Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model parameters and operational memory, which increases the burden on computational resources. To solve this problem, we propose a Parametric Efficient Image InPainting Network (PEIPNet) for efficient and effective image-inpainting. Unlike other state-of-the-art methods, the proposed model has a one-stage inpainting framework in which depthwise and pointwise convolutions are adopted to reduce the number of parameters and computational costs. To generate semantically appealing results, we selected three unique components: spatially-adaptive denormalization (SPADE), dense dilated convolution module (DDCM), and efficient self-attention (ESA). The SPADE was adopted to conditionally normalize activations according to the mask to distinguish between damaged and undamaged regions. The DDCM was employed at every scale to overcome the gradient-vanishing obstacle and gradually fill-in pixels by capturing global information along the feature maps. The ESA was utilized to obtain clues from unmasked areas by extracting long-range information. In terms of efficiency, our model has the lowest operational memory compared with other state-of-the-art methods. Both qualitative and quantitative experiments demonstrate the generalized inpainting of our method on three public datasets: Paris StreetView, CelebA, and Places2.

This study presents the solution of the second type of a two-dimensional nonlinear integral equation in Banach space. Also, the existence and uniqueness of this equation’s solution are discussed. We utilize a numerical approach involving hybrid and block-pulse functions to obtain the approximate solution of a two-dimensional nonlinear integral equation. Nonlinear integral equation in two dimensions is reduced numerically to a system of nonlinear algebraic equations that can be solved using numerical methods. This study focuses on showing the convergence analysis for the numerical approach and obtaining an error estimate. Some numerical examples have been provided to demonstrate the approach’s viability and efficacy

This paper presents results on tribological characteristics for polymer blends made of polybutylene terephthalate (PBT) and polytetrafluoroethylene (PTFE). This blend is relatively new in research as PBT has restricted processability because of its processing temperature near the degradation one. Tests were done block-on-ring tribotester, in dry regime, the variables being the PTFE concentration (0%, 5%, 10% and 15%wt) and the sliding regime parameters (load: 1 N, 2.5 N and 5 N, the sliding speed: 0.25 m/s, 0.5 m/s and 0.75 m/s, and the sliding distance: 2500 m, 5000 m and 7500 m). Results are encouraging as PBT as neat polymer has very good tribological characteristics in terms of friction coefficient and wear rate. SEM investigation reveals a quite uniform dispersion of PTFE drops in the PBT matrix.

Proteins are the workhorses of the cell that perform biological functions by interacting with other proteins. Many statistical methods for protein-protein interaction (PPI) have been studied without considering time-dependent changes in networks and the functionalities. I introduced a novel method that models PPI networks as being dynamic in nature and evolving time-varying multivariate distribution with Conditional Random Fields (CRF). This research is directed towards implementing this new combinatorial algorithm on massively parallel architectures such as Graphics Processing Units (GPUs) for efficient computations for large scale bioinformatics datasets. I compared Conditional Random Fields (CRF) and the proposed novel method using CRF combined with the Block Coordinate Descent algorithm for human protein-protein interaction data set. Both are implemented on GPU-Accelerated Computing Architecture and the proposed novel method showed the advantages in predicting protein-protein interaction sites. I also show that the proposed approach is more efficient in 6.13% than standalone CRF++ in predicting protein-protein interaction sites.

Objetives: Adequate donor-recipient matching is necessary to achieve the best renal function after transplantation. The nephron load received from the donor, which can be reflected in variables such as age, sex and body mass index, may influence the evolution of the graft. Our objetive was develop a combined score according to the donor-recipient pairing in age, sex and body mass index, and assess its influence on the evolution of the renal graft. Materials and methods: Transplant patients between 2013 and 2020 who had at least 1 year of post-transplant follow-up. A ranking of 0 to 2 points was awarded according to the match between donor and recipient for sex, age and body mass index individually, with higher rank to greater mismatch. Subsequently, a Score with value of 0 to 6 points was created with the combined ranking of the indivudual variables, analyzing its influence on the evolution of the graft by multivariate linear regression. Results: 280 patients followed for 4.3 years of median. Worse renal function was observed at the end of follow-up as the range of each variable increased separately, and the Score created, with a worsening of renal function as the combined Score increased in the multivariate analysis: Coefficient B= -2.4 ml/min, p=0.018. Conclusions: An adequate match between donor and recipient in age, sex and body mass index using this combined Score can help achieve better kidney function after transplantation.

