Miracle-dz/UniFAR-FTUs huggingface dataset:
Symbiotic Dinoflagellates of the Giant Clam, Tridacna squamosa, Express Ammonium Transporter 2 at the Plasma Membrane and Increase Its Expression Levels During Illumination

Giant clams harbor dinoflagellates generally of the three genera (Symbiodinium, Cladocopium, and Durusdinium) of phototrophic Symbiodiniaceae. Coccoid dinoflagellates (alias zooxanthellae) are found mainly inside zooxanthellal tubules located in the colorful outer mantle. The symbionts need to obtain carbon, nitrogen and phosphorus from the host for growth and metabolism. The host can absorb exogenous ammonia through the ctenidium and assimilate it into glutamine. Although the host does not normally excrete ammonia, its hemolymph contains only low concentrations of ammonia, indicating that the symbionts can absorb and recycle the ammonia produced metabolically by the host. In this study, we had obtained from the outer mantle of the giant clam, Tridacna squamosa, three major ammonium transporter 2 (AMT2) sequences, one each for Symbiodinium spp. (Symb-AMT2), Cladocopium spp. (Clad-AMT2), and Durusdinium spp. (Duru-AMT2), which comprised 1341 bp, 1308 bp, and 1296 bp, respectively. The respective deduced amino acid sequences contained 447 (~ 46.5 kDa), 436 (~ 45.5 kDa), and 432 (~ 45.0 kDa) residues. Phenogramic and sequence similarity analyses confirmed that these sequences were derived from dinoflagellates. Zooxanthellae-AMT2 (Zoox-AMT2), which represented comprehensively AMT2 of Symbiodinium spp. , Cladocopium spp. , and Durusdinium spp. was localized at the dinoflagellates’ plasma membranes, indicating that it could partake in the absorption of ammonia from the luminal fluid of the zooxanthellal tubules. Zoox-AMT2 expression was detected in the outer mantle, inner mantle, foot muscle, hepatopancreas and ctenidium of T. squamosa, indicating that the coccoid dinoflagellates residing in all five organs had the potential of ammonia absorption. The outer mantle had the highest transcript level of Zoox-AMT2, and illumination upregulated the protein abundance of Zoox-AMT2 therein. Therefore, it can be deduced that the coccoid dinoflagellates residing in the outer mantle could augment the potential of ammonia absorption in alignment with photosynthesis as the assimilation of ammonia required an increased supply of carbon chains.
Spatio-Temporal Disparity and Driving Forces of the Supply Level of Healthcare Service in the Yangtze River Delta

The equalization of healthcare supply is not only related to the people's need for a better life, but can also provide a strong guarantee for the high-quality and sustainable development of the Yangtze River Delta integration. By using exploratory spatial analysis techniques, this study analyzed the spatio-temporal evolution characteristics and heterogeneous influence effects of the supply level of healthcare service in the Yangtze River Delta from 2007 to 2019. It was found that the supply level of healthcare service in the Yangtze River Delta had improved significantly. The differences in the supply level of healthcare service between cities had tended to narrow without polarization, and the supply level of healthcare service generally showed a high spatial pattern in the south delta and low spatial pattern in the north delta. The higher the supply level of healthcare service was, the weaker the interannual variability was. The supply level of healthcare service in the Yangtze River Delta region presented obvious spatial association and differentiated tendency of local high and low spatial clusters. The relative length and curvature of the supply level of healthcare service in the Yangtze River Delta generally presented a spatial pattern with low values in the northeast and high values in the southwest. Population density and urban-rural income gap generally exhibited negative spatio-temporal impact on the supply level of healthcare service across most cities. On the other hand, urbanization level and per capita disposable income generally had positive spatio-temporal impact on the supply level of healthcare service across most cities. Per capita gross domestic product (GDP) showed an increasingly positive spatio-temporal impact on the supply level of healthcare service across most cities. While per capita fiscal expenditure exhibited significantly negative impact on the supply level of healthcare service across most cities in space.
Parameter Estimation Algorithm of Surface-PMSM for FOC, Rotor Angle and Speed Estimation

The basic V/$f$ control algorithm is not sufficient for Permanent Magnet Synchronous Machines (PMSM) in various industrial applications. Complex control strategies like Field-oriented Control (FOC) and modified V/$f$ are used for applications where high-performance dynamic control of motors is essential. FOC requires tuning of current and speed PI controllers which is hugely dependent on the accurate measurement or estimation of motor parameters. These parameters are mandatory for speed & rotor angle estimation process for sensor less applications in various observers like Sliding-mode and Back-EMF. This paper deals with the estimation of essential motor parameters. This process is a combination of standstill and rotational methods to estimate parameters with the help of motor current sensors, motor terminal voltage sensors and a DC voltage sensor. The algorithm was simulated using MATLAB-Simulink and tested on hardware implemented using BOOSTXL-3PhGaNINV EVM and TMS320F280049C Texas Instruments microcontroller on 3kW Surface mount PMSM (SPM).
Inducing Emotional Stress From The Intensive Care Context Using Storytelling In VR

