Low-temperature processed solar cells with formamidinium tin halide perovskite/fullerene heterojunctions

A new type of lead-free, formamidinium (FA)-based halide perovskites, FASnI₂Br, are investigated as light-harvesting materials for low-temperature processed p–i–n heterojunction solar cells with different configurations. The FASnI₂Br perovskite, with a band-gap of 1.68 eV, exhibits optimal photovoltaic performance after low-temperature annealing at 75 °C. By using C₆₀ as electron-transport layer, the device yields a hysteresis-less power conversion efficiency of 1.72%. The possible use of an inorganic MoO _x film as a new type of independent hole-transport layer for the present tin-based perovskite solar cells is also demonstrated.

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Manipulation of polygonal objects with two wheeled-tip fingers: Planning in the presence of contact position error

This paper addresses the planning problem of object manipulation using wheeled-tip robots considering the wheel-object contact positioning error. The term wheeled-tip refers to a new mechanism that incorporates active wheels at robot’s fingertips and allows the grasp contact-point to move along the object’s surface. The benefits of unlimited rolling contact is achieved at the cost of contact positioning error that may cause the manipulation to fail. We propose a probabilistic based algorithm for robot motion planning that in addition to being collision free, guarantees the stability of the grasp throughout the planned path. To do so, first we introduce an algorithm that ensures the kinematical stability of the grasp during manipulation by respecting the force closure constraint. Further we extend the algorithm to address the practical uncertainties involved in the position of wheel-object contact points. The proposed algorithms can be employed for manipulators with limited rolling contacts, as well. The algorithms have been tested and the results prove that the planned path can be trusted in uncertain situations.     

Significance of Peel Test Speed on Interface Strength in Cohesive Zone Modeling

The analysis of the experimental peel test data for obtaining the adhesion fracture energy of an adhesively laminated polymer to the sheet metal surface is considered. The experimental results of the 180° peel test on two types of polymer laminated sheet metal at three different peel speeds are analyzed by two methodological approaches in cohesive zone modeling. These approaches are linear-elastic stiffness approach and critical maximum stress approach. Comparing the results of these two approaches reveals the significance of the peel test speed on the interface strength determination for cohesive zone modeling. It is concluded that a “reference” peel speed may exist at which the interface strength is equal to the yield strength of the peel arm material. A constitutive equation has been proposed which relates the interface strength to the peel test velocity by using the reference peel speed and its corresponding peel arm yield strength.     

Estimation of boundary conditions in the presence of unknown moving boundary caused by ablation

Ablative materials can sustain very high temperatures in which surface thermochemical processes are significant enough to cause surface recession. Existence of moving boundary over a wide range of temperatures, temperature-dependent thermophysical properties of ablators, and no prior knowledge about the location of the moving surface augment the difficulty for predicting the exposed heat flux at the receding surface of ablators. In this paper, the conjugate gradient method is proposed to estimate the unknown surface recession and time-varying net surface heat flux for these kinds of problems. The first order Tikhonov regularization is employed to stabilize the inverse solution. Considering the complicated phenomena that are taking place, it is shown via simulated experiment that unknown quantities can be obtained with reasonable accuracy using this method despite existing noises in the measurement data.     

Simulation of the urban thermal comfort in a high density tropical city: Analysis of the proposed urban construction rules for Dhaka, Bangladesh

