Operant Paradigm Research Articles

Analysis of Current Situation of Green Finance and Sustainable Development in China

Amidst escalating global environmental challenges, green finance, serving as a pivotal instrument for advancing sustainable growth, is garnering heightened interest from the international arena. This financial mechanism seeks to channel societal capital into eco-friendly sectors including environmental stewardship, energy efficiency, decarbonization, and renewable energy, all the while adeptly mitigating ecological hazards and fostering harmonious development dynamics. The primary objective of this piece is to delve into the conceptual framework, historical evolution, predominant operational paradigms, hurdles, and prospective growth trajectories of green finance. Moreover, it seeks to dissect the interplay between green finance and pertinent governmental strategies, alongside its influence on various facets of the economy. Through this approach, the endeavor seeks to furnish both theoretical benchmarks and practical insights that can propel the development and refinement of the global green finance framework. Moreover, it intends to catalyze more profound investigation and hands-on experimentation within this domain, thereby accelerating the evolution and enhancement of green finance mechanisms to more effectively support the overarching objective of global sustainability.

Nuclear Magnetic Resonance with a Levitating Microparticle.

Nuclear magnetic resonance (NMR) spans diverse fields from biology to quantum science. Employing NMR on a floating object could unveil novel possibilities beyond conventional operational paradigms. Here, we observe NMR within a levitating microdiamond using the nuclear spins of nitrogen-14 atoms. By tightly confining the angular degrees of freedom of the diamond in a Paul trap, we achieve efficient hyperfine interaction between optically polarized electronic spins of nitrogen-vacancy centers and the ^{14}N nuclear spin, enabling nuclear spin polarization and quantum state readout revealing coherence times up to hundreds of microseconds. This represents the longest recorded spin coherence time in a levitated system, surpassing previous records by 3 orders of magnitude. Our results offer promise for various applications, including cooling macroscopic particles to their motional ground state and exploring geometric phases for gyroscopy.

Adapting the Future VET Curriculum in Response to Emerging Challenges: A Model for Evaluating the Impact of AI Integration in VET for Armed Conflicts and Warfare

This article examines the profound implications and the transformative impact of artificial intelligence (AI) on vocational education and training (VET) within military contexts, underscoring the imperative for curriculum reform to address the evolving demands of contemporary warfare. As AI technologies progress, they present both significant opportunities and challenges, reshaping defense strategies and operational frameworks, fundamentally altering defense strategies and operational paradigms. The integration of AI into military applications necessitates a workforce proficient in specialized competencies, including technical expertise in AI ethics, system design, and human-centered methodologies. This paper delineates the essential competencies required for future VET professionals, emphasizing the necessity for ongoing upskilling and reskilling to effectively navigate the ethical and legal complexities associated with AI in military operations. By incorporating and embedding AI education into VET curricula, stakeholders can cultivate a new generation of defense professionals equipped to utilize AI technologies in a responsible and effective manner, ensuring adherence to and compliance with international humanitarian standards while enhancing operational efficacy. The findings highlight the critical role of VET in developing a skilled workforce prepared to confront the dynamic landscape of military technologies driven by AI.

Chromosomal and gonadal sex have differing effects on social motivation in mice.

