Miracle-dz/UniFAR-FTUs huggingface dataset:
Yarışan bağımlı risklerle sağkalım analizinde Archimedean Kapula Yaklaşımı

Sagkalim analizi, baslama ani ile calismanin basinda belirlenen ve basarisizlik olarak adlandirilan bir sonucun ortaya cikmasina kadar gecen sure olarak elde edilen verilerin analizidir. Sagkalim analizinde birey icin basarisizlik birden fazla nedenden meydana gelebilmektedir. Bu tur calismalarda, bireye ait sagkalim suresi ve meydana gelebilecek basarisizligin olasi nedeni ise kesikli rasgele degiskeni ile gosterilir. Burada, toplam tane farkli neden vardir ve bunlar neden, neden, … , neden biciminde ifade edilir. Basarisizligin bu olasi nedenleri yarisan risk olarak adlandirilmaktadir. Yarisan risklerin var oldugu durumlarda temel varsayim her bireye ait potansiyel sagkalim suresi vektorunun oldugudur. Bu sagkalim sureleri ayni anda gozlenemeyeceginden ortuk (latent) bir yapiya sahiptir. Tum risklerin varliginda gozlenen gercek sagkalim suresi olmaktadir. Risklerin bagimliligi ile bu ortuk sagkalim surelerinin bagimliligi anlatilmaktadir. Calismada iki bagimli yarisan riskin varliginda, bu riskler arasindaki bagimliligi en iyi sekilde temsil eden Archimedean kapula fonksiyonunun tahmin edilmesi amaclanmistir. Bu amac icin Genest ve Rivest (1993) ‘in onerdigi parametrik olmayan yontemin duruma uyarlamasi yapilmistir. Son olarak yontem gercek bir veri seti uzerinde uygulanmis, elde edilen sonuclar ve oneriler verilmistir. Abstract Survival analysis is related to analyze the data which are obtained as a time until a prespesified event is called failure event. In survival analysis, failure may occur from several causes for any individual. In such studies, survival time that is assigned to an indivual and potential cause of failure are represented by and , respectively. There are diffrent causes and these are represented by 1st cause, 2nd cause, … , kth cause. These potential causes are called as competing risks. In the presence of competing risks, the basic assumption is there is a potential survival time vector that is assigned to every individual. Since these survival times are not observable random variables they have a latent structure. When all the risks are present the actual observed survival time is . Dependent risks means dependent these latent survial times. The aim of this study is to estimate the Archimedean copula function that provide best fit for the dependency between two dependent competing risks. For the purpose, the method that is suggested by Genest and Rivest (1993) is adapted for the case. At the end of the study, the method is applied to a real-life data set, the obtained results and suggestions are presented.
MFANet: Mixed Feature Attention Network for Port Lane Detection

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario.
Distance Awared:Adaptive Voxel Resolution to help 3D Object Detection Networks See Farther

Point cloud is the most widely used data input for modern 3D object detection methods, however, due to the complexity of the environment in which data is collected, it suffers from inevitable loss of information, which is extremely serious for distant objects. In this paper, we improved the backbone of voxel-based 3D object detection methods, which help to better detect distant targets. The improvements we proposed can help to process raw points in different resolutions according to its distance to lidar. Specifically, the farther point is away from lidar, the finer feature extraction and aggression conduction we will adopt. Our purpose is to find a balance between information loss and memory consumption. Moreover, inspired by the success of Transformer in computer vision, we adopted Multi-headed Self-attention(MHSA) structure to our modified backbone. MHSA offers the ability of global receptive field, which helps to get more informative Bird's Eye View(BEV) representation of the point cloud. Our modifications are plug and play, and can be used in any 3D object detection method based on voxels and sparse 3D convolution. We evaluated the performance of our modifications in KITTI, experiments demonstrate the effectiveness of our efforts.
Improvement and Analysis of Kinematic Model for Cable-Driven Snake Manipulator Considering Cable Length Errors

In this paper, an improved kinematic model is proposed and analyzed for a cable-driven snake manipulator. Firstly, the fundamental motor-screw-cable cascaded driven characteristics are briefly described for this type of snake manipulator. Then, a theoretical kinematic model is established based on the D-H method. The coupling relationship between the ball screw error, cable flexibility error, and cable offset error of the manipulator is considered, and the reasons for these errors are specifically analyzed. The modeling of these errors and their impact on the cable length error are discussed, which serves as the theoretical foundation for improving the kinematic mapping relationship between cable length and joint angle. Finally, the joint angle and its error are simulated which consider the relationship between the ball screw error, cable flexibility error and cable offset error. The impacts of these errors on joint angle error are discussed separately, and the reasons are explained for these impacts.
Time Binning Method for Nonpulsed Sources Characterization With a Superconducting Photon Number Resolving Detector

