Miracle-dz/UniFAR-FTUs huggingface dataset:
Induced pluripotent stem cell generation using mRNAs: the effect of valproic acid, 5-azacytidine and ascorbic acid

In the bourgeoning fields of tissue engineering and regenerative medicine, induced pluripotent stem cells (iPSCs) technology with gene therapy are promising candidates for alternative stem cell source and cell transplantation. In this study, small molecules as anti-oxidant; ascorbic acid (ASA), histone deacetylase inhibitors (HDACi); Valproic acid (VPA), and DNA methyltransferase inhibitors (DNMTi); 5-Azacytidine (5-AzaC) were examined during the generation of murine iPSCs using mRNA of Yamanaka factors from mouse embryonic fibroblasts (MEFs). These modulators were selected based on their well-known effect on the epigenetic status and chromatin modification during early reprogramming. iPSC generation was performed by using synthesized mRNAs of Yamanaka factors Oct4, Sox2, c-Myc, and Klf4 (OSCK) as a standard reprogramming strategy. Both morphological changes and the expression level of the pluripotency markers were examined. 5-AzaC with 1 µM concentration has a slightly toxic effect on the cells, affecting its proliferation and growing efficiency. In contrast, the use of VPA or ASA led to a two-fold increase in the number of iPSC colonies. The iPSCs cultured with the addition of VPA or ASA showed a high expression of the tested pluripotency markers, with a significant increase, more than that of the cells cultured with the addition of 5-AzaC. These findings shed light on the role of ASA, VPA, and 5-AzaC during murine iPSCs generation using a mRNA reprogramming strategy.
Symmetric self-electrooptic effect device : optical set-reset latch, defferential logic gate and differential modulator/detector

The symmetric self-electrooptic-effect device (S-SEED), a structure consisting of two p-i-n diodes electrically connected in series and acting as an optically bistable set-reset latch, is discussed. Applications and extensions of this device are also discussed. The devices do not require the critical biasing that is common to most optically bistable devices and thus is more useful for system applications. They have been optically cascaded in a photonic ring counter and have been used to perform different NOR, OR, NAND, and AND logic functions. Using the same device, a differential modulator that generates a set of complementary output beams with a single voltage control lead and a differential detector that gives an output voltage dependent on the ratio of the two optical input powers have been demonstrated. >
Fibre optic sensor network for spacecraft health monitoring

Close meshed instrumentation or sensor networks applying conventional sensors for temperature and strain monitoring may result in excessive penalties in terms of weight constraints, reliability and sensitivity to environmental conditions, and complex interfaces. The fibre-optic sensor network described in this paper is a multiplexed system of fibre Bragg grating (FBG) strain and temperature sensors and was designed and developed for a demanding space environment, but it can also be emphasized as a promising sensor technology with high potential for non-space applications. The FBG sensor network measures both strain and temperature at the measuring conditions of the structural core of the X-38 spacecraft, by means of wavelength shifts due to tensile stress on a Bragg grating. Dependent on the fixation of the fibre, either isolated from or mechanically coupled to the structure, local thermal or mechanical loads can be determined in the temperature range from -40 to +190°C, and in the strain range from -0.1 % to +0.3%. Short-term resolution and repeatability of the strain measurement amount to 5 μe and 25 μe, respectively. The FBG sensor network is very suitable for structural health monitoring of large structures, i.e. to determine thermal and mechanical load profiles during operation, to assess residual strength of structural elements or to detect irregular working conditions. In comparison to conventional sensors like thermocouples and strain gauges, an FBG sensor network significantly reduces the amount of required front end electronics (FEE) and harness.
Intelligent Road Tracking and Real-time Acceleration-deceleration for Autonomous Driving Using Modified Convolutional Neural Networks

Neural network is one of the most widely used method in autonomous driving. Current researchers use only steering angle to train artificial neural networks, ignoring the importance of acceleration and deceleration for autonomous vehicles. We used an intelligent driving platform built with the Raspberry Pi 4 Model B, a front wide-angle camera, and a 1:16 scale model car to achieve real-time acceleration and deceleration while performing road tracking. Existing models cannot learn steering angle and throttle values well. This research proposed a novel architecture CNN model (PBLM-CNN21) to achieve real-time acceleration and deceleration while achieving road tracking. The PBLM-CNN21 model can learn steering angle and throttle value. The training loss value of our proposed PBLM-CNN21 model was 35% lower than the current TDD model, and the stability of our proposed road tracking model was 82% greater than that of the current TDD model. Furthermore, we tested the impact of different hyper-parameters on training model loss and road tracking performance. In addition, we also tested the effectiveness of varying lighting conditions and speed ratios on road tracking performance. The PBLM-CNN21 model proved more robust than the existing TDD models. Moreover, the PBLM-CNN21 model achieved road tracking under different lighting conditions and was more suitable for high-speed ratios.
Production and Characterization of Polyhydroxyalkanoates from Wastewater via Mixed Microbial Cultures and Microalgae

