Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Proactive Resource Allocation at the Extreme Edge

Edge Computing (EC) has emerged as a key enabling paradigm for latency-critical and/or data-intensive applications. Recently, recycling abundant yet underutilized computational resources of the Extreme Edge Devices (EEDs), such as smartphones, laptops, connected vehicles, etc, has been explored. This is since EEDs can bring the computation service much closer to the edge, which can drastically reduce the delay. However, resource allocation in such environments typically follows a reactive approach, which can lead to increased delay and wasted resources. In this paper, we introduce the Optimal Proactive Resource Allocation (OPRA) benchmark to quantify the potential gains of proactive resource allocation in EC environments. OPRA exploits the predictability of request patterns to proactively perform resource allocation and create compute clusters that take future task and resource dynamics into consideration. Specifically, OPRA formulates the resource allocation problem as a Binary Integer Linear Program (BILP) problem, where it aims to minimize the total delay under full task assignment and computation capacity constraints. The optimal solution acquired under perfect knowledge acts as the upper bound on the achievable potential of predictive proactive resource allocation schemes. The effect of erroneous predictions on the performance of OPRA is also investigated. Extensive simulation results show that OPRA outperforms a reactive baseline by yielding a 50% decrease in the subtask dropping rate and 97% decrease in the service capacity.
Hydrodynamic Investigation of a Concentric Cylindrical OWC Wave Energy Converter

A fixed, concentric, cylindrical oscillating water column (OWC) wave energy converter (WEC) is proposed for shallow offshore sites. Compared with the existing shoreline OWC device, this wave energy device is not restricted by the wave directions and coastline geography conditions. Analytical solutions are derived based on the linear potential-flow theory and eigen-function expansion technique to investigate hydrodynamic properties of the device. Three typical free-surface oscillation modes in the chamber are discussed, of which the piston-type mode makes the main contribution to the energy conversion. The effects of the geometrical parameters on the hydrodynamic properties are further investigated. The resonance frequency of the chamber, the power extraction efficiency, and the effective frequency bandwidth of the device is discussed, amongst other topics. It is found that the proposed OWC-WEC device with a lower draft and wider chamber breadth has better power extraction ability.
Optimal Sizing of Energy Storage Systems for Shipboard Applications

The recent worldwide effort on the environmental issue has led to new regulations on greenhouse gases emissions (GHG), both for land and marine applications. Nowadays, the extensive electrification of transportation systems is a promising choice for this purpose. In this perspective, algorithms for the optimum sizing and management of energy storage systems (ESSs) integrated into already operating shipboard power systems are proposed in this paper. The main aim of this method is to reduce the power generation system fuel oil consumption, GHG emissions, and management costs. This is applied to two case studies (i.e., a ferry and a platform supply vessel), of which load power profiles are available from the on-board integrated automation system. The results yielded show remarkable savings close to 6% and 32% along the whole ship's life horizon for the ferry and the platform supply vessel, respectively. These results prove that an optimal sizing combined with an optimum management of ESSs may significantly reduce the operative costs of shipboard power systems.
The implementation of bichronous online learning: EFL students’ perceptions and challenges

The COVID-19 pandemic has made online learning as a common instructional delivery today. Since various methods, approaches, and technologies are involved, it is important to understand students’ perceptions for taking further decision in teaching. The current research aimed to investigate Indonesian students’ perceptions of the implementation of bichronous online learning along with the challenges in the level of higher education. Bichronous online learning refers to the blending between asynchronous and synchronous online learning. In this study, asynchronous learning was carried out through Edmodo while the synchronous mode was implemented via Zoom. The participants were 57 first-year university students who experienced full bichronous online learning in the English for Specific Purposes class. This research applied explanatory sequential mixed-methods as suggested by Creswell (2014). The quantitative data were obtained by Likert-scale questionnaires while the qualitative data were acquired through interviews and observations. The results indicated that students had positive perceptions of bichronous online learning in terms of motivations, academic achievement, and communication and interactions. Bichronous online learning promoted students’ engagement, active interaction, and feedback exchange which highly contributed to their learning outcome. However, technical issues such as poor internet connection and limited data plan were the biggest challenges faced by the students. It is expected that educators can adjust which online learning tools are convenient to use and cater to students’ needs.
Research on Load Control Strategy of Aggregate Water Heater Based on Artificial Immune Algorithm

