Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Canadian Regulations and Remediation Methods for Diesel Oil Contaminated Soils

Diesel fuel released into the environment can contaminate ground water, degrade potable water supplies and cause the collapse of fisheries. They are toxic to both animals and humans and can affect the liver, lungs, kidneys, and nervous system leading to cancer as well as immunological and reproductive effects. The objectives of this study were to review current Canadian regulations pertaining to diesel fuel and to evaluate the current remediation methods using five criteria: efficiency, applicability, cost, time and cleanliness. PAHs are deemed toxic under the Canadian Environmental Protection Act but no standards have been set for PAHs in diesel. The Canadian Council of Ministers of the Environment (CCME) has developed Canada-Wide Standards for Petroleum Hydrocarbons in Soil (CWS PHCS) while the Atlantic PIRI has implemented a Risk Based Corrective Action (RBCA) for the Atlantic region. The remediation methods included soil washing, landfilling, incineration, thermal desorption, radio frequency heating, chemical addition, landfarming, biopiling, composting, bioventing, liquid delivery and bioreactors. The bioreactors studied included: static bed, continuous mix, horizontal drum, fungal compost, slurry-phase, DITS, biofilters and packed bed bioreactors. The results showed that the biological methods were more effective than nonbiological ones and the bioreactors scored the highest among the biological methods. Eight criteria were then used for the evaluation of bioreactors: efficiency, time, cost, maintenance, simplicity, release of VOCs to the atmosphere, containment of contaminants and control of operating parameters The results showed that the continuous mix bioreactor was the most effective system.
Multimodal Image Reconstruction of Electrical Impedance Tomography Using Kernel Method

The inverse problem of electrical impedance tomography (EIT) is nonlinear and severely ill-posed, resulting in low image quality, which explicitly involves the aspects of structure preservation and conductivity contrast differentiation. This article reports a kernel method-based multimodal EIT image reconstruction approach to tackle this challenge. The kernel method performs image-level segmentation-free information fusion and incorporates the structural information of an auxiliary high-resolution image into the EIT inversion process through the kernel matrix, leading to an unconstrained least square problem. We describe this approach in a general way so that the high-resolution images from various imaging modalities can be adopted as the auxiliary image if they contain sufficient structural information. Compared with some state-of-the-art algorithms, the proposed kernel method generates superior EIT images on challenging simulation and experimental phantoms. It also presents the advantage of suppressing the interference of imaging-irrelevant objects in the auxiliary image. Simulation and experiment results suggest the kernel method has great potential to be applied to more complex tissue engineering applications.
Drought could promote the heartwood formation in Dalbergia odorifera by enhancing the transformation of starch to soluble sugars

ABSTRACT The heartwood of Dalbergia odorifera T. Chen (D. odorifera) is highly valuable. It was speculated that drought could promote heartwood formation in D. odorifera. To test this hypothesis, we artificially simulated conditions of low and high soil moisture content and investigated whether D. odorifera trees living in habitats with contrasting soil water conditions display distinct heartwood formation. Besides, we performed a preliminary study to explore the possible mechanism of drought affecting heartwood formation in D. odorifera in terms of non-structural carbohydrates (NSC). Higher heartwood formation percentage and heartwood size were observed in drought-treated trees. The heartwood density of drought-treated trees was significantly greater than that of control and the oil content of heavy drought-treated heartwood was significantly higher than that of the other treatments. None of water treatments had a significant effect on the essential oil components of heartwood. Furthermore, the effects of water treatments on the total amount of NSC were very limited; however drought treatments enhanced the transformation of starch to soluble sugars compared to control. This study confirmed the hypothesis that drought could promote the heartwood formation in D. odorifera, not by changing the total amount of NSC but by changing the component proportion of NSC.
Relationship between Freeway Flow Parameters and Safety and Its Implication for Adding Lanes

The decision to add lanes to a freeway is motivated by the need to relieve congestion. Practicing engineers and planners generally believe that the decreased congestion that results from adding lanes is associated with some degree of improved safety, yet the majority opinion of researchers is that accident rates increase as the number of lanes increases. In more than 70 years of modern road building, these conflicting views have not been reconciled. This paper first examines the relationship of traffic flow parameters, such as volume, density, and speed, with safety by calibrating corridor-specific safety performance functions. On the basis of an understanding of this relationship, a possible explanation of the effect that adding lanes has on safety is formulated. An empirical examination of the relationship of flow, density, and speed to the crash rate on selected freeways in Colorado suggests that, as the flow increases, the crash rate initially remains constant until a certain critical threshold combination of speed and density is reached. Once this threshold is exceeded, the crash rate rapidly rises. The rise in the crash rate may be because an increase in density without a notable reduction in speed produces headways so small that it becomes difficult or impossible to compensate for driver error. This model suggests that, after the construction of additional lanes, crash rates initially decline because of the lower traffic volume and density per lane. However, as development and rerouting occur, freeways with more lanes are expected to have higher crash rates that are attributable to the increased opportunities for lane change–related conflicts.
Electroelastomers: applications of dielectric elastomer transducers for actuation, generation, and smart structures

