Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Scale Fusion Localization Based on Magnetic Trajectory Sequence

Magnetic fingerprint has a multitude of advantages in the application of indoor positioning, but as a weak magnetic field, the dynamic range of the data is limited, which exerts direct influence on the positioning accuracy. Aiming at resolving the problem wherein the indoor magnetic positioning results tremendously rest with the magnetic characteristics, this paper puts forward a method based on deep learning to fuse the temporal and spatial characteristics of magnetic fingerprints, to fully explore the magnetic characteristics and to obtain stable and trustworthy positioning results. First and foremost, the trajectory of the acquisition area is extracted by adopting the ameliorated random waypoint model, and the simulation of pedestrian trajectory is completed. Then, the magnetic sequence is obtained by mapping the magnetic data. Aside from that, considering the scale characteristics of the sequence, a scale transformation unit is designed to obtain multi-scale features. At length, the neural network self-attention mechanism is adopted to fuse multiple features and output the positioning results. By probing into the positioning results of dissimilar indoor scenes, this method can adapt to diverse scenes. The average positioning error in a corridor, open area and complex area reaches 0.65 m, 0.93 m and 1.38 m respectively. The addition of multi-scale features has certain reference value for ameliorating the positioning performance.
Experiments on Attenuation of Weak Shock Waves in High-Speed Train Tunnels

Experimental studies on weak shock wave attenuation in high- speed train tunnels were conducted using a 1/250-scaled train tunnel simulator and a shock tube. Three kinds of porous/perforated walls installed in the test section of the simulator near its exit were examined. Installing aluminum foam or porous plastic on the wall clearly decreased the pressure gradient of the wave front. The processes of the shock wave attenuation were visualized by holographic interferometer. A significant decrease in the overpressure was obtained by installing perforations along the simulato wall. However, with the short perforation section of 0.5 m, the pressure gradient of the shock wave remained steep. Through the series of the experiment and theoretical considerations, it is found that the length of the perforated/porous section is one of the most essential parameters to effectively attenuate the shock wave.
Improving Concrete Crack Segmentation Networks through CutMix Data Synthesis and Temporal Data Fusion

A key element in an automated visual inspection system for concrete structures is identifying the geometric properties of surface defects such as cracks. Fully convolutional neural networks (FCNs) have been demonstrated to be powerful tools for crack segmentation in inspection images. However, the performance of FCNs depends on the size of the dataset that they are trained with. In the absence of large datasets of labeled images for concrete crack segmentation, these networks may lose their excellent prediction accuracy when tested on a new target dataset with different image conditions. In this study, firstly, a Transfer Learning approach is developed to enable the networks better distinguish cracks from background pixels. A synthetic dataset is generated and utilized to fine-tune a U-Net that is pre-trained with a public dataset. In the proposed data synthesis approach, which is based on CutMix data augmentation, the crack images from the public dataset are combined with the background images of a potential target dataset. Secondly, since cracks propagate over time, for sequential images of concrete surfaces, a novel temporal data fusion technique is proposed. In this technique, the network’s predictions from multiple time steps are aggregated to improve the recall of predictions. It is shown that application of the proposed improvements has increased the F1-score and mIoU by 28.4% and 22.2%, respectively, which is a significant enhancement in performance of the segmentation network.
Protective Role of Chronic Exercise Training in Modulating the Impact of Hyperglycemia on Vascular Sensitivity to Ischemia-Reperfusion