Abstract: The aim of this study is to emphasize on the clinical uses of amniotic membrane (AM) in multiple ocular disorders. AM possesses many properties including promotion of epithelialization, anti-fibrotic, anti-apoptotic, anti-angiogenic properties. Epithelial wound healing on the eye surface is promoted due to several epidermal and keratocyte growth factors as well as by its anti-inflammatory and anti-scarring effects due to interleukin-10, interleukin-1 receptor antagonists and inhibition of transforming growth factor- beta (TGF-β) signal transduction by AM in the corneal and conjunctival fibroblasts respectively. It plays a crucial role by functioning as a substitute of basement membrane (BM) and as a temporary graft material. It is not only being used as an eye graft but also its extract can be used as an eye drop for corneal and external eye diseases. So its proper preparation, preservation and clinical application can bring a revolutionary change in the treatment of different ocular disorders.

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art network of object detection, YOLOv5, has been widely used on insulator and defect detection. However, YOLOv5 network has some limitations like poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced Ghost module into YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance in unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention module (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) 0.5 and the mAP0.5:0.95 of our model can reach 99.4% and 91.7%, the parameters and model weight are reduced to 3807372 and 8.79M, which can easily deploy to embedded devices like UAVs. And the speed of detection can reach 10.9ms/image, which can meet the real-time detection requirement.

Abstract: Ambulatory cancer centers face fluctuating patient demand and deploy specialized personnel who have variable availability. This undermines operational stability through misa-lignment of resources to patient needs, resulting in overscheduled clinics, budget deficits, and wait times exceeding provincial targets. We describe deployment of a Learning Health System framework for operational improvements within the entire ambulatory center. Known methods of value stream mapping, operations research and statistical process control were applied to achieve organizational high performance that is data-informed, agile and adaptive. We transitioned from a fixed template model by individual physician to a caseload management by disease site model that is realigned quarterly. We adapted a block schedule model for the ambulatory oncology clinic to align the regional demand for specialized services with optimized human and physical resources. We demonstrated improved utilization of clinical space, increased weekly consistency and im-proved distribution of activity across the workweek. Increased value, represented as the ratio of monthly encounters per nursing worked hours, and increased percentage of services delivered by full-time nurses were benefits realized in our cancer system. The creation of a data-informed demand capacity model enables application of predictive analytics and business intelligence tools that will further enhance clinical responsiveness..

The article suggests methods that allow creating the most complicated type of polygonal masonry found in Peru. This masonry type consists of large stone blocks weighing from several hundred kilograms to several tons fitted close to each other almost without a gap between complicated curved surfaces over a large area. The work provides a description of techniques, which apparently were used by builders who arrived from Europe. The techniques under discussion are based on the use of a reduced clay model, 3D-pantograph, topography translator and replicas. The use of the topography translator, reduced clay model and pantograph provides not only the unique appearance and high quality of masonry of large blocks, but also allows to increase the productivity of the builders significantly. As machines coping-scaling three-dimensional objects are known since the beginning of the 18th century, the stone structures under consideration should be approximately dated by this time. The remaining simpler types of polygonal masonry, when the stones are small or the fitted surfaces are almost flat, or the stones contact each other over a small area, or there are significant gaps between the stones, are quite consistent with the well-known methods of stone processing at that time or earlier, and, therefore, they do not require any additional explanations. The Fortress Sacsayhuaman is considered as an example of early star fortresses that has survived to our time. The polygonal structures in Peru, the polygonal Face Towers and polygonal bas-reliefs in Cambodia, symmetrical statues of pharaohs in Egypt are based on the same construction technologies, working methods, tools and technical contrivances. Therefore, with a high probability one can state that all these monuments were created by the same group of architects, sculptors, builders, and could not have appeared before the 17th century.

