Miracle-dz/UniFAR-FTUs huggingface dataset:
High Performance Frequency Up-Conversion Energy Harvester Based on PZT Thick Film Technology for IoT Applications

The development of Internet of Things (IoT) technology has greatly contributed to the development of smart cities, from smart homes to smart factories. The quality of human life is enriched by the comprehensive sensing and seamless communication of IoT devices in different spaces. As a foundation, wireless sensor networks, which currently consist of various fixed sensors, are experiencing sustainability and compatibility issues. Here, we report a broadband vibrational piezoelectric energy harvester (PEH) with frequency up-conversion (FUC) function, consisting of a high-frequency piezoelectric cantilever (HF-PC) with a resonant frequency of 214 Hz and a low-frequency stainless-steel cantilever (LF-SC) with a resonant frequency of 36 Hz, and the HF-PC is fabricated by MEMS PZT thick film technology. Under the excitation of external low-frequency vibration, the LF-SC at the bottom impinges on the HF-PC during its motion, and in this way converts the external low-frequency vibration into high-frequency piezoelectric cantilever vibration. The maximum output power of a single HF-PC is 521.5 ȝ: at 214 Hz and 1.0 g acceleration. On the other hand, the FUC-PEH is capable of achieving a maximum voltage output of 26.6 V over a drive frequency range of 26.2 Hz to 48.4 Hz, with an operating bandwidth of 22.2 Hz and a maximum output power of 775.3 μW. The output power is sufficient to power the Bluetooth temperature and humidity sensor, demonstrating the high power output and excellent charging capability of the FUC-PEH.
A THREE-DIMENSIONAL MODEL OF ACTIVE REGION 7986: COMPARISON OF SIMULATIONS WITH OBSERVATIONS

In the present study, we use a forward modeling method to construct a 3D thermal structure encompassing active region 7986 of 1996 August. The extreme ultraviolet (EUV) emissions are then computed and compared with observations. The heating mechanism is inspired by a theory on Alfvén wave turbulence dissipation. The magnetic structure is built from a Solar and Heliospheric Observatory (SOHO)/MDI magnetogram and an estimated torsion parameter deduced from observations. We found that the solution to the equations in some locations is in a thermal nonequilibrium state. The time variation of the density and temperature profiles leads to time dependent emissions, which appear as thin, loop-like structures with uniform cross-section. Their timescale is consistent with the lifetime of observed coronal loops. The dynamic nature of the solution also leads to plasma flows that resemble observed coronal rain. The computed EUV emissions from the coronal part of the fan loops and the high loops compare favorably with SOHO/EIT observations in a quantitative comparison. However, the computed emission from the lower atmosphere is excessive compared to observations, a symptom common to many models. Some factors for this discrepancy are suggested, including the use of coronal abundances to compute the emissions and the neglect of atmospheric opacity effects.
Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case–control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case–control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10−6). Telomere length was also significantly reduced (p = 6.6 × 10−5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10−8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.
A Priori Knowledge-Based Dual Hierarchical RNN for Spatial–Temporal Process Modeling: Using a Multitubular Reactor as a Case Study

Deep learning methods have been rapidly developed in recent decades. In this article, they are extended to model spatial-temporal industrial processes. Instead of pure black-box data-driven modeling approaches, the proposed model encodes the domain knowledge and physical rules governing the spatiotemporal system, called a dual-hierarchical recurrent neural network (DH-RNN). Both spatial and temporal relationships are modeled by multiple RNNs with diverse structures, which need correct specifications of all the interactions between spatial and temporal variables with a priori knowledge of the real process. A more accurate prediction can be obtained with fewer parameters employed in the network. And the effectiveness of the proposed DH-RNN is verified via a real ethylene oxychlorination process.
Intervention Health Volunteer During the COVID-19 Pandemic: Online Education Practices to Pregnant Women

