Miracle-dz/UniFAR-FTUs huggingface dataset:
ISO 9000: marketing motivations and benefits

Reports a survey of the largest‐ever national survey of the international quality management system, ISO 9000 (BS EN ISO 9000), which has been installed in 95,000 companies internationally. Specifically, reports the marketing considerations which motivate companies to seek certification and the marketing benefits which accrue from certification. Mail surveys were carried out on 4,250 certificated organizations; 1,220 (28.7 per cent) responded. Marketing considerations were secondary in seeking registration, and outcomes related to profitability and process improvement were more highly valued than marketing benefits.
Analysis on influence of long vertical grounding electrodes on grounding system for substation

A three dimensional grounding system is introduced to decrease the grounding resistance, step and touch voltages of grounding systems in areas with high soil resistivity or with limited areas for grounding systems. The relationship between the number of vertical grounding rods and the grounding resistance is discussed. The new means to add grounding rods to the grounding system with an explosion grounding technique, have efficiently decreased the grounding resistance in the actual grounding engineering. Reasons why vertical grounding rods can efficiently decrease the maximum touch and step voltages of the earth surface above the grounding system are analyzed. Calculated results shows the vertical grounding rods can also effectively decrease the influence of seasonal factor on safety of grounding system. This paper provides the rule to choose the vertical grounding rods in multi-layer soil based on the relationship analysis between the number and the length of the vertical grounding rods.
Performance of a Cooperative NOMA/OMA Scheme with Energy Buffer-Aided Near-User

Cooperative downlink non-orthogonal multiple access (NOMA) is a spectrally-efficient signalling technique that can enhance throughput to an Internet of Things (IoT) distant from the access point, termed the far-user (FU) IoT, while simultaneously serving a closer IoT node, termed the near-user (NU) IoT. However, it requires relaying by the NU IoT, which can drain its battery energy. In this paper, we consider an energy-buffer equipped NU IoT that uses only harvested energy to relay to the FU IoT. To further enhance performance with the energy buffer, and allow it to accumulate energy, we switch between NOMA, cooperative NOMA and orthogonal multiple access (OMA) signalling modes. This hybrid scheme results in better FU IoT throughput performance as compared to individual modes, while simultaneously enhancing NU IoT performance, without requiring the latter to expend its battery energy. The energy buffers are modelled precisely using a continuous-state space Markov chain. The best-effort policy (BEP) energy management policy is considered. The limiting distribution of BEP policy is provided for stable buffers, and then use this to derive expression for throughput. Simulation results confirm accuracy of the derived analytical expressions.
Joint Calibration and Direct Position Determination for Moving Array Sensors

This paper addresses the problem of joint calibration and direct position determination (DPD) for moving array sensors. DPD techniques often rely on high-resolution direction finding (DF) methods like MUltiple SIgnal Classification (MUSIC). These methods require precise knowledge of the array response and are sensitive to model perturbations. Self-calibration uses sources of opportunity to estimate both, the unknown directions of arrival (DOAs) as well as the model perturbations.In this paper we propose a new technique that combines the aforementioned self-calibration and the DPD approach for a single moving array sensor. By fully exploiting the source position, gain and phase imperfections can be uniquely determined, using a single source of opportunity. We derive the Cramér-Rao lower bound for the problem of joint calibration and localization for a deterministic signal model and show that the proposed estimator is asymptotically efficient in our numerical experiments. Finally, the proposed technique is verified using measurements collected during field trials.
Availability and Propulsion

The first and second laws applied to steady-flow systems are expressed in forms which emphasize the distinction between energy, which is conserved, and available energy, which is depleted in real processes. These forms are applied to propulsive systems using, as a velocity datum, the propulsion unit itself and, alternatively, the atmosphere at rest. The maximum thrust power obtainable from the combustion of the fuel is shown to be dependent on the composition, state and velocity of the fuel and also on the composition and state of the environment in which the unit works. An illustrative calculation of the losses in a turbojet engine in flight reveals that in this case 16.64 per cent of the fuel's available energy is obtained as thrust power, 54.25 per cent is rejected by the exhaust, 2.33 per cent is dissipated by aerodynamic losses and the remainder (26.78 per cent) is lost by combustion. Available-energy calculations are seen as providing a consistent framework within which losses can be compared within.
Steady-state Performance Assessment of Induction Motor under Unbalanced Voltage Condition

