Miracle-dz/UniFAR-FTUs huggingface dataset:
PENELOPE 2018: A code system for Monte Carlo simulation of electron and photon transport

The computer code system penelope (version 2018) performs Monte Carlo simulation of coupled electron-photon transport in arbitrary materials for a wide energy range, from a few hundred eV to about 1 GeV. Photon transport is simulated by means of the standard, detailed simulation scheme. Electron and positron histories are generated on the basis of a mixed procedure, which combines detailed simulation of hard events with condensed simulation of soft interactions. A geometry package called pengeom permits the generation of random electron-photon showers in material systems consisting of homogeneous bodies limited by quadric surfaces, i.e., planes, spheres, cylinders, cones, etc. This report is intended not only to serve as a manual of the penelope code system, but also to provide the user with the necessary information to understand the details of the Monte Carlo algorithm.
Category $\mathcal{O}$ for the Lie algebra of vector fields on the line

Let $\mathfrak{W}$ be the Lie algebra of vector fields on the line. Via computing extensions between all simple modules in the category $\mathcal{O}$, we give the block decomposition of $\mathcal{O}$, and show that the representation type of each block of $\mathcal{O}$ is wild using the Ext-quiver. Each block of $\mathcal{O}$ has infinite simple objects. This result is very different from that of $\mathcal{O}$ for complex semisimple Lie algebras. To find a connection between $\mathcal{O}$ and the module category over some associative algebra, we define a subalgebra $H_1$ of $U(\mathfrak{b})$. We give an exact functor from $\mathcal{O}$ to the category $\Omega$ of finite dimensional modules over $H_1$. We also construct new simple $\mathfrak{W}$-modules from Weyl modules and modules over the Borel subalgebra $\mathfrak{b}$ of $\mathfrak{W}$.
Collaborative Control of Autonomous Swarms with Resource Constraints

Title of Dissertation: Collaborative Control of Autonomous Swarms with Resource Constraints Wei Xi, Doctor of Philosophy, 2006 Dissertation directed by: Professor John S. Baras Department of Electrical and Computer Engineering This dissertation focuses on the collaborative control of homogeneous UAV swarms. A two-level scheme is proposed by combining the high-level path planning and the lowlevel vehicle motion control. A decentralized artificial potential function (APF) based approach, which mimics the bacteria foraging process, is studied for the high-level path planning. The deterministic potential based approach, however, suffers from the local minima entrapment dilemma, which motivate us to fix the “flaw” that is naturally embedded. An innovative decentralized stochastic approach based on the Markov Random Filed (MRF) theory is proposed; this approach traditionally used in statistical mechanics and in image processing. By modeling the local interactions as Gibbs potentials, the movements of vehicles are then decided by using Gibbs sampler based simulated annealing (SA) algorithm. A two-step sampling scheme is proposed to coordinate vehicle networks: in the first sampling step, a vehicle is picked through a properly designed, configuration-dependent proposal distribution, and in the second sampling step, the vehicle makes a move by using the local characteristics of the Gibbs distribution. Convergence properties are established theoretically and confirmed with simulations. In order to reduce the communication cost and the delay, a fully parallel sampling algorithm is studied and analyzed accordingly. In practice, the stochastic nature of the proposed algorithm might lead to a high traveling cost. To mitigate this problem, a hybrid algorithm is developed by combining the Gibbs sampler based method with the deterministic gradient-flow method to gain the advantages of both approaches. The robustness of the Gibbs sampler based algorithm is also studied. The convergence properties are investigated for different types sensor errors including range-error and random-error. Error bounds are derived to guarantee the convergence of the stochastic algorithm. In the low-level motion control module, a model predictive control (MPC) approach is investigated for car-like UAV model. Multiple control objectives, for example, minimizing tracking error, avoiding actuator/state saturation, and minimizing control effort, are easily encoded in the objective function. Two numerical optimization approaches, gradient descendent approach and dynamic programming approach, are studied to strike the balance between computation time and complexity. Collaborative Control of Autonomous Swarms with Resource Constraints
The Determinant of Adoption of Enterprise Resource Planning for Small and Medium Enterprises in Iran