Clinical and analytical differences between adult patients monoinfected with Borrelia burgdorferi sensu lato (s.l.) and patients infected with B. burgdorferi s.l. who also had IgG antibodies against Babesia divergens have not been reported so far. Both Lyme disease caused by B. burgdorferi and babesiosis caused by B. divergens, endemic in Asturias, Northwestern Spain, are transmitted by Ixodes tick bites. Clinical, laboratory and other diagnostic tests characteristics (imaging, electro-cardiographic-ECG) of 120 residents of Asturias with confirmed B. burgdorferi s.l. infection, di-agnosed during 2015-2017, of whom 47 (39.2%) had B. divergens IgG antibodies, were retrospec-tively compared. Cardiorespiratory symptoms were reported in 9/47 (19.2%) patients with Lyme disease and B. divergens IgG antibodies compared to 4/73 (5.5%) patients with B. burgdorferi s.l. monoinfection (P=0.02). Dyspnea was recorded in 4/47 (8.5%) patients with Lyme disease and B. divergens IgG antibodies compared to 1/73 (1.4%) monoinfected (P=0.07). In addition ECG atri-oventricular (AV) block, mostly 1st degree, was detected in 5/47 (15.6%) patients with Lyme dis-ease and B. divergens IgG antibodies compared to 1/73 (2.6%) B. burgdorferi s.l. monoinfected (P=0.09). No other clinical, laboratory or other tests differences were observed between B. burgdorferi and B. divergens IgG antibodies carriers and B. burgdorferi monoinfected. We con-cluded that patients with Lyme disease and B. divergens IgG antibodies had more frequent car-diorespiratory symptoms, mainly dyspnea, compared to B. burgdorferi monoinfected patients. These symptoms were unrelated to anemia. ECG AV block, perhaps induced by summative myo-cardial damage due to either infections or B. divergens induced lung microcirculation slow-ing-down, might play a role in the cardiorespiratory dysfunction.

The final assembly of appliances and automotive industries is a rigorous process, that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies The many parts and sub-assemblies that compose the final assembly, make full traceability a challenging fit, that is almost unsurmountable. Such full traceability along the entire supply-chain is not existent today and must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has a great value. This article expounds a framework that harnesses block-chain and smart-contract technology to offer traceable and protected documentation of the assembly process. We expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities’ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system.

The article presents the results and conclusions of a series of tests of wedge-shaped blocks armoring carried out on a new experimental facility (‘Hydraulic Experimentation Facility - Luis Ruano’) in quasi-prototype conditions. The article describes the singular testing facility that was built at the inlet channel of the Acequia de Sora channel, immediately upstream of the Laverné reservoir (Zaragoza, Spain). The inflow of the testing facility has a maximum discharge of 9 m³s^-1. The aim is to perform experimental research on technologies to protect embankment dams and dikes against overflowing erosion under quasi-prototype conditions. The results of the tests showed the ability of the WSBs to withstand high unit discharges under extremely negative conditions. Furthermore, conclusions were drawn about potential threats to the sound behavior of the armor.

Reverse-order laws for generalized inverses of matrix products is a classic object of study in the theory of generalized inverses. One of the well-known reverse-order laws for a matrix product AB is (AB)^(i,...,j) = B^(i,...,j)A^(i,...,j), where (·)^i,...,j denotes an {i,...,j}-generalized inverse of matrix. Because {i,...,j}-generalized inverse of a general matrix is not necessarily unique, the relationships between both sides of the reverse-order law can be divided into four situations for consideration. In this article, we first introduce a linear mixed model y = ABβ + Aγ + ε, present two least-squares methodologies to estimate the fixed parameter vector  in the model, and describe the connections between the two least-squares estimators and the reverse-order laws for generalized inverses of the matrix product AB. We then prepare some valued matrix analysis tools, including a general theory on linear or nonlinear matrix identities, a group of expansion formulas for calculating ranks of block matrices, two groups of explicit formulas for calculating the maximum and minimum ranks of B^(i,...,j)A^(i,...,j), as well as necessary and sufficient conditions for B^(i,...,j)A^(i,...,j) to be invariant with respect to the choice of A^(i,...,j) and B^(i,...,j). We then present a unied approach to the 512 set inclusion problems {(AB)^(i,...,j) ⊇ {B^(i,...,j)A^(i,...,j)}for the eight commonly-used types of generalized inverses of A, B, and AB using the block matrix representation method (BMRM), matrix equation method (MEM), and matrix rank method (MRM), where {(·)^(i,...,j)} denotes the collection of all {i,...,j}-generalized inverse of a matrix.

