Search Results (29)

Grounded in coupling theory, this study investigates the interplay among three key elements of economic growth, namely the digital economy, carbon emissions efficiency, and high-quality economic development. Drawing on data from 30 Chinese provinces from 2000 to 2023, we employ exploratory spatiotemporal data analysis and the GeoDetector model to examine the spatial–temporal evolution and underlying driving forces of coupling coordination. This research enriches the theoretical framework of multi-system synergistic development in a green transition context and offers empirical insights and policy recommendations for fostering regional coordination and sustainable development. The results reveal that (1) both the digital economy and high-quality economic development show a steady upward trend, while carbon emissions efficiency has a “U-shaped” curve pattern; (2) at the national level, the degree of coupling coordination has evolved over time from “mild disorder” to “on the verge of disorder” to “barely coordinated,” while at the regional level, this pattern of coupling coordination shifts over time from “Eastern–Northeastern–Central–Western” to “Eastern–Central–Northeastern–Western”; (3) although spatial polarization in coupling coordination has improved, disparities fluctuate in a “decline–rise” pattern, with interregional differences being the main source of that variation; (4) the degree of coupling coordination has a positive spatial correlation, but with a declining trend with fluctuations; and (5) improvements in the level of economic development, human capital, industrial structure, green technological innovation, and market development capacity all contribute positively to coupling coordination. Among them, green technological innovation and market development capacity are the most influential drivers, and the interactions among all driving factors further enhance their collective impact. Full article

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy. Full article

Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government which seeks to create a sustainable human-centric society by putting to work recent advances in technology. One of the key challenges in implementing Society 5.0 is providing trusted and secure services for everyone to use. Motivated by this challenge, this paper makes three contributions that we summarize as follows: Our first main contribution is to propose a novel blockchain and smart contract-based trust and reputation service design to reduce the uncertainty associated with buyer feedback in marketplaces that we expect to see in Society 5.0. Our second contribution is to extend Laplace’s Law of Succession in a way that provides a trust measure in a seller’s future performance in terms of their past reputation scores. Our third main contribution is to illustrate three applications of the proposed trust and reputation service. Here, we begin by discussing an application to a multi-segment marketplace, where a malicious seller may establish a stellar reputation by selling cheap items, only to use their excellent reputation score to defraud buyers in a different market segment. Next, we demonstrate how our trust and reputation service works in the context of sellers with time-varying performance due, say, to overcoming an initial learning curve. We provide a discounting scheme where older reputation scores are given less weight than more recent ones. Finally, we show how to predict trust and reputation far in the future, based on incomplete information. Extensive simulations have confirmed the accuracy of our analytical predictions. Full article

Amidst the growing imperative to address carbon emissions, aiming to improve energy utilization efficiency, optimize equipment operation flexibility, and further reduce costs and carbon emissions of regional integrated energy systems (RIESs), this paper proposes a low-carbon economic operation strategy for RIESs. Firstly, on the energy supply side, energy conversion devices are utilized to enhance multi-energy complementary capabilities. Then, an integrated demand response model is established on the demand side to smooth the load curve. Finally, consideration is given to the RIES’s participation in the green certificate–carbon trading market to reduce system carbon emissions. With the objective of minimizing the sum of system operating costs and green certificate–carbon trading costs, an integrated energy system optimization model that considers electricity, gas, heat, and cold coupling is established, and the CPLEX solver toolbox is used for model solving. The results show that the coordinated optimization of supply and demand sides of regional integrated energy systems while considering multi-energy coupling and complementarity effectively reduces carbon emissions while further enhancing the economic efficiency of system operations. Full article

Currently, fault identification in most photovoltaic systems primarily relies on experienced engineers conducting on-site tests or interpreting data. However, due to limited human resources, it is challenging to meet the vast demands of the solar photovoltaic market. Therefore, we propose to identify fault types through the current–voltage curves of solar arrays, obtaining curves for various conditions (normal, aging faults, shading faults, degradation faults due to potential differences, short-circuit faults, hot-spot faults, and crack faults) as training data for the model. We employ a multi-input model architecture that combines convolutional neural networks with deep neural networks, allowing both the imagery and feature values of the current–voltage curves to be used as input data for fault identification. This study demonstrates that by inputting the current–voltage curves, irradiance, and module specifications of solar string arrays into the trained model, faults can be identified quickly using actual field data. Full article

