Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nonlinear Gauss-Helmert Model and Its Robust Solution for Seafloor Control Point Positioning

Abstract Using GNSS-Acoustic (GNSS-A) technology to establish the seafloor geodetic datum is both feasible and flexible and thus has become an important way to obtain the absolute positions of seafloor control points. However, numerous errors are inevitable in marine surveying, including systematic errors and gross errors caused by GNSS dynamic positioning, inaccurate sound velocity profile measurements, and ocean ambient noise, and their interference will be directly reflected in the positioning results. To accurately calculate the seafloor control point coordinates, this paper first notes that the general error propagation law (EPL) method is defective in dealing with various error factors in GNSS-A positioning. A more rigorous method incorporates the time-varying term of the sound velocity ranging error into the coefficient matrix of the underwater observation equation, and the transducer position error should be considered. Therefore, a Gauss-Helmert (GH) model is used for seafloor control point positioning. Then, considering the dual nonlinearity of the model, a Lagrange objective function is constructed to derive its solution algorithm. On this basis, considering the gross errors polluting of the observations, the robust estimation principle is introduced, and the robust solution steps are given. Finally, simulation experiments and a testing experiment in the sea area near Jiaozhou Bay are used to verify the performance of the new method. The results show that the functional relationship and stochastic model of the nonlinear GH model for seafloor point positioning are reasonably described. Under ideal conditions with no gross errors and either different water depths or different transducer position errors, the accuracy and stability of the new method are both higher than those of the EPL method. When the observations are polluted by gross errors, the robust algorithm of the new method can accurately identify the abnormal information. By improving the robustness of the observation and structure spaces, the positioning precision of the 3D point deviation results can be optimized, and the solution performance of the new method is superior to that of the general method.
A Novel Immune-Related Prognostic Signature Predicting Survival in Patients with Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) carries the lowest survival rate of all major organ cancers, which is of dismal prognosis and high mortality rate. Thus, the present study attempted to identify a few novel prognostic biomarkers and establish an immune-related prognostic signature which could predict the prognosis of PAAD. Four prognostic immune-related genes (IRGs) including S100A6, S100A10, S100A16, and SDC1 were screened by differentially expressed gene (DEG) identification and weighted gene coexpression network analysis (WGCNA). Subsequent analysis proved the high expression of these IRGs in PAAD tissues, suggested by TCGA-PAAD data, merged microarray-acquired dataset (MMD), GEPIA, and Oncomine webtool. By using MMD and TCGA-PAAD data, S100A6 (MMD: AUC = 0.897; TCGA: AUC = 0.843), S100A10 (MMD: AUC = 0.880; TCGA: AUC = 0.780), S100A16 (MMD: AUC = 0.878; TCGA: AUC = 0.838), and SDC1 (MMD: AUC = 0.885; TCGA: AUC = 0.812) exhibited excellent diagnostic efficiency for PAAD. By conducting connectivity map (CMap) analysis, we concluded that three molecule drugs (sulpiride, famotidine, and nalidixic acid) might have worked in the treatment of PAAD. Then, an immune-related prognostic index was constructed, which was validated as an independent prognostic factor for PAAD patients (P=0.004). We further constructed a nomogram by using this immune-related signature and age, the prognostic value of which was validated by using concordance index (C-index = 0.780) and area under curve (AUC = 0.909). Moreover, the immune-related prognostic signature was associated with response to anti-PD-1/L1 immunotherapy. To sum up, four IRGs were screened out and verified to be novel immune-related prognostic biomarkers in PAAD. Besides, sulpiride, famotidine, and nalidixic acid might be potential choices in the treatment of PAAD. An immune-related signature was established to show great potential for prognosis prediction for PAAD, independently, which might guide more effective immunotherapy strategies. A nomogram is further established by using this immune-related prognostic index, which might contribute to more effective prognosis prediction in PAAD patients.
Broadband light trapping in a-Si:H based plasmonic solar cells using Au core-Al2O3 shell composite nanospheres using FDTD method