Hyperglycemia (HG) is associated with increased mortality and morbidity in acute ischemic events. Regardless of the tissue or organs involved, the vascular endothelium is a key target of ischemia-reperfusion (I/R) injury severity. Among endothelium-protective strategies, exercise has been widely described as useful. However, whether this strategy is able to impact the deleterious effect of HG on endothelial function during I/R has never been challenged. For this, 48 male Wistar rats were randomized into 4 groups: sedentary (Sed) or exercised (Ex, 45 min/day, 5 days/week for 5 weeks) rats, treated (hyperglycemic, HG) or not (normoglycemic, NG) with streptozotocin (40 mg/kg, 48 h before procedure). Vascular I/R (120/15 min) was performed by clamping the femoral artery. Arterial and downstream muscular perfusions were assessed using laser speckle contrast imaging. Vascular endothelial function was assessed in vivo 15 min after reperfusion. HG was responsible for impairment of reperfusion blood flow as well as endothelial function. Interestingly exercise was able to prevent those impairments in the HG group. In agreement with the previous results, HG increased reactive oxygen species production and decreased nitric oxide bioavailability whereas exercise training normalized these parameters. It, therefore, appears that exercise may be an effective prevention strategy against the exacerbation of vascular and muscular damage by hyperglycemia during I/R.
In Situ Study of Twin Boundary Stability in Nanotwinned Copper Pillars under Different Strain Rates

The nanoscopic deformation of 〈111〉 nanotwinned copper nanopillars under strain rates between 10−5/s and 5 × 10−4/s was studied by using in situ transmission electron microscopy. The correlation among dislocation activity, twin boundary instability due to incoherent twin boundary migration and corresponding mechanical responses was investigated. Dislocations piled up in the nanotwinned copper, giving rise to significant hardening at relatively high strain rates of 3–5 × 10−4/s. Lower strain rates resulted in detwinning and reduced hardening, while corresponding deformation mechanisms are proposed based on experimental results. At low/ultralow strain rates below 6 × 10−5/s, dislocation activity almost ceased operating, but the migration of twin boundaries via the 1/4 〈101¯ 〉 kink-like motion of atoms is suggested as the detwinning mechanism. At medium strain rates of 1–2 × 10−4/s, detwinning was decelerated likely due to the interfered kink-like motion of atoms by activated partial dislocations, while dislocation climb may alternatively dominate detwinning. These results indicate that, even for the same nanoscale twin boundary spacing, different nanomechanical deformation mechanisms can operate at different strain rates.
Surface Defects Improved SERS Activity of Nanoporous Gold Prepared by Electrochemical Dealloying

Nanoporous metals possess excellent catalytic and optical properties that are related with surface morphology. Here, we modulated the ligament surface of nanoporous gold (NPG) by controlling electrochemical dealloying and obtained NPG with an improved enhancement of its surface-enhanced Raman scattering (SERS) property. We found that both high-density atomic steps and kinks on the curved surfaces and high-content silver atoms close to the ligament surface contributed to the high SERS ability. The presented strategy will be useful for the fabrication of nanoporous metal with an excellent surface that is needed for sensing, conversion, and catalytic.
Antiacne and Anti-Inflammatory Effects of Phenolic Compounds from Quercus acutissima Carruth. Leaves

Quercus plants are widely distributed in Korea and have been used for their antiallergic and anti-inflammatory properties to treat dermatitis. The phenolic compounds of Quercus acutissima Carruth (QA) are estimated to have antioxidant and anti-inflammatory activities, based on the results of previous studies with Quercus mongilica, Quercus stenophylla, Quercus gilva Blame. , and Quercus acuta Thunb. We yield QA extract and the isolated phenolic compounds (hyperoside (1), astragalin (2), kaempferol 3–O-(6″- galloyl)–β–D–glucopyranoside (KGG) (3), quercetin 3–O-(6″-O-galloyl)-β–D–glucopyranoside (QGG) (4), pedunculagin (5), and casuarinin (6)) and were identified using NMR. Among them, KGG (3) and QGG (4) were isolated for the first time from QA. QA extract and the isolated phenolic compounds demonstrated antioxidative, anti-inflammatory, and antiacne activities in RAW 264.7 mouse macrophage cells in vitro. 3–6 demonstrated strong inhibitory activities in the DPPH scavenging and NO production assay and anti-inflammatory and antiacne activities through western blotting (NLRP3, IL-1β, and 5α-reductase). The most outstanding activity in all experiments was casuarinin (6). The study findings suggest potential therapeutic candidates for acne.
First Order Rate Law Analysis for Reset State in Vanadium Oxide Thin Film Resistive Random Access Memory Devices

