A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity.

Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable efficient detection and response to a wide range of movements. Current recognition methods rely on calculating changes in joint distance to classify activity patterns. Therefore, a different approach is required to identify the direction of movement to distinguish activities exhibiting similar joint distance changes but differing motion directions, such as sitting and standing. The research conducted in this study focused on determining the direction of movement using an innovative joint angle shift approach. By analyzing the joint angle shift value between specific joints and reference points in the sequence of activity frames, the research enabled the detection of variations in activity direction. The joint angle shift method was combined with a Deep Convolutional Neural Network (DCNN) model to classify 3D datasets encompassing spatial-temporal information from RGB-D video image data. Model performance was evaluated using the confusion matrix. The results show that the model successfully classified nine activities in the Florence 3D Actions dataset, including sitting and standing, obtaining an accuracy of (96.72 ± 0.83)%. In addition, to evaluate its robustness, this model was tested on the UTKinect Action3D dataset, obtaining an accuracy of 97.44%, proving that state-of-the-art performance has been achieved.

Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.

An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance.

Socio-economic impacts of the COVID-19 pandemic on waste bank closed-loop system in Surabaya, Indonesia.

The COVID-19 pandemic has caused most waste recycling activities to be terminated due to several factors, such as concerns about the spread of coronavirus through the collected solid waste. This study investigates the socio-economic impact of the situation of the closed-loop system of solid waste recycling. Several recommendations for tackling this problem are presented in this research. Primary data collection for the waste bank and informal recycling sector was carried out in the eastern part of Surabaya during large-scale social restrictions. In-depth interviews were conducted with waste bank customers, waste bank unit representatives and the informal recycling sector to understand the pandemic's socio-economic impact on the closed-loop system. Results show that this pandemic has significant impacts on individuals and stakeholders engaged in waste recycling activities. Customers of waste banks, who mostly belong to low-income communities, mentioned that the waste bank closure gave rise to social and economic problems, such as increasing unmanaged solid waste and decreasing income. This result also applied to the informal recycling sector. The government can use the recommendations in this study to generate related policies, such as enforcing the health protocol within solid waste management to keep the recycling system in place and the business alive.

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative.

Femoral-tibial alignment is a prominent risk factor for Knee Osteoarthritis (KOA) incidence and progression. One way of assessing alignment is by determining the Femoral-Tibial Angle (FTA). Several studies have investigated FTA determination; however, methods of assessment of FTA still present challenges. This paper introduces a new method for semi-automatic measurement of FTA as part of KOA research. Our novel approach combines preprocessing of X-ray images and the use of Active Shape Model (ASM) as the femoral and tibial segmentation method, followed by a thinning process. The result of the thinning process is used to predict FTA automatically by measuring the angle between the intersection of the two vectors of branching points on the femoral and tibial areas. The proposed method is trained on 10 x-ray images and tested on 50 different x-ray images of the Osteoarthritis Initiative (OAI) dataset. The outcomes of this approach were compared with manually obtained FTA measurements from the OAI dataset as the ground truth. Based on experiments, the difference in measurement results between the FTA of the OAI and the FTA obtained using our method is quite small, i.e., below 0.81° for the right FTA and below 0.77° for the left FTA with minimal average errors. This result indicates that this method is clinically suitable for semi-automatic measurement of the FTA.

Multi-level anomalous Hall resistance in a single Hall cross for the applications of neuromorphic device.

We demonstrate the process of obtaining memristive multi-states Hall resistance (RH) change in a single Hall cross (SHC) structure. Otherwise, the working mechanism successfully mimics the behavior of biological neural systems. The motion of domain wall (DW) in the SHC was used to control the ascend (or descend) of the RH amplitude. The primary synaptic functions such as long-term potentiation (LTP), long-term depression (LTD), and spike-time-dependent plasticity (STDP) could then be emulated by regulating RH. Applied programmable magnetic field pulses are in varying conditions such as intensity and duration to adjust RH. These results show that analog readings of DW movement can be closely resembled with the change of synaptic weight and have great potentials for bioinspired neuromorphic computing.

Role of RKKY torque on domain wall motion in synthetic antiferromagnetic nanowires with opposite spin Hall angles.

We experimentally show the effect of enhanced spin-orbit and RKKY induced torques on the current-induced motion of a pair of domain walls (DWs), which are coupled antiferromagnetically in synthetic antiferromagnetic (SAF) nanowires. The torque from the spin Hall effect (SHE) rotates the Néel DWs pair into the transverse direction, which is due to the fact that heavy metals of opposite spin Hall angles are deposited at the top and the bottom ferromagnetic interfaces. The rotation of both DWs in non-collinear fashion largely perturbs the antiferromagnetic coupling, which in turn stimulates an enhanced interlayer RKKY exchange torque that improved the DW velocity. The interplay between the SHE-induced torque and the RKKY exchange torque is validated via micromagnetic simulations. In addition, the DW velocity can be further improved by increasing the RKKY exchange strength.

