Single Scenario Research Articles

Topography of the Young Galactic Disk: Spatial and Kinematic Patterns of Clustered Star Formation in the Solar Neighborhood

The accuracy in determining the spatial-kinematical parameters of open clusters makes them ideal tracers of the Galactic structure. Young open clusters (YOCs) are the main representatives of the clustered star formation mode, which identifies how most of the stars in the Galaxy form. We apply the Kriging technique to a sample of Gaia YOCs within a 3.5 kpc radius around the Sun and log(age) ≤ 7.5, as the age in years, to obtain Z(X, Y) and V Z (X, Y) maps. Previous work by Alfaro et al. has shown that Kriging can provide reliable results even with small data samples (N ∼ 100). We approach the 3D spatial and vertical velocity field structure of the Galactic disk defined by YOCs and analyze the hierarchy of the stellar cluster formation, which shows a rich hierarchical structure, displaying complexes embedded within each other. We discuss the fundamental characteristics of the methodology used to perform the mapping and point out the main results obtained in phenomenological terms. Both the 3D spatial distribution and the vertical velocity field reveal a complex disk structure with a high degree of substructures. Their analysis provides clues about the main physical mechanisms that shape the phase space of the clustered star formation in this Galactic area. Warp, corrugations, and high local deviations in Z and V Z appear to be intimately connected, in a single but intricate scenario.

Multi-RIS aided 3D secure precise wireless transmission

In this paper, multiple reconfigurable intelligent surfaces (RIS) aided secure precise wireless transmission (SPWT) schemes are proposed in the three-dimensional (3D) wireless communication scenario. Unavailable direct path channels from transmitter to receivers are considered when the direct paths are obstructed by obstacles. Then, multiple RISs are utilized to achieve SPWT through the reflection path among transmitter, RISs and receivers in order to enhance the communication performance and energy efficiency simultaneously. First, a maximum-signal-to-interference-and-noise ratio (MSINR) scheme is proposed in a single user scenario. Then, the multi-user scenario is considered where the illegitimate users are regarded as eavesdroppers. A maximum-secrecy-rate (MSR) scheme and a maximum-signal-to-leakage-and-noise ratio (MSLNR) are proposed. The former achieves a better secrecy rate (SR) performance but incurs a higher complexity. The latter has a lower complexity than the MSR scheme with an SR performance loss. Simulation results show that both singleuser scheme and multi-user scheme can achieve SPWT which transmits confidential message precisely to location of desired users. Moreover, MSLNR scheme has a lower complexity than the MSR scheme, while the SR performance is close to that of the MSR scheme.

Inducing flooding index for vegetation mapping in water-land ecotone with Sentinel-1 & Sentinel-2 images: A case study in Dongting Lake, China

Water-land ecotone is a core area of wetlands which play an important role in ecological function. The information of vegetation type on the water-land ecotone is important for detecting the health of the wetland, and its protection. Seasonal flooding is a key factor in controlling the environment in water-land ecotone. Even preventing direct detection of the flooded vegetation with satellites, seasonal flooding has effects on the distribution of plants, which may provide potential information on vegetation classification with satellites. Therefore, the study induces flooding index in vegetation mapping in water-land ecotone with satellites. We applied Sentinel-1 and Sentinel-2 images to map vegetation in the Dongting Lake with random forest method. The Sentinel-1 SAR images were used to obtain the flooding index (i.e. inundation frequency), and Sentinel-2 images were applied to acquire the optical characteristic of vegetation. A comparative experiment based on whether the flooding index was added or not during vegetation mapping in the Dongting Lake was carried out to explore the role of flooding index in vegetation mapping. The results concluded that (1) seasonal flooding will hinder vegetation mapping using a single phase of optical information; (2) the classification accuracy of single phase scenario was significantly improved with the aid of flooding index especially in wet season; (3) the classification accuracy can be somewhat improved by adding flooding index for the multi-temporal scenario. The study implies that inundation frequency information can be used for vegetation mapping in water-land ecotone, especially for complex regions with insufficient cloud-free satellite images.

