Trapped in the Tiebout model : the impact of federal affordable housing programs on migration of wealthier residents.

Housing mobility programs have been implemented in America since the 1990s to solve poverty concentration and to improve the economic self-sufficiency of low-income families through housing subsidies. This policy was based on the assumption that mixed-income neighborhoods provide better resources and opportunities to low-income families so that program participants who relocate to low-poverty neighborhoods improve their economic status. Currently, few studies examine the effectiveness of housing mobility programs under a decentralized fiscal system. Specifically, the migration of program participants may stimulate Tiebout’s vote-with-feet mechanisms and may limit the duration of the newly-created mixed-income environment, leaving poverty concentration and poverty unsolved. This research uses a dynamic economic model to analyze the impacts of housing mobility programs on local taxes and public goods in both the sending and receiving municipalities and the impacts of this change on further migration of different economic classes. An ANOVA model and a MANCOVA model were used to support the findings from the economic model. The ANOVA results indicate that residents of higher-poverty municipalities did not pay more taxes for welfare and health than residents of lower-poverty municipalities did because the expenses mostly came from intergovernmental funds. However, the MANCOVA results show that the percentage of population growth between 2000 and 2012 in the low-poverty municipalities with more low-income affordable housing program participants was still significantly smaller than that in the low-poverty municipalities with no/fewer low-income affordable housing program participants. These findings accord with the dynamic economic model, which suggests that even if the non-poor living with the poor do not pay more taxes for the public goods used exclusively by the poor, like welfare and health, further migration may still occur under a decentralized fiscal system. Property taxation requires wealthier residents to pay more for each unit of other nonexclusive public goods than the poor do. The research implies that funding anti-poverty programs at the local level rather than the national level may stimulate the out-migration of wealthier residents. As wealthier residents exit the newly mixed-income municipalities, poverty may re-concentrate, limiting the effectiveness of housing mobility programs

COMPARISON OF PLAYER’S CENTER OF MASS MOVEMENT BETWEEN HIGH AND LOW IMPACT POSITIONS IN TENNIS FOREHAND STROKE

During the tennis forehand stroke, the displacement of body center of mass (COM) changes with the body movement. The COM movement influences the recovery from one stroke to the next. Therefore, the purpose of this study is to investigate the differences of COM movement and joint kinematics between high and low-impact positions on different skilled players. This study adopted a 3-D motion analysis system for recording and tracing the advanced (n = 5; level 3-4) and intermediate (n = 7; level 5-6) athletes’ motion of whole body during high and low-impact positions in tennis forehand stroke. The results showed that significant difference was not found between both impact positions and level groups in ball velocity. Advanced group showed greater anterior/posterior displacement than the intermediate group in low-impact position that increased the kinetic energy

Synthesis and crystal structure of the first 6a-thiathiophthen metal complex [Mo(CO)_5PPh_(2]2)(µ-C_5H_2S_3)

The first 6a-thiathiophthen metal complex was prepared by treating M(CO_)5[PPh_2CS_2CH_2C≡CH] with a catalytic amount of secondary amine or tertiary amine; the structure of the 6a-thiathiophthen molybdenum complex is confirmed by an X-ray diffraction analysis

From Isovist to Spatial Perception: Wayfinding in Historic Quarter

Based on the assumption that human behaviours are mainly affected by physical and animate environments, this empirical research takes the changeful and complex historical district in Tainan to observe wayfinding behaviours. An a priori analysis of the isovist fields is conducted to identify spatial characteristics. Three measures, the relative area, convexity, and circularity, are applied to scrutinize the possible stopping points, change of speed, and route choices. Accordingly, an experiment is carried out to observe spatial behaviours and different influences of social stimuli. Results show that social interactions afford groups and pairs to perform better than individual observers in wayfinding.© 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creative commons.org/licenses/by-nc-nd/4.0/).Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.Keywords: wayfinding; isovist; spatial perception and social stimuli; historic quarte

Long-range mechanical force enables self-assembly of epithelial tubular patterns

Enabling long-range transport of molecules, tubules are critical for human body homeostasis. One fundamental question in tubule formation is how individual cells coordinate their positioning over long spatial scales, which can be as long as the sizes of tubular organs. Recent studies indicate that type I collagen (COL) is important in the development of epithelial tubules. Nevertheless, how cell–COL interactions contribute to the initiation or the maintenance of long-scale tubular patterns is unclear. Using a two-step process to quantitatively control cell–COL interaction, we show that epithelial cells developed various patterns in response to fine-tuned percentages of COL in ECM. In contrast with conventional thoughts, these patterns were initiated and maintained by traction forces created by cells but not diffusive factors secreted by cells. In particular, COL-dependent transmission of force in the ECM led to long-scale (up to 600 μm) interactions between cells. A mechanical feedback effect was encountered when cells used forces to modify cell positioning and COL distribution and orientations. Such feedback led to a bistability in the formation of linear, tubule-like patterns. Using micro-patterning technique, we further show that the stability of tubule-like patterns depended on the lengths of tubules. Our results suggest a mechanical mechanism that cells can use to initiate and maintain long-scale tubular patterns

A parametrized three-dimensional model for MEMS thermal shear-stress sensors

This paper presents an accurate and efficient model of MEMS thermal shear-stress sensors featuring a thin-film hotwire on a vacuum-isolated dielectric diaphragm. We consider three-dimensional (3-D) heat transfer in sensors operating in constant-temperature mode, and describe sensor response with a functional relationship between dimensionless forms of hotwire power and shear stress. This relationship is parametrized by the diaphragm aspect ratio and two additional dimensionless parameters that represent heat conduction in the hotwire and diaphragm. Closed-form correlations are obtained to represent this relationship, yielding a MEMS sensor model that is highly efficient while retaining the accuracy of three-dimensional heat transfer analysis. The model is compared with experimental data, and the agreement in the total and net hotwire power, the latter being a small second-order quantity induced by the applied shear stress, is respectively within 0.5% and 11% when uncertainties in sensor geometry and material properties are taken into account. The model is then used to elucidate thermal boundary layer characteristics for MEMS sensors, and in particular, quantitatively show that the relatively thick thermal boundary layer renders classical shear-stress sensor theory invalid for MEMS sensors operating in air. The model is also used to systematically study the effects of geometry and material properties on MEMS sensor behavior, yielding insights useful as practical design guidelines

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Developing Mobile BIM/2D Barcode-Based Automated Facility Management System

Facility management (FM) has become an important topic in research on the operation and maintenance phase. Managing the work of FM effectively is extremely difficult owing to the variety of environments. One of the difficulties is the performance of two-dimensional (2D) graphics when depicting facilities. Building information modeling (BIM) uses precise geometry and relevant data to support the facilities depicted in three-dimensional (3D) object-oriented computer-aided design (CAD). This paper proposes a new and practical methodology with application to FM that uses an integrated 2D barcode and the BIM approach. Using 2D barcode and BIM technologies, this study proposes a mobile automated BIM-based facility management (BIMFM) system for FM staff in the operation and maintenance phase. The mobile automated BIMFM system is then applied in a selected case study of a commercial building project in Taiwan to verify the proposed methodology and demonstrate its effectiveness in FM practice. The combined results demonstrate that a BIMFM-like system can be an effective mobile automated FM tool. The advantage of the mobile automated BIMFM system lies not only in improving FM work efficiency for the FM staff but also in facilitating FM updates and transfers in the BIM environment

TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention

The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.Comment: Published to IEEE Internet of things Journal but haven't been accepted yet (under review