Background: In regenerative dentistry the graft material influences the success. It should act as an osteoconductive scaffold, providing a mineral substrate during resorption and inducing the activity of osteoinductive cells capable of producing new bone, platelet growth factors, and cell differentiation factors inducing the differentiation of undifferentiated mesenchymal cells into. Given that dentin shares many biochemical characteristics with bone tissue, it has recently attracted considerable interest as a biomaterial for bone repair. The aim of this study is to compare two grinder types in order to determine the optimal method for producing dentinal particles using a mechanical grinder. Materials and methods: A sample of 40 natural human teeth without restorations, prostheses or root canal treatments was used and divided into two groups subjected to two different grinder speeds (high-speed and low-speed). The high-speed showed a greater dispersion (53.5+-9.89% of the tooth) due to the pulverisation (highly thin granules) of part of the tooth. The low-speed grinder does not pulverize the dentin and the percentage of tooth loss is 9.16+/-2.34%. Conclusion: The low-speed gringer allows to save a major part of the tooth and to have a maximum quantity of graft material but requires more time. Further studies must be promoted to optimise the grinding procedures.

The enzyme-coupled transient receptor potential channel subfamily M member 7, TRPM7, has been associated with immunity and immune cell signalling. Here, we review the role of this remarkable signalling protein in lymphocyte proliferation, differentiation, activation and survival. We also discuss its role in mast cell, neutrophil and macrophage function and highlight the potential of TRPM7 to regulate immune system homeostasis. Further, we shed light on how the cellular signalling cascades involving TRPM7 channel and/or kinase activity culminate in pathologies as diverse as allergic hypersensitivity, arterial thrombosis, and graft versus host disease (GVHD), stressing the need for TRPM7 specific pharmacological modulators.

Fetuses of pregnant women affected by anti-Ro/anti La antibodies are at risk to develop complete atrioventricular heart block (CAVB) and other potentially life-threatening cardiac affections. CAVB can develop in less than 24 hours. Treatment with anti-inflammatory drugs and immuno-globulins (IVIG) can restore normal rhythm if applied in the transition period. Routine weekly echocardiography as often recommended will seldomly detect emergent AVB. Surveillance of these pregnancies is controversial. To obtain an overview of the current practice in Germany we have developed a web-based survey sent by the DEGUM (German Society of Ultrasound in Med-icine) to ultrasound specialists. Home-monitoring by hand-held Doppler is a promising new approach. With the intention to evaluate practicability, we instructed pregnant women at risk to use hand-held Doppler in the vulnerable period between 18-26 weeks at our university center.

Post-training quantization is pivotal in deploying convolutional neural networks for mobile applications. Block-wise reconstruction with adaptive rounding, as employed in prior works like BrecQ and Qdrop, facilitates acceptable 4-bit quantization accuracy. However, adaptive rounding is time-intensive, and its constraint on weight optimization space curtails the potential for quantization performance. The optimality of block-wise reconstruction hinges on the quantization status of subsequent network blocks. In this investigation, we delve into the theoretical underpinnings of the limitations inherent in adaptive rounding and block-wise reconstruction. Our exploration leads to the development of a post-training quantization methodology, designated as AE-Qdrop. This algorithm operates in two distinct phases: block-wise reconstruction and global fine-tuning. The block-wise reconstruction phase introduces a progressive optimization strategy, superseding adaptive rounding, which not only augments quantization precision but also significantly improves quantization efficiency. To mitigate the risk of overfitting, we introduce a random weighted quantized activation mechanism. During the global fine-tuning phase, we account for interdependencies among quantized network blocks. The weight of each network block will be corrected with logit matching and feature matching. Extensive experiments validate that AE-Qdrop achieves high-precision and efficient quantization. For instance, in the case of 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop by achieving a 6.26% enhancement in quantization accuracy and quintupling the quantization efficiency.

