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With the rapid development of autonomous driving technology, target detection, as the core link of its environmental perception, has become a research hotspot in the field of computer vision This article systematically reviews the application status, key challenges, and future trends of computer vision-based object detection technology in autonomous driving. This article provides a comprehensive review of computer vision-based object detection technology in autonomous driving scenarios. It discusses the importance of object detection in autonomous driving, analyzes key challenges such as adapting to complex environments and balancing real-time performance with accuracy, and summarizes current solutions like context learning and algorithm optimization. The paper also highlights future trends, including the development of lightweight deep learning models and the application of computer vision in special environments such as urban logistics and mining areas.

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world’s largest CO 2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world’s largest wind power market. Offshore wind power may play a key role in decarbonising energy supplies. Here the authors evaluates current grid integration capabilities for wind power in China and find that investment levels should be doubled for 2030, and that long-term storage and transmissions are key to accelerated developments of offshore wind in 2050.

NF-κB signaling has been discovered for nearly 40 years. Initially, NF-κB signaling was identified as a pivotal pathway in mediating inflammatory responses. However, with extensive and in-depth investigations, researchers have discovered that its role can be expanded to a variety of signaling mechanisms, biological processes, human diseases, and treatment options. In this review, we first scrutinize the research process of NF-κB signaling, and summarize the composition, activation, and regulatory mechanism of NF-κB signaling. We investigate the interaction of NF-κB signaling with other important pathways, including PI3K/AKT, MAPK, JAK-STAT, TGF-β, Wnt, Notch, Hedgehog, and TLR signaling. The physiological and pathological states of NF-κB signaling, as well as its intricate involvement in inflammation, immune regulation, and tumor microenvironment, are also explicated. Additionally, we illustrate how NF-κB signaling is involved in a variety of human diseases, including cancers, inflammatory and autoimmune diseases, cardiovascular diseases, metabolic diseases, neurological diseases, and COVID-19. Further, we discuss the therapeutic approaches targeting NF-κB signaling, including IKK inhibitors, monoclonal antibodies, proteasome inhibitors, nuclear translocation inhibitors, DNA binding inhibitors, TKIs, non-coding RNAs, immunotherapy, and CAR-T. Finally, we provide an outlook for research in the field of NF-κB signaling. We hope to present a stereoscopic, comprehensive NF-κB signaling that will inform future research and clinical practice.

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. India currently relies heavily on fossil-based sources for its power needs. Here the authors show that renewable energy in India could be cheaper than fossil-based alternatives and could reduce CO 2 emissions by 85% by 2040.

Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data. Transfer learning strategies are useful to increase the accuracy of data-driven predictions in low-data regimes. Here the authors present a hybrid framework integrating transfer learning and expert knowledge to predict carrier mobility of 2D materials from bulk properties.

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.

Individuals with depression often exhibit cognitive distortions in socioeconomic decision-making, particularly in interpreting fairness. However, the role of subjective socioeconomic status in shaping these distortions remains underexplored. This study investigates how depression influences fairness perception and rejection behavior in the Ultimatum Game. Specifically, it is to examine whether the fairness perception of unfair offers mediates the association between depression and the rejection rate of unfair offers, and whether subjective socioeconomic status moderates this relationship. 274 participants completed the CES-D scale to assess depressive symptoms, the MacArthur Scale to measure subjective socioeconomic status, and participated in a modified UG to evaluate fairness perception and rejection rates. Mediation and moderated mediation analyses were conducted using PROCESS Model 7. The results showed that individuals with higher levels of depression tended to perceive unfair offers as more fair, which subsequently led to fewer rejections. Crucially, this mediation effect was significant only among individuals with high subjective socioeconomic status. For low subjective socioeconomic status individuals, depression did not significantly alter the fairness perception of unfair offers. These findings suggest that subjective socioeconomic status shapes the cognitive consequences of depression, highlighting the importance of accounting for socio-cognitive contextual factors in understanding how depression affects social decision-making processes.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the Gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

The misuse and abuse of antibiotics in livestock and poultry seriously endanger both human health and the continuously healthy development of the livestock and poultry breeding industry. Plant-derived bioactive compounds (curcumin, capsaicin, quercetin, resveratrol, catechin, lignans, etc.) have been widely studied in recent years, due to their extensive pharmacological functions and biological activities, such as anti-inflammatory, antioxidant, antistress, antitumor, antiviral, lowering blood glucose and lipids, and improving insulin sensitivity. Numerous studies have demonstrated that plant-derived bioactive compounds are able to enhance the host’s ability to resist or diminish diseases by regulating the abundance of its gut microbiota, achieving great potential as a substitute for antibiotics. Recent developments in both humans and animals have also highlighted the major contribution of gut microbiota to the host’s nutrition, metabolism, immunity, and neurological functions. Changes in gut microbiota composition are closely related to the development of obesity and can lead to numerous metabolic diseases. Mounting evidence has also demonstrated that plant-derived bioactive compounds, especially curcumin, can improve intestinal barrier function by regulating intestinal flora. Furthermore, bioactive constituents can be also directly metabolized by intestinal flora and further produce bioactive metabolites by the interaction between the host and intestinal flora. This largely enhances the protective effect of bioactive compounds on the host intestinal and whole body health, indicating that the bidirectional regulation between bioactive compounds and intestinal flora has great application potential in maintaining the host’s intestinal health and preventing or treating various diseases. This review mainly summarizes the latest research progress in the bioregulation between gut microbiota and plant-derived bioactive compounds, together with its application potential in humans and animals, so as to provide theoretical support for the application of plant-derived bioactive compounds as new feed additives and potential substitutes for antibiotics in the livestock and poultry breeding industry. Overall, based on this review, it can be concluded that plant-derived bioactive compounds, by modulating gut microbiota, hold great promise toward the healthy development of both humans and animal husbandry.

Many studies have demonstrated that moral philosophies, such as idealism and relativism, could be used as robust predictors of judgements and behaviours related to common moral issues, such as business ethics, unethical beliefs, workplace deviance, marketing practices, gambling, etc. However, little consideration has been given to using moral philosophies to predict environmentally (un)friendly attitudes and behaviours, which could also be classified as moral. In this study, we have assessed the impact of idealism and relativism using the Ethics Position Theory. We have tested its capacity to predict moral identity, moral judgement of social vs. environmental issues, and self-reported pro-environmental behaviours. The results from an online MTurk study of 432 US participants revealed that idealism had a significant impact on all the tested variables, but the case was different with relativism. Consistently with the findings of previous studies, we found relativism to be a strong predictor of moral identity and moral judgement of social issues. In contrast, relativism only weakly interacted with making moral judgements of environmental issues, and had no effects in predicting pro-environmental behaviours. These findings suggest that Ethics Position Theory could have a strong potential for defining moral differences between environmental attitudes and behaviours, capturing the moral drivers of an attitude-behaviour gap, which continuously stands as a barrier in motivating people to become more pro-environmental.