ABSTRACT In this work, two different geometries of Au core-Al2O3 shell composite nanospheres (CNS) in ITO/a-Si:H based solar cell structure have been considered. A finite-difference time domain (FDTD) analysis of optical and electrical properties for both geometries has been conducted using open-source software MEEP. When embedded in the active layer of the cell, Au core-Al2O3 shell CNS exhibit relatively high scattering efficiency over a broad spectrum. Maximum normalized scattering efficiency for ‘CNS on top’ geometry has been found 18.16 while that for ‘embedded CNS’ geometry it boosts up to 19.62. The localized surface plasmon resonance wavelength achieves a value of 738.9nm for ‘CNS on top’ and 835.8nm for ‘embedded CNS’ configurations. Furthermore, reflectance of the incident radiation reduces significantly with the shell thickness leading to effective light trapping. This study provides an important design to enhance the efficiency of silicon based plasmonic solar cells.
Taxonomy and systematics of the enigmatic emerald moth Xenochlorodes graminaria (Kollar, 1850) (Lepidoptera: Geometridae), and its assignment to a new genus

Abstract A new genus, Sabzia Wanke & Rajaei, gen. n. , is described based on the Iranian species Phorodesma graminaria Kollar, 1850, which in recent taxonomy was combined with the genus Xenochlorodes Warren, 1897. Moreover, Xenochlorodes albicostaria Brandt, 1938, syn. n. is synonymized with Sabzia graminaria (Kollar, 1850), comb. n. The new genus is established based on external and internal characters of the adult specimens. Morphological traits including wing pattern, wing venation and male and female genitalia are described and illustrated.
Role of sweet potato GST genes in abiotic stress tolerance revealed by genomic and transcriptomic analyses

Glutathione S-transferases (GSTs) are proteins synthesized in plants and responsible for their tolerance to environmental stresses. However, little information is available on the GST gene family of sweet potato, a globally important crop. The genetic evolution of GSTs in sweet potato remains unclear. The present study investigated the GST gene family in sweet potato by transcriptomic and comparative genomic analyses. A total of 51 GSTs were identified. Gene expression analysis showed differential expression patterns of the GSTs between two investigated varieties. Some GST expression levels were either upor downregulated under oxidative, salinity and drought stresses. The results of the investigation provided new insights on the GST gene family in sweet potato, which may further the understanding of the roles of these genes in regulating abiotic stresses.
Investigating the Critical Success Factors of Excellence Model Implementation in the Public Sector “Egypt Government Excellence Award”

The purpose of this paper is to outline the structure of the Egypt Government Excellence Award, as well as to investigate the role of adopting an excellence framework in developing public sector work nature and improving public services, as well as to attempt to identify and propose solutions to the practical problems and challenges posed by excellence awards.The paper will highlight and investigate critical success factors (CSFs) that influence the success of the excellence model's implementation and adoption in the Egyptian public sector. A qualitative method will be used to build a model for CSF.The paper has summarized potential CSF that has been analyzed in previous literature that developed different models with different methodologies according to industry context. The majority of literature has discussed and analyzed CSF regarding TQM principles, with very little literature having discovered CSF of Excellence Models.The paper uses a qualitative narrative approach for an exploratory purpose. The data has been collected and analyzed using human interactive data sources by using unstructured interviews with experts in excellence from Egypt and other countries that have a similar context to EGEA.The results have shown that the main top five CSF, which are considered the main factors that will help any public organization in Egypt to successfully implement excellence models, are leadership, human assets, culture, excellence model, and performance management system.
Automated fractured femur segmentation using CNN

Fracture fixation surgeries require a careful and well thought out surgical plan, mainly due to the wide range of possibilities in the fracture types and available choices in fixation constructs. There is considerable interest in virtual 3D planning tools ranging from 3D visualization, interactive fracture reduction and bio-mechanical analysis of fracture fixation construct stability to arrive at optimal plan. One of the key steps prior to reconstructing 3D fractures is accurate fracture segmentation which can be tedious and time consuming even with semi-automated tools. In this paper, we report preliminary results from our attempt to fully automate the segmentation of fractured bone using deep learning. We performed experiments using the widely used 3D segmentation model called 3D U-Net on a dataset of 14 CT volumes. The dataset is randomly divided into train, validation and test splits comprising 7, 3 and 4 volumes respectively. Even with a small training set of femur fractures, we were able to achieve a mean dice score of 0.861 with a mean sensitivity of 0.899. The model was able to capture the challenging fracture regions and could cleanly separate the femur head and socket. Apart from this, we also studied the impact of different loss functions on the network’s performance. The results indicate that deep learning based segmentation methodologies have good potential in automating the challenging task of fractured femur segmentation. Further improvement is expected with a larger collection of such fractured samples
Association between Androgenetic Alopecia and Psychosocial Disease Burden: A Cross-Sectional Survey among Polish Men

