Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models

Background: Recurrent events analysis plays an important role in many applications, including the study of chronic diseases or recurrence of infections. Historically, most models for the analysis of time-to-event data, including recurrent events, have been based on Cox proportional hazards regression. Recently, however, the Piece-wise exponential Additive Mixed Model (PAMM) has gained popularity as a flexible framework for survival analysis. While many papers and tutorials have been presented in the literature on the application of Cox based models, few papers have provided detailed instructions for the application of PAMMs and to our knowledge, none exist for recurrent events analysis. Methods: The PAMM is introduced as a framework for recurrent events analysis. We describe the application of the model to unstratified and stratified shared frailty models for recurrent events. We illustrate how penalized splines can be used to estimate non-linear and time-varying covariate effects without a priori assumptions about their functional shape. The model is motivated for both, analysis on the gap timescale ("clock-reset") and calendar timescale ("clock-forward"). The data augmentation necessary for the application to recurrent events is described and explained in detail. Results: Simulations confirmed that the model provides unbiased estimates of covariate effects and the frailty variance, as well as equivalence to the Cox model when proportional hazards are assumed. Applications to recurrence of staphylococcus aureus and malaria in children illustrates the estimation of seasonality, bivariate non-linear effects, multiple timescales and relaxation of the proportional hazards assumption via time-varying effects. The R package pammtools has been extended to facilitate estimation, visualization and interpretation of PAMMs for recurrent events analysis. Conclusion: PAMMs provide a flexible framework for the analysis of time-to-event and recurrent events data. The estimation of PAMMs is based on Generalized Additive Mixed Models and thus extends the researcher’s toolbox for recurrent events analysis.
Interlaboratory Evaluation of Proficiency Testing in Urinary δ-Aminolevulinic Acid Analyses

Twenty-six European laboratories have participated in a study of various methods used for determination of δ-aminolevulinic acid in urine, to determine to what extent they give comparable results when performed by different laboratories. None of the methods as used in the program appeared to be precise and accurate enough to allow each of several laboratories to detect the small differences in urinary δ-aminolevulinic acid that would result from differences in exposure to environmental lead. Variation among results obtained by different laboratories using the same technique probably reflects differences in the practical experience each laboratory had with the various techniques, rather than intrinsic problems with the methods themselves.
Emergence of social media as new normal during COVID-19 pandemic: a study on innovative complaint handling procedures in the context of banking industry

Purpose: Banking industry has no option but to remain observant and redraft the contagion playbook as per the situation evolves owing to COVID-19. Therefore, this study aims to develop a model to examine empirically how effectively complaints are handled to control customer retaliation and negative e-word of mouth (eWOM) in such a pandemic situation, where banking institutions are using social media as a key platform. Design/methodology/approach: A self-administered questionnaire was distributed among 309 participants who had encountered service failure and experienced complaint handling on Facebook. This study draws upon prevailing literature to test a series of hypotheses through structural equation modeling. Findings: The findings show that effective complaint handling has a negative influence on customer retaliation in the social media environment. As a result, customer retaliation was found to have a positive association with negative eWOM. Therefore, this study has revealed that effective complaint handling will lead to decrease in customer retaliation and negative eWOM. Practical implications: This study carries an understanding of effective complaint handling efforts by leading banks in the social media environment during the COVID-19 pandemic. This study recommends that customized and effective efforts by the banks can influence customer retaliation and negative e-WOM. Originality/value: The study is conducted during COVID-19 pandemic considering the impact of COVID-19 on banking sector, which is a new phenomenon. This study has highlighted how banks have modified their working methodology during pandemic situation by using Facebook as a prominent platform to redress customers’ issues and complaints. © 2021, Emerald Publishing Limited.
Minimal Processing of Papaya for Quality Maintenance and Shelf Life

The effect of different types of semi permeable films, sanitizing agents and antioxidant solutions on quality maintenance and shelf life of minimally processed freshly cut papaya cubes was evaluated during storage at 5±1°C (RH 85%) and ambient temperature (20±1°C). Storage life of papaya cubes stored at ambient temperature irrespective of treatment with antioxidants and sanitizing agents was maintained up to 2 days. Papaya cubes treated with antioxidants showed minimum changes in colour with least browning. In the initial stages of storage, TSS of the cubes packed in semi permeable films with out antioxidants increased. Total soluble solids, acidity, vitamin C, β-carotenes, firmness of the cubes was maintained up to 16 days with good physico chemical characteristics and nutritional composition when sanitized with sodium hypochlorite (100 ppm) for 2-3 min, treated with antioxidant (citric acid 1% W/V) for 30 s and packed in 19 μ semi permeable film and stored at 5±1°C.
Determination of free amino acids, saccharides, and selected microbes in biogenic atmospheric aerosols – seasonal variations, particle size distribution, chemical and microbial relations