In the context of circular economy and sustainable production of materials, this project investigated the feasibility of producing sustainable polyhydroxyalkanoates (PHA) from microalgae and sludge used in the treatment of municipal wastewater. The overall process was studied looking at the main steps: microalgae production, fermentation of the biomass, production and characterization of the PHAs. It was possible to obtain blends of hydroxybutyrate-hydroxyvalerate copolymers with high molecular weights and different compositions depending on the nature of the feedstock (mixed volatile fatty acids). In some cases, almost completely amorphous PHA materials were obtained, suggesting a potential diversification of uses and applications.
A Low-Jitter and Low-Reference-Spur 320 GHz Signal Source With an 80 GHz Integer-N Phase-Locked Loop Using a Quadrature XOR Technique

This article reports a 320-GHz low-jitter and low-reference-spur signal source consisting of an 80-GHz integer-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> phase-locked loop (PLL) and a 320-GHz frequency quadrupler. The 80-GHz PLL features a novel dual-path quadrature exclusive-OR (QXOR) technique to cancel the spurs at the reference frequency and its harmonics, enabling low-spur and low-noise phase locking. The proposed phase detector (PD) also enables frequency detection and lock detection (LD), rendering the band-searching to be decoupled from the loop components. Implemented in a 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SiGe BiCMOS technology, the proposed signal source shows a −73.1-dBc reference spur, −113.7-dB/Hz phase noise at 1-MHz offset at 40.96 GHz, and −90.3-dB/Hz phase noise at 1-MHz offset at 311.8 GHz. It achieves an integrated jitter of 66.9 fs<sub>rms</sub> at 40.96 GHz and 122 fs<sub>rms</sub> (both integrated from 10 kHz to 100 MHz) beyond 300 GHz, with a total division ratio of 512. The LD time is at the microsecond level. The maximum output power is −3.24 dBm, and the power consumption is 372 mW.
Evaluating Seasonal Wildfire Susceptibility and Wildfire Threats to Local Ecosystems in the Largest Forested Area of China

The frequent occurrence of wildfires presents a serious threat to human livelihoods and local ecosystems. The use of machine learning (ML) methods to assess wildfire susceptibility can provide decision support for disaster prevention. However, most current ML‐based wildfire susceptibility assessments overly focus on spatially evaluating the disaster threat, while ignoring the potential threats of wildfires to local ecosystems. This situation makes it difficult to determine seasonal variations in wildfire susceptibility and limits the value of assessment results. We present a framework to assess wildfire susceptibility and wildfire threats seasonally to local ecosystems. The ecosystem service value (ESV) was used as a proxy for the economic value of an ecosystem, the random forest algorithm was used to evaluate wildfire susceptibility, and the Daxinganling region, the largest forested area in China, was selected as the study area, and the dynamic equivalent coefficient factor method was used to calculate the ESV of each cell. Our main findings were as follows: (a) wildfire susceptibility exhibited obvious disparities in terms of spatial distribution across the four seasons; (b) each ecosystem in the study area faced a different magnitude of wildfire disturbance; and (c) the expected ESV loss (USD 10.8 billion) due to wildfires was much higher than the region’s total GDP (USD 2 billion) in 2019. This study was repeatable, and all data required were obtained freely. The methodologies used can be applied directly to other regions. Our study will be of particular interest to developing counties where intensive wildfire monitoring is limited.
Implementing an Online Program to Change Benzodiazepine Prescription: Protocol of a Hybrid Type 1 Cluster-Randomised Trial

Introduction: Excessive benzodiazepine (BZD) prescription has long been considered a serious mental health concern in many countries. Many interventions using different methodologies have been implemented to change BZD prescription patterns in primary health care settings, with limited positive results. Objectives: The primary objective of our study was to analyse the effectiveness and implementation process of an intervention aimed at changing BZD prescription patterns in a primary health care setting in Portugal. Methodology: We chose as methodology an effectiveness-implementation hybrid type 1 intervention. Our intervention was based on the development of an online platform, named ePrimaPrescribe, which was delivered using a Digital Behaviour Change Intervention (DBCI), using a two-arm cluster-randomised clinical trial. Results: We primarily aimed to evaluate the effectiveness of our DBCI in changing BZD prescription patterns using the frequency of BZD prescriptions issued per month as an outcome measure. Secondarily, we aimed to analyse the effect of ePrimaPrescribe on antidepressant prescriptions, to study the effect of the platform on diagnosis registration associated with BZDs and antidepressant prescription, and to perform a cost analysis considering the monthly National Health Service spending on BZD co-payments. Finally, we aimed to analyse the implementation process using quantitative and qualitative methods. Conclusion: With this study, we expect to contribute with a cost-effective intervention to change the complex matter of excessive BZD prescriptions, and also to improve insight into the challenges to intervention implementation processes in primary health care settings. We believe that our findings are relevant not only to the specific setting where the study was implemented, but also to all countries where primary health care plays a central role in care provision.
On Scheduling Policy for Multi-process Cyber-Physical System with Edge Computing