As our brain develops, many factors influence how we behave later in life. The brain forms differently in males and females, potentially leading to sex variation seen in many behaviors including sociability. In addition, conditions defined by differences in social behaviors, such as autism, are diagnosed more in males than females. However, researchers don't know exactly how distinct sex factors, such as hormones and sex chromosome genes, lead to different behaviors in males and females. In this study, we used mouse models and tests of mouse behavior to explore these differences. Results show that sex hormones primarily contributed to differences in social motivation between sexes. Yet when we repeated these same assays in a mouse model of genetic liability for a human neurodevelopmental syndrome, we found that sex chromosome genes rather than sex hormones played a larger role in the behavioral consequences of impaired neurodevelopment. These insights can inform future research on the biological mechanisms of social behavior in the context of genetic liability for neurodevelopmental disorders. Four-core genotype mouse model crossed with MYT1L heterozygous mouse revealed independent effects of chromosomal and gonadal sex on social motivation. Myt1l haploinsufficiency was associated with increased activity in both males and females. While females are more active, contributions of chromosomes and gonadal hormones to this sex effect are environment dependent.Presence of ovaries was associated with increased measures of social seeking and orienting regardless of genotype.Chromosomal sex interacted with MYT1L genotype, with increased social orienting and seeking specifically in XX MYT1L heterozygous mice. Sex differences in brain development are thought to lead to sex variation in social behavior. Sex differences are fundamentally driven by both gonadal (i.e., hormonal) and chromosomal sex, yet little is known about the independent effects of each on social behavior. Further, mouse models of the genetic liability for the neurodevelopmental disorder MYT1L Syndrome have shown sex specific deficits in social motivation. In this study, we aimed to determine if hormonal or chromosomal sex primarily mediate the sex differences seen in mouse social behavior, both at baseline and in the context of Myt1l haploinsufficiency. Four-core genotype (FCG) mice, which uncouple gonadal and chromosomal sex, were crossed with MYT1L heterozygous mice to create eight different groups with unique combinations of sex factors and MYT1L genotype. A total of 131 mice from all eight groups were assayed for activity and social behavior via the open field and social operant paradigms. Measures of social seeking and orienting were analyzed for main effects of chromosome, gonads, and their interactions with Myt1l mutation. The FCGxMYT1L cross revealed independent effects of both gonadal and chromosomal sex on activity and social behavior. Specifically, the presence of ovaries, and by extension the presence of ovarian hormones, increased overall activity, social seeking, and social orienting regardless of genotype. In contrast, sex chromosomes affected social behavior mainly in the MYT1L heterozygous group, with XX sex karyotype when combined with MYT1L genotype contributing to increased social orienting and seeking. Gonadal and chromosomal sex have independent mechanisms of driving increased social motivation in females. Additionally, sex chromosomes may interact with neurodevelopmental mutations to influence sex variation in atypical social behavior.

Disconnected Connections of Learning Beyond Formal Schooling Through Human–Computer–Human Interactions

This article originated from a working group on “Learning beyond formal schooling through human–computer–human interaction (HCHI)” convened at the UNESCO EDUSummIT 2023 in Kyoto (Japan). A polylogue approach was adopted by engaging eight co-authors whose diverse perspectives culminated in propositions that addressed the pivotal question: How should the connections between formal, non-formal, and informal learning be considered in a digitally mediated world? Formal learning is typically structured, organized and chronologically arranged institutional learning, whereas informal learning is associated with everyday learning across contexts throughout one’s life, and non-formal learning is a hybrid of these forms of learning. Considering the growing prominence of informal and non-formal learning in a digitally mediated world, the evolving learning ecosystem calls for a recalibration of the emphasis on formal learning. In this regard, HCHI has the potential to mediate human–human interactions, thereby bridging formal and informal learning. Our articulated position is to preserve the distinct boundaries and inherent complexities of each type of learning while creating opportunities or ‘bridges’ to authentically draw on the processes of each through meaningful actions. The polylogue yielded three propositions to bridge the connections between formal, non-formal and informal learning spaces in a digitally mediated world: (a) formal education institutions can establish strategic alliances and collaborations with learning organizations beyond the formal educational system, (b) the creation of digital learning communities within formal and non-formal spaces exemplify a paradigm of operation in an unregulated, student-centric cohesive space where the educator is a co-participant, (c) the recognition of the value of informal learning experiences by formal education institutions is critical, with emphasis on the learning process rather than the product.

Light‐Broadened Faradaic Regime of Organic Electrochemical Transistors for Accelerated Amperometric Biodetection

AbstractFaradaic‐mode organic electrochemical transistors (OECT) are promising but usually need hundreds of millivolts to sustain redox reactions. Decrease or even removal of the voltage penalty is highly desirable. Herein, the Faradic regime of the OECT is broadened toward zero bias by integrating a p‐n heterojunction of Cu2S‐diethylenetriamine (DETA)‐CdS for efficient photogating of poly(3,4‐ethylenedioxythiophene): poly(styrene sulfonate) channel. Upon light illumination, it is found that an obvious Faradaic process is evolved at the gate/electrolyte interface under zero gate bias, suggesting the potential of sensitive amperometric biodetection with enhanced signal resolution. At the Cu2S‐DETA‐CdS/liquid interface, a biosensing process is introduced, combining with a DNA walker and enzymatic biocatalytic precipitation to produce a target‐dependent diffusion barrier, modulating the amperometric output with enhanced signal variations under light irradiation compared to that in the dark. The proposed system achieves the desired analytical performance for representative target miRNA‐10b with a low detection limit of 0.21 fM. This work features a light‐mediated OECT device with enhanced signal resolution and provides new operational paradigms and insights for novel optoelectronics interfacing with biological systems.

