Miracle-dz/UniFAR-FTUs huggingface dataset:
Wave Power Density Hotspot Distribution and Correlation Pattern Exploration in the Gulf of Mexico

Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns.
Social appropriateness perception of dynamic interactions

ABSTRACT The current study explored the judgment of communicative appropriateness while processing a dialogue between two individuals. All stimuli were presented as audio-visual as well as audio-only vignettes and 24 young adults reported their social impression (appropriateness) of literal, blunt, sarcastic, and teasing statements. On average, teasing statements were rated as more appropriate when processing audio-visual statements compared to the audio-only version of a stimuli, while sarcastic statements were judged as less appropriate with additional visual information. These results indicate a rejection of the Tinge Hypothesis for audio-visual vignettes while confirming it for the reduced, audio-only counterparts. We also analyzed time-frequency EEG data of four frequency bands that have been related to language processing: alpha, beta, theta and low gamma. We found desynchronization in the alpha band literal versus nonliteral items, confirming the assumption that the alpha band reflects stimulus complexity. The analysis also revealed a power increase in the theta, beta and low gamma band, especially when comparing blunt and nonliteral statements in the audio-only condition. The time-frequency results corroborate the prominent role of the alpha and theta bands in language processing and offer new insights into the neural correlates of communicative appropriateness and social aspects of speech perception.
A Two-Stage Scheduling Model for the Tunnel Collapse under Construction: Rescue and Reconstruction

In the process of transportation system construction, the tunnel is always an indispensable part of the traffic network due to terrain constraints. A collapse of the tunnel under construction may give rise to a potential for significant damage to the traffic network, complicating the road conditions and straining relief services for construction workers. To cope with the variety of vehicle types during the rescue effort, this paper divides them into small, medium, and large sizes, herein correcting the corresponding speed considering six road condition factors on account of the previous research. Given the influence of different special road conditions on the speed of different sized vehicles, a multi-objective model which contains two stages is presented to make decisions for rescue vehicle scheduling. Under the priority of saving human life, the first-stage objective is minimizing the arrival time, while the objective of the second stage includes minimizing the arrival time, unmet demand level, and scheduling cost. To solve the currently proposed model, a non-dominated sorting genetic algorithm II (NSGA-II) with a real number coding method is developed. With a real tunnel example, the acceptability and improvement of the model are examined, and the algorithm’s optimization performance is verified. Moreover, the efficiency of applying real number coding to NSGA-II, the multi-objective gray wolf algorithm (MOGWO), and the traditional genetic algorithm (GA) is compared. The result shows that compared with the other two methods, the NSGA-II algorithm converges faster.
From Data Mining to Expert Mining

In a brief analysis the disadvantage of traditional data mining,the importance and inevitability of expert mining are pointed out.As a new field of research,fundamental concept of expert mining is introduced at first,then the model of mining process,the main methods and technology are proposed,and through comparison with traditional data mining,we analyze the advantage of mutual driven knowledge discovery by integrating expert mining and data mining.Finally,an example is given to prove the feasibility of expert mining.
Managing Chronic Conditions: Lessons Learnt from a Comparative Analysis of Seven Years’ Policies for Chronic Care Patients in Italy

This policy paper aims to compare what policies are developed in Italy for the management of chronic patients in order to improve population health, quality of care and patient experience and reduce per-capita cost. The paper also aims to identify the key trends and evolutionary trajectories across the Country. Methodology: The analysis focuses on 10 Italian Regions and the time span of observation is 7 years (from 2014 to 2020). Data collection and analysis adopts mixed methods in order to have a more in-depth picture of the contextual factors, mechanisms and outcomes. It includes a desk research of the literature and documentary analysis; semi-structured interviews; a theory driven evaluation of 12 programmes identified at the regional level; and a Consensus Conference to discuss and validate the results with an Expert Panel Group. Conclusions: The paper firstly describes the main policies developed in Italy in the last seven years; secondly, it discusses six main trends and clusters them into three strategies: demand management strategies; strategies to improve the management of comorbid and frail patients; and strategies to improve the coordination between levels of care and the patient journey; thirdly, it discusses eight trends and evolutionary trajectories which are now emerging.
A novel image quality metrices for evaluation of optical coherence tomography images of skin