In order to study the collapse failure of shaft wall of high depth inclined ore-pass under ore impact wear behavior, 25 ore-passes in a deep mine in Yunnan, China were investigated. Considering the ore-pass structural characteristics and the impact wear effect of ore on shaft wall, the shaft wall damage model of high depth inclined ore-pass is constructed based on the contact mechanics theory. Furthermore, No. 1#, 2#, 3# and 9# ore-passes of the mine are taken as examples for analysis, and the three results of theoretical analysis, numerical simulation and engineering case are used for comparison and verification. The shaft wall damage degree is predicted, and the damage mechanism between total shaft wall damage volume Qtol and ore-pass depth H, ore-pass dip angle θ, inclined angle of chute α, shaft diameter D and ore block size P is quantitatively investigated. The results show that the rationality of the proposed model is verified by numerical analysis and field engineering situations. Moreover, the deeper ore-pass depth H, the more shaft wall damage volume Qtol with the increase of collision frequency. Qtol increases exponentially with P and increases gradually with D. With the increase of θ or α, Qtol increases generally first and then decreases. Therefore, Qtol can be effectively reduced by selecting the appropriate structure parameters of ore-pass and adopting the technology of drilling straight ore-pass. This work provides technical support for long period safe operation of high depth inclined ore-pass.

Craters are the most prominent geomorphological features on the surface of celestial bodies, which is playing a crucial role in studying the formation and evolution of celestial bodies, as well as in landing and planning for surface exploration. Currently, the main automatic crater detection models and datasets focus on the detection of large and medium craters. In this paper, we created 23 small lunar crater datasets for model training based on the Chang’E-2 (CE-2) DOM, DEM, Slope, and integrated data with 7 kinds of visualization stretching methods. And then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater samples imbalance problem and introducing a CBAM attention mechanism to mitigate interference from the complex extraterrestrial environment. The results show that the accuracy (P = 87.86%, R = 66.04%, and F1 = 75.41%) of the Lunar YOLO-Crater model based on the DOM-MMS (Maximum-Minimum Stretching) dataset is the highest and better than that of YOLOX model. And the Martian YOLO-Crater, trained by the Martian dataset from the 2022 GeoAI Martian Challenge, achieves good performance with P = 88.37%, R = 69.25%, and F1 = 77.65%. It indicates that the YOLO-Crater model has a strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different type: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using continuum removal methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account change of support. Secondly, the two-sensor data were integrated with soil granulometric properties by using the multivariate geostatistical techniques of multi-collocated cokriging and factor cokriging, in order to achieve a more exhaustive and finer-scale soil characterisation. The paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalised factors at two different scales (two per each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterised by different granulometry and clay composition. The applied method is sufficiently flexible and could be applied to any number and type of sensors.

Background: The aim of the present investigation was to evaluate the clinical success of horizontal ridge augmentation in the severely atrophic maxilla (Cawood and Howell class IV) using freeze-dried custom made bone harvested from cadaver donors tibial hemiplateau and to analyze the marginal bone level gain prior dental implants placement at 9 months after bone grafting and before prosthetic rehabilitation. Methods: A 52-year-old woman received custom made bone grafts. Patient underwent CT scans 2 weeks prior and 9 months after surgery for graft volume and density analysis. Results: The clinical and radiographic bone observations showed a very low rate of resorption after bone graft and implant placement. Conclusions: The custom-made allograft material was a highly effective modality for restoring the alveolar horizontal ridge, resulting in this way to reduce the need to obtain autogenous bone from a secondary site with predictable procedure. Further studies are needed to investigate its behavior at longer time points.

