Miracle-dz/UniFAR-FTUs huggingface dataset:
Putting the theory into ‘burstlet theory’: A biophysical model of bursts and burstlets in the respiratory preBötzinger complex

Inspiratory breathing rhythms arise from synchronized neuronal activity in a bilaterally distributed brainstem structure known as the preBötzinger complex (preBötC). In in vitro slice preparations containing the preBötC, extracellular potassium must be elevated above physiological levels (to 7 − 9 mM) to observe regular rhythmic respiratory motor output in the hypoglossal nerve to which the preBötC projects. Reexamination of how extracellular K+ affects preBötC neuronal activity has revealed that low amplitude oscillations persist at physiological levels. These oscillatory events are sub-threshold from the standpoint of transmission to motor output and are dubbed burstlets. Burstlets arise from synchronized neural activity in a rhythmogenic neuronal subpopulation within the preBötC that in some instances may fail to recruit the larger network events, or bursts, required to generate motor output. The fraction of subthreshold preBötC oscillatory events (burstlet fraction) decreases sigmoidally with increasing extracellular potassium. These observations underlie the burstlet theory of respiratory rhythm generation. Experimental and computational studies have suggested that recruitment of the non-rhythmogenic component of the preBötC population requires intracellular Ca2+ dynamics and activation of a calcium-activated non-selective cationic current. In this computational study, we show how intracellular calcium dynamics driven by synaptically triggered Ca2+ influx as well as Ca2+ release/uptake by the endoplasmic reticulum in conjunction with a calcium-activated non-selective cationic current can explain all of the key observations underlying the burstlet theory of respiratory rhythm generation. Thus, we provide a mechanistic basis to unify the experimental findings on rhythm generation and motor output recruitment in the preBötC.
Control of Traffic Signals in a Model Predictive Control Framework

Abstract This paper presents a signal control method of urban road traffic by using model predictive control (MPC), where online adjustments of all free parameters of traffic signals, i.e., cycle times, split times and offsets, are focused on simultaneously. A simple model of macroscopic traffic, whose dynamics are nonlinear involving binary representation corresponding to green and red signals, is formulated. The parameters of the model are calibrated by using data obtained from detailed microscopic simulation that yields realistic statistics. The simple model is transformed into a mixed logical dynamical system (MLDS), and a mixed integer optimization is applied in the MPC framework. The proposed control scheme makes the signals flexibly turn to red and green depending on traffic conditions in the road network. By adapting quickly to any changes in traffic conditions, the scheme generates appropriate traffic signals, and the traffic flow is kept optimum regardless of such changes. Results of optimization are also verified by microscopic traffic simulation using a detailed model. It is demonstrated that the proposed control scheme reduces the average number of vehicles and their average stay time in the network by outperforming other ones that control only the frequency and the phase of each traffic signal.
Gaussian mixture models-based control chart pattern recognition

Abnormal patterns exhibited in control charts can be associated with certain assignable causes for process variation. Hence, accurate and fast control chart pattern recognition (CCPR) is essential for significantly narrowing down the scope of possible causes that must be investigated, and speeds up the troubleshooting process. This study proposes a Gaussian mixture models (GMM)-based CCPR model that employs a collection of several GMMs constructed for CCPR. By using statistical features and wavelet energy features as the input features, the proposed CCPR model provides a more simple and effective training procedure and better generalisation performance than using a single CCPR recogniser, and hence is easier to be used by quality engineers and operators. Furthermore, the proposed model is capable of adapting novel control chart patterns (CCPs) by applying a dynamic modelling scheme. The experimental results indicate that the GMM-based CCPR model shows good detection and recognition performance for current CCPs and adapts further novel CCPs effectively. Moreover, the proposed model provides a promising way for the on-line recognition of CCPs because of its efficient computation and good pattern recognition performance. Analysis from this study provides guidelines for developing GMM-based statistical process control (SPC) recognition systems.
Design considerations of a broad band microstrip antenna with embedded negative capacitor loading

In this article, effects of negative capacitor loading on the bandwidth (BW) and efficiency of a rectangular microstrip antenna (RMSA) have been investigated. The active negative capacitor is made of a field‐effect transistor that exhibits negative resistance as well as capacitance. It can, therefore, compensate the loss of an inductor having antenna. A single negative‐capacitor‐loaded RMSA gives broadband frequency operation with broadband radiation. A microstrip patch operating at 10.5 GHz having 12.22% BW has been utilized as a reference antenna. Theoretical results for the impedance and the radiation patterns have been presented. It is also shown that the impedance BW of the antenna has been enhanced by using active negative capacitance networks from 12.22% to 16.96%, respectively. In addition, it has been shown that active compensation significantly improves the radiation pattern. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2651–2656, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22806
Tension stiffening and cracking of concrete reinforced with glass fiber reinforced polymer (GFRP) bars