In the reset state, the decay reaction mechanism and bipolar switching properties of vanadium oxide thin film RRAM devices for LRS/HRS are investigated and discussed here. To discover the properties of I-V switching curves, the first order rate law behaviors of the reset state between the resistant variety properties and the reaction time were observed. To verify the decay reaction mechanism in the reset state, vanadium oxide thin films from RRAM devices were measured by different constant voltage sampling and exhibited the same decay reaction rate constant. Finally, the electrical conduction transfer mechanism and metallic filament forming model described by I-V switching properties of the RRAM devices were proven and investigated.
On the Optimality of Huffman Trees

Huffman’s well-known algorithm for the construction of optimal search trees is shown to have a more general optimality property than was previously known. A corollary of this general optimality property yields explicitly the optimal policy for the dynamic programming recurrence $F( n ) = f( n ) + \min _{1\leqq j\leqq n - 1} [ {F( j ) + F( {n - j} )} ]$, where f is concave nondecreasing. Several one-dimensional search problems are described which give rise to this recurrence.
The Protective Effect of Theaflavins on the Kidney of Mice with Type II Diabetes Mellitus

Diabetic nephropathy, primarily caused by advanced glycation end products (AGEs), is a serious complication resulting from type 2 diabetes mellitus (T2DM). Reportedly, theaflavins (TFs) can improve diabetic nephropathy; however, the underlying molecular mechanism is not fully clear. In this study, T2DM mice were treated with different concentrations of TFs by gavage for 10 weeks to investigate the effect of TFs on diabetic nephropathy and their potential molecular mechanism of action. Biochemical and pathological analysis showed that the TFs effectively improved blood glucose, insulin resistance, kidney function, and other symptoms in diabetic mice. The mechanism studies indicated that TFs inhibited the formation of AGEs, thereby inhibiting the activation of the MAPK/NF-κB signaling pathway. Therefore, our study suggested that TFs improved diabetic nephropathy by inhibiting the formation of AGEs.
Biosensing of Haemorheological Properties Using Microblood Flow Manipulation and Quantification

The biomechanical properties of blood have been used to detect haematological diseases and disorders. The simultaneous measurement of multiple haemorheological properties has been considered an important aspect for separating the individual contributions of red blood cells (RBCs) and plasma. In this study, three haemorheological properties (viscosity, time constant, and RBC aggregation) were obtained by analysing blood flow, which was set to a square-wave profile (steady and transient flow). Based on a simplified differential equation derived using a discrete circuit model, the time constant for viscoelasticity was obtained by solving the governing equation rather than using the curve-fitting technique. The time constant (λ) varies linearly with respect to the interface in the coflowing channel (β). Two parameters (i.e., average value: , linear slope: dλdβ) were newly suggested to effectively represent linearly varying time constant. exhibited more consistent results than dλdβ. To detect variations in the haematocrit in blood, we observed that the blood viscosity (i.e., steady flow) is better than the time constant (i.e., transient flow). The blood viscosity and time constant exhibited significant differences for the hardened RBCs. The present method was then successfully employed to detect continuously varying haematocrit resulting from RBC sedimentation in a driving syringe. The present method can consistently detect variations in blood in terms of the three haemorheological properties.
High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.
Single-photon emission from cubic GaN quantum dots

We report the demonstration of single-photon emission from cubic GaN/AlN quantum dots grown by molecular beam epitaxy. We have observed spectrally clean and isolated emission peaks from these quantum dots. Clear single-photon emission was detected by analyzing one such peak at 4 K. The estimated g(2)[0] value is 0.25, which becomes 0.05 when corrected for background and detector dark counts. We have also observed the single-photon nature of the emission up to 100 K (g(2)[0] = 0.47). These results indicate that cubic GaN quantum dots are possible candidates for high-temperature operating UV single-photon sources with the possibility of integration into photonic nanostructures.
Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network

The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.
Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning’s quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.
Sigma-1 Receptor Activation Improves Oligodendrogenesis and Promotes White-Matter Integrity after Stroke in Mice with Diabetic Mellitus

