Miracle-dz/UniFAR-FTUs huggingface dataset:
Passing Extracranial Artery Occlusion by Intermediate Catheter With Expanding Microballoon (PEACE): A Novel Endovascular Therapy in Acute Tandem Occlusion Stroke

Purpose: Endovascular treatment of atherosclerotic tandem occlusions in acute ischemic stroke (AIS) is a matter of debate. This article reports a single-center experience using an intermediate catheter with microballoon for treatment of tandem occlusions. Methods: A total of 151 AIS patients with large vessel occlusion received endovascular therapy and a consecutive series of patients (n = 26) who suffered from tandem cervical intracranial occlusions were treated using the Passing Extracranial Artery Occlusion by Intermediate Catheter with Expanding Microballoon (PEACE) technique. Intracranial recanalization was achieved by aspiration or stent retriever and then emergency stenting was performed for extracranial internal carotid artery (ICA) lesion. Demographic, clinical characteristics, procedural details of endovascular therapy, and prognosis outcome were assessed. The outcomes of tandem occlusion group were compared with isolated intracranial occlusion group (n = 122) and previous studies. Results: As compared to isolated intracranial occlusion groups, only a few patients suffered from atrial fibrillation (7.7% vs 38.5%, p<0.01) in tandem occlusions group. A larger proportion of patients (61.5% vs 29.5%) had tandem occlusions in which extracranial ICA occlusion was combined with intracranial terminus occlusion in ICA (p<0.01). 46.2% of tandem occlusions patients achieved intracranial recanalization by aspiration alone versus 15.6% in patients with isolated intracranial occlusion (p<0.01). In tandem occlusion patients treated with PEACE, 92.3% achieved successful reperfusion (thrombolysis in cerebral infarct [TICI] ≥2b). The median time from puncture to recanalization was 51 minutes (interquartile range [IQR], 41–66). 67.6% favorable functional prognosis (modified Rankin score [mRS], 0–2) was seen, with 11.5% mortality and 3.8% of symptomatic intracerebral hemorrhage (sICH) at 90 days. These outcomes are all consistent or better than previously reported studies performed for tandem occlusion. Conclusions: Endovascular therapy using the PEACE technique with intermediate catheter and lined expanding microballoon is safe, efficient, and fast in the treatment of atherosclerotic tandem occlusion patients.
Positive-Unlabeled Learning-Based Hybrid Deep Network for Intelligent Fault Detection

Intelligent fault detection methods based on deep learning have been developed rapidly in recent years. However, most of these methods are based on supervised learning which requires a fully labeled training set. It is difficult to obtain massive labeled samples in real applications incredibly accurately labeled fault samples from an operating system. The lack of labels and label noise becomes a great challenge for fault detection. To tackle this problem, in this article, we propose a positive-unlabeled learning based hybrid network (PUHN). It only needs part of the normal operating samples to be labeled. All other samples (including the rest of the normal samples and all fault samples) are unlabeled, which greatly reduces the labeling cost. PUHN consists of three modules: a nonnegative risk positive-unlabeled (PU) network for training the classifier, a feature extraction module, and a clustering layer for improving data separability and estimating the class priors of PU learning. The three are optimized as a whole and the corresponding optimization strategy is designed. The monitoring data of 24 wind turbines are used to verify the effectiveness and robustness of the proposed method. The experimental results indicate that the proposed method is superior to the benchmark methods, and the performance is significantly better than the supervised learning method when there exists label noise.
Influence of Temperature on Methods for Determining Silicon Solar Cell Series Resistance

Series resistance (R s ) is considered to be one of the most important parameters affecting solar cell performance, especially those operating under concentrated solar radiation. Many methods have been proposed where the estimated R s values do not coincide or even come close to each other. In this paper, seven methods for determining R s are reviewed and verified experimentally using a commercial single-crystal silicon solar cell (104 cm 2 of total area). Their differences lie principally in: (1) number of diodes quoted in the solar cell model; (2) other assumptions (constant ideality factor or not, infinite, or finite shunt resistance); and (3) simultaneous determination of other parameters or not. Based on these methods, R s values were derived by extracting the necessary parameters from the measured I-V characteristics of the cell at different illuminations and cell temperatures. According to these methods, the obtained R s values varied greatly in comparison with each other as well as its trend with temperature variation.
Buck-Based Active-Clamp Circuit for Current-Fed Isolated DC–DC Converters

