Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidential Diversity and the Triangulation of Phenomena

The article argues for the epistemic rationale of triangulation, namely, the use of multiple and independent sources of evidence. It claims that triangulation is to be understood as causal reasoning from data to phenomenon, and it rationalizes its epistemic value in terms of controlling for likely errors and biases of particular data-generating procedures. This perspective is employed to address objections against triangulation concerning the fallibility and scope of the inference, as well as problems of independence, incomparability, and discordance of evidence. The debate on the existence of social preferences is used as an illustrative case.
Influence of Lubricant on the Nucleate Boiling Heat Transfer Performance of Refrigerant—A Review

This review provides an overview of the lubricant on the heat transfer performance pertaining to nucleate boiling. It appears that the effect of individual parameter on the heat transfer coefficient may be different from study to study. This is associated with the complex nature of lubricant and some compound effects accompanying the heat transport process. Some important effects such as oil concentration, heat flux, geometric configuration, saturation temperature, thermodynamic and transport properties, miscibility, foaming, and additional surface active agent are reported and discussed. In general, the heat transfer coefficient is impaired provided the oil concentration is sufficient high (e.g., >7%), and this is applicable to smooth and structured tubes. But normally structured tubes tend to suffer more from lubricant, yet this is especially conspicuous when the size of the reentrant channel is small. On the other hand, foaming and partial miscibility seem to benefit the heat transfer coefficient.
On cross-waves

Cross-waves are standing waves with crests at right angles to a wave-maker. They generally have half the frequency of the wave-maker and reach a steady state at some finite amplitude. A second-order theory of the modes of oscillation of water in a tank with a free surface and wave-makers at each end leads to a form of Mathieu's equation for the amplitude of the cross-waves, which are thus an example of parametric resonance and may be excited at half the wave-maker frequency if this is within a narrow band. The excitation depends on the amplitude of the wave-maker at the surface and the integral over depth of its amplitude. Cross-waves may be excited even if the mean free surface is stationary. The effects of finite amplitude are that the cross-waves approach a steady state such that a given amplitude is achieved at a frequency greater than that for free waves by an amount proportional to the amplitude of the wave-maker. The theory agrees reasonably well with the experimental results of Lin & Howard (1960). The amplification of the cross-waves may be understood in terms of the rate of working of the wave-maker against transverse stresses associated with the cross-waves, one located at the surface and the other equal to Miche's (1944) depth-independent second-order pressure. The theory applies to the situation where the primary motion consists of standing waves and the cross-waves are constant in amplitude away from the wave-maker, but certain generalizations may be made to the situation where the primary waves are progressive and the cross-waves decay away from the wave-maker.
The role of macrophage subsets in and around the heart in modulating cardiac homeostasis and pathophysiology

Cardiac and pericardial macrophages contribute to both homeostatic and pathophysiological processes. Recent advances have identified a vast repertoire of these macrophage populations in and around the heart - broadly categorized into a CCR2+/CCR2- dichotomy. While these unique populations can be further distinguished by origin, localization, and other cell surface markers, further exploration into the role of cardiac and pericardial macrophage subpopulations in disease contributes an additional layer of complexity. As such, novel transgenic models and exogenous targeting techniques have been employed to evaluate these macrophages. In this review, we highlight known cardiac and pericardial macrophage populations, their functions, and the experimental tools used to bolster our knowledge of these cells in the cardiac context.
A Propensity-Matched Retrospective Comparative Study with Historical Control to Determine the Real-World Effectiveness of Durvalumab after Concurrent Chemoradiotherapy in Unresectable Stage III Non-Small Cell Lung Cancer

