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,

Q-enhanced fold-and-bond MEMS inductors

This work presents a novel coil fabrication technology to enhance quality factor (Q factor) of microfabricated inductors for implanted medical wireless sensing and data/power transfer applications. Using parylene as a flexible thin-film device substrate, a post-microfabrication substrate folding-and-bonding method is developed to effectively increase the metal thickness of the surface-micromachined inductors, resulting in their lower self-resistance so their higher quality factor. One-fold-and-bond coils are successfully demonstrated as an example to verify the feasibility of the fabrication technology with measurement results in good agreements with device simulation. Depending on target specifications, multiple substrate folding-and-bonding can be extensively implemented to facilitate further improved electrical characteristics of the coils from single fabrication batch. Such Q-enhanced inductors can be broadly utilized with great potentials in flexible integrated wireless devices/systems for intraocular prostheses and other biomedical implants

Human Electrical Brain Dynamics During Locomotor Obstacle Avoidance in Virtual Reality

Visually identifying and avoiding obstacles encountered during walking is crucial for navigating real world environments. Motor deficits that affect gait and balance, and changes due to aging, can increase fall risk. There is a needed to better understand the complex relationships between gaze behaviors of the eye and electrical brain dynamics during locomotor obstacle avoidance. Virtual reality provides nearly limitless opportunities to create experimentally controlled, complex, realistic environments to study human behaviors, such as locomotion. PURPOSE: Our aim was to identify human electrocortical dynamics during walking and obstacle avoidance in virtual reality, to better understand visually guided human locomotor control. METHODS: We recorded 64-channel mobile high-density electroencephalography (EEG), lower-limb motion capture, ground reaction forces, and eye gaze behavior from participants navigating virtual environments on a treadmill with obstacles to step over. Eighteen (8F and 10M) participants completed nine obstacle avoidance conditions lasting 3-4 minutes each, including 3 gait speeds (1.0 m/s, 1.25 m/s, 1.5 m/s) and 3 obstacle-approach speeds (0.75x, 1x, 1.25x gait speed). Baseline walking conditions without virtual obstacles present were also recorded at each gait speed. RESULTS: Based on preliminary analysis, we identified increased gamma band power (\u3e30 Hz) from the visual cortex, posterior parietal cortex, and frontal cortex when compared to walking without virtual obstacles present. At faster walking speeds, beta (13-30 Hz) and low gamma band power (30-60 Hz) decreased from the prefrontal cortex. CONCLUSION: Changes in human electrical spectral power dynamics among cortical regions during walking in virtual reality, at different speeds, and with or without obstacles to avoid, provides possible biomarkers for assessing cortical processing during real world locomotor navigation. These findings may be used to better understand cortical networks affected by aging, neurological disease, or disorder, providing objective measure for tracking gait rehabilitation, or developing assistive brain computer interfaces

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Case Report: Rare percutaneous coronary intervention for “right” main bifurcation

We presented the case of a patient with non-ST-elevation myocardial infarction with coronary arteries of an anomalous origin, an interarterial course of the LMCA, a unique wide-angle “right” main bifurcation lesion, and a high SYNTAX score. Management with contemporary PCI and imaging may be an alternative to surgery

An All Deep System for Badminton Game Analysis

The CoachAI Badminton 2023 Track1 initiative aim to automatically detect events within badminton match videos. Detecting small objects, especially the shuttlecock, is of quite importance and demands high precision within the challenge. Such detection is crucial for tasks like hit count, hitting time, and hitting location. However, even after revising the well-regarded shuttlecock detecting model, TrackNet, our object detection models still fall short of the desired accuracy. To address this issue, we've implemented various deep learning methods to tackle the problems arising from noisy detectied data, leveraging diverse data types to improve precision. In this report, we detail the detection model modifications we've made and our approach to the 11 tasks. Notably, our system garnered a score of 0.78 out of 1.0 in the challenge.Comment: Golden Award for IJCAI CoachAI Challenge 2023: Team NTNUEE AIoTLa

ANALYSIS OF GRIP FORCE DURING GOLF PUTTING AT DIFFERENT DISTANCES - PILOT STUDY

The purpose of this study was to explore the grip force performance at different distances during putting stroke. Four golfers (2 professionals and 2 novices) as accurately as possible executed a putt to reach 1, 2 and 3 m target distance, respectively. Putting motions were recorded by JVC video and grip pressure measurement sensor placed on two hands, allowing the force output of all regions of the hands to be measured. The grip force trace among 1 to 3 m distance was repeatable across putting strokes for each golfer but between golfers was inconsistent. Dominant forces appear to arise primarily from the left hand. In this study, the grip force and force distribution were preliminarily discovered during putting stroke at different distances. This research has suggested a potentially important influence of grip force on the golf putting performance in long distance