Abstract. Primary biological aerosol particles (PBAPs) play an important role in the interaction between biosphere, atmosphere, and climate, affecting cloud and precipitation formation processes. The presence of pollen, plant fragments, spores, bacteria, algae, and viruses in PBAPs is well known. In order to explore the complex interrelationships between airborne and particulate chemical tracers (amino acids, saccharides), gene copy numbers (16S and 18S for bacteria and fungi, respectively), gas phase chemistry, and the particle size distribution, 84 size-segregated aerosol samples from four particle size fractions (< 1.0, 1.0–2.5, 2.5–10, and > 10 µm) were collected at the SMEAR II station, Finland, in autumn 2017. The gene copy numbers and size distributions of bacteria, Pseudomonas, and fungi in biogenic aerosols were determined by DNA extraction and amplification. In addition, free amino acids (19) and saccharides (8) were analysed in aerosol samples by hydrophilic interaction liquid chromatography–mass spectrometry (HILIC-MS). Different machine learning (ML) approaches, such as cluster analysis, discriminant analysis, neural network analysis, and multiple linear regression (MLR), were used for the clarification of several aspects related to the composition of biogenic aerosols. Clear variations in composition as a function of the particle size were observed. In most cases, the highest concentration values and gene copy numbers (in the case of microbes) were observed for 2.5–10 µm particles, followed by > 10, 1–2.5, and < 1.0 µm particles. In addition, different variables related to the air and soil temperature, the UV radiation, and the amount of water in the soil affected the composition of biogenic aerosols. In terms of interpreting the results, MLR provided the greatest improvement over classical statistical approaches such as Pearson correlation among the ML approaches considered. In all cases, the explained variance was over 91 %. The great variability of the samples hindered the clarification of common patterns when evaluating the relation between the presence of microbes and the chemical composition of biogenic aerosols. Finally, positive correlations were observed between gas-phase VOCs (such as acetone, toluene, methanol, and 2-methyl-3-buten-2-ol) and the gene copy numbers of microbes in biogenic aerosols.
XLNet-Caps: Personality Classification from Textual Posts

Personality characteristics represent the behavioral characteristics of a class of people. Social networking sites have a multitude of users, and the text messages generated by them convey a person’s feelings, thoughts, and emotions at a particular time. These social texts indeed record the long-term psychological activities of users, which can be used for research on personality recognition. However, most of the existing deep learning models for multi-label text classification consider long-distance semantics or sequential semantics, but problems such as non-continuous semantics are rarely studied. This paper proposed a deep learning framework that combined XLNet and the capsule network for personality classification (XLNet-Caps) from textual posts. Our personality classification was based on the Big Five personality theory and used the text information generated by the same user at different times. First, we used the XLNet model to extract the emotional features from the text information at each time point, and then, the extracted features were passed through the capsule network to extract the personality features further. Experimental results showed that our model can effectively classify personality and achieve the lowest average prediction error.
The Role of HOX Genes as Potential Biomarkers in Colorectal Cancer: A Systematic Review

Background: Colorectal cancer (CRC) is worldwide the third leading cause of cancer-related death, and despite therapeutic advances, survival remains low. Emerging evidence shows that Homeobox (HOX) genes are important in carcinogenesis, and their dysregulation has been linked with metastatic potential and poor prognosis. This systematic review aims to present the current evidence on the role of HOX genes as biomarkers in CRC and the impact of their modulation in tumour growth and progression. Methods: MEDLINE, EMBASE, Web of Science and Cochrane databases were searched by following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Eligible studies investigated two research questions: a) the clinicopathological and prognostic significance of HOX gene dysregulation in patients with CRC and b) the functional role of HOX genes in CRC progression. This study was registered in the international prospective register of systematic reviews (PROSPERO), CRD42020190953. Results: Twenty-five studies enrolling 3003 patients with stage I-IV CRC, showed that 26 out of 39 HOX genes were dysregulated in cancerous versus normal colon. Aberrant expression of HOX proteins was significantly related to tumour depth, nodal invasion, distant metastases, advanced stage and poor prognosis. Twenty-two preclinical studies showed that HOX proteins are crucially related to tumour growth and metastatic potential by affecting cell proliferation and altering the expression of epithelial-mesenchymal transition modulators. Conclusions: In conclusion, our findings suggest that HOX proteins play vital roles in CRC progression and significantly affect survival. Further research, though, is required to elucidate their potential role as biomarkers in CRC.
ICP-AES Determination of 16 Elements of Microamounts in 8 Kinds of Vegetables

