bluuebunny/crossref_metadata_2025 huggingface dataset:
Modification of the Structure and Linear/Nonlinear Optical Characteristics of PVA/Chitosan Blend through CuO Doping for Eco-Friendly Applications
Authors: Alharthi Sami S. , Badawi Ali
Published: May 2023

The solution casting technique is utilized to fabricate blank and CuO-doped polyvinyl alcohol/chitosan (PVA/CS) blends for eco-friendly applications. The structure and surface morphologies of prepared samples were explored by Fourier transform infrared (FT-IR) spectrophotometry and scanning electron microscopy (SEM), respectively. FT-IR analysis reveals the incorporation of CuO particles within the PVA/CS structure. SEM analysis exposes the well-dispersion of CuO particles in the host medium. The linear/nonlinear optical characteristics were found on the basis of UV-visible-NIR measurements. The transmittance of the PVA/CS decreases upon CuO increasing to 20.0 wt%. The optical bandgap (Eg dir./Eg ind.) decreases from 5.38/4.67 eV (blank PVA/CS) to 3.72/3.12 eV (20.0 wt% CuO-PVA/CS). An obvious improvement in the optical constants of the PVA/CS blend is achieved by CuO doping. The Wemple-DiDomenico (WDD) and Sellmeier oscillator models were utilized to examine the CuO role dispersion behavior of the PVA/CS blend. The optical analysis shows clear enrichment of the optical parameters of the PVA/CS host. The novel findings in the current study nominate CuO-doped PVA/CS films for applications in linear/nonlinear optical devices.
Thermal and Oxidative Aging Effects of Polyamide-11 Powder Used in Multi-Jet Fusion
Authors: Pandelidi Chrysoula , Blakis Ryan , Lee Kok Peng Marcian , Bateman Stuart , Brandt Milan , Kajtaz Mladenko
Published: May 2023

The transition of additive manufacturing (AM) from a technique for rapid prototyping to one for manufacturing of near net or net components has been led by the development of methods that can repeatedly fabricate quality parts. High-speed laser sintering and the recently developed multi-jet fusion (MJF) processes have seen quick adoption from industry due to their ability to produce high-quality components relatively quickly. However, the recommended refresh ratios of new powder led to notable amounts of used powder being discarded. In this research, polyamide-11 powder, typically used in AM, was thermally aged to investigate its properties at extreme levels of reuse. The powder was exposed to 180 °C in air for up to 168 h and its chemical, morphological, thermal, rheological, and mechanical properties were examined. To decouple the thermo-oxidative aging phenomena from AM process related effects, such as porosity, rheological and mechanical properties characterisation was performed on compression-moulded specimens. It was found that exposure notably affected the properties of both the powder and the derived compression-moulded specimens within the first 24 h of exposure; however, consecutive exposure did not have a significant effect.
Study on the Thermogravimetric Kinetics of Dehydrated Sewage Sludge Regulated by Cationic Polyacrylamide and Sawdust
Authors: Yang Kai , Sun Jianqi , Liu Hongning , Yang Weichao , Dong Lei
Published: May 2023

With the continuous increase in sewage-sludge production worldwide, the pyrolytic disposal of sludge has received great attention. To build knowledge on the kinetics of pyrolysis, first, sludge was regulated using appropriate amounts of cationic polyacrylamide (CPAM) and sawdust to study their enhancing effect on dehydration. Due to the effects of the charge neutralization and skeleton hydrophobicity, a certain dose of CPAM and sawdust reduced the sludge’s moisture content from 80.3% to 65.7%. Next, the pyrolysis characteristics of the dehydrated sludge regulated by CPAM and sawdust were investigated at a heating rate of 10~40 °C/min by using TGA method. The addition of sawdust enhanced the release of volatile substances and reduced the apparent activation energy of the sample. The maximum weight-loss rate decreased with the heating rate, and the DTG curves moved in the direction of high temperature. A model-free method, namely the Starink method, was adopted to calculate the apparent activation energies, which ranged from 135.3 kJ/mol to 174.8 kJ/mol. Combined with the master-plots method, the most appropriate mechanism function ultimately obtained was the nucleation-and-growth model.
Natural Rubber Composites Using Hydrothermally Carbonized Hardwood Waste Biomass as a Partial Reinforcing Filler—Part II: Mechanical, Thermal and Ageing (Chemical) Properties
Authors: Lubura Jelena , Kočková Olga , Strachota Beata , Bera Oskar , Pavlova Ewa , Pavličević Jelena , Ikonić Bojana , Kojić Predrag , Strachota Adam
Published: May 2023