Electroactive polymers (EAPs) can overcome many limitations of traditional smart material and transducer technologies. A particularly promising class of EAP is dielectric elastomer, also known as electroelastomer. Dielectric elastomer transducers are rubbery polymer materials with compliant electrodes that have a large electromechanical response to an applied electric field. The technology has been developed to the point where exceptional performance has already been demonstrated: for example, actuated strains of over 300 percent. These strains and the corresponding energy densities are beyond those of other field-activated materials including piezoelectrics. Because of their unique characteristics and expected low cost, dielectric elastomer transducers are under development in a wide range of applications including multifunctional (combined actuation, structure, and sensing) muscle-like actuators for biomimetic robots; microelectromechanical systems (MEMS); smart skins; conformal loudspeakers; haptic displays; and replacements for electromagnetic and pneumatic actuators for industrial and commercial applications. Dielectric elastomers have shown unique performance in each of these applications; however, some further development is required before they can be integrated into products and smart-materials systems. Among the many issues that may ultimately determine the success or failure of the technology for specific applications are durability, operating voltage and power requirements, and the size, cost, and complexity of the required electronic driving circuitry.
A MPPT Control Algorithm Based on Extreme Learning Machine in PV System

At present, there are many commonly used maximum power point tracking (MPPT) control methods for solar photovoltaic system which have different degrees of output fluctuations. In order to solve the problem and improve the response speed further, a novel MPPT algorithm based on the extreme learning machine was put forward in this paper. Using the extreme learning machine which has small error, fast speed and simple structure train a mathematical model with the environmental temperature and light intensity as input and the maximum power point as output, the model can be used as the controller of the photovoltaic cells. The simulation results show that the error of the model meets the requirements, when the ambient temperature and light intensity change, it can respond quickly so as to photovoltaic cells work at the maximum power point and operate stably.
Multigoal Programming for Structure/Control Design Synthesis with Fuzzy Goals

This paper considers the simultaneous structure/control design synthesis of a wing with an active control system called the gust load alleviation (GLA) system. The introduction of fuzzy goals for both the gust-induced stress with the control system off and the structural mass is the primary focus in the present paper. A simple wind tunnel model excited by a random gust is studied. The thickness of a FEM beam element modeling the wing spar and the feedback control gain are optimized to maximize the fuzzy decision with the prioritized design requirements, e. g. , the control system's stability characteristics, and the standard deviation of the gust-induced stress and control surface deflection angle with the control system on. The sequential linear approximation method is proposed to obtain the optimum solutions for nonlinear multigoal problems with fuzzy goals.
Generation of high-power subnanosecond pulses

The generation of short (0.1-1.0 ns) pulses based on spark gaps remained for a long time the only type of low impedance (<100 Ohm) subnanosecond devices capable of producing an output voltage of 100 kV and more. The developments in subnanosecond pulse generation are briefly outlined. The results of fundamental importance obtained in a series of experiments on studying the operating modes of subnanosecond high-pressure gas gaps are as follows: repetitive pulse modulators with highly reproducible and tunable parameters have been realized, subnanosecond jitters have been achieved for the starting of electrically triggered gas switches, and compact module systems have been created. New experimental data on the electric strength of various types of insulation exposed to pulsed electric and microwave fields have made it possible to create rather compact and high power superwideband generators. The studies on the generation of subnanosecond high-current beams have led to the realization of new high-power sources of millimeter microwaves operating in the superradiation mode based on semiconductor opening switches.
Application of the Higher Modes to Crack Identification in a Cantilever Beam

Damage detection by the wavelet transform of the fundamental vibration mode receives much attention recently. However, the higher vibration modes were hardly discussed in past literatures. As we know they are more sensitive to the crack comparing with the fundamental mode, which bring abundant damage information. The objective of this study is to show that the first four modes are available using wavelet transform for crack parameter identification. Specially, using the higher mode could result in an excellent quality of crack identification when the mode data was contaminated by noise. But too higher modes have possibly adverse effects on the quality of crack detection.
Regular Issue Papers Computer Model for Harmonic Ultrasound Imaging