Tension stiffening and cracking of axial tension members is evaluated for concrete reinforced with steel (reinforcing ratio ρ = 2.0%) and glass fiber reinforced polymer (GFRP) bars (1.3%, 2.0%, and 2.9%), with shrinkage included in the analysis of the member response. Results show that because of a lower bar stiffness the GFRP-reinforced concrete exhibits greater tension stiffening than steel-reinforced concrete for any given value of axial member strain. Transverse cracking in the GFRP-reinforced concrete does not stabilize until much higher values of axial strain are reached, and longitudinal splitting cracks are also evident before cracking has stabilized. Crack widths in concrete reinforced with GFRP bars are larger because of their lower bar stiffness in combination with an increased crack spacing during the crack development stage. Tension stiffening of cracked reinforced concrete is taken into account using an average stress-strain response with a descending branch to model the concrete in tension.
Network-Level Pavement Deflection Testing and Structural Evaluation

Structural evaluation provides valuable information about the expected behavior of pavements and can be very useful at the network level of pavement management for project prioritization purposes. However, due to the expense involved in data collection and analysis, structural capacity is usually not determined at the network level. In Kansas, due to limited resources and the large size of the network (10 971 miles), annual network-level structural data collection at the same rate (five to ten tests per mile) as the project level is not realistic. This study was initiated to determine the sample size (% mileage) at the network level as well as the test intervals and frequency that can be used as guides for network-level deflection testing and structural evaluation of pavements. Falling-weight deflectometer (FWD) data, collected on 571 km (357 miles) of non-interstate asphalt pavements of the Kansas Department of Transportation from 1993 to 1996, were used in the analysis. Three response variables were studied: (1) normalized and temperature-corrected first sensor deflection, (2) backcalculated subgrade resilient modulus (Mr), and (3) effective pavement modulus (Ep). Statistical analysis of these variables showed that the FWD tests up to a three-year interval at the network level yield statistically similar pavement responses and layer properties. At the network level, FWD tests on 20% mileage appear to be a valid statistical choice, and three tests per mile is the minimum test frequency required. An analysis procedure for assessing structural deterioration of pavements at the network level is also demonstrated.
Deep multi-instance transfer learning for pneumothorax classification in Chest X-Ray images.

PURPOSE Pneumothorax is a life-threatening emergency that requires immediate treatment. Frontal-view chest X-ray images are typically used for pneumothorax detection in clinical practice. However, manual review of radiographs is time-consuming, labor-intensive, and highly dependent on the experience of radiologists, which may lead to misdiagnosis. Here, we aim to develop a reliable automatic classification method to assist radiologists in rapidly and accurately diagnosing pneumothorax in frontal chest radiographs. METHODS A novel residual neural network (ResNet)-based two-stage deep-learning strategy is proposed for pneumothorax identification: local feature learning (LFL) followed by global multi-instance learning (GMIL). Most of the nonlesion regions in the images are removed for learning discriminative features. Two datasets are used for large-scale validation: a private dataset (27,955 frontal-view chest X-ray images) and a public dataset (the National Institutes of Health (NIH) ChestX-ray14; 112,120 frontal-view X-ray images). The model performance of the identification was evaluated using the accuracy, precision, recall, specificity, F1-score, receiver operating characteristic (ROC), and area under ROC curve (AUC). 5-fold cross validation is conducted on the datasets, and then the mean and standard deviation of the above-mentioned metrics are calculated to assess the overall performance of the model. RESULTS The experimental results demonstrate that the proposed learning strategy can achieve state-of-the-art performance on the NIH dataset with an accuracy, AUC, precision, recall, specificity, and F1-score of 94.4 ± 0.7%, 97.3 ± 0.5%, 94.2 ± 0.3%, 94.6 ± 1.5%, 94.2 ± 0.4%, and 94.4 ± 0.7%, respectively. CONCLUSIONS The experimental results demonstrate that our proposed CAD system is an efficient assistive tool in the identification of pneumothorax. This article is protected by copyright. All rights reserved.
Productivity, changes and resilience in New Zealand grassland agriculture over the last three decades

This paper discusses key changes in New Zealand pastoral agriculture over the last three decades at the national scale, and how these have influenced the performance of grasslands, animal productivity, and the resilience of pastoral livestock systems. It assesses the positive and negative impacts of land-use change, changes in pasture production and supplementary feeding and dry matter (DM) consumed, and the key management practices implemented by farmers to enhance farm system resilience. It also notes environmental and other policy changes and examines how sectors and Government have started to respond. The largest estimated increase in DM consumed by livestock from 1990 to 2018 was from increased supplementary feed in the dairy sector. The largest estimated decline in DM production was due to pasture-land conversion to planted forests, followed by weed and pest impacts. In 2018, the dairy sector consumed the most DM at an estimated 25.0M t/yr, followed by sheep at 16.6M t/yr and beef at 10.1M t/yr. The total consumed DM in 2018 was 51.9 M t/yr which corresponds well with the independently estimated national pasture DM production of 64M t/yr. The environmental impacts of managements to enhance resilience in DM availability are becoming evident and future regulations may limit the extent some of these practices can expand.
Quality Assessment of Knowledge Graph Hierarchies using KG-BERT