Simple Summary The PACIFIC trial demonstrated the survival benefits of durvalumab consolidation (DC) in patients with unresectable stage III non–small cell lung cancer (NSCLC). In this retrospective cohort study, using a propensity score-matched analysis, we investigated the effectiveness of DC after concurrent chemoradiotherapy (CCRT) and compared DC after CCRT with a historical control in this regard. DC was tolerable and consistently associated with survival benefits (compared with a lack of DC) in real-world contexts. This study suggested that the outcomes of the PACIFIC trial could be successfully translated into real practice and that DC after CCRT could be established as a new standard of care for stage III NSCLC. Abstract This study aimed to add real-world evidence to the literature regarding the effectiveness and safety of durvalumab consolidation (DC) after concurrent chemoradiotherapy (CCRT) in the treatment of unresectable stage III non-small cell lung cancer (NSCLC). Using a hospital-based NSCLC patient registry and propensity score matching in a 2:1 ratio, we conducted a retrospective cohort study of patients with unresectable stage III NSCLC who completed CCRT with and without DC. The co-primary endpoints were 2-year progression-free survival and overall survival. For the safety evaluation, we evaluated the risk of any adverse events requiring systemic antibiotics or steroids. Of 386 eligible patients, 222 patients—including 74 in the DC group—were included in the analysis after propensity score matching. Compared with CCRT alone, CCRT with DC was associated with increased progression-free survival (median: 13.3 vs. 7.6 months, hazard ratio[HR]: 0.63, 95% confidence interval[CI]: 0.42–0.96) and overall survival (HR: 0.47, 95% CI: 0.27–0.82) without an increased risk of adverse events requiring systemic antibiotics or steroids. While there were differences in patient characteristics between the present real-world study and the pivotal randomized controlled trial, we demonstrated significant survival benefits and tolerable safety with DC after the completion of CCRT.
Ag–Cellulose Hybrid Filler for Boosting the Power Output of a Triboelectric Nanogenerator

The triboelectric nanogenerator (TENG) is a newly developed energy harvesting technology that can convert mechanical energy into electricity. The TENG has received extensive attention due to its potential applications in diverse fields. In this work, a natural based triboelectric material has been developed from a natural rubber (NR) filled with cellulose fiber (CF) and Ag nanoparticles. Ag nanoparticles are incorporated into cellulose fiber (CF@Ag) and are used as a hybrid filler material for the NR composite to enhance the energy conversion efficiency of TENG. The presence of Ag nanoparticles in the NR-CF@Ag composite is found to improve the electrical power output of the TENG by promoting the electron donating ability of the cellulose filler, resulting in the higher positive tribo-polarity of NR. The NR-CF@Ag TENG shows significant improvement in the output power up to five folds compared to the pristine NR TENG. The findings of this work show a great potential for the development of a biodegradable and sustainable power source by converting mechanical energy into electricity.
Effects of Coconut Oil and Palm Oil on Growth, Rumen Microbiota, and Fatty Acid Profile of Suckling Calves

This study aimed to evaluate the effects of coconut oil and palm oil in milk replacer (MR) on the growth performance, blood lipids, rumen fermentation, rumen microbiota, and fatty acid profile of hepatic and muscle of suckling calves. Thirty-six Holstein male calves were randomly assigned to three treatments. Three milk replacers containing different fat sources were as follows: control group (CON, milk fat), coconut oil group (CCO, coconut oil powder as fat), and palm oil group (PLO, palm oil powder as fat). Calves were weighed and blood sampled at 14, 28, 42, and 56 days old, respectively, and the feed intake and fecal score were recorded daily. Fat sources in milk replacers had no effects on body weight, ADG, DMI, fecal score, or days of abnormal fecal in suckling calves among the three groups, while the PLO group tended to decrease starter intake compared with the other groups. Serum concentrations of TC, HDL-C, LDL-C, and VLDL-C in the CCO group increased compared with those of the CON group. Palm oil also decreased the serum GLU concentration of calves but had no effects on serum lipids compared with milk fat. Coconut oil or palm oil had no effects on rumen fermentation, rumen chyme enzyme activity, rumen bacterial community richness and diversity, and dominant phyla and genera when compared with milk fat. However, compared with the CON group, the CCO group increased the proportion of MCFAs and n-6 PUFAs, and decreased the proportion of UFAs and MUFAs in liver tissue, while the PLO group increased the proportion of PUFAs and decreased the proportion of n-3 PUFAs in liver tissue. In addition, compared with the CON group, the CCO group increased the proportion of MCFAs, and decreased the proportion of UFAs and n-3 PUFAs in longissimus dorsi, while the PLO group increased the proportion of PUFAs and decreased the proportion of n-3 PUFAs in longissimus dorsi. In conclusion, compared with milk fat, coconut oil or palm oil in MR had no effects on growth performance, rumen fermentation, and rumen microflora but significantly increased serum lipids concentration and changed some proportions of MCFAs and PUFAs in liver and longissimus dorsi in suckling calves. These results indicate that coconut oil or palm oil as the sole fat source for MRs has no adverse effect on calf rumen fermentation and rumen microbiota but has a detrimental effect on n-3 PUFAs deposition in the liver and longissimus dorsi muscle.
Breastfeeding Is Associated with Higher Adherence to the Mediterranean Diet in a Spanish Population of Preschoolers: The SENDO Project