Abstract This article discusses the performance assessment of a 3-Φ induction motor under supply voltage unbalance. It has been emphasized that not only degree but the knowledge of conditions of voltage unbalance is also required to obtain unique value of performance parameters, viz., efficiency, copper loss, power factor, derating factor, etc. of a 3-Φ induction motor operating under supply voltage unbalance. Further, it has been shown that different conditions of unbalances are exactly reflected in the magnitude and phase angle of the complex current unbalance factor, whereas the voltage unbalance factor gives the degree or severity of unbalance only. Hence, the inclusion of magnitude and phase angle of the complex current unbalance factor along with the voltage unbalance factor has been asserted for the precise and complete assessment of the 3-Φ induction motor. Additionally, it has been revealed that the “derating factor” and “peak copper loss” are highly sensitive to the conditions of voltage unbalance that depends on the angle of complex current unbalance factor. Hence, to find the unique values of these two performance parameters, the incorporation of complex current unbalance factor angle to define the conditions of unbalance has been emphasized. Finally, to show the accuracy of the proposed method for the precise and complete assessment of the performance of the 3-Φ induction motor, the simulation and experimental results for efficiency, total loss, and derating factor are presented.
A Useful Equivalence for a Coaxial-Waveguide Junction

The junction formed by a coaxial line entering the broad wall of a rectangular waveguide is commonly used, but has not been accurately analyzed due to the electromagnetic-field complexity. An excellent correlation has been established between this complex structure and a similar one which has already been thoroughly characterized. This close correlation permits use of the equivalent circuit of the previously characterized structure as a representation for the coax-waveguide junction as well. This usage is made possible by study of the relationship which exists between the two configurations, thus allowing accurate circuit analysis of components utilizing the coax-waveguide junction.
Continuity and credibility in the Cognitive Science of Religion

reading Hearing Voices. Consequently, after having benefited from the philosophical rigor McCauley and Graham apply to such a large array of topics in Hearing Voices, I wish to take this opportunity to ask them to explicate the scientific utility of labeling some mental disorders or symptoms as “religious” and, if they find scientific utility in such labeling, to detail whose labeling criteria should be used and why. Such an explication would help once again demonstrate the important contributions of philosophy to not only the intersection of religion and mental illness but to the cognitive science of religion as a whole.
Life Cycle Costing Analysis of Solar Photo Voltaic Generation System in Indian Scenario

ABSTRACT This paper aims at the application of Life-Cycle-Costing (LCC) analysis of sustainable renewable energy like solar photo voltaic generation system in India to find out its viability and techno-economic feasibility. Life-Cycle-Costinganalysis is employed to evaluate the long term benefit of the huge investments in India’s ambitious plan of setting up nearly 100 GW installed solar power capacity by December 2022. For renewable energy systems, this methodology shows the cost-effectiveness of being used as an alternative source compared to conventional power generations. Case studies of six solar power plants in India in the state of Karnataka, Gujarat, Andhra Pradesh, Tamil Nadu, New Delhi and Rajasthan were considered to find out the economic viability of the feasibility of the solar power plants in India. It has been observed that in an emerging economy like India, the feasibility and economic viability of solar power plants are quite high. The cash flow analysis for all six cases confirms that project payback depends on initial investment and rate of Power–Purchase-Agreement (PPA). It has been observed that the payback period is generally less than eight years. Thereby the techno-economic feasibility of the solar power plant projects in India is quite high.
Polarimetric spatio-temporal light transport probing