Clean energy has strong randomness, high volatility, high wind and light abandonment rate and low energy utilization rate. However, demand side water heater has rich resources, wide control range and good energy storage function. Therefore, it is of great significance to carry out reasonable polymerization control on water heater. To solve this problem, this paper constructs the model of single water heater and aggregate water heater group, and studies the control method of power transfer of aggregate water heater group on this basis. The method first establishes an evaluation method for the adjustable capacity of aggregate water heaters, and then optimizes the allocation of clean energy absorption capacity with the constraints of user comfort (adjustable temperature range) and water heater physical parameters. Secondly, through artificial immune algorithm, iterative optimization is carried out to appropriately adjust the temperature set point of water heater load, so as to achieve the purpose of absorbing clean energy in advance at a proper time. Finally, it is verified by experiments that the algorithm in this paper can effectively consume clean energy, and at the same time, the intervention on the water heater is small, which ensures the user’s experience and economy.
Framing post-disaster collective action as ‘good news’: Possibilities and tensions

This article examines the development and implications of positive news media coverage of a crisis volunteer group across a decade of disaster responses. We investigate the case of the Student Volunteer Army in Aotearoa New Zealand, a group that has been positioned as a potential blueprint for youth-led disaster response. Drawing on in-depth interviews and news media sources, we trace how a distinct framing of the group as ‘good news’ consolidated across successive disasters, initially in media reporting and then through active cultivation by the group. The findings demonstrate the potential for positive media coverage of disaster volunteerism to assist people’s recovery and provide crisis volunteer groups with important leverage to further their operational abilities and challenge exclusionary power structures in post-disaster environments. However, our analysis also warns that simplifying accounts of post-disaster collective action to create ‘good news’ can produce internal tensions within crisis volunteer groups and reinforce the hierarchies and inequities that characterize disaster response.
Pollinator research provides conservation management implications in North Dakota

Pollinator declines have driven research and increased monitoring efforts. Within North Dakota, USA, our research group initiated research in 2015 on pollinator conservation and management. We synthesized results across five projects, producing 12 publications and providing baseline data on pollinator diversity and rangeland management to improve conservation efforts while land‐sharing with livestock. We detected 76 species of butterflies and ∼318 bee species. Butterfly diversity and relative abundance were driven by floral resources and less exotic plant invasions, with a positive relationship between flowers and pollinators. Invasive forbs were visited by pollinators, primarily honey bees. We also found management influenced vegetation characteristics within pastures, but landscape context was important for determining the specific outcome. Although pollinator abundance did not distinctly respond to management, diversity was affected by regime and grazer type. Using fire and grazing may benefit flowers to support pollinators. Our research will help guide rangeland management decisions that promote land sharing and benefit pollinator conservation efforts.
Captorhinid reptiles from the Early Permian of New Mexico, with description of a new genus and species

A new genus and species of single-tooth-rowed captorhinid, Rhiodenticulatus heatoni, is based on two skulls and partial postcranial skeletons collected from the Lower Permian Cutler Formation near Arroyo de Agua, north-central New Mexico. A cladistic analysis of its relationships to other single-tooth-rowed captorhinids suggests that it is a primitive sister taxon to Labidosaurus and Eocaptorhinus. The dentition of R. heatoni, however, exhibits several unique derived features which are interpreted as representing an adaptation to a specialized diet. Puercosaurus obtiisidens Williston, 1916, the only previously described captorhinid from New Mexico, is declared a nomen dubium because the holotypic left dentary is indeterminate, and there is no basis for accepting that it and the two poorly preserved captorhinid skulls found at a different locality and referred to the species by Williston (1916) are conspecific. Additional captorhinid remains have been collected recently from the Lower Permian Cutler, Abo, and Sangre de Cristo formations at widely scattered localities in central and northern New Mexico. Though these specimens, as well as the skulls referred to ^‘‘Puercosaurus obtusidens,"’’ are too poorly preserved to be assigned to existing or new taxa, they do indicate that the Captorhinidae was diverse and widely distributed in the Lower Permian of New Mexico. ‘ Address: Department of Biology, Erindale Campus, Univer Ontario L5L 1C6, Canada. Submitted 2 August 1985. i I M
Non-intrusive Load Disaggregation Method Considering State Probability Factors

Non-intrusive load monitoring plays an important role in obtaining electrical power information, which is conducive to strengthening demand-side management, guiding users to use electricity, and realizing energy conservation and consumption reduction. In terms of the variety of electrical appliances and the changeable working state, the means-shift clustering algorithm is utilized to construct load power templates. Based on the load power characteristics, the state probability factors are introduced as a new feature into the fitness function, which can be solved by the genetic algorithm. Furthermore, abnormal state correction is performed on the disaggregation results. Simulation results show that the proposed method can be well applied to non-invasive load identification.
Expert Review of Contextual Learning through Extensive Reading in a Digital Game-Based System