In this article, a buck-based active-clamp circuit for current-fed isolated dc–dc converters is proposed. The proposed active-clamp circuit can not only suppress the voltage spikes caused by the current difference between the current-fed inductor and leakage inductor of the transformer but also transfer the energy of the above process to the input voltage source. Different from the traditional active-clamp circuit, the proposed active-clamp circuit needs only one switch regardless of the number of input phases in the main circuit. Moreover, the control of the proposed active-clamp circuit is decoupled with the main circuit, which makes the design and implementation of the system controller easier. A 400-W experimental prototype has been built to verify the effectiveness of the proposed circuit.
Artificial Intelligence Applications in Civil/Construction/Architectural Engineering Education

It is increasingly important to go beyond traditional departmental course curriculum boundaries for some areas of science and engineering education. Artificial Intelligence (AI) is one such field; its applications are very extensive and interdisciplinary. The graduate students should especially be encouraged to learn various applications of contemporary computing techniques including artificial neural network (ANN), genetic algorithm (GA), etc. Civil/construction/ architectural Engineering has exercised a rapidly growing interest in the application of neurally inspired computing techniques. The motive for this interest was the promises of certain information processing characteristics similar to those of the human brain. Soft computing (SC) is an emerging approach to computing, which parallels to remarkable ability of the human mind to reason and learn in an environment of certainty and precision. This paper highlights various applications of AI in civil/construction/architectural engineering especially in SC areas. As an example of a graduate project, this paper demonstrated an ANN and GA based knowledge model where the customer’s preferences regarding comfort and safety issues in a large residential multistory flat housing scheme was studied. Architecture/engineering is an applied science where many lessons can be learned from existing structures, their successes and failures, and incorporating them to find out new techniques for a better structure. This implies that the designer should be able to derive from each previous design some qualitative values, especially on user’s approval regarding building’s safety and comfort quality as to assure a successful design. Architects/design engineers are quite often challenged with soft data, which are linguistic qualitative in nature, and needed to interpret and integrate into their design decision making processes. They should know much about their customer’s desires and requirements, and especially customer’s preferences when it comes to specific design issues. Hence, post-Proceedings
DePo: Dynamically Offload Expensive Event Processing to the Edge of Cyber-Physical Systems

Event processing is one of the cornerstones to manage massive data streams in Cyber-Physical Systems (CPS). Due to CPS applications' increasing complexity, detecting highly complicated events (<italic>aka</italic>. “expensive” events) leads to significant performance degradation, particularly harmful to mission-critical systems. To tackle this challenge, we define a new task - dynamic event processing offloading to CPS-edges. This paper proves the problem NP-hard and proposes a solution - <monospace>DePo</monospace>. <monospace>DePo</monospace> splits the expensive events into sub-models and offloads them to CPS edges. We design a long and short-term event memory mechanism in <monospace>DePo</monospace> that enables the edges and server to process expensive events collaboratively within their capabilities. Besides, we propose a concept called Edge Utility to measure the optimality of offloading schemes. A heuristic algorithm is presented in this study to guide how to dispatch events to edges, thereby helping <monospace>DePo</monospace> generate a sub-optimal solution in polynomial computational complexity. Our extensive experiments show that the performance gap between <monospace>DePo</monospace> and the optimal benchmark is less than 5%. <monospace>DePo</monospace> effectively reduces more than 40% redundant states and provides over 100% higher throughput than state-of-the-art approaches. Experimental results verified the high stability and scalability of <monospace>DePo</monospace>, especially when dealing with a large number of expensive events.
The high osmolarity glycerol (HOG) pathway in fungi†

Abstract While fungi are widely occupying nature, many species are responsible for devastating mycosis in humans. Such niche diversity explains how quick fungal adaptation is necessary to endow the capacity of withstanding fluctuating environments and to cope with host-imposed conditions. Among all the molecular mechanisms evolved by fungi, the most studied one is the activation of the phosphorelay signalling pathways, of which the high osmolarity glycerol (HOG) pathway constitutes one of the key molecular apparatus underpinning fungal adaptation and virulence. In this review, we summarize the seminal knowledge of the HOG pathway with its more recent developments. We specifically described the HOG-mediated stress adaptation, with a particular focus on osmotic and oxidative stress, and point out some lags in our understanding of its involvement in the virulence of pathogenic species including, the medically important fungi Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus, compared to the model yeast Saccharomyces cerevisiae. Finally, we also highlighted some possible applications of the HOG pathway modifications to improve the fungal-based production of natural products in the industry.
Meteoric water contribution to sea ice formation and its control of the surface water carbonate cycle on the Wandel Sea shelf, northeastern Greenland