Background: The ageing process starts in the center of the face, in the periocular region and around the mouth, with a combination of volume loss and tissue descent, wrinkles deepen, and loss of skin structure and quality. Recently, several studies proved the efficacy of therapies based on the autologous adipose tissue grafting that leverages the properties of stromal vascular fraction (SVF) and adipose derived mesenchymal stem cells (ADSCs) to accelerate the regenerative processes of the skin. This study aimed to verify the ability of the guided Superficial Enhanced Fluid Fat Injection (SEFFI) in the facial area to correct volume loss and skin aging and proved the very low rate of complications of this standardized procedure . Methods: we retrospectively collected data from 2,365 procedures performed in Italian centers between 2019 and 2021. Guided SEFFI was performed alone or combined with cosmetic treatments, including the use of hyaluronic acid filler, suspension threads, synthetic calcium hydroxylapatite, botulin toxin, and microneedling. Results: SEFFI was used alone in more than 60% of the patients, and in all face areas. In about one tenth of patients SEFFI was combined with botulin toxin treatment or hyaluronic acid filling. Other procedures were used more rarely. Ecchymosis in the donor or injection sites was the most frequent event, and yet observed in only 14.2% and 38.6% of patients, respectively. Conclusions: The SEFFI technique is standardized and minimally invasive, leading to very few complications. It can be a promising antiaging medical treatment that combines effectiveness, safety, and simplicity.

Investigations reporting positive effects of Extracorporeal Shock Wave Therapy (ESWT) on nerve regeneration are limited to the rat sciatic nerve model. The effects of ESWT on muscle-in-vein conduits (MVCs) have also not been investigated yet. This study aimed to evaluate the effects of ESWT after repair of the rat median nerve with either autografts (ANGs) or MVCs. In male Lewis rats, a 7-mm segment of the right median nerve was reconstructed either with an ANG or MVC. For each reconstructive technique, one group of animals received one application of ESWT while the other rats served as controls. Animals were observed for 12 weeks and nerve regeneration was assessed via computerized gait analysis, the grasping test, electrophysiological evaluations and histological quantification of axons, blood vessels and lymphatic vasculature. Here we provide for the first time a comprehensive analysis of ESWT effects on nerve regeneration in a rat model of median nerve injury. Furthermore, this study is among the first reporting the quantification of lymphatic vessels following peripheral nerve injury and reconstruction in vivo. While we found no significant direct positive effects of ESWT on peripheral nerve regeneration, results following nerve repair with MVCs were significantly inferior to those after ANG repair.

Understanding and predicting damage progression within thin plain-woven CFRP components is an essential issue for scheduling maintenance intervals and structural inspections, and typically require extensive test series. However, uncertainties always remain and result in conservative design and unnecessary downtimes due to the repeated inspections of a healthy structure. SHM can be used to reduce these uncertainties, as it allows to evaluate the condition of a mechanical structure during operation. Numerous SHM methods are developed , that achieve different levels of damage identification but are often investigated at small coupons with non-realistic structural damages and loading conditions on both, structure and sensor. The present research investigates the electrical impedance tomography (EIT) featuring an elastoresistive thin-film sensor to evaluate damage that propagates from a circular hole in a large plate under cyclic tensile-tensile multi-block loading. Applied fatigue loads were increased multiple times up to a total number of two million cycles. During the loading damage initiated at the hole and continuously propagated into the plate. Repeated EIT evaluations of the applied sensor and validating evaluations by means of digital image correlation clearly revealed the robustness of the hardware and the potential of the SHM method for damage evaluation of plain-woven CFRP components.

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area ratio of ship targets is small and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed detection and false detection. In this study, we propose an improved-YOLOv5 algorithm. The improvement strategies mainly include: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce cross-layer connection channel and lightweight GSConv structure into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) The Wise-IoU loss function is used to cal-culate the localization loss in the Output, and assign reasonable gradient gains to cope with dif-ferences in image quality. In addition, during the preprocessing stage of experimental data, a me-dian and bilateral filter method is used for noise reduction to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Im-proved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; Compared to the original YOLOv5s, the mean Average Precision (mAP) has improved by 3.2% and the Frames Per Second (FPN) has accelerated by 8.7%.