Exploring the convergence of Metaverse, Blockchain, and AI: A comprehensive survey of enabling technologies, applications, challenges, and future directions

AbstractThe Metaverse, distinguished by its capacity to integrate the physical and digital realms seamlessly, presents a dynamic virtual environment offering diverse opportunities for engagement across innovation, entertainment, socialization, and commercial endeavors. However, the Metaverse is poised for a transformative evolution through the convergence of contemporary technological advancements, including artificial intelligence (AI), Blockchain, Robotics, augmented reality, virtual reality, and mixed reality. This convergence is anticipated to revolutionize the global digital landscape, introducing novel social, economic, and operational paradigms for organizations and communities. To comprehensively elucidate the future potential of this technological fusion and its implications for digital innovation, this research endeavors to undertake a thorough analysis of scholarly discourse and research pertaining to the Metaverse, AI, Blockchain, and associated technologies. This survey delves into various critical facets of the Metaverse ecosystem, encompassing component analysis, exploration of digital currencies, assessment of AI utilization in virtual environments, and examination of Blockchain's role in enhancing digital content and data security. Leveraging articles retrieved from esteemed digital repositories including ScienceDirect, IEEE Xplore, Springer Nature, Google Scholar, and ACM, published between 2017 and 2023, this study adopts an analytical approach to engage with these materials. Through rigorous examination and discourse, this research aims to provide insights into the emerging trends, challenges, and future directions in the convergence of the Metaverse, Blockchain, and AI.This article is categorized under: Application Areas > Industry Specific Applications

Parallel intelligence in three decades: a historical review and future perspective on ACP and cyber-physical-social systems

Recent advances in human-in-the-loop or human-centric research have sparked a new wave of scientific exploration. These studies have enhanced the understanding of complex social systems and contributed to more sustainable artificial intelligence (AI) ecosystems. However, the incorporation of human or social factors increases system complexity, making traditional approaches inadequate for managing these complex systems and necessitating a novel operational paradigm. Over decades of work, a mature and comprehensive theory of parallel intelligence (PI) has been established. Rooted in cyber-physical-social systems (CPSS), PI adapts flexibly to various situations within complex systems through the ACP framework (Artificial systems, Computational experiments, and Parallel execution), ensuring system reliability. This paper provides a detailed review and a novel perspective on PI, beginning with the historical and philosophical origins of CPSS and proceeding to present both the fundamental framework and technological implementations of PI. PI-based Industry 5.0 is highlighted, where three pillars are adopted to help realize the supposed vision. Additionally, the paper outlines applications of PI in multiple fields, such as transportation, healthcare, manufacturing, and agriculture, and discusses the opportunities and challenges for imaginative intelligence. The continuous exploration of PI is expected to eventually facilitate the realization of “6S”-based (safe, secure, sustainable, sensitive, service, and smart) parallel ecosystems.

Implementing Component Degradations into a Modelica Model of an iPWR System to Develop Health Monitoring Techniques

The economic operation of small modular reactors will partly rely on managing and reducing inspection and maintenance activities while supporting new operational paradigms like load-following. Turbine control valves throttle the steam from the steam generator into the steam turbine while maintaining the pressure within the steam generator at a constant set point. Degradation of these components could impact the ability to manage electrical power production. Utilizing the Idaho National Laboratory Hybrid repository and the Oak Ridge National Laboratory TRANSFORM library developed for multiphysics simulations in Dymola/Modelica, an integral pressurized water reactor system was modeled based on the available specifications of the NuScale power module. The effects of various component degradation modes have been implemented into the model in order to simulate faulted plant data during both steady-state and load-following operations. The fault modes resemble different physical fault modes that may occur at an operating nuclear power plant; a leaking turbine control valve and a valve actuator failure due to loss of hydraulic pressure have been implemented. A neural network autoencoder is employed in conjunction with statistical analysis, namely, simple signal thresholding (SST) or sequential probability ratio testing (SPRT), to identify the presence of a fault. Fuzzy logic is additionally employed in a novel and promising manner to classify the state of the system based on the cumulative sum of the neural network residuals. SST and SPRT are both successfully validated using healthy data and proved capable of identifying both fault types; fuzzy logic identified the false positives and classified the faulted data correctly.

