The occurrence and spread of antibiotic resistance have become a pressing global health concern. Understanding the genetic elements that facilitate the dissemination of antibiotic resistance genes (ARGs) in marine environments is crucial for effective microbial surveillance and management strategies. This study aimed to reveal the presence of mobilizable multiresistance clusters, consisting of ARGs associated with mobile genetic elements (MGEs), in marine bacterial communities. Water samples were collected from two beaches in Jeju, South Korea, and screened to identify multi-drug resistant bacteria. A total of 20 bacterial isolates were selected for whole genome sequencing, and through comprehensive genomic analysis, we identified and characterized nine such clusters primarily composed of betalactams, aminoglycosides, and tetracycline antibiotic resistance genes associated with IS6, IS9, and Tn3. Additionally, an extensive analysis of 900 marine bacterial genomes from the National Center for Biotechnology Information (NCBI) database was conducted to gain a broader perspective. Our results provide valuable insights into the prevalence and diversity of mobilizable multiresistance clusters in marine bacterial communities. Unveiling the genetic basis of resistance dissemination in marine environments contributes to our understanding of the mechanisms underlying the persistence and spread of antibiotic resistance in the oceans. These findings are pivotal for developing targeted interventions and management strategies to mitigate the impact of antibiotic resistance in marine ecosystems and safeguard human and environmental health.

ABSTRACT

Software fault prediction (SFP) is a critical process in ensuring the reliability of software systems by identifying and eliminating faults. Machine learning techniques have emerged as effective methods for addressing SFP challenges. However, the large size of fault data obtained from mining software historical repositories poses a dimensionality problem due to the abundance of features (metrics). Feature selection (FS) is a valuable solution to reduce data dimensionality by identifying the most relevant features. In this research, an enhanced version of the Whale Optimization Algorithm (WOA) is proposed, by incorporating truncation selection by combining it with a single point crossover method to improve the exploration process and avoid local optima. The performance of the proposed enhancement is evaluated on 14 SFP datasets obtained from the PROMISE repository. Our comprehensive analysis demonstrates that the proposed approach outperforms the original WOA and other variants of the WOA.

In this paper, a generalized estimator of finite population variance using the auxiliary information under stratified random sampling is proposed. The expressions for bias and mean square error equations of the proposed estimator are derived up to the first degree of approximation. The theoretical efficiency conditions under which the proposed estimator is better than some existing estimators are obtained. The performances of the existing and proposed estimators were assessed using three real datasets based on the criteria of minimum mean square error and supreme percentage relative efficiency. Evidence from the study showed that the proposed estimator performed better and is more efficient than some existing estimators considered.

Photonic crystal fibers (PCFs) are promising candidates for 5G communication systems, as they offer low loss and high bandwidth. In this paper, we use COMSOL Multiphysics software to design and optimize PCF structure suitable for optical fiber communication in the terahertz (THz) frequency range. The proposed model and simulation analysis based on light propagation, dispersion, and polarization effects in PCFs helps to explore how different parameters affects the performance . Hence, COMSOL simulation of the proposed model helps to design and meet the requirements of 5G networks and enables wireless communication.

The Kumaraswamy distribution is a continuous model with broad applications in many areas, such as environment, medicine, and finance. The present study proposes a new compound continuous distribution named the odd beta prime-Kumaraswamy (OBPK) distribution. The proposed distribution is an extension of the Kumaraswamy distribution by incorporating the odd beta prime generalized family of distributions from T-X family. Various statistical properties of the OBPK distribution were derived. This distribution can be unimodal, and its shape is right-skewed or left-skewed. We employed the maximum likelihood estimation method to estimate the model parameters. The application of the new OBPK distribution is illustrated by applying two real-life data sets concerning the COVID-19 mortality rate during different periods, and its performance is compared with other well-known extended versions of the Kumaraswamy distribution. The adequacy of the new OBPK model is verified based on various statistical metrics. The findings indicate that the OBPK model outperforms other competitive models by providing the best fit to the COVID-19 data sets. This study updates the traditional Kumaraswamy distribution and provides a vital tool for modeling various phenomena in different domains.

The work "Mapping and Navigation of Autonomous Robot with LiDAR" focuses on the development of an advanced robotic system capable of autonomously navigating and mapping its environment using LiDAR technology. LiDAR (Light Detection and Ranging) has emerged as a powerful sensing technology that enables precise and detailed 2D mapping of the surroundings by emitting laser beams and measuring their reflections. By leveraging LiDAR, this work aims to enhance the capabilities of autonomous robots and unlock their potential in various applications.
The main objectives of this paper encompass several key aspects. Firstly, it involves integrating a LiDAR sensor with the robot's existing hardware and software systems, establishing seamless communication and data exchange. Secondly, the article focuses on developing algorithms and techniques for environment mapping using LiDAR data. This entails utilizing laser-based measurements to construct accurate and detailed 2D maps of the robot's surroundings.
Real-time operation and robustness are vital considerations in this work. The system will be optimized to ensure fast and responsive decision-making based on LiDAR data, allowing the robot to adapt to dynamic environments and changing conditions. It will be designed to handle various scenarios, such as different lighting conditions, diverse terrain, and the presence of moving objects.
Ultimately, the successful completion of this task will lead to the development of an autonomous robot that can independently explore unknown environments, map them in detail, and navigate through them safely and efficiently using LiDAR technology.

