Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote Sensing Image Classification: A Comprehensive Review and Applications

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.
A Performance-Based Window Design and Evaluation Model for Naturally Ventilated Offices

This study proposes a performance-based window design model for optimised natural ventilation potential by reducing the level of indoor carbon dioxide (CO2) concentration and improving thermal comfort, consequently minimising supplementary heating/cooling loads. The model consists of several stages: (1) Knowledge acquisition, (2) establishing a relationship between window design and natural ventilation, (3) identifying performance criteria and the design of experiments (DOE), (4) conducting performance-based dynamic simulations, (5) evaluation of findings, and (6) making informed design decisions. The study also proposed an evaluation method by which assessments of indoor CO2 concentration and adaptive thermal comfort are performed using the threshold suggested by the World Health Organisation (WHO, Geneva, Switzerland) and the acceptability categories of the British/European standard BS EN 15251:2007. The proposed model was applied to a single office inspired by the staff offices at the Department of Architecture, Eastern Mediterranean University, Famagusta, North Cyprus. The findings show that the developed model of performance-based window design enables the handling of various window design variables along with different performance criteria to determine the near-optimal window design alternatives for effective natural ventilation (NV) and mixed-mode (MM) offices. This model can guide architects in making informed decisions in the early stages of office window design.
Are Psychophysiological Wearables Suitable for Comparing Pedagogical Teaching Approaches?

This study describes how wearable devices can be used in elementary schools to compare some aspects of different teaching approaches. Upper arm wearables were used as an objective tool to compare three approaches when teaching science: (i) classical frontal teaching, (ii) embodied (kinesthetic) teaching, and (iii) a distance teaching approach. Using the wearables, the approaches were compared in terms of their impact on students’ psychological arousal and perceived well-being. In addition, short-term and long-term knowledge gain and physiological synchronization between teacher and students during the lecture were assessed. A synchronization index was defined to estimate the degree of physiological synchronization. During distance teaching, by means of measurements with wearables, students were significantly less physically active and significantly less psychologically aroused. Embodied teaching allowed significantly higher physical activation than during the other two approaches. The synchronization index for all three teaching approaches was positive with the highest values for distance and frontal teaching. Moreover, knowledge gain immediately after the embodied lessons was higher than after frontal lessons. No significant differences in the long-term knowledge retention between the three different teaching methods were found. This pilot study proved that wearables are a useful tool in research in the field of education and have the potential to contribute to a deeper understanding of the mechanisms involved in learning, even in complex environments such as an elementary school classroom.
Student teachers’ development through a first-time teaching practicum and challenges: a qualitative case study approach

ABSTRACT Japan enacted a new teacher education curriculum in 2019, following which it became possible to include school experiences and internships within one’s practicum credits. However, the contents of the practicum are not systematically arranged, and discussions on student-teachers’ (STs) learning and their resulting professional development achieved through the practicum are limited. The purpose of this study is to identify and examine the challenges STs faced in their initial school experience, with a particular focus on STs who began their teacher education programme. Twenty-eight STs attending the early stages of a teacher education programme participated in this qualitative case study. The findings suggest that STs have recognised their own challenges regarding understanding students, teaching skills and methods, and active communication. In addition, the results re-confirm the value of early school experience in teacher training. Particularly, the results suggest that the first school experience can help STs realise the importance of acquiring the knowledge and skills necessary to know and act like a teacher, and the importance of theoretical learning at university.
A Simple Method of Trend Construction

SUMMARY The principle adopted here in the construction of a trend for a time series consists in minimizing a linear combination of two sums of squares, of which one refers to the second differences of the trend values, the other to the deviations of the observations from the trend values. Properties of the general solution are deduced, and the solution is explicitly obtained for up to 7 observations. In the special case in which the sum of the two sums of squares is minimized, the exact solution is derived for up to 15 observations. An approximation formula, suitable for practical use when there are 8 or more observations, is also given. The method is illustrated by examples, in which it is applied to an artificially constructed and to an actual time series.
Flows on 2-dimensional Manifolds: An Overview