Optical coherence tomography is a three-dimensional imaging modality that captures microstructures of the tissue. The application of OCT in dermatology is limited due to the low visibility in these images. Numerous image denoising and enhancement algorithms have been implemented for quality improvement of the OCT skin images. One way to evaluate the performance of these algorithms is to quantify the quality of the processed images using different image quality metrices. Current image quality metrics though do not fairly represent the visual quality of the images. We propose an algorithm to quantify the quality of OCT images compatible with human visual perception, and the diagnostically important features in skin images. We implement a new metric called Signal to Noise Ratio. The metric is assessed on different number of averaged OCT images taken from the same cross section of the skin.
Do Online Classes help EFL Learners Improve their English Language skills? A Qualitative Study at a Saudi University

The unprecedented time of COVID-19 has called schools and educators worldwide to think of transferring education from face-to-face based to become more digital oriented. This transformation has pros and cons. Scholars in the field are questioning investigating the challenges that online learning entails; however, one of the issues still not fully investigated in the literature is how effective online learning on improving the EFL learners’ language skills. Thus, the purpose of this study is to explore the perspectives of EFL learners toward how effective online learning on improving their English language skills. The participants involved were three students from the Community College at King Saud University. Semi-structured interviews were used to gather the data in the study. The results from the qualitative data showed that the participants reported some flaws associated with online learning. However, they preferred such learning methodology to face-to-face instruction as they feel more comfortable when attending their classes from home, saving their time and costs. Moreover, the findings showed that online learning helps the learners develop all their language skills, makes them more concentrated, confident, and encourages them to become autonomous learners. The results also suggested that online learning helps EFL learners develop their online learning skills. The outcomes of this research build on the current literature on using online learning in EFL classrooms during the COVID-19 pandemic.
Machine Learning of Cutting Conditions in Drilling Using Artificial Neural Network

Machining has traditionally been one of the major operations within most manufacturing systems and intelligent machining will play an important role in feature manufacturing systems. This paper concerns the machine learning, specifically classification and recognition of cutting conditions in drilling process. Awareness of the cutting conditions can enhance the auto-diagnosis of an intelligent machining system. In this paper, features representing the drilling process are generated from the converted forms of thrust force and torque and extracted with wavelet packet transform (WPT) and then selected by principal component analysis (PCA). Data instances are generated from experiments with different cutting conditions including workpiece material, drill diameter, feed rate, and spindle speed. A feed-forward network trained with back-propagation method (BPNN) is applied to distinguish between the patterns of each cutting condition. The different contributions of features and the recognition results of cutting conditions are discussed.
Prequel to the Stories of Warm Conveyor Belts and Atmospheric Rivers: The Moist Tongues Identified by Rossby and His Collaborators in the 1930s

The model of atmospheric river (AR) has been around since the 1990s. A closely related model is the warm conveyor belt (WCB) developed in the 1970s. A look further back in time, a phenomenon known as “moist tongue” was intensively investigated in the late 1930s and early 1940s by Rossby and his collaborators using the innovation of isentropic analysis. This article aims to establish a historical perspective on the development of moist tongue model and its relevance to the current models of WCBs and ARs. As it turns out, moist tongue was identified as an extension of moist air into a region of lower moisture content on the selected isentropic charts. Most moist tongues are driven by large-scale cyclonic and anticyclonic eddies, and are often accompanied by surface cold fronts in close proximity. Ahead of the moist tongues, areas of continuous precipitation are caused mainly by the motion of moist air up the steep isentropic slopes over warm fronts or topographical features. In the warm season, the mere presence of a moist tongue could be sufficient to give thunderstorms. A reanalysis dataset is used to re-examine the structures and evolutions of two moist tongue events in 1936. It is shown that not all but some of the moist tongues fit well with the modern conceptual models of WCB and AR. These two case studies also serve to elucidate the usefulness of using the reanalysis data to investigate historical high-impact weather events that were poorly understood due to the lack of observational data.
The Promoter of the Immune-Modulating Gene TIR-Containing Protein C of the Uropathogenic Escherichia coli Strain CFT073 Reacts to the Pathogen’s Environment