Natural rubber composites were reinforced by the co-fillers ‘hydrochar’ (HC), obtained by hydrothermal carbonization of hardwood sawdust and commercial carbon black (CB). The content of the combined fillers was kept constant while their ratio was varied. The aim was to test the suitability of HC as a partial filler in natural rubber. Due to its larger particle size and hence smaller specific surface area, large amounts of HC reduced the crosslinking density in the composites. On the other hand, due to its unsaturated organic character, HC was found to display interesting chemical effects: if it was used as the exclusive filler component, it displayed a very strong anti-oxidizing effect, which greatly stabilized the rubber composite against oxidative crosslinking (and hence embrittlement). HC also affected the vulcanization kinetics in different ways, depending on the HC/CB ratio. Composites with HC/CB ratios 20/30 and 10/40 displayed interesting chemical stabilization in combination with fairly good mechanical properties. The performed analyses included vulcanization kinetics, tensile properties, determination of density of permanent and reversible crosslinking in dry and swollen states, chemical stability tests including TGA, thermo-oxidative aging tests in air at 180 °C, simulated weathering in real use conditions (‘Florida test’), and thermo-mechanical analyses of degraded samples. Generally, the results indicate that HC could be a promising filler material due to its specific reactivity.
The Potential Applications of Reinforced Bioplastics in Various Industries: A Review
Authors: Kong Uwei , Mohammad Rawi Nurul Fazita , Tay Guan Seng
Published: May 2023

The introduction of bioplastics has been an evolution for plastic industry since conventional plastics have been claimed to cause several environmental issues. Apart from its biodegradability, one of the advantages can be identified of using bioplastic is that they are produced by renewal resources as the raw materials for synthesis. Nevertheless, bioplastics can be classified into two types, which are biodegradable and non-biodegradable, depending on the type of plastic that is produced. Although some of the bioplastics are non-biodegradable, the usage of biomass in synthesising the bioplastics helps in preserving non-renewable resources, which are petrochemical, in producing conventional plastics. However, the mechanical strength of bioplastic still has room for improvement as compared to conventional plastics, which is believed to limit its application. Ideally, bioplastics need to be reinforced for improving their performance and properties to serve their application. Before 21st century, synthetic reinforcement has been used to reinforce conventional plastic to achieve its desire properties to serve its application, such as glass fiber. Owing to several issues, the trend has been diversified to utilise natural resources as reinforcements. There are several industries that have started to use reinforced bioplastic, and this article focuses on the advantages of using reinforced bioplastic in various industries and its limitations. Therefore, this article aims to study the trend of reinforced bioplastic applications and the potential applications of reinforced bioplastics in various industries.
Robust, Flame-Retardant, and Anti-Corrosive Waterborne Polyurethane Enabled by a PN Synergistic Flame-Retardant Containing Benzimidazole and Phosphinate Groups
Authors: Zhang Li-Ping , Zhao Zhen-Guo , Huang Yuan-Yuan , Zhu Chang-Jian , Cao Xing , Ni Yan-Peng
Published: May 2023

Waterborne polyurethanes (WPUs) have attracted great interest owing to their environmentally friendly properties, and are wildly applied in production and daily life. However, waterborne polyurethanes are flammable. Up to now, the challenge remains to prepare WPUs with excellent flame resistance, high emulsion stability, and outstanding mechanical properties. Herein, a novel flame-retardant additive, 2-hydroxyethan-1-aminium (2-(1H-benzo[d]imidazol-2-yl)ethyl)(phenyl)phosphinate (BIEP-ETA) has been synthesized and applied to improve the flame resistance of WPUs, which has both phosphorus nitrogen synergistic effect and the ability to form hydrogen bonds with WPUs. The WPU blends (WPU/FRs) exhibited a positive fire-retardant effect in both the vapor and condensed phases, with significantly improved self-extinguishing performance and reduced heat release value. Interestingly, thanks to the good compatibility between BIEP-ETA and WPUs, WPU/FRs not only have higher emulsion stability, but also have better mechanical properties with synchronously improved tensile strength and toughness. Moreover, WPU/FRs also exhibit excellent potential as a corrosion-resistant coating.
Poly(3-hydroxybutyrate) (PHB) and Polycaprolactone (PCL) Based Blends for Tissue Engineering and Bone Medical Applications Processed by FDM 3D Printing
Authors: Krobot Štěpán , Melčová Veronika , Menčík Přemysl , Kontárová Soňa , Rampichová Michala , Hedvičáková Věra , Mojžišová Ema , Baco Andrej , Přikryl Radek
Published: May 2023