Light emitted from a source into a scene can undergo complex interactions with multiple scene surfaces of different material types before being reflected towards a detector. During this transport, every surface reflection and propagation is encoded in the properties of the photons that ultimately reach the detector, including travel time, direction, intensity, wavelength and polarization. Conventional imaging systems capture intensity by integrating over all other dimensions of the incident light into a single quantity, hiding this rich scene information in these aggregate measurements. Existing methods are capable of untangling these measurements into their spatial and temporal dimensions, fueling geometric scene understanding tasks. However, examining polarimetric material properties jointly with geometric properties is an open challenge that could enable unprecedented capabilities beyond geometric scene understanding, allowing for material-dependent scene understanding and imaging through complex transport, such as macroscopic scattering. In this work, we close this gap, and propose a computational light transport imaging method that captures the spatially- and temporally-resolved complete polarimetric response of a scene, which encodes rich material properties. Our method hinges on a novel 7D tensor theory of light transport. We discover low-rank structure in the polarimetric tensor dimension and propose a data-driven rotating ellipsometry method that learns to exploit redundancy of polarimetric structure. We instantiate our theory with two imaging prototypes: spatio-polarimetric imaging and coaxial temporal-polarimetric imaging. This allows us, for the first time, to decompose scene light transport into temporal, spatial, and complete polarimetric dimensions that unveil scene properties hidden to conventional methods. We validate the applicability of our method on diverse tasks, including shape reconstruction with subsurface scattering, seeing through scattering media, untangling multi-bounce light transport, breaking metamerism with polarization, and spatio-polarimetric decomposition of crystals.
Pragmatic restaurant tipping in star-rated hotels in Kenya

ABSTRACT Tipping can be traced to Tudor England in the sixteenth century. This act transformed into a custom, spreading to many countries. However, tipping is observed differently from one country to the other and thus is not homogeneous from a global perspective. The act of tipping is thought to be motivated by various predictors, which were studied and are thought to influence peoples’ tipping behaviour. They include gender, income level, religion, nationality, hospitality exposure, alcohol consumption and the weather conditions of the day. The study applied a cross-sectional survey design and was carried out in Kenya during from December 2019 to February 2020 in eight star-rated hotels. A questionnaire was applied in this study. The study has results showing that data is distributed close to the mean values. Further, the study results show that the independent variable explains 78.4% of the variability in the tipping practice. Thus the variable of “Patronage frequency” gave the strongest significant and unique contribution in explaining the dependent variable (B = 0.515, Sig. = 0.000, t = 15.363). However, religion gave the weakest unique and non-significant contribution (B = 0.013, Sig. = 0.770, t = 0.293). For hotel restaurants to achieve high levels of tipping, there is a need to adjust these predictors depending on the effect of each on the outcome variable. Thus, the study established a significant relationship between the determinants of tipping and the tipping practices in the sampled star-rated hotels in Kenya.
PBNS

We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to un-supervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth. While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar.
Soluble Non-Starch Polysaccharides From Plantain (Musa x paradisiaca L.) Diminish Epithelial Impact of Clostridioides difficile

Clostridioides difficile infection (CDI) is a leading cause of antibiotic-associated diarrhoea. Adhesion of this Gram-positive pathogen to the intestinal epithelium is a crucial step in CDI, with recurrence and relapse of disease dependent on epithelial interaction of its endospores. Close proximity, or adhesion of, hypervirulent strains to the intestinal mucosa are also likely to be necessary for the release of C. difficile toxins, which when internalized, result in intestinal epithelial cell rounding, damage, inflammation, loss of barrier function and diarrhoea. Interrupting these C. difficile-epithelium interactions could therefore represent a promising therapeutic strategy to prevent and treat CDI. Intake of dietary fibre is widely recognised as being beneficial for intestinal health, and we have previously shown that soluble non-starch polysaccharides (NSP) from plantain banana (Musa spp.), can block epithelial adhesion and invasion of a number of gut pathogens, such as E. coli and Salmonellae. Here, we assessed the action of plantain NSP, and a range of alternative soluble plant fibres, for inhibitory action on epithelial interactions of C. difficile clinical isolates, purified endospore preparations and toxins. We found that plantain NSP possessed ability to disrupt epithelial adhesion of C. difficile vegetative cells and spores, with inhibitory activity against C. difficile found within the acidic (pectin-rich) polysaccharide component, through interaction with the intestinal epithelium. Similar activity was found with NSP purified from broccoli and leek, although seen to be less potent than NSP from plantain. Whilst plantain NSP could not block the interaction and intracellular action of purified C. difficile toxins, it significantly diminished the epithelial impact of C. difficile, reducing both bacteria and toxin induced inflammation, activation of caspase 3/7 and cytotoxicity in human intestinal cell-line and murine intestinal organoid cultures. Dietary supplementation with soluble NSP from plantain may therefore confer a protective effect in CDI patients by preventing adhesion of C. difficile to the mucosa, i.e. a “contrabiotic” effect, and diminishing its epithelial impact. This suggests that plantain soluble dietary fibre may be a therapeutically effective nutritional product for use in the prevention or treatment of CDI and antibiotic-associated diarrhoea.
Analyzing Complex Problem Solving by Dynamic Brain Networks