Nurses in intensive care units are exposed to permanent stress. Emotional stress contributes to psycho-physiological exhaustion symptoms such as chronic sleep disturbances, restlessness or burnout, which negatively affects the quality of care and interaction with patients. A promising method for learning coping strategies to deal with stress is Stress Inoculation Training, which involves practicing stressors in a controlled environment at increasing intensity. In this work, using virtual reality, the stressor of having to comply with the patient’s or family’s wishes, even if one does not agree with them, was selected. The stressor was defined based on a literature review and interviews with experts which was then implemented in three intensity levels. In a Wizard-of-Oz trial, participants interacted with virtual characters using natural speech. The stress response of the induced stress was measured using objective (heart rate, time between R peaks, skin conductance, and respiratory rate) and subjective measures (Perceived Stress Questionnaire and a 7-point Likert item). While objective results do not show significant differences between intensity levels, a significant increase is detected in the subjective measures. We show that emotional stress can be induced by increasing the intensity of a stressor in VR using virtual characters.
Youtube Data Analysis using Linear Regression and Neural Network

The popularity of YouTube provides an effective way to propagate epidemic prevention knowledge by analyzing the video preferences of viewers from different locations. However, it is challenging to analyze video preferences due to the dispersed geographical locations of the YouTube viewers and the indistinguishable video categories and subcategories. This paper combines linear regression and neural networks to unravel both geographical and categorical difficulties and improve the accuracy of task-solving models. First, the YouTube dataset and extract variables are preprocessed, including categories, subcategories, countries, number of subscribers, and view counts of each YouTubers. Then, linear regression and neural networks are trained to classify and find the correlation between these variables. Finally, Matplotlib, google chart, and Tableau are utilized to visualize the result based on video categories and geographical locations. The accuracies of linear regression and neural network models are verified through the R-squared estimation. Both linear regression and neural network models show the trending types of videos and a positive correlation between the number of viewers and subscribers. The experimental results show a remarkable user’s tendency of watching films and listening to music, a concentration of YouTube users from India and the U.S., and propose targeted Covid-19 prevention propaganda based on the above two characteristics.
Automatic Virtual Portals Placement for Efficient VR Navigation

Portals placement in a large virtual scene can help users improve navigation efficiency, but determining the number and the positions of the portals has some challenges. In this paper, we proposed two automatic virtual portals placement methods for efficient VR navigation. To reduce the number of reverse redirections, we also proposed a real-time portal orientation determination algorithm. For any given single-floor outdoor virtual scene, our methods can automatically place the portals.
Formulation of Improved ANFIS for Function Approximation in Theoretical Wireless Signal Propagation Modelling

Currently, it is evident that the use of wireless systems is growing at an unprecedented rate more so in relation to Internet of Things (IoT). Because of this fact, there is need for accelerated studies on these systems to improve on the quality of service (QoS) provided to the users of these systems. In the process of wireless signal prediction modelling, a number of methods have been used that include deterministic and empirical models among others. This study is aimed at explaining the use Adaptive Neural Fuzzy Inference System (ANFIS) in wireless signal propagation modelling. It gives details on step-by-step use of ANFIS in the modelling process. It also shows how the improved ANFIS, that is, the six layered LOG10D-ANFIS is used to increase its performance for a single input. The root mean square error (RMSE), mean error (ME) and standard deviation (SD) of the predicted signal were used in the process of training where the LOG10D-ANFIS model had very low values in the range of 10−6 to 10−8 for the RMSE. The data used in this research is based on the one slope model (OSM) and multiwall model (MWM) though all the other theoretical models' data showed similar results.
Optimal Dispatch of Wind-hydro-thermal Power System With Priority Given to Clean Energy

Large-scale integration of wind power has brought profound challenge to traditional power generation dispatch.It becomes necessary to effectively coordinate the operation of wind power and traditional power sources.Considering the nature of wind,hydro and thermal power,a multi-objective optimal dispatch model was established for wind-hydro-thermal power system aiming at fully using clean energy,reducing operation cost and ensuring efficient operation of thermal generators.In order to avoid frequent starting and shutting or part-load operation of thermal generators that caused by capacity redundancy and wind randomness,a heuristic search method was applied to determine the number of dispatched thermal units.By introducing the antibody reproduction stratagem in artificial immune algorithm,the conventional particle swarm algorithm was improved through adding particle concentration cognition item and has been proved its superiority on a 10-unit test system.The modified IEEE 24-bus test system was used to verify the rationality and feasibility of the proposed model and algorithm,meanwhile the coordinated operation strategy of wind,hydro and thermal power was obtained.
Multi-Modal Lesion Segmentation Using Deep Convolution Graph-Based Network

