PTSD and depressive symptoms in Chinese adolescents exposed to multiple stressors from natural disasters, stressful life events, and maltreatment: A dose-response effect

ObjectivesLittle is known about the effects and the extent that childhood adversity has on post-traumatic stress disorder (PTSD) and depression.Study designA population-based, epidemiological study from the Wenchuan earthquake.MethodsA total of 5,195 Wenchuan Earthquake adolescent survivors aged 11–18 years from nine high schools in southwest China completed questionnaires that assessed their PTSD and depression symptoms due to childhood maltreatment, stressful life events, and childhood earthquake exposure.ResultsThe PTSD and depression prevalences were 7.1 and 32.4%. After controlling for age and gender, the multiple linear regressions revealed that stressful life events had the most significant direct effect on depression (β = 0.491), followed by childhood emotional abuse (β = 0.085), and earthquake exposure (β = 0.077). Similarly, stressful life events (β = 0.583) were found to have more significant direct effects on PSTD, followed by earthquake exposure (β = 0.140); however, childhood emotional abuse was not found to have an effect. The structural equation modeling (SEM) revealed that there were interactions between the three childhood adversities, with all three concurrently affecting both PTSD and depression.ConclusionThese findings add weight to the supposition that psychological maltreatment, negative life events, and earthquake exposure contribute to PTSD and depression. In particular, the identification of subgroups that have a high prevalence of these childhood adversities could assist professionals to target populations that are at high risk of mental health problems

Engineering oxygen vacancies in hierarchically Li-rich layered oxide porous microspheres for high-rate lithium ion battery cathode

Abstract(#br)Lithium-rich layered oxides always suffer from low initial Coulombic efficiency, poor rate capability and rapid voltage fading. Herein, engineering oxygen vacancies in hierarchically Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 porous microspheres (L@S) is carried out to suppress the formation of irreversible Li 2 O during the initial discharge process and improve the Li + diffusion kinetics and structural stability of the cathode mateiral. As a result, the prepared L@S cathode delivers high initial Coulombic efficiency of 92.3% and large specific capacity of 292.6 mA h g −1 at 0.1 C. More importantly, a large reversible capacity of 222 mA h g −1 with a capacity retention of 95.7% can be obtained after 100 cycles at 10 C. Even cycled at ultrahigh rate of 20 C, the L@S cathode can..

Surface Ni-rich engineering towards highly stable Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 cathode materials

Abstract(#br)Li-rich layered oxide cathode materials (LLOs) are regarded as promising next-generation cathode candidate in high-energy-density lithium ion batteries due to their high specific capacity over 250 mA h g −1 . However, LLOs always suffer from a series of severe issues, such as rapid voltage fading, fast capacity decay and bad cycling stability. In this work, Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 -Li 1.2 Mn 0.44 Ni 0.32 Co 0.04 O 2 (LLO-111@111/811) hybrid layered-layered cathode is constructed via facilely increasing surface Ni content. Profiting from this special design, the prepared LLO-111@111/811 cathode exhibits a remarkable specific capacity of 249 mA h g −1 with a high capacity retention of 89.3% and a high discharge voltage of 3.57 V with a voltage retention of 83.0% after cycling 350 times at 0.5 C. As a result, the specific energy of LLO-111@111/811 cathode is 887 Wh Kg −1 at 0.5 C and it keeps as high as 658 Wh Kg −1 after 350 cycles. LLO-111@111/811 also exhibits an initial high capacity of 169 mA h g −1 at a high rate of 5 C and maintains a good capacity retention of 90.0% after 200 cycles. This strategy can successfully improve structural stability, suppress capacity decay and restrain voltage fading of LLOs, which is beneficial for their practical application

Analytical Model of Subthreshold Drain Current Characteristics of Ballistic Silicon Nanowire Transistors

A physically based subthreshold current model for silicon nanowire transistors working in the ballistic regime is developed. Based on the electric potential distribution obtained from a 2D Poisson equation and by performing some perturbation approximations for subband energy levels, an analytical model for the subthreshold drain current is obtained. The model is further used for predicting the subthreshold slopes and threshold voltages of the transistors. Our results agree well with TCAD simulation with different geometries and under different biasing conditions

A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit

The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO is further proposed to optimize the BiGRU parameters to build an optimal diagnosis model based on BiGRU. For the ISCSO, four improvement methods are proposed. The traditional sand cat swarm algorithm is improved using logistic chaotic mapping, the water wave dynamic factor, adaptive weighting, and the golden sine strategy. Then, benchmarking functions are used to test the optimization performance of ISCSO and the four algorithms, and the results show that ISCSO has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on L-Isomap and ISCSO-BiGRU is obtained. Using the model for fault diagnosis, the example simulation results show that using L-ISOMP to filter and downscale the model inputs can better improve model performance. The results are compared with the SCSO-BiGRU, WOA-BiGRU, GWO-BiGRU, and PSO-BiGRU fault diagnosis models. The results show that the fault diagnosis rate of ISCSO-BiGRU is 94.8%, which is 11.69%, 10.39%, 7.14%, and 5.9% higher than that of PSO-BiGRU, GWO-BiGRU, WOA-BiGRU, and SCSO-BiGRU, respectively, and validate that the proposed method can effectively improve the fault diagnosis performance of transformers

