Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Request Scheduling and Service Caching for MEC-Assisted IoT Networks: An Online Learning Approach

Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-hungry and latency-sensitive services by enabling the placement of services and execution of computing tasks at the edge of radio access networks much closer to resource-constrained devices. However, how to serve more requests while reducing service latency by exploiting limited resources (storage capacities, CPU cycles, communication bandwidth) is still a critical issue in the multidevice MEC-assisted IoT networks, since the time-varying computing demands of devices and unavailability of future information make it difficult to determine where to handle computation tasks and which services to cache. In this article, we propose a twin-timescale framework to jointly optimize adaptive request scheduling (RS) and cooperative service caching (SC) in the multidevices and MEC-assisted networks, in order to explore request dynamic, MECs heterogeneity, service difference. To accommodate the unavailability of future information and unknown system dynamics, we, respectively, formulate RS and SC as partially observable Markov decision process (POMDP) problems. Then, we propose a deep reinforcement learning (DRL)-based online algorithm to improve the service latency reduction ratio and hit rate, which do not require a priori knowledge such as service popularity. Moreover, we give the optimal CPU cycles and communication bandwidth allocations in order to further minimize the average service latency. Extensive and trace-driven simulation results demonstrate the efficacy of the proposed approach.
Weighted Subsethood Fuzzy Time Series towards Energy-Water Efficiency for Water Treatment Plant

Forecasting is the process that uses statistical tools to make predictions based on past and present data. In the water treatment plant systems, forecasting is useful to increase the efficiency of energy-water units by forecasting the amount of energy used in the current and future time. Fuzzy time-series (FTS) employs an algorithm or statistical model to predict the future condition. By analysing the patterns of data, fuzzy time-series forecasting can forecast the future condition of a system. The FTS approaches have been increasingly popular in recent years, as there are so many methods available for a researcher to apply to forecast the future based on the needs of the user. This paper proposed the modification of the classical fuzzy time series by applying weighted subsethood Fuzzy Time Series (WeSuFTS) in the water management industry. 2 sets of monthly data, which are water production and energy consumption data from January 2017 to December 2020, are used in this study. To measure the performance of the proposed model, the forecast results of the proposed model were compared with the forecast results of Chen and Cheng models. This study compares 3 different models using Absolute Percentage Error (APE) and Mean Absolute Percentage Error (MAPE). The WeSuFTS model delivers a good performance in the evaluation part with MAPE 0.9252% and APE value between 0.02226% and 1.7877%.
Extracting Lakes and Reservoirs From GF-1 Satellite Imagery Over China Using Improved U-Net

Lakes and reservoirs (LaR) are important parts of water resources and their rapid and accurate monitoring is an essential guarantee for maintaining ecological health and social development. The existing waterbody extraction methods are mostly targeted at local water bodies, with little attention on the national scale. In this letter, an improved U-Net method is proposed for LaR extraction from GF-1 satellite imagery. First, 21 scenes of GF-1 images are evenly selected across China, and the training set and validation set are produced by image processing, cropping, and augmentation. Second, a deep learning network is constructed by modifying the U-Net, deepening the network and introducing multiple skip connections, which is suitable for extracting LaR China-wide. Experiments on the GF-1 imagery demonstrate that the superiority of the improved U-Net when compared with other deep learning methods (U-Net, UNet++, FastFCN, DeepLabv3+) and traditional methods [the normalized difference water index (NDWI), maximum likelihood method (MLM)]. In addition, 20 LaR are selected for further evaluation of the model, and all of them achieve good extraction results, showing excellent generalization of the model.
Structural and functional development of twelve newly established floodplain pond mesocosms