In the presented work, poly(3-hydroxybutyrate)–PHB-based composite blends for bone medical applications and tissue engineering are prepared and characterized. PHB used for the work was in two cases commercial and, in one case, was extracted by the chloroform-free route. PHB was then blended with poly(lactic acid) (PLA) or polycaprolactone (PCL) and plasticized by oligomeric adipate ester (Syncroflex, SN). Tricalcium phosphate (TCP) particles were used as a bioactive filler. Prepared polymer blends were processed into the form of 3D printing filaments. The samples for all the tests performed were prepared by FDM 3D printing or compression molding. Differential scanning calorimetry was conducted to evaluate the thermal properties, followed by optimization of printing temperature by temperature tower test and determination of warping coefficient. Tensile test, three-point flexural test, and compression test were performed to study the mechanical properties of materials. Optical contact angle measurement was conducted to determine the surface properties of these blends and their influence on cell adhesion. Cytotoxicity measurement of prepared blends was conducted to find out whether the prepared materials were non-cytotoxic. The best temperatures for 3D printing were 195/190, 195/175, and 195/165 °C for PHB-soap/PLA-SN, PHB/PCL-SN, and PHB/PCL-SN-TCP, respectively. Their mechanical properties (strengths ~40 MPa, moduli ~2.5 GPa) were comparable with human trabecular bone. The calculated surface energies of all blends were ~40 mN/m. Unfortunately, only two out of three materials were proven to be non-cytotoxic (both PHB/PCL blends).
A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization
Authors: Peng Funan , Lv Li , Chen Weiru , Wang Jun
Published: May 2023

Many-objective optimization problems (MaOPs) are challenging optimization problems in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides the objective space into projection plane and free dimension(s). The balance between convergence and diversity is maintained using a Bi-Elite queue. The MOEA/PII is not only an algorithm, but also an algorithm framework. We can choose a decomposition-based or dominance-based algorithm to be the free dimension algorithm. When it is an algorithm framework, it exhibits a better performance. We compare the performance of the algorithm and the algorithm with the MOEA/PII framework. The performance is evaluated by benchmark test instances DTLZ1-7 and WFG1-9 on 3, 5, 8, 10, and 15 objectives using IGD-metric and HV-metric. In addition, we investigated its superior performance on the wireless sensor networks deployment problem using C-metric. Moreover, determining objective domain for the objects of the wireless sensor networks deployment problem reduces the time and makes the solution set more responsive to user needs.
Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making
Authors: Shao Chong , Wei Bo , Liu Wenfei , Yang Yong , Zhao Yihang , Wu Zhaoyuan
Published: May 2023

The power sector may reduce carbon emissions and reach carbon neutrality by accelerating the energy transition and lowering its reliance on fossil fuels. However, there are limitations on the new power system’s ability to operate safely and steadily due to the randomness, volatility, and intermittent nature of renewable energy supply. The key to solving this issue is to harness the flexible resources that energy storage systems (ESSs) represent; however, ESSs have more than a value for providing system flexibility. Thus, this study suggested a flexible, technical, economic, and environmental value index system based on multi-criteria decision-making models for evaluating the multi-dimensional value of ESSs. First, the objective and subjective weights are obtained using the CRITIC model and best–worst method; then, the weights are combined using the minimum relative entropy model. The complete values of five typical ESSs are then evaluated using the TOPSIS model. Three scenarios that reflect the traits of the new power system are then created and analyzed. The outcome demonstrates the following: (1) as the new power system continues to grow, the value of ESSs’ flexibility has become ever more crucial; (2) as the need for system flexibility increases, the value of electrochemical ESSs gradually manifests.
Sedimentary Evolution and Geological Characteristics of the Jurassic in the South Qiangtang Basin, China
Authors: Cai Zhanhu , You Hong , Wu Qilin
Published: May 2023

Based on the analysis of lithology, lithofacies combinations, sedimentary structures, and fossil types in five geological sections measured through fieldwork, this paper comprehensively elucidates the sedimentary evolution characteristics of the Jurassic period in the South Qiangtang area. The South Qiangtang Basin is renowned for preserving the most complete Jurassic marine sedimentary strata in China, and it primarily consists of a mixed platform environment of carbonate and clastic rocks. The Jurassic strata in the South Qiangtang Basin range from the Quse Formation at the base to the Suowa Formation at the summit, with sedimentary facies evolving from the outer shelf to the subtidal zone, and seawater depth gradually becoming shallower. This trend may be associated with the division and expansion of the Bangong–Nujiang suture zone during the Late Triassic, subduction in the Middle Jurassic, and the suture and splicing of the Qiangtang–Lhasa plate during the Late Jurassic tectonic movements. In conclusion, this research presents a comprehensive analysis of the sedimentary evolution of the Jurassic period in the South Qiangtang Basin for the first time. The findings offer significant contributions to the understanding of the region’s geological history and serve as a valuable foundation for future investigations.
Study on Internal Flow Characteristics and Abrasive Wear of Pelton Turbine in Sand Laden Water
Authors: Huang Yu , Deng Fangxiong , Deng Huiming , Qing Qiwei , Qin Mengjun , Liu Jitao , Yu Zhishun , Pang Jiayang , Liu Xiaobing
Published: May 2023