Experimental Study on the Scouring Rate of Cohesive Soil in the Lower Yellow River

The different soil anti scourability in the lower reaches of the Yellow River leads to different scouring and retreating speeds, which has a great influence on river regime evolution. Through the incipient motion scouring test of cemented cohesive soil in the lower reaches of the Yellow River, the physical phenomena of the incipient motion of cohesive soil were expounded, the scouring rate of cohesive soil was calculated, and the relationship between the scouring rate and its influencing factors was established. The results show that when the moisture content of cohesive soil is 43%~61%, the scouring rate is about 0.001~0.03 kg/(m2·s). The scouring rate of cohesive soil with the same particle size varies with the flow shear stress under different deposition duration conditions. Under the same flow rate, the scouring rate of cohesive soil increases with the increase of water content, showing an exponential relationship of increment. Under the same shear stress condition, the scouring rate decreases with the increase of dry density, while the exponential relationship between dry density and scouring rate is not clear when the shear stress is small. With the increase of shear stress, there is an obvious exponential relationship between dry density and scouring rate. Finally, the relationship between the scouring rate and relative residual shear stress was established, and the scouring rate formula suitable for cohesive extremely fine sediment was fitted. The formula can better estimate the scouring rate of the riverbank composed of very fine cohesive sediment and provide support for predicting the scouring and retreating rate of riverbanks in natural rivers

Deep learning algorithm for feature matching of cross modality remote sensing images

Focusing on the problem of difficulty in matching due to the differences in imaging modality, time phases, and resolutions of cross modality remote sensing images, a new deep learning feature matching method named CMM-Net is proposed. First, a convolutional neural network is used to extract high-dimensional feature maps of the cross modality remote sensing images. The key points are selected according to the conditions that both the channel maximum and local maximum are met, and the 512-dimensional descriptors in corresponding location are extracted on the feature map to complete the feature extraction. In the matching stage, after completing the fast-nearest neighbor searching, in order to solve the problem of lots of mismatched points, a purification algorithm with dynamic adaptive Euclidean distance and RANSAC constraints is proposed to ensure that the mismatches are effectively eliminated while retaining the correct matches. The algorithm was tested using multiple sets of cross modality remote sensing images and compared with other algorithms. The results show that the proposed algorithm has the ability to extract similar scale invariant features in cross modality images, and has strong adaptability and robustness

Review on Riverbank Soil Collapse

Bank slope collapse is a kind of natural phenomenon which commonly existed on both sides of alluvial plain rivers. The mechanism of bank collapse is complex, and it is an interdisciplinary frontier research subject. The collapse of the bank slope will lead to the instability of river regime and frequent changes of erosion and siltation, which will cause great harm to river regulation and people's livelihood. Through review of river bank soil collapse at home and abroad, it is concluded that the main influencing factors of river bank soil collapse are the action of water flow and the soil structure of river bank. In addition, the stability of river bank and the numerical simulation of river bank collapse are also studied by scholars. In view of the above research results, the deficiencies of the current research are pointed out and the research directions that should be followed in the future are put forward

Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

The problem of the low accuracy of Dissolved Gas Analysis (DGA) in diagnosing transformer faults is addressed by proposing an Improved Golden Jackal Optimization (IGJO) based Stochastic Configuration Network (SCN) method. The method of transformer fault diagnosis based on IGJO optimized SCN is proposed. Firstly, Kernel Principal Component Analysis (KPCA) is used to reduce the dimensionality of the gas data and extract the effective feature quantities. Secondly, the L2 parametric penalty term is introduced into the SCN to improve the generalisation ability of SCN in practical applications. The elite backward learning and golden sine algorithms are incorporated into the golden jackal algorithm, and the IGJO performance is tested using 13 typical test functions, demonstrating that the IGJO has greater stability and merit-seeking capability. The penalty term coefficient C of the SCN is optimised using the IGJO to develop a transformer fault diagnosis model with an Improved Golden Jackal algorithm optimised Random Configuration Network (IGJO-SCN). Finally, the feature quantities extracted by KPCA are used as the input set of the model and the different transformer fault diagnosis models are simulated and validated. The results show that the IGJO-SCN has higher transformer fault diagnosis accuracy compared to other models