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Extreme heavy rainfall due to Typhoon Talas on September 2–4, 2011 in the Kii Peninsula, Japan, triggered numerous floods and landslides. This study investigates the mechanism and the entire process of rainfall-induced deep-seated landslides forming two massive dams in the Kuridaira and Akatani valleys, respectively. The mechanism of the rapid deep-seated landslides is examined through a series of laboratory experiments on samples from sliding surfaces by using undrained high-stress dynamic-loading ring-shear apparatus. The test results indicate that the failure of samples is triggered by excess pore water pressure generation under a shear displacement from 2 to 7 mm with a pore pressure ratio ranging from 0.33 to 0.37. The rapid movement of landslides is mainly attributed to high mobility due to the liquefaction behavior of both sandstone-rich and shale samples. Geomorphic settings and landslide mobility are major contributing factors to the dam formation. Additionally, shear displacement control tests show that a certain amount of shear displacement between 2 and 7 mm along the sliding surfaces of the gravitationally deformed slopes might have led to the failures. Importantly, computer simulation with LS-RAPID software using input parameters obtained from physical experiments is employed to interpret the entire formation process of the abovementioned two landslide dams. The simulation results are examined in accordance with the observed on-site geomorphic features and recorded data to explain the possibility of sliding processes. The results further point out that local failures are initiated from the lower middle part of the landslide bodies where the geological boundary exists. This condition most probably influences the landslide initiation in the two case studies. This research is therefore helpful for hazard assessment of slopes that are susceptible to deep-seated landslides and other sequential processes in areas with geology and geomorphology similar to that of the Kii Peninsula.

Osteoarthritis (OA) is a leading cause of disability, often resulting from overuse or injury, but inactivity can also contribute to cartilage degeneration. Conventional in vivo models struggle to isolate and study the specific effects of mechanical stress on cartilage health. To address this limitation, a microphysiological system (MPS) is established to examine how varying levels of shear stress impact cartilage homeostasis. The system allows for the cultivation of 3D chondrogenic microconstructs (CMCs) derived from human mesenchymal stromal cells, simulating both physiological and pathophysiological shear stress. Inflammation is induced via TNF‐α or activated peripheral blood mononuclear cells to model cartilage damage, enabling the evaluation of therapeutic interventions. The study demonstrates the development of an arthritis‐like phenotype and successful restoration of cartilage conditions through a JAK inhibitor under physiological shear stress. Physiological shear stress is identified as a critical factor in maintaining cartilage integrity. This MPS offers a standardized method to study shear stress, replicate cytokine‐induced cartilage damage, and simulate key features of arthritis, providing a valuable alternative to animal models. Osteoarthritis (OA) arises from mechanical stress or inactivity, impacting cartilage health. A microphysiological system (MPS) to investigate shear stress effects on 3D chondrogenic constructs, modeling cartilage damage with TNF‐α or immune cells is established. Physiological shear stress preserved construct integrity under these conditions, while therapeutic interventions restored cartilage health, offering a platform for preclinical drug testing.

Around hundred landslides were triggered by the Kumamoto earthquakes in April 2016, causing fatalities and serious damage to properties in Minamiaso village, Kumamoto Prefecture, Japan. The landslides included many rapid and long-runout landslides which were responsible for much of the damage. To understand the mechanism of these earthquake-triggered landslides, we carried out field investigations with an unmanned aerial vehicle to obtain DSM and took samples from two major landslides (Takanodai landslide and Aso-ohashi landslide) to measure parameters of the initiation and the motion of landslides. A series of ring-shear tests and computer simulations were conducted using a measured Kumamoto earthquake acceleration record from KNet station KMM005, 10 km west of Aso-ohashi landslide. The research results supported our assumed mechanism of sliding-surface liquefaction for the rapid and long-runout motion of these landslides.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

Shallow landslides have posed significant threats to humans around the world. In order to reduce landslide disaster risk, the effectiveness of early warning systems and hazard zonation work needs to be improved. This research attempted to couple a landslide simulation model (LS-RAPID model) and a hydrological model (Rainfall-Runoff-Inundation (RRI) model) to exploit the advantages of each model for simulating and predicting landslide hazard (location and timing). The pilot area is a small catchment where a shallow landslide happened in July 2015 after 2 days of heavy rain. The landslide buried 3 houses and killed 8 people in Cao Thang Ward, Halong City, Vietnam. A soil sample was collected from the sliding surface and tested using an undrained ring-shear apparatus ICL-2 in the undrained condition. A thickness map of the potential sliding material was interpolated from the relationship between the depth of sandy soil layer and slope (based on 12 soil drill locations) and updated through field surveys in the study area. Different pore water pressure ratio scenarios were applied in the LS-RAPID model to simulate in 3D the initiation and motion of the rapid shallow landslide to create different hazard maps. The subsurface water level was monitored at two locations on the top of the shallow landslide. Based on the observed subsurface water and rainfall data, the RRI model was calibrated and then integrated with the LS-RAPID scenarios to generate Risk Index maps. The simulation results from the newly proposed coupled hydrological-geotechnical framework were compared with those from the observed landslide hazard and showed the reliability to predict the spatial and temporal occurrence of landslide hazard. This could be very useful for supporting decision-makers in rainfall-induced landslide hazard early warning and land use planning.