Abstract Ecosystems are complex structures with interacting abiotic and biotic processes evolving with ongoing succession. However, limited knowledge exists on the very initial phase of ecosystem development and colonization. Here, we report results of a comprehensive ecosystem development monitoring for twelve floodplain pond mesocosms (FPM; 23.5 m × 7.5 m × 1.5 m each) located in south‐western Germany. In total, 20 abiotic and biotic parameters, including structural and functional variables, were monitored for 21 months after establishment of the FPMs. The results showed evolving ecosystem development and primary succession in all FPMs, with fluctuating abiotic conditions over time. Principal component analyses and redundancy analyses revealed season and succession time (i.e., time since ecosystem establishment) to be significant drivers of changes in environmental conditions. Initial colonization of both aquatic (i.e., water bodies) and terrestrial (i.e., riparian land areas) parts of the pond ecosystems occurred within the first month, with subsequent season‐specific increases in richness and abundance for aquatic and terrestrial taxa over the entire study period. Abiotic environmental conditions and aquatic and terrestrial communities showed increasing interpond variations over time, that is, increasing heterogeneity among the FPMs due to natural environmental divergence. However, both functional variables assessed (i.e., aquatic and terrestrial litter decomposition) showed opposite patterns as litter decomposition rates slightly decreased over time and interpond differences converged with successional ecosystem developments. Overall, our results provide rare insights into the abiotic and biotic conditions and processes during the initial stages of freshwater ecosystem formation, as well as into structural and functional developments of the aquatic and terrestrial environment of newly established pond ecosystems.
Analysis of Longitudinal Forest Data on Individual-Tree and Whole-Stand Attributes Using a Stochastic Differential Equation Model

This paper focuses on individual-tree and whole-stand growth models for uneven-aged and mixed-species stands in Lithuania. All the growth models were derived using a single trivariate diffusion process defined by a mixed-effect parameters trivariate stochastic differential equation describing the tree diameter, potentially available area, and height. The mixed-effect parameters of the newly developed trivariate transition probability density function were estimated using an approximate maximum likelihood procedure. Using the relationship between the multivariate probability density and univariate marginal (conditional) densities, the growth equations were derived to predict or forecast the individual-tree and whole-stand variables, such as diameter, potentially available area, height, basal area, and stand density. All the results are illustrated using an observed dataset from 53 permanent experimental plots remeasured from 1 to 7 times. The computed statistical measures showed high predictive and forecast accuracy compared with validation data that were not used to find parameter estimates. All the results were implemented in the Maple computer algebra system.
CHARACTERIZATION OF THE VERY LOW CONTACT RESISTANCE ON HEAVILY BORON DOPED (113) CVD DIAMOND

The low resistance of ohmic contacts on diamond layers is important for the fabrication of diamond power electronic devices with fast switching capabilities for future high voltage applications. The low barrier height between the metal and diamond, high level of boron doping and annealing at elevated temperatures are the most critical parameters to reach the lowest contact resistivity. In this work, we report on titanium/gold ohmic contacts prepared on the heavily boron-doped (113) epitaxial diamond layers. The contact resistance has been characterized by the Circular Transmission Line Model (cTLM) structures. We used the analytical model of field enhanced emission, tunneling and the image force influence including Fermi level position dependence on the boron concentration for theoretical Ti/Au contact analysis and the Silvaco TCAD 2D simulation to estimate the measurement error associated with the nonzero metal resistance. We show that the resulting simulation values are consistent with the experimental results.
Frequent Eating Out and 10-Year Cardiovascular Disease Risk: Evidence from a Community Observatory in Malaysia

Despite increasing mortality rates from cardiovascular diseases (CVDs) in low- and middle-income countries, information on the estimation of 10-year CVD risk remains to be sparse. Therefore, this study was aimed at predicting the 10-year CVD risk among community dwellers in Malaysia and at identifying the association of distal (socioeconomic characteristics) and proximal (lifestyle practices) factors with 10-year CVD risk. We calculated the 10-year CVD risk score among 11,897 eligible respondents from the community health survey conducted by the South East Asia Community Observatory (SEACO) using the Framingham risk score (FRS). The findings indicate that 28% of respondents have a high chance of having CVD within the next ten years. After adjusting for the age of respondents, demographic and socioeconomic factors such as gender, ethnicity, marital status, education, income, and occupation had an association with the 10-year CVD risk. In addition, frequent eating out had an association with 10-year CVD risk, while physical activity was found to have no association with predicted CVD risk. CVD remained among the top five mortality causes in Malaysia. Health promotion strategies should emphasize the importance of having home-cooked meals as a healthy dietary behavior, to reduce the mortality rate among Malaysians due to CVDs.
Streamflow Response to Wildfire Differs With Season and Elevation in Adjacent Headwaters of the Lower Colorado River Basin

