Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of a model for an enclosing structure with a heat-accumulating material with phase transition taking into account the process of solar energy accumulation

This paper proposes a mathematical model and a procedure for calculating the thermal state of the enclosing structure of the building, which includes an energy-active panel that accumulates solar radiation due to the phase transition of the heat-accumulating material. The mathematical model is based on a two-dimensional non-stationary nonlinear equation of thermal conductivity, which describes the process of heat transfer in the bearing layer of the enclosing structure and the energy-active panel. The model also includes equations describing radiant heat transfer between opaque and translucent bodies. To correctly describe solar insolation, the ASHRAE 2009 model was used in conjunction with the daily change in the position of the Sun in the sky. To solve the system of equations that make up the mathematical model, an iterative procedure has been developed, which involves alternating solution at each time step of the two-dimensional equation of thermal conductivity and a set of algebraic equations of convective and radiant heat transfer. The study’s result established that the amount of accumulated energy in the heat-accumulating material of the phase transition during daylight hours increases significantly, from 15 to 35 %. At night, the surface temperature of the heat-accumulating element in structures using a material with a phase transition is greater than in the case of heat accumulation only in the bearing layer. As a result, it is possible to select from 70 to 120 % more accumulated heat while the presence of high-thermal partitions in a heat-accumulating material with a phase transition contributes to an increase in accumulated heat and usable heat
Room-temperature ferromagnetic and ferroelectric behavior in polycrystalline ZnO-based thin films

Polycrystalline ZnO-based thin films with Li and/or Co doping have been prepared by a sol-gel spin-coating method on silicon substrates. Magnetization measurements reveal that Li-doped ZnO film shows paramagnetic behavior. However, the Co-doped ZnO thin films show obvious room-temperature ferromagnetic properties, and ferromagnetic properties can be enhanced by the Li codoping, which may be ascribed to indirect exchange via Li-related defects. All ZnO-based films exhibit ferroelectric behavior, and ferroelectric properties can be tuned by the dopants.
A Proteomic Analysis for the Red Seaweed Asparagopsis taxiformis

Simple Summary There is increasing interest in the red seaweed Asparagopsis taxiformis due to its potential use as a feed supplement to knock out methane emissions of ruminants. Yet, there is little understanding of its molecular biology, including the proteins that facilitate photosynthesis, metabolite biosynthesis, growth and reproduction. This study used different extraction procedures to elucidate proteins from A. taxiformis (Lineage 6) sporophytes (diploid stage) and gametophytes (haploid stage). The outcome is the most comprehensive overview of expressed proteins in a seaweed, with interesting insights into the potential functions of proteins. This provides a foundation for future targeted protein extraction from A. taxiformis in search of potential bioactive molecules relevant to health and ecology. Abstract The red seaweed Asparagopsis taxiformis is a promising ruminant feed additive with anti-methanogenic properties that could contribute to global climate change solutions. Genomics has provided a strong foundation for in-depth molecular investigations, including proteomics. Here, we investigated the proteome of A. taxiformis (Lineage 6) in both sporophyte and gametophyte stages, using soluble and insoluble extraction methods. We identified 741 unique non-redundant proteins using a genome-derived database and 2007 using a transcriptome-derived database, which included numerous proteins predicted to be of fungal origin. We further investigated the genome-derived proteins to focus on seaweed-specific proteins. Ontology analysis indicated a relatively large proportion of ion-binding proteins (i.e., iron, zinc, manganese, potassium and copper), which may play a role in seaweed heavy metal tolerance. In addition, we identified 58 stress-related proteins (e.g., heat shock and vanadium-dependent haloperoxidases) and 44 photosynthesis-related proteins (e.g., phycobilisomes, photosystem I, photosystem II and ATPase), which were in general more abundantly identified from female gametophytes. Forty proteins were predicted to be secreted, including ten rhodophyte collagen-alpha-like proteins (RCAPs), which displayed overall high gene expression levels. These findings provide a comprehensive overview of expressed proteins in A. taxiformis, highlighting the potential for targeted protein extraction and functional characterisation for future biodiscovery.
Stretchable conducting materials with multi-scale hierarchical structures for biomedical applications

