Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Dealing with high-dimensional data has always been a major problem in many pattern recognition and machine learning applications. Trace ratio criterion is a criterion that can be applicable to many dimensionality reduction methods as it directly reflects Euclidean distance between data points of within or between classes. In this paper, we analyze the trace ratio problem and propose a new efficient algorithm to find the optimal solution. Based on the proposed algorithm, we are able to derive an orthogonal constrained semi-supervised learning framework. The new algorithm incorporates unlabeled data into training procedure so that it is able to preserve the discriminative structure as well as geometrical structure embedded in the original dataset. Under such a framework, many existing semi-supervised dimensionality reduction methods such as SDA, Lap-LDA, SSDR, SSMMC, can be improved using our proposed framework, which can also be used to formulate a corresponding kernel framework for handling nonlinear problems. Theoretical analysis indicates that there are certain relationships between linear and nonlinear methods. Finally, extensive simulations on synthetic dataset and real world dataset are presented to show the effectiveness of our algorithms. The results demonstrate that our proposed algorithm can achieve great superiority to other state-of-art algorithms.     

Incorporation of a Probabilistic Monotonic Strain Energy Analysis to a Lifting Method

The proposed work analyzes the possibility of improving the capabilities of an energy-based fatigue life prediction method. The improvement being addressed is regarding the variation of empirical monotonic strain energy density calculations and the effects on the energy-based fatigue life prediction capability. Since the prediction method was developed from the concept that the strain energy accumulated during both monotonic failure and an entire fatigue process are equal, meaning the strain energy accumulated during monotonic failure is a physical damage quantity, it was important to understand the variation of monotonic strain energy density. The process for incorporating this variation into the prediction method explores a probabilistic, Three-Sigma analysis that is applicable for all deterministic methods of measuring experimental monotonic strain energy density. The accuracy of the probabilistic energy-based lifing method was admirably assessed by comparison with experimental fatigue life results, between 10³ and 10⁵ cycles, conducted on Titanium 6Al–4V specimens at room temperature.     

Quantile regression on the nonlinear relationship between land use and trip time

This study conceptually confirms and empirically tests the potential that the significance and magnitude of the compact land use–trip time relationship differ by the degree of compactness and trip time. Based on a quantile regression of about 25,000 commuters in Seoul, Korea, the empirical analysis suggests a significant level for each neighbourhood land use variable and a magnitude change within the level. Weekend trip and school densities are significant for shorter trip time commuters, population density is significant for longer trip time commuters, and pedestrian density is significant for all commuters. Wherever significant, the land use variables exert stronger effects as their values increase.     

Charge Transfer Variability in Misfit Layer Compounds: Comparison of SnS-SnS2 and LaS-TaS2

The present paper compares and discusses two selected misfit (layer) compounds exemplarily, namely SnS-SnS₂ and LaS-TaS₂. We have employed a density-functional theory-based approach to calculate structural, energetic, and electronic properties of these structures. We have put emphasis on the difference between single layers, combined double-layer systems and periodically stacked bulk structures. Especially the varying magnitudes of charge transfer between the sublayers were studied. We demonstrate how the chemical constitution of the sublayers affects the interlayer interactions: these may be a weak non-bonding van-der-Waals dominated interlayer interaction as in SnS-SnS₂ and many other layered structures or a strong interaction related to a remarkable charge transfer between the layers as in LaS-TaS₂.     

Synthesis of a PPV‐fluorene derivative: Applications in luminescent devices

Synthesis of a polyfluorene/poly(p‐phenylene vinylene) derivative, the Poly [(9,9′‐di‐hexylfluorenediylvinylene‐alt‐1,4‐phenylenevinylene)‐co‐((9,9′‐(3‐t‐butylpropanoate) fluorene‐1,4‐phenylene)] (LaPPS 42) was performed following Wittig and Suzuki routes. Polyfluorenes and derivatives have been used in electroluminescent devices, and the synthesis described here has the advantage in pave the way to get distinct structures having different emission spectra. An extensive study of its electrochemical, thermomechanical, optical, and structural properties was carried out, as well as its application in electroluminescent devices. Polymer light‐emitting diodes (PLEDs) and polymer light‐emitting electrochemical cells (PLECs) were built using LaPPS 42 as active layer, and their electric and optical characterizations confirm they have a potential as active element in electroluminescent devices. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42579.     

Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia

Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For example, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further hampered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements.     

Enhanced O2 Flux of CaTi0.85Fe0.15O3−δ Based Membranes by Mn Doping

Dense symmetric membranes of CaTi_0.85?xFe_0.15Mn_xO_3?δ (x = 0.1, 0.15, 0.25, 0.4) are investigated in order to determine the optimal Mn dopant content with respect to highest O₂ flux. O₂ permeation measurements are performed as function of temperature between 700°C–1000°C and as function of the feed side ranging between 0.01 and 1 bar. X‐ray photoelectron spectroscopy is utilized to elucidate the charge state of Mn, and synchrotron radiation X‐ray powder diffraction (SR‐XPD) is employed to investigate the structure symmetry and cell volume of the perovskite phase at temperatures up to 800°C. The highest O₂ permeability is found for x = 0.25 over the whole temperature and ranges, followed by x = 0.4 above 850°C. The O₂ permeability for x = 0.25 reaches 0.01 mL(STP) min^?1 cm^?1 at 925°C with 0.21 bar feed side and Ar sweep gas. X‐ray photoelectron spectroscopy indicates that the charge state of Mn changes from approx. +3 to +4 when x > 0.1, which implies that Mn mainly improves electronic conductivity for x > 0.1. The cell volume is found to decrease linearly with Mn content, which coincides with an increase in the activation energy of O₂ permeability. These results are consistent with the interpretation of the temperature and dependency of O₂ permeation. The sintering behavior and thermal expansion properties are investigated by dilatometry, which show improved sinterability with increasing Mn content and that the thermal expansion coefficient decreases from 12.4 to 11.9 × 10^?6 K^?1 for x = 0 and x = 0.25, respectively.     

The Effects of Exercise and Activity-Based Physical Therapy on Bone after Spinal Cord Injury

Spinal cord injury (SCI) produces paralysis and a unique form of neurogenic disuse osteoporosis that dramatically increases fracture risk at the distal femur and proximal tibia. This bone loss is driven by heightened bone resorption and near-absent bone formation during the acute post-SCI recovery phase and by a more traditional high-turnover osteopenia that emerges more chronically, which is likely influenced by the continual neural impairment and musculoskeletal unloading. These observations have stimulated interest in specialized exercise or activity-based physical therapy (ABPT) modalities (e.g., neuromuscular or functional electrical stimulation cycling, rowing, or resistance training, as well as other standing, walking, or partial weight-bearing interventions) that reload the paralyzed limbs and promote muscle recovery and use-dependent neuroplasticity. However, only sparse and relatively inconsistent evidence supports the ability of these physical rehabilitation regimens to influence bone metabolism or to increase bone mineral density (BMD) at the most fracture-prone sites in persons with severe SCI. This review discusses the pathophysiology and cellular/molecular mechanisms that influence bone loss after SCI, describes studies evaluating bone turnover and BMD responses to ABPTs during acute versus chronic SCI, identifies factors that may impact the bone responses to ABPT, and provides recommendations to optimize ABPTs for bone recovery.     

Plastic-containing materials as alternative reductants for base metal production

Shredder residue materials are produced after the removal of ferrous and non-ferrous fractions from end-of-life electronic equipment. Despite the high plastic content and metal value in the ash, high percentages of these materials are currently sent to landfills. In this study, the potential of utilising shredder residue material and other plastic-containing materials as reducing agents was studied. Plastic-containing materials were co-injected with coal into a zinc-fuming furnace in Boliden-Rönnskär smelter. The data obtained from the trial, such as the data from the chemical analysis of the slag and the steam production, are discussed. The observations indicate that plastic-containing material can replace up to 1?ton?h^?1 of coal without a significant decrease in the zinc reduction rate.     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.