1 Definitions and Examples: Preliminaries Basic constructions Basic examples. 2 Poncare-Bendixon's theory: Existence of closed transversal Absence of non-trivial recurrent trajectories on some surfaces Hilmy's and Cherry's theorems on quasiminimal sets Gutierrez's structure theorem Limit set of individual trajectory 3 Decomposition of flows: Decomposition theorems Center of flow Blowing-down of flows Regular flows Application: smoothing of flows. 4 Local Theory: Topological normal forms Analytical normal forms Smooth normal forms Finitely smooth normal forms, Degenerate critical points C1 normal forms of degenerate singularities. 5 Space of Flows and vector fields: Structural stability Classification of Morse-Smale flows Lyapunov's method, Connected components of Morse-Smale flows Degrees of non-stability, Typical properties of non-stable flows. 6 Ergodic theory: Liouville's theorem Kolmogorov's theorem for flows on torus Non-trivial invariant measures Ergodicity Mixing Entropy. 7 Invariants of surface flows: Topological classification of torus flows Oriented surfaces of higher genus . z 2 Application of geodesic laminations Transitive flows on non-orientable surfaces Classification of exceptional minimal sets Classification of regular flows Classification of non-wandering flows Cayley graph of a flow Homology and cohomology invariants Rotation sets of surface flows Smooth classification of flows. 8 C*-algebras of Surface Flows: Irrational rotation algebra Artin's rotation algebra K-theory C*-algebra of Morse-Smale flows. 9 Semi-local Theory: Denjoy's and Schwarz's theorems Cherry's problem Local structure preventing quasiminimality. 10 Anosov-Weil Problem: Theorems of Weil and Anosov Asymptotic direction of individual curves Approximation of curve by trajectories of a flow Limit sets of curves and trajectories at the absolute Deviation of curves from the geodesics Examples of unbounded deviation. 11 Non-compact Surfaces: Kaplan classification Level curves of harmonic functions Markus's classification Structural stability Neumann's example Inaba example and Beniere-Meigniez theorem, Beniere-Hector theorem Aranson-Zhuzhoma's example. 12 Triptych: Geodesic frameworks revisited On continuity and collapse of geodesic frameworks Cr-Closing lemma.
Change Captioning: A New Paradigm for Multitemporal Remote Sensing Image Analysis

Change detection (CD) is among the most important applications in remote sensing (RS) that allows identifying the changes that occurred in a given geographical area across different times. Even though CD systems have seen a lot of progress in RS, their output is either a binary map highlighting the changing area or a semantic change map that indicates the type of change for each pixel. The change maps are often difficult to interpret by end users, and they omit important information such as relationships and attributes of the changed areas. Motivated by the recent advancement of image captioning in the RS community, in this article, we propose to describe the changes over bitemporal images through change sentence descriptions. The aim of this article is to provide a user-friendly interpretation of the occurred changes. To this end, we propose two change captioning (CC) systems that take bitemporal images as input and generate coherent sentence descriptions of the occurred changes. Convolutional neural networks (CNNs) are used to extract discriminative features from the bitemporal images and recurrent neural networks (RNNs) or support vector machines (SVMs) are exploited to generate coherent change descriptions. Furthermore, in the absence of a CC dataset to test our systems, we propose two new datasets. One is based on very high-resolution RGB images, and the other one is based on multispectral RS images. The obtained experimental results show promising capabilities of the proposed systems to generate coherent change descriptions from the bitemporal images. The datasets are available at the following link: https://disi.unitn. it/~melgani/datasets.html.
The impact of chief executive officer (CEO) and deal characteristics on mergers and acquisitions (M&A) duration: A quantile regression evidence from an emerging market