Electrogenetic tissues in human body such as central and peripheral nerve systems, muscular and cardiomuscular systems are soft and stretchable materials. However, most of the artificial materials, interfacing with those conductive tissues, such as neural electrodes and cardiac pacemakers, have stiff mechanical properties. The rather contradictory properties between natural and artificial materials usually cause critical incompatibility problems in implanting bodymachine interfaces for wide ranges of biomedical devices. Thus, we developed a stretchable and electrically conductive material with complex hierarchical structures; multi-scale microstructures and nanostructural electrical pathways. For biomedical purposes, an implantable polycaprolactone (PCL) membrane was coated by molecularly controlled layer-bylayer (LBL) assembly of single-walled carbon nanotubes (SWNTs) or poly(3,4-ethylenedioxythiophene) (PEDOT). The soft PCL membrane with asymmetric micro- and nano-pores provides elastic properties, while conductive SWNT or PEDOT coating preserves stable electrical conductivity even in a fully stretched state. This electrical conductivity enhanced ionic cell transmission and cell-to-cell interactions as well as electrical cellular stimulation on the membrane. Our novel stretchable conducting materials will overcome long-lasting challenges for bioelectronic applications by significantly reducing mechanical property gaps between tissues and artificial materials and by providing 3D interconnected electro-active pathways which can be available even at a fully stretched state.
Status of Additive Manufacturing Capabilities for Processing Refractory Alloys Under the Molybdemum-99 Program

The enriched 100 Mo target, denoted as aMo , that Northstar Medical Radioisotopes is developing to produce 99 Mo from an accelerator-based method is a disk-type geometry with a 0.5–0.715 mm thickness and a 29 mm outer diameter. A press and sinter method was adopted to fabricate these samples from aMo powder feedstock. Inherent porosity from the press and sinter method makes the disks more easily dissolvable during 99 Mo postprocessing. However, the disk-type geometry will be subject to thermal stresses due to temperature variations once the geometry is in line with the accelerated electron beam, causing warping and possible failure. Additive manufacturing (AM) processes such as laser powder bed fusion (LPBF) enable the manufacturing of custom, complex geometries that may otherwise be difficult to produce with more conventional methods (e.g., press and sinter approach). Therefore, the National Nuclear Security Administration’s Molybdenum-99 Program invested in developing an AM facility dedicated to processing and printing refractory powders. This report details the LPBF process, how irregular refractory powder can be prepared for LPBF, and the equipment that is available to support the program.
Covert Communication for Spatially Sparse mmWave Massive MIMO Channels

Covert communication, also known as communication with low probability of detection, aims to provide reliable communication for legal users and prevent any other user from detecting the occurrence of legal communication. Motivated by the strong need of security links of the next generation communication systems, we study covert communication with millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) hybrid beamforming. Consistent with existing studies on covert communication, we use the Kullback-Leibler (KL) divergence and the total variation (TV) distance as the covertness measure. Under both covertness measures, for block fading channels, we derive the covert transmission rate with and without artificial noise. These results are obtained by optimizing the transmit power and the jamming power to satisfy the covertness constraints and to maximize the transmission rate. Specifically, when artificial noise is allowed, we show that there exists an optimal jamming power to achieve the covert transmission rate given the transmit signal power. Furthermore, we propose a metric to measure the inherent sparsity of the mmWave massive MIMO channel in the spatial domain, and study its effect on the covertness measures and the corresponding covert transmission rates. Our results provide insights and benchmarks for the design of practical covert communication systems with mmWave massive MIMO.
A proposal of fuzzy thesaurus generated by fuzzy covering

