We present a new reward design for the deep reinforcement learning (DRL)-based routing, modulation and spectrum assignment in the elastic optical networks (EONs). The performance of the EONs is highly related to the spectrum fragmentation, and entropy can be used to quantify the fragmentation level. In this paper, we propose entropy-based reward design for the DRL agent. By doing this, the impact on the spectrum fragmentation of different actions can be perceived to facilitate the learning of the agent. Simulation results show that our proposed approach outperforms previous simple reward-based DRL approaches.
Survivability of Lactobacillus plantarum in nutmeg (Myristica fragrans Houtt) flesh extract and its effect on the performance of broiler chicken

Objective: This study aimed to see if increasing the concentration of nutmeg flesh extract in vitro could increase the growth of Lactobacillus plantarum bacteria and if it had any effect on broiler chicken performance. Materials and Methods: Different concentrations of nutmeg flesh extract (5, 10, 15, and 20/100 ml distilled water) were combined with 10 ml L. plantarum (bacterial concentration 1 × 109 cfu/ml) to produce synbiotics. A total of 250 unsexed Lohmann broiler chickens were reared together from 0 to 7 days of age in the in vivo study. Beginning on day 8, synbiotics nutmeg flesh extract and L. plantarum were added to the ration in amounts of 0.5, 1, 1.5, and 2 ml/kg for T1, T2, T3, and T4, respectively, while no synbiotics were added to the control diet (T0). Results: The levels of nutmeg flesh extract had a significant (p < 0.05) effect on L. plantarum growth. In the survival test against gastric acid, bile salts, and temperature, the addition of nutmeg flesh extract (20/100 ml distilled water) significantly (p < 0.05) maintained the population of L. plantarum. In vivo studies showed that the T1,T2,T3, and T4 groups gained more body weight (p < 0.05) than the T0 group during the rearing period but had no effect (p > 0.05) on the internal organ weight and carcass of broiler chickens. Conclusions: Nutmeg flesh extract could stimulate the growth of L. plantarum bacteria, and using it as a synbiotic could improve broiler chicken performance.
Models for handling coarsening and non-monotone missingness in clinical trials.

Longitudinal studies often generate incomplete response patterns according to a missing not at random mechanism. Shared parameter models provide an appealing framework for the joint modelling of the measurement and missingness processes, especially in the non-monotone missingness case, and assume a set of random effects to induce the inter-dependence. Parametric assumptions are typically made for the random effects distribution, violation of which leads to model misspecification with a potential effect on the parameter estimates and standard errors. In this chapter we avoid any parametric assumption for the random effects distribution and leave it completely unspecified. The estimation of the model is then made using a semi-parametric maximum likelihood method. Our proposal is illustrated on a randomized longitudinal study on patients with Rheumatoid Arthritis exhibiting non-monotone missingness.
Effect of Different Accumulative Temperate Zones in Heilongjiang on Glycine Soja Metabolites as Analyzed by Non-Target Metabolomics

To study the effect of growth temperature on the nutritional components and metabolites of the wild soybean (Glycine soja), we analyzed the nutritional components and metabolic gases of the wild soybean in six accumulated temperature regions of the Heilongjiang Province, China, by gas chromatography–time-of-flight mass spectrometry (GC-TOF-MS). A total of 430 metabolites, including organic acids, organic oxides, and lipids, were identified and analyzed using multivariate statistical analysis, orthogonal partial least squares discriminant analysis, principal component analysis, and cluster analysis. Eighty-seven metabolites significantly differed in the sixth accumulated temperature region compared with the other five accumulated temperature regions. The 40 metabolites (such as threonine (Thr) and lysine (Lys)) were found to be elevated in soybeans from the sixth accumulated temperature zone compared with the other five accumulated temperature zones. Through analyzing the metabolic pathways of these metabolites, amino acid metabolism had the greatest influence on wild soybean quality. The results of the amino acid analysis were consistent with those of the GC-TOF-MS and showed that amino acids in wild soybeans from the sixth accumulated temperature zone significantly differed from those of the other zones. Threonine and lysine were the main substances driving these differences. The growth temperature affected the type and concentrations of metabolites in wild soybeans, and the GC-TOF-MS analysis of the effect of growth temperature on wild soybean metabolites was shown to be feasible.
Respiration extraction and atrial fibrillation detection from clinical data based on single RGB camera

In this work, we investigated the feasibility of extracting continuous respiratory parameters from a single RGB camera stationed in a short-stay ward. Based on the extracted respiration parameters, we further investigated the feasibility of using respiratory features to aid in the detection of atrial fibrillation (AF). To extract respiration, we implemented two algorithms: chest optical flow (COF) and energy variance maximization (EVM). We used COF to extract respiration from the patient’s thoracic area and EVM from the patient’s facial area. Using capnography as the reference, for average breath-to-breath rate estimation (i.e., 15-second sliding windows with 50% overlap), we achieved errors within 3 breaths per minute with COF and within 3.5 breaths per minute with EVM. To detect the presence of AF in the respiratory signal, we extracted three respiratory features from the derived COF measurements. We fed these features to a logistic regression model and achieved an average AUC value of 0.64. This result showcases the potential of using camera-based respiratory parameters as predictors for AF, or as surrogate predictors when there is no sufficient facial area in the camera’s field of view for the extraction of cardiac measurements.