A decline in quality of life in men with androgenetic alopecia (AGA) is frequently reported, so we aimed to evaluate the psychosocial burden related to AGA in Polish male patients with AGA. We enrolled 75 adult patients with AGA. The study was conducted in an outpatient dermatology clinic in Poland. Each participant answered 23 dedicated questions about demographic data, history of the disease, and a psychosocial condition. Overall, 38.7% of patients did not notice any impact of AGA on their contact with other people or activity in their free time; 50.7% of patients observed little or no effect on their relationship with their partner; 60% of patients often or sometimes felt embarrassed by their baldness (mainly those aged 18–25 years; p=0.002); 66.7% of patients reported a rather large negative impact on their self-esteem; and 81.3% of patients sometimes experienced stress in everyday life. We conclude that AGA impairs the emotional condition and social functioning of men of all ages, but particularly of younger men. Broader interventions should be planned to allow access to a psychological and psychosocial support, starting treatment at an early stage of the disease, and involving family physicians in the treatment of AGA.
Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.
Weld defects detection for X-ray linear array real-time imaging

A method of the noise reduction,region segmentation and welding defects extraction was proposed accerding to weld image of X-ray linear array detector real-time scanning.The adaptive median filtering was applied to reduce noise,and the weld region was intersected by the variance ratio of interclass and intraclass segmentation with mathematical morphology method,and then the welding defects were extracted by high frequency enhancement.Simulation results show that the adaptive median filter can effectively remove noise while the weld and defects edge detail was reserved.By variance ratio of interclass and intraclass together with mathematical morphology method,the weld metal region can be distinguished accurately from the base metal;the grey changes in weld center can be extruded with the high frequency enhancement filtering,consequently the defects can be detected.
Multi-objective optimal design of hybrid active power filter

In dealing with the parameter design and the investment optimization of hybrid active power filter, we com- prehensively consider the price, the reactive power compensation and the filter effect in a new mixed algorithm of genetic theory and particle swarm optimization. On this basis, the multi-objective mathematical model of capacity assignment is created and the penalty function theory is applied; and then, the multi-objective design is converted into a single-objective design, making the capacity assignment simple and flexible. Secondly, simulation analysis is carried on the power systems computer aided design/electromagnetic transients including DC(PSCAD/EMTDC); the results demonstrate that the cost performance of the hybrid active power filter is improved by using the proposed method. Finally, experimental results and comparison analysis are presented to confirm the above-mentioned method.
Framework on Outlier Sequential patterns for Outbreak Detection

There are many outbreak detection that available with various techniques being introduced ranging from statistic to data mining including machine learning. With the direction of spatial-temporal data the research under public health surveillance especially outbreak detection or anomalies detection are promising research. In this paper we applied data mining techniques in detecting outbreak in public health surveillance. The phase involves learning, detecting and repository. An extracted sequential pattern method, outlier set was identified using outlier detection algorithm methods. 1.0 Introduction The main objective of a health surveillance system is to reduce the impact of an outbreak by enabling officials to detect it quickly and implement timely, appropriate interventions. Identifying an outbreak days to weeks earlier than traditional surveillance will result in a reduction in morbidity, mortality, and its economic consequences. This is likely obtainable by improvements in data collection and associate analysis. Ideas express by Stoto in 'Syndromic surveillance: Is it Worth the Effort? ' as to be useful for early detection of natural disease outbreaks (80). With a spectrum of technologies from statistics, computer science and data mining that can help, there are numbers of analysis methods and informal reasoning for the early detection such that being uses in the Biosurveillance system. (56) The field of surveillance, generally on biomedical informatics has drawn increasing popularity and attention, and has been growing rapidly over the past two decades. In particular, knowledge management, data mining, and text mining techniques have been adopted in various successful biomedical applications in recent years (60). Paradigms for data mining or machine learning and data analysis including: probabilistic and statistical models, symbolic learning and rule induction, neural networks, evolution-based algorithms, and analytic learning and fuzzy logic to be merging into health informatics focusing on health surveillance. This paper, which is divided into three main sections, presents sequence outlier detection based on data mining theory and surveillance perspective applied into public health domain. The first section discusses the basic concept and theory of data mining sequential pattern and outlier detection. The second section presents the framework of clustering-based outlier detection according to the outlier set produced by sequence pattern generated. The third section discusses the potential of propose techniques for outbreak detection.
Optimized Dispatch Schedule for Autonomous Grids in Isolated Islands

