Search Results (32,648)

The rice leaf folder Cnaphalocrocis medinalis is an important migratory pest in Asia. Although this pest possesses diverse bacterial communities in its gut, functions of these bacteria in modulating host fitness, including development durations, pupal weight, adult longevity, and fecundity, remain unknown. We isolated gut bacteria from field–collected C. medinalis larvae using a culture–dependent method and identified 15 bacterial isolates. Six of the isolates (Klebsiella aerogenes, Klebsiella pneumoniae, Enterobacter ludwigii, Enterobacter asburiae, Pantoea dispersa, and Pantoea ananatis) were newly discovered in C. medinalis. When larvae were orally inoculated with individual bacterial isolates, 15 isolates showed varying degrees of effects on C. medinalis fitness. Importantly, we found that 10 bacterial isolates induced significant larval mortality. Specifically, the inoculation of Pseudomonas mosselii, P. dispersa, Chryseobacterium culicis, P. ananatis, and Myroides odoratus caused high mortality ranging from 40.0% to 56.7%. However, reducing the entire gut bacterial community with antibiotic treatment negatively impacted C. medinalis fitness, while the reinoculation of a bacterial community to antibiotic–treated larvae recovered some of the adverse effects. In particular, control and bacterial community–inoculated C. medinalis laid approximately 37.6% more eggs than antibiotic–treated C. medinalis. This suggests that these bacteria affect their hosts differently when they are together as compared to alone. Our results reveal that C. medinalis harbors gut bacteria capable of both mutualistic and pathogenic interactions, suggesting their potential as biocontrol agents and indicating that targeting the gut bacterial community could be an effective strategy for controlling C. medinalis infestations. Full article

This study examines the trends in the literature about adopting digital banking in emerging economies. It is based on the concepts of digital transformation and technological adoption, which significantly impact the development of the banking industry. A quantitative approach was used through a bibliometric analysis using data from Scopus to achieve the objective. The search equation allowed 118 publications to be extracted and analyzed. The results show that digital banking in emerging countries is a growing field of research that has driven the introduction of new information technologies. The perceived usefulness of digital banking is a key factor in promoting its adoption in the market. Attributes such as security and trust were identified as affecting the level of user satisfaction. Most studies are based on technological adoption, where perceived risk, usefulness, and ease of use are key to understanding the intention to use these technologies. Some countries’ concerns about financial inclusion, cyber security, and trust in financial technology are evident. While digital banking has the potential to increase the coverage of financial services, there are concerns about cybersecurity risks and user data protection. Full article

Flow past one or multiple bluff bodies is almost ubiquitous in nature and industrial applications, and its rich underlying physics has made it one of the most typical problems in fluid mechanics and related disciplines. The search for ways to control such problems has attracted extensive attention from both the scientific and engineering fields, as this could potentially bring about benefits such as reduced drag, mitigated noise, suppressed vibration, and enhanced heat transfer. Flow control can be generally categorized into passive and active approaches, depending on whether there is an external energy input to the flow system. Active control is further divided into open-loop approaches and closed-loop approaches, depending on whether the controller depends on feedback signals extracted from the flow system. Unlike in many other applications of passive flow control and open-loop active flow control, theoretically advantageous closed-loop controls are quite rare in this area, due to the complicated features of flow systems. In this article, we review the recent progress in and future perspectives of flow past a single or multiple bluff bodies using model-free closed-loop control so as to outline the state-of-the-art research, determine the physical rationale, and point to some future research directions in this field. Full article

With advancements in small-scale research fields, precision manipulation has become crucial for interacting with small objects. As research progresses, the demand for higher precision in manipulation has led to the emergence of ultrahigh-precision engineering (UHPE), which exhibits significant potential for various applications. Traditional rigid-body manipulators suffer from issues like backlash and friction, limiting their effectiveness at smaller-scale applications. Smart materials, particularly piezoelectric materials, offer promising solutions with their rapid response and high resolution, making them ideal for creating efficient piezoelectric transducers. Meanwhile, compliant mechanisms, which use elastic deformation to transmit force and motion, eliminate inaccuracies induced by rigid-body mechanisms. Integrating piezoelectric transducers and compliant mechanisms into piezoelectric compliant devices enhances UHPE system performance. This paper reviews the recent advances in piezoelectric compliant devices. By focusing on the utilization of piezoelectric transducers and compliant mechanisms, their applications in perception, energy harvesting, and actuation have been surveyed, and future research suggestions are discussed. Full article