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.
Speeding Up a Large-Scale Filter

Abstract Wolfson et al. introduced a storm tracking algorithm, called the Growth and Decay Storm Tracker, in which the large-scale features were extracted from radar data fields by using an elliptical filter. The elliptical filter as introduced was computationally too expensive to be performed in real time. In this paper, it is shown that although the elliptical filter is nonlinear, it can be decomposed into two parts, one of which is, under some simplifying assumptions, linear and shift-invariant. The linear component can be accelerated using fast algorithms available to compute the digital Fourier transform (DFT). Furthermore, it is shown that the nonlinear part can be written as an update equation, thus reducing the amount of computer memory required. With these improvements to the basic large-scale filtering technique, this paper reports that the large-scale filtering can be done 1–2 orders of magnitude faster. The improvement makes it possible to use the large-scale filtering technique in situations .
Studies of phosphorus Gaussian profile emitter silicon solar cells

Considering recent modifications on n-type highly doped silicon parameters, an emitter optimization was made based on one-dimensional models with analytical solutions. In order to get good accuracy, a fifth order approximation has been considered. Two kinds of emitters, homogeneous and non-homogeneous, with phosphorus Gaussian profile emitter solar cells were optimized. According to our results: homogeneous emitter solar cells show their maximum efficiencies (h @ 21.60-21.74%)with doping levelsNs = 1x1019 - 5x1018 (cm-3) and (1.2-2.0) mm emitter thickness range. Non-homogeneous emitter solar cells provide a slightly higher efficiency (h = 21.82-21.92%), with Ns = 1x1020 (cm-3) with 2.0 mm thickness under metal-contacted surface and Ns = 1x1019 - 5x1018 (cm-3) with (1.2-2.0) mm thickness range, (sheet resistance range 90-100 W/ ) under passivated surface. Although non-homogeneous emitter solar cells have a higher efficiency than homogeneous emitter ones, the required technology is more complex and their overall interest for practical applications is questionable.
Machine Learning in Financial Market Surveillance: A Survey

The use of machine learning for anomaly detection is a well-studied topic within various application domains. However, the detection problem for market surveillance remains challenging due to the lack of labelled data and the nature of anomalous behaviours, which are often contextual and spread over a sequence of anomalous instances. This paper provides a comprehensive review of state-of-the-art machine learning methods used, particularly in financial market surveillance. We discuss the research challenges and progress in this field, mainly applied in other related application domains. In particular, we present a case of machine learning-based surveillance system design for a physical power trading market and discuss how the nature of input data affects the effectiveness of the methods on detecting anomalous market behaviours. Overall, our findings indicate that the regression tree-based ensemble algorithms robustly and effectively predict day-ahead future prices, showing their capability to detect abnormal price changes.
Motorcycle Helmet Retention Devices: Convenience and Comfort

Motorcycle helmets have been proven to be very effective in reducing or preventing head injuries in accidents. For full protection, the helmet must be secured on the wearer's head. This is accomplished by the use of a strap under the wearer's jaw. These retention straps are secured by different devices which have different methods of fastening. Experiments were conducted with the three most common fastening devices on thirty-two subjects to determine measures of fastening and unfastening speed, tightness and the effect of instructions. Mixed ANOVA shows main effects of gender and fastener type as well as interactive effects. No effects were found for instructions. Subjects preferred the buckle-type fastener over slide-bar over D-ring devices.