Digital Deforestation: Comparing Automated Approaches to the Production of Digital Terrain Models (DTMs) in Agisoft Metashape

This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeological sites. DTMs are often required for various forms of archaeological mapping and analysis. The suite of tools provided by Agisoft are relatively user-friendly as compared to many point cloud classification algorithms and do not require the use of additional software. Based on a case study from the Mycenaean site of Kastrouli, Greece, the mostly-automated, geometric classification tool “Classify Ground Points” provides the best results and produces a quality DTM that is sufficient for mapping and analysis. Each of the methods tested in this paper can likely be improved through manual editing of point cloud classification.

The utility of the Edmonton Obesity Staging System for the prediction of COVID-19 outcomes: a multi-centre study

Background Patients with obesity have an increased risk for adverse COVID-19 outcomes. Body mass index (BMI) does not acknowledge the health burden associated this disease. The performance of the Edmonton Obesity Staging System (EOSS), a clinical classification tool that assesses obesity-related comorbidity, is compared with BMI, with respect to adverse COVID-19 outcomes. Methods 1071 patients were evaluated in 11 COVID-19 hospitals in Mexico. Patients were classified into EOSS stages. Adjusted risk factors for COVID-19 outcomes were calculated and survival analysis for mechanical ventilation and death was carried out according to EOSS stage and BMI category. Results The risk for intubation was higher in patients with EOSS stages 2 and 4 (HR 1.42, 95% CI 1.02–1.97 and 2.78, 95% CI 1.83–4.24), and in patients with BMI classes II and III (HR 1.71, 95% CI 1.06–2.74, and 2.62, 95% CI 1.65–4.17). Mortality rates were significantly lower in patients with EOSS stages 0 and 1 (HR 0.62, 95% CI 0.42–0.92) and higher in patients with BMI class III (HR 1.58, 95% CI 1.03–2.42). In patients with a BMI ≥ 25 kg/m2, the risk for intubation increased with progressive EOSS stages. Only individuals in BMI class III showed an increased risk for intubation (HR 2.24, 95% CI 1.50–3.34). Mortality risk was increased in EOSS stages 2 and 4 compared to EOSS 0 and 1, and in patients with BMI class II and III, compared to patients with overweight. Conclusions EOSS was associated with adverse COVID-19 outcomes, and it distinguished risks beyond BMI. Patients with overweight and obesity in EOSS stages 0 and 1 had a lower risk than patients with normal weight. BMI does not adequately reflect adipose tissue-associated disease, it is not ideal for guiding chronic-disease management.

Knowledge of ICU Nurses Regarding the Ongoing Nursing Assessment of ICU Patients in Khartoum City, 2020

Background: An ongoing nursing assessment is the most significant point in the nursing process to be executed in the beginning of every shift which can be accomplished by using different approaches. It needs to be conducted accurately to guide professional nurses’ decision-making ability to further provide holistic nursing care to patients in the intensive care units (ICUs). This study was aimed to assess the ICU nurses’ knowledge regarding ongoing nursing assessment of ICU patients. Methods: This descriptive cross-sectional hospital-based study was conducted in Khartoum city, and included 86 out of the 135 participants working in the critical care units of the main governmental hospitals in Khartoum city. Data were collected using a structured self-administered questionnaire after being tested for validity and then analyzed using mean, standard deviation, and correlation. Data were then presented as frequencies and percentages. Results: The study participants were aged between 20 and 40 years with a female to male ratio of 3:1, and varied levels of experience. Overall, 71.7% of the studied participants scored good on the standardized knowledge classification tool used, with few areas of knowledge gap, impacted by increased experience. Moreover, 36% of the participants used the ABCDE approach for ongoing nursing assessment, followed by the head-to-toe assessment approach (21%). Conclusion: The nurses’ knowledge regarding ongoing nursing assessment was good with a few areas of weakness raising the need for continuous educational and training programs.

Predictive Analysis in Business Analytics: Application of Decision Tree in Business Decision Making

Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on hand data with the final objective of improving performance of the corporation. Predictive analysis has been gaining much attention in the late 20th century and it has been around for decades, but as technology advances, so does this technique and the techniques include data mining, big data analytics, and prescriptive analytics. Last but not least, the decision tree methodology (DT) which is a supervised simple classification tool for predictive analysis which be fully scrutinized below for applying predictive business analytics and DT in business applications

Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

Distinguishing COVID-19 infection and vaccination history by T cell reactivity

SARS-CoV-2 infection and COVID-19 vaccines elicit memory T cell responses. Here, we report the development of two new pools of Experimentally-defined T cell epitopes derived from the non-spike Remainder of the SARS-CoV-2 proteome (CD4RE and CD8RE). The combination of T cell responses to these new pools and Spike (S) were used to discriminate four groups of subjects with different SARS-CoV-2 infection and COVID-19 vaccine status: non-infected, non-vaccinated (I-V-); infected and non-vaccinated (I+V-); infected and then vaccinated (I+V+); and non-infected and vaccinated (I-V+). The overall classification accuracy based on 30 subjects/group was 89.2% in the original cohort and 88.5% in a validation cohort of 96 subjects. The T cell classification scheme was applicable to different mRNA vaccines, and different lengths of time post-infection/post-vaccination. T cell responses from breakthrough infections (infected vaccinees, V+I+) were also effectively segregated from the responses of vaccinated subjects using the same classification tool system. When all five groups where combined, for a total of 239 different subjects, the classification scheme performance was 86.6%. We anticipate that a T cell-based immunodiagnostic scheme able to classify subjects based on their vaccination and natural infection history will be an important tool for longitudinal monitoring of vaccination and aid in establishing SARS-CoV-2 correlates of protection.

Deep Learning Applied to SEM Images for Supporting Marine Coralline Algae Classification

The classification of coralline algae commonly relies on the morphology of cells and reproductive structures, along with thallus organization, observed through Scanning Electron Microscopy (SEM). Nevertheless, species identification based on morphology often leads to uncertainty, due to their general plasticity. Evolutionary and environmental studies featured coralline algae for their ecological significance in both recent and past Oceans and need to rely on robust taxonomy. Research efforts towards new putative diagnostic tools have recently been focused on cell wall ultrastructure. In this work, we explored a new classification tool for coralline algae, using fine-tuning pretrained Convolutional Neural Networks (CNNs) on SEM images paired to morphological categories, including cell wall ultrastructure. We considered four common Mediterranean species, classified at genus and at the species level (Lithothamnion corallioides, Mesophyllum philippii, Lithophyllum racemus, Lithophyllum pseudoracemus). Our model produced promising results in terms of image classification accuracy given the constraint of a limited dataset and was tested for the identification of two ambiguous samples referred to as L. cf. racemus. Overall, explanatory image analyses suggest a high diagnostic value of calcification patterns, which significantly contributed to class predictions. Thus, CNNs proved to be a valid support to the morphological approach to taxonomy in coralline algae.

Motives for Studying and Student Wellbeing: Validation of the Motivational Mindset Model

Research on the joint effect of multiple motives for studying was recently given a push in a new direction with the introduction of the motivational mindset model (MMM). This model contributes to a better understanding of study success and student wellbeing in higher education. The aim of the present study is to validate the newly developed model and the associated mindset classification tool (MCT). To this end, 662 first-year university students were classified in one of the four types of motivational mindset using the classification tool and three exploratory validation procedures were conducted through sense of purpose, study engagement, and students’ background characteristics in terms of gender and ethnicity. Both purpose and study engagement are central dimensions of student wellbeing and predictors of study success. The results show that (1) sense of purpose and study engagement differ across the four types of mindset, (2) students in the low-impact mindset show the least optimal pattern of study engagement and sense of purpose, (3) sense of purpose and study engagement are positively related and this relationship is consistent across mindsets, and (4) overall differences in purpose and study engagement between gender and ethnic subgroups stem from one specific type of motivational mindset. The results provide support for the validity of the MMM and the usefulness of the MCT. The implications of the findings are discussed as well as promising avenues for future research.

No Need of Data Pre-processing

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

The Use of Electronic Nose as Alternative Non-Destructive Technique to Discriminate Flavored and Unflavored Olive Oils

Cv. Arbequina extra virgin olive oils (EVOO) were flavored with cinnamon, garlic, and rosemary and characterized. Although flavoring significantly affected the physicochemical quality parameters, all oils fulfilled the legal thresholds for EVOO classification. Flavoring increased (20 to 40%) the total phenolic contents, whereas oxidative stability was dependent on the flavoring agent (a slight increase for rosemary and a decrease for cinnamon and garlic). Flavoring also had a significant impact on the sensory profiles. Unflavored oils, cinnamon, and garlic flavored oils had a fruity-ripe sensation while rosemary flavored oils were fruity-green oils. Fruit-related sensations, perceived in unflavored oils, disappeared with flavoring. Flavoring decreased the sweetness, enhanced the bitterness, and did not influence the pungency of the oils. According to the EU regulations, flavored oils cannot be commercialized as EVOO. Thus, to guarantee the legal labelling requirement and to meet the expectations of the market-specific consumers for differentiated olive oils, a lab-made electronic nose was applied. The device successfully discriminated unflavored from flavored oils and identified the type of flavoring agent (90 ± 10% of correct classifications for the repeated K-fold cross-validation method). Thus, the electronic nose could be used as a practical non-destructive preliminary classification tool for recognizing olive oils’ flavoring practice.