When a Pelton turbine operates in sand laden water, the abrasive wear of its overflow components by high-speed jets is serious. Based on the VOF (volume of fluid) multiphase flow model, the SST (shear stress transfer) k-ω turbulence model, the particle motion Lagrangian model, the generic wear model, and the SIMPLEC (Semi-Implicit Method for Pressure Linked Equations Consistent) algorithm, the liquid–air–solid three-phase flow in the key overflow components of a Pelton turbine were simulated, the abrasive wear was predicted, and the internal sand-water flow characteristics and the abrasive wear of the overflow components were analyzed. The results show that the trailing edge at the root of the runner bucket, the leading face of the bucket near the root, the notch, and the splitter are severely worn. The abrasive wear of the splitter and the notch is more severe than that of the leading face of the bucket. The wear rate from the splitter to the trailing edge increases first and then decreases. The wear pattern of the needle tip is mainly “dotted”, while that of the nozzle opening is “flaky”, and the abrasive wear of the nozzle opening is more severe than that of the needle. The predicted results are consistent with the actual conditions at the site of the power station. This study provides a technical method for the prediction of abrasive wear of the Pelton turbine and a technical basis for the operation and maintenance of the power station.
Combining Reinforcement Learning Algorithms with Graph Neural Networks to Solve Dynamic Job Shop Scheduling Problems
Authors: Yang Zhong , Bi Li , Jiao Xiaogang
Published: May 2023

Smart factories have attracted a lot of attention from scholars for intelligent scheduling problems due to the complexity and dynamics of their production processes. The dynamic job shop scheduling problem (DJSP), as one of the intelligent scheduling problems, aims to make an optimized scheduling decision sequence based on the real-time dynamic job shop environment. The traditional reinforcement learning (RL) method converts the scheduling problem with a Markov process and combines its own reward method to obtain scheduling sequences in different real-time shop states. However, the definition of shop states often relies on the scheduling experience of the model constructor, which undoubtedly affects the optimization capability of the reinforcement learning model. In this paper, we combine graph neural network (GNN) and deep reinforcement learning (DRL) algorithm to solve DJSP. An agent model from job shop state analysis graph to scheduling rules is constructed, thus avoiding the problem that traditional reinforcement learning methods rely on scheduling experience to artificially set the state feature vectors. In addition, a new reward function is defined, and the experimental results prove that our proposed reward method is more effective. The effectiveness and feasibility of our model is demonstrated by comparing with general deep reinforcement learning algorithms on minimizing the earlier and later completion time, which also lays the foundation for solving the DJSP later.
Assessment of Swarm Kinematic Orbit Determination Using Two Different Double-Difference Methods
Authors: Zhang Rui , Xiong Yongliang , Xu Shaoguang , Chen Weiwei , Li Xinzhong , Zhao Ban
Published: May 2023

The Swarm mission aims to study the principle and change regularities of the Earth’s magnetic field. Precise orbit determination is essential to the successful implementation of the mission and relevant scientific research. This article focuses on using two different double-difference methods to improve the accuracy of Swarm kinematic orbit determination. The accuracy of the kinematic orbit determination relies entirely on the space-borne observation data, independent of any dynamic parameters. The article analyzes the data quality of the Swarm space-borne global positioning system (GPS) receiver and presents a detailed introduction to the data pre-processing algorithms. The double-difference observation gathering and the applied orbit determination strategy using two different double-difference methods are discussed. The results of the kinematic orbits under different solar cycle conditions are presented, along with an evaluation based on analysis of GPS carrier phase residuals, subtracting from the post-processed orbits, and assessment with satellite laser ranging (SLR) measurements. The results show that the accuracy of the kinematic orbit determination is at the centimeter level for the three Swarm satellites’ orbit solutions. The daily root mean square (RMS) values of the three satellites’ phase residuals remain at around the 6 mm level. The RMS values of the position residuals between the kinematic orbits and the reduced dynamic orbits released by the European Space Agency (ESA) are at about the 2–3 cm level. The external evaluation with SLR measurements shows a good agreement with the ESA level, with the RMS values of the SLR residuals for kinematic orbits around 2 cm.
Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed
Authors: Lee Ju , Lindenschmidt Karl-Erich
Published: May 2023

Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data collected in the field, it is almost impossible to reorganize backscattering responses at pixel scales. Considering the influence of soil storage on watershed streamflow, we thus suggested watershed-scale hydrological validation. In addition, to overcome the limitations of backscattering models that are widely used for C-band Synthetic Aperture Radar (SAR) soil moisture but applied to bare soils only, in this study, RADARSAT-2 soil moisture was stochastically retrieved to correct vegetation effects arising from agricultural lands. Roughness-corrected soil moisture retrievals were assessed at various spatial scales over the Brightwater Creek basin (land cover: crop lands, gross drainage area: 1540 km2) in Saskatchewan, Canada. At the point scale, local station data showed that the Root Mean Square Errors (RMSEs), Unbiased RMSEs (ubRMSEs) and biases of Radarsat-2 were 0.06~0.09 m3/m3, 0.04~0.08 m3/m3 and 0.01~0.05 m3/m3, respectively, while 1 km Soil Moisture Active Passive (SMAP) showed underestimation at RMSEs of 0.1~0.22 m3/m3 and biases of −0.036~−0.2080 m3/m3. Although SMAP soil moisture better distinguished the contributing area at the catchment scale, Radarsat-2 soil moisture showed a better discharge hysteresis. A reliable estimation of the soil storage dynamics is more important for discharge forecasting than a static classification of contributing and noncontributing areas.
Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters
Authors: Chen Hao , Lin Xingwen , Sun Yibo , Wen Jianguang , Wu Xiaodan , You Dongqin , Cheng Juan , Zhang Zhenzhen , Zhang Zhaoyang , Wu Chaofan , Zhang Fei , Yin Kechen , Jian Huaxue , Guan Xinyu
Published: May 2023

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472.
Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning
Authors: Gurumoorthy Kambatty Bojan , Rajasekaran Arun Sekar , Kalirajan Kaliraj , Gopinath Samydurai , Al-Turjman Fadi , Kolhar Manjur , Altrjman Chadi
Published: May 2023

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals. Therefore, considering the significance of continuous data gathering and transmission sequence for WS, this article introduces a Concerted Sensor Data Transmission Scheme (CSDTS). This scheme endorses aggregation and transmission that aims at generating continuous data sequences. The aggregation is performed considering the overlapping and non-overlapping intervals from the WS sensing process. Such concerted data aggregation generates fewer chances of missing data. In the transmission process, allocated first-come-first-serve-based sequential communication is pursued. In the transmission scheme, a pre-verification of continuous or discrete (missing) transmission sequences is performed using classification tree learning. In the learning process, the accumulation and transmission interval synchronization and sensor data density are matched for preventing pre-transmission losses. The discrete classified sequences are thwarted from the communication sequence and are transmitted post the alternate WS data accumulation. This transmission type prevents sensor data loss and reduces prolonged wait times.
PNANet: Probabilistic Two-Stage Detector Using Pyramid Non-Local Attention
Authors: Zhang Di , Zhang Weimin , Li Fangxing , Liang Kaiwen , Yang Yuhang
Published: May 2023

Object detection algorithms require compact structures, reasonable probability interpretability, and strong detection ability for small targets. However, mainstream second-order object detectors lack reasonable probability interpretability, have structural redundancy, and cannot fully utilize information from each branch of the first stage. Non-local attention can improve sensitivity to small targets, but most of them are limited to a single scale. To address these issues, we propose PNANet, a two-stage object detector with a probability interpretable framework. We propose a robust proposal generator as the first stage of the network and use cascade RCNN as the second stage. We also propose a pyramid non-local attention module that breaks the scale constraint and improves overall performance, especially in small target detection. Our algorithm can be used for instance segmentation after adding a simple segmentation head. Testing on COCO and Pascal VOC datasets as well as practical applications demonstrated good results in both object detection and instance segmentation tasks.
Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System
Authors: Li Yan , Wang Yubo , Chen Shiyong , Huang Xinyu , Huang Tiancong
Published: May 2023

Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility.
High-Temperature Fiber-Optic Fabry–Perot Vibration Sensor Based on Single-Crystal Sapphire
Authors: Liu Hua , Jia Pinggang , Su Chengxin , Zhao Aihao , Liu Jia , Ren Qianyu , Xiong Jijun
Published: May 2023