Automatic Identification System (AIS) Data Supported Ship Trajectory Prediction and Analysis via a Deep Learning Model

Automatic Identification System (AIS) data-supported ship trajectory analysis consistently helps maritime regulations and practitioners make reasonable traffic controlling and management decisions. Significant attentions are paid to obtain an accurate ship trajectory by learning data feature patterns in a feedforward manner. A ship may change her moving status to avoid potential traffic accident in inland waterways, and thus, the ship trajectory variation pattern may differ from previous data samples. The study proposes a novel ship trajectory exploitation and prediction framework with the help of the bidirectional long short-term memory (LSTM) (Bi-LSTM) model, which extracts intrinsic ship trajectory features with feedforward and backward manners. We have evaluated the proposed ship trajectory performance with single and multiple ship scenarios. The indicators of mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) suggest that the proposed Bi-LSTM model can obtained satisfied ship trajectory prediction performance.

Optical Beams Switching-Based Coverage Enhancement Scheme for Compact Visible Light Communications

Visible light communication (VLC) has been recognized as one promising candidate to satisfy the unprecedented throughput requirement of near-future mobile wireless networks. Due to the directional Lambertian optical beam-based design approach, multiple visible light transmitters are essential to provide sufficient coverage. However, these schemes could not meet spatial compactness requirements and fail to match harsher constrained transmitters or even single transmitter scenarios. To address these challenges, this article proposes a non-Lambertian optical beam switching-based dynamic coverage scheme for VLC mobility enhancement. Unlike the conventional Lambertian beam-based technique paradigm, the proposed scheme tentatively utilizes the commercially available asymmetric non-Lambertian beam to configure the VLC links with azimuth selectivity. Numerical results show that, for the solo visible light transmitter scenario, compared with the approximate 39.22 dB average signal to noise ratio (SNR) of the conventional Lambertian configuration, up to 5.27 dB average SNR gain is derived through the proposed dynamic scheme with four candidate non-Lambertian beams. Moreover, this potential gain will be further enhanced to about 13.24 dB when up to four visible light transmitters are available.

Efficient scenario analysis for optimal adaptation of bridge networks under deep uncertainties through knowledge transfer

Due to deep uncertainties associated with climate change and socioeconomic growth, managing bridge networks faces the challenge to perform optimization for different scenarios. There exist a large number of scenarios when various sources of uncertainties, such as population growth and the increasing magnitude and frequency of natural hazards due to climate change, are compounded. Traditionally, scenarios are analyzed sequentially. However, when optimization for one single scenario is time-consuming, only a limited number of scenarios can be considered. To accelerate scenario analysis, this paper proposes a novel scheme through knowledge transfer between scenarios. Specifically, after finishing the optimization of a certain number of scenarios, the analyses of any new scenarios are accelerated by utilizing the knowledge obtained from optimization of previous scenarios. To implement the novel scheme, a proper definition of similar scenarios for adaptation of bridge networks under deep uncertainties is first given to stipulate the situation when knowledge transfer can occur. Then an approach based on surrogate modeling and meta-learning is used to realize the concept of knowledge transfer and perform the optimization. A bridge network is used as an illustrative example to demonstrate the computational efficiency of the proposed novel scheme.

Self-backhauling failure mitigation using 5G new radio

5G network operators will consider the dense deployment of small cells to increase network coverage and capacity. However, in order to install a large number of gNBs, operators will face the challenge of backhauling their traffic to the core network in a cost-effective manner. Although Integrated Access and Backhaul (IAB) using 5G new radio is a promising solution to solve the dilemma of small cells’ backhauling, densifying the network will increase the probability of failure of these links. To cope with this problem, a self-healing scheme is used to mitigate or at least alleviate the effect of backhaul failure. We propose to use IAB with the collaboration of neighboring gNBs to mitigate the failed backhaul link(s). Hence, our goal is to design a pre-planned network capable of providing the minimum rate requirements to its users in the presence of backhaul failure. We formulate a joint resource allocation/backhaul outage compensation optimization problem as a non-convex mixed integer non-linear program. Due to the difficulty of this problem, we divide this problem into two sub-problems, in addition to the use of different approximation and relaxation techniques to find an approximated sub-optimal solution. Simulation results show that the proposed scheme is capable of providing the network users an acceptable continuous, albeit slightly degraded, service during backhaul failure. The degradation percentage is inversely proportional to the network densification for single and multiple failures. Meanwhile, the Degree of Recovery (DoR), i.e., subtracting the degradation percentage from 100, of single failure scenarios range from 95% (for the best case) to 54% (for the worst case). For the multiple failures scenario, the DoR is much lower and can reach zero in case of three concurrent failures