This paper discusses preciseness of data in terms of obtaining degree of similarity in which a fuzzy set can be used as an alternative to represent imprecise data. Degree of similarity between two imprecise data represented in two fuzzy sets is approximately determined by using a fuzzy conditional probability relation. More-over, the degree of similarity relationship between fuzzy sets corresponding to fuzzy classes as results of a fuzzy partition on a given finite set of data is examined. Related to a well known fuzzy partition, called fuzzy pseudopartition or fuzzy c-partition where c designates the number of fuzzy classes in the partition, we introduced fuzzy symmetric c-partition regarded as a special case of the fuzzy c-partition. In addition, we also introduced fuzzy covering as a generalization of fuzzy partition. Similarly, two fuzzy coverings, namely fuzzy c-covering and fuzzy symmetric c-covering are proposed corresponding to the fuzzy c-partition and the fuzzy symmetric c-partition, respectively. In this paper, special attention will be given to apply the concept of fuzzy c-covering in generating a fuzzy thesaurus.
Discovery of a Closterovirus Infecting Jujube Plants Grown at Aksu Area in Xinjiang of China

Chinese jujube (Ziziphus jujuba Mill.) is a widely grown fruit crop at Aksu in Xinjiang Uygur Autonomous Region of China. Viral disease-like symptoms are common on jujube plants. Here, for the first time, we report a virus tentatively named persimmon ampelovirus jujube isolate (PAmpV-Ju) infecting jujube plants. The virus was identified using high-throughput sequencing from a jujube plant (ID: AKS15) and molecularly related to viruses in the family Closteroviridae. The genomic sequences of two PAmpV-Ju variants named AKS15-20 and AKS15-17 were determined by RT-PCR amplifications. The genome structure of PAmpV-Ju was identical to that of a recently reported persimmon ampelovirus (PAmpV) and consisted of seven open reading frames. The genomes of AKS15-20 and AKS15-17 shared 83.7% nt identity with each other, and the highest nt sequence identity of 79% with two variants of PAmpV. The incidence of PAmpV-Ju on Aksu jujube plants was evaluated by RT-PCR assays. The phylogenetic analysis of amplified partial sequences coding for polymerase, HSP70h, and CP revealed two phylogenetic clades represented by AKS15-20 and AKS15-17. Our study provides important evidence for understanding viruses infecting jujube plants and establishing efficient measures to prevent virus spread.
A solution approach to minimum spanning tree problem under fermatean fuzzy environment

In classical graph theory, the minimal spanning tree (MST) is a subgraph with no cycles that connects each vertex with minimum edge weights. Calculating minimum spanning tree of a graph has always been a common problem throughout ages. Fuzzy minimum spanning tree (FMST) is able to handle uncertainty existing in edge weights for a fuzzy graph which occurs in real world situations. In this article, we have studied the MST problem of a directed and undirected fuzzy graph whose edge weights are represented by fermatean fuzzy numbers (FFN). We focus on determining an algorithmic approach for solving fermatean fuzzy minimum spanning tree (FFMST) using the modified Prim’s algorithm for an undirected graph and modified optimum branching algorithm for a directed graph under FFN environment. Since the proposed algorithm includes FFN ranking and arithmetic operations, we use FFNs improved scoring function to compare the weights of the edges of the graph. With the help of numerical examples, the solution technique for the proposed FFMST model is described.
Potential Use of Brazilian Green Propolis Extracts as New Photosensitizers for Antimicrobial Photodynamic Therapy against Cariogenic Microorganisms