This paper examines the impact of chief executive officer (CEO) and deal characteristics on mergers and acquisitions (M&A) duration in Malaysia. Univariate analysis and quantile regression (QR) are performed on 556 completed M&As transactions undertaken by Malaysian public firms from 2001 to 2019. In line with the upper echelons theory, which states that organizational outcomes can be predicted by looking at the characteristics of top-level executives, the findings from QR show that CEO characteristics significantly affect acquisition duration. This effect is conditional on the duration quantiles for CEO tenure and CEO duality but non-conditional for foreign CEO. Specifically, the findings reveal that the degree of influence by CEO characteristics gets stronger when the transactions are longer and complicated. CEO tenure can decrease M&A duration when a transaction falls in longer duration quantile. M&A transactions tend to take a longer duration when there is CEO duality. Foreign CEOs show more ability to execute transactions in a short duration compared to local CEOs. Deal characteristics such as deal size, merger transaction, hiring a financial advisor and conducting multiple acquisitions are main factors that prolong duration. The findings of this study may benefit policymakers, managers, and investors who involve directly and indirectly in an M&A process.
Optimal Control of Automated Vehicles for Autonomous Intersection Management With Design Specifications

In this article, we study optimal control of automated vehicles (AVs) at an autonomous intersection with design specifications, where the intersection manager (computer) controls the acceleration/deceleration of AVs within a control region to determine their conflict-free trajectories. We develop a mathematical model to describe the cooperative dynamics of vehicles within the control zone of a signal-free intersection. Further, we formulate an optimization problem with the objective of maximizing traffic throughput while minimizing passenger discomfort. We then address the problem using mathematical programming to yield the optimal control input for each vehicle inside the control zone. We apply the proposed control mechanism for optimally coordinating the movement of AVs at an autonomous intersection subject to physical and safety constraints. In other words, the solution, if implemented, would allow vehicles to pass the intersection with safety guarantees (avoiding collisions), high-level efficiency (maximizing throughput), and good comfort (minimizing discomfort) without deploying traffic lights. In addition, we derive analytical specifications for the design and deployment of autonomous intersections, which appears to have been largely ignored in prior studies. Finally, we conduct a series of numerical experiments to show the effectiveness of the methodology proposed in this article. As the framework developed here is fairly general and can be extended to cover many complex traffic scenarios, we believe that the procedure presented in this article will be useful with the advent of AVs in the near future.
Incarcerated students' experiences of UNISA’s open distance e-learning at one Medium Correctional Centre

Technology mediated pedagogies of the 21st century present new and unprecedented challenges for incarcerated tertiary students. The researchers, employing a qualitative exploratory research design, sought to explore the learning experiences of UNISA students incarcerated at Baviaanspoort Medium Correctional Centre. Six participants were sampled purposively and recruited to participate during a focus group interview. Qualitative content analysis revealed the ever-changing landscape of higher education due to the advent of digitised e-learning; the unique challenges encountered by incarcerated students studying at tertiary level; strategies for overcoming barriers associated with ODeL; and that lastly, in the context of imprisonment, student-centeredness is still primarily informed by a constructivist approach to ODL. The study concluded that students’ learning experiences, and by extension, academic success, can be enhanced provided learner support is augmented for incarcerated tertiary students.
Selfish but Socially Approved: The Effects of Perceived Collision Algorithms and Social Approval on Attitudes toward Autonomous Vehicles

Abstract An online experiment was conducted to investigate how perceived collision algorithm types (selfish vs. utilitarian) and social approval for the algorithms (weak approval vs. strong approval) jointly affect individuals’ attitudes toward automated vehicles (AVs). The results showed a discrepancy between what individuals consider socially desirable and what they trust or would use. Although participants regarded AVs with utilitarian collision algorithms as more ethical and socially beneficial, they personally preferred AVs with selfish algorithms: they trusted selfish AVs more and showed higher intention to use and pay a premium for selfish AVs. Also, participants evaluated AVs as more ethical and socially beneficial when strong social approval was given to the algorithms. However, in utilitarian algorithms, strong social approval did not increase trust or behavioral intention to use AVs. Only strong social approval of selfish algorithms increased participants’ trust and intention to use AVs.
TECHNOLOGICAL INNOVATIONS AND STRUCTURAL TRANSFORMATION IN AFRICAN ECONOMIES