Guided current-induced skyrmion motion in 1D potential well.

Magnetic skyrmions are particle-like magnetization configurations which can be found in materials with broken inversion symmetry. Their topological nature allows them to circumvent around random pinning sites or impurities as they move within the magnetic layer, which makes them interesting as information carriers in memory devices. However, when the skyrmion is driven by a current, a Magnus force is generated which leads to the skyrmion moving away from the direction of the conduction electron flow. The deflection poses a serious problem to the realization of skyrmion-based devices, as it leads to skyrmion annihilation at the film edges. Here, we show that it is possible to guide the movement of the skyrmion and prevent it from annihilating by surrounding and compressing the skyrmion with strong local potential barriers. The compressed skyrmion receives higher contribution from the spin transfer torque, which results in the significant increase of the skyrmion speed.

Coupled Néel domain wall motion in sandwiched perpendicular magnetic anisotropy nanowires.

The operating performance of a domain wall-based magnetic device relies on the controlled motion of the domain walls within the ferromagnetic nanowires. Here, we report on the dynamics of coupled Néel domain wall in perpendicular magnetic anisotropy (PMA) nanowires via micromagnetic simulations. The coupled Néel domain wall is obtained in a sandwich structure, where two PMA nanowires that are separated by an insulating layer are stacked vertically. Under the application of high current density, we found that the Walker breakdown phenomenon is suppressed in the sandwich structure. Consequently, the coupled Néel domain wall of the sandwich structure is able to move faster as compared to individual domain walls in a single PMA nanowire.

Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.

Reproducibility of standing posture for X-ray radiography: a feasibility study of the BalancAid with healthy young subjects.

Unreliable spinal X-ray radiography measurement due to standing postural variability can be minimized by using positional supports. In this study, we introduce a balancing device, named BalancAid, to position the patients in a reproducible position during spinal X-ray radiography. This study aimed to investigate the performance of healthy young subjects' standing posture on the BalancAid compared to standing on the ground mimicking the standard X-rays posture in producing a reproducible posture for the spinal X-ray radiography. A study on the posture reproducibility measurement was performed by taking photographs of 20 healthy young subjects with good balance control standing on the BalancAid and the ground repeatedly within two consecutive days. We analyzed nine posterior-anterior (PA) and three lateral (LA) angles between lines through body marks placed in the positions of T3, T7, T12, L4 of the spine to confirm any translocations and movements between the first and second day measurements. No body marks repositioning was performed to avoid any error. Lin's CCC test on all angles comparing both standing postures demonstrated that seven out of nine angles in PA view, and two out of three angles in LA view gave better reproducibility for standing on the BalancAid compared to standing on the ground. The PA angles concordance is on average better than that of the LA angles.

Stimulation of submucosal glands by nicotine applied locally to the airway mucosa.

Chronic bronchitis is characterized by hypersecretion of mucus and is caused by cigarette smoking. We investigated the effect of nicotine on mucus secretion from tracheal submucosal glands, applying nicotine to the airway mucosa in vitro. We anesthetized 50 ferrets with pentobarbital (66 mg/kg), excised their tracheae and mounted tracheal segments in Ussing chambers. We added 50 microCi Na(2)35SO4 (35S) to the submucosal side and determined nondialyzable 35S in medium collected from the luminal side at 15 min intervals. Nicotine was a powerful stimulant of mucus secretion with threshold effects at 10(-5) M and peak effects at 3 x 10(-4) M. Percentage increases were the same for males and females, but absolute increases in mucus secretion were significantly larger in males than in females. Luminal nicotine was more effective than submucosal nicotine, especially when nicotine bitartrate was used (increase above baseline, 150 +/- 54 vs 22 +/- 12 cpm/15 min, 10(-4) M, n = 4, bitartrate). Effects of luminal nicotine sulfate were larger than those of luminal nicotine bitartrate (303 +/- 88 vs 120 +/- 38 cpm/15 min, P less than 0.05, n = 6, 10(-4) M). Two applications of nicotine 1.5 h apart had similar effects up to 3 x 10(-5) M. At higher concentrations the second response was significantly weaker than the first (tachyphylaxis). Secretory effects of nicotine were prevented completely by atropine and were reduced significantly by hexamethonium.(ABSTRACT TRUNCATED AT 250 WORDS)