Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple is best: A single-CNN method for classifying remote sensing images

Recently, researchers have proposed a lot of methods to boost the performance of convolutional neural networks (CNNs) for classifying remote sensing images (RSI). However, the methods' performance improvements were insignificant, while time and hardware costs increased dramatically due to re-modeling. To tackle this problem, this study sought a simple, lightweight, yet more accurate solution for RSI semantic classification (RSI-SC). At first, we proposed a set of mathematical derivations to analyze and identify the best way among different technical roadmaps. Afterward, we selected a simple route that can significantly boost a single CNN's performance while maintaining simplicity and reducing costs in time and hardware. The proposed method, called RE-EfficientNet, only consists of a lightweight EfficientNet-B3 and a concise training algorithm named RE-CNN. The novelty of RE-EfficientNet and RE-CNN includes the following: First, EfficientNet-B3 employs transfer learning from ImageNet-1K and excludes any complicated re-modeling. It can adequately utilize the easily accessible pre-trained weights for time savings and avoid the pre-training effect being weakened due to re-modeling. Second, RE-CNN includes an effective combination of data augmentation (DA) transformations and two modified training tricks (TTs). It can alleviate the data distribution shift from DA-processed training sets and make the TTs more effective through modification according to the inherent nature of RSI. Extensive experimental results on two RSI sets prove that RE-EfficientNet can surpass all 30 cutting-edge methods published before 2023. It gives a remarkable improvement of 0.50% to 0.75% in overall accuracy (OA) and a 75% or more reduction in parameters. The ablation experiment also reveals that RE-CNN can improve CNN OA by 0.55% to 1.10%. All the results indicate that RE-EfficientNet is a simple, lightweight and more accurate solution for RSI-SC. In addition, we argue that the ideas proposed in this work about how to choose an appropriate model and training algorithm can help us find more efficient approaches in the future.
Entrepreneurial Mindset as a Facilitator and Barrier to Entrepreneurship Development: The Mediating Role of Entrepreneurial Culture

Entrepreneurship has been globally identified as an important factor for economic development. The importance of entrepreneurship has encouraged policymakers to invest in entrepreneurship development, and entrepreneurial mindset and culture are being assumed as important factors to consider in developing entrepreneurship success and growth. This study focused on the role of entrepreneurial mindset and culture on the entrepreneurship success or failure in South Africa. The objective of this study was to understand whether entrepreneurship culture can enhance entrepreneurial mindset and whether both entrepreneurial mindset and culture enhance entrepreneurship success and development. A qualitative research approach was used for this study, where a systematic literature review was followed as the method of review. Various online databases and peer-reviewed journal articles were used in analysing appropriate and relevant literature. The findings revealed that culture and mindset have a robust contribution towards entrepreneurship development and success. The findings further revealed that entrepreneurship culture makes an important contribution to the development of an entrepreneurial mindset, which encourages entrepreneurial intentions and venture success. This study indicates that the creation of an entrepreneurial mindset that drives creativity and innovation, resulting in new ventures being created and entrepreneurship culture being cemented, is very important in encouraging entrepreneurship growth and success.
The Impact of Prenatal Vitamin D on Enamel Defects and Tooth Erosion: A Systematic Review

Prenatal Vitamin D has been suggested to be critical for dental health in children, affecting outcomes including the prevalence of enamel defects and tooth erosion. This systematic review aimed to evaluate the potential impact of prenatal Vitamin D levels on these dental health outcomes. A total of seven studies, involving 6978 participants, were included after a comprehensive search of PubMed, Web of Science, and Scopus from 2013 to June 2023. The average age of mothers varied across studies, with Vitamin D levels or supplementation practices displaying significant variation among the study populations. The age of children at examination ranged from 3.6 to 6.6 years. The analysis demonstrated a diverse association between Vitamin D levels and dental outcomes, with enamel defects reported in 21.1% to 64% of the children and opacities ranging from 36% to 79.5% across studies. Maternal Vitamin D insufficiency was identified as a significant risk factor for enamel defects in one study (OR: 3.55), whereas high prenatal Vitamin D levels indicated a protective effect against Hypomineralized Second Primary Molars (OR: 0.84) and Molar Incisor Hypomineralization (OR: 0.95) in another. Conversely, low Vitamin D levels increased the risk of enamel hypoplasia (OR: 1.29) and dental decay. The maternal and child demographics varied greatly across the studies, and the assessment and prevalence of Vitamin D deficiency or insufficiency were heterogenous. This review illuminates the potential influence of prenatal Vitamin D on dental health in children, underscoring the importance of adequate Vitamin D levels during pregnancy. However, more robust research is required to establish the optimal Vitamin D intake during pregnancy to ensure healthy dental outcomes in children.
Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

