Miracle-dz/UniFAR-FTUs huggingface dataset:
The GEOFON Program

Following the long tradition in broadband seismology in Germany, the GedForschungsZentrum (GFZ) at Potsdam, an institution for interdisciplinary research in geosciences, founded in 1992 by the Federal Ministry of Research and Technology, has started a program for the establishment of a network of globally distributed broadband seismic stations. The program, called GEOFON (GEOFOrschungsNetz), is dedicated to Emst von Rebeur-Paschwitz who recorded the first teleseismic seismogram, 1889 in Potsdam and proposed a global seismograph network and an earthquake reporting system. Our program will, after its completion, consist of three parts: a permanent network of about 30 stations, a portable broadband network and a comprehensive data archive. It is planned for two three-year periods (1993-1995 and 1996-1998). The funding for the first period has already been provided almost completely. An Advisory Board with members from German Universities and the GFZ guides the operation of GEOFON. The main task of the program is to serve the seismological community with high quality broadband data for all kinds, of scientific tasks. The research projects at the GFZ itself, to be carried out with GEOFON and other broadband data, are presently dealing mainly with lithospheric and upper and lower mantle 3D structure.
First Report and Complete Genome Characterization of Cherry Virus A and Little Cherry Virus 1 from Russia

Virus diseases affect the yield and fruit quality and shorten the productive life of stone fruits (Prunus spp. in the family Rosaceae). Of over fifty known viruses infecting these crops, cherry virus A (CVA) is among the most common, and little cherry virus 1 (LChV1) is one of the most economically important. Using high-throughput sequencing, full-length genomes of CVA and LChV1 isolates, found on interspecies hybrids in the Prunus collection of the Nikita Botanical Gardens, Russia, were sequenced, assembled, and characterized. CVA was found in the P. cerasifera × P. armeniaca hybrid and in phylogenetic analysis clustered with non-cherry virus isolates. The LChV1 isolate Stepnoe was detected in ((P. cerasifera Ehrh. × P. armeniaca L.) × P. brigantiaca Vill. ) trihybrid suggesting that both P. cerasifera and P. brigantiaca potentially can be the LChV1 hosts. The isolate Stepnoe was most closely related to the Greece isolate G15_3 from sweet cherry, sharing 77.3% identity at the nucleotide level. Possibly, the highly divergent Russian isolate represents one more phylogroup of this virus. This is the first report of CVA and LChV1 from Russia, expanding the information on their geographical distribution and genetic diversity.
Bioinspiration and Biomimetic Art in Robotic Grippers

The autonomous manipulation of objects by robotic grippers has made significant strides in enhancing both human daily life and various industries. Within a brief span, a multitude of research endeavours and gripper designs have emerged, drawing inspiration primarily from biological mechanisms. It is within this context that our study takes centre stage, with the aim of conducting a meticulous review of bioinspired grippers. This exploration involved a nuanced classification framework encompassing a range of parameters, including operating principles, material compositions, actuation methods, design intricacies, fabrication techniques, and the multifaceted applications into which these grippers seamlessly integrate. Our comprehensive investigation unveiled gripper designs that brim with a depth of intricacy, rendering them indispensable across a spectrum of real-world scenarios. These bioinspired grippers with a predominant emphasis on animal-inspired solutions have become pivotal tools that not only mirror nature’s genius but also significantly enrich various domains through their versatility.
Development of a predictive nomogram for acute respiratory distress syndrome in patients with acute pancreatitis complicated with acute kidney injury