In this paper, a fiber-optic Fabry–Perot (F–P) vibration sensor that can work at 800 °C is proposed. The F–P interferometer is composed of an upper surface of inertial mass placed parallel to the end face of the optical fiber. The sensor was prepared by ultraviolet-laser ablation and three-layer direct-bonding technology. Theoretically, the sensor has a sensitivity of 0.883 nm/g and a resonant frequency of 20.911 kHz. The experimental results show that the sensitivity of the sensor is 0.876 nm/g in the range of 2 g to 20 g at an operating frequency of 200 Hz at 20 °C. The nonlinearity was evaluated from 20 °C to 800 °C with a nonlinear error of 0.87%. In addition, the z-axis sensitivity of the sensor was 25 times higher than that of the x-axis and y-axis. The vibration sensor will have wide high-temperature engineering-application prospects.
Signal Property Information-Based Target Detection with Dual-Output Neural Network in Complex Environments
Authors: Shen Lu , Su Hongtao , Mao Zhi , Jing Xinchen , Jia Congyue
Published: May 2023

The performance of traditional model-based constant false-alarm ratio (CFAR) detection algorithms can suffer in complex environments, particularly in scenarios involving multiple targets (MT) and clutter edges (CE) due to an imprecise estimation of background noise power level. Furthermore, the fixed threshold mechanism that is commonly used in the single-input single-output neural network can result in performance degradation due to changes in the scene. To overcome these challenges and limitations, this paper proposes a novel approach, a single-input dual-output network detector (SIDOND) using data-driven deep neural networks (DNN). One output is used for signal property information (SPI)-based estimation of the detection sufficient statistic, while the other is utilized to establish a dynamic-intelligent threshold mechanism based on the threshold impact factor (TIF), where the TIF is a simplified description of the target and background environment information. Experimental results demonstrate that SIDOND is more robust and performs better than model-based and single-output network detectors. Moreover, the visual explanation technique is employed to explain the working of SIDOND.
Autonomous Robots for Services—State of the Art, Challenges, and Research Areas
Authors: Misaros Marius , Stan Ovidiu-Petru , Donca Ionut-Catalin , Miclea Liviu-Cristian
Published: May 2023

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted.
An Analysis of the Mixed IEEE 802.11ax Wireless Networks in the 5 GHz Band
Authors: Natkaniec Marek , Bieryt Natalia
Published: May 2023

This paper presents an analysis of the IEEE 802.11ax networks’ coexistence with legacy stations, namely IEEE 802.11ac, IEEE 802.11n, and IEEE 802.11a. The IEEE 802.11ax standard introduces several new features that can enhance network performance and capacity. The legacy devices that do not support these features will continue to coexist with newer devices, creating a mixed network environment. This usually leads to a deterioration in the overall performance of such networks; therefore, in the paper, we want to show how we can reduce the negative impact of legacy devices. In this study, we investigate the performance of mixed networks by applying various parameters to both the MAC and PHY layers. We focus on evaluating the impact of the BSS coloring mechanism introduced to the IEEE 802.11ax standard on network performance. We also examine the impact of A-MPDU and A-MSDU aggregations on network efficiency. Through simulations, we analyze the typical performance metrics such as throughput, mean packet delay, and packet loss of mixed networks with different topologies and configurations. Our findings indicate that implementing the BSS coloring mechanism in dense networks can increase throughput by up to 43%. We also show that the presence of legacy devices in the network disrupts the functioning of this mechanism. To address this, we recommend using an aggregation technique, which can improve throughput by up to 79%. The presented research revealed that it is possible to optimize the performance of mixed IEEE 802.11ax networks.
Fast High-Resolution Phase Diversity Wavefront Sensing with L-BFGS Algorithm
Authors: Zhang Haoyuan , Ju Guohao , Guo Liang , Xu Boqian , Bai Xiaoquan , Jiang Fengyi , Xu Shuyan
Published: May 2023

The presence of manufacture error in large mirrors introduces high-order aberrations, which can severely influence the intensity distribution of point spread function. Therefore, high-resolution phase diversity wavefront sensing is usually needed. However, high-resolution phase diversity wavefront sensing is restricted with the problem of low efficiency and stagnation. This paper proposes a fast high-resolution phase diversity method with limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm, which can accurately detect aberrations in the presence of high-order aberrations. An analytical gradient of the objective function for phase-diversity is integrated into the framework of the L-BFGS nonlinear optimization algorithm. L-BFGS algorithm is specifically suitable for high-resolution wavefront sensing where a large phase matrix is optimized. The performance of phase diversity with L-BFGS is compared to other iterative method through simulations and a real experiment. This work contributes to fast high-resolution image-based wavefront sensing with a high robustness.
Creating the Current and Riding the Wave: Persistence and Change in Community-Engaged Health Sciences Research
Authors: Haapanen Krista A. , London Jonathan K. , Andrade Karen
Published: May 2023

