Isotopic Application in High Saline Conditions

Evaporite minerals record the hydrogeochemical conditions in which they precipitated. And therefore they can be used to reconstruct the paleoclimate and paleoenvironments. Evaporite minerals are also major sources of industrial minerals including gypsum, halite, borates, lithium concentrates, and others. Because of their scientific significance and economic importance, evaporite minerals and their isotopic hydrochemical processes linked to their formation have been the focus of many geologists and paleoclimatologists. This chapter will discuss the application of isotopes of hydrogen, oxygen, sulfur, strontium, and boron in saline conditions. This will include the following: the δ18O and δD of hydrated water of gypsum and their paleoclimate since 2.2 Ma in the Qaidam Basin, NE Tibetan Plateau; the δ18O and δD of the interlayer water of clay minerals in salar lacustrine sediments; and the 87Sr/86Sr, δ34S, and δ11B of halite from evaporite deposits in Khorat Plateau, Laos, and Yunnan and their application in the origins of brine

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface

The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies

SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding.

It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Effects of Combined Physical Activity and Cognitive Training on Cognitive Function in Older Adults with Subjective Cognitive Decline: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background. Subjective cognitive decline (SCD) is recognized as the earliest prodromal stage of Alzheimer’s disease (AD). Emerging studies explored the effects of combined physical activity and cognitive training interventions on cognitive ability, psychological well-being, and emotion of older adults with SCD, but the results are now still controversial. Objective. This study systematically evaluated the enhancement effects of the combined physical-cognitive interventions on memory self-efficacy, objective cognitive function, psychological well-being, and emotion of older adults with SCD. Methods. Data sources PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang degree and conference papers database, Chinese Science and Technology Periodical (VIP) databases from their inception to 28 February 2020, the Cochrane Central Register of Controlled Trials (Cochrane Library, 2020, Issue 3), and the reference lists of all retrieved articles were searched. Data analysis and bias risk evaluation were conducted in 2020. Two reviewers (SG and YY) independently evaluated the risk of bias of the included studies using the RoB 2 tool. Results. Eleven RCTs involving 1713 participants with SCD (age 68.0 ± 6.1) were included for review and meta-analysis. The interventions in the included studies were physical activity combined with cognitive training. Multiple-modality exercise with mind-motor training, supervised strategy-based memory training sequentially after stationary cycling, Dejian Mind-Body intervention, and physical activity and cognitive stimulation were also practiced. Conclusions. Compared to the active or nonactive control groups, the combined interventions are effective in improving objective cognitive function in SCD which may show the potential value of combined physical-cognitive interventions in improving objective cognitive ability and preventing the conversion of SCD to MCI or AD and no adverse effects. However, owing to the limitations of the included studies, these findings should be interpreted cautiously

A Distribution Model for Shared Parking in Residential Zones that Considers the Utilization Rate and the Walking Distance

Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments

Emergency response to hazardous gases in the environment is an important research field in environmental monitoring. In recent years, with the rapid development of sensor technology and mobile device technology, more autonomous search algorithms for hazardous gas emission sources are proposed in uncertain environment, which can avoid emergency personnel from contacting hazardous gas in a short distance. Infotaxis is an autonomous search strategy without a concentration gradient, which uses scattered sensor data to track the location of the release source in turbulent environment. This paper optimizes the imbalance of exploitation and exploration in the reward function of Infotaxis algorithm and proposes a mobile strategy for the three-dimensional scene. In two-dimensional and three-dimensional scenes, the average steps of search tasks are used as the evaluation criteria to analyze the information trend algorithm combined with different reward functions and mobile strategies. The results show that the balance between the exploitation item and exploration item of the reward function proposed in this paper is better than that of the reward function in the Infotaxis algorithm, no matter in the two-dimensional scenes or in the three-dimensional scenes

Road Network Extraction from SAR Images with the Support of Angular Texture Signature and POIs

Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China’s Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information