ABSTRACT Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification.
Thinking by metaphor, fast and slow: Deliberate Metaphor Theory offers a new model for metaphor and its comprehension

The immense increase in metaphor theory and research over the past decades is posing a threat of fragmentation to the field, which has been responded to by calls for new and more encompassing approaches to virtually all aspects metaphorical. This article argues that the opposite response may be more productive. By focusing on a different way of theorizing metaphor and its comprehension, existing theories and data can be re-ordered in an alternative and coherent way, which moreover breaks new grounds in tying up both with a general theory for all utterance comprehension as well as a general theory for all cognition as involving fast and slow thinking. The core of the new theory highlights the differentiation between deliberate and non-deliberate metaphor use, related to how people see the use of a metaphor as a metaphor in communication, that is, as a metaphor that counts as a metaphor between language users. It shows how this distinction can be employed to make sense of many insights about metaphor and its comprehension in innovative ways. The article outlines the foundations of the new theory and discusses how existing data, old and new, can be seen as supporting the new proposals.
New Zealand acceleration response spectrum attenuation relations for crustal and subduction zone earthquakes

Attenuation relations are presented for peak ground accelerations (pga) and 5% damped acceleration response spectra in New Zealand earthquakes. Expressions are given for both the larger and the geometric mean of two randomly-oriented but orthogonal horizontal components of motion. The relations take account of the different tectonic types of earthquakes in New Zealand, i.e., crustal, subduction interface and dipping slab, and of the different source mechanisms for crustal earthquakes. They also model the faster attenuation of high-frequency earthquake ground motions in the volcanic region than elsewhere. Both the crustal and subduction zone attenuation expressions have been obtained by modifying overseas models for each of these tectonic environments to better match New Zealand data, and to cover site classes that relate directly to those used for seismic design in New Zealand codes. The study used all available data from the New Zealand strong-motion earthquake accelerograph network up to the end of 1995 that satisfied various selection criteria, supplemented by selected data from digital seismographs. The seismographs provided additional records from rock sites, and of motions involving propagation paths through the volcanic region, classes of data that are sparse in records produced by the accelerograph network. The New Zealand strong-motion dataset lacks records in the near-source region, with only one record from a distance of less than 10 km from the source, and at magnitudes greater than M w 7.23. The New Zealand data used in the regression analyses ranged in source distance from 6 km to 400 km (the selected cutoff) and in moment magnitude from 5.08 to 7.23 for pga, with the maximum magnitude reducing to 7.09 for response spectra data. The required near-source constraint has been obtained by supplementing the New Zealand dataset with overseas peak ground acceleration data (hut not response spectra) recorded at distances less than 10 km from the source. Further near-source constraints were obtained from the overseas attenuation models, in terms of relationships that had to be maintained between various coefficients that control the estimated motions at short distances. Other coefficients were fitted from regression analyses to better match the New Zealand data. The need for different treatment of crustal and subduction zone earthquakes is most apparent when the effects of source mechanism are taken into account. For crustal earthquakes, reverse mechanism events produce the strongest motions, followed by strike-slip and normal events. For subduction zone events, the reverse mechanism interface events have the lowest motions, at least in the period range up to about Is, while the slab events, usually with normal mechanisms, are generally strongest. The attenuation relations presented in this paper have been used in many hazard studies in New Zealand over the last five years. In particular, they have been used in the derivation of the elastic site spectra in the new Standard for earthquake loads in New Zealand, NZS 1170.5:2004.
Clinical application of a fully automated blood collection robot and its assessment of blood collection quality of anticoagulant specimens

Background and objectives To investigate the application of intelligent puncture blood collection robots in anticoagulated blood specimens, the satisfaction of subjects with the two blood collection methods, and the feasibility of intelligent blood collection devices to replace manual blood collection methods in clinical work. Materials and methods A total of 154 volunteers from Zhongshan Hospital Fudan University were recruited to compare the test results of anticoagulant blood samples between blood collection robot and manual blood collection, a questionnaire was used to inquire about the volunteers’ feelings about the two blood collection methods; the blood collection data of 6,255 patients willing to use the robot for blood collection were collected to analyze the success rate of blood collection. Results The blood collection robot is superior to manual specimen collection in terms of volume and pain of specimen collection, and the puncture success rate is 94.3%. The anticoagulated blood specimens collected by the robot had 11 indexes statistically different from the results of manual blood collection, but the differences did not affect the clinical diagnosis and prognosis. Conclusion The intelligent robotic blood collection is less painful and has better acceptance by patients, which can be used for clinical anticoagulated blood specimen collection.
Perceived Injustice in Cancer Survivors: Population-Specific Cut-Off Score and Relations with Personal Factors, Symptoms and Quality of Life—A Cross-Sectional Study