There are many factors involved in language acquisition, but immersive language experiences and consistent exposure are two crucial components. Creating these learningconducive settings can be challenging in a traditional classroom. However, learning a new language through context to connect words and definitions can be fun and engaging with a gamebased approach. Consequently, it’s important to create opportunities for this approach in a curriculum to diversify studies and excite learners about the language itself. Adding games based on their entertainment value may not translate into effective learning. Therefore, in this paper, a proposal for a gameful approach using a visual novel was evaluated. Ten industry experts from various fields of education were asked to review a game-based system that focused on acquiring language through reading and comprehension. The educational experts graded the system based on perceived acceptance and usability, learner experience and, qualitatively, its impact on education. The experts concluded that the system is an innovative and exciting implementation for language learning.
On the Properties of Time-Varying SNR Process in Cellular-Enabled UAV Networks

The unmanned aerial vehicle (UAV) based communication is expected to play an important role in enabling a variety of applications in future cellular networks. However, because of the mobility of the UAVs, the communications links involving UAVs undergo large-scale temporal variations in the received signal quality, which may affect the quality-of-service of the underlying application. Therefore, it is crucial to characterize the time-varying process of signal quality observed by the UAVs. In this paper, we consider a scenario in which a cellular-connected UAV acts as a user equipment (UAV-UE), where the locations of base stations (BSs) follow a Poisson point process (PPP) and the UAV-UE is moving along a 3GPP-inspired straight-line trajectory. For this setting, we study the properties of the time-varying successful transmission process that is defined in terms of the time-varying signal-to-noise ratio (SNR) observed at the UAV. In particular, we show that this process is a wide sense stationary (WSS) process and derive its first- and second-order statistics. Finally, we establish an equivalence between the successful transmission processes observed by a UAV-UE served by terrestrial BSs and a terrestrial user served by UAV mounted BSs (UAV-BSs) each moving along an independent straight-line trajectory.
Fluorsigaiite, Ca2Sr3(PO4)3F, a new mineral of the apatite supergroup from the Saima alkaline complex, Liaoning Province, China

Abstract Fluorsigaiite, ideally Ca2Sr3(PO4)3F, is a new Sr analogue of fluorphosphohedyphane and a new member of the apatite supergroup. It was discovered in lujavrite from the Saima alkaline complex, Liaoning Province, China. Fluorsigaiite commonly occurs as individual prismatic, columnar and platy crystals of 10 to 50 μm in size, associated with microcline, nepheline, aegirine, natrolite, eudialyte, fluorapatite, a fluorstrophite-like mineral, stronadelphite and calcite. Occasionally, crystals of fluorsigaiite form prismatic aggregates in the interstices of lujavrite. Fluorsigaiite is translucent to transparent, colourless to yellowish white with a vitreous lustre and without fluorescence. The estimated Mohs hardness is 5, and the tenacity is brittle with uneven fractures. The calculated density is 3.842 g/cm3. Optically, fluorsigaiite is uniaxial (–) with ω = 1.64(1) and ɛ = 1.63(1) in white light and without dispersion. The mean chemical composition (in wt.%) of fluorsigaiite is Na2O 0.75, CaO 15.17, SrO 44.44, La2O3 3.64, Ce2O3 2.22, Pr2O3 0.19, Nd2O3 0.13, Sm2O3 0.05, Gd2O3 0.23, P2O5 31.87, F 1.91, H2O 0.46, sum 100.26, giving the empirical formula (Sr2.82Ca1.79Na0.16La0.15Ce0.09Pr0.01Nd0.01Gd0.01)Σ5. 04P2.97O12[F0.66(OH)0.34]Σ1, which is calculated on the basis of 13 total anions and F+(OH) = 1. The strongest eight lines of its powder X-ray diffraction pattern [d, Å (I, %) (hkl)] are: 3.563 (15) (002), 3.275 (15) (102), 3.144 (19) (120), 2.876 (100) (121), 2.861 (96) (112), 2.772 (27) (300), 1.991 (17) (222) and 1.895 (23) (213). Fluorsigaiite is hexagonal, in the space group P63/m and unit-cell parameters refined from single-crystal X-ray diffraction data are: a = 9.6101(2) Å, c = 7.1311(1) Å, V = 570.35(3) Å3 and Z = 2. It is isostructural with hedyphane-group minerals, and contains different prevailing (species-defining) Ca and Sr cations at the Ca1 and Ca2 sites, respectively. Fluorsigaiite was probably formed from Sr-rich fluids at late-magmatic or hydrothermal stage of the Saima lujavrite.
Rational design of non-noble-metal-based alloy catalysts for hydrogen activation: a density functional theory study