Exposure to incivility does not hinder speaking up: a randomised controlled high-fidelity simulation-based study

BackgroundSpeaking up with concerns is critical for patient safety. We studied whether witnessing a civil (i.e. polite, respectful) response to speaking up would increase the occurrence of further speaking up by hospital staff members as compared with witnessing a pseudo-civil (i.e. vague and slightly dismissive) or rude response. MethodsIn this RCT in a single, large academic teaching hospital, a single simulated basic life support scenario was designed to elicit standardised opportunities to speak up. Participants in teams of two or three were randomly assigned to one of three experimental conditions in which the degree of civility in reacting to speaking up was manipulated by an embedded simulated person. Speaking up behaviour was assessed by behaviour coding of the video recordings of the team interactions by applying 10 codes using The Observer XT 14.1. Data were analysed using multilevel modelling. ResultsThe sample included 125 interprofessional hospital staff members (82 [66%] women, 43 [34%] men). Participants were more likely to speak up when they felt psychologically safe (γ=0.47; standard error [se]=0.19; 95% confidence interval [CI], 0.09–0.85; P=0.017). Participants were more likely to speak up in the rude condition than in the other conditions (γ=0.28; se=0.12; 95% CI, 0.05–0.52; P=0.019). Across conditions, participants spoke up most frequently by structuring inquiry (n=289, 31.52%), proactive (n=240, 26.17%), and reactive (n=148, 16.14%) instruction statements, and gestures (n=139, 15.16%). ConclusionOur study challenges current assumptions about the interplay of civility and speaking up behaviour in healthcare.

Enhancing performance of multi-temporal tropical river landform classification through downscaling approaches

ABSTRACT Multi-temporal remote sensing imagery has the potential to classify river landforms to reconstruct the evolutionary trajectory of river morphologies. Whilst open-access archives of high spatial resolution imagery are increasingly available from satellite sensors, such as Sentinel-2, there remains a fundamental challenge of maximising the utility of information in each band whilst maintaining a sufficiently fine resolution to identify landforms. Although image fusion and downscaling methods on Sentinel-2 imagery have been investigated for many years, there is a need to assess their performance for multi-temporal object-based river landform classification. This investigation first compared three downscaling methods: area to point regression kriging (ATPRK), super-resolution based on Sen2Res, and nearest neighbour resampling. We assessed performance of the three downscaling methods by accuracy, precision, recall and F1-score. ATPRK was the optimal downscaling approach, achieving an overall accuracy of 0.861. We successively engaged a set of experiments to determine an optimal training model, exploring single and multi-date scenarios. We find that not only does remote sensing imagery with better quality improve river landform classification performance, but multi-date datasets for establishing machine learning models should be considered for contributing higher classification accuracy. This paper presents a workflow for automated river landform recognition that could be applied to other tropical rivers with similar hydro-geomorphological characteristics.

MedUCC: Medium-Driven Underwater Camera Calibration for Refractive 3-D Reconstruction

Underwater camera calibration has attracted much attentions due to its significance in high-precision three-dimensional (3-D) pose estimation and scene reconstruction. However, most existing calibration methods focus on calibrating the underwater camera in a single scenario [e.g., air-glass-water], which can not well formulate the geometry constraint and further result in the complex calibration process. Moreover, the calibration precision of these methods is low, since multilayer transparent refractions with unknown layer orientation and distance make the task more difficult than that in air. To address these challenges, we develop a novel and efficient medium-driven method for underwater camera calibration (MedUCC), which can calibrate the underwater camera parameters, including the orientation and position of the transparent glass accurately. Our key idea of this article is to leverage the light-path changes formed by medium refractions between different media to acquire calibration data, which can better formulate the geometry constraint, and estimate the initial value of the underwater camera parameters. To improve the calibration accuracy of the underwater camera system, a quaternion-based solution is developed to refine the underwater camera parameters. To the end, we evaluate the calibration performance on an underwater camera system. Extensive experiment results demonstrate that our proposed method can obtain a better performance in comparison to the existing works. We also validate our proposed MedUCC method on our designed 3-D scanner prototype, which illustrates the superiority of our proposed calibration method.