Photon number resolving detectors find space in many fields, such as quantum optics, boson sampling, and fluorescence spectroscopy. In particular, the reconstruction of the input photon distribution is essential in quantum communications to detect photon-number-splitting attacks. In this work, we discuss the operation configurations of a photon number resolving detector based on superconducting nanostrips at a wavelength of 1550 nm from a temporal point of view. We set a time binning and acquired the number of recorded pulses per bin by means of a time-to-digital converter. We studied the predictions of two theoretical models and compared them to the experimental data in order to analyze their operation regimes depending on the binwidth and to employ them for the reconstruction of the input photon distribution. We applied this method to a continuous-wave laser source, showing that the former can be used for the characterization of nonpulsed light sources, even with a photon emission rate so low that the dark count rate of a superconducting nanostrip is not negligible.
Natat in Cerebro: Intelligent Information Retrieval for “The Guillotine” Language Game

This paper describes OTTHO (On the Tip of my THOught), a system designed for solving a language game, called Guillo-tine. The rule of the game is simple: the player observes ﬁve words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others. The system performs retrieval from several knowledge sources, such as a dictionary, a set of proverbs, and Wikipedia to realize a knowledge infusion process. The main motivation for designing an artiﬁcial player for Guil-lotine is the challenge of providing the machine with the cultural and linguistic background knowledge which makes it similar to a human being, with the ability of interpreting natural language documents and reasoning on their content. Our feeling is that the approach presented in this work has a great potential for other more practical applications besides solving a language game.
Short-term Electricity Load Forecasting Based on Dual Attention Mechanism and BiLSTM

Power load forecasting is the foundation of smart grid construction, and short-term load forecasting can further optimize energy dispatching. To address the problem that the traditional load forecasting methods cannot fully utilize the correlation of data and thus lead to low accuracy, a short-term electric load forecasting model based on Double Attention Mechanism (DAM) and Bidirectional Long and Short Term Memory Neural Network (BILSTM) is proposed. The model first uses the feature attention mechanism to analyze the correlation between the electric load and the influencing factors of the load (temperature, electricity price, etc.), extracts the important features and then uses the BILSTM network for training. The temporal attention mechanism further combines temporal influences such as holidays with the BILSTM network to improve the stability of the forecast. Through the experimental comparison of electricity datasets from a Belgian country and a domestic region, the results show that the Attention-BiLSTM model is more accurate in prediction compared to neural network models such as CNN, RNN, DNN, and GRU.
Phylogenetic relationship and characterization of the complete chloroplast genome of Rhododendron przewalskii subsp. przewalskii Maximowicz 1877, an ornamental and medicinal plant in China

Abstract As one of the top 10 famous flowers in Chinese tradition, Rhododendron przewalskii subsp. przewalskii known as ‘beauty in flowers,’ which has high ornamental and medicinal value. The complete chloroplast (cp) genome of R. przewalskii subsp. przewalskii was determined in this study. The complete chloroplast genome of R. przewalskii subsp. przewalskii was 201,233 bp in length and contained a large single-copy region (LSC, 108,077 bp), and a small single-copy region (SSC, 2624 bp) and a pair of inverted repeat regions (IRa and IRb, 45,266 bp). A total of 142 functional genes were observed in this cp genome, including 91 protein-coding genes (PCGs), 43 transfer RNA genes (tRNAs), and eight ribosomal RNA genes (rRNAs). The R. przewalskii subsp. przewalskii cp genome has an A + T content of 64.06% and presents a positive AT-skew (0.53%) and a negative GC-skew (−1.56%). The maximum likelihood phylogenetic analysis based on the concatenated nucleotide sequences of 13 PCGs strongly supported the monophyletic relationship of R. przewalskii subsp. przewalskii the clade of R. henanense subsp. lingbaoense. This study provides genomic evidence for the vegetation classification of Rhododendron.
Synthesis of Robust Model Predictive Control for Vehicle Path Following with Uncertainties

In the problem of path following of autonomous vehicles, the complexity of vehicle model and road surface will bring time-varying parametric and unstructured uncertainties to the system. In our study, we design a controller based on synthesis of model predictive control method, which is robust to the uncertainties of vehicle longitudinal velocity, tire cornering stiffness and path curvature. According to the vehicle dynamics model, the vehicle linear uncertain system model is established, where road curvature is modeled as system state rather than system noise. A vehicle path following controller based on synthesis robust model predictive control (SRMPC) method is proposed, which is robust to uncertain parameters and has low online computational expense. The results of co-simulation conducted on Prescan and MATLAB/Simulink show that, compared with offline robust model predictive control method, the lateral error of path following controller based on SRMPC method is smaller, and the robustness is better.
BPDS4,7-diphenyl-1,10-phenantrolinedisulfonic acidCTABcetyl trimethyl ammonium bromideDAB3,3′-diaminobenzidine tetrahydrochlorideDWdry weightMS mediumMurashige Skoog mediumSHAMsalicylhydroxamic acidFWfresh weightROSReactive Oxygen SpeciesTETris-EDTAT- DNAtransferred DNAWTwild-type Keywords