The synergic effect of Streptococcus mutans and Candida albicans increases dental caries severity. Antimicrobial photodynamic therapy (aPDT) is a non-invasive treatment for antimicrobial aspects. However, the current photosensitizers (PS) have many downsides for dental applications. This study aimed to evaluate the efficiency of two different Brazilian green propolis (BGP-AF and BGP-AG) as PS for aPDT against these microorganisms. A single-species biofilm was irradiated with crude extracts and their fractions and controls. Such extracts showed the best results and were evaluated in dual-species biofilms. Photodegradation, reactive oxygen species (ROS), cytotoxicity, and color stability assays were also investigated. Reductions higher than 3 log10 CFU/mL (p < 0.0001) occurred for crude BGP in single- and dual-species biofilms. Singlet oxygen was produced in BGP (p < 0.0001). BGP-mediated aPDT delayed S. mutans and C. albicans regrowth after 24 h of treatment (p < 0.0001). Both BGP did not change the color of dental materials (p > 0.05). BGP-AF-mediated aPDT showed 72.41% of oral keratinocyte viability (p < 0.0001). BGP extracts may be used in aPDT against S. mutans and C. albicans. Specifically, BGP-AF may represent a promising PS for dental applications.
A Knowledge Transfer Method for Unsupervised Pose Keypoint Detection Based on Domain Adaptation and CAD Models

Vision‐based pose estimation is a basic task in many industrial fields such as bin‐picking, autonomous assembly, and augmented reality. One of the most commonly used pose estimation methods first detects the 2D pose keypoints in the input image and then calculates the 6D pose using a pose solver. Recently, deep learning is widely used in pose keypoint detection and performs excellent accuracy and adaptability. However, its over‐reliance on sufficient and high‐quality samples and supervision is prominent, particularly in the industrial field, leading to high data cost. Based on domain adaptation and computer‐aided‐design (CAD) models, herein, a virtual‐to‐real knowledge transfer method for pose keypoint detection to reduce the data cost of deep learning is proposed. To address the disorder of knowledge flow, a viewpoint‐driven feature alignment strategy is proposed to simultaneously eliminate interdomain differences and preserve intradomain differences. The shape invariance of rigid objects is then introduced as constraints to address the large assumption space problem in the regressive domain adaptation. The multidimensional experimental results demonstrate the superiority of the method. Without real annotations, the normalized pixel error of keypoint detection is reported as 0.033, and the proportion of pixel errors lower than 0.05 is up to 92.77%.
Assessment of learning in university mathematics

The important issues in assessment practices for university undergraduates are identified. The way in which assessment can be used to enhance student learning, the impact of external factors on assessment methods and the barriers that inhibit change are discussed. The paper also discusses the various ways in which changes in assessment practices have have been implemented and studies that have been carried out to gauge the effect of different methods of assessment.
Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks

Non-terrestrial network (NTN) services using low-Earth-orbit (LEO) satellites are expanding. Interference management of NTN services with other terrestrial wireless services is emerging as a critical issue owing to the inherent international and vast coverage nature of NTN. This study develops a multi-agent deep reinforcement learning (DRL) framework to establish a multi-beam uplink channel allocation strategy that minimizes interference with incumbent stations under the given quality of service (QoS) constraints. We propose a novel framework with the sequential training of agents in a specific order to overcome the inherent non-stationarity of multi-agent DRL. To improve learning efficiency, we design the training sequence in accordance with reward function and initial state. As a result, taking actions in the order of the largest interference to the incumbent station provides superior performance to taking actions in an arbitrary order. Moreover, the proposed channel allocation performs close to the optimal exhaustive search and outperforms conventional greedy graph coloring method.
Under Pressure: Social Capital and Trust in Government After Natural Disasters

In response to the increasing threats posed by natural hazards, both disaster managers and researchers have recognized social networks and trust between communities and government as fundamental building blocks for resilience. However, these efforts often overlook the fact that the same network ties to family and friends that can help households weather a storm may also extend households’ exposure through collective trauma, reshaping their trust in and perceptions of government. Utilizing two restricted-access data sets gathered in Houston, Texas, following Hurricane Harvey, this study investigates the frequency with which households experienced a direct and/or close-tie impact and how such impacts affect households’ trust in local, state, and federal government. Results indicate that households experience close-tie impacts pervasively and that experiencing a close-tie impact is significantly correlated with lower trust in government at all levels, net of experiencing a direct impact and other statistical controls. Implications for a more nuanced approach to social capital and trust in disaster mitigation and research are discussed.
Leisure-Related Social Work Interventions for Patients with Cognitive Impairment: A Systematic Review and Meta-Analysis