Diabetes mellitus (DM) is a major risk factor for stroke and exacerbates white-matter damage in focal cerebral ischemia. Our previous study showed that the sigma-1 receptor agonist PRE084 ameliorates bilateral common-carotid-artery occlusion-induced brain damage in mice. However, whether this protective effect can extend to white matter remains unclear. In this study, C57BL/6 mice were treated with high-fat diets (HFDs) combined with streptozotocin (STZ) injection to mimic type 2 diabetes mellitus (T2DM). Focal cerebral ischemia in T2DM mice was established via injection of the vasoconstrictor peptide endothelin-1 (ET-1) into the hippocampus. Three different treatment plans were used in this study. In one plan, 1 mg/kg of PRE084 (intraperitoneally) was administered for 7 d before ET-1 injection; the mice were sacrificed 24 h after ET-1 injection. In another plan, PRE084 treatment was initiated 24 h after ET-1 injection and lasted for 7 d. In the third plan, PRE084 treatment was initiated 24 h after ET-1 injection and lasted for 21 d. The Y-maze, novel object recognition, and passive avoidance tests were used to assess neurobehavioral outcomes. We found no cognitive dysfunction or white-matter damage 24 h after ET-1 injection. However, 7 and 21 d after ET-1 injection, the mice showed significant cognitive impairment and white-matter damage. Only PRE084 treatment for 21 d could improve this white-matter injury; increase axon and myelin density; decrease demyelination; and increase the expressions of myelin regulator 2‘-3‘-cyclic nucleotide 3‘-phosphodiesterase (CNpase) and myelin oligodendrocyte protein (MOG) (which was expressed by mature oligodendrocytes), the number of nerve/glial-antigen 2 (NG2)-positive cells, and the expression of platelet-derived growth factor receptor-alpha (PDGFRα), all of which were expressed by oligodendrocyte progenitor cells in mice with diabetes and focal cerebral ischemia. These results indicate that maybe there was more severe white-matter damage in the focal cerebral ischemia of the diabetic mice than in the mice with normal blood glucose levels. Long-term sigma-1 receptor activation may promote oligodendrogenesis and white-matter functional recovery in patients with stroke and with diabetes.
First Report of Clonostachys rosea as a Mycoparasite on Sclerotinia sclerotiorum Causing Head Rot of Cabbage in India

Clonostachys rosea, an ascomycetous, omnipresent, cellulose-decaying soil fungus, has been reported to be a well-known mycoparasitic biological control agent. In this study, we isolated C. rosea, a mycoparasitic fungus for the first time in India from sclerotia of the notorious plant pathogen Sclerotinia sclerotiorum, causing head rot disease in cabbage. A total of five mycoparasitic fungi were isolated from the sclerotial bodies of S. sclerotiorum (TNAU-CR 01, 02, 03, 04 and 05). All the isolates were tested under morpho-molecular characterization. Among them, TNAU-CR 02 showed the greatest mycelial inhibition of 79.63% over the control. Similarly, the SEM imaging of effective C. rosea isolates indicated the presence of numerous conidia destroying the outer cortex layers of sclerotia. Metabolite fingerprinting of C. rosea TNAU-CR 02 identified 18 chemical compounds using GC-MS analysis. The crude antibiotics of C. rosea TNAU-CR 02 were verified for their antifungal activity against S. sclerotiorum and the results revealed 97.17% mycelial inhibition compared with the control. Similarly, foliar application of TNAU-CR 02 at 5 mL/litre on 30, 45 and 60 days after transplanting showed the lowest disease incidence of 15.1 PDI compared to the control. This discovery expands our understanding of the biology and the dissemination of C. rosea, providing a way for the exploitation of C. rosea against cabbage head rot pathogens.
CYCLIC PROPERTIES OF TRIANGULAR GRID GRAPHS

Abstract It is known that all 2-connected, linearly convex triangular grid graphs, with only one exception, are hamiltonian (Reay and Zamfirescu, 2000). In the paper, it is shown that this result holds for a wider class of connected, locally connected triangular grid graphs and, with more exceptions, even for some general class of graphs. It is also shown that the HAMILTONIAN CYCLE problem is NP-complete for triangular grid graphs.