The graph-based network aims to increase the visualization of grid-based data into the graph domain. In this paper, a novel fusion architecture of graph con-volutional network (GCN) and features from convolution network based on end to end deep learning for medical lesion segmentation. For multi-modal analysis, features have been captured from each modality of magnetic resonance imaging (MRI). Graph module is learned from neighborhood connectivity with the correlation between modalities and nodes present in the graph. Multi-modal analysis improved segmentation performance as it integrate the independent and individual information from imaging. This improved the segmentation performance as dice similarity coefficient (DSC) was evaluated to 87.4% for the multiple sclerosis (MS) patient datasets.
User Preference for Navigation Instructions in Mixed Reality

Current solutions for providing navigation instructions to users who are walking are mostly limited to 2D maps on smartphones and voice-based instructions. Mixed Reality (MR) holds the promise of integrating navigation instructions directly in users’ visual field, potentially making them less obtrusive and more expressive. Current MR navigation systems, however, largely focus on using conventional designs such as arrows, and do not fully leverage the technological possibilities. While MR could present users with more sophisticated navigation visualizations, such as in-situ virtual signage, or visually modifying the physical world to highlight a target, it is unclear how such interventions would be perceived by users. We conducted two experiments to evaluate a set of navigation instructions and the impact of different contexts such as environment or task. In a remote survey (n = 50), we collected preference data with ten different designs in twelve different scenarios. Results indicate that while familiar designs such as arrows are well-rated, methods such as avatars or desaturation of non-target areas are viable alternatives. We confirmed and expanded our findings in an in-person virtual reality (VR) study (n = 16), comparing the highest-ranked designs from the initial study. Our findings serve as guidelines for MR content creators, and future MR navigation systems that can automatically choose the most appropriate navigation visualization based on users’ contexts.
Numerical study of ablation-dominated arcs in polyamide enclosure

The breaking capability of a circuit breaker depends on the duration it takes for the switching arc to quench. In order to reduce this duration, outgassing polymers are used in circuit breakers. These polymers release gases upon interaction with the switching arc which further acts as an energy sink and quenches the arc quicker than non-gassing materials. The outgassing of polymer occurs as a result of ablation of these materials; hence these arcs are called ablation-dominated or ablation-controlled arcs. This work focuses on the numerical study of arc formed between two metal electrodes inside a polyamide enclosure performed using VizSpark®, a high-fidelity thermal plasma simulation software. The results are validated against the experimental research literature for current amplitude 1.1 kA and 3 kA respectively. We report temperature, pressure and electrical conductivity maps during arcing.
Research of city fire disposal experts system based on fuzzy neural network

According to the theory of system science and behavior science, combining artifical neural network and fuzzy reason method with computer technique, this paper proposes fire disposal experts system based on fuzzy neural network that provides the decision on fire disposal for fire commander during fire. Firstly, this paper proposes general frame of fire disposal experts system based on fuzzy neural network which comprises of task management module, management and maintenance of knowledge base and database, management system of fire model, interpretive mechanism, control system of reason machine, collect and dispose of signal and result output module, and shows function of each module. Secondly, it introduces the method of knowledge expression and knowledge acquisition in the experts system that combines calculation intelligence with symbol intelligence to solve knowledge acquisition bottleneck question of experts system. Finally, it introduces the method of constructing repository and reason that combines fuzzy reason with rule reason.
Investigating how speech and animation realism influence the perceived personality of virtual characters and agents

The portrayed personality of virtual characters and agents is understood to influence how we perceive and engage with digital applications. Understanding how the features of speech and animation drive portrayed personality allows us to intentionally design characters to be more personalized and engaging. In this study, we use performance capture data of unscripted conversations from a variety of actors to explore the perceptual outcomes associated with the modalities of speech and motion. Specifically, we contrast full performance-driven characters to those portrayed by generated gestures and synthesized speech, analysing how the features of each influence portrayed personality according to the Big Five personality traits. We find that processing speech and motion can have mixed effects on such traits, with our results highlighting motion as the dominant modality for portraying extraversion and speech as dominant for communicating agreeableness and emotional stability. Our results can support the Extended Reality (XR) community in development of virtual characters, social agents and 3D User Interface (3DUI) agents portraying a range of targeted personalities.
DaSoR: Data-driven State-of-Health & Remaining Useful Life Estimation of Li-ion Batteries in a Closed-loop system of Electric Vehicles