ABSTRACT The alloying of precious metals with non-noble metals in the active components for hydrogen activation during regeneration of the solid acid catalyst in hydrogenation conditions, can reduce the cost of the catalyst and improve the catalytic efficiency of precious metals. However, it lacks a theoretical method to guide the development of non-noble-metal-based alloy catalysts. In this work, the dependence of hydrogen adsorption and dissociation of alloy models on various non-noble-metal bases has been investigated by first-principles calculations. When the surface metal elements are in the same line of periodic table, the adsorption strength of hydrogen on the alloy weakens with the increase of the atomic number of the surface metal element. Importantly, we establish a screening model to make high-throughput predictions for non-noble-metal-based alloy modified with precious metal. This work provides a reasonable design strategy to guide the development of alloy catalysts used in hydrogen activation by identifying specific non-noble-metal bases. GRAPHICAL ABSTRACT
Multiple Configured-Grants Optimization in Grant-Free NOMA for mURLLC Service

Realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in massive Ultra-Reliable and Low-Latency Communications (mURLLC) is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced grant-free non-orthogonal multiple access (GF-NOMA) with multiple configured-grants (MCGs), where UE can choose any of these grants as soon as the data arrives. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems. We first design the MCG-GF-NOMA model by characterizing each CG. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances for mURLLC.
MEU Convolutional Neural Network and Random Noise Suppression of Seismic Data

In allusion to the strong interference problem of random noise in seismic exploration, this paper proposed a Multiscale enhancement U-Net (MEU-Net) for the first time. First, the network carries out multiple convolution and pooling on the data in the backbone feature extraction network, then conducts channel addition, convolution and upsampling in the enhancement feature extraction network for extracting and restoring data information, finally, further improve the denoising effect through the dilated convolution, residual module and attention mechanism in the multi-scale enhancement module. The actual data application shows that the method in this paper can achieve good denoising effect under noise with different intensities, and can be widely used in data denoising processing.
Energy-efficient Online Data Sensing and Processing Optimization in Wireless Powered Edge Computing Systems

This paper considers a wireless powered mobile edge computing (MEC) system consisting of multiple wireless devices (WDs) and one hybrid access point (HAP) broadcasting radio frequency (RF) energy to the WDs. Relying on the harvested energy, the WDs senses data from the monitored environment and execute the task data locally or offload the task to the HAP for edge processing. Given an average power constraint at the HAP, we aim to design an energy-efficient online algorithm under random fading channels to maximize the long-term average data sensing rate of WDs while meeting the system data queue stability. We formulate the target problem as a multi-stage stochastic optimization, where the major difficulty lies in the uncertainty of future channel state and the tight couplings among control decisions over different time slots. To solve this problem, we propose a Lyapunov optimization-based online algorithm named LEESE. Specifically, LEESE equivalently transforms the multi-stage stochastic optimization into per-slot deterministic problems. For each per-slot problem, we derive the optimal closed-form solution. We show that the optimal control on WPT and data processing follows an interesting threshold-based manner decided by the battery state and data queue backlog. Numerical simulations show that the proposed LEESE algorithm can achieve more than 21.9% performance improvement over the considered benchmark methods.
Fast RSS-based Tuning of RF Multi-Tap Feedforward Self-Interference Cancellation Filter using Coordinate Descent

In this work a novel method for tuning the variable phase shifters and attenuators in a multi-tap feedforward self-interference cancellation (SIC) filter is presented. The proposed method uses perturbations of the filter weights and received signal strength (RSS) measurements to implement a least squares coordinate descent that converges rapidly to the optimal solution. It is shown that when the cancellation tap delays are uniformly spaced at the Nyquist critical sampling interval, the optimal phase shift and attenuation values can be found in a single iteration per tap, requiring 5 RSS measurements each. We find that the required tuning time increases substantially when closer tap spacings are used, and that the proposed method can significantly outperform conventional gradient descent in these settings. The method does not require any manual selection of parameters, and represents an attractive solution for practical systems.
Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods

According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people’s productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80–86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.