Abstract Background Acute respiratory distress syndrome (ARDS) is a common complication in patients with acute pancreatitis (AP), especially when patients complicated with acute kidney injury (AKI), resulting in increased duration of hospitalization and mortality. It is of potential clinical significance to develop a predictive model to identify the the high-risk patients. Method AP patients complicated with AKI from January 2019 to March 2022 were enrolled in this study and randomly divided into training cohort and validation cohort at a ratio of 2:1. The Least absolute shrinkage and selection operator(LASSO) regression and machine learning algorithms were applied to select features. A nomogram was developed based on the multivariate logistic regression. The performance of the nomogram was evaluated by AUC, calibration curves, and decision curve analysis. Results A total of 292 patients were enrolled in the study, with 206 in the training cohort and 86 in the validation cohort. Multivariate logistic analysis showed that IAP (Odds Ratio (OR)=4.60, 95%CI:1.23-18.24, p = 0.02), shock (OR = 12.99, 95%CI:3.47-64.04, p < 0.001), CRP(OR= 26.19, 95%CI:9.37-85.57, p < 0.001), LDH (OR = 13.13, 95%CI:4.76-40.42, p < 0.001) were independent predictors of ARDS. The nomogram was developed based on IAP, shock, CRP and LDH. The nomogram showed good discriminative ability with an AUC value of 0.954 and 0.995 in the training and validation cohort, respectively. The calibration curve indicating good concordance between the predicted and observed values. The DCA showed favorable net clinical benefit. Conclusion This study developed a simple model for predicting ARDS in AP patients complicated with AKI. The nomogram can help clinicians identify high-risk patients and optimize therapeutic strategies.
Recent progress in carbon dots for anti-pathogen applications in oral cavity

Background Oral microbial infections are one of the most common diseases. Their progress not only results in the irreversible destruction of teeth and other oral tissues but also closely links to oral cancers and systemic diseases. However, traditional treatment against oral infections by antibiotics is not effective enough due to microbial resistance and drug blocking by oral biofilms, along with the passive dilution of the drug on the infection site in the oral environment. Aim of review Besides the traditional antibiotic treatment, carbon dots (CDs) recently became an emerging antimicrobial and microbial imaging agent because of their excellent (bio)physicochemical performance. Their application in treating oral infections has received widespread attention, as witnessed by increasing publication in this field. However, to date, there is no comprehensive review available yet to analyze their effectiveness and mechanism. Herein, as a step toward addressing the present gap, this review aims to discuss the recent advances in CDs against diverse oral pathogens and thus propose novel strategies in the treatment of oral microbial infections. Key scientific concepts of review In this manuscript, the recent progress of CDs against oral pathogens is summarized for the first time. We highlighted the antimicrobial abilities of CDs in terms of oral planktonic bacteria, intracellular bacteria, oral pathogenic biofilms, and fungi. Next, we introduced their microbial imaging and detection capabilities and proposed the prospects of CDs in early diagnosis of oral infection and pathogen microbiological examination. Lastly, we discussed the perspectives on clinical transformation and the current limitations of CDs in the treatment of oral microbial infections.
Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain

ABSTRACT Land degradation, soil erosion and illegal occupation in mountainous terrain of southern China have led to an ever-decreasing stock of cultivated land. Small unmanned aerial vehicles (UAVs) are used to collect images with very fine spatial and temporal resolutions. However, acquired image pairs of the same scene often contain scale changes, noises and rotated changes at different temporal scales. To address these problems, we propose a small UAV-based multi-temporal change detection for cultivated land cover in mountainous terrain which contains the following contributions. First, the multi-scale feature description includes convolutional neural network (CNN)-based feature descriptor (CFD) and neighbouring structure descriptor (NSD), where CFD is generated using layers formed via a pretrained Visual Geometry Group (VGG)-16 architecture. Second, a gradually increasing selection of inliers is defined for improving the robustness of feature point registration. Finally, intuitionistic fuzzy C-Means (IFCM) classifier is adopted to generate a similarity matrix between image pair of geometric correction process. The performance of proposed method is validated on multi-temporal image pairs taken by the small UAV. Experimental results show that the proposed method can detect cultivated land cover change at small size and scattered distribution landscapes, obtain satisfactory change detection results.
An automatic classi�cation grading of spinach seedlings water stress based on N-MobileNetXt