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models. Full article

In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated option price data given the illiquid nature of the South African market, and the out-of-sample performance of the optimized ANNs was evaluated using an implied volatility surface constructed from published volatility skews. The results from this paper show that ANNs trained on artificially generated input data are able to accurately approximate the explicit solutions to the respective option price sensitivities of both a classical and a modern multi-curve framework in a real-world out-of-sample application to the South African market. Full article

Solid-state lighting (SSL) devices are ubiquitous in several markets, including architectural, automotive, healthcare, heritage conservation, and entertainment lighting. Fine control of the LED light output is crucial for applications where spectral precision is required, but dimming LEDs can cause a nonlinear response in its output, shifting the chromaticity. The nonlinear response of a multi-color LEDs can be corrected by curve-fitting the measured data to input dimming controls. In this study, the spectral output of an RGB LED projector was corrected using polynomial curve fitting. The accuracy of four different measurement methods was compared in order to find the optimal correction approach in terms of the time and effort needed to perform measurements. The results suggest that the curve fitting of very high-resolution dimming steps (n = 125) significantly decreased the chromaticity shifts between measured (actual) and corrected spectra. The effect size between approaches indicates that the curve-fitting of the high-resolution approach (n = 23) performs equally well as at very high resolution (n = 125). The curve-fitting correction can be used as an alternative approach or in addition to existing methods, such as the closed-loop correction. The curve fitting method can be applied to any tunable multi-color LED lighting system to correct the nonlinear dimming response. Full article

China’s electricity market reform has posed a real challenge to the large-scale hydropower system. Taking the world’s largest watershed hydropower system, the Three Gorges large hydropower system (TGLHS), as the engineering background, this study analyzes the issues and strategies of dispatching and trading in the electricity market. The analysis indicates that the TGLHS exhibits unique difficulties because of transprovincial and transregional power transmission. Major issues including the multi-dimensional and multi-time-scale nested allocation of hydropower energy, the bidding and performance of cascaded hydropower plants in multiple electricity markets, as well as multiple uncertainties in the runoff; electricity prices in multiple markets are also elaborated upon. The corresponding suggested strategies are proposed to cope with the aforementioned issues: (1) for multi-dimensional and multi-scale nested allocation problems, it is necessary to comprehensively consider monthly market transactions and priority generation plans, and establish a profit maximization model; (2) propose a bidding decision-making linkage and segmented bidding optimization model for cascades upstream and downstream hydropower stations; (3) construct a model for decomposing the annual and monthly planned electricity consumption curves and developing operational plans for giant cascade power stations that are suitable for cross-provincial and cross-regional power transmission and transformation; (4) a runoff, electricity price, and market distribution model has been proposed, laying the foundation for further research on multi-scale optimization models for hydropower. Finally, prospects for research on the participation of large-scale hydropower systems in the electricity market are summarized, expecting to promote the marketization of large cascaded hydropower systems. The dispatching and trading of the TGLHS implies that it is important and necessary to explore market theories and methods considering hydropower characteristics and operation needs. Full article

Increasing wind capacity and capacity factors (CF) are essential for achieving the goals set by the Paris Climate Agreement. From 2010–2012 to 2018–2020, the 3-year mean CF of the global onshore wind turbine fleet rose from 0.22 to 0.25. Wind turbine siting, wind turbine technology, hub height, and curtailed wind energy are well-known CF drivers. However, the extent of these drivers for CF is unknown. Thus, the goal is to quantify the shares of the four drivers in CF development in Germany as a case. Newly developed national power curves from high-resolution wind speed models and hourly energy market data are the basis for the study. We created four scenarios, each with one driver kept constant at the 2010–2012 level, in order to quantify the share of a driver for CF change between 2010–2012 and 2019–2021. The results indicated that rising hub heights increased CF by 10.4%. Improved wind turbine technology caused 7.3% higher CF. However, the absolute CF increase amounted to only 11.9%. It is because less favorable wind turbine sites and curtailment in the later period moderated the CF increase by 2.1% and 3.6%, respectively. The drivers are mainly responsible for perennial CF development. In contrast, variations in wind resource availability drive the enormous CF inter-annual variability. No multi-year wind resource change was detected. Full article