This research article investigates the recycling of end-of-life solar photovoltaic (PV) panels by analyzing various mechanical methods, including Crushing, High Voltage Pulse Crushing, Electrostatic Separation, Hot Knife Cutting, Water Jet Cutting, and Magnetic Separation. Each method’s effectiveness in extracting materials such as glass, silicon, metals (copper, aluminum, silver, tin, lead), and EVA was evaluated. The analysis reveals that no single method is entirely sufficient for comprehensive material recovery. Based on the data analysis, a new hypothetical hybrid method, Laser and High Voltage Pulse (L&HVP), is proposed, which integrates the precision of laser irradiation with the robustness of high voltage pulse crushing. The laser irradiation step would theoretically facilitate the removal of the ethylene-vinyl acetate (EVA) encapsulant, preparing the materials for subsequent separation. The high high-voltage pulse crushing would then selectively fragment and separate the remaining components, potentially enhancing material recovery efficiency while minimizing contamination. The proposed approach is grounded in the observed limitations of existing techniques. This method aims to offer a more comprehensive and sustainable solution for solar PV module recycling. Further research and experimentation are necessary to validate the effectiveness of the L&HVP method and its potential impact on the field of solar PV recycling. Full article

Micro- and nanorobots (MNRs) have attracted significant interest owing to their promising applications in various fields, including environmental monitoring, biomedicine, and microengineering. This review explores advances in the synthetic routes used for the preparation of MNRs, focusing on both top-down and bottom-up approaches. Although the top-down approach dominates the field because of its versatility in design and functionality, bottom-up strategies that utilize template-assisted electrochemical deposition and bioconjugation present unique advantages in terms of biocompatibility. This review investigates the diverse propulsion mechanisms employed in MNRs, including magnetic, electric, light, and biological forces, which enable efficient navigation in various fluidic environments. The interplay between the synthesis and propulsion mechanisms of MNRs in the development of colorimetric and fluorescence detection platforms is emphasized. Additionally, we summarize the recent advancements in MNRs as sensing and biosensing platforms, particularly focusing on colorimetric and fluorescence-based detection systems. By utilizing the controlled motion of MNRs, dynamic changes in the fluorescent signals and colorimetric responses can be achieved, thereby enhancing the sensitivity and selectivity of biomolecular detection. This review highlights the transformative potential of MNRs in sensing applications and emphasizes their role in advancing diagnostic technologies through innovative motion-driven signal transduction mechanisms. Subsequently, we provide an overview of the primary challenges currently faced in MNR research, along with our perspective on the future applications of MNR-assisted colorimetric and fluorescence biosensing in chemical and biological sensing. Moreover, issues related to enhanced stability, biocompatibility, and integration with existing detection systems are discussed. Full article

Chip-scale optical waveguide-assisted surface-enhanced Raman spectroscopy (SERS) that used nanoparticles (NPs) was demonstrated. The Raman signals from Raman reporter (RR) molecules on NPs can be efficiently excited by the waveguide evanescent field when the molecules are in proximity to the waveguide surface. The Raman signal was enhanced by plasmon resonance due to the NPs close to the waveguide surface. The optical waveguide mode and the NP-induced field enhancement were calculated using a finite difference method (FDM). The sensing performance of the waveguide-assisted SERS device was experimentally characterized by measuring the Raman scattering from various RRs, including 4-mercaptobenzoic acid (4-MBA), 5,5′-dithio-bis-(2-nitrobenzoic acid) (DTNB), and malachite green isothiocyanate (MGITC). The observed Raman spectral features were identified and assigned to the complex vibrational modes associated with different reporters. A low detection limit of 1 nM was achieved. In addition, the device sensing method was applied to the detection of the biomarker cardiac troponin I (cTnI) using an aptamer sandwich assay immobilized on the device surface. Overall, the optical waveguides integrated with SERS show a miniaturized sensing platform for the detection of small molecules and large proteins, potentially enabling multiplexed detection for clinically relevant applications. Full article