BACKGROUND: The COVID-19 pandemic condition limits pregnant women from getting health services, especially continuum care-based health services. Pregnant women need access to antenatal care services to get screening and assistance related to efforts to get good pregnancy outcomes. AIM: The purpose of this study is to identify factors related to information exposure and the role of health volunteers in providing health education to pregnant women. METHODS: This study also applies the preparation of health volunteers in conducting online education practices during the COVID-19 pandemic for pregnant women. Pregnant women who have been given education by the health volunteers are asked to assess their acceptance of the health volunteer’s educational practices. Collect data about need assessment online education in pregnant women. Health volunteers who attended debriefing related to online education practices and pregnant, women who were given online education by health volunteers. The number of samples of health volunteers and pregnant women in this study was 32 health volunteers and then 32 pregnant women giving assessment about online education practices by health volunteers. RESULTS: The results of the intervention on the application of online education by health volunteers are quite good, according to pregnant women, namely, screening and mentoring, health volunteers have provided information. Submission of information is done through WhatsApp by 76% and pregnant women understand the health information of pregnant women delivered by health volunteers. CONCLUSION: It is necessary to strengthen the role of health volunteers in providing online assistance and education so that it can continue to be carried out optimally.
On the Choice of a Model for Representing Data Flow Parameters in a Digital System

The paper considers the problem of analyzing the characteristics of a digital control system as a black box based on the input and output flows characteristics. The Network calculus method is proposed as a system model. The method makes possible to describe the system as a set of deterministic constraints or arrival curve on the flow. The paper proposes to extend the Network Calculus for the case when the flow is described by more than two parameters and extend Network Calculus for multidimensional feature space. An example the feature space may be consist from a packet sequence number, packet size and packet arrival time. The multidimensional flow might be described in a framework of the Network Calculus by some multidimensional arrival cure used to compute boundaries on the flow behavior and characteristics. The proposed extension of the network Calculus makes possible to model a wider class of systems, for example, considering non-uniform data flows.
Gut Microbiome and Small RNA Integrative-Omic Perspective of Meconium and Milk-FED Infant Stool Samples

The human gut microbiome plays an important role in health, and its initial development is conditioned by many factors, such as feeding. It has also been claimed that this colonization is guided by bacterial populations, the dynamic virome, and transkingdom interactions between host and microbial cells, partially mediated by epigenetic signaling. In this article, we characterized the bacteriome, virome, and smallRNome and their interaction in the meconium and stool samples from infants. Bacterial and viral DNA and RNA were extracted from the meconium and stool samples of 2- to 4-month-old milk-fed infants. The bacteriome, DNA and RNA virome, and smallRNome were assessed using 16S rRNA V4 sequencing, viral enrichment sequencing, and small RNA sequencing protocols, respectively. Data pathway analysis and integration were performed using the R package mixOmics. Our findings showed that the bacteriome differed among the three groups, while the virome and smallRNome presented significant differences, mainly between the meconium and stool of milk-fed infants. The gut environment is rapidly acquired after birth, and it is highly adaptable due to the interaction of environmental factors. Additionally, transkingdom interactions between viruses and bacteria can influence host and smallRNome profiles. However, virome characterization has several protocol limitations that must be considered.
A Compact Photochromic Occlusion Capable See-through Display with Holographic Lenses

Occlusion is a crucial visual element in optical see-through (OST) augmented reality, however, implementing occlusion in OST displays while addressing various design trade-offs is a difficult problem. In contrast to the traditional method of using spatial light modulators (SLMs) for the occlusion mask, using photochromic materials as occlusion masks can effectively eliminate diffraction artifacts in see-through views due to the lack of electronic pixels, thus providing superior see-through image quality. However, this design requires UV illumination to activate the photochromic mate-rial, which traditionally requires multiple SLMs, resulting in a larger form factor for the system. This paper presents a compact photochromic occlusion-capable OST design using multilayer, wavelength-dependent holographic optical lenses (HOLs). Our approach employs a single digital mi-cromirror display (DMD) to form both the occlusion mask with UV light and a virtual image with visible light in a time-multiplexed man-ner. We demonstrate our proof-of-concept system on a bench-top setup and assess the appearance and contrasts of the displayed image. We also suggest potential improvements for current prototypes to encourage the community to explore this occlusion approach.
Separating Anthropogenically‐ and Naturally‐Caused Temperature Trends: A Systematic Approach Based On Climate Memory Analysis