Knowledge graphs in both public and corporate settings need to keep pace with the constantly growing amount of data being generated. It is, therefore, crucial to have automated solutions for assessing the quality of Knowledge Graphs, as manual curation quickly reaches its limits. This research proposes the use of KG-BERT for a triple (binary) classification task that assesses the quality of a Knowledge Graphs’s hierarchical structure. The use of KG-BERT allows the textual as well structural aspects of a Knowledge Graph to be leverage for this quality assessment (QA) task. The performance of our proposed approach is measured using four different Knowledge Graphs: two branches (Physics and Mathematics) of a corporate Knowledge Graph OmniScience, a WordNet subset, and the UMLS Semantic Network. Our method yields high-performance scores on all four KGs (88-92% accuracy) making it a relevant tool for quality assessment and knowledge graph maintenance.
IPAC2021 — 12th International Particle Accelerator Conference Campinas, SP, Brazil 24-28 May 2021

In this work, particle-in-cell simulations were carried out to investigate the role of the down-ramp length of a H2 gas jet in accelerating electrons ionized by the laser pulse. The laser and plasma density were chosen so that the system is operating in the self-modulated regime. Preliminary results show how the down-ramp length can control the injection of electrons in the first bubble induced in the plasma by the laser pulse.
Key Exchange and Management Schemes for Automotive Control Units

Today's vehicles are typically equipped with dozens of electronic control units that control one or more of the electrical systems or subsystems. Due to the rapid development towards autonomous driving, vehicle functionalities strongly depend on the communication between diverse control units (xCUs). Herein, tampering to manipulate e. g. the emission values of environmental protection systems (EPSs) to gain financial benefits poses a cyber risk to the various xCUs. To protect the communication channels from tampering/cyber-attacks, advanced key exchange and key management schemes are required. Within the framework of the Smart Adaptive Remote Diagnostic Anti-Tampering Systems (DIAS) project, two key exchange and management schemes are developed to enable a secure communication between xCUs and provide protection against tampering. For this purpose, two main communication protocols within a modern vehicle are considered: The Controller Area Network (CAN) protocol and the Single Edge Nibble Transmission (SENT) protocol. The DIAS project mainly aims at hardening EPSs against tampering, and it is funded by European Union's Horizon 2020 research and innovation program. The proposed key exchange and management schemes will be demonstrated in an experimental desktop environment.
Research on the global matching relationship between the key components of engine cylinder heads

The motivation of this paper is to develop a straightforward approach to provide an optimal solution to cylinder head design. In this research, a topological plot is first defined to represent the load bearing topology of the cylinder head. A simplified model enabling a designer to clearly understand the global matching relationship between the key components of a cylinder head is then identified from the topological plot. After characterizing the stress/strain behavior through parametric analysis, an interesting phenomenon associated with stress convergence in the cylinder head is observed. A concise mathematical explanation for this phenomenon is then derived from the well-known Kuhn–Tucker conditions, leading to a novel optimization criterion for cylinder head design. In this criterion, the optimal solution is described as an ideal “balanced point” among the main design parameters in terms of weight distribution of the key components. The benefits and advantages of the proposed method are finally confirmed by a concrete design case.
Quantum vacuum experiments with nonlinear optical crystals in microwave cavities: A feasibility study

We present an idea for parametric amplification of the quantum fluctuations of the electromagnetic vacuum in a three-dimensional microwave resonator and report preliminary measurements to test its feasibility. In the presented experimental scheme, the fundamental mode of a microwave cavity is non-adiabatically perturbed by modulating the index of refraction of a nonlinear optical crystal enclosed therein. Intense, multi-GHz laser pulses as those delivered by a mode-locked laser source impinge on the crystal to accomplish the n-index modulation. We theoretically analyse the process of parametric generation, which is related to the third order nonlinear coefficient χ of the nonlinear crystal, and assess the suitable experimental conditions for generating real photons from the vacuum. We substantiate the role of the second-order nonlinear coefficient χ as a source of spurious photons, which can arise during the process of laser excitation. The interplay between a real nonlinear crystal endowed by a χ coefficient and a real mode-locked laser source (i.e. featuring an offset-fromcarrier noise) is also experimentally investigated. This work opens up the possibility of alternative experimental approaches to observe the DCE in three dimensional cavities.
Effect of poison and evasion attack on machine learning algorithms