Autoimmune limbic encephalitis (LE) is a rare, but devastating complication of allogeneic hem-atopoietic stem cell transplantation (HSCT). There is currently limited evidence describing the risk factors, laboratory features, and underlying mechanisms of this neurologic adverse event. We retrospectively reviewed available clinical, imaging, and laboratory data from adult patients with hematological malignancies who underwent haploidentical HSCT with cyclophosphamide (PTCy) at Chungnam National University Hospital from June 2016 to May 2020. Patients who developed LE were compared to those who did not based on clinical assessment, serum inflam-matory biomarkers, and reconstitution of various T cell populations. Of 35 patients, four devel-oped LE. There were no differences in patient demographics, donor demographics, or treatment conditions between patients that did and did not develop LE. Overall, patients with LE had worse clinical outcomes and overall survival than those without. In addition, they tended to have higher markers of systemic inflammation in the early post-transplant period, including fever, C-reactive protein (CRP), and cytokines. Remarkably, baseline interleukin-6 levels before HSCT were found to be higher in patients who developed LE than those who did not. In addition, analysis of T cell subsets showed impaired expansion of CD25+FOXP3+ regulatory T (Treg) cells in LE compared to non-LE patients despite appropriate reconstitution of the total CD4+ T cell population. Patients that developed LE within the first 30 days of HSCT were likely to have high serum IL-6 among other inflammatory cytokines coupled with suppression of regulatory T cell differentiation. Further work is needed on the mechanisms underlying impaired Treg expansion following HSCT and potential therapies.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (<0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

A plenty of theories on the origin of genetic codes have been proposed so far, yet all ignored the energetic driving force, its relation to the biochemical system, and most importantly, the missing “matchmaker” between proteins and nucleic acids. Here, a new hypothesis is proposed, according to which ATP is at the origin of the primordial genetic code by driving the coevolution of the genetic code with the pristine biochemical system. This hypothesis aims to show how the genetic code was produced e.g. by photochemical reactions in a protocell that derived from a lipid vesicle enclosing various life’s building blocks (e.g. nucleotides and peptides). At extant cell, ATP is the only energetic product of photosynthesis, and is at the energetic heart of the biochemical systems. ATP could energetically form and elongate chains of both polynucleotides and polypeptides, thus acting a “matchmaker” between these two bio-polymers and eventually mediating precellular biochemical innovation from energy transformation to informatization. ATP was not the only one that could drive the formation of polynucleotides and polypeptides, but favored by precellular selection. The protocell innovated a photosynthesis system to produce ATP efficiently and regularly with the aids of proteins and RNA/DNA. The completion of permanently recording the genetic information by DNA marked the dawn of cellular life operated by Darwinian evolution. The ATP hypothesis assumes or supports the photochemical origin of life, shedding light on the origins of both photosynthetic and biochemical systems, which remains largely unknown thus far. Based on ATP hypothesis, virus (like the new coronavirus) could not be the earliest life on Earth, as it has neither biochemical systems nor lipid bilayer membrane that provided relatively isolated environment for the development of protobiochemical reactions, although it owns the genetic code of a cellular life. Virus could not be a bridge between life and non-life, but is an anti-life substance, as it depletes cellular material for its own replication, and then spreads by destroying the host cells. It can be imagined that if cellular life are completely wiped out by the virus, the complete destruction of life on Earth would be inevitable.

Alzheimer’s Disease (AD) has no cure. Earlier, we showed that partial inhibition of mitochondrial complex I (MCI) with small molecule CP2 induces adaptive stress response activating multiple neuroprotective mechanisms. Chronic treatment reduced inflammation, improved synaptic and mitochondrial functions, and blocked neurodegeneration in symptomatic APP/PS1 mice, a translational model of AD. Here, using serial block-face scanning electron microscopy (SBFSEM) and three-dimensional (3D) EM reconstructions combined with Western blot analysis and next-generation RNA sequencing, we demonstrate that CP2 treatment also restores mitochondrial morphology and mitochondria-endoplasmic reticulum (ER) communication in the APP/PS1 mouse brain. Using 3D EM volume reconstructions, we show that mitochondria in AD dendrites exist primarily as mitochondria-on-a-string (MOAS). Compared to other morphological phenotypes, MOAS are extensively enveloped in the ER membranes forming multiple mitochondria-ER contact sites (MERCS) known to contribute to abnormal lipid and calcium homeostasis. CP2 treatment specifically reduced MOAS formation, consistent with improved energy homeostasis in the brain, with concomitant reduction in MERCS, ER stress, and improved lipid homeostasis. These data provide novel information on the role MOAS play in AD and additional support for further development of partial MCI inhibitors as disease modifying strategy for AD.