Separating anthropogenic and natural contributions to global warming is crucial for understanding and predicting climate change, but it is challenging due to the complexity of the climate system. In this study, we try to address this issue by estimating the anthropogenic forcing impacts with its long‐term persistence features properly considered. In this way, a data‐driven approach is proposed to decompose the observed trends from global and regional scales into anthropogenically‐ and naturally‐caused trends. We find that there is a continuous anthropogenic global warming trend since the beginning of the last century, even during the recent global warming hiatus period. On regional scales, the anthropogenically‐forced trends among regions are found at a similar level, while their unevenly distributed warming trends among regions may be attributed to natural causes. Our findings provide new insight into climate warming, and our approach based on persistence analysis provides a new perspective for attribution studies.
Basic equations of electro-mechanical coupling problem in the Hamiltonian system

To the piezoelectric material with electro-mechanical property, couple equations of electro-mechanical coupling problem in the Hamiltonian system is derived by using the variational principle with multi-variables, and the general expressions of eigenvalue zero and non-zero eigenvalue in the problem are gotten. Full analysis and calculation are made for an idiographic problem that two-dimension piezoelectric slab is subjected to well-distributed load, and exact solutions of stress and electric displacement are obtained, which are consistent with those obtained by the general theory of elasticity and rather precise. The results indicate that the Hamiltonian system is applicable to the mechanics analysis in piezoelectric materials.
Effect of Chain Structure on the Various Properties of the Copolymers of Fluorinated Norbornenes with Cyclooctene

Fluorinated polymers are attractive due to their special thermal, surface, gas separation, and other properties. In this study, new diblock, multiblock, and random copolymers of cyclooctene with two fluorinated norbornenes, 5-perfluorobutyl-2-norbornene and N-pentafluorophenyl-exo-endo-norbornene-5,6-dicarboximide, are synthesized by ring-opening metathesis copolymerization and macromolecular cross-metathesis in the presence of the first- to third-generation Grubbs’ Ru-catalysts. Their thermal, surface, bulk, and solution characteristics are investigated and compared using differential scanning calorimetry, water contact angle measurements, gas permeation, and light scattering, respectively. It is demonstrated that they are correlated with the chain structure of the copolymers. The properties of multiblock copolymers are generally closer to those of diblock copolymers than of random ones, which can be explained by the presence of long blocks capable of self-organization. In particular, diblock and multiblock fluorine-imide-containing copolymers show a tendency to form micelles in chloroform solutions well below the overlap concentration. The results obtained may be of interest to a wide range of researchers involved in the design of functional copolymers.
Efficient Elitist Cooperative Evolutionary Algorithm for Multi-Objective Reinforcement Learning

Sequential decision-making problems with multiple objectives are known as multi-objective reinforcement learning. In these scenarios, decision-makers require a complete Pareto front that consists of Pareto optimal solutions. Such a front enables decision-makers to understand the relationship between objectives and make informed decisions from a broad range of solutions. However, existing methods may be unable to search for solutions in concave regions of the Pareto front or lack global optimization ability, leading to incomplete Pareto fronts. To address this issue, we propose an efficient elitist cooperative evolutionary algorithm that maintains both an evolving population and an elite archive. The elite archive uses cooperative operations with various genetic operators to guide the evolving population, resulting in efficient searches for Pareto optimal solutions. The experimental results on submarine treasure hunting benchmarks demonstrate the effectiveness of the proposed method in solving various multi-objective reinforcement learning problems and providing decision-makers with a set of trade-off solutions between travel time and treasure amount, enabling them to make flexible and informed decisions based on their preferences. Therefore, the proposed method has the potential to be a useful tool for implementing real-world applications.
An Optimization-Based Model for A Hybrid Photovoltaic-Hydrogen Storage System for Agricultural Operations in Saudi Arabia

