In the era of artificial intelligence, ChatGPT, as an advanced language model technology, has the potential for radical innovation. Despite its significant advantages, ChatGPT poses specific ...potential social and ethical issues. Therefore, we need responsible innovation to mitigate these risks and enable ChatGPT to benefit the global community truly. By embedding responsible innovation throughout the various stages of ChatGPT, we can ensure the practical realisation of public trust in governments and expectations from enterprises, thus achieving compliance and successful implementation. Through such a healthy development approach, we can ensure that ChatGPT positively impacts society and continues to foster its healthy growth.
Both adaptive learning and semiotics play crucial roles in learning English as a foreign language. This paper reviews adaptive learning and semiotics, respectively, by analyzing the technical support ...and function of adaptive learning, as well as the concept of semiotics. Then, this paper further explores the relationship between the two, finding out that adaptive learning is an embodiment of semiotics, and semiotics is a mediator in adaptive learning. Among a number of applications of adaptive learning, the author chooses one of the representative applications and analyzes its strengths and weaknesses. Since there are scant articles that cover adaptive learning and semiotics and focus on learning English as a foreign language at the same time, this paper aims to demonstrate the significance of linking the two aspects with foreign language learning and provide practical pedagogical implications to language teachers.
With the largest installed capacity in the world, wind power in China is experiencing a ~20% curtailment. The inflexible combined heat and power (CHP) has been recognized as the major barrier for ...integrating the wind source. The approach to reconcile the conflict between inflexible CHP units and variable wind power in Chinese energy system is yet unclear. This paper explores the technical and economic feasibility of deploying the heat storage tanks and electric boilers under typical power grids and practical operational regulations. A mixed integer linear optimization model is proposed to simulate an integrated power and heating energy systems, including a CHP model capable of accounting for the commitment decisions and nonconvex energy generation constraints. The model is applied to simulate a regional energy system (Jing-Jin-Tang) covering 100-million population, with hourly resolution over a year, incorporating actual data, and operational regulations. The results project an accelerating increase in wind curtailment rate at elevated wind penetration. Investment for wind breaks even at 14% wind penetration. At such penetration, the electric boiler (with heat storage) is effective in reducing wind curtailment. The investment in electric boilers is justified on a social economic basis, but the revenues for different stakeholders are not distributed evenly.
The ship-timber beetle
Cretoquadratus engeli
gen. et sp.n. has been described and classified based on a moderately well-preserved fossil specimen found inside mid-Cretaceous amber from the Hukawng ...Valley in northern Myanmar. The newly created taxon is placed within Atractocerinae, and the new species can be easily distinguished from all other extinct and recent members of the subfamily due to the presence of media veins and branches, including M1, M
2
, M
3
, and M
4
.
Cretoquadratus engeli
is the oldest known representative of (Atractocerinae) Lymexylidae, with the exception of
Cratoatractocerus grimaldii
Wolf-Schwenninger, 2011, which was deposited in the Lower Cretaceous of Brazil. The palaeobiomigratory significancehas been briefly discussed.
•Presented a probabilistic tensor learning approach for SHM data imputation and forecasting.•Proposed an efficient incremental learning scheme to deal with streaming data.•Developed a Bayesian tensor ...decomposition framework for missing data recovery.•Integrated tensor decomposition and vector autoregression for response forecasting.•Demonstrated effectiveness of the approach on a concrete bridge with over three-year records.
There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by sensor malfunction. Recent development in Bayesian temporal factorization models for high-dimensional time series analysis has provided an effective tool to solve both imputation and prediction problems. However, for large datasets, the default Bayesian temporal factorization model becomes inefficient since the model has to be fully retrained when new data arrives. A potential solution is to train the model using a short time window covering only most recent data; however, by doing so, we may miss some critical dynamics and long-term dependencies which can only be identified from a longer time window. To address this fundamental issue in temporal factorization models, this paper presents an incremental Bayesian matrix/tensor learning scheme to achieve efficient imputation and prediction of structural response in long-term SHM. In particular, a spatiotemporal tensor is first constructed followed by Bayesian tensor factorization that extracts latent features for missing data imputation. To enable structural response forecasting based on long-term and incomplete sensing data, we develop an incremental learning scheme to effectively update the Bayesian temporal factorization model. The performance of the proposed approach is validated on continuous field-sensing data (including strain and temperature records) of a concrete bridge, based on the assumption that strain time histories are highly correlated to temperature recordings. The results indicate that the proposed probabilistic tensor learning framework is accurate and robust even in the presence of large rates of random missing, structured missing and their combination. The effect of rank selection on the imputation and prediction performance is also investigated. The results show that a better estimation accuracy can be achieved with a higher rank for random missing whereas a lower rank for structured missing.
Despite rapid development of adhesive hydrogels, the typical double‐sided adhesives fail to adhere to wet tissues and concurrently prevent postoperative tissue adhesion, thus severely limiting their ...applications in repair of internal tissues. Herein, a negatively charged carboxyl‐containing hydrogel is gradiently, electrostatically complexed with a cationic oligosaccharide by a one‐sided dipping method to form a novel Janus hydrogel wet adhesive whose two‐side faces demonstrate strikingly distinct adhesive and nonadhesive properties. The lightly complexed surface demonstrates instant robust adhesion to various wet biological tissues even under water since the phase separation induced by electrostatic complexation increases the hydrophobicity and water drainage capacity. Intriguingly, the highly complexed surface is non‐adhesive due to complete neutralization of carboxyls in the hydrogels. The Janus hydrogel can be used to replace traditional sutures to treat gastric perforation of rabbits. Animal experiment outcomes reveal that one side of the Janus hydrogel is firmly glued to the stomach tissue, and other side facing outward can efficiently prevent the postoperative adhesion. Molecular simulation elucidates the importance for selecting cationic polymer species. It is believed that gradient polyelectrolyte complexation establish a new direction to create Janus adhesives for internal tissue/organ repair and simultaneous prevention of post‐operative adhesion.