Abstract This study proposed an automatic classiﬁcation method for water stress level of spinach seedlings based on N-MobileNetXt network, solving ineﬃcient water stress classiﬁcation of spinach seedlings under complex background. Firstly, this study reconstructed the Sandglass Block in MobileNetXt model to eﬀectively increase the model accuracy by adopting up-dimension and down-dimension, and introduced NAM and two-dimensional adaptive average pooling; secondly, this study introduced the group convolution module and a two-dimensional adaptive average pool, which can signiﬁcantly compress the model parameters and enhance the model robustness separately; ﬁnally, this study innovatively proposed the NCAM to enhance the image features obviously. The experimental results showed that the classiﬁcation accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%, and the number of parameters was decreased by 66% compared with the original MobileNetXt model. While, the model size is only 3.89 MB. Comparing the N-MobileNetXt model with the deep network models proposed in the past ﬁve years, the N-MobileNetXt model was superior to other network models in terms of parameters and accuracy of identiﬁcation. The N-MobileNetXt exhibited a considerable advantage
Risk of cesarean delivery in ongoing pregnancies of nulliparous women after 37 weeks of gestation: a prospective cohort study

Objective: To study the risk of CD for each gestational week among ongoing pregnancies of nulliparous women at term. Design: A retrospective, population-based cohort study from January 1, 2016, through December 31, 2017 Setting: a French perinatal network of the Yvelines district, France Population: 11 308 nulliparous women with a singleton fetus in a cephalic presentation and delivered at term (≥37-week +0 day) Methods: for each week of gestation at term, we defined ongoing pregnancies as all pregnancies undelivered at the start of each week. Regression models adjusted by maternal characteristics and hospital status were used to compare the CD risk between ongoing pregnancies and the pregnancies delivered the preceding week. The same methods were applied to subgroups defined by the mode of labor onset. Main outcome measure: The caesarean delivery rate (CD) Results: Ongoing pregnancies > 40 weeks+0 days were associated with a higher risk of CD compared with pregnancies delivered the previous week: 24.3% in ongoing pregnancies ≥ 40 weeks +0 days versus 19.9% in deliveries between 39 weeks +0 days and 39 weeks+6 days (Odd ratio adjusted of 1.28, 95%CI [1.15-1.44]; 30.4% in ongoing pregnancies ≥ 41 weeks +0 days versus 19.6% in deliveries between 40 weeks +0 days and 40 weeks+6 days (OR 1.73, 95%CI [1.51-1.96]). This was also shown for all modes of labor onset and in every maternity unit. Conclusions: CD rates increased starting at 40 weeks +0 days in ongoing pregnancies, regardless of the mode of labor onset.
Efektivitas Pemanfaatan Alat Permainan Edukatif (Ape) Bahan Lokal untuk Meningkatkan Kemampuan Bahasa pada Anak Usia 5-6 Tahun di Taman Kanak-kanak Melati Dharma Wanita Air Tiris, Kecamatan Kampar