Lyα Emitting Galaxies (LAEs) at Cosmic Dawn: Implications and Predictions

The detection of Lyα emitting galaxies (LAEs) puts severe constraints on the reionization history. In this paper, we derive the properties of very high-z LAEs predicted in the only two reionization scenarios shown in a previous paper to be consistent with the current data on 15 independent evolving global (or averaged) cosmic properties regarding luminous objects and the intergalactic medium and the optical depth to electron scattering of ionized hydrogen to cosmic microwave background photons: one with a monotonic behavior, which is completed by z = 6, as commonly considered, and another one with a nonmonotonic behavior with two full ionization events at z = 6 and z = 10. We find that the Lyα luminosity functions of very high-z LAEs are very distinct in those two scenarios. Thus, comparing these predictions to the observations that will soon be available thanks to new instruments such as the James Webb Space Telescope, it should be possible to unveil the right reionization scenario. In the meantime, we can compare the predicted redshift distribution and UV (or Lyα) luminosities of very high-z LAEs to those of the few objects already observed at z > 7.5. By doing that, we find that such data are in tension with the single reionization scenario, while they are fully compatible with the double reionization scenario.

Engineering the Main Structures of the DEMO Fusion Reactor Magnet System

The Roadmap to Fusion Electricity draws on innovative experiments pivotal for the accomplishment of the European DEMO, i.e., the demonstration fusion power plant by the EUROfusion Consortium. At the end of the pre-conceptual design phase, the machine comprises three main magnetic systems: 6 poloidal field (PF) coils, 16 toroidal field (TF) coils and the central solenoid (CS), primary member of the ideal transformer in which the induced plasma is the secondary. Tokamak reactor operates under extremely heterogeneous and demanding loading conditions, leading structures, and components near to the material mechanical limits. All the structures must be assessed under the static and fatigue structural viewpoint. This work presents the advanced global model for the DEMO machine. This model consists of the main magnetic system, structures, auxiliary components, and joint connections. All the updated timepoints of the actual Single Null scenario have been analysed via a dedicated electromagnetic FEM model. The routine adopted allows to analyse each magnet and each component with a fully detailed submodel and/or with a homogenised model and equivalent thermoelastic material properties.

On the Performance of Partial RIS Selection vs. Partial Relay Selection for Vehicular Communications

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work.

Simulating Prophylactic Fixation Methods for Osteoporotic Femoral Neck Fracture Prevention.

Geriatric patients who suffer femoral neck fractures have high morbidity and mortality. Prophylactic fixation of the femoral neck is a potential avenue to reduce the incidence of femoral neck fractures. We studied 3 different implants traditionally used to stabilize the femoral neck: 6.5mm cannulated screws (CANN), the femoral neck system (FNS) (Depuy Synthes), and the dynamic hip screw (DHS) (Depuy Synthes). Five osteoporotic Sawbone femurs were used for each model and a control group. Two scenarios were investigated: single leg stance to measure construct stiffness and lateral impact to measure construct stiffness, energy to fracture, and qualitative examination of fracture patterns. Stiffness for each femur and energy to fracture for the lateral impact scenario were calculated and compared between groups using one-way ANOVA. DHS showed significantly higher stiffness than the other 2 implants and the control in single leg stance. In the lateral impact scenario, the DHS and CANN were significantly stiffer FNS and the control. Femurs implanted with CANN tended to fracture at the greater trochanter while FNS fractured in a transverse subtrochanteric pattern, and DHS fractured obliquely in the subtrochanteric region. FNS and DHS experienced fracture patterns less amenable to surgical correction. CANN and DHS proved better able to resist external forces in the lateral fall scenario. CANN also proved better able to resist external forces in the single leg stance scenario and experienced a more amenable fracture pattern in the lateral fall scenario. FNS was less able to resist external forces compared with the other implants. This work informs the potential implications between the choice of implants that, although historically have not been used prophylactically, may be considered in the future for prophylactic stabilization of the femoral neck. Cadaveric study and clinical trials are recommended for further study.