The TIR-containing protein C (TcpC) of the uropathogenic Escherichia coli strain CFT073 modulates innate immunity by interfering with the Toll-like receptor and NALP3 inflammasome signaling cascade. During a urinary tract infection the pathogen encounters epithelial and innate immune cells and replicates by several orders of magnitude. We therefore analyzed whether these cell types and also the density of the pathogen would induce the recently defined promoter of the CFT073 tcpC gene to, in time, dampen innate immune responses. Using reporter constructs we found that the uroepithelial cell line T24/83 and the monocytic cell line THP-1 induced the tcpC promoter. Differentiation of monocytic THP-1 cells to macrophages increased their potential to switch on the promoter. Cell-associated CFT073 displayed the highest promoter activity. Since potassium represents the most abundant intracellular ion and is secreted to induce the NLRP3 inflammasome, we tested its ability to activate the tcpC promoter. Potassium induced the promoter with high efficiency. Sodium, which is enriched in the renal cortex generating an antibacterial hypersalinity, also induced the tcpC promoter. Finally, the bacterial density modulated the tcpC promoter activity. In the search for promoter-regulating proteins, we found that the DNA-binding protein H-NS dampens the promoter activity. Taken together, different cell types and salts, present in the kidney, are able to induce the tcpC promoter and might explain the mechanism of TcpC induction during a kidney infection with uropathogenic E. coli strains.
Design and characterization of long path absorption cell and digital lock-in amplifier for multi-channel infrared gas sensor

A high-precision three-gas detection system based on non-dispersive infrared (NDIR) technique was designed to accurately detect multiple gas concentrations in a complex environment. This system consists of an infrared source, a quad channel pyroelectric detector, a signal processing circuit and an optical gas chamber. A multiplexed digital lock-in amplification algorithm based on MCU was realized to improve accuracy, it has low cost of multiplex, there is potential to be applied to the measurement of multi-channel data. And a compact gas cell with the size of 40mmx110mmx22mm was designed, in which the average optical path length is 475mm. Initial measurement was done to one channel for SF6 gas concentration, and the feasibility of gas cell structure and signal processing circuit was demonstrated.
PP1 phosphatases control PAR-2 localization and polarity establishment in C. elegans embryos

Cell polarity relies on the asymmetric distribution of the conserved PAR proteins, which is regulated by phosphorylation/dephosphorylation reactions. While the kinases involved have been well studied, the role of phosphatases remains poorly understood. In C. elegans zygotes, phosphorylation of the posterior PAR-2 protein by the atypical protein kinase PKC-3 inhibits PAR-2 cortical localization. Polarity establishment depends on loading of PAR-2 at the posterior cortex. We show that the PP1 phosphatases GSP-1 and GSP-2 are required for polarity establishment in embryos. We find that co-depletion of GSP-1 and GSP-2 abrogates the cortical localization of PAR-2 and that GSP-1 and GSP-2 interact with PAR-2 via a PP1 docking motif in PAR-2. Mutating this motif in vivo, to prevent binding of PAR-2 to PP1, abolishes cortical localization of PAR-2, while optimizing this motif extends PAR-2 cortical localization. Our data suggest a model in which GSP-1/-2 counteract PKC-3 phosphorylation of PAR-2 allowing its cortical localization at the posterior and polarization of the one-cell embryo. SUMMARY Calvi et al. identify PP1 protein phosphatases as regulators of cell polarity in C. elegans embryos. Their results show that two redundant phosphatases, GSP-1 and GSP-2, interact with the polarity protein PAR-2 and control its localization and polarity establishment.
Dynamic Changes of Peripheral Blood Markers Predict Prognosis of Patients with Advanced Non-Small Cell Lung Cancer Receiving Anti-PD-1/PD-L1 Treatment

Background Immune checkpoint inhibitors have been routinely used in the treatment of advanced non- small-cell lung cancer (aNSCLC) with significantly improved survival rate. However, the identification of reliable prognositic and predictive biomarkers is lacking. The aim of the study was to investigate the prognostic value of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) and their dynamic changes for aNSCLC. Methods We retrospectively analyzed 60 patients with aNSCLC who received immunotherapy. The NLR and PLR were assessed at baseline (NLR0 and PLR0) and after 4 cycles of treatment (NLR4c and PLR4c). Results Of all aNSCLC patients on immunotherapy, 47 (78.3%) cases were in the response group. Whether at baseline or after 4 cycles of treatment, the levels of NLR and PLR in the response group were both significantly lower than those in the non-response group (p < 0.05). Patients with a high NLR4c had poor progression-free survival (PFS) and overall survival (OS) independently in multivariate analysis (3.90 vs. 7.50 months, p = 0.005; 7.20 vs. 15.90 months, p = 0.041, respectively). Patients with high NLR0-highNLR4c were associated with poor PFS and OS (p = 0.009, p = 0.001 respectively). Results are similar for PLR. Conclutions NLR after 4 cycles of treatment and dynamic changes of NLR and PLR during immunotherapy might help to predict a more accurate prognosis in aNSCLC patients.
Spontaneous early pregnancy with very low levels of progesterone during controlled ovarian stimulation-A case report

