Progress and summary of reinforcement learning on energy management of MPS-EV

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS

Remote sensing and environmental assessment of wetland ecological degradation in the Small Sanjiang Plain, Northeast China

IntroductionThe plain marsh wetland ecosystems are sensitive to changes in the natural environment and the intensity of human activities. The Sanjiang Plain is China’s largest area of concentrated marsh wetland, the Small Sanjiang Plain is the most important component of the Sanjiang Plain. However, with the acceleration of the urbanization and development of large-scale agricultural reclamation activities in the Small Sanjiang Plain in Northeast China, the wetland has been seriously damaged. In light of this degradation this study examines the Small Sanjiang Plain.MethodsFrom the four aspects of area, structure, function, and human activities, we try to construct a wetland degradation comprehensive index (WDCI) in cold region with expert scoring methods and analytic hierarchy process (AHP), coupled with network and administrative unit. The objective was to reveal the degradation of wetlands in Northeast China over three decades at a regional scale.ResultsThe results showed that (1) the overall wetland area decreased between 1990 and 2020 by 39.26×103 hm2. Within this period a significant decrease of 336.56×103 hm2 occurred between 1990 and 200 and a significant increase of 214.62×103 hm2 occurred between 2010 and 2020. (2) In terms of structural changes, the fractal dimension (FRAC) has the same trend as the Landscape Fragmentation Index (LFI) with little change. (3) In terms of functional changes, the average above-ground biomass (AGB) increased from 1029.73 kg/hm2 to 1405.38 kg/hm2 between 1990 and 2020 in the study area. (4) In terms of human activities, the average human disturbance was 0.52, 0.46, 0.57 and 0.53 in 1990, 2000, 2010 and 2020, with the highest in 2010. (5) The composite wetland degradation index shows that the most severe wetland degradation was 49.61% in 2010 occurred between 1990 and 2020. (6) Among the severely deteriorated trajectory types in 2010–2020, mild degradation → serious degradation accounted for the largest area of 240.23×103 hm2, and the significant improvement trajectory type in 1990–2000 accounted for the largest area of 238.50×103 hm2.DiscussionIn brief, we conclude that the degradation of the Small Sanjiang Plain wetland was caused mainly by construction, overgrazing, deforestation, and farmland reclamation. This study can also provide new views for monitoring and managing wetland degradation by remote sensing in cold regions

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment

Vector Analysis of the Effects of FS-LASIK and Toric ICL for Moderate to High Astigmatism Correction

Purpose. To estimate the treatment effectiveness of femtosecond-assisted laser in situ keratomileusis (FS-LASIK) and Toric implantable collamer lens (Toric ICL) for moderate and high astigmatism via vector analysis. Materials and Methods. The study involved 44 eyes from 44 patients who had a preoperative refractive cylinder ≥1.0 diopters (D) and underwent bilateral FS-LASIK or Toric ICL surgery. The examinations included corrected distance visual acuity measurement and subjective refraction before and 3 months after surgery. The astigmatic changes were estimated using vector analysis. Results. No statistically significant differences were found in cylindrical refraction and percentage of spherical equivalent within 0 D, ±0.50 D, ±1.00 D, and ±1.50 D between the FS-LASIK and Toric ICL groups at 3 months after surgery. The parameters of the vector analysis included intended refractive correction, surgically induced refractive correction, error vector, correction ratio, error ratio, error of magnitude, and error of angle, with no significant differences between the groups. However, error ratio the of the off-axis correction in the FS-LASIK and Toric ICL groups was 4.11 ± 3.02 and 8.11 ± 3.82, respectively, and the difference was significant (t = −2.46, p=0.02). Conclusion. Both FS-LASIK and Toric ICL were effective for correcting moderate and high astigmatism, although Toric ICL might produce a larger error of angle than FS-LASIK when an off-axis correction occurs

Evolution of the Groundwater Flow System since the Last Glacial Maximum in the Aksu River Basin (Northwest China)

Thoroughly investigating the evolution of groundwater circulation and its controlling mechanism in the Aksu River Basin, where human activities are intensifying and the groundwater environment is increasingly deteriorating, is highly urgent and important for promoting the theory, development and implementation of groundwater flow systems (GFSs) and protecting groundwater resources. Based on a detailed analysis of the sediment grain size distribution, chronology, electrofacies, glacial sedimentary sequence, palaeoclimate indicators and existing groundwater age, this paper systematically reconstructs the palaeosedimentary environment of the basin-scale aquifer system in the study area and scientifically reveals the evolutionary pattern and formation mechanism of the GFS. The results showed that the later period of the late Pleistocene experienced a rapid downcutting erosional event caused by tectonic uplift, and the sedimentary environment transitioned from a dry–cold deep downcutting environment in the Last Glacial Maximum (LGM) to a coarse-grained fast-filling fluvial facies sedimentary environment in the Last Glacial Deglaciation (LDP) as the temperature rose; then, it shifted to an environment of fine-grained stable alternating accumulation of fluvial facies and lacustrine facies that was dominated by the warm and arid conditions of the Holocene megathermal period (HMP); this process changed the previous river base level via erosion, glacier elongation or shortening and river level, thus resulting in a complex coupling relationship between the palaeosedimentary environment, palaeoclimate and basin GFS. Furthermore, the existing GFS pattern in the basin exhibits a vertically unconformable groundwater age distribution, which indicates that it is the outcome of the complex superposition of groundwater flow controlled by the palaeosedimentary environment in different periods. Therefore, neotectonic movement and climate fluctuation have jointly acted on the variation in the river level, resulting in the “seesaw” effect, thereby fundamentally controlling the strength of the driving force of groundwater and resulting in the gradual evolution of the GFS from the fully developed regional GFS pattern during the LGM to the current multihierarchy nested GFS pattern

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm

Determining the three-dimensional atomic structure of an amorphous solid.

Amorphous solids such as glass, plastics and amorphous thin films are ubiquitous in our daily life and have broad applications ranging from telecommunications to electronics and solar cells1-4. However, owing to the lack of long-range order, the three-dimensional (3D) atomic structure of amorphous solids has so far eluded direct experimental determination5-15. Here we develop an atomic electron tomography reconstruction method to experimentally determine the 3D atomic positions of an amorphous solid. Using a multi-component glass-forming alloy as proof of principle, we quantitatively characterize the short- and medium-range order of the 3D atomic arrangement. We observe that, although the 3D atomic packing of the short-range order is geometrically disordered, some short-range-order structures connect with each other to form crystal-like superclusters and give rise to medium-range order. We identify four types of crystal-like medium-range order-face-centred cubic, hexagonal close-packed, body-centred cubic and simple cubic-coexisting in the amorphous sample, showing translational but not orientational order. These observations provide direct experimental evidence to support the general framework of the efficient cluster packing model for metallic glasses10,12-14,16. We expect that this work will pave the way for the determination of the 3D structure of a wide range of amorphous solids, which could transform our fundamental understanding of non-crystalline materials and related phenomena