The enterprise resource planning system (ERP) is a new class of packaged application software that has been appeared through the past decade, seemingly merging under a same banner with several of the largest software firms. These packaged software solutions tries to complete the range of a business's functions and process to be able to present a whole view of the business from singular information technology (IT)architecture. The small and medium size enterprise (SME) does not possess sufficient financial and human recourse, in comparison to the large size organizations; hence, it will not allow them to have a sufficient chance to compete with powerful competitors, in a business environment. However, the adoption rate of ERP in Iran is still in the beginning stage with not many adopters. As a result, this study focuses on the adoption of ERP among Iran companies. Seven factors were identified as determinants of ERP adoption by combining the two widely applied IT theories i.e.Diffusion on Innovation (DOI) and Technology-Organization- Environment (TOE) framework. In the future with eight factors we will conduct survey, factor analysis and logistic regression to determine factors affecting ERP adoption.
Factorial Models for Noise Robust Speech Recognition

Noise compensation techniques for robust automatic speech recognition (ASR) attempt to improve system performance in the presence of acoustic interference. In feature-based noise compensation, which includes speech enhancement approaches, the acoustic features that are sent to the recognizer are first processed to remove the effects of noise (see Chapter 9). Model compensation approaches, in contrast, are concerned with modifying and even extending the acoustic model of speech to account for the effects of noise. A taxonomy of the different approaches to noise compensation is depicted in Figure 12.1, which serves as a road map for the present discussion. The two main strategies used for model compensation approaches are model adaptation and model-based noise compensation. Model adaptation approaches implicitly account for noise by adjusting the parameters of the acoustic model of speech, whereas model-based noise compensation approaches explicitly model the noise and its effect on the noisy speech features. Common adaptation approaches include maximum likelihood linear regression (MLLR) [56], maximum a posteriori (MAP) adaptation [32], and their generalizations [17, 29, 47]. These approaches, which are discussed in Chapter 11, alter the speech acoustic model in a completely data-driven way given additional training data or test data. Adaptation methods are somewhat more general than model-based approaches in that they may handle effects on the signal that are difficult to explicitly model, such as nonlinear distortion and changes in the voice in reaction to noise (the Lombard effect [53]). However, in the presence of additive noise, failing to take take into account the known interactions between speech and noise can be detrimental to performance. Model-based noise compensation approaches, in contrast to adaptation approaches, explicitly model the different factors present in the acoustic environment: the speech, the various sources of acoustic interference, and how they interact to form the noisy speech
IRC + 10°216 mass loss properties through the study of λ3 mm emission

Low-mass evolved stars are major contributors to interstellar medium enrichment as a consequence of the intense mass-loss process these stars experience at the end of their lives. The study of the gas in the envelopes surrounding asymptotic giant branch (AGB) stars through observations in the millimetre wavelength range provides information about the history and nature of these molecular factories. Here we present ALMA observations at subarsecond resolution, complemented with IRAM-30 m data, of several lines of SiO, SiS, and CS towards the best-studied AGB circumstellar envelope, IRC + 10°216. We aim to characterise their spatial distribution and determine their fractional abundances mainly through radiative transfer and chemical modelling. The three species display extended emission with several enhanced emission shells. CS displays the most extended distribution reaching distances up to approximately 20′′. SiS and SiO emission have similar sizes of approximately 11′′, but SiS emission is slightly more compact. We have estimated fractional abundances relative to H2, which on average are equal to f(SiO) ~10−7, f(SiS) ~10−6, and f(CS) ~10−6 up to the photo-dissociation region. The observations and analysis presented here show evidence that the circumstellar material displays clear deviations from an homogeneous spherical wind, with clumps and low density shells that may allow UV photons from the interstellar medium (ISM) to penetrate deep into the envelope, shifting the photo-dissociation radius inwards. Our chemical model predicts photo-dissociation radii compatible with those derived from the observations, although it is unable to predict abundance variations from the starting radius of the calculations (~10 R*), which may reflect the simplicity of the model. We conclude that the spatial distribution of the gas proves the episodic and variable nature of the mass loss mechanism of IRC + 10°216, on timescales of hundreds of years.
A DEADLY FAITH IN FAKES: Trademark Theft and the Global Trade in Counterfeit Automotive Components