The rapid development of wind power provides a new solution for power supply of isolated island. However, due to the intermittent and stochastic nature of renewable energy resources (RES), the energy storage unit (ESU) is required for power grid reliability. This paper proposed an automatic programming method for autonomous grid in isolated islands. The sea water pumped storage plant serves as ESU to counter-balance the fluctuations of RESs. The penetration level of RES and the profit of the Island system operator (ISO) increase significantly. With the geographical and historical data of an island in China, the effectiveness of the proposed method is testified.
Cat Scratch Disease-associated Encephalitis Followed by Parkinsonism

Cat scratch disease (CSD) is a zoonotic infection caused by Bartonella henselae typically resulting in self-limited regional lymphadenopathy. Encephalitis is a complication with a supposedly benign prognosis, but we encountered an exceptional case. A 19-year-old Japanese woman presented with status epilepticus. She was diagnosed with CSD-associated encephalitis based on her history of contact with a kitten and a high titre of serum IgG to B. henselae. Multimodal treatment ameliorated her encephalitis, but neurological sequelae including spastic paraparesis, persisted. After several months, she developed age-disproportionate parkinsonism inconsistent with a neurodegenerative disease. In conclusion, CSD-associated encephalitis can result in severe neurological sequelae and post-encephalitic parkinsonism.
A New Look at Crocodylopodus meijidei: Implications for Crocodylomorph Locomotion

ABSTRACT A review of the type material of the crocodylomorph ichnotaxon Crocodylopodus meijidei Fuentes Vidarte and Meijide Calvo, 2001 from the Berriasian of Spain is carried out. The review allows a better characterization of this type ichnotaxon and provides interesting new data on the candidate trackmakers and especially on their locomotion. Three different size classes possibly related to different ontogenetic states or sexual dimorphism of the same small- to medium-sized crocodylomorph trackmaker are distinguished. Morphological differences within the sample such as digital impression lengths might be a consequence of differences in allometric growth, assuming similarities with extant crocodylians. Other differences are a consequence of variation in the morphological quality and mode of preservation across the sample. Some trackway features (intermediate-gauge trackways with high pace angulation, absence of tail, belly or drag marks) indicate the trackmakers, presumed neosuchian crocodylomorphs, were walking in a ‘high-walk’ mode with a semi-erect posture at a moderate speed. The trackmaker may have walked with more erect limb posture and with the center of mass located more anteriorly than occurs in extant species, albeit not as erect as quadrupedal animals such as mammals or other extinct archosaurs including trackmakers of other crocodylomorph ichnotaxa (e.g., Batrachopus).
DWANet: focus on foreground features for more accurate location

Object detection can locate objects in an image using bounding boxes, which can facilitate classification and image understanding, resulting in a wide range of applications. Both knowing how to mine useful features from images and detect objects of different scales have become the focus for object-detection research. In this paper, considering the importance of foreground features in the process of object detection, a foreground feature extraction module, based on deformable convolution, is proposed, and the attention mechanism is integrated to suppress the interference from the background. To learn effective features, considering that different layers in a convolutional neural network have different contributions, we propose methods to learn the weights for feature fusion. Experiments on the VOC datasets and COCO datasets show that the proposed algorithm can effectively improve the object detection accuracy, which is 12.1% higher than two-stage detection algorithms, such as Faster R-CNN, 1.5% higher than the single-stage object detection algorithm, RefineDet, and 2.3% higher than the Hierarchical Shot Detector (HSD).
New beam position detectors for NA61/SHINE experiment

NA61/SHINE is a multi-purpose fixed-target experiment located at the Super Proton Synchrotron at CERN. The main goals of the experiment include studies for physics of strong interactions, neutrino physics, and cosmic-rays physics. After the upgrade of the detector system, the experiment will collect data up to 1 kHz event rate. The development of new detectors, used to measure the positions of incoming beam particles in the transverse plane, is a crucial part of the upgrade. Two new kinds of beam position detectors are prepared and tested. One of them is the scintillating fiber detector with a multi-anode photomultiplier readout. It is built of two perpendicularly arranged ribbons, each consisting of two shifted layers of green-emitting scintillating fibers with a diameter of 250 μm. The second type of detector is based on single-sided silicon strip detector (Hamamatsu S13804). The paper gives an overview of both detectors’ design and readout concepts.