Recent decades have seen considerable increases in funding and support for community-engaged research (CER) in the health sciences, including the introduction of community engagement requirements into federally funded research infrastructure programs. This paper asks why, despite these supports and incentives, even the best-intentioned researchers and research organizations may struggle to design, implement, and sustain successful community engagement strategies. This question is examined using an exploratory case study of an environmental health sciences research center whose strategies were influenced in part by a requirement by the funder to incorporate community engagement into its research activities. This study utilizes multiple sources of qualitative data collected between the research center’s second and fifth years of operation, including participant observation, interviews, and focus groups. The analysis employs an organizational perspective, yielding insights into the factors hindering and facilitating the development of practices that integrate community perspectives and control into academic structures. The findings point to an ongoing dialectic between support for innovative community engagement practices and persistence of conventional academic structures. We highlight the interconnected effects of environmental influences, organizational structures, and individual agency on the development of innovative community engagement practices. The implications for future research and practice are discussed.
Coupling and Coordination Relationship between Human Settlement Environments and Resident Demand: An Empirical Study from China
Authors: Zhang Ying , Fang Zhiqiang , Xie Zhongqi
Published: May 2023

The harmonious development of human settlement environments (HSEs) and resident demands (RD) contributes to the sustainable development of areas. However, few studies have focused on development-level evaluations and analyses of coupling coordination between the two. This study used a comprehensive evaluation index model and a coupling coordination degree model to measure the development level, coupling coordination degrees, and spatial characteristics of HSE and RD in 31 regions of China. The results display the following: (1) The developing degree of HSE and RD continued to improve, with spatial heterogeneity, whereas the development level of RD and the regional economy was positively correlated; (2) the degree of coupling between the two was relatively high, with high-level coupling areas accounting for 77%, and the coupling coordination level included a medium level and primary levels; (3) in the coupling and coordination relationship between subsystems, living needs > security requirements > respected needs > social needs > self-realization needs, and environmental safety > ecological environment. This study offered a new research perspective for the sustainable development of areas and put forward suggestions for promoting development.
Study on a Stomatal Conductance Model of Grape Leaves in Extremely Arid Areas
Authors: Sun Ruifeng , Ma Juanjuan , Sun Xihuan , Bai Shijian , Zheng Lijian , Guo Jiachang
Published: May 2023

Stomata are essential for regulating the exchange of water and energy between plants and the atmosphere. In the context of climate warming, especially in extremely arid regions, the knowledge of stomatal conductance variation patterns is fundamental to the study of crop evapotranspiration, productivity and drought resistance characteristics. The accurate simulation of stomatal conductance in this region is an important prerequisite for the optimal regulation of the crop growth environment. In this study, a two-year field experiment was carried out in vineyards in an extremely arid region. The Jarvis model and BWB model were used to evaluate the daily changes in stomatal conductance. The results showed that stomatal conductance was significantly correlated with environmental factors (temperature difference between leaf and air (ΔT), photosynthetically active radiation and air temperature). The Jarvis and BWB models performed well. However, the response function of the environment factor in the Jarvis model can affect the model performance. The ΔT effectively improved the model, and the modified Jarvis model outperformed the modified BWB model. The R2 and model slope b of the modified Jarvis model increased by 45.18–70.37% and 2.51–3.12%, respectively. RMSE and MAE decreased by 38.98–43.12% and 42.69–44.35%, respectively. Overall, the Jarvis3–ΔT model had a good effect on the simulation of the daily change of stomatal conductance during the critical period of grape growth, and the Jarvis3–ΔT model was the best stomatal conductance model in this study. The results of the study are of great significance for further improving the sustainable use of water resources in grapevines in extremely arid regions.
Comparative Study on Low-Carbon Strategy and Government Subsidy Model of Pharmaceutical Supply Chain
Authors: Wen Yan , Liu Lu
Published: May 2023