Machine learning has significantly advanced traffic surveillance and management, with YOLO (You Only Look Once) being a prominent Convolutional Neural Network (CNN) algorithm for vehicle detection. This study utilizes YOLO version 7 (YOLOv7) combined with the Kalman-based SORT (Simple Online and Real-time Tracking) algorithm as one of the models used in our experiments for real-time vehicle identification. We developed the “ISTraffic” dataset. We have also included an overview of existing datasets in the domain of vehicle detection, highlighting their shortcomings: existing vehicle detection datasets often have incomplete annotations and limited diversity, but our “ISTraffic” dataset addresses these issues with detailed and extensive annotations for higher accuracy and robustness. The ISTraffic dataset is meticulously annotated, ensuring high-quality labels for every visible object, including those that are truncated, obscured, or extremely small. With 36,841 annotated examples and an average of 32.7 annotations per image, it offers extensive coverage and dense annotations, making it highly valuable for various object detection and tracking applications. The detailed annotations enhance detection capabilities, enabling the development of more accurate and reliable models for complex environments. This comprehensive dataset is versatile, suitable for applications ranging from autonomous driving to surveillance, and has significantly improved object detection performance, resulting in higher accuracy and robustness in challenging scenarios. Using this dataset, our study achieved significant results with the YOLOv7 model. The model demonstrated high accuracy in detecting various vehicle types, even under challenging conditions. The results highlight the effectiveness of the dataset in training robust vehicle detection models and underscore its potential for future research and development in this field. Our comparative analysis evaluated YOLOv7 against its variants, YOLOv7x and YOLOv7-tiny, using both the “ISTraffic” dataset and the COCO (Common Objects in Context) benchmark. YOLOv7x outperformed others with a [email protected] of 0.87, precision of 0.89, and recall of 0.84, showing a 35% performance improvement over COCO. Performance varied under different conditions, with daytime yielding higher accuracy compared to night-time and rainy weather, where vehicle headlights affected object contours. Despite effective vehicle detection and counting, tracking high-speed vehicles remains a challenge. Additionally, the algorithm’s deep learning estimates of emissions (CO, NO, NO₂, NO_x, PM_2.5, and PM₁₀) were 7.7% to 10.1% lower than ground-truth. Full article

In this study, we explore the impact of stochastic resetting on the dynamics of random walks on a T-fractal network. By employing the generating function technique, we establish a recursive relation between the generating function of the first passage time (FPT) and derive the relationship between the mean first passage time (MFPT) with resetting and the generating function of the FPT without resetting. Our analysis covers various scenarios for a random walker reaching a target site from the starting position; for each case, we determine the optimal resetting probability ${\gamma }^{*}$ that minimizes the MFPT. We compare the results with the MFPT without resetting and find that the inclusion of resetting significantly enhances the search efficiency, particularly as the size of the network increases. Our findings highlight the potential of stochastic resetting as an effective strategy for the optimization of search processes in complex networks, offering valuable insights for applications in various fields in which efficient search strategies are crucial. Full article

Generative Artificial Intelligence (GAI) holds significant potential to advance the field of social work, yet it brings forth considerable challenges and risks. Key concerns include the legal and ethical ramifications of GAI application, as well as its effects on the vital human connections inherent in social work. Nonetheless, educators in this field must ready their students for the evolving digital environment, ensuring they are adept at employing GAI thoughtfully, skillfully, and ethically. This article will explore the integration of GAI knowledge and skills within educational settings. It will feature a case study detailing the author’s redesign community welfare and social work degree assignments to include GAI within a community welfare/social work undergraduate course in Queensland, Australia. The discussion will extend to curriculum and assessment development processes aimed at leveraging GAI to enhance student learning, knowledge retention, and confidence in applying GAI within their academic and professional pursuits. Furthermore, the article will examine the implications for curriculum and assessment design, emphasizing the importance of clear learning objectives, the creation of specific, intricate, and contextualized assessments, the necessity for students to critically evaluate GAI outputs, and the challenge of presenting GAI with tasks beyond its capabilities. Full article

Abstract: In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. This methodology represents a significant advancement over conventional approaches in UAV technology. Our experimental design involved comprehensive field data collection across two distinct altitude environments, employing a high-precision Digital Laser Distance Meter as the reference standard (Class II). This rigorous approach facilitated a thorough evaluation of our model’s performance across varied terrains, ensuring robust and reliable results. The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. These results not only indicate significant improvements over traditional methods, but also establish a new benchmark in UAVs altitude estimation accuracy. A key innovation in our approach is the elimination of costly additional hardware such as Light Detection and Ranging (LiDAR), offering a cost-effective, software-based solution. This advancement has broad implications, enhancing the accessibility of advanced UAVs technology and expanding its potential applications across diverse sectors including precision agriculture, urban planning, and emergency response. This research represents a significant contribution to the integration of AI and UAVs technology, potentially unlocking new possibilities in UAVs applications. By enhancing the capabilities of lightweight UAVs, we are not merely improving a technical aspect, but revolutionizing the potential applications of UAVs across industries. Our work sets the stage for safer, more reliable, and precise UAVs operations, marking a pivotal moment in the evolution of aerial technology in an increasingly UAV-dependent world. Full article