Concept of operations for increasingly autonomous space operations

Concept of Operations (ConOps) documents provide a common view of future system functions to all stakeholders. This ConOps focuses on deep space missions, such as a mission to Mars. While Earth experts will be continuously monitoring operations during crewed deep-space missions, there will be communication delays and disruptions that will impede the rapid assistance required by the crew in time- and safety-critical situations. An argument will be made that the crew will require (some kind of) assistance to quickly understand the situation enough to safe the system. This document describes a notional vision of the operational processes, practices and capabilities needed by a deep space mission crew for them to autonomously respond to anticipated and unanticipated, time-critical anomalies. A descriptive model of a Crew Performance Support System (CPSS) is used to illustrate what will be required for a safe and successful manned mission to Mars. Scenarios will address crew, Earth-Support and technology roles/responsibilities, task prioritization, teaming strategies, complex procedure development and execution, assumptions, asynchronous collaboration under communication time delay and limited data exchange to illustrate potential operational needs and approaches. Scenarios are responsive to known human risks identified during and after long duration spaceflight and incorporate transition plans as space travel moves from ISS to Lunar to Mars operations specifically identifying test bed and research activity needs. The envisioned CPSS will alter the current operational paradigm of crew reliance on Earth experts to resolve anomalies. The intent of this ConOps is to advance the research and development of a CPSS.

A comprehensive study on modern optimization techniques for engineering applications

Rapid industrialization has fueled the need for effective optimization solutions, which has led to the widespread use of meta-heuristic algorithms. Among the repertoire of over 600, over 300 new methodologies have been developed in the last ten years. This increase highlights the need for a sophisticated grasp of these novel methods. The use of biological and natural phenomena to inform meta-heuristic optimization strategies has seen a paradigm shift in recent years. The observed trend indicates an increasing acknowledgement of the effectiveness of bio-inspired methodologies in tackling intricate engineering problems, providing solutions that exhibit rapid convergence rates and unmatched fitness scores. This study thoroughly examines the latest advancements in bio-inspired optimisation techniques. This work investigates each method’s unique characteristics, optimization properties, and operational paradigms to determine how revolutionary these approaches could be for problem-solving paradigms. Additionally, extensive comparative analyses against conventional benchmarks, such as metrics such as search history, trajectory plots, and fitness functions, are conducted to elucidate the superiority of these new approaches. Our findings demonstrate the revolutionary potential of bio-inspired optimizers and provide new directions for future research to refine and expand upon these intriguing methodologies. Our survey could be a lighthouse, guiding scientists towards innovative solutions rooted in various natural mechanisms.

Applications of the Fourier neural operator in a regional ocean modeling and prediction

In this paper, we apply the Fourier neural operator (FNO) paradigm to ocean circulation and prediction problems. We aim to show that the complicated non-linear dynamics of an ocean circulation can be captured by a flexible, efficient, and expressive structure of the FNO networks. The machine learning model (FNO3D and the recurrent FNO2D networks) trained by simulated data as well as real data takes spatiotemporal input and predicts future ocean states (sea surface current and sea surface height). For this, the double gyre ocean circulation model driven by stochastic wind stress is considered to represent an ideal ocean circulation. In order to generate the training and test data that exhibits rich spatiotemporal variability, the initial states are perturbed by Gaussian random fields. Experimental results confirm that the trained models yield satisfactory prediction accuracy for both types of FNO models in this case. Second, as the training set, we used the HYCOM reanalysis data in a regional ocean. FNO2D experiments demonstrated that the 5-day input to 5-day prediction yields the averaged root mean square errors (RMSEs) of 5.0 cm/s, 6.7 cm/s, 7.9 cm/s, 8.9 cm/s, and 9.4 cm/s in surface current, calculated consecutively for each day, in a regional ocean circulation of the East/Japan Sea. Similarly, the RMSEs for sea surface height were 2.3 cm, 3.5 cm, 4.2 cm, 4.6 cm, and 4.9 cm, for each day. We also trained the model with 15-day input and 10-day prediction, resulting in comparable performance. Extensive numerical tests show that, once learned, the resolution-free FNO model instantly forecasts the ocean states and can be used as an alternative fast solver in various inference algorithms.