Despite the growing urgency to curb carbon emissions worldwide, the healthcare industry, particularly the pharmaceutical industry, has received little attention from the sustainability community in terms of its contribution to the global carbon footprint. This paper constructs a differential game model of the secondary pharmaceutical supply chain consisting of pharmaceutical enterprises and medical institutions in the context of centralized drug procurement policy, considering the effects of health insurance reimbursement and consumers’ low-carbon preferences, and compares and analyzes the feedback equilibrium strategies of low-carbon inputs and marketing efforts, supply chain profits, and social welfare levels under four government subsidy models and further discusses them with arithmetic examples. The results illustrated that government subsidies have a significant impact on the low-carbon investment of pharmaceutical enterprises and the low-carbon marketing of medical institutions; subsidies for pharmaceutical enterprises can significantly increase the low-carbon investment and profit level of pharmaceutical enterprises; subsidies for medical institutions can effectively promote the implementation of the “zero-rate” policy and the realization of the emission reduction target under the centralization policy of medical institutions, increase the market demand for low-carbon drugs, and thus gain higher profits; the dual-subsidy model of the government brings higher social welfare than the single-subsidy model, and under a reasonable subsidy ratio, the profit and social welfare of the whole supply chain can be maximized.
Exploring the Curvilinear Relationship between Academic-Industry Collaboration Environment and Innovation Performance: A Multilevel Perspective
Authors: Daradkeh Mohammad
Published: May 2023

Academic institutions play a crucial role in knowledge production and driving innovation and economic growth. To enhance their capacity to deliver on these responsibilities, they are increasingly urged to establish academic–industry collaboration (AIC) environments to support research and innovation activities by their faculty and students. Despite the recognized importance of AIC in stimulating innovation performance, there is limited research exploring the cross-level impact of AIC on innovation performance. This study aims to address this research gap by investigating the relationship between the AIC environment and innovation performance while specifically examining the cross-level mediating role of researchers’ technological capability in this relationship. The study used a hierarchical linear model (HLM) approach, drawing on data from 187 researchers at 14 universities and academic institutions in the United Arab Emirates. The study’s results reveal a curvilinear (inverted U-shaped) effect of the AIC environment on innovation performance and a positive correlation between researchers’ technological capability and innovation performance. Furthermore, the AIC environment demonstrates a curvilinear effect on researchers’ technological capability, with researchers’ technological capability partially mediating the relationship between the AIC environment and innovation performance. These findings have significant theoretical and practical implications for policymakers in government agencies and university management seeking to develop evidence-based policies for effective science and technology management that enhance innovation performance.
AI- and IoT-Assisted Sustainable Education Systems during Pandemics, such as COVID-19, for Smart Cities
Authors: Kamruzzaman M. M. , Alanazi Saad , Alruwaili Madallah , Alshammari Nasser , Elaiwat Said , Abu-Zanona Marwan , Innab Nisreen , Mohammad Elzaghmouri Bassam , Ahmed Alanazi Bandar
Published: May 2023

The integration of AI and the IoT in education has the potential to revolutionize the way we learn. Personalized learning, real-time feedback and support, and immersive learning experiences are some of the benefits that AI and the IoT can bring to the education system. In this regard, this research paper aims to investigate how AI and the IoT can be integrated into sustainable education in order to provide students with personalized and immersive learning experiences during pandemics, such as COVID-19, for smart cities. The study’s key findings report that AI can be employed in sustainable education through personalized learning. AI-powered algorithms can be used to analyze student data and create personalized learning experiences for each student. This includes providing students with tailored content, assessments, and feedback that align with their unique learning style and pace. Additionally, AI can be used to communicate with students in a more natural and human-like way, making the learning experience more engaging and interactive. Another key aspect of the integration of AI and the IoT in education obtained from this research is the ability to provide real-time feedback and support. IoT-enabled devices, such as smart cameras and microphones, can be used to monitor student engagement and provide real-time feedback. AI algorithms can then use these data to adapt the learning experience in real time. IoT-enabled devices, such as tablets and laptops, can be used to collect and process student work, allowing for the automatic grading of assignments and assessments. Additionally, IoT technology can facilitate remote monitoring and grading of student work, which would be particularly useful for students who cannot attend traditional classroom settings. Furthermore, AI and the IoT can also be used to create intelligent personal learning environments (PLEs) that provide students with personalized, adaptive, and engaging learning experiences. IoT-enabled devices, such as smart cameras and microphones, combined with AI-powered algorithms, can provide real-time feedback and support, allowing the PLE to adapt to the student’s needs and preferences. It is concluded that integrating AI and the IoT in sustainable education can revolutionize the way people learn, providing students with personalized, real-time feedback and support and opening up new opportunities for remote and disadvantaged students. However, it will be important to ensure that the use of AI and the IoT in education is ethical and responsible to ensure that all students have equal access to the benefits of these technologies.