Miracle-dz/UniFAR-FTUs huggingface dataset:
SPATIAL DISTRIBUTION AND INTERSPECIFIC ASSOCIATION PATTERNS BETWEEN SHOREA ROXBURGHII G. DON AND OTHER TREE SPECIES IN A SOUTH VIETNAM EVERGREEN FOREST

To understand the ecology and spatial distribution patterns of Shorea roxburghii, a vulnerable plant on the IUCN’s Red List, this study was conducted in a Vietnamese evergreen forest. To collect data, a 100 m × 100 m plot was established in the Tan Phu protected forest, in South Vietnam. Relative density, relative basal area, and importance value index (IVI) were determined for each species. Individuals were categorized by juvenile (dbh < 5 cm), subadult (5 cm ≤ dbh < 10 cm) and adult (dbh ≥ 10 cm) life-history stages. We used Excel, Programita Noviembre 2018 and R to analyze data. Spatial analyses focused on overall univariate patterns, dispersion patterns, and hetero-specific association of the species. A total of 111 species was found. Shorea roxburghii has higher basal area, relative density, maximum dbh, IVI and lower median main neighbour. Shorea roxburghii has cluster distribution in the juvenile (5f) and subadult stage (5g), regular or clustered pattern in the adult stage (5h). However, the general trend is a regular pattern as the dbh increases. With the achieved results, this study contributes to the simulation method of forest species ecological characteristics, including distribution characteristics, intra and interspecific relationships in the space of Shorea roxburghii.
Lunar landing : dynamic operator interaction with multi-modal automation systems

The ability of operators to “gracefully transition” (maintaining control and awareness of the system without excessive workload or decrements in flight performance) between levels of automation (LOA) in several case studies and in a simulated lunar landing was investigated in anticipation of future lunar missions. Endsley’s situation awareness model (extended to apply to supervisory control systems) and the Sheridan/Verplank and Proud/Hart LOA scales were used to analyze six maritime, aviation, and aerospace case studies and formulate design guidelines for enhancing mode transitions. These motivated an experiment in which thirteen subjects with flight simulator experience flew 24 approach trajectories (half including a landing point redesignation) that transitioned from a fully automatic flight control mode to either: pitch rate-control/attitude hold (RC/AH) with automatic rate-of-descent (ROD), roll-pitch-yaw (RPY) RC/AH with automatic ROD, or RPY RC/AH with incremental ROD. Subjective and objective workloads were measured using a Modified Bedford Scale and secondary task response time, respectively. A tertiary task verbal callouts of altitude, fuel, and location, provided a measure of pilot situation awareness. Flight performance was evaluated using the pitch axis tracking error. Friedman pairwise tests demonstrated that secondary task response time significantly increased following the mode transition. Subjects’ workload ratings, when ranked, showed unanimous agreement that workload was lowest prior to the transition, and highest during. The accuracy of the situation awareness verbal callouts decreased significantly after the transition. The immediate effect of redesignation was statistically concordant across subjects. Pitch axis tracking mean square error following a mode transition was greater in trials with redesignations (p = 0.0005), and increased consistently with control mode difficulty
Designing Effective Online Courses: Exploring the Relationships Amongst Online Teaching Self-efficacy, Professional Development, Online Teaching Experience, and Reported Implementation of Effective Higher Education Online Course Design Practices

Abstract How best to prepare and support higher education faculty to design and teach effective online courses is a topic of great significance to higher education institutional leaders and faculty developers. This study explored how hours of professional development along with online teaching and learning experiences were related to online teaching self-efficacy and the extent to which participants reported implementation of effective online course design practices. Using a non-experimental quantitative correlational explanatory research study design, data were collected using a questionnaire. Participants included 104 online faculty from a large public higher education system located in the upper Midwest that includes both community colleges and universities. The findings suggested that both online teaching self-efficacy and self-reported ratings of implementation of effective online course design practices were higher when individuals have completed at least 20 hours of professional development meant to prepare them to teach online, have experience as an online instructor and/or online learner, and have participated in a peer review of their online course. The findings offer insights into how those with varying levels of online teaching self-efficacy rate their online course design practices and suggest that faculty may not accurately self-assess their course design abilities. The results and implications for those who are planning for and providing professional development meant to prepare faculty to teach online are discussed.
Large-scale comparative review and assessment of computational methods for phage virion proteins identification

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.
Imprecise Information and Second-Order Beliefs ∗