Objective: To assess whether breastfeeding during the first months of life is associated with adherence to the Mediterranean dietary (MedDiet) pattern in preschool children. Design: The Seguimiento del Niño para un Desarrollo Óptimo (SENDO) project is an ongoing pediatric cohort with open recruitment, started in 2015 in Spain. Participants, recruited when they are 4 to 5 years old at their primary local health center or school, are followed annually through online questionnaires. For this study, 941 SENDO participants with full data on study variables were included. Breastfeeding history was collected retrospectively at baseline. Adherence to the Mediterranean diet was assessed with the KIDMED index (range −3 to 12). Results: After accounting for multiple sociodemographic and lifestyle confounders, including parental attitudes and knowledge about dietary recommendations for children, breastfeeding was independently associated with a higher adherence to the MedDiet. Compared with children who were never breastfed, children breastfed for ≥6 months had a one-point increase on their mean KIDMED score (Mean difference +0.93, 95%confidence interval [CI]. 0.52–1.34, p for trend <0.001). The odds ratio of high adherence to the MedDiet (KIDMED index ≥8) was 2.94 (95%CI 1.50–5.36) in children who were breastfed for at least 6 months, as compared to their peers who were never breastfeed. Children who were breastfed for less than 6 months exhibited intermediate levels of adherence (p for trend <0.01). Conclusion: Breastfeeding for 6 months or longer is associated with a higher adherence to the Mediterranean diet during the preschool years.
Computational Fluid Dynamics Modelling of Two-Phase Bubble Columns: A Comprehensive Review

Bubble columns are used in many different industrial applications, and their design and characterisation have always been very complex. In recent years, the use of Computational Fluid Dynamics (CFD) has become very popular in the field of multiphase flows, with the final goal of developing a predictive tool that can track the complex dynamic phenomena occurring in these types of reactors. For this reason, we present a detailed literature review on the numerical simulation of two-phase bubble columns. First, after a brief introduction to bubble column technology and flow regimes, we discuss the state-of-the-art modelling approaches, presenting the models describing the momentum exchange between the phases (i.e., drag, lift, turbulent dispersion, wall lubrication, and virtual mass forces), Bubble-Induced Turbulence (BIT), and bubble coalescence and breakup, along with an overview of the Population Balance Model (PBM). Second, we present different numerical studies from the literature highlighting different model settings, performance levels, and limitations. In addition, we provide the errors between numerical predictions and experimental results concerning global (gas holdup) and local (void fraction and liquid velocity) flow properties. Finally, we outline the major issues to be solved in future studies.
Preference of black soldier fly larvae for feed substrate previously colonised by conspecific larvae

The black soldier fly Hermetia illucens Linnaeus (Diptera: Stratiomyidae; BSF) is gaining interest as an alternative protein ingredient for livestock feed. Larval aggregation behaviour occurs commonly in larvae of various dipteran species. However, the cues initiating aggregation behaviour and its occurrence in subsequent larval instars are still unknown in BSF larvae. Here, we focus on understanding the attraction of larvae to cues left behind in the substrate by conspecific larvae. We developed a dual‐choice test in the dark and examined the preference behaviour of different instars with the use of video recordings. Larval choices were identified based on first substrate contact, cumulative number of substrate contacts, entry of the substrate and number of larvae present in a substrate over time. Our results demonstrate that BSF larvae discriminated between similar substrates with or without cues released by conspecifics, with a strong preference for previously colonised substrates. Our results also showed a difference in behaviour between head‐capsule classes. Substrate contacts occurred more frequently in larvae from a lower head‐capsule class and the number of larvae present in a substrate over time differed between the head‐capsule classes 0.71–0.80 mm and 0.91–1.00 mm. Demonstrating arrestment in response to chemical cues from conspecifics is the first step of understanding aggregation behaviour of BSF larvae and offers opportunities to identify the chemical cues involved.
A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking

The airline industry strives to maximize the revenue obtained from the sale of tickets on every flight. This is referred to as revenue management and it forms a crucial aspect of airline logistics. Ticket pricing, seat or discount allocation, and overbooking are some of the important aspects of a revenue management problem. Though ticket pricing is usually heavily influenced by factors beyond the control of an airline company, significant amount of control can be exercised over the seat allocation and the overbooking aspects. A realistic model for a single leg of a flight should consider multiple fare classes, overbooking of the flight, concurrent demand arrivals of passengers from the different fare classes, and class-dependent, random cancellations. Accommodating all these factors in one optimization model is a challenging task because that makes it a very large-scale stochastic optimization problem. Almost all papers in the existing literature either accommodate only a subset of these factors or use a discrete approximation in order to make the model tractable. We consider all these factors and cast the single leg problem as a semi-Markov Decision Problem (SMDP) under the average reward optimizing criterion over an infinite time horizon. We solve it using a stochastic optimization technique called Reinforcement Learning. Not only is Reinforcement Learning able to scale up to a huge state-space but because it is simulation-based it can also handle complex modeling assumptions such as the ones mentioned above. The state-space of the numerical test problem scenarios considered here is non-denumerable; its countable part being of the order of 109. Our solution procedure involves a multi-step extension of the SMART algorithm which is based on the one-step Bellman equation. Numerical results presented here show that our approach is able to outperform a heuristic, namely the nested version of the EMSR heuristic, which is widely used in the airline industry. We also present a detailed study of the sensitivity of some modeling parameters via a full factorial experiment.
Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer

Background: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Materials and Methods: Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student’s t-test or Mann–Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. Results: Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10−3 mm2/s vs. 1.193 ± 0.337 ×10−3 mm2/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. Conclusion: APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.
Free-surface Simulations Using An Interface-tracking Finite-volume Method With 3D Mesh Movement

Abstract In the numerical solution of a free-surface fluid flow problem, an additional conservation equation (called the space conservation law) has to be solved along with the mass, momentum and energy conservation equations. In this paper a surface adaptive curvilinear finite volume method for solving free-surface flows is presented. The computational mesh defines hexahedra that are used as the control volumes in a finite-volume method in which the mesh moves to adapt to the free-surface. Dependent variables are located at the centre of each volume. Code verification is carried out on a number of inviscid, laminar and turbulent flow cases and the results show good agreement with experimental data and theoretical solutions.
Exploring the transition between water- and wind-dominated landscapes in Deep Springs, California, as an analog for transitioning landscapes on Mars

Abstract. Many planetary surfaces have been shaped by eolian and fluvial processes, and understanding the resulting landscape is of critical importance to understanding changes in climate. Surface features on Earth and Mars are commonly observed using a variety of remote sensing methods. The observed geomorphology provides evidence of present processes and paleo-processes, but interpretations are limited by the resolution of the data and similarity to well-understood systems on Earth. In this work, we study a complex fluvio-lacustrine and eolian landscape at Deep Springs playa, California, using field measurements and remote sensing as an analog for a wet-to-dry-transitioning landscape on Mars. The playa system in arid Deep Springs reflects fluvio-lacustrine processes in its interior but transitions to eolian-dominated processes along the playa margin. Weather station data and field observations collected over 34 months illustrate the interplay between eolian and lacustrine processes and provide context for interpreting the observed geomorphology in aerial images. Our results showed a consistent distal-to-proximal geomorphic transition in the landscape defined by the changing expression of polygonal fractures, wave ripples, and evaporite deposits. Crescent-shaped sedimentary deposits, originally suspected to be related to barchan dunes, proved unrelated to eolian processes. We discuss the processes, sedimentary features, and climate drivers at Deep Springs to provide a potential framework for identifying and interpreting similar interactions between fluvio-lacustrine and eolian geomorphology elsewhere on Earth, on Mars, and beyond.
Volumetric Model Genesis in Medical Domain for the Analysis of Multimodality 2-D/3-D Data Based on the Aggregation of Multilevel Features

The automatic and accurate classification of medical imaging data has potential applications in computer-aided disease diagnosis, prognosis, and treatment. However, it remains a challenge to optimize recent deep learning algorithms in the medical domain for the accurate classification of large-scale three-dimensional (3-D) volumetric data. To address these challenges, we propose an efficient deep volumetric classification network based on the aggregation of multilevel deep features for the accurate classification of large-scale medical 2-D/3-D imaging data. To perform a detailed quantitative analysis of our method, 26 different datasets were fused to construct a single large-scale multimodal database that comprises a total of seventy different classes, including 151,095 data samples. Additionally, 15 different baseline methods were configured under the same experimental protocol for volumetric model genesis and extensive performance comparison with our method. The experimental results of our method exhibited promising performance as an area under the curve of 93.66% and outperformed various state-of-the-art methods.