An influx of glacial meltwater has the ability to alter the properties of marine surface waters and their ability to exchange CO2 through changes to water column stratification and the inorganic carbon system. Here, we report how inputs of meteoric water affect the physical and biogeochemical properties of both the water column and the sea ice cover on the Wandel Sea shelf, northeastern Greenland, during spring 2015. The observed depleted δ18O–H2O in the water column, with surface water values as low as –16.3 ‰, suggests a strong input of meteoric water (i.e., water derived from atmospheric precipitation). Depleted δ18O–H2O observed within sea ice (from –21.5 to –8.0 ‰) reflects its formation from surface water that was already depleted isotopically. In addition, a thick snow cover, as present during the study, promotes the formation of snow ice as well as insulates the ice cover. Within sea ice, the resulting relatively warm temperature and low salinity impedes ikaite formation. However, measurements of total dissolved inorganic carbon and total alkalinity indicate that, in both sea ice and the water column, the dissolution of calcium carbonate was the main process affecting the carbonate system. This finding suggests that inputs of glacial meltwater deliver glacier-derived carbonate minerals to the ocean which become incorporated within the ice structure, increasing calcium carbonate dissolution in the water column in the absence of ikaite precipitation within the sea ice. If widespread in glacial-fed waters, bedrock carbonate minerals could increase CO2 sequestration in glacial catchments despite the weakening of the sea ice carbon pump.
Ethical fashion project: partnering with industry

This article shares a platform of apparel design that considers ethical implications within the product development process, which were incorporated into a senior level apparel design course during Spring 2009 and expanded upon inSpring 2010. In a problem-based learning format, students were challenged to develop ethical fashions from design development through final fashion garment in collaboration with the Fair Labor Association (FLA), Gildan Activewear and El Instituto Politecnico Centroamericano (IPC) in Honduras. The project demonstrates a broad range of considerations that should go into designing and producing a garment that is defined as ‘ethical’, including choice of materials that benefit the environment, choice of suppliers with verifiable fair labour practices, and responsible interactions and exchanges between the buyers/designers and suppliers. Use of communication and apparel industry-specific technology was essential in this project with international partners.
Algorithms for optimizing shared mobility systems

Bikes sharing systems have known a growing success all over the world. Several attempts have been made since the 1960s. The latest developments in ICT have enabled the system to become efficient. People can obtain real-time information about the position of the vehicles. More than 200 cities have already introduced the system and this trend keeps on with the launching of the NYC system in spring 2013. A new avatar of these means of transportation has arrived with the introduction of Autolib in Paris end of 2011.The objective of this thesis is to propose algorithms that may help to improve this system efficiency. Indeed, operating these systems induces several issues, one of which is the regulation problem. Regulation should ensures users that a right number of vehicles are present at any station anytime in order to fulfill the demand for both vehicles and parking racks. This regulation is often executed thanks to trucks that are travelling the city. This regulation issue is crucial since empty and full stations increase users' dissatisfaction. Finding the optimal strategy for regulating a network appears to be a difficult question. This thesis is divided into two parts. The first one deals with the “static” case. In this part, users' impact on the network is neglected. This is the case at night or when the system is closed. The operator faces a given repartition of the vehicles. He wants the repartition to match a target one that is known a priori. The one-truck and multiple-truck balancing problems are addressed in this thesis. For each one, an algorithm is proposed and tested on several instances. To deal with the “dynamic” case in which users interact with the system, a simulator has been developed. It is used to compare several strategies and to monitor redistribution by using trucks. Strategies not using trucks, but incentive policies are also tested: regularly updated prices are attached to stations to deter users from parking their vehicle at specified stations. At last, the question to find the best initial inventory is also addressed. It corresponds to the case when no truck are used within the day. Two local searches are presented and both aim at minimizing the total time lost by users in the system. The results obtained can be used as inputs for the target repartitions used in the first part. During my thesis, I participated to two EURO-ROADEF challenges, the 2010 edition proposed by EDF and the 2012 one by Google. In both case, my team reached the final phase. In 2010, our method was ranked fourth over all the participants and led to the publication of an article. In 2012, we ranked eighteenth over all the participants. Both works are added in the appendix
Efficient Energy Dissipating Steel-Braced Frame to Resist Seismic Loads