A Janus hydrogel wet adhesive with strikingly distinct adhesive/nonadhesive properties on its two sides is fabricated by gradient polyelectrolyte complexation via one‐sided dipping of a carboxyl‐containing hydrogel in cationic oligosaccharide solution. This Janus hydrogel demonstrates an instantly robust adhesion to soft tissue under water, and is successfully used for repairing perforated stomachs of rabbits, while preventing post‐operative tissue adhesion in vivo.
•We develop a new tensor completion framework-LSTC-for large-scale traffic data.•LSTC transforms the large problem into a series of small subproblems for each day.•We introduce unitary linear ...transforms to preserve the correlation among subproblems.•We use Tubal nuclear norm minimization to achieve global consistency.•We use quadratic variation minimization to achieve local smoothness.
Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large data tensors. In this paper, we focus on addressing the missing data imputation problem for large-scale spatiotemporal traffic data. To achieve both high accuracy and efficiency, we develop a scalable tensor learning model—Low-Tubal-Rank Smoothing Tensor Completion (LSTC-Tubal)—based on the existing framework of Low-Rank Tensor Completion, which is well-suited for spatiotemporal traffic data that is characterized by multidimensional structure of location × time of day × day. In particular, the proposed LSTC-Tubal model involves a scalable tensor nuclear norm minimization scheme by integrating linear unitary transformation. Therefore, tensor nuclear norm minimization can be solved by singular value thresholding on the transformed matrix of each day while the day-to-day correlation can be effectively preserved by the unitary transform matrix. Before setting up the experiment, we consider some real-world data sets, including two large-scale 5-min traffic speed data sets collected by the California PeMS system with 11160 sensors: 1) PeMS-4W covers the data over 4 weeks (i.e., 288×28 time points), and 2) PeMS-8W covers the data over 8 weeks (i.e., 288×56 time points). We compare LSTC-Tubal with some state-of-the-art baseline models, and find that LSTC-Tubal can achieve competitively accuracy with a significantly lower computational cost. In addition, the LSTC-Tubal will also benefit other tasks in modeling large-scale spatiotemporal traffic data, such as network-level traffic forecasting.
•Accurate and interpretable method for city-wide missing traffic speed data recovery.•It automatically discovers traffic speed patterns from partially observed data.•Different initialization ...strategies for tensor decomposition are tested.•Element-like and fiber-like missing scenarios are investigated.
Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation systems. While there are several studies on the missing traffic data recovery in the last decade, it is still an open issue of making full use of spatial-temporal traffic patterns to improve recovery performance. In this paper, due to the multi-dimensional nature of traffic speed data, we treat missing data recovery as the problem of tensor completion, a three-procedure framework based on Tucker decomposition is proposed to accomplish the recovery task by discovering spatial-temporal patterns and underlying structure from incomplete data. Specifically, in the missing data initialization, intrinsic multi-mode biases based traffic pattern is extracted to perform a robust recovery. Thereby, the truncated singular value decomposition (SVD) is introduced to capture main latent features along each dimension. Finally, applying these latent features, the missing data is eventually estimated by the SVD-combined tensor decomposition (STD). Empirically, relying on the large-scale traffic speed data collected from 214 road segments within two months at 10-min interval, our experiment covers two missing scenarios – element-like random missing and fiber-like random missing. The impacts of different initialization strategies for tensor decomposition are evaluated. From numerical analysis, a sensitivity-driven rank selection can not only choose an appropriate core tensor size but also determine how much features we actually need. By comparison with two baseline tensor decomposition models, our method is shown to successfully recover missing data with the highest accuracy as the missing rate ranges from 20% to 80% under two missing scenarios. Moreover, the results have also indicated that an optimal initialization for tensor decomposition could suggest a better performance.
Abstract
This paper considers options for a future Indian power economy in which renewables, wind and solar, could meet 80% of anticipated 2040 power demand supplanting the country’s current reliance ...on coal. Using a cost optimization model, here we show that renewables could provide a source of power cheaper or at least competitive with what could be supplied using fossil-based alternatives. The ancillary advantage would be a significant reduction in India’s future power sector related emissions of CO
2
. Using a model in which prices for wind turbines and solar PV systems are assumed to continue their current decreasing trend, we conclude that an investment in renewables at a level consistent with meeting 80% of projected 2040 power demand could result in a reduction of 85% in emissions of CO
2
relative to what might be expected if the power sector were to continue its current coal dominated trajectory.
With the largest installed capacity in the world, wind power in China is experiencing a ~ 20% curtailment during operation. The large portion of the generation capacity from inflexible combined heat ...and power (CHP) is the major barrier for integrating this variable power source. This paper explores opportunities for increasing the flexibility of CHP units using electrical boilers and heat storage tanks for better integration of wind power. A linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks. The model balances heat and power demands in multiple areas and time periods with various energy sources, including CHP, wind power, electrical boilers, and heat storage. The impact of introducing electrical boilers and heat storage systems is examined using a simple test system with characteristics similar to those of the power systems in Northern China. Our results show that both electrical boilers and heat storage tanks can improve the flexibility of CHP units: introducing electrical boilers is more effective at reducing wind curtailment, whereas heat storage tanks save more energy in the energy system as a whole, which reflect a different heating efficiency of the two solutions.
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