ICP-AES was applied to the determination of microamounts of 16 elements(Ca,Al,Cu,Fe,Mn,Zn,Cr,Hg,P,Se,Mg,As,Cd,Pb,K,Na) in 8 kinds of vegetables.Working conditions of the instrument was given and the choice of analytical spectral lines was described.Microwave heating was used in the sample digestion.Detection limits for these elements were found in the range of 0.3-30.0 μg·L-1.Results of recovery test obtained were in the range of 94.3%-104.8%.
MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.
Deep Neural Models for Illumination Estimation and Relighting: A Survey

Scene relighting and estimating illumination of a real scene for insertion of virtual objects in a mixed‐reality scenario are well‐studied challenges in the computer vision and graphics fields. Classical inverse rendering approaches aim to decompose a scene into its orthogonal constituting elements, namely scene geometry, illumination and surface materials, which can later be used for augmented reality or to render new images under novel lighting or viewpoints. Recently, the application of deep neural computing to illumination estimation, relighting and inverse rendering has shown promising results. This contribution aims to bring together in a coherent manner current advances in this conjunction. We examine in detail the attributes of the proposed approaches, presented in three categories: scene illumination estimation, relighting with reflectance‐aware scene‐specific representations and finally relighting as image‐to‐image transformations. Each category is concluded with a discussion on the main characteristics of the current methods and possible future trends. We also provide an overview of current publicly available datasets for neural lighting applications.
Removal of Orange II Dye in Water by Visible Light Assisted Photocatalytic Ozonation Using Bi2O3 and Au/Bi2O3 Nanorods

This paper presents the results of visible light assisted photocatalytic ozonation and other related oxidation processes for the removal of Orange II dye from water using Bi2O3 and Au/Bi2O3 nanorods prepared using microwave irradiation which was found effective compared to Bi2O3 particles prepared by conventional heat treatment method at 90 °C. Particularly, in the photocatalytic ozonation using Au/Bi2O3 nanorods, a fourfold increase in the rate was observed as compared to that in the absence of Au/Bi2O3. This is because tuning semiconductor materials into nanosize could create high quantum confinement of electron and hole to increase the illumination efficiency. In addition, the deposition of nanomaterial can also be employed to enhance the mechanical strength of devices. That is, deposited Au nanoparticles act as electron traps to impede electron/hole recombination.
Mobile Edge Computing Assisted Green Scheduling of On-Move Electric Vehicles

Mobile edge computing (MEC) has been proposed as a promising solution, which enables the content processing at the edges of the network helping to significantly improve the quality of experience (QoE) of end users. In this article, we aim to utilize the MEC facilities integrated with time-varying renewable energy resources for charging/discharging scheduling known as green scheduling of on-move electric vehicles (EVs) in a geographical wide area comprising of multiple charging stations (CSs). In the proposed system, the charging/discharging demands and the contextual information of EVs are first transmitted to nearby edge servers. With instantaneous electricity load/pricing and the availability of renewable energy at nearby CSs collected by aggregators, a weighted social-welfare maximization problem is then solved at the edges using greedy-based algorithms to choose the best CS for the EV’s service. From the system point of view, our results reveal that compared to cloud-based scheme, the proposed MEC-assisted EVs scheduling system significantly improves the complexity burden, boosts the satisfaction (QoE) of EVs’ drivers by localizing the traffic at nearby CSs, and further helps to efficiently utilize the renewable energy across CSs. Furthermore, our greedy-based algorithm, which utilizes the internal updating heuristics, outperforms some baseline solutions in terms of social welfare and power grid ancillary services.
Outage performance analysis of non-orthogonal multiple access systems with RF energy harvesting