Pendidikan Anak Usia Dini merupakan upaya menstimulasi perkembangan anak secara efektifdan komprehensif. Salah satu aspek perkembangan anak usia dini yang perlu mendapatperhatian di Taman Kanak-Kanak adalah aspek perkembangan bahasa anak sebagai alatkomunikasi antar manusia di sekitar anak. Berdasarkan hasil survey awal yang peneliti lakukandi TK Melati Dharma Wanita Air Tiris terlihat perkembangan aspek bahasa anak bervariasi danpaling banyak berada dalam kategori belum berkembangan (BB) dan mulai berkembang (MB). Peneliti tertarik untuk mengetahui lebih dalam tentang perkembangan bahasa anak dan upayaefektif untuk mengembangkannya ke tingkat perkembangan sesuai harapan (BSH) danberkembang sangat baik (BSB) dengan memberikan perlakuan pemanfaatan APE dari bahanlocal sekitar anak. Peneliti menggunakan metode eksperimen dalam bentuk rancangan “onegroup pretes posttest design”. Hasil penelitian terbukti bahwa (1) Sebagian besar kemampuanbahasa pada anak usia 5-6 tahun sebelum diberikan perlakuan (treatment) pemanfaatan APEbahan local Di TK Melati Dharma Wanita Air Tiris, Kecamatan Kampar dalam kategori sedang(MB) dan rendah (BB); (2) Sesudah diberikan perlakuan (treatment) pemanfaat APE bahan localDi TK Melati Dharma Wanita Air Tiris, Kecamatan Kampar ternyata seluruh indicator yang diukur(posttest) berada pada kategori BSH dan BSB (baik dan sangat baik). Dan (3) Uji t menunjukkanefefektivitas pemanfaatan alat permainan edukatif (APE) bahan lokal untuk meningkatkankemampuan bahasa pada anak usia 5-6 tahun di Taman Kanak-Kanak Melati Dharma WanitaAir Tiris, Kecamatan Kampar dengan besar peningkatan 36% dari pretest ke posttest.Implikasinya, pemanfaatan APE bahan lokal cukup efektif (sedang) untuk meningkatkankemampuan bahasa anak usia 5-6 tahun di PAUD.
Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics

ABSTRACT The current concerns about reproducibility have focused attention on proper use of statistics across the sciences. This gives statisticians an extraordinary opportunity to change what are widely regarded as statistical practices detrimental to the cause of good science. However, how that should be done is enormously complex, made more difficult by the balkanization of research methods and statistical traditions across scientific subdisciplines. Working within those sciences while also allying with science reform movements—operating simultaneously on the micro and macro levels—are the key to making lasting change in applied science.
Characterisation of

sc-GISH. Through a combination of molecular and cytological analysis of all the 25 lines containing Th. bessarabicum recombinants and chromosome aberrations we were able to physically map 1150 SNP markers onto seven Th. bessarabicum J chromosomes which were divided into 36 segmental blocks. Comparative analysis of the physical map of Th. bessarabicum and the wheat genome showed that synteny between the two species is highly conserved at the macrolevel and confirmed that Th. bessarabicum contains the 4/5 translocation also present in the A genome of wheat. These wheat–Th. bessarabicum recombinant lines and SNP markers provide a useful genetic resource for wheat improvement with the latter having a wider impact as a tool for detection of introgressions from other Thinopyrum species containing the J or a closely-related genome such as Thinopyrum intermedium (JrJrJvsJvsStSt) and Thinopyrum elongatum (EeEe), respectively.
Selective Trials, Information Production and Technology Diffusion

This paper explores how to run randomized experiments when outcomes depend significantly on unobserved effort decisions taken by agents. Our first set of results show that making selection explicit rather than implicit can generate greater information. This information comes at the cost of oversampling agents with high valuation for the treatment and undersampling agents with low valuation. These sampling costs disappear if the sample size is large, or agents are very responsive to incentives. Our second set of results study the effect of information on future adoption. Because effort responds to beliefs, which in turn respond to information, informative experiments can potentially encourage the adoption of useful technologies. We show that while information can increase the expected take-up of a high-return technology under fairly broad conditions, this is not always the case. Whether more information increases usage depends on the quality of the information, on the shape of the mapping between agents’ beliefs and usage, and on how agents interpret data.
Bipartite Tracking Consensus of Second-Order Sampled-Data Multi-Agent Systems under Switching Topology

A bipartite tracking consensus problem is investigated in this paper for sampled-data second-order multi-agent systems under switching topology, where the speed of each agent is not required to be synchronized to zero value. First, the bipartite tracking consensus problem is transformed into a convergence problem of the product of infinite time-varying matrices. Then by utilizing the tools from graph theory and matrix analysis, a sufficient condition on the bipartite tracking consensus is derived. It is shown that the bipartite tracking consensus can be achieved under the proposed protocol through appropriately choosing control parameters. Finally, several simulation examples are illustrated for the effectiveness of the theoretical result.
Structural Design, Control and Experimental Validation of a Novel Hexacopter with Synchronized-Tilting Rotors