: The evaluation of the Poison and Evasion attack poses a serious threat to Machine learning model security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data or injecting Adverse data into classification data. In this article, we will consider the possibility of developing an example of crafted malware. Explore how machine learning classifiers can be confused with different threat models. therefore, to conclude that incorporating carefully processed data into training data can significantly reduce detection accuracy. We have attacked SVM, Decision Tree, XG Boost, Random Forest, KNN. We are using the YALE Face Recognition database for performance evaluation of Algorithms.
Multi-period home health care routing and scheduling problem with the medical grouping of patients

Home health care service has signi(cid:12)cant importance in modern societies. In most of the active institutions in this (cid:12)eld, the traditional procedure is used for planning and managing health personnel and determining patient visit sequence. This procedure usually causes an increase in costs and reduces patients’ satisfaction. This paper, for the (cid:12)rst time, groups the patients in a model according to the level of emergency and discriminating in their examination. Considering dependency and independence of patient visits to each other, assuming multi-depot and multi-period issues are attractive aspects of the proposed model. The model is solved with General Algebraic Modeling System (GAMS) for small scale and two variable neighborhood search algorithm and simulated annealing algorithm are used to solve large scale problems and their performances are compared. The results indicate minimizing total cost and also increasing patients’ satisfaction by the proposed model.
Treatments for Children with Severe Aggressive and Antisocial Behavior

Please describe your current position and research interests. I am the John M. Musser Professor of Psychology and Child Psychiatry and the Director of the Yale Parenting Center. My interests are in developmen​tal psychopathology, which consists of the study of psychiatric disorders and impairment among children and adolescents. My research has focused primarily on aggressive and antisocial behavior and depression. The study of child dysfunction invariably leads to other topics including parent, fam​ily, and contextual factors that are often linked with child functioning. For example, we have also studied parent psychiatric dysfunction, interpersonal violence in the home, child-rearing practices, and stress to help understand child and parent functioning.
In-service fiber line identification based on high-resolution fiber length measurement

An in-service fiber line identification scheme based on high-resolution fiber line length measurement is proposed and demonstrated for single star, survivable, and passive double star architectures to meet the demands of in-service cable reconstruction. In the proposed scheme, a reference fiber is assigned just after the fiber network has been constructed and differences in length between the constructed fiber lines and the reference fiber are measured by OFDR. The measured values are recorded as identification ranges, which include the measurement errors, as unique values of the actual lines. The OFDR length measurement system can measure a wide range of length differences from 4 cm to 3 km. The lines were successfully identified in all three kinds of architecture by the proposed scheme. The scheme is based on the line length, which is an actual property representing the whole line. Therefore, the line data can be verified simply and effectively, according to the actual information on the line. >
Computer Use of Science Teachers and Their Attitudes toward Computer Supported Teaching: A Sample from Turkey

The aim of this study was to examine (i) science teachers‟ level of computer use, (ii) their attitudes toward Computer Supported Teaching (CST), and (iii) to investigate the correlation between science teachers‟ level of computer use and their attitudes toward CST. 75 female and 97 male totally 172 science teachers (91 science and technology, 26 physics, 26 chemistry, and 29 biology teachers) from 96 primary and secondary schools participated in the research during the 2008-2009 semester in Gaziantep, Turkey. Computer Supported Teaching Questionnaire was used to collect data. The questionnaire has three parts, which consists of demographic items, level of computer use items, and Attitude toward Computer Supported Teaching items. The data gathered were analyzed by SPSS 14 software. Cronbach‟s Alpha value was 0.93. Mean, standard deviation, and Pearson correlation analyses were used. Results indicated that science teachers‟ level of computer use was high and their attitudes toward CST were positive. There was a moderate positive correlation between teachers‟ computer use level and their attitudes toward CST. Findings were discussed in the framework of the related literature, and some suggestions were made.
An Efficient Differential Privacy Method with Wavelet Transform for Edge Weights of Social Networks

With more and more attention paid to privacy preservation in social networks, many effective methods based on differential privacy have been presented. To preserve the sensitive information of edge weights in a weighted social network, we combine the differential privacy with wavelet transform and devise a DPEW (Differential Privacy based Edge weight with Wavelet Transform) method. The proposed method satisfies differential privacy and provides better data utility. In this method, WTDP (Wavelet Transform based on Differential Privacy) algorithm can achieve differential privacy preserving while less noise is added on the edge weights. In addition, the properties of the original graph are maintained by EDU (Enhancing Data Utility) algorithm. The experimental results show that the DPEW method achieves ϵ -differential privacy and reduces the information loss of the edge weight than other methods.