One of the most widely accepted axioms of reproductive biology is that pregnancy requires the sole support of progesterone, without which pregnancy cannot be established or maintained. We report a rare case of ongoing third trimester pregnancy in a 41-year-old woman, where early gestational period was maintained despite extremely low progesterone levels of <1 ng/ml, and was discovered during controlled ovarian hyperstimulation (COS) for in vitro fertilization (IVF). She was started on ovarian stimulation (OS) with gonadotrophins after a withdrawal bleed during lactational amenorrhea. Baseline investigations on day 2 of the menstruation confirmed low serum estradiol and progesterone levels (<1 ng/ml). After 5 days of stimulation, on ultrasound scan, a sac-like structure was seen in the uterine cavity. Beta hCG levels were measured and confirmed the presence of early pregnancy despite progesterone levels below 1 ng/ml. COS was stopped, and progesterone support was started. Subsequent scan confirmed live intrauterine pregnancy and the fetus is currently growing uneventfully at 31 weeks of gestation (at the time of writing this report).
Thiolated Chitosan Microneedle Patch of Levosulpiride from Fabrication, Characterization to Bioavailability Enhancement Approach

In this study, a first attempt has been made to deliver levosulpiride transdermally through a thiolated chitosan microneedle patch (TC-MNP). Levosulpiride is slowly and weakly absorbed from the gastrointestinal tract with an oral bioavailability of less than 25% and short half-life of about 6 h. In order to enhance its bioavailability, levosulpiride-loaded thiolated chitosan microneedle patches (LS-TC-MNPs) were fabricated. Firstly, thiolated chitosan was synthesized and characterized by nuclear magnetic resonance (1HNMR) spectroscopy, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, differential scanning calorimetry (DSC), and X-ray diffraction (XRD). Thiolated chitosan has been used in different drug delivery systems; herein, thiolated chitosan has been used for the transdermal delivery of LS. LS-TC-MNPs were fabricated from different concentrations of thiolated chitosan solution. Furthermore, the levosulpiride-loaded thiolated chitosan microneedle patch (LS-TC-MNP) was characterized by FTIR spectroscopic analysis, scanning electron microscopy (SEM) study, penetration ability, tensile strength, moisture content, patch thickness, and elongation test. LS-TC-MNP fabricated with 3% thiolated chitosan solution was found to have the best tensile strength, moisture content, patch thickness, elongation, drug-loading efficiency, and drug content. Thiolated chitosan is biodegradable, nontoxic and has good absorption and swelling in the skin. LS-TC-MNP-3 consists of 100 needles in 10 rows each with 10 needles. The length of each microneedle was 575 μm; they were pyramidal in shape, with sharp pointed ends and a base diameter of 200 µm. The microneedle patch (LS-TC-MNP-3) resulted in-vitro drug release of 65% up to 48 h, ex vivo permeation of 63.6%, with good skin biocompatibility and enhanced in-vivo pharmacokinetics (AUC = 986 µg/mL·h, Cmax = 24.5 µg/mL) as compared to oral LS dispersion (AUC = 3.2 µg/mL·h, Cmax = 0.5 µg/mL). Based on the above results, LS-TC-MNP-3 seems to be a promising strategy for enhancing the bioavailability of levosulpiride.
Building on Solid Bedrock: Embedding Language Development in First-year Geology Curriculum

Collaboration between experts in language learning and non-language disciplines is a practical approach for reshaping assessments to ensure student language development within the discipline context. While embedding language into curriculum is by itself a valuable application, our professional partnership yielded broader positive impacts. This alliance supported professional reflective practice by providing a unique perspective on means to overcome challenges of first-year assessment and feedback, and support for students to become independent learners. As part of a larger community of professional practice, this cooperative work promotes student success across multiple disciplines and faculties.
Generating Characteristic Summaries for Entity Descriptions

Graph-structured data describing entities and their properties has become a notable component of the Web. With the increasing size of data graphs, an entity is often associated with too many property values to be entirely shown to the user, thereby requiring a compact but characteristic summary to present its most distinguishing features. This paper aims to automatically generate such characteristic entity summaries for human users. To achieve it, we exploit the informativeness of property values by analyzing the data graph using information theory. To improve the utility of information carried by a summary, we learn it from a text corpus. To reduce the information redundancy of a summary, we perform logical reasoning and measure similarity with statistical support. We formalize the entity summarization problem considering these factors as combinatorial optimization problems to solve. Experiments based on a real data graph and hand-crafted gold standards show that our approach improves on two state-of-the-art approaches in F-measure by 20.63%–38.79%.