A new tsunami hazard assessment for eastern Makran subduction zone by considering splay faults and applying stochastic modeling

ABSTRACT Tsunami hazard imposed by possible rupture of splay faults is important as it may significantly intensify tsunami heights locally. The Makran Subduction Zone (MSZ) in the northwestern Indian Ocean can generate large thrust earthquakes that could trigger significant tsunamis. In this paper, the effects of possible rupture of splay faults on the tsunami hazards of eastern MSZ are studied by developing a framework that uses stochastic earthquake rupture models and considers uncertainties related to rupture location, rupture geometry, seismic moment split ratio, earthquake slip asperity location within a fault plane, and earthquake slip heterogeneity. To quantify these uncertainties, 484 different parameter combinations of tsunami sources are considered systematically. The geometry of splay faults is developed using the most recent marine seismic surveys of the tectonic structure of the MSZ. A moment magnitude of 8.6 is considered as a scenario magnitude. The results of this study are generated in two parts, by considering average sources and stochastic sources. Results show significant local amplification of the maximum tsunami heights due to splay faults. For instance, the maximum wave height in Pasni, Pakistan can be amplified by a factor of four due to a single splay fault rupture scenario of average sources.

Distance and age of the massive stellar cluster Westerlund 1. I. Parallax method using Gaia-EDR3

ABSTRACT Westerlund 1 (Wd 1) is one of the most massive young star clusters in the Milky Way. Although relevant for star formation and evolution, its fundamental parameters are not yet very well constrained. We aim to derive an accurate distance and provide constraints on the cluster age. We used the photometric and astrometric information available in the Gaia Early Data Release 3 (Gaia-EDR3) to infer its distance of 4.06$^{+0.36}_{-0.34}$ kpc. Modelling of the eclipsing binary system W36, reported in Paper II, led to the distance of 4.03 ± 0.25 kpc, in agreement with the Gaia-EDR3 distance and, therefore, validating the parallax zero-point correction approach appropriate for red objects. The weighted average distance based on these two methods results in dwd1 = 4.05 ± 0.20 kpc (m − M = 13.04$^{+0.11}_{-0.12}$ mag), which has an unprecedented accuracy of 5 per cent. Using the Binary Population and Spectral Synthesis (BPASS) models for the Red Supergiants with solar abundance, we derived an age of 10.7 ± 1 Myr, in excellent agreement with recent work by Beasor & Davies (10.4$^{+1.3}_{-1.2}$ Myr) based on MIST evolutionary models. In Paper II, W36B was reported to be younger than 7.1 Myr, supporting recent claims of a temporal spread of several Myrs for the star-forming process within Wd 1 instead of a single monolithic starburst episode scenario.

A Detection-Service-Mobile Three-Terminal Software Platform for Point-of-Care Infectious Disease Detection System

The traditional infectious disease detection process is cumbersome, and there is only a single application scenario. In recent years, with the development of the medical industry and the impact of the epidemic situation, the number of infectious disease detection instruments based on nursing point detection has been increasing. Due to this trend, many detection instruments and massive detection data urgently need to be managed. In addition, the experiment failed due to the abnormal fluorescence curve generated by a human operator or sample impurities. Finally, the geographic information system has also played an active role in spreading and preventing infectious diseases; this paper designs a “detection-service-mobile” three-terminal system to realize the control of diagnostic instruments and the comprehensive management of data. Machine learning is used to classify the enlarged curve and calculate the cycle threshold of the positive curve; combined with a geographic information system, the detection results are marked on the mobile terminal map to realize the visual display of the positive results of nucleic acid amplification detection and the early warning of infectious diseases. In the research, applying this system to portable field pathogen detection is feasible and practical.

Dynamic Mass Balance Modeling for Chemical Distribution Over Time in In Vitro Systems With Repeated Dosing.

As toxicologists and risk assessors move away from animal testing and more toward using in vitro models and biological modeling, it is necessary to produce tools to quantify the chemical distribution within the in vitro environment prior to extrapolating in vitro concentrations to human equivalent doses. Although models predicting chemical distribution in vitro have been developed, very little has been done for repeated dosing scenarios, which are common in prolonged experiments where the medium needs to be refreshed. Failure to account for repeated dosing may lead to inaccurate estimations of exposure and introduce bias into subsequent in vitro to in vivo extrapolations. Our objectives were to develop a dynamic mass balance model for repeated dosing in in vitro systems; to evaluate model accuracy against experimental data; and to perform illustrative simulations to assess the impact of repeated doses on predicted cellular concentrations. A novel dynamic in vitro partitioning mass balance model (IV-MBM DP v1.0) was created based on the well-established fugacity approach. We parameterized and applied the dynamic mass balance model to single dose and repeat dosing scenarios, and evaluated the predicted medium and cellular concentrations against available empirical data. We also simulated repeated dosing scenarios for organic chemicals with a range of partitioning properties and compared the in vitro distributions over time. In single dose scenarios, for which only medium concentrations were available, simulated concentrations predicted measured concentrations with coefficients of determination (R 2) of 0.85–0.89, mean absolute error within a factor of two and model bias of nearly one. Repeat dose scenario simulations displayed model bias <2 within the cell lysate, and ∼1.5-3 in the medium. The concordance between simulated and available experimental data supports the predictive capacity of the IV-MBM DP v1.0 tool, but further evaluation as empirical data becomes available is warranted, especially for cellular concentrations.