In this paper, we consider a cyber-physical system (CPS) with multiple Internet of Things (IoT) devices. There are multiple independent linear time-invariant processes in the system, which are sampled by sensors, scheduled by controllers and controlled by actuators. In the literature of wireless control CPS, commonly assume that the system just have one controller and ignore the processing time on server. In this work we employ the edge computing, the controllers are facilitated by edge server and cloud server. The processing time of status update depends on the characteristic of different processes and servers. By taking into account such conditions, we mainly investigate how to choose the destination of status updates (i.e., edge server or cloud server) to minimize the average Mean Square Error (MSE) of the entire system. To address this issue, we formulate a Markov Decision Process (MDP) problem and obtain the optimal scheduling policy. The threshold property of the optimal scheduling policy is proved, and a suboptimal policy is proposed to overcome the curse of dimensionality. The simulation results illustrate that the selection of controller is related to the timeliness of process and show the superiority of the proposed policies.
The Nao Robot as a Platform for Evolutionary Robotics

The Nao robot seemingly has capabilities of doing interesting Evolutionary Robotics research and experiments. Implementing an evolutionary learning platform capable of such research has therefore been realised in this project. After this, finding suitable optimisation problems for the Nao was done, before conducting evolutionary experiments such as locomotion gait optimisation involving both a physical and simulated version of the robot. The results from a preliminary experiment showed a clear difference in performance between the real robot and the one in the simulator. Appropriate methods and techniques were therefore investigated and conducted, resulting in more or less satisfying results in the end. The overall impression from the experiments is that the Nao robot is highly capable of doing Evolutionary Robotics research, and the implemented evolutionary learning platform should be a great start for conducting further and more interesting experiments in the future. Suggestions on further experiments and improvements can be found in the last chapter, along with a more detailed discussion of the already done experiments and results.
UCDNet: A Deep Learning Model for Urban Change Detection From Bi-Temporal Multispectral Sentinel-2 Satellite Images

Change detection (CD) from satellite images has become an inevitable process in earth observation. Methods for detecting changes in multi-temporal satellite images are very useful tools when characterization and monitoring of urban growth patterns is concerned. Increasing worldwide availability of multispectral images with a high revisit frequency opened up more possibilities in the study of urban CD. Even though there exists several deep learning methods for CD, most of these available methods fail to predict the edges and preserve the shape of the changed area from multispectral images. This article introduces a deep learning model called urban CD network (UCDNet) for urban CD from bi-temporal multispectral Sentinel-2 satellite images. The model is based on an encoder–decoder architecture which uses modified residual connections and the new spatial pyramid pooling (NSPP) block, giving better predictions while preserving the shape of changed areas. The modified residual connections help locate the changes correctly, and the NSPP block can extract multiscale features and will give awareness about global context. UCDNet uses a proposed loss function which is a combination of weighted class categorical cross-entropy (WCCE) and modified Kappa loss. The Onera Satellite Change Detection (OSCD) dataset is used to train, evaluate, and compare the proposed model with the benchmark models. UCDNet gives better results from the reference models used here for comparison. It gives an accuracy of 99.3%, an $F1$ score ( $F1$ ) of 89.21%, a Kappa coefficient (Ka) of 88.85%, and a Jaccard index (JI) of 80.53% on the OSCD dataset.
The Benefits and Barriers of Sport for Children From Low-Income Settings: An Integrative Literature Review

The purpose of this integrative review was to examine the existing literature about the emotional and social benefits, as well as barriers and facilitators to sport participation for children from low-income settings. Thematic analysis was performed yielding three major themes: (1) emotional benefits of sport participation; (2) social benefits of sport participation; and (3) barriers and facilitators to sport participation. Overall, the thirteen studies showed positive emotional and social benefits for sport participation; however, one study found decreased mental health and one reported no significant findings. Facilitators such as psychological safety and social support are needed to encourage sport participation as significant barriers to sport participation continue to exist for children from low-income settings. Future areas of research include more longitudinal studies related to the role of sport, the environment, coaching style, and investigation into why participation rates continue to be decreased for children from low-income settings despite added social supports.
Do Spoken Vocabulary and Gestural Production Distinguish Children with Transient Language Delay from Children Who Will Show Developmental Language Disorder? A Pilot Study