Harmonic ultrasound imaging has received great attention from ultrasound scanner manufacturers and researchers. In this paper, we present a computer model that can generate realistic harmonic images. In this model, the incident ultrasound is modeled after the "KZK" equa- tion, and the echo signal is modeled using linear propaga- tion theory because the echo signal is much weaker than the incident pulse. Both time domain and frequency domain nu- merical solutions to the "KZK" equation were studied. Re- alistic harmonic images of spherical lesion phantoms were generated for scans by a circular transducer. This model can be a very useful tool for studying the harmonic buildup and dissipation processes in a nonlinear medium, and it can be used to investigate a wide variety of topics related to B- mode harmonic imaging.
Speech Classification to Recognize Emotion Using Artificial Neural Network

-This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centers, banks, education, and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral, and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.
Knowledge Management of Social Networks

Emma Tonkin was originally educated as a physicist, but has since diversified. She works as a researcher at UKOLN, University of Bath, United Kingdom and can be reached at e.tonkin ukoln.ac. uk. Heather D. Pfeiffer is college research faculty at New Mexico State University in the Klipsch School of Electrical and Computer Engineering. She is interested in knowledge engineering and management, database management and modeling of domains of information using automatic ontology building. She can be reached at hdp cs.nmsu. edu. Greg Tourte is a research associate at the School of Geographical Sciences at the University of Bristol, United Kingdom. He currently specializes in large climate dataset management, distribution and visualization. He also maintains a supercomputer and the associated data processing server farm in his spare time. Greg can be reached at g.j.l.tourte bris.ac. uk.
Signal quality and BER performance improvement by wavelength conversion with an integrated three-port Mach-Zehnder interferometer

2.5 Gbit/s all-optical wavelength conversion is reported with a novel monolithically integrated Mach-Zehnder interferometer realised in InGaAs/InP MQW technology. The three-port MZI wavelength converter enables strong improvement of extinction ratios up to 11 dB in dynamic operation and signal chirp reduction by optical crossphase modulation. This improvement of signal quality by wavelength conversion is confirmed by a gain of 2.3 dB in system sensitivity after transmission over 100 km (nondispersion-shifted) fibre compared to the standard 2.5 Gbit/s direct modulation performance of a DFB laser. >
A Global Max-Flow-Based Multi-Resolution Next-Best-View Method for Reconstruction of 3D Unknown Objects

Many robot tasks, such as grasping and inspection, may require complete 3D models with enough surface details. Fully autonomous environment exploration and 3D reconstruction of unknown objects are challenging for a robot when little or no knowledge about an object is known as a priori. Previous work updated sensor measurements from a greedy ordered set of views into a map with an unchangeable voxel size, leading to the lack of details on the object surface, sampling problems, and no adaption to small objects. We propose a global max-flow-based multi-resolution next-best-view (NBV) method to improve performance on these problems. In particular, it utilizes a max-flow-based global view quality function to obtain optimal NBVs, and a multi-resolution strategy to optimize the reconstruction quality and efficiency. Results of comparative simulation experiments with state-of-the-art (SOTA) methods show that our method achieves a higher voxel coverage under the same resolution. Parametric and ablation studies confirm that the global view function and multi-resolution strategy are effective. Results of real-world experiments show that our method achieves complete reconstruction of small objects with only 6–8 views.
MESS- UND ÜBERWACHUNGSAUFGABEN BEI CARDIOCHIRURGISCHEN OPERATIONEN

Im Verlauf eines cardiochirurgischen Eingriffes am eröffneten Herzen tanter Anwendung der Herz-Lungen-Maschine, erfährt der Organismus des Patienten schwerwiegende Abweichungen vom Normalzustand vitaler Funktionssysteme und ihres natürlichen Zusammenspiels. Die Aufeinanderfolge derartiger induzierter unphysiologischer Phasen (z.B.: hypothermer Kreislaufstillstand Extrakorporale Zirkulation Wiederbelebung des Herzens) bishin zur Wiederherstellung stabiler Verhältnisse geschieht ausserordentlich dynamisch. Entsprechend ungeteilt und konzentriert ist die Aufmerksamkeit von Chirurgen, Anästhesisten und medizinisch-technischem Personal auf den Zustand des Patienten gerichtet: Die Dichte von 6-12 Ärzten und Hilfspersonen, die ausschliesslich mit der Behandlung eines Patienten beschäftigt sind, dürfte sonst in der Medizin selten erreicht werden.
Structural Seismic Demand Analysis: Consideration of "Collapse"