Split computing: DNN inference partition with load balancing in IoT-edge platform for beyond 5G

In the era of beyond 5G technology, it is expected that more and more applications can use deep neural network (DNN) models for different purposes with minimum inference time which is highly important for providing a great user experience in the Internet of Things (IoT) use-cases. However, due to hardware limitations and low computational capabilities, it is almost impossible to execute complex DNN models on IoT devices. To provide a better user experience and mitigate these problems, we introduce split computing technology where the DNN model inference task is partially offloaded from an IoT device to a nearby dependable device called edge. While splitting the DNN between IoT-edge, it is unclear how much the DNN model needs to be partitioned according to varying network conditions to ensure a satisfactory user experience. To address these issues, we propose the following mechanisms that provide a trade-off between the computation and communication, namely: (1) dynamic split computation (DSC) mechanism: finds an optimal partitioning of the DNN model to be executed between IoT and edge to reduce the computation overhead (i.e., overall inference time); (2) reliable communication network switching (RCNS) mechanism: intelligently finds a suitable network to connect to either Wi-Fi/Cellular/Bluetooth to provide reliable data transport between IoT and edge. Further, as the edge devices perform DNN tasks extensively, it is possible that they run out of battery, or get interrupted by high-priority user applications executing on them due to which edge devices cannot complete the assigned DNN task in the desired time. To address these issues, we also propose a load-balancing mechanism between IoT and edge called task load balancing with prioritization (TLBP) model for single and multiple DNN task scenarios. We conduct extensive experiments with Raspberry Pi as an IoT device and Samsung Galaxy S20 smartphones as edge devices to test the proposed mechanism. The results show that the proposed algorithms substantially reduce the inference time of DNN models as compared to the on-device inference time and balance the tasks across the edge devices to minimize excessive battery drainage.

Multi-optimal design and dispatch for a grid-connected solar photovoltaic-based multigeneration energy system through economic, energy and environmental assessment

Solar photovoltaic-based multigeneration energy systems (SPVMES) which can use the excess energy of photovoltaic (PV) systems for heating and hydrogen production to improve the self-consumption of solar PV, have gained wide attention as a promising solution for clean and efficient production. To reliably meet diverse energy demands, gird system is integrated into a SPVMES as a backup energy source. In this paper, a grid-connected SPVMES is established to meet the electricity, heating and hydrogen loads, including PV, electrolysis, solid oxide fuel cell, electric heater for small-scale centralized heating and energy storage devices for electricity, heating and hydrogen storage, respectively. The emphasis to focus on when designing and dispatching the SPVMES lies in system performance evaluation. The innovation of this paper is to design and dispatch such a grid-connected SPVMES in a preliminary design stage through economic, energy and environmental assessment by considering cost of energy (COE), energy rate (ER) and renewable fraction (RF). Simultaneously, to explore which single objective/multi-objective optimization has better performance in comprehensive system evaluation, four different scenarios named COE optimization, RF optimization, ER optimization and multi-objective optimization are analyzed and compared. The specific application processes of component sizing and system dispatching with different optimization objectives are presented in detail. The results show that the proposed system has the COE of 0.0343$ in COE optimization scenario, the ER of 0.9128 in ER optimization scenario and the RF of 0.8811 in RF optimization scenario, which shows its significant potential in economic, energy efficiency and environmental performance. By comprehensively scoring the four optimization scenarios, it can be found that the multi-objective optimization scenario has better performance in system performance evaluation compared to the other three single objective optimization scenarios. Additionally, the multi-objective optimization could balance the tradeoff of COE, RF and ER and compromise the results of the single objective optimization scenarios. Potentially, sensitivity analysis is conducted to point out the effect of discharging depth of battery, PV subsidy, linear increasement of loads and seasonality effect on system performances.