The thermal comfort in urban canyons of a high density city is a very challenging issue for urban planners and designers, especially in hot humid tropical zone. The present study aims to evaluate the effects of a newly promulgated building construction rules in respect of thermal climate for Dhaka, Bangladesh, a region characterized by high density area with tropical climate. Three different urban canyons from three areas were considered which almost represents the whole city. Measurements were carried out to assess the existing thermal climate in the city canyons. Further, three model canyons were configured according to the new rules of ground coverage, floor area ratio (FAR) and site setback. A three-dimensional numerical model with high spatial and temporal resolution was used to investigate the microclimatic changes within the urban environments. Model calculations were run for a typical summer day. The thermal climate was evaluated based on air temperature T _a, surface temperature T _s, relative humidity RH, and wind speed in the existing and model canyons. For thermal comfort assessment the temperature humidity index (THI) was considered in this study. The results showed that the outdoor spaces of the study areas are thermally uncomfortable and the new building construction rules hardly improve the conditions in general. However, THI decreases in a canyon where sky view factor (SVF) decreases in the model canyon than the existing one. In addition, the surface temperature T _s and air temperature T _a do not show the same picture. Furthermore, the wind speed increases in all the model canyons as the site setback allows wind flow uniformly inside the canyons.     

Lead time control in multi-server multi-stage assembly system

In this paper, two multi-objective models to optimally control the lead time in multi-server multi-stage assembly system by considering server allocation problem (SAP) and also service rate control problem (SCP) are presented, where new product orders, including all their operations, are entered to the system according to a Poisson process, only one type of products also is produced by the production system, and multi-servers can be settled in each service station. Each operation of any work is operated at a devoted service station with only one of the servers located at a node of the network based on a first-come-first-serve discipline, while the processing times are independent random variables with exponential distributions. Furthermore, it is also assumed that the transport times between each pair of service stations are independent random variable with generalized Erlang distributions. Such system can be modeled as a queueing network, where the system is in the steady state and the lead time is controllable. For modeling of multi-server multi-stage assembly system, initially the network of queues is transformed into an appropriate stochastic network with exponentially distributed arc lengths. A differential equations system is organized to solve and obtain approximate lead time distribution for any particular wok by applying a proper finite-state continuous-time Markov model. Then, two multi-objective models including four conflicted objectives are presented to optimally control the servers allocated to the service stations in SAP and also service rate of service stations in SCP. For solving a discrete-time approximation of the primary multi-objective problems, the goal attainment technique is employed. In this research, reactive controlling in a multi-server multi-stage assembly system also is discussed.     

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

As intelligent sensing and sensor network systems have made progress and low‐cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large‐scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high‐accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deep‐learning‐enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one‐dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high‐accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long‐span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.     

Bismuth doped TiO2 as an excellent photocathode catalyst to enhance the performance of microbial fuel cell

Bismuth impregnation on pure TiO₂ (BiTiO₂) was carried out and tested in microbial fuel cell (MFC) as photocathode catalyst. UV–Visible spectral observation confirmed higher catalytic activity of BiTiO₂ under visible light irradiation with reduced band gap of 2.80 eV as compared to pure TiO₂ (3.26 eV). Electrochemical impedance spectroscopy also showed two times higher exchange current density with lower charge transfer resistance for BiTiO₂ (1.90 Ω) than pure TiO₂ (3.95 Ω), thus confirming it as superior oxygen reduction reaction catalyst. MFC operated with BiTiO₂ could generate a maximum power density of 224 mW m^?2, which was higher than MFC with Pt as cathode catalyst (194 mW m^?2) and much higher than MFCs with TiO₂ catalyzed cathode (68 mW m^?2) and without any cathode catalyst (60 mW m^?2). The results thus promote Bi doped TiO₂ as a superior low-cost alternative to the costly Pt catalyst to take this MFC technology forward for field application.     

Making supply chain design the rational differentiating characteristic of the OEMs

For more complex products that require customers to undertake learning in order to retrieve or realize the products’ true values, the product design and supply management decisions are different from those of less complex products. Nonetheless, in spite of the undeniable significance of coordinating decisions across product design and supply chain structures, we know very little about how to do so in practical ways. In this paper, we build on previous findings and offer a framework to enable the application of the supply chain structure design early in the new product introduction process. The proposed framework is based on a field study of the common practices in one of the major North American automakers, but we believe that it is widely applicable in other industries.     

Unaligned training for voice conversion based on a local nonlinear principal component analysis approach

During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at first, performance of the network is described by means of an artificial dataset and then, the developed method is applied to perform VC.