Intellectual property (IP) crime remains as yet a marginal topic in sociological and criminological investigation. This neglect is due in part to the perception that such offences are ‘non-serious’ and/or ‘victimless’. This paper sets out to challenge such assumptions by examining a particular instance of IP crime, namely the counterfeiting of dangerous goods, and in particular the counterfeiting of automotive components. It is argued that such activities, now globally widespread, carry both significant economic costs, and that they pose substantial risks to public health and safety. Some of the key drivers of this trade are analysed, along with recent developments in law, policing, crime control and technological innovation that aim to curtail the counterfeiting of these and other dangerous counterfeit products. It is argued that IP crime comprises a socially significant and sociologically challenging phenomenon, one that deserves concerted attention from those working in the sociology of crime, law, health, risk, technology and political economy.
Ultrasonic Spot Welding of Similar and Dissimilar Alloys for Automotive Applications

Lightweighting has been regarded as a key strategy in the automotive industry to improve fuel efficiency and reduce anthropogenic environment-damaging, climate-changing, and costly emissions. Magnesium (Mg) alloys and Aluminum (Al) alloys are progressively more used in the transportation industries to reduce the weight of vehicles due to their high strength-to-weight ratio. Similarly, high strength low alloy (HSLA) steel is widely used to reduce gauge thickness and still maintain the same strength, and thereby reduce vehicle weight as well. A multi-material design of automotive structures and parts inevitably involve similar Mg-to-Mg and dissimilar Mg-to-Al, Al-to-steel, and Mg-to-Cu joints. Ultrasonic spot welding (USW) – a solid-state joining technique has recently received significant attention due to its higher efficiency in comparison with conventional fusion welding techniques. In this study, USW was used to generate similar joints of low rare-earth containing ZEK100 Mg alloy sheets and dissimilar ZEK100-to-Al5754, Al6111-to-HSLA steel, and Mg-to-Cu joints at different levels of welding energy or welding time. To optimize welding process and identify key factors affecting the weld strength, microstructural evolution, microhardness test, tensile lap shear test, fatigue test, and fracture analysis were performed on similar and dissimilar ultrasonic spot welded (USWed) joints. Dynamic recrystallization and grain coarsening were observed during Mg-to-Mg similar welding while rapid formation and growth of interface diffusion layer were observed in all dissimilar joints in the present study. It was due to significantly high strain rate (~103 s-1) and high temperature generated via frictional heating during USW. The interface diffusion layer was analyzed by SEM, EDS and XRD phase identification techniques which showed the presence of eutectic structure containing intermetallic compounds (IMCs). As a result, brittleness at the interface increased. The Zn coating in dissimilar USWed Al-to-steel joints eliminated the formation of brittle IMCs of Al-F, which were replaced by relatively ductile AlZn eutectic. The optimum welding energy or welding time during similar and dissimilar USW of lightweight alloys with a sheet thickness of 1-2 mm was in the range of ~500 J to 2000 J (~0.25 s to 1 s).
Birth asphyxia and its contributing factors among the neonates delivered at Negest Eleni Mohammed memorial teaching hospital, Southern Ethiopia: A cross-sectional study