A decision problem under uncertainty is often given with a piece of objective but imprecise information about the states of the world such as in the Ellsberg urn. By incorporating such information into the smooth ambiguity model of Seo (2009), we characterize a class of smooth ambiguity representations whose second-order beliefs are consistent with the objective information. As a corollary, we provide an axiomatization for the second-order expected utility, which has been studied by Nau (2001), Neilson (2009), Grant, Polak, and Strzalecki (2009), Strzalecki (2011), and Ghirardato and Pennesi (2019). In our model, attitude toward uncertainty can be disentangled from a perception about uncertainty and connected with attitude toward reduction of compound lotteries. ∗An earlier version of this paper was presented under the title “State-Wise Dominance and Subjective Probabilities. ” We would like to thank Koji Abe, Simone Cerreia-Vioglio, Andrew Ellis, Larry Epstein, Spyros Galanis, Takashi Hayashi, Youichiro Higashi, Mayumi Horie, Atsushi Kajii, Peter Klibanoff, Igor Kopylov, Jawwad Noor, Pietro Ortoleva, Kyoungwon Seo, Peter Wakker, Jingyi Xue, and the audiences at RUD 2017 (London Business School), and the seminar participants at Iwate Prefectural University and Kyoto University for their useful comments. Ui acknowledges financial support by MEXT, Grant-in-Aid for Scientific Research 18H05217. All remaining errors are the authors’ responsibility.
FS Resident ' s backpack Recurrent anteroinferior dislocation of the shoulder . Essential articles

255 ABSTRACT Anterior glenohumeral dislocation is one of the most common disorders in orthopaedic traumatology. Knowing its natural history helps us to know what we can offer our patients according to the phase of the condition they are in. The resident's backpack does not intend to offer a protocolised review of this disease condition but to provide a brief review of a series of articles that are essential for the study and understanding of this condition. Through the comments, we can set the basis of aspects that are crucial to the study of shoulder instability. It is of no use to know the latest techniques available to restore shoulder stability if we are not familiar with aspects as basic as the functioning of the shoulder ligaments or the implication of bone defects and how they are related, together with an understanding of middle range stability and end of range stability. We must know what to do in the case of a first traumatic dislocation episode, understanding why it is essential in such a common disorder, and must moreover take into account that patient age is one of the most important factors. Likewise, knowing how to describe the concept of glenoid track and what it is used for is of great help when we are beginning, in order to know the different treatment alternatives open to us. RESUMEN Luxación recidivante anteroinferior del hombro. Artículos imprescindibles
Factors Influencing the Usage of Online Shopping in Albania

The increase in the use of the internet worldwide has shifted consumers’ attention to online shopping. The increased confidence in technology, ability to use information and communication technology and online payment methods are changing consumer buying behaviour away from traditional methods. Online shopping is showing encouraging progress among customers in Albania. An incentive for the increase in online shopping is the impact of the pandemic situation of COVID-19 that forced foreign and domestic retailers to offer their products online as well. The purpose of this research was to study the online shopping behaviour of individual customers in Albania. The objectives of this study were: to identify the benefits and challenges of online shopping, the products that are bought more often, and the most frequently used websites; and to determine the demographic and socio-economic factors that influence the usage on online shopping, the experience with online shopping, and the frequency of online shopping. Descriptive analysis and binary logistic regression analysis are used to analyse the collected data for 213 respondents. The results indicated that age, region, and income level significantly influence the usage of online shopping; age and income level significantly influence the experience with online shopping, and marital status and income level significantly influence the frequency of online shopping. The findings of this study provide insights for online shoppers, delivery companies, and online retailers for market segmentation and planning marketing strategies.
RECOGNITION OF MAN-MADE OBJECT IN UNDERWATER USING TRANSFER LEARNING AND DEEP LEARNING

Underwater image processing has always been a promising and thrilling task due to the natural condition and the lighting effect for taking the image requires good artificial lights. While taking underwater images lots of difficulties are faced by photographers such as the shadows, non-uniform lighting, color shading, etc. Recognizing the object underwater is very difficult in order to the environmental condition. Man-made object recognition was made with underwater optical sensors to capture underwater images that have gained more attention from the users. Deep learning methods have demonstrated impressive performance in object recognition tasks from natural images. Anyhow it is hard to collect all the labelled underwater optical images for training the model. It is possible to acquire labelled images. Based on the assumption that it is possible to acquire sufficient labeled in-air images, the proposed work leverages a combination of deep learning and transfer learning to develop a novel recognition system for the man-made object from underwater optical images. The extracted features from the proposed network have high representative power and demonstrate robustness in both in-air and underwater imaging modalities. Therefore, our proposed framework has the ability to recognize underwater man-made objects using only labeled in-air images. The results of experiments on simulated data demonstrate that the proposed method outperforms traditional deep learning methods in the task of underwater man-made object recognition.
Risk factors, time to onset and recurrence of delirium in a mixed medical-surgical ICU population: A secondary analysis using Cox and CHAID decision tree modeling