Fatigue and pain are the most common side effects impacting quality of life (QoL) in cancer survivors. Recent insights have shown that perceived injustice (PI) can play a substantial role in these side effects, but research on cancer survivors is scarce. Furthermore, guidelines for recognizing clinically relevant levels of PI in cancer survivors are missing. The aims of this study are to provide a clinically relevant cut-off for PI and to explore relationships between personal characteristics, symptoms, and QoL with PI. This multicenter, cross-sectional study uses the Injustice Experience Questionnaire (IEQ), Numeric Pain Rating Scale (NPRS), Patient-Specific Complaints (PSC), Multidimensional Fatigue Index (MFI), and European Organization for Research and Treatment of Cancer QoL Questionnaire-C30 (EORTC-QLQ-C30). A clinical cut-off for PI was identified based on the 75th percentile of IEQ scores. Univariate and multivariate regressions explored the relationship between PI and personal characteristics (sex, age, cancer type, treatment type), symptoms (pain intensity, fatigue), and QoL (daily activity complaints, cancer-related QoL). Cancer survivors (n = 121) were included, and a cut-off of 20 was identified. Significant indirect associations were found between chemotherapy, NPRS, PSC, MFI, and EORTC-QLQ-C30 with PI. In the multivariate model, only MFI (B = 0.205; 95% CI: 0.125–0.018) and age (B = 0.086; 95% CI: −0.191–0.285) maintained a significant association with PI.
Perceptions of resident physicians towards antibiotic prescribing during the COVID-19 pandemic: a qualitative study

Background: The COVID-19 pandemic affected physicians’ practices of prescribing antibiotics for Upper Respiratory Tract Infections (URTIs). Aim: This study aims to explore the perceptions of resident physicians in a teaching hospital towards antibiotic prescribing for Upper Respiratory Tract Infections (URTIs) during the COVID-19 pandemic. Methods: This was a qualitative study based on conducting in-depth face-to-face interviews with resident physicians. Interviews were audio-recorded, transcribed verbatim in Arabic, translated to English, anonymised, and analysed thematically. Results: Data analysis produced five broad themes: determinants of prescribing antibiotics for URTIs before and after the pandemic, uncertainty in differentiating between COVID-19 cases and other URTIs, the shift in antibiotic prescribing for URTIs following the pandemic, the place of azithromycin in the treatment of COVID-19, and patients’ expectations of antibiotic prescribing. Conclusion: The study demonstrated that the COVID-19 pandemic added to the complexity of controlling the use of antibiotics in a country that is already striving against inappropriate antibiotics utilization.
Bisphosphonates and Dental Implants: A Systematic Review and Meta-Analysis

The purpose of the present systematic review was to investigate the influence of bisphosphonates (BPs) on the dental implant failure rate and marginal bone loss (MBL). An electronic search was undertaken in three databases, plus a manual search of journals. Meta-analyses were performed, besides a meta-regression in order to verify how the log odds ratio (OR) was associated with follow-up time. The five- and ten-year estimated implant survivals were calculated. The review included 33 publications. Altogether, there were 1727 and 21,986 implants placed in patients taking and not taking BPs, respectively. A pairwise meta-analysis (26 studies) showed that implants in BP patients had a higher failure risk in comparison to non-BP patients (OR 1.653, p = 0.047). There was an estimated decrease of 0.004 in log OR for every additional month of follow-up, although it was not significant (p = 0.259). The global estimated implant survival in patients taking BPs after 5 and 10 years was 94.2% (95% CI, 94.0–94.4) and 90.1% (95% CI, 89.8–90.3), respectively. It was not possible to make any reliable analysis concerning MBL, as only two studies reported MBL results separated by groups. There is a 65.3% higher risk of implant failure in patients taking BPs in comparison to patients not taking this class of drugs.
Spectral reflectances of natural targets for use in remote sensing studies

A collection of spectral reflectances of 156 natural targets is presented in a uniform format. For each target both a graphical plot and a digital tabulation of reflectance is given. The data were taken from the literature and include laboratory, field, and aircraft measurements. A discussion of the different measurements of reflectance is given, along with the changes in apparent reflectance when targets are viewed through the atmosphere. The salient features of the reflectance curves of common target types are presented and discussed.
The Role of Career Development and Commitment to Mediate the Effect of Competence on the Employees Performance in Dinas Kependudukan & Pencatatan Sipil Kabupaten Sidoarjo