Background : Birth asphyxia is a major contributor to neonatal mortality worldwide. In Ethiopia, birth asphyxia remains a severe condition that leads to significant mortality and morbidity. This study aimed to assess the prevalence and contributing factors of birth asphyxia among the neonates delivered at the Negest Eleni Mohammed Memorial Teaching Hospital, Southern Ethiopia. Methods: A hospital based cross-sectional study design was employed from June 1- 30, 2019. Two hundred seventy nine study participants were selected using the systematic sampling method. Data were collected using a pretested structured interviewer administered questionnaire, check list and chart review . Data were entered into Epi-data version 3.1 and analyzed using statistical package for social sciences version 24. Descriptive and binary logistic regression analysis were made and P value of < 0.05 was considered significant. Result: The overall prevalence of birth asphyxia among newborns was 15.1%. Factors that were significantly associated with birth asphyxia included mothers aged ≥ 35 [AOR=6.4, 95% CI (2.0, 20.5)], primigravida [AOR=5.1, 95% CI (2.0, 13.3)], prolonged second stage of labor [AOR=4.6, 95%CI (1.6, 13.3)], preterm birth [AOR=4.7, 95% CI (1.5, 14.1)], meconium stained amniotic fluid [AOR=7.5, 95% CI (2.5, 21.4)]and tight nuchal [AOR=3.1, 95% CI (1.2, 9.3)]. Conclusion: Birth asphyxia is still prevalent in the study setting. The results of this study show the need for better maternal care, creating awareness about contributing factors of birth asphyxia to the maternity health professionals, careful monitoring of labor, and identifying and taking proper measures that could help in reducing the occurrence of birth asphyxia.
Accurate Multi-Site Daily-Ahead Multi-Step PM2.5 Concentrations Forecasting Using Space-Shared CNN-LSTM

: Accurate multi-step PM 2.5 (particulate matter with diameters ≤ 2.5 um ) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM 2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM 2.5 concentration series. In which, CNN and LSTM are used to extract each site’s rich hidden feature representations in a stack mode. Especially, CNN is to extract the hidden short-time gap PM 2.5 concentration patterns; LSTM is to mine the hidden features with long-time dependency. Each channel extracted features are merged as the comprehensive features for future multi-step PM 2.5 concentration forecasting. Besides, the space-shared mechanism is implemented by multi-loss functions to achieve space information sharing. Therefore, the final features are the fusion of short-time gap, long-time dependency, and space information, which enables forecasting more accurately. To validate the proposed method’s effectiveness, the authors designed, trained, and compared it with various leading methods in terms of RMSE, MAE, MAPE, and R 2 on four real-word PM 2.5 data sets in South Korea. The massive experiments proved that the proposed method could accurately forecast multi-site multi-step PM 2.5 concentration only using self-historical PM 2.5 concentration time series and running once. Specifically, the proposed method obtained averaged RMSE of 8.05, MAE of 5.04, MAPE of 23.96%, and R 2 of 0.7 for four-site daily ahead 10-hour PM 2.5 concentration forecasting.
Column Grid Array Rework for High Reliability

The probability of removal and replacement of these devices on the actual flight printed wiring board assemblies is deemed to be very high because of last minute discoveries in final test which will dictate changes in the firmware. The questions and challenges presented to the manufacturing organizations engaged in the production of high reliability electronic assemblies are, “Is the reliability of the PWBA adversely affected by rework (removal and replacement) of the CGA package? ” and “How many times can we rework the same board without destroying a pad or degrading the lifetime of the assembly? ” To answer these questions, the most complex printed wiring board assembly used by the project was chosen to be used as the test vehicle, the PWB was modified to provide a “daisy chain” pattern, and a number of bare PWB’s were acquired to this modified design. Non-functional 624 pin CGA packages with internal “daisy chained” matching the pattern on the PWB were procured. The combination of the modified PWB and the daisy chained packages enables continuity measurements of every soldered contact during subsequent testing and thermal cycling. Several test vehicles boards were assembled, reworked and then thermal cycled to assess the reliability of the solder joints and board material including pads and traces near the CGA. The details of rework process and results of thermal cycling are presented in this paper.
IDK, From Identification to Dissemination of Knowledge : Paper presented at the 11th Annual ANZSYS Conference/Managing the Complex, Christchurch, December 5 - 7, 2005