Fires increasingly impact forested watersheds, with uncertain water resources impacts. While research has revealed higher peak flows, longer‐term yields may increase or decrease following fire, and the mechanisms regulating post‐fire streamflow are little explored. Hydrologic response to disturbance is poorly understood in the Lower Colorado River Basin (LCRB), where snowmelt often occurs before the growing season. Here, we quantify annual streamflow changes following what have been, before 2020, two of the largest wildfires in the modern history of the contiguous United States. We evaluate nine nested watersheds with >50 years records within the Salt River Basin to evaluate fire impact over ranges of elevation, climate, vegetation, burned area, and spatial scale. We employ double‐mass comparison of paired watersheds, pre‐ and post‐fire runoff ratio comparison, multiple linear regression of climate and fire, and time‐trend analysis. Precipitation and streamflow are decoupled during dry periods; therefore we conduct separate change detection for wet and dry periods. Post‐fire summer streamflow increased by 24%–38% at all elevations. While winter/spring streamflow remained constant in the highest, coldest headwaters, winter flows declined in lower‐elevation headwaters. As a result, basin annual streamflow declined. These results support emerging understanding that warm semiarid watersheds respond differently to disturbance than well‐studied, colder watersheds. Asynchrony between winter snowmelt and summer evaporative demand is likely important when considering long‐term impacts of forest management and disturbance on water supply in the LCRB.
TextureMe: High-Quality Textured Scene Reconstruction in Real Time

Three-dimensional (3D) reconstruction using an RGB-D camera has been widely adopted for realistic content creation. However, high-quality texture mapping onto the reconstructed geometry is often treated as an offline step that should run after geometric reconstruction. In this article, we propose TextureMe, a novel approach that jointly recovers 3D surface geometry and high-quality texture in real time. The key idea is to create triangular texture patches that correspond to zero-crossing triangles of truncated signed distance function (TSDF) progressively in a global texture atlas. Our approach integrates color details into the texture patches in parallel with the depth map integration to a TSDF. It also actively updates a pool of texture patches to adapt TSDF changes and minimizes misalignment artifacts that occur due to camera drift and image distortion. Our global texture atlas representation is fully compatible with conventional texture mapping. As a result, our approach produces high-quality textures without utilizing additional texture map optimization, mesh parameterization, or heavy post-processing. High-quality scenes produced by our real-time approach are even comparable to the results from state-of-the-art methods that run offline.
“You feel a little bit embarrassed, but you get over it”: EFL students’ beliefs and emotions about speaking

Drawing on positive psychology, i.e. the study of how people flourish and thrive, (MacIntyre, Gregersen and Mercer, 2016), this paper aims to analyse the elements which learners regard as most important to have positive and enjoyable experiences in the English classroom. The participants are first-year university students enrolled in an English for Specific Purposes course that were inquired about their life-long trajectories studying English and their experiences in an ESP course which followed a communicative approach. The data were obtained through an open-ended question from a questionnaire (pre- and post-); eight semi-structured interviews; and four focus groups. The answers from the open-response items were analysed at a macro-level through Domain and Taxonomic Coding (Saldaña, 2016) and afterwards the interviews and focus groups were content-analysed to deepen the understanding of students’ responses at a micro-level. The analysis reveals that oral activities are the aspect which students value most positively; yet, some report negative emotions due to lack of practice and to negative beliefs about speaking tasks and about their own capabilities. Findings suggest that extensive speaking practice along the course, as well as a supportive teacher, a positive classroom atmosphere and an effective classroom management can increase students’ self-confidence and positivity.
Multi-modal physiological sensing on the upper arm

Wearable technology provides fitness information and, in some cases, continual access to tracking and monitoring of cardiac health to physicians and patients. However, wearable methods suffer from motion artifacts. To address this issue, we have developed a multi-modal system using the combination of (1) PPG with three wavelengths of light (green, red, and IR at 527, 660, and 880 nm), (2) bioimpedance, and (3) single-sided ECG to measure heart rate (HR) on the upper arm. This study investigated measuring HR under conditions of sedentary motion and micro-motion (e.g., typing). We compared the system with signals acquired from commercial wrist and chest devices, verifying the accuracy of the HR measurement for each anatomical location. This multi-modal approach investigated the wavelength of light chosen to provide the most accurate HR measurement and assessed cross-correlation to minimize motion artifacts. Results indicated the green PPG and SS-ECG modalities have the lowest mean absolute error (1.0 and 2.0 bpm, respectively) relative to the chest device during typing conditions compared with the wrist device and other upper arm modalities.
WoT Search Engine based on Multi Agent System: A Conceptual Framework