In this research, the seismic performance of a proposed efficient energy dissipating steel-braced frame (EEDBF) in relation to that of a moment-resisting frame (MRF) and chevron braced frame (CBF) is studied. The frame is intended to combine the advantages of MRF and CBF and eliminate most of the disadvantages pertinent to these frames. Nonlinear static pushover, time history, and damage analyses of the three frames are conducted to assess the performance of the EEDBF compared to that of MRF and CBF. The analyses results revealed that the EEDBF has a more stable lateral force-deformation behavior compared to CBF. The energy dissipation capacity of the EEDBF is comparable to that of the MRF. The drift of the EEDBF at small to medium intensity ground motions is comparable to that of the CBF and smaller than that of the MRF. At high intensity ground motions, the drift of the EEDBF is smaller than those of both CBF and MRF. Furthermore, the EEDBF is found to experience less damage compared to other frames.
A new smart traffic monitoring method using embedded cement-based piezoelectric sensors

Cement-based piezoelectric composites are employed as the sensing elements of a new smart traffic monitoring system. The piezoelectricity of the cement-based piezoelectric sensors enables powerful and accurate real-time detection of the pressure induced by the traffic flow. To describe the mechanical-electrical conversion mechanism between traffic flow and the electrical output of the embedded piezoelectric sensors, a mathematical model is established based on Duhamel’s integral, the constitutive law and the charge-leakage characteristics of the piezoelectric composite. Laboratory tests show that the voltage magnitude of the sensor is linearly proportional to the applied pressure, which ensures the reliability of the cement-based piezoelectric sensors for traffic monitoring. A series of on-site road tests by a 10 tonne truck and a 6.8 tonne van show that vehicle weight-in-motion can be predicted based on the mechanical-electrical model by taking into account the vehicle speed and the charge-leakage property of the piezoelectric sensor. In the speed range from 20 km h−1 to 70 km h−1, the error of the repeated weigh-in-motion measurements of the 6.8 tonne van is less than 1 tonne. The results indicate that the embedded cement-based piezoelectric sensors and associated measurement setup have good capability of smart traffic monitoring, such as traffic flow detection, vehicle speed detection and weigh-in-motion measurement.
Earthquake Mitigation Decisions and Consequences

A primary aim of recent earthquake engineering research has been to integrate analyses of social and technical systems, to take into account the potential consequences of earthquakes for society as a whole. In some cases the resulting decision frameworks treat acceptable consequences and risks as fixed objectives. This paper describes the rationale for abandoning a priori assessment of the acceptability of earthquake consequences as conceived in consequence-based engineering (CBE). Support for an alternative approach comes from behavioral and policy research including an examination of seismic retrofit decision processes for U.S. General Services Administration-owned federal buildings. Additional review of research on earthquake mitigation policy making at the local government level also supports the case. We then suggest dynamic decision structuring that would better support the integration of engineering expertise and decision makers’ preferences.
Uncertainty Forecasts Improve Decision Making Among Nonexperts

The objective of this research was to evaluate the impact of weather uncertainty information on decision making in naturalistic settings. Traditional research often reveals deficits in human decision making under uncertainty as compared with normative models of rational choice. However, little research has addressed the question of whether people in naturalistic settings make better decisions when they have uncertainty information as compared with when they have only a deterministic forecast. Two studies investigated the effect of several types of weather uncertainty information on the quality of decisions to protect roads against icing and on temperature predictions and compared them with a control condition that provided deterministic forecast only. Experiment 1 was a Web-based questionnaire that included a single trial. Experiment 2, conducted in lab, included 120 trials and provided outcome feedback and a reward based on performance. Both studies indicated enhanced performance with uncertainty information. The best kind of uncertainty information tested here was the one that provided the probability at the threshold for the task at hand. We conclude that uncertainty information can be used advantageously, even when it does not result in perfectly rational performance, and that uncertainty can be communicated effectively to nonexpert end users, resulting in improved decision making.
Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular “patterns” of these privileged structures for combinatorial design or target selectivity.