Non-orthogonal multiple access (NOMA) has drawn enormous attention from the research community as a promising technology for future wireless communications with increasing demands of capacity and throughput. Especially, in the light of fifth-generation (5G) communication where multiple internet-of-things (IoT) devices are connected, the application of NOMA to indoor wireless networks has become more interesting to study. In view of this, we investigate the NOMA technique in energy harvesting (EH) half-duplex (HD) decode-and-forward (DF) power-splitting relaying (PSR) networks over indoor scenarios which are characterized by log-normal fading channels. The system performance of such networks is evaluated in terms of outage probability (OP) and total throughput for delay-limited transmission mode whose expressions are derived herein. In general, we can see in details how different system parameters affect such networks thanks to the results from Monte Carlo simulations. For illustrating the accuracy of our analytical results, we plot them along with the theoretical ones for comparison.
Relative Position Representation over Interaction Space for Natural Language Inference

In natural language inference (NLI), attention mechanism has achieved great success, but it does not explicitly model order information between sequential elements. Besides, existing models for NLI tend to neglect positional information of inter-sentence words. In this paper, we propose a novel relative position representation over interaction space, which is called Distance and Direction based Relative Position Presentation (D2RPR). It can capture relative distance and relative direction information simultaneously, but also establish potential correlations between non-central words while representing the position of the central word. We incorporate D2RPR into self-attention over space to enhance intra-sentence contextual connections. Moreover, D2RPR is used to capture relative position relationships between inter-sentence words. In this way, our model can strengthen the semantic connection be-tween sentences. Experiment results show the effectiveness of D2RPR and our model achieves competitive performance on SNLI dataset and Quora dataset.
The Influence of Wildfire Smoke on Cloud Microphysics during the September 2020 Pacific Northwest Wildfires

During late summer 2020, large wildfires over the Pacific Northwest produced dense smoke that impacted the region for an extended period. During this period of poor air quality, persistent low-level cloud coverage was poorly forecast by operational numerical weather prediction models, which dissipated clouds too quickly or produced insufficient cloud coverage extent. This deficiency raises questions about the influence of wildfire smoke on low-level clouds in the marine environment of the Pacific Northwest.This paper investigates the effects of wildfire smoke on the properties of low-level clouds, including their formation, microphysical properties, and dissipation. A case study from 12-14 September 2020 is used as a testbed to evaluate the impact of wildfire smoke on such clouds. Observations from satellites and surface observing sites, coupled with mesoscale model simulations, are applied to understand the influence of wildfire smoke during this event. Results indicate that the presence of thick smoke over Washington led to decreased temperatures in the lower troposphere which enhanced low-level cloud coverage, with smoke particles altering the microphysical structure of clouds to favor high concentrations of small droplets. Thermodynamic changes due to smoke are found to be the primary driver of enhanced cloud lifetime during these events, with microphysical changes to clouds as a secondary contributing factor. However, both the thermodynamic and microphysical effects are necessary to produce a realistic simulation.
Taxonomy and Distribution of Deep-Sea Bigscales and Whalefishes (Teleostei: Stephanoberycoidei) Collected off Northeastern Brazil, Including Seamounts and Oceanic Islands

Despite the increasing number of studies on the systematics of the Stephanoberycoidei (bigscales, pricklefishes, gibberfishes, hispidoberycids, and whalefishes) globally, knowledge about the diversity and distribution of the group in the western South Atlantic still remains fragmentary. In this study, we present new anatomical (meristic and morphometric) and distributional data for 18 species of the Stephanoberycoidei based on the examination of 150 specimens recently collected during the ABRACOS (Acoustics along the BRAzilian COaSt) expeditions off northeastern Brazil, including the Rocas Atoll, Fernando de Noronha Archipelago, and the seamounts off Rio Grande do Norte State, and additional museum specimens. In the Melamphaidae, remarks on the taxonomy of Melamphaes polylepis and of the Poromitra crassiceps and P. megalops species groups are made based on specimens examined. In addition, Scopeloberyx opercularis, currently considered as a junior synonym of Scopeloberyx robustus, is recognized as a valid species. Among the species identified, nine have their distributions extended in the western South Atlantic based on confirmed records: Melamphaes polylepis, M. typhlops, Poromitra megalops, Poromitra sp. , Scopeloberyx opercularis, Scopeloberyx opisthopterus, Scopelogadus mizolepis, Cetostoma regani, and Rondeletia loricata. Eight further species are reported for the first time in Brazilian waters: Cetomimus sp. 1, Cetomimus sp. 2, Ditropichthys storeri, Gyrinomimus bruuni, Melamphaes eulepis, M. leprus, M. longivelis, and Melamphaes sp. Additional remarks on the taxonomy and distribution of the Stephanoberycoidei in the western South Atlantic are also provided.