In plants, the iron storage protein ferritin can be targeted to both chloroplasts and mitochondria. To investigate the role of Arabidopsis ATFER4 ferritin in mitochondrial iron trafficking, atfer4-1 and atfer4-2 mutant knock-outs for the AtFer4 gene were grown in heterotrophic suspension cultures. Both mutants showed altered cell size and morphology, reduced viability, higher H2O2 content and reduced O2 consumption rates when compared to wt. Although no reduction in total ferritin or in mitochondrial ferritin was observed in atfer4 mutants, total iron content increased in atfer4 cells and in atfer4 mitochondria. Transcript correlation analysis highlighted a partial inverse relationship between the transcript levels of the mitochondrial ferric reductase oxidase FRO3, putatively involved in mitochondrial iron import/export, and AtFer4. Consistent with this, FRO3 transcript levels were higher in atfer4 cells. We propose that the complex molecular network maintaining Fe cellular homeostasis requires, in Arabidopsis heterotrophic cells, a proper balance of the different ferritin isoforms, and that alteration of this equilibrium, such as that occurring in atfer4 mutants, is responsible for an altered Fe homeostasis resulting in a change of intraorganellar Fe trafficking. Abbreviations BPDS4,7-diphenyl-1,10-phenantrolinedisulfonic acidCTABcetyl trimethyl ammonium bromideDAB3,3′-diaminobenzidine tetrahydrochlorideDWdry weightMS mediumMurashige Skoog mediumSHAMsalicylhydroxamic acidFWfresh weightROSReactive Oxygen SpeciesTETris-EDTATDNAtransferred DNAWTwild-type
Standalone Optode for Functional Near-Infrared Spectroscopy Acquisition

Functional near-infrared spectroscopy(fNIRS) is a non-invasive modality that can measure brain activation based on its hemodynamic response. It has high spatial resolution and it is robust from motion artifacts and safe due to the absence of radiation. This study investigates the ability to create an optode module to acquire fNIRS signals. Several such optodes can be connected using a central controller to obtain signals from several regions of the scalp simultaneously but the main focus of this paper is on the optode module. It has a photodiode, near-infrared light emitting diodes, and a Cortex M4 microcontroller. Its operation is verified using the Valsalva maneuver, The readings obtained from this prototype along with possible improvements are also discussed in detail.
Repeated Short-Term Bouts of Hyperoxia Improve Aerobic Performance in Acute Hypoxia

Abstract Faulhaber, M, Schneider, S, Rausch, LK, Dünnwald, T, Menz, V, Gatterer, H, Kennedy, MD, and Schobersberger, W. Repeated short-term bouts of hyperoxia improve aerobic performance in acute hypoxia. J Strength Cond Res 37(10): 2016–2022, 2023—This study aimed to test the effects of repeated short-term bouts of hyperoxia on maximal 5-minute cycling performance under acute hypoxic conditions (3,200 m). Seventeen healthy and recreationally trained individuals (7 women and 10 men) participated in this randomized placebo-controlled cross-over trial. The procedures included a maximal cycle ergometer test and 3 maximal 5-minute cycling time trials (TTs). TT1 took place in normoxia and served for habituation and reference. TT2 and TT3 were conducted in normobaric hypoxia (15.0% inspiratory fraction of oxygen). During TT2 and TT3, the subjects were breathing through a face mask during five 15-second periods. The face mask was connected through a nonrebreathing T valve to a 300-L bag filled with 100% oxygen (intermittent hyperoxia) or ambient hypoxic air (placebo). The main outcome was the mean power output during the TT. Statistical significance level was set at p < 0.05. The mean power output was higher in the intermittent hyperoxia compared with the placebo condition (255.5 ± 49.6 W vs. 247.4 ± 48.2 W, p = 0.001). Blood lactate concentration and ratings of perceived exertion were significantly lower by about 9.7 and 7.3%, respectively, in the intermittent hyperoxia compared with the placebo condition, whereas heart rate values were unchanged. IH application increased arterial oxygen saturation (82.9 ± 2.6% to 92.4 ± 3.3%, p < 0.001). Repeated 15-second bouts of hyperoxia, applied during high-intensity exercise in hypoxia, are sufficient to increase power output. Future studies should focus on potential dose-response effects and the involved mechanisms.
The Interactive Role of Macrophages in Innate Immunity