A retrospective secondary analysis of 4,200 patients was collected from two academic medical centers. Delirium was assessed using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in all patients. Univariate and multivariate Cox models, logistic regression analysis, and Chi-square Automatic Interaction Detector (CHAID) decision tree modeling were used to explore delirium risk factors. Increased delirium risk was associated with exposed only to artificial light (AL) hazard ratio (HR) 1.84 (95 % CI: 1.66-2.044, P<0.001), physical restraint application 1.11 (95 % CI: 1.001-1.226, P=0.049), and high nursing care requirements (>8 hours per 8-hour shift) 1.18 (95 % CI: 1.048-1.338, P=0.007). Delirium incidence was inversely associated with greater family engagement 0.092 (95 % CI: 0.014-0.596, P=0.012), low staff burnout and anticipated turnover scores 0.093 (95 % CI: 0.014-0.600, P=0.013), non-ICU length-of-stay (LOS)<15 days 0.725 (95 % CI: 0.655-0.804, P<0.001), and ICU LOS ≤15 days 0.509 (95 % CI: 0.456-0.567, P<0.001). CHAID modeling indicated that AL exposure and age <65 years were associated with a high risk of delirium incidence, whereas SOFA score ≤11, APACHE IV score >15 and natural light (NL) exposure were associated with moderate risk, and female sex was associated with low risk. More rapid time to delirium onset correlated with baseline sleep disturbance (P=0.049), high nursing care requirements (P=0.019), and prolonged ICU and non-ICU hospital LOS (P<0.001). Delirium recurrence correlated with age >65 years (HR 2.198; 95 % CI: 1.101-4.388, P=0.026) and high nursing care requirements (HR 1.978, 95 % CI: 1.096-3.569), with CHAID modeling identifying AL exposure (P<0.001) and age >65 years (P=0.032) as predictive variables. Development of ICU delirium correlated with application of physical restraints, high nursing care requirements, prolonged ICU and non-ICU LOS, exposure exclusively to AL (rather than natural), less family engagement, and greater staff burnout and anticipated turnover scores. ICU delirium occurred more rapidly in patients with baseline sleep disturbance, and recurrence correlated with the presence of delirium on ICU admission, exclusive AL exposure, and high nursing care requirements.
Correlating Laboratory Fatigue Endurance Limits to Field-Measured Strains

The most commonly used technique for predicting the fatigue performance of asphalt mixtures in the field is the bending beam fatigue test. However, there has been some difficulty in developing meaningful relationships between the laboratory and the field, sometimes extending to perpetual pavement design. To examine this issue, three analysis techniques compared measured strain data from embedded asphalt strain gauges under dynamic loading at the 2006 National Center for Asphalt Technology (NCAT) Test Track to laboratorymeasured endurance limits. These analysis techniques were originally used to evaluate fatigue endurance limits of two mixtures placed at the 2003 NCAT Test Track. This research was designed to further validate previous results. The first analysis compared the laboratory fatigue endurance limits of the mixtures to field-measured strains at a reference temperature (20C). The analysis was developed to determine if pavements that performed well in the field had field-measured strains less than the endurance limit. As with the previous study, a strong relationship between field-measured strains at a relative temperature and the laboratory fatigue endurance limit did not exist. Some test sections with field-measured strains lower than the laboratory fatigue endurance limit cracked. The converse was also true. The second analysis technique compared the fatigue endurance limit to a percentile on each section’s cumulative strain distribution. If a greater percentage of strains fell below the fatigue endurance limit, then one would hypothesize better performance in the field. As in 2003, the findings from the 2006 Test Track using this analysis technique were inconsistent. Test sections which showed no evidence of fatigue cracking had only 10% of their strain events below the fatigue endurance limit while other test sections which cracked had 50% of their strains repetitions below the laboratory threshold. The final analysis, using the concept of a laboratory to field strain ratio, showed the best relationship between laboratory fatigue endurance limits, field-measured strain data, and field performance. The strain ratio represented the nth percentile strain of a section’s cumulative strain distribution divided by the laboratory endurance limit. For example, the fatigue ratio for the 95th percentile strain is 2.76. The results of this final analysis verified prior findings that showed the fatigue ratio was the most appropriate analysis technique to use to correlate pavement performance with the laboratory fatigue endurance limit.
Genome-wide association study of nociceptive musculoskeletal pain treatment response in UK Biobank