A novel hexacopter with tiltable rotors is presented to tackle challenges of fully-actuated vehicles such as low ef-ficiency and excessive actuators. The tilting motion of all rotors is coupled together by bevel gears, which requires only one additional servo motor to synchronize the tilting of all rotors and change the rotor configurations of the vehicle. When these rotors are coplanar, the vehicle becomes a conventional hexacopter with high flight efficiency. Meanwhile, the non-coplanar rotor configurations that make the vehicle a fully-actuated system, allow the vehicle to decouple its translational and rotational dynamics. Its fully-actuated property can be changed by adjusting the servo motor rotation angle. Subsequently, based on the reachable set, we propose a 6-D trajectoryrR3 and SO(3)) tracking control algorithm applicable to the vehicle with different rotor configurations, which prioritizes position tacking over attitude tracking, takes a reference 6-D trajectory as an input and adjusts the desired attitude of the vehicle in real time. The algorithm is suitable for seamless switching between under-actuated and fully-actuated configurations. Finally, A series of experiments are designed to verify the feasibility of the vehicle design and the practicability of the introduced control algorithm.
University of Groningen THE ROLE OF ASYMPTOMATIC INFECTIONS IN THE COVID-19 EPIDEMIC VIA COMPLEX NETWORKS AND STABILITY ANALYSIS Stella,

Italy was the first country to be affected by the COVID-19 epidemic in Europe. In the past months, predictive mathematical models have been used to understand the proportion of this epidemic and identify effective policies to control it, but few have considered the impact of asymptomatic or paucisymptomatic infections in a structured setting. A critical problem that hinders the accuracy of these models is indeed given by the presence of a large number of asymptomatic individuals in the population. This number is estimated to be large, sometimes between 3 and 10 times the diagnosed patients. We focus on this aspect through the formulation of a model that captures two types of interactions---one with asymptomatic individuals and another with symptomatic infected. We also extend the original model to capture the interactions in the population via complex networks, and, in particular, the Watts--Strogatz model, which is the most suitable for social networks. The contributions of this paper include (i) the formulation of an epidemic model, which we call SAIR, that discriminates between asymptomatic and symptomatic infected through different measures of interactions and the corresponding stability analysis of the system in feedback form through the calculation of the \scrR 0 as H\infty gain; (ii) the analysis of the corresponding structured model involving the Watts and Strogatz interaction topology, to study the case of heterogeneous connectivity in the population; (iii) a case study on the Italian case, where we take into account the Istat seroprevalence study in the homogeneous case first, and then we analyze the impact of summer tourism and of the start of school in September in the heterogeneous case.
Long-Term Map-Based Visual Localization: Analysis of Individual Components of a Hierarchical Pipeline

As a low-cost localization solution with great application potential, visual localization has attracted a lot of attention from academia and industry in recent years. Currently, a hierarchical visual localization pipeline, from global to local, has been established and validated through numerous robotic experiments and applications. However, the problem of robust visual localization under long-term changing conditions has not yet been solved, and the upper bound of visual localization performance still needs to be explored. This requires an analysis of the possible enhancement space in each stage of the pipeline. In this paper, we review the hierarchical visual localization pipeline and discuss possible strategies to improve localization performance in each stage. Additionally, for the pose estimation stage, we propose a unified 2-correspondence RANSAC framework that maximizes the feature matching set and adapts well to changing environmental characteristics. Finally, we design a comprehensive experimental evaluation using a public real-world localization dataset to investigate the impact of these strategies on the final localization performance. This evaluation can provide experimental guidance for future work.
Learning Compliant Assembly Strategy From Demonstration

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process.