The social work profession has been exploring nonpharmacological interventions for patients with cognitive impairment, but there are few evidence-based research outputs. Systematically evaluating the effectiveness of social work interventions for people with cognitive impairment can shed light on the matter to further improve similar interventions. Randomized controlled trials of nonpharmacological interventions for patients with cognitive impairment were selected from key literature databases in both English and Chinese from 2010 to 2021. A systematic review and meta-analysis with Revman 5.4 were performed. Seven trials were included, involving 851 patients with cognitive impairment. The meta-analysis showed that, in terms of overall cognitive function, the Montreal Cognitive Assessment score (MD = 1.64, 95% CI [0.97, 2.30], p < 0.001) of the intervention group was superior to the control group, but there was no significant difference in the Mini-Mental State Examination score between the two groups (MD = 0.33, 95% CI [−0.16, 0.82], p = 0.18). Compared with the control group, nonpharmacological intervention can effectively improve the neuropsychiatric condition of patients (SMD = −0.42, 95% CI [−0.64, −0.20], p = 0.0002). In summary, the current evidence shows that nonpharmacological social work interventions had a positive effect on the cognitive function and neuropsychiatric status of patients with cognitive impairment. Suggestions for future nonpharmacological intervention practice are discussed.
Blockchain based Sensor System Design For Embedded IoT

ABSTRACT The Internet of Things (IoT) is a network of interconnected computing devices with unique IDs and the potential to exchange data over the network. Most current IoT solutions have highly centralized designs vulnerable to cyber-attacks and have a single point of failure. A new solution path is required to improve data access by enhancing privacy and security standards. In this research, the Block Chain-based sensor system was designed for an embedded IoT platform to improve the integrity of sensor data with their open issues and challenges. The main objective of this research is to enhance sensor data security, resolve single points of failures and improve the trust between users to make the framework more scalable for devices. In this review paper, BC-based IoT network-based techniques are analyzed based on the security and privacy-preservation aspects. Also, discuss current research obstacles and suggest some study possibilities for future work.
Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic Computing

From rate to temporal encoding, spiking information processing has demonstrated advantages across diverse neuromorphic applications. In the aspects of data capacity and robustness, multiplexing encoding outperforms alternative encoding schemes. In this work, we aim to implement a new class of multiplexing temporal encoders, patterning stimuli in multiple timescales to improve the information processing capability, and robustness of systems deployed in noisy environments. Benefitted by the internal reference frame using subthreshold membrane oscillation (SMO), the encoded spike patterns are less sensitive to the input noise, increasing the encoder’s robustness. Our design results in a tremendous saving on power consumption and silicon area compared with the power-hungry analog-to-digital converters. Furthermore, a working prototype of the multiplexing temporal encoder built based on an interspike interval (ISI) encoding scheme is implemented on a silicon chip using the standard 180-nm CMOS process. To the best of our knowledge, our introduced encoder demonstrates the first integrated circuit (IC) implementation of neural encoding with multiplexing topology. Finally, the accuracy and efficiency of our design are evaluated through standard machine learning benchmarks, including Modified National Institute of Standards and Technology (MNIST), Canadian Institute For Advanced Research (CIFAR)-10, Street View House Number (SVHN), and spectrum sensing in high-speed communication networks. While our multiplexing temporal encoder demonstrates a higher classification accuracy across all the benchmarks, the power consumption and dissipated energy per spike reach merely $2.6~\mu \text {W}$ and 95 fJ/spike, respectively, with an effective frame rate of 300 MHz. Compared with alternative encoding schemes, our multiplexing temporal encoder achieves at most 100% higher data capacity, 11.4% more accurate in classification, and 25% more robust against noise. Compared with the state-of-the-art designs, our work achieves up to $105 \times $ power efficiency without significantly increasing the silicon area.