Macrophages are critical effector cells of the innate immune system that play central roles in the initiation and resolution of inflammation. They mediate resistance in response to pathogens and “danger” signals mainly through phagocytosis. Macrophages and other cells co-operate and complement one another in host defense. As innate immune cells, they also contribute to the initiation of adaptive immune responses. Therefore, appropriate activation of macrophages would aid effective immune response in curbing many infections. This chapter explores how the interaction and roles of macrophages influence outcomes during infections. It is expected that understanding these fundamental mechanisms may help stimulate research to exploit macrophages for therapeutic benefits.
Time of Day Effects on Anaerobic Performance Using a Nonmotorized Treadmill

Abstract Renziehausen, JM, Bergquist, AM, Park, J-H, Hill, EC, Wells, AJ, Stout, JR, and Fukuda, DH. Time of day effects on anaerobic performance using a nonmotorized treadmill. J Strength Cond Res 37(10): 2002–2007, 2023—The purpose of this study was to determine the effects of time of day on performance during a maximal effort sprinting assessment (30nmt) and determine potential differences based on chronotype and sex. Twenty-six recreationally active men (n = 12) and women (n = 14) between the ages of 18 and 35 years old (21.5 ± 2.4 years) completed the 30nmt at 9:00 am, 2:00 pm, and 7:00 pm in a randomized order over a 24-hour period. Resting heart rate and temperature assessments were taken at each visit. A dietary recall and the Morningness-Eveningness Questionnaire were used to assess kilocalories (kcals) and chronotype, respectively. Two-way (time x sex) repeated measures analyses of variance were conducted to determine differences in peak/mean power, peak/mean velocity, distance, resting heart rate, temperature, and kcals at each time point. Paired sample t tests were used to assess peak and nadir of each performance variable. A significance level was set at p < 0.05. There was a significant main effect for temperature (p < 0.001), resting heart rate (p = 0.007), and pre-exercise caloric intake (p = 0.021) throughout the day. No significant main effects for time were found for peak power (p = 0.766), mean power (p = 0.094), peak velocity (p = 0.497), mean velocity (p = 0.193), or distance (p = 0.262). There were no significant time × sex interactions for any dependent performance variables (p > 0.05). Significant differences were shown between the peak and nadir of each performance variable (p < 0.001). There were no significant differences in performance during maximal effort anaerobic assessments shown throughout the day; however, peak/nadir of performance times may be individualized and differ between morning types and intermediate types.
A data-driven method for multi-step-ahead degradation prediction and long-term prognostics of proton exchange membrane fuel cell using conditional dilated convolutional neural network

Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high eﬃciency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitalization digitization is happening to oﬀer possibilities to increase the productivity and the ﬂexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitalization digitization measures, how and where they can be applied, and their beneﬁts. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the ﬁrst time applied for predicting the time-dependent fuel cell performance and is proved to be more eﬃcient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behavior dynamics.
Work-Related Burnout on Psychological Well-Being among Public School Teachers: Resilience as Moderating Factor

Education being one of the cornerstones of the Philippines, teachers may experience pressure and burnout from carrying the workload and responsibilities of being second to parents being the steppingstone for the development of young children. This study used a moderation analysis research design to examine if resilience moderates the relationship between work-related burnout and psychological well-being. Using a non-probability purposive sampling technique, over 233 Filipino public elementary school teachers from Quezon City, Philippines, participated. Results revealed a substantial negative relationship between psychological well-being and work-related burnout, and a positive relationship exists between resilience and psychological well-being. Furthermore, linear regression analysis showed that work-related burnout negatively predicted psychological well-being. Moreover, this study found that resilience does not moderate the association between work-related burnout and psychological well-being among public elementary school teachers.
Towards 6G Holographic Localization: Enabling Technologies and Perspectives

In the last years, we have experienced the evolution of wireless localization from being a simple add-on feature for enabling specific applications to become an essential characteristic of wireless cellular networks, as for sixth-generation (6G) cellular networks. This paper illustrates the importance of radio localization and its role in all the cellular generations, from first-generation (1G) to 6G. Also, it speculates about the idea of holographic localization where the characteristics of electromagnetic (EM) waves, including the spherical wavefront in the near-field, are fully controlled and exploited to achieve better wireless localization. Along this line, we briefly overview possible technologies, such as large intelligent surfaces, and challenges to realize holographic localization. To corroborate our vision, we also include a numerical example that confirms the potentialities of holographic localization.