It is well known that a structure generally responds well beyond its elastic capacity at high seismic loading; nonlinear time-history analysis is therefore the most accurate procedure for the seismic demand calculations. Such analyses are being used systematically, for example, in the current SAC steel momentresisting frame project. There is apparently no methodology currently available to characterize the seismicdemand calculations when we observe that in some cases structures, especially tall structures, “collapse” because of strength degradation and/or significant effects. In this paper we will propose a novel way to calculate the seismic demand by considering a three-parameter probability distribution model. The parameters of the distribution at any ground-motion intensity level can be calculated easily from the response results when the ground-motion records are scaled to that intensity level. More generally, the parameters can be estimated economically from (1) a conventional-regression analysis of the deformations/drifts of a structure versus the intensity of ground motion conditioned on no collapse and (2) a binary-regression analysis of observations of “collapse” or “no collapse” versus intensity level. We illustrate the seismic-demand calculations through an example problem of a 20-story steel structure that collapses in a subset of the nonlinear time-history analyses under high-intensity ground motions. These regression results can be used with the conventional seismic hazard analysis to calculate the annual response-exceedance probabilities.
Location as a basis of competitive advantage for grain milling firms in Nairobi and Mombasa

Businesses worldwide are faced with different kinds o f challenges which if not properly handled can lead to the business making losses or even to wind up. It is in this context thus that businesses try to develop strategies and attributes which will enable them become competitive in order to outperform the competition and make higher profits. The aim of this study was to establish if location is used as a basis of competitive advantage by grain milling firms in Nairobi and Mombasa. To this end, this exploratory study was conducted to establish if the locations picked by grain millers to set up their business gives an advantage over businesses located in different locations. The study was a census of the grain milling firms located in Nairobi and Mombasa. Data was collected from a total of 31 firms. The grain milling firms purposively selected for this study constituted of 19 [61.3%] from Nairobi and 12 [38.7%] from Mombasa. Data was collected using a comprehensive questionnaire which was administered to respondents. The study used both structured and close ended questions. The respondents included different line managers who had different academic qualifications and business experience concerning developing business strategies for the milling firms to enable the business remain competitive and grow to new levels. These included sales, operations and marketing managers. Generally, the findings o f this study was that location o f a firm was only a competitive advantage if that location was near factors like relevant technology and provided access to a good transportation network. Otherwise, location did not act as basis for competitive advantage. Many respondents stated that factors like brand power and packaging offered a potent strategy for competitive advantage.
Simulation Research on Vehicle Handling Inverse Dynamics Based on Radial Basis Function Neural Networks

Based on a brief review of the developing history of vehicle steering inverse dynamics, and using radial basis function neural network, the nonlinear mapping relation between lateral angular velocity and steering angle input is found. The identification results of step and sine steering angle input show that the proposed identification method is not only practical, but also with high accuracy.
Impact of distance on stroke inpatients’ mobility in rehabilitation clinics: a shadowing study

ABSTRACT Stroke inpatients in rehabilitation clinics are highly inactive in their free time and often depend on staff members to transport them to scheduled therapies. This study examines how distances between spaces in rehabilitation clinics impact patients’ mobility. Seventy patients were shadowed over the course of one ordinary day in rehabilitation. Shadowing was accompanied by patient and staff questionnaires. Both patients and staff members described the labyrinthine built environment with long corridors that all look similar. Patients covered substantial daily distances in the clinics, and longer distances were significantly related to encountering more mobility barriers and dependence on staff. Compact layouts with vertically separate wards and main therapy areas resulted in reduced travel distances compared to more complex building layouts. Patients’ mobility abilities were occasionally observed to change on different distances and even throughout the day. As distances result from the building's layout, greater attention needs to be paid to this aspect of the built environment in the early design stages. This is especially the case since other built-environment barriers were found to be intertwined with long distances. All patients may be independently mobile if distances between their most important areas (wards, therapy areas and dining spaces) are carefully planned.
Improved location estimation with pulse-ranging in presence of shadowing and multipath excess-delay effects

The authors describe an alternative to the method of least squares (LS) for estimating the location of a subscriber in a mobile radio system through hyperbolic trilateration with multipath corrupted ranging estimates. In simulation trials, the new approach reduced the multipath limited error by 30 – 500% relative to the LS method. This contributes to location finding applications without needing GPS receivers in mobiles or phased arrays at the basestations to combat multipath.