Pesticide residues in food have become an important factor seriously threatening human health. Therefore, this study was conducted to determine the pesticide residues in fruits and vegetables commonly found in Fujian, China, with the aim of constructing a simple and rapid method for pesticide residue monitoring. We collected 5607 samples from local markets and analyzed them for the presence of 45 pesticide residues. A fast, easy, inexpensive, effective, robust, and safe (QuEChERS) multi-residue extraction method followed by liquid chromatography equipped with triple-quadrupole mass spectrometry (LC-MS/MS) was successfully established. This 12-min-long analytical method detects and quantifies pesticide residues with acceptable validation performance parameters in terms of sensitivity, selectivity, linearity, the limit of quantification, accuracy, and precision. The linear range of the calibration curves ranged from 5 to 200 mg/L, the limits of detection for all pesticides ranged from 0.02 to 1.90 μg/kg, and the limits of quantification for the pesticides were 10 μg/kg. The recovery rates for the three levels of fortification ranged from 72.0% to 118.0%, with precision values (expressed as RSD%) less than 20% for all of the investigated analytes. The results showed that 726 (12.95%) samples were contaminated with pesticide residues, 94 (1.68%) samples exceeded the maximum residue limit (MRL) of the national standard (GB 2763-2021, China), 632 (11.23%) samples were contaminated with residues below the MRL, and 4881 (87.05%) samples were pesticide residue-free. In addition, the highest number of multiple pesticide residues was observed in bananas and peppers, which were contaminated with acetamiprid, imidacloprid, pyraclostrobin, and thiacloprid. Full article

The modern credit market is actively changing under the influence of digitalization processes. Some of the drivers of these changes are financial companies that carry out, among other things, online lending. Online lending is objectively focused on short-term small loans, both payday loans (PDL) and short-term loans for SMEs. In our research, we applied a special segmentation of borrowers based on the whale-curve approach. Such segmentation leads to four segments of borrowers (A, B, C, and D) which are characterized by the specific features of profitability, risk, recurrent loan granting, and others. The model of optimal correspondence between “risk–return-marketing efforts” is elaborated in the mentioned segments. Marketing efforts are considered in the context of the optimization of the marketing-budget allocation. Our approach was essentially grounded in special scoring-tools that allow multi-layer assessment. A scheme of assessment of profitability, risk, and marketing-resources allocation for borrower’s inflow is constructed. The results can be applied to the customer relationship management (CRM) of online non-banking lenders. Full article

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as $O\left(n\right)$ for real-time person re-ID applications. Full article

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO₂ emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers. Full article

An analytical method was developed to simultaneously determine pyridate, quizalofop-ethyl, and cyhalofop-butyl in brown rice, soybean, potato, pepper, and mandarin using LC-MS/MS. Purification was optimized using various sorbents: primary–secondary amine, octadecyl (C18) silica gel, graphitized carbon black, zirconium dioxide-modified silica particles, zirconium dioxide-modified silica particles (Z-SEP), and multi-walled carbon nanotubes (MWCNTs). Three versions of QuECHERS methods were then tested using the optimal purification agent. Finally, samples were extracted using acetonitrile and QuEChERS EN salts and purified using the Z-SEP sorbent. A six-point matrix-matched external calibration curve was constructed for the analytes. Good linearity was achieved with a determination coefficient ≥0.999. The limits of detection and quantification were 0.0075 mg/kg and 0.01 mg/kg, respectively. The method was validated after fortifying the target standards to the blank matrices at three concentration levels with five replicates for each concentration. The average recovery was within an acceptable range (70–120%), with a relative standard deviation <20%. The applicability of the developed method was evaluated with real-world market samples, all of which tested negative for these three herbicide residues. Therefore, this method can be used for the routine analysis of pyridate, quizalofop-ethyl, and cyhalofop-butyl in agricultural products. Full article