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"Zhang, Xiaoyu"
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Pythagorean fuzzy MAIRCA CRITIC for energy price and demand forecasting involving eco economic factors for sustainable economy
Sustainable economies require effective energy planning that goes beyond relying on functioning forecasting models to comprehend energy dynamics, and also provides well-defined decision-making (DM) models that can address risk, ambiguity, and conflicting eco-economic objectives. This type of strategic planning requires an integrated assessment approach that can evaluate forecasting choices in an uncertain and dynamic environment. This paper presents a new and modified methodology for ranking energy forecasting models within a Pythagorean Fuzzy Set (PFS) system by integrating the CRITIC (Criteria Importance Through Inter-Criteria Correlation) weighting framework and the MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) ranking scheme. In the suggested framework, expert uncertainty and vagueness are represented by the PFS environment. In contrast, some of the leading eco-economic indicators are objectively weighted using CRITIC, and forecasting model alternatives are prioritized based on MAIRCA. A comparative study is conducted on a hypothetical data set that represents realistic energy system capabilities, including adaptability, carbon policy integration, and computing efficiency. The findings suggest that the framework contributes to consistent, interpretable, and uncertainty-aware rankings, and the Deep Q-Network (DQN) model was ranked to be the most effective alternative. The study contributes to the development of more sophisticated decision-support mechanisms to facilitate sustainable energy planning, enabling informed and balanced decisions as the eco-economic climate evolves rapidly.
Journal Article
Sustainable development in African countries: evidence from the impacts of education and poverty ratio
This paper uses the autoregressive-distributed lag model to investigate the effects of education, poverty, trade volume, ICT development, and GDP per capita on sustainability in the 15 largest African economies from 1999 to 2019. The results show a positive correlation between higher tertiary education rates and sustainability, emphasizing the importance of investing in education for sustainable development. In contrast, higher poverty rates are linked to lower sustainability, highlighting the need for poverty reduction efforts. Increased trade volumes are associated with reduced sustainability, indicating the challenges of trade liberalization policies in achieving sustainability goals. However, ICT development has a significant positive impact on sustainability. Interestingly, higher GDP per capita is linked to lower sustainability, potentially due to unsustainable consumption patterns and social inequalities. Policy recommendations for promoting sustainable development in African countries include targeted measures focusing on education and poverty reduction. Gender-friendly policies and initiatives to eliminate educational disparities, especially among marginalized groups, can enhance human capital development. Additionally, promoting e-businesses and sustainable entrepreneurship, along with attracting foreign investment for sustainable education and employment initiatives, can drive economic growth while minimizing environmental impact and fostering inclusive development.
Journal Article
Epigenetic Landscape of Plants
In plants, DNA methylation, histone modifications, and RNA interference play critically important roles in regulating chromatin structure, thereby profoundly affecting transcription and other molecular events. Recent advances in microarray and high-throughput sequencing technologies have enabled genome-wide studies of these pathways in great detail. The vast amounts of \"epigenomic\" data generated so far have provided new insights into the mechanisms and functions of these pathways and have broadened our understanding of the structure and organization of plant chromatin as a whole.
Journal Article
Causal Debiasing in Recommender Systems: Principles and Prospects
The recommender system (RS) directly influences users’ experience in e-commerce, video, social media and service platforms. However, it performs restrictively due in part to the inevitable various biases. Although traditional debiasing methods such as Inverse Propensity Score (IPS) and Double Robustness (DR) can mitigate the problems to some extent, new glitches (idealization of exposure mechanism and high variation) still have an effect on the performance of RS. Nowadays, causal inference, which can facilitate the robustness and fairness of RS models, has become one crucial method to handle such bias problems. This paper initially outlines the connotation, significance and restrictions of RS and causal inference respectively. Depending on the procedure of RS, it then classifies and analyzes some popular causal debiasing methods in the past three years. Finally, potential future prospects are provided according to the characteristics of those popular methods, aiming at giving a guide to subsequent research and study.
Journal Article
Cultural Continuity and Transition
This article explores the adaptation of the Grand Song of the Dong Ethnic Group [侗族大歌] to modern socio-cultural challenges using the ‘music sustainability’ framework. It examines tradition bearers in Beijing, university integration, and government efforts, highlighting both challenges and revitalization, offering models for traditional culture’s adaptation in contemporary society. Ta članek raziskuje prilagajanje velike pesmi, načina petja etnične skupine Dong [侗族大歌] sodobnim družbenim in kulturnim izzivom z uporabo okvira ‘trajnosti glasbe’. Preučuje nosilce tradicije v Pekingu, integracijo univerz in prizadevanja vlade, poudarja izzive in oživitev ter ponuja modele za prilagajanje tradicionalne kulture sodobni družbi.
Journal Article
OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data
High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.
Journal Article
Point-Plane SLAM Using Supposed Planes for Indoor Environments
Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.
Journal Article
Smoothing the energy transfer pathway in quasi-2D perovskite films using methanesulfonate leads to highly efficient light-emitting devices
Quasi-two-dimensional (quasi-2D) Ruddlesden–Popper (RP) perovskites such as BA
2
Cs
n
–1
Pb
n
Br
3
n
+1
(BA = butylammonium,
n
> 1) are promising emitters, but their electroluminescence performance is limited by a severe non-radiative recombination during the energy transfer process. Here, we make use of methanesulfonate (MeS) that can interact with the spacer BA cations via strong hydrogen bonding interaction to reconstruct the quasi-2D perovskite structure, which increases the energy acceptor-to-donor ratio and enhances the energy transfer in perovskite films, thus improving the light emission efficiency. MeS additives also lower the defect density in RP perovskites, which is due to the elimination of uncoordinated Pb
2+
by the electron-rich Lewis base MeS and the weakened adsorbate blocking effect. As a result, green light-emitting diodes fabricated using these quasi-2D RP perovskite films reach current efficiency of 63 cd A
−1
and 20.5% external quantum efficiency, which are the best reported performance for devices based on quasi-2D perovskites so far.
Owing to large exciton binding energy, quasi-2D perovskite is promising for light-emitting application, yet inhomogeneous phases distribution limits the potential. Here, the authors improve the performance by using MeS additive to regulate the phase distribution and to reduce defect density in the films.
Journal Article
Trifluoroacetate induced small-grained CsPbBr3 perovskite films result in efficient and stable light-emitting devices
Quantum efficiencies of organic-inorganic hybrid lead halide perovskite light-emitting devices (LEDs) have increased significantly, but poor device operational stability still impedes their further development and application. All-inorganic perovskites show better stability than the hybrid counterparts, but the performance of their respective films used in LEDs is limited by the large perovskite grain sizes, which lowers the radiative recombination probability and results in grain boundary related trap states. We realize smooth and pinhole-free, small-grained inorganic perovskite films with improved photoluminescence quantum yield by introducing trifluoroacetate anions to effectively passivate surface defects and control the crystal growth. As a result, efficient green LEDs based on inorganic perovskite films achieve a high current efficiency of 32.0 cd A
−1
corresponding to an external quantum efficiency of 10.5%. More importantly, our all-inorganic perovskite LEDs demonstrate a record operational lifetime, with a half-lifetime of over 250 h at an initial luminance of 100 cd m
−2
.
All-inorganic cesium lead bromide perovskite based light-emitting diodes show improved operational stability but the film quality limits their performance. Here Wang et al. use trifluoroacetate anions to passivate defects and achieve excellent device performance and stability.
Journal Article