The rising incursions with embed and linked sensors cause the construction of the Internet of Things (IoT). It’s presenting a wide access to the physical world. We support that on head of this data, a Web of Things (WoT) is required. A framework that permits the achievement of programs related to sensors in the IoT. In this paper we present a conceptual model that describes Web of Things Search engine (WoTSE). Our WoTSE is based on artificial multi agent system with a modular architecture, addressing the key challenge of scalable search for rapidly changing content while leveraging existing Web infrastructure. Also, a model for searching in the WoT, namely WoTmAS2E, is proposed and experimentally evaluated using a prototype.
Social Media and the COVID-19: South African and Zimbabwean Netizens’ Response to a Pandemic

Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa. The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social media
Quelle phénoménologie pour quels phénomènes?

L’adoption de l’approche phénoménologique par les chercheurs en sciences humaines et sociales est problématique. A mon avis, cela tient à la méprise quant à la signification qu’ils donnent aux termes phénomènes et phénoménologie. L’examen de ces deux notions mettra en lumière le sens phénoménologique du phénomène et de la phénoménologie, à ne pas confondre avec un sens phénoménal ou empirique. Il montrera
Synthesis of Clock Networks with a Mode-Reconfigurable Topology

Modern digital circuits are often required to operate in multiple modes to cater to variable frequency and power requirements. Consequently, the clock networks for such circuits must be synthesized, meeting different timing constraints in different operational modes. The overall power consumption and robustness to variations of a clock network are determined by the topology. However, state-of-the-art clock networks use the same topology in every mode, despite that timing constraints in low- and high-performance modes can be very different. In this article, we propose a clock network with a mode-reconfigurable topology (MRT) for circuits with positive-edge-triggered sequential elements. In high-performance modes, the MRT structure is reconfigured into a near-tree to provide the required robustness to variations. In low-performance modes, the MRT structure is reconfigured into a tree to save power. Non-tree (or near-tree) structures provide robustness to variations by appropriately constructing multiple alternative paths from the clock source to the clock sinks, which neutralizes the negative impact of variations. In MRT structures, OR-gates are used to join multiple alternative paths into a single path. Hence, the MRT structures consume no short-circuit power because there is only one gate driving each net. Moreover, it is straightforward to reconfigure an MRT structure into a tree topology using a single clock gate. In high-performance modes, the experimental results demonstrate that MRT structures have \( 25\% \) lower power consumption than state-of-the-art near-tree structures. In low-performance modes, the power consumption of the MRT structure is similar to the power consumption of a clock tree.
Robust Sliding-Mode Control for Inverted Pendulum System

The Inverted Pendulum is a highly nonlinear, unstable, and fast dynamic system. These characteristics make it a popular benchmark for building and testing novel controllers. Therefore, in this study, a sliding mode controller is proposed and tested on the inverted pendulum system. According to the results of the simulation experiments with a sine signal as a reference, the proposed controller can stabilize the system well and has so fast response. Moreover, we have tuned the parameters of the proposed sliding mode controller in order to eliminate the chattering effect, the overshoot, and the steadystate error.
Failure in psychotherapy: a qualitative comparative study from the perspective of patients diagnosed with depression

ABSTRACT This qualitative study’s objective was to understand how failure in psychotherapy develops from depressed patients’ perspective. Forty-seven patients were interviewed after brief psychotherapy. Data analysis was conducted according to Grounded Theory. Patients evaluated their own psychotherapies’ outcome according to their subjective criteria. Then, negative, positive and mixed-results evaluations were compared in the main categories. Results showed that patients reporting negative evaluations considered null or adverse outcomes as failure. Distrust in their psychotherapists (both as persons and professionals) developed in early stages of the process and was apparently hard to revert. This early onset of distrust led patients to a lack of collaboration and an unreceptive attitude. They perceived their psychotherapist as not understanding, distant, and uninterested, losing focus on relevant problems and not providing new information. The relationship was experienced as uncomfortable and distant, and sometimes became harmful. Therefore, distrust led patients to regard their psychotherapies as an unhelpful experience, in contrast to what occurred in patients with positive or mixed results evaluations. Conclusions contribute to a clarification of how patients conceptualize failure and suggest reevaluating the relevance of their perspective, which seems not to be fully reflected in current outcome measures. Clinicians should consider building trust as a baseline and encourage patients to disclose even the earliest negative feelings about treatment and psychotherapist