This paper examines the role of technological innovations on structural transformation on economies of Africa. To attain this objective, it uses a sample of 32 African countries. The estimation techniques are system GMM and two stage least Square. Results indicate that technological innovations, as measured by total patents applications, have a significant effect on per sector value added. This result supports the almost nonlinear relationship of structural transformation since this study shows that manufacturing is the dominant sector in the promotion of structural changes as far as the implementation of technological innovations is concerned. Also, our results highlight that with regards to the African context, manufacturing is the sector in which technological innovation has the greatest impact on per sector value added and by so doing, it promotes structural transformation than other sectors (services or agriculture sector). As policy implication, governments need to encourage technological infrastructure through private and public spending on research and development especially for agricultural development to foster structural transformation.
Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals

Learning journals are increasingly used in vocational education to foster self-regulated learning and reflective learning practices. However, for many apprentices, documenting working experiences is a difficult task. In this article, we profile apprentices' learning behavior in an online learning journal. Based on a pedagogical framework, we propose a novel multistep clustering pipeline that integrates different learning dimensions into a combined profile. Specifically, the profiles are described in terms of effort, consistency, regularity, help-seeking behavior, and quality of the written entries. Our results on two populations of chef apprentices (183 apprentices) interacting with an online learning journal (over 121K entries) show that our pipeline captures changes in learning patterns over time and yields interpretable profiles that can be related to academic performance. The obtained profiles can be used as a basis for personalized interventions, with the ultimate goal of improving the apprentices' learning experience.
Momentum particle swarm optimizer

The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO (LDW-PSO) equipped with a boundary checking strategy is also discussed, which is denoted as LDWBC-PSO. LDW-PSO, LDWBC-PSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities.
Mitochondrial Dysfunction and Cardiovascular Disease: Pathophysiology and Emerging Therapies

Mitochondria ensure the supply of cellular energy through the production of ATP via oxidative phosphorylation. The alteration of this process, called mitochondrial dysfunction, leads to a reduction in ATP and an increase in the production of reactive oxygen species (ROS). Mitochondrial dysfunction can be caused by mitochondrial/nuclear DNA mutations, or it can be secondary to pathological conditions such as cardiovascular disease, aging, and environmental stress. The use of therapies aimed at the prevention/correction of mitochondrial dysfunction, in the context of the specific treatment of cardiovascular diseases, is a topic of growing interest. In this context, the data are conflicting since preclinical studies are numerous, but there are no large randomized studies.
Intellectual Capital Performance, Prestasi Reputasi Perusahaan dan Financial Distress

The company's going concern ability should be able to compete and survive in business activities to avoid financial distress. This study aims to determine the effect of intellectual capital performance, company achievement, company reputation, profitability, and leverage on financial distress. This study uses samples from manufacturing companies listed on the Indonesia Stock Exchange in 2015-2019. The sample used was 125 observations taken using purposive sampling technique. Data analysis was performed using logistic regression analysis. The results showed that the performance of intellectual capital and profitability had a negative effect on financial distress and leverage had a positive effect on financial distress. Meanwhile, company achievement and company reputation have no effect on financial distress. Keywords: Intellectual Capital Performance; Company Achievement; Company Reputation; Financial Distress.
CALL-Mediated task-based language teaching: A speaking project with online audience in Indonesia

Tasks and technology are reciprocally related since the integration of technology can benefit task-based language teaching (TBLT) implementation. This study investigates students’ speaking performance, learning experiences, and perceptions in a speaking course specially designed with video recording activities involving online audiences based on the principles of TBLT. Thirty-eight undergraduate students majoring in English and enrolled in the speaking class served as the respondents. The data were collected through speaking tests, Likert scale questionnaires, and interviews. The findings indicated that the students developed their speaking performance after experiencing speaking class through a computer-assisted language learning (CALL)-mediated TBLT with video recording activities. They were also able to find their strengths and weaknesses by watching their own video recordings on YouTube. The findings also showed that the students had positive learning experiences and perceptions of video recording activities with online audiences on YouTube. Nevertheless, a further investigation of the online interactions toward the use of tasks and digital learning platforms with online audiences is highly advised.