Understanding the State-of-Health (SoH) and the Remaining Useful Life (RUL) of the commonly used Lithiumion Batteries (LIBs) within the emerging sector of Electric Vehicles (EVs) is crucial for assuring their efficiency, stability, and safety. However, predicting SoH precisely is challenging due to the intricacies of electrochemical processes within the cells and especially the variability of real-world operating circumstances of an EV. As a result, numerous data-driven approaches for estimating SoH with resilient and adaptable characteristics have been advocated in the literature. However, there is a lack of real-time closed-loop systems, extensive algorithm analysis across multiple datasets, and efficient hyperparameter optimization in the existing works. Hence in this paper, we proposed a robust closed-loop system to report user/ OEM of the battery health, quantitatively compared the performance of several ML algorithms on both CALCE and NASA Ames PCoE Battery Datasets and used AutoML for optimal hyperparameter selection. Furthermore, time series modelling of the intrinsic parameters of the LIBs with Long Short Term Memory (LSTM) for estimating the SoH yields promising results in general, with a mean absolute error (MAE) of 0.031 on the NASA Ames PCoE Battery Dataset and a best MAE of 0.026 on the CALCE Dataset for MLP.
Minimization of Line Current Harmonics in Multiple Single-Phase Active FEC fed MW Railways Propulsion System

Single-phase active front-end converters (AFEC), operating at 1.5MW power level, are deployed in parallel for feeding propulsion systems in Indian Railways. To curb the high switching energy losses, these converters are typically operated at low switching frequency $(\text{i.e}\ f_{sw}=250\text{Hz})$ • Such converters generate high line current harmonics due to low $f_{sw}$ operation. This paper focuses on the minimization of line current harmon-ics through Carrier interleaving. Cases of parallel connected two, three and four single-phase AFECs are considered. Total harmonic distortion in line side current is plotted against the interleaving angle for all three cases. It is observed that line current harmonics up to $29^{th}$ order are minimized to very low values for a four converter system. Simulation results, pertaining to all three cases, are presented at a power level of 2MW for validation of the proposed concepts.
Role of Artificial Intelligence in Renewable Energy and its Scope in Future

The use of air and the sunlight considering significant sources of the renewable energy have the study focus and development in past years. The presence of air, sunlight and the other commodities in Earth used wisely for human welfare while also maintaining the living beings and the environment. The development of revolutionary technology for best output from natural resources is the backbone of current and future efforts in RE and increased environmental awareness to improve management and distribution systems. Artificial Intelligence (AI) could help the RE to achieve its future goals, just as it has in other fields (food, health, lodging, safety, and so on). Biologically inspired AI approaches have been used in a number of projects to meet the RE’s common and future goals. Despite the fact that many researches has been performed for scaling and for optimize renewable energy systems. Literature study provides a comprehensive review of the best plans of Artificial Intelligence, highlighting the importance of penetrating literature. Artificial Intelligence utilizes the features of renewable energy in order to improve the systems economic functioning. This study shows a complete review as well as modern research findings in the fields of wind, solar, geothermal, bioenergy, ocean, hybrid energy, hydro, and hydrogen energy. The role of hybrid and single artificial intelligence techniques for the study and the development for RE sources mentioned above will thoroughly be analyzed.
Highly compact wideband double-section rat-race hybrid with harmonic suppression using series and shunt stepped impedance transmission lines

This paper presents the design of a size miniaturized and harmonic suppressed wideband double-section rat-race coupler (RRC). Series and shunt stepped-impedance transmission line units are proposed to replace the quarter wavelength transmission lines in the conventional design of wideband (50% fractional bandwidth) two-stage rat-race coupler. Design equations are derived using the lossless transmission line model. The proposed double-section RRC occupies only 14% area of the conventional coupler, with good return loss and isolation performances. In addition, the full-wave simulation and measured responses exhibit harmonic suppression up to at least fifth harmonic. The proposed design can be easily implemented using the standard printed circuit board etching process without any via-holes, bonding wires, and lumped elements, which make it very useful for wireless communication systems
Real-Time Implementation of Deep Learning Technique in Microcontroller-Based DC-DC Boost Converter- A Design Approach

This article proposes a design methodology to implement an online based deep learning (DL) algorithm in a microcontroller for DC-DC boost converter. This technique may pave a new futuristic dimension to prevent cyber-attacks in DC microgrid (DCMG) systems as well. The presence of constant power loads (CPL), sudden load changes, source or input variations makes the control system complicated and prone to being unstable. Hence, there's a necessity to develop a robust control methodology for dc-dc converters to achieve voltage stabilization with low ripples. This article proposes a model-free and data driven approach which efficiently substitutes the conventional controller methodologies and also eliminates the need for the usage of a current sensor. Active compensation technique (ACT) is designed to overcome the negative impedance effect produced by CPLs. The effectiveness of the proposed scheme is validated by conducting the real-time experiments and its results are explored.