Globally, a great number of development projects are continuously going on in order to improve human economic and living con-ditions. During those pro-jects immense multi-tudes of valuable know-ledge, in-sights, and experiences are created. The problem, however, is that that knowledge will disintegrate and disappear as soon as the project has come to its end. Hence, the next project has to restart from zero without any possibility of taking advan-tage of the experiences already achieved. In other words, we will just have learning on a local and temporal basis but no global learning for improving the total de-velopment endeavour. However, the situation may be improved with the help of Multi-modal Knowledge Modelling as a tool for identifying and organising knowledge, Talkative Knowledge Gathering as a procedure for gathering knowledge and also as a trigger of thinking and knowledge intensification. Other proposed tools and procedures include Systemic Knowledge Instruments and Questionnaires, Process Simulation Seminaries, and Dissemination Courses. The proposals are based on several years of empirical and pragmatic work with gathering, improving, and dissemination of knowledge
A CRISPR/Cas9 method facilitates efficient oligo-mediated gene editing in Debaryomyces hansenii

Abstract Halophilic and osmotolerant yeast Debaryomyces hansenii has a high potential for cell factory applications due to its resistance to harsh environmental factors and compatibility with a wide substrate range. However, currently available genetic techniques do not allow the full potential of D. hansenii as a cell factory to be harnessed. Moreover, most of the currently available tools rely on the use of auxotrophic markers that are not suitable in wild-type prototrophic strains. In addition, the preferred non-homologous end-joining (NHEJ) DNA damage repair mechanism poses further challenges when precise gene targeting is required. In this study, we present a novel plasmid-based CRISPRCUG/Cas9 method for easy and efficient gene editing of the prototrophic strains of D. hansenii. Our toolset design is based on a dominant marker and facilitates quick assembly of the vectors expressing Cas9 and single or multiple single-guide RNAs (sgRNAs) that provide the possibility for multiplex gene engineering even in prototrophic strains. Moreover, we have constructed NHEJ-deficient D. hansenii that enable our CRISPRCUG/Cas9 tools to support the highly efficient introduction of point mutations and single/double gene deletions. Importantly, we also demonstrate that 90-nt single-stranded DNA oligonucleotides are sufficient for direct repair of DNA breaks induced by sgRNA-Cas9, resulting in precise edits reaching 100% efficiencies. In conclusion, tools developed in this study will greatly advance basic and applied research in D. hansenii. In addition, we envision that our tools can be rapidly adapted for gene editing of other non-conventional yeast species including the ones belonging to the CUG clade.
COMPREHENSIVE DIAGNOSTIC AND IMPROVEMENT TOOLS FOR HVAC-SYSTEM INSTALLATIONS IN LIGHT COMMERCIAL BUILDINGS

Proctor Engineering Group, Ltd. (PEG) and Carrier-Aeroseal LLP performed an investigation of opportunities for improving air conditioning and heating system performance in existing light commercial buildings. Comprehensive diagnostic and improvement tools were created to address equipment performance parameters (including airflow, refrigerant charge, and economizer operation), duct-system performance (including duct leakage, zonal flows and thermal-energy delivery), and combustion appliance safety within these buildings. This investigation, sponsored by the National Energy Technology Laboratory, a division of the U.S. Department of Energy, involved collaboration between PEG and Aeroseal in order to refine three technologies previously developed for the residential market: (1) an aerosol-based duct sealing technology that allows the ducts to be sealed remotely (i.e., without removing the ceiling tiles), (2) a computer-driven diagnostic and improvement-tracking tool for residential duct installations, and (3) an integrated diagnosis verification and customer satisfaction system utilizing a combined computer/human expert system for HVAC performance. Prior to this work the aerosol-sealing technology was virtually untested in the light commercial sector--mostly because the savings potential and practicality of this or any other type of duct sealing had not been documented. Based upon the field experiences of PEG and Aeroseal, the overall product was tailored to suit the skill sets of typical HVAC-contractor personnel.