Efficiency and Returns to Scale Measurements with Shared Inputs in Multi-Activity Data Envelopment Analysis: An Application to Farmers' Organizations in Taiwan

This paper addresses the question how team production promotes efficiency of a firm when some inputs can be rewarded on the basis of outputs but some cannot because they are shared among outputs and non-separable. A multi-activity DEA model with variable returns to scale is proposed to provide information on the efficiency performance for organizations with inputs shared among several closely related activities. The model is applied to study the case of 279 farmers' associations in Taiwan. The result suggests that it is important to improve the efficiency of the non-profit oriented activities to improve their overall performances. Three out of four departments of TFAs can gain from economies of scale through expansion, while the remaining one gains through contraction. Thus, policies promoting structural adjustment and consolidations of TFAs would not be inconsistent with public interests.multi-activity DEA, shared inputs, efficiency measure, directional distance function, Productivity Analysis,

Many-Body Coherence in Quantum Transport

In this study, we propose the concept of harnessing quantum coherence to control electron transport in a many-body system. Combining an open quantum system technique based on Hubbard operators, we show that many-body coherence can eliminate the well-known Coulomb staircase and cause strong negative differential resistance. To explore the mechanism, we analytically derive the current-coherence relationship in the zero electron-phonon coupling limit. Furthermore, by incorporating a gate field, we demonstrate the possibility of constructing a coherence-controlled transistor. This development opens up a new direction for creating quantum electronic devices based on many-body coherence.Comment: 5 pages, 3 figure

A Comparison of Residual-based Methods on Fault Detection

An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over time, and isolate faults. As faults are typically rare occurrences, it is essential to perform this monitoring in an unsupervised manner. Various approaches have been proposed not only to detect faults in an unsupervised manner but also to distinguish between different potential fault types. In this study, we perform a comprehensive comparison between two residual-based approaches: autoencoders, and the input-output models that establish a mapping between operating conditions and sensor readings. We explore the sensor-wise residuals and aggregated residuals for the entire system in both methods. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. To perform the comparison, we utilize the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a subset of the turbofan engine dataset containing three different fault types. All models are trained exclusively on healthy data. Fault detection is achieved by applying a threshold that is determined based on the healthy condition. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate. While the fault detection performance is similar for both models, the input-output model provides better interpretability regarding potential fault types and the possible faulty components.Comment: 10 pages, submitted to the 15th Annual Conference of the Prognostics and Health Management Societ

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

Partial discharge (PD) is a common indication of faults in power systems, such as generators, and cables. These PD can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions. First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.Comment: 13 pages, 4 figures, 2 table

SPATIAL EQUILIBRIUM MODELING WITH IMPERFECTLY COMPETITIVE MARKETS: AN APPLICATION TO RICE TRADE

A general imperfect competition spatial equilibrium model is developed to estimate the trading country behaviors in the international rice market using a conjectural variation approach. Such a model allows the possibility of an imperfect competitive market to exit on both the export and import sides without any assumption of market structure. The empirical results show that the major exporting countries, Thailand, Vietnam, and the U.S. acted as high degree of imperfect competitors(or oligopolies) while Pakistan acted as a lower degree of imperfect competitor. The importing countries such as Japan, the Philippines, Europe, Brazil, and the former USSR behaved as high degree of imperfect competitors (or oligopsonies). The empirical results also show that there are welfare gains of $1,492 million when all trading countries comply with the free trade agreement.Marketing,

Productivity Change in Taiwan's Farmers' Credit Unions: A Nonparametric Risk-Adjusted Malmquist Approach

This article proposes an extended three-stage DEA methodology similar to Fried et al. (2002) to improve the measurement of productivity growth then the assumption of free disposability of undesirable outpu t does not apply. A directional distance function is used to construct adjusted Malmquist-Luenberger productivity indexes which simultaneously account for the impacts of undesirable outputs, environmental variables, and statistical noise. Panel data for 264 farmers' credit unions (FCUs) in Taiwan covering the 1998-2000 period are employed to illustrate the advantages of this method. On average, the productivity of Taiwan's FCUs is found to have deteriorated over the 1998-2000 period. Although an improvement in efficiency has been observed, the major reason for the deterioration is found to be due to the regression of techno logy.Malmquist-Luenberger productivity index, three-stage DEA, undesirable outputs, directional distance function, Agricultural Finance, Productivity Analysis,

Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study

Circuit breakers (CBs) play an important role in modern society because they make the power transmission and distribution systems reliable and resilient. Therefore, it is important to maintain their reliability and to monitor their operation. A key to ensure a reliable operation of CBs is to monitor their condition. In this work, we performed an accelerated life testing for mechanical failures of a vacuum circuit breaker (VCB) by performing close-open operations continuously until failure. We recorded data for each operation and made the collected run-to-failure dataset publicly available. In our experiments, the VCB operated more than 26000 close-open operations without current load with the time span of five months. The run-to-failure long-term monitoring enables us to monitor the evolution of the VCB condition and the degradation over time. To monitor CB condition, closing time is one of the indicators, which is usually measured when the CB is taken out of operation and is completely disconnected from the network. We propose an algorithm that enables to infer the same information on the closing time from a non-intrusive sensor. By utilizing the short-time energy (STE) of the vibration signal, it is possible to identify the key moments when specific events happen including the time when the latch starts to move, and the closing time. The effectiveness of the proposed algorithm is evaluated on the VCB dataset and is also compared to the binary segmentation (BS) change point detection algorithm. This research highlights the potential for continuous online condition monitoring, which is the basis for applying future predictive maintenance strategies.Comment: 7 page

Critical quality attributes (CQAs) of a therapeutic antibody produced from integrated continuous bioprocessing

The integrated continuous bioprocess provides an innovative way to produce protein drugs with flexibility and efficiency. However, during the long-term cultivation and complicated production, how to ensure the process stability and product quality is critically important. In this study, the monoclonal antibody (mAb) was produced in a bioreactor operated in a perfusion mode utilizing the ATF cell retention system for up to 32 days. The 2L harvest per day starting at day 10 was continuously purified using the 3-column periodic counter-current (PCC) chromatography system. The first protein A capture purification was performed with the dynamic binding capacity of 50% breakthrough around 60 mg mAb/mL of resin (vs 20 mg/mL resin for batch purification) for 120 cycles or 360 column operations followed by a polishing step of mixed mode chromatography for 20 cycles. The process and quality attributes were monitored daily. The results demonstrate consistency in both the purification process and the mAb qualities (in the aspects of product integrity, aggregates, and glycan profile) between PCC and batch purifications. Culture-related charge heterogeneity was observed accompanied by an increase of bioreactor harvest time using both batch and PCC purification processes. In addition, the impurities such as endotoxin and HCP were also monitored while under this high capacity utilization of chromatography resins. By sharing the insights of process and quality attributes, we hope to provide better understanding on the process-related heterogeneity between batch and continuous production and/or purification