Comparative Studies On Culture Method, Biological Parameters, And Climbing Abilities Of Tropical Bed Bug, Cimex Hemipterus (F.) And Common Bed Bug, Cimex Lectularius L. (Hemiptera: Cimicidae)

This thesis focuses on the effects of artificial and natural feeding system on the biological parameters of both common (Cimex lectularius Linnaeus) and tropical bed bugs (Cimex hemipterus [Fabricius]), and the climbing ability of both species on pitfall style monitoring traps. The feeding effectiveness of an artificial feeding system (Hemotek® membrane feeding system) for Cimex lectularius (Monheim [MH] and Sydney [SYD] strains) and C. hemipterus (Madam Mo [MM], Tanjung Tokong [TT], Queensland [QSL] and Kuala Lumpur [KL] strains) using two types of rabbit blood (defibrinated [Defibrinated Rabbit Blood, DRB] and heparinized [Heparinized Rabbit Blood, HRB]) were compared with natural feeding on a human host

Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors.

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach

Differences in Climbing Ability of Cimex lectularius and Cimex hemipterus (Hemiptera: Cimicidae)

The climbing abilities of two bed bug species, Cimex lectularius L. and Cimex hemipterus (F.), were determined by evaluating their escape rates from smooth surface pitfall traps using four commercial bed bug monitors (Verifi Bed Bug Detector, ClimbUp Insect Interceptor, BlackOut Bed Bug Detector, and SenSci Volcano Bed Bug Detector). All detectors were used in the absence of lures or attractants. Unlike C. lectularius, adult C. hemipterus were able to escape from all traps. On the other hand, no or a low number nymphs of both species escaped, depending on the evaluated traps. Examination of the vertical friction force of adults of both species revealed a higher vertical friction force in C. hemipterus than in C. lectularius. Scanning electron microscope micrograph observation on the tibial pad of adult bed bugs of C. hemipterus showed the presence of a greater number of tenent hairs on the tibial pad than on that of adult C. lectularius. No tibial pad was found on the fourth and fifth instars of both species. Near the base of the hollow tenent hairs is a glandular epithelium that is better developed in adult C. hemipterus than in adult C. lectularius. This study highlights significant morphological differences between C. lectularius and C. hemipterus, which may have implications in the monitoring and management of bed bug infestations

Boosting Learning for LDPC Codes to Improve the Error-Floor Performance

Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error rate stops decreasing rapidly at a certain level, presents challenges for achieving extremely low error rates and deploying LDPC codes in scenarios demanding ultra-high reliability. In this work, we propose training methods for neural min-sum (NMS) decoders to eliminate the error-floor effect. First, by leveraging the boosting learning technique of ensemble networks, we divide the decoding network into two neural decoders and train the post decoder to be specialized for uncorrected words that the first decoder fails to correct. Secondly, to address the vanishing gradient issue in training, we introduce a block-wise training schedule that locally trains a block of weights while retraining the preceding block. Lastly, we show that assigning different weights to unsatisfied check nodes effectively lowers the error-floor with a minimal number of weights. By applying these training methods to standard LDPC codes, we achieve the best error-floor performance compared to other decoding methods. The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor .Comment: 17 pages, 10 figure