Paving blocks are concrete pieces placed in exposed places to the weather, which are sub-jected to loads and wear. Hence, quality control in the manufacture of paving blocks is es-sential to guarantee the properties and durability of the product in construction projects. In Ecuador, the requirements are described in the Ecuadorian technical standard "NTE INEN 3040", and tensile splitting strength is a fundamental requirement to guarantee product quality. It is analyzed using quality control measurements such as dimensions, the weight of the fresh paving block in the vibro-compacted process, and the percentage of water absorption in order to know how the variables influence and manage to predict the tensile splitting strength to avoid product non-conformity in advance, having a timely and better control of the manufacturing process. The data was obtained from a company that can produce 30 000 units per day of rectangular paving blocks with 6 cm thickness. Mul-tivariate models such as multiple linear regression, regression trees, random forests and neural networks are performed to predict the tensile splitting strength variable through two groups of predictors; the first group is the thickness mm, width mm, length mm, mass of fresh paving block g and percentage of water absorption %. The second group of pre-dictor variables is the density of the fresh paving block kg/m3 and the percentage of water absorption %. It is concluded that the multiple linear regression method performs better in predicting the first group of predictor variables with a mean square error (MSE) of 0.110086, followed by the neural network without hidden layers resulting in an MSE of 0.112198. The best method for the second set of predictors was the neural network with-out hidden layers with a mean square error ( MSE ) of 0.112402, closely followed by the multiple linear regression model with an MSE of 0.115044.

Perhaps the greatest challenge in the graft copolymerization of acrylic acid onto starch initiated by Fenton’s is the selectivity of the grafting reaction. Low selectivity means that too much of unwanted homopolymer polyacrylic acid is produced. In this paper, it has been analyzed why with this reaction system, involving a water soluble monomer and indirect initiation, the graft selectivity is such a challenge. Several other grafting reactions, especially with monomers of low water solubility or when direct initiation is applied, show a better grafting performance. A scheme is presented based on reaction engineering principles, that supports an explanation of the observed differences. A more selective activation of starch is a factor, but perhaps even more important is a low monomer to starch ratio at the starting sites of graft reactions. Based on these insights, several methods to improve the graft selectivity are evaluated, with the help of literature data but mostly by experimental explorations in our laboratory. Most promising appears to be a dedicated dosage method, to add monomer in portions over time, tested in a batch reactor with gelatinized cassava starch. This way, both grafting percentage and graft selectivity could be improved. Such could not be realized with the more conventional dosage, which is to add all monomer before the start of the reaction. This can be considered a principal breaktrough. Still, more research and development would be needed to refine the method and to implement the idea also in a continuous reactor at larger scale.

SARS-CoV-2 Omicron variant spike RBD epitopes in complex with spike (S) -protein D614G mutations now predominate globally and increase infectivity by assembling more functional S proteins into the virion. On average, homeopathic medicine contains less than one molecule per dose on average. Single variable SphericalHarmonicY, LaguerreL, WhittakerM, SphericalBesselJ, LegendreP, LegendreQ, LaguerreL, ChebyshevT, and Hypergeometric1F1 Functions pFqarise in connection with the power series solution of the Schrodinger equation or in the summation of perturbation expansions in quantum mechanics. A quantum interpretation of the homeopathic method is presented here through a novel algebraic topology approach to supersymmetry and symmetry breaking in quantum fields and quantum gravity theory with the aim of developing a wide range of physical-based drug design applications in a Small Molecule Quantized Water Memory Network suggesting that the Hidden Black Hole Paradox of Information Loss might be solved under suitable conditions.