The literature on the role of gestures in children with language delay (LD) is partial and controversial. The present study explores gestural production and modality of expression in children with LD and semantic and temporal relationships between gestures and words in gesture + word combinations. Thirty-three children participated (mean age, 26 months), who were recruited through a screening programme for LD. Cognitive skills, lexical abilities, and the use of spontaneous gestures in a naming task were evaluated when the children were 32 months old. When the children were 78 months old, their parents were interviewed to collect information about an eventual diagnosis of developmental language disorder (DLD). According to these data, the children fell into three groups: children with typical development (n = 13), children with LD who did not show DLD (transient LD; n = 9), and children with LD who showed DLD (n = 11). No significant differences emerged between the three groups for cognitive and lexical skills (comprehension and production), for number of gestures spontaneously produced, and for the sematic relationships between gestures and words. Differences emerged in the modality of expression, where children with transient LD produced more unimodal gestural utterances than typical-development children, and in the temporal relationships between gestures and words, where the children who would show DLD provided more frequent representational gestures before the spoken answer than typical-development children. We suggest a different function for gestures in children with T-LD, who used representational gestures to replace the spoken word they were not yet able to produce, and in children with LD-DLD, who used representational gestures to access spoken words.
The Effect of Self-Service Technologies on Customer Experiences in Banking: The Case of Ghana

The surge in self-service technologies (SSTs) adoption in the banking industries across the globe has changed customers' banking experiences. Customers have developed a passion for SSTs which has affects their intentions to save with a bank. The positive effects of employing SSTs in the banking industries are evident in literature however, little is known on how SSTs affect customer experiences in the banking industries of developing countries like Ghana. The study involved a sample of 384 banking customers in Ghana. The study confirms the positive effects of SSTs adoption on customer experiences. The study establishes that customers' adoption of SSTs is influenced by their perception of SSTs usefulness and ease of use which increases their satisfaction level. The study finds out that when SSTs are deployed individually, they have no effect on customer experiences in the banking sector. However, when the individual SSTs are combined in usage, they collectively produce a positive effect on customer banking experience.
Association of Mannose-Binding Lectin 2 Gene Polymorphism with Tuberculosis Based on Mycobacterium tuberculosis Lineages

Purpose Polymorphisms in MBL2 may contribute to the susceptibility to tuberculosis. The aim of the present study was to determine the associations of the polymorphisms of five loci (rs1800450, rs1800451, rs7096206, rs7095891, and rs11003125) in the MBL2 gene with susceptibility to tuberculosis and specific lineages of Mycobacterium tuberculosis causing tuberculosis in the Uyghur population of Xinjiang, China. Methods From January 2019 to January 2020, we enrolled 170 Uyghur tuberculosis patients as the case group and 147 Uyghur staff with no clinical symptoms as the control group from four designated tuberculosis hospitals in southern Xinjiang, China. The polymorphisms of five loci in MBL2 of human were detected by sequencing. Whole-genome sequencing was applied in 68 M. tuberculosis isolates from the case group and the data were used to perform genealogy analysis. Results The distributions of allele and genotype frequencies of five loci in MBL2 varied little between the case and control groups and varied little among the groups, including those infected with different lineages of M. tuberculosis and the control (except those of rs11003125), the P values were all >0.05. The distribution of alleles of rs11003125 was statistically different between patients infected with lineages 3 and 4 M. tuberculosis (χ2=7.037, P=0.008). The C allele and CC genotype of rs11003125 were found to be protective factors against lineage 4 infection when compared to lineage 3 (ORs were 0.190 and 0.158, respectively; 95% confidence intervals were 0.053~0.690 and 0.025~0.999, respectively). Conclusion Our results suggested that human’s susceptibility to tuberculosis is affected both by the host genetic polymorphisms and the lineage of the M. tuberculosis that people were exposed to. However, due to the limitation of the sample size in the present study, larger sample size and more rigorous design should be guaranteed in future studies.
Interdecadal Modulation of ENSO‐Related Anomalous Equatorial Intermediate Currents in the Western Pacific by the PDO

As the most prominent decadal variability in the Pacific Ocean, the Pacific Decadal Oscillation (PDO) can influence the El Niño‐Southern Oscillation‐related atmospheric and upper oceanic motions. Based on results from ocean reanalysis products and linear continuously stratified model experiments, we reveal that the PDO can further extend its interdecadal modulation effect to the equatorial intermediate currents below the thermocline. For the maximum El Niño‐related intermediate eastward transport anomaly in the western Pacific, its magnitude during the warm PDO is nearly twice as large as that during the cold PDO, and its occurrence time in the warm PDO leads that in the cold PDO by about 2 months. Such modulation is achieved through westward and downward propagation of the reflected Kelvin‐to‐Rossby waves, triggered by enhanced and eastward‐displaced anomalous westerly wind. The decisive wind forcing region for the modulation is located over the equatorial central Pacific of 5°S–5°N and 180°–140°W.