Drug treatment for nociceptive musculoskeletal pain (NMP) follows a three-step analgesic ladder proposed by the World Health Organization (WHO), starting from non-steroidal anti-inflammatory drugs (NSAIDs), followed by weak or strong opioids until the pain is under control. However, effective pain treatment is challenged by inter-individual differences, and unsatisfied pain treatment response (PTR) rates ranging from 34 to 79% in those suffering from NMP. To investigate the underlying genetic component of PTR, we performed a genome-wide association study (GWAS) in ~ 23,000 participants with NMP from the UK Biobank. In our primary analysis, we compared NSAID vs. opioid users as a reflection of (non)response to NSAIDs, adjusting for age, sex, BMI, population substructure, and study-specific covariates. One genome-wide significant hit was identified in an intergenic region on chromosome 4, rs549224715 (P = 3.88x10-8), and seven signals pass the suggestively significant threshold (P < 1x10-6). All identified loci were in non-coding regions, but most variants showed potential regulatory functions. SNPs in LD (r2 > 0.6) with the lead SNPs passing the nominal significant threshold (P < 0.05) were mapped to 28 target genes in FUMA. Eight of these 28 genes are involved in processes linked to neuropathic pain and musculoskeletal development. Pathway and network analyses with Ingenuity resulted in the identification of immunity-related processes and a (putative) central role of EGFR. Genetic correlation analysis including 596 traits resulted in the identification of 67 nominally significant (P < 0.05) genetic correlations, and these traits were significantly enriched for chronic pain and socioeconomic status traits (P = 3.35 x 10-12). Additionally, we conducted a subtype GWAS for inflammatory NMP and a secondary GWAS for participants with NMP disease history, but no significant hits or overlap with the primary analysis were observed. Overall, we identified one genome-wide significant association in this first GWAS focusing on pain treatment using the analgesic ladder as phenotype. However, we realize that this study lacked power and should be viewed as a first step to elucidate the genetic background of NMP treatment.
Ab initio study of dynamical properties of U–Nb alloy melt

The U–Nb alloy, as a kind of nuclear material with good corrosion resistance and mechanical properties, plays an important role in the nuclear industry. However, the experimental measurements and theoretical calculations of many parameters which are essential in describing the dynamical properties of this alloy melt, including density, diffusivity, and viscosity, have not been carried out yet. The lack of data on the dynamical properties of nuclear materials seriously hinders the high-performance nuclear materials from being developed and applied. In this work, the dynamical properties of the U–Nb alloy melt are systematically studied by means of ab initio molecular dynamics simulations and their corresponding mathematical models are established, thereby being able to rapidly calculate the densities, diffusion coefficients, viscosities, and their activation energies in the whole U–Nb liquid region. This work provides a new idea for investigating the dynamical properties of binary alloy melts, thereby promoting the development of melt research.
A HIF1-alpha Regulatory Loop Links Hypoxiaand Mitochondrial Signals in Pheochromocytomas

are neural crest–derived tumors that arise from inherited or sporadic mutations in at least six independent genes. The proteins encoded by these multiple genes regulate distinct functions . We show here a functional link between tumors with VHL mutations and those with disruption of the genes encoding for succinate dehydrogenase (SDH) subunits B (SDHB) and D (SDHD). A transcription profile of reduced oxidoreductase is detected in all three of these tumor types, together with an angiogenesis/hypoxia profile typical of VHL dysfunction. The oxidoreductase defect, not previously detected in VHL -null tumors, is explained by suppression of the SDHB protein, a component of mitochondrial complex II. The decrease in SDHB is also noted in tumors with SDHD mutations. Gain-of-function and loss-of-function analyses show that the link between hypoxia signals (via VHL) and mitochondrial signals (via SDH) is mediated by HIF1 a . These findings explain the shared features of pheochromocytomas with VHL and SDH mutations and suggest an additional mechanism for increased HIF1 a activity in tumors.