Renewable energy technologies and resources, particularly solar photovoltaic systems, provide cost-effective and environmentally friendly solutions for meeting the demand for electricity. The design of such systems is a critical task, as it has a significant impact on the overall cost of the system. In this paper, a mixed-integer linear programming-based model is proposed for designing an integrated photovoltaic-hydrogen renewable energy system to minimize total life costs for one of Saudi Arabia’s most important fields, a greenhouse farm. The aim of the proposed system is to determine the number of photovoltaic (PV) modules, the amount of hydrogen accumulated over time, and the number of hydrogen tanks. In addition, binary decision variables are used to describe either-or decisions on hydrogen tank charging and discharging. To solve the developed model, an exact approach embedded in the general algebraic modeling System (GAMS) software was utilized. The model was validated using a farm consisting of 20 greenhouses, a worker-housing area, and a water desalination station with hourly energy demand. The findings revealed that 1094 PV panels and 1554 hydrogen storage tanks are required to meet the farm’s load demand. In addition, the results indicated that the annual energy cost is $228,234, with a levelized cost of energy (LCOE) of 0.12 $/kWh. On the other hand, the proposed model reduced the carbon dioxide emissions to 882 tons per year. These findings demonstrated the viability of integrating an electrolyzer, fuel cell, and hydrogen tank storage with a renewable energy system; nevertheless, the cost of energy produced remains high due to the high capital cost. Moreover, the findings indicated that hydrogen technology can be used as an energy storage solution when the production of renewable energy systems is variable, as well as in other applications, such as the industrial, residential, and transportation sectors. Furthermore, the results revealed the feasibility of employing renewable energy as a source of energy for agricultural operations.
A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area.
Machine-Learning-Based Generative Optimization Method and Its Application to an Antenna Decoupling Design

A machine-learning-based generative optimization method using masked autoencoders (MAE) is proposed and applied to multiobjective antenna decoupling structure design. The machine learning method contains <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means algorithm and MAE neural network structure. The <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means is used for label-free classification of decoupling structure samples, and MAE is used for the intelligent optimization design of decoupling structures. By applying the machine-learning-based method, MAE optimization models for designing decoupling structures are obtained. An antenna decoupling example using neutralization line is selected to validate the effectiveness of the proposed optimization method. Measurement results show that the neutralization line designed by the proposed method improves the antenna isolation by at least 6 dB, that is, <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> reaches below at least -18 dB between 3.5 and 9.7 GHz, while requiring little manual intervention during the optimization progress.
Variable property model for natural convection in a square cavity with temperature differences of 20～720 K

A variable property fluid model was used to analyze laminar natural convection with large temperature differences (20～720 K) with the incompressible Navier-Stokes equations with variable density, viscosity, and thermal conductivity. A modified SIMPLEC solver was used with nonuniform collocated grids. The numerical solutions for the velocity, temperature distributions, and the average Nusselt number at the hot wall for natural convection in a square cavity were calculated for temperature differences of 20 K and 720 K and Rayleigh numbers from 10~3 to 10~5. These solutions were compared with benchmark solutions and those using the Boussinesq approximation. The results show that the model and solver are more exact and simpler than those using the Boussinesq approximation.
Safe Data-Driven Model Predictive Control of Systems With Complex Dynamics

In this article, we address the task and safety performance of data-driven model predictive controllers (DD-MPC) for systems with complex dynamics, i.e., temporally or spatially varying dynamics that may also be discontinuous. The three challenges we focus on are the accuracy of learned models, the receding horizon-induced myopic predictions of DD-MPC, and the active encouragement of safety. To learn accurate models for DD-MPC, we cautiously, yet effectively, explore the dynamical system with rapidly exploring random trees (RRT) to collect a uniform distribution of samples in the state-input space and overcome the common distribution shift in model learning. The learned model is further used to construct an RRT tree that estimates how close the model's predictions are to the desired target. This information is used in the cost function of the DD-MPC to minimize the short-sighted effect of its receding horizon nature. To promote safety, we approximate sets of safe states using demonstrations of exclusively safe trajectories, i.e., without unsafe examples, and encourage the controller to generate trajectories close to the sets. As a running example, we use a broken version of an inverted pendulum where the friction abruptly changes in certain regions. Furthermore, we showcase the adaptation of our method to a real-world robotic application with complex dynamics: robotic food-cutting. Our results show that our proposed control framework effectively avoids unsafe states with higher success rates than baseline controllers that employ models from controlled demonstrations and even random actions.