Virtual reality has emerged as a transformative tool in education, offering unique opportunities to enhance teaching and learning experiences. This study explores the application of virtual reality in the context of textile materials, with a particular focus on its potential to enhance the study and understanding of textile structures. By immersing learners in 3D virtual environments, virtual reality enables an unprecedented exploration of textiles that goes beyond traditional microscopic observations. This research emphasises the historical significance of the 1950s vintage car era and brings together experts from diverse fields including communication design, clothing technology, conservation and excavation techniques. Advanced digital microscopy techniques were used to examine textile surfaces, gain knowledge of standard testing methods, convert the results into dynamic 3D data, and visualise the ageing processes of textiles. The interdisciplinary collaboration promoted knowledge exchange, enabled teachers and learners to discover innovative teaching approaches and paved the way for new ways of thinking. This study explores immersive and transformative learning experiences and can open up new areas of research and understanding in the field of textile materials through the power of virtual reality.

The present work is based on a mathematical model describing a single acoustic cavitation bubble evolving under an ultrasonic field of 200 and 300 kHz and an acoustic amplitude of 1.8 atm within 1-Butyl- 3-methylimidazolium Acetate. The model integrates the dynamics of bubble oscillation, the thermodynamics within the bulk volume of the bubble and at its interface, as well as the sonophysical and sonochemical events occurring in the presence of dissolved cellulose in the ionic liquid. The performed simulations shed light on the major physical effects of acoustic cavitation, namely shockwave and microjet, as well as the sonochemical effects in terms of the degradation rate of the dissolved cellulose in the secondary reactional site, i.e., the interface. The predominance of the effects and its dependency on the acoustic frequency is tackled from an energetic point of view, it has been demonstrated that 300 kHz offers the lowest heat flow across the bubble interface, lowering the chances for the sonochemical degradation of cellulose, whilst 200 kHz offers a significant degradation rate attaining 17 mol.dm^-3.s^-1, and harsher microjets and shockwaves with powers of 3300 and 900 mW at the collapse, respectively.

Product engineering involves the design and development of new products or the improvement of existing products to efficiently meet market needs and ensure high quality. In this, the implementation of Design for Excellence (DfX) concepts (namely Design for Manufacturing, Design for Assembly, Design for Reliability, Design for User Experience, Design for Testability, and Design for Security) is essential to enhance manufacturability, ease of assembly, and serviceability, thereby improving overall product performance and user experience. However, growing concerns about the global environment and climate changes, resource depletion, pollution, and dynamic user requirements limit the fruitfulness of product engineering. This necessitates the integration of sustainability principles with the traditional DfX concepts as it is emerging as a significant factor for businesses and government policies worldwide. By implementing sustainability practices, businesses can create innovative and marketable products that minimize environmental impact while meeting consumer demands. In this view, this paper discusses a new approach called “Design for Sustainability (DfS)” in product engineering that focuses on developing sustainable products. The paper discusses various steps involved in implementing DfS (such as material selection, modular design principles, alternative energy sources, serviceable products, circular economy, long service life, transportation impact, and simulation) in the product engineering process and also highlights its importance and benefits. All these steps contribute to responsible resource use, waste management, and emission reduction, and lessen the social and economic impacts.

The paper presents an indoor positioning system that uses ultra-wideband technology. For this purpose, we used four anchors and one tag that is fixed on one small car. The anchors and the tag are DWM1001 modules. The configuration of the five DW1001 modules is performed through a smartphone application provided by the manufacturer. The car has been built by us. The controller of the car is a Raspberry Pi microcomputer which controls the driver of the car DC motors by generating PWM signals. In addition, Raspberry Pi communicates with the tag by UART interface. The Raspberry Pi is powered by two 3.7 V rechargeable batteries that are connected in series, followed by a DC-DC converter that generates a 5 V DC voltage. Thus, our goal is to control the car to reach a certain destination (xd,yd) requested by the user via Wi-Fi. Because the car hosts the tag, it continuously updates its (x,y) coordinates according to the four anchors. We implemented two scenarios, considering the initial (x,y) coordinates of the car. In the first scenario, either xd=x or yd=y, so the car will move to destination either along the y-axis or along the x-axis. In the second scenario, xd is different to x and yd is different to y, so the car moves along one of the axes and then turns 90 degrees to reach its destination. Experimental results that show the precision of positioning system are presented.