A rapid and long-traveling landslide was triggered at Aranayaka, Kegalle district, Sri Lanka on 17 May 2016 by exceptionally heavy rainfall associated with a slow-moving tropical cyclone. The precipitation that accumulated within the last 3 days from May 14 to 17 reached 446.5 mm. The landslide mass traveled over an approximately 2-km distance killing 127 people and destroying 75 houses. To deduce the failure mechanism of the Aranayaka landslide, shear behavior of two samples taken from the initial landslide area were examined through ring-shear tests. The first sample (S1) was taken from the weathered soil layer on the left scarp of the landslide. The second sample (S2) was taken from the weathered granitic gneiss at the bottom of the depression in the middle part of the landslide area. The layer was affected by intense tectonic crushing and subsequent deep weathering. A high value of shear resistance at steady state was measured on the sample S1 while the sample S2 obtained a much smaller steady state shear resistance. This indicated that the sliding surface of the landslide was located in the weathered granitic gneiss associated with the sample S2. A series of computer simulations of this landslide was then carried out given the soil parameters from the ring-shear tests and pore-water pressure ratio estimated from the rainfall records using the “SLIDE” model. In the simulation, the landslide initiated from the middle part of the source area, close to the location from where sample S2 was taken. Moreover, the time of occurrence from the simulation was similar to that observed in the real event. This is a very important information to assess further rapid landslides in areas with similar conditions. This study also indicates the importance of selecting soil samples and suggests that the ring-shear apparatus and computer simulations are effective tools to reproduce the process of landslides.

This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech coefficients. The constraint imposed by dynamic features (i.e., the time derivatives of the speech coefficients) are used to enhance the smoothness of predicted coefficient trajectories in two ways. One is to obtain the enhanced speech coefficients with a least square estimation from the coefficients and dynamic features predicted by DNN. The other is to incorporate the constraint of dynamic features directly into the DNN training process using a sequential cost function. In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space. The transform is estimated to maximize the likelihood of the transformed coefficients given a model of clean speech coefficients. Unlike the DNN approach, no parallel corpus is used and no assumption on distortion types is made. The two approaches are evaluated on the REVERB Challenge 2014 tasks. Both speech enhancement and automatic speech recognition (ASR) results show that the DNN-based mappings significantly reduce the reverberation in speech and improve both speech quality and ASR performance. For the speech enhancement task, the proposed dynamic feature constraint help to improve cepstral distance, frequency-weighted segmental signal-to-noise ratio (SNR), and log likelihood ratio metrics while moderately degrades the speech-to-reverberation modulation energy ratio. In addition, the cross transform feature adaptation improves the ASR performance significantly for clean-condition trained acoustic models.

The paper presents methods for solving the distribution network reconfiguration problem based on heuristic algorithms. In particular, the study solved the distribution network reconfiguration problem for active power losses reduction. The application of the method to a sample network proved effective compared to other algorithms, especially in case of large-scale and complex systems. In addition, the paper considers the influence of the location and capacity of distributed generations on the distribution network reconfiguration problem in different cases. The results show that the distribution network reconfiguration problem combined with optimization location and size of distributed generations is the most efficient solution for minimizing power loss and enhancing voltage profile. The proposed methods have been also successfully applied in the small- and medium-scale practical radial distribution networks. The simulation results show that the proposed methods can be used as reference materials for network reconfiguration problems with single and multi-objectives.

This paper develops a neural network based adaptive backstepping sliding mode control for trajectory tracking problems of uncertain nonlinear Euler-Lagrange systems. Firstly, the conventional sliding mode control is conducted based on the backstepping technique. Then, the uncertain components are approximated by neural networks to overcome the drawbacks in system dynamics, system parameters, and external disturbances. One advantage of the proposed controller is that the upper bound of the uncertainties and disturbances is not required as usual, it will be estimated directly by the neural network instead. Moreover, the chattering phenomenon, which is typical of the sliding mode control, is countably reduced. The stability of the closed loop and the convergence of the approximation are proven mathematically via Lyapunov stable theory. Finally, the simulation and the comparison with the recent existing works are employed to demonstrate the effectiveness of the introduced control scheme.

There is a growing burden of hypertension (HTN) among adults living in rural Vietnam, which is associated with reduced health related quality of life (HRQoL). Few community-based interventions have, however, attempted to improve the quality of life in patients with uncontrolled HTN. This study aimed to examine the impact of a multi-component intervention on HRQoL in adults with uncontrolled HTN. This cluster-randomized controlled trial was conducted in sixteen communities (8 intervention and 8 comparison) living in a rural setting in Vietnam (2017-2022). Consenting adults with uncontrolled HTN were enrolled. The comparison arm received training sessions about HTN prevention and management and patient education materials. The intervention arm received information similar to the comparison group and three enhancement components, namely a storytelling intervention, home blood pressure (BP) self-monitoring, and expanded community health worker services. The primary outcome was the differential change in HRQoL over 12-month follow-up period, measured using the 12-Item Short Form Health Survey (SF-12), which generates a Physical Component Summary (PCS-12) and a Mental Component Summary (MCS-12). The total HRQoL score was calculated as the sum of PCS-12 and MCS-12, with higher scores indicating better HRQoL. A total of 671 patients were studied; their mean age was 66 years and 55% were women. At the 12-month follow-up, the intervention group showed a significant increase in their PCS-12 with a multivariable-adjusted difference of 4.2 points (95% CI: 2.0-6.4) compared with the control group. While the MCS-12 scores increased for both groups, their differential change over 12 months was not statistically significant (multivariable-adjusted difference: 1.4 points; 95% CI: -0.6; 3.5). Our results demonstrate that a multicomponent intervention effectively improved overall HRQoL with a significant impact on physical health-related HRQoL in individuals with uncontrolled HTN. ClinicalTrials.gov, Registration number: https://clinicaltrials.gov/study/NCT03590691, (registration date July 17, 2018).