An algorithm to create model file for Partially Observable Markov Decision Process for mobile robot path planning

The Partially Observable Markov Decision Process (POMDP), a mathematical framework for decision-making in uncertain environments suffers from the curse of dimensionality. There are various methods that can handle huge sizes of POMDP matrices to create approximate solutions, but no serious effort has been reported to effectively control the size of the POMDP matrices. Manually creating the high-dimension matrices of a POMDP model is a cumbersome and sometimes even impossible task. The PCMRPP (POMDP file Creator for Mobile Robot Path Planning) software package implements a novel algorithm to programmatically generate these matrices such that: •The sizes of the matrices can be controlled by configuring the granularity of discretization of the components of the state and•The sparseness of the matrices can be controlled by configuring the spread of the observation probability distribution. This kind of flexibility allows one to achieve a trade-off between time complexity and the level of robustness of the POMDP solution.

Detection of SARS-CoV-2 in Environment: Current Surveillance and Effective Data Management of COVID-19

Since diagnostic laboratories handle large COVID-19 samples, researchers have established laboratory-based assays and developed biosensor prototypes. Both share the same purpose; to ascertain the occurrence of air and surface contaminations by the SARS-CoV-2 virus. However, the biosensors further utilize internet-of-things (IoT) technology to monitor COVID-19 virus contamination, specifically in the diagnostic laboratory setting. The IoT-capable biosensors have great potential to monitor for possible virus contamination. Numerous studies have been done on COVID-19 virus air and surface contamination in the hospital setting. Through reviews, there are abundant reports on the viral transmission of SARS-CoV-2 through droplet infections, person-to-person close contact and fecal-oral transmission. However, studies on environmental conditions need to be better reported. Therefore, this review covers the detection of SARS-CoV-2 in airborne and wastewater samples using biosensors with comprehensive studies in methods and techniques of sampling and sensing (2020 until 2023). Furthermore, the review exposes sensing cases in public health settings. Then, the integration of data management together with biosensors is well explained. Last, the review ended with challenges to having a practical COVID-19 biosensor applied for environmental surveillance samples.

Single and Bunch Soliton Generation in Optical Fiber Lasers Using Bismuth Selenide Topological Insulator Saturable Absorber

In this work, we present the generation of two distinct types of soliton pulses using a Bismuth Selenide (Bi₂Se₃) saturable absorber (SA) synthesized in our laboratory. The soliton pulses were generated in two different laser cavity configurations, resulting in two types of solitons: a soliton pulse with Kelly sidebands and a bunched soliton pulse with peak-dip sidebands. Both solitons operated at the fundamental repetition rate-23.3 MHz (for the soliton with Kelly sidebands) and 13 MHz (for the bunched soliton with peak-dip sidebands). We observed that the accumulation of nonlinear phase shift from the added single mode fiber (SMF) split the single soliton pulse into 44 pulses in a bunched oscillation envelope. At the same time, peak-dip sidebands were imposed on the bunched soliton spectrum due to constructive and destructive interferences between soliton pulse and dispersive waves. The measured pulse width for both solitons were 0.63 ps (for the soliton with Kelly sidebands) and 1.52 ps (for the bunched soliton with peak-dip sidebands), respectively. Our results demonstrate the potential of Bi₂Se₃ SAs in generating different types of soliton pulses, which could have potential applications in various areas of optical communication and spectroscopy.