This review offers a detailed examination of modern ECG signal processing techniques employed in the prediction and detection of ventricular fibrillation (VF). It contains a thorough analysis of recent advancements in the field, exploring the strengths, limitations, and real-world applications of these techniques. By evaluating the current state of research, the review seeks to identify the most effective approaches and highlight key areas where further investigation is needed, ultimately guiding future research efforts toward improving VF prediction and detection. Overall, AI has shown significant potential in a range of VF-related tasks. However, real-world implementation encounters several challenges, including difficulties in accurately interpreting ECG signals, the variability in individual physiological responses, and the infrequency of ventricular fibrillation events. Additionally, there are issues related to the critical timing required for detecting VF, the presence of similar arrhythmias, the need for adaptation to new ECG devices, energy consumption concerns, and the complex process of obtaining regulatory and legislative approvals for integrating software components into medical equipment. We consider that the present work might be useful in approaching the above challenges. Full article

Ionic liquids have been utilized in numerous significant applications within the field of chemistry, particularly in organic chemistry, due to their unique physical and chemical properties. In the realm of asymmetric transition-metal-catalyzed transformations, chiral ionic-liquid-supported ligands and their corresponding transition-metal complexes have facilitated these processes in unconventional solvents, especially ionic liquids and water. These innovative reaction systems enable the recycling of transition-metal catalysts while producing optically active organic molecules with comparable or even higher levels of chemo-, regio-, and stereoselectivity compared to their parent catalysts. In this short review, we aim to provide an overview of the structures of chiral ionic-liquid-supported ligands and the synthetic pathways for these ligands and catalysts. Various synthetic methodologies are demonstrated based on the conceptual frameworks of diverse chiral ionic-liquid-supported ligands. We systematically present the structures and comprehensive synthetic pathways of the chiral ionic-liquid-supported ligands and the typical corresponding transition-metal complexes that have been readily applied to asymmetric processes, categorized by their parent ligand framework. Notably, the crucial experimental procedures are delineated in exhaustive detail, with the objective of enhancing comprehension of the pivotal aspects involved in constructing chiral ionic-liquid-tagged ligands and compounds for both scholars and readers. Considering the current limitations of such ligands and catalysts, we conclude with remarks on several potential research directions for future breakthroughs in the synthesis and application of these intriguing ligands. Full article

Optical fiber sensors have emerged as a critical sensing technology across various fields due to their advantages, including high potential bandwidth, electrical isolation that is safe for utilization in electrically hazardous environments, high reliability, and ease of maintenance. However, conventional optical fiber sensors face limitations in achieving high sensitivity and precision. The integration of nanostructures with advanced coating technology is one of the critical solutions to enhancing sensor functionality. This review examined nanostructure coating techniques that are compatible with optical fiber sensors and evaluated etching techniques for the improvement of optical fiber sensing technology. Techniques such as vapor deposition, laser deposition, and sputtering to coat the nanostructure of novel materials on the optical fiber sensors are analyzed. The ability of optical fiber sensors to interact with the environment via etching techniques is highlighted by comparing the sensing parameters between etched and bare optical fibers. This comprehensive overview aims to provide a detailed understanding of nanostructure coating and etching for optical fiber sensing and offer insights into the current state and future prospects of optical fiber sensor technology for sensing performance advancement, emphasizing its potential in future sensing applications and research directions. Full article

Titanium–Niobium alloys have garnered extensive interest in various fields, such as aerospace, medical equipment, and scientific research instruments, due to their superior properties. Particularly, their anti-magnetic characteristics render them high potential in the watchmaking industry. The temperature coefficient of the elastic modulus of balance spring materials is a crucial parameter for assessing the impact of temperature on the properties of TiNb alloys. This study aims to explore the influence of heat treatment on the microstructural and elastic modulus temperature coefficient of the Ti-45Nb alloy. The results indicate that after short-term aging treatment, ω particles are enriched at grain boundaries and defects and are distributed in a necklace shape after erosion. After long-term aging treatment, the α phase appears in the material. The phase transformation process of low-temperature aging is β → β + β′ → β + ω → β + ω + α. The grain size of the material does not change significantly after different treatments. Additionally, the effect of heat treatment on material properties was studied by a low-temperature dynamic elastic modulus tester. The results showed that the temperature coefficient of the elastic modulus of the material in its original state was relatively high, ranging from 50~220 × 10⁻⁶·°C⁻¹. After long-time aging treatment, the temperature coefficient of the elastic modulus of the material decreased significantly due to the appearance of the α phase. The temperature coefficient of the elastic modulus of the material after 48 h of heat preservation treatment fluctuated at 0 ± 30 × 10⁻⁶·°C⁻¹. The internal control standard of excellent products in the industry is −11~35 × 10⁻⁶/°C. This study provides significant practical implications for the application of Ti-45Nb alloy in the watchmaking industry by adjusting the heat treatment temperature and time to study the effects on organizational evolution and the temperature coefficient of the elastic modulus. Full article