This study aims to determine and analyze the influence of the effect (1) competence on employee performance, (2) competence mediate career development on employee performance, and (3) competence mediate commitment on employee performance at Dinas Kependudukan & Pencatatan Sipil Kabupaten Sidoarjo. This is quantitative research of the associative variety. All civil servants at the Dinas Kependudukan & Pencatatan Sipil in Kabupaten Sidoarjo were included in the sample with saturation sampling approach. Forty-four civil servants were surveyed using questionnaires to collect the data. The quantitative data analysis technique uses PLS (Partial Least Square) with smartPLS 3.0 software. This research at the Dinas Kependudukan & Pencatatan Sipil Kabupaten Sidoarjo as a government agency in the field of public services in improving the performance of its employees by providing competency, career development and commitment to civil servants of Dinas Kependudukan & Pencatatan Sipil in Kabupaten Sidoarjo. The results indicated that competence has a significant positive effect on employee performance, career development has a non-significant effect in mediating competence's effect on employee performance, and commitment has a significant positive effect in mediating competence's effect on employee performance.
Particulate matter science for policy makers : a NARSTO assessment

1. Perspective for managing PM Marjorie Shepherd 2. Health effects context Roger McClellan 3. Atmospheric aerosol processes Spyros Pandis 4. Emission characterization George Hidy and Thompson Pace 5. Particle and gas measurements Fred Fehsenfeld, Don Hastie, Paul Solomon and Judy Chow 6. Spatial and temporal characterization of PM Charles Blanchard 7. Receptor methods Jeff Brook, Elizabeth Vega and John Watson 8. Chemical transport models Christian Seigneur and Michael Moran 9. Visibility and radiative balance effects Ivar Tombach and Karen McDonald 10. Conceptual models of PM for North American areas James Vickery 11. Recommended research to inform public policy Peter McMurry.
Modelos predictivos para el estudio del abandono en centros universitarios

El abandono universitario es un problema que siempre ha afectado a las distintas universidades publicas. La Universidad Politecnica de Madrid, en concreto la Escuela Tecnica Superior de Ingenieros Informaticos, pretende reducir dicho abandono aplicando tecnicas de Data Mining para detectar aquellos alumnos que abandonan antes de que lo hagan. Este proyecto, a traves de la metodologia CRISP-DM (CRoss Industry Standard Process for Data Mining) y con los datos obtenidos del SIIU (Sistema Integrado de Informacion Universitaria), tiene como fin identificar a los alumnos que abandonan el primer anoo para poder aplicar distintas politicas en ellos y asi evitarlo. ---ABSTRACT---Early university abandonment is a problem which has affected different public universities. The Universidad Polit´ecnica de Madrid (Polytechnic University of Madrid), inparticular the Escuela Tecnica Superior de Ingenieros Informaticos (Computer Science Engineering School), wants to reduce this abandonment rate using Data Mining techniques to find students who are likely to leave before they do. The purpose of this project, through CRISP-DM (CRoss Industry Standard Process for Data Mining) methodology and using the data from SIIU (Sistema Integrado de Informaci´on Universitaria), is to identify those students who leave the first year in order to apply different policies and avoid it.
School-Based Teacher Professional Development Framework (SBTPDF): A Blueprint for School Principals in Nigeria

There is a growing body of knowledge on effective Teacher Professional Development (TPD), with a consensus that skews in favour of decentralised, school-based and teacher-centred approach to TPD as opposed to the centralised, traditional and top-down approach. On the contrary, developing countries such as Nigeria still practise a centralised education system where traditional top-down TPD remains the norm, with the attendant consequence of professionally deficient teachers in the classroom. This situation has left a deficiency in centralised district/cluster-based teacher development. This study explored teachers’ and school administrators’ perceptions on the existing effectiveness TPD, with a view to developing a school-based TPD framework that caters for teachers’ professional needs. The study was a qualitative multiple-case study informed by the interpretivist paradigm. The study used, as its lenses, two complementary theories: Distributed Leadership Theory (DLT) and Adult Learning Theory (ALT). Three schools were selected using the purposive sampling technique. Four participants were selected from each school using a combination of purposive and snowball techniques. Data were collected through semi-structured interviews and analysed using the Thematic Analysis (TA) procedure. The findings revealed that one-size-fit-all centralised schools cluster TPD no longer meet the professional needs of teachers. The result also showed that participants preferred school-based teachers’ professional development to centralised approach. Hence, the study proposed a data-informed and theory-driven model known as School-Based Teacher Professional Development Framework (SBTPDF) as a blue-print for the implementation of school-based TPD for school administrators.