Remote sensing of electron precipitation mechanisms enabled by ELFIN mission operations and ADCS

The Electron Loss and Fields INvestigation (ELFIN) mission comprising two 3U+ CubeSats was developed, built, and operated by several generations of undergraduate students at UCLA. The spin-stabilized CubeSats (spin-rate: 21 RPM) produced high-resolution measurements of precipitating, trapped, and backscattered fluxes of electrons and ions in the radiation belts. Launched in September 2018, ELFIN operated successfully until its deorbit just over four years later. Initially, however, mission operations was very challenging and only tapped the full mission potential after a thorough redesign of the operations paradigm. This mid-mission adjustment yielded the higher data downlink volume necessary to acquire a comprehensive data set, therefore enabling ensemble studies with sufficient statistical significance. The new operational framework also led to additional improvements across the mission. Most notably, the Attitude Determination and Control System (ADCS) benefited from more reliable collections of magnetometer data for attitude determination, enabling knowledge and control to <1°. This allowed high pitch-angle resolution (especially within the loss cone) and high energy resolution spectrograms, both powerful diagnostics of radiation belt electron and ion precipitation. This paper highlights the scientific advancements made possible by ELFIN’s efficient mission operations and ADCS design, unique for CubeSats, and emphasizes the role of electron precipitation measurements for future studies in magnetospheric, ionospheric, and atmospheric physics.

Analytical game strategies for active UAV defense considering response delays

In the realm of aerial warfare, the protection of Unmanned Aerial Vehicles (UAVs) against adversarial threats is crucial. In order to balance the impact of response delays and the demand for onboard applications, this paper derives three analytical game strategies for the active defense of UAVs from differential game theory, accommodating the first-order dynamic delays. The targeted UAV executes evasive maneuvers and launches a defending missile to intercept the attacking missile, which constitutes a UAV-Missile-Defender (UMD) three-body game problem. We explore two distinct operational paradigms: the first involves the UAV and the defender working collaboratively to intercept the incoming threat, while the second prioritizes UAV self-preservation, with independent maneuvering away from potentially sacrificial engagements. Starting with model linearization and order reduction, the Collaborative Interception Strategy (CIS) is first derived via a linear quadratic differential game formulation. Building upon CIS, we further explore two distinct strategies: the Informed Defender Interception Strategy (IDIS), which utilizes UAV maneuvering information, and the Unassisted Defender Interception Strategy (UDIS), which does not rely on UAV maneuvering information. Additionally, we investigate the conditions for the existence of saddle point solutions and their relationship with vehicle maneuverability and response agility. The simulations demonstrate the effectiveness and advantages of the proposed strategies.

Modular vehicles can reduce greenhouse gas emissions for departure flight baggage transportation

Rapid and on-time baggage transportation plays a crucial role in ensuring customer satisfaction and is an important source of greenhouse gas (GHG) emissions in the aviation sector. Modular Vehicle (MV) is an emerging transportation technology that allows vehicles to adjust their capacity flexibly by assembling or disassembling identical detachable units. This innovative technology offers a new perspective to decarbonize the aviation sector, as it holds promise for reducing GHG emissions in flight baggage transportation. To investigate this possibility, this study proposes an MV operation paradigm and a corresponding “greenest” MV scheduling problem that aims to minimize MV-relevant GHG emissions while transporting baggage from the terminal to the aircraft without delay. To solve the problem efficiently, a fast construction-merging heuristic is proposed based on the theoretical properties of the problem. A series of case studies at the Tampa International Airport were conducted to evaluate the performance of the proposed MV operation and the construction-merging heuristic. The results indicate that the proposed MV operation effectively reduces GHG emissions, and the heuristic solves near-optimal solutions to the investigated problem much faster than Gurobi, a state-of-the-art commercial solver for integer programs, without much loss of the optimality of the solutions. Results from this study provide important managerial and operational insights into decarbonizing baggage transportation for airport operators.

Jacdac: Service-Based Prototyping of Embedded Systems

The traditional approach to programming embedded systems is monolithic: firmware on a microcontroller contains both application code and the drivers needed to communicate with sensors and actuators, using low-level protocols such as I2C, SPI, and RS232. In comparison, software development for the cloud has moved to a service-based development and operation paradigm: a service provides a discrete unit of functionality that can be accessed remotely by an application, or other service, but is independently managed and updated. We propose, design, implement, and evaluate a service-based approach to prototyping embedded systems called Jacdac. Jacdac defines a service specification language, designed especially for embedded systems, along with a host of specifications for a variety of sensors and actuators. With Jacdac, each sensor/actuator in a system is paired with a low-cost microcontroller that advertises the services that represent the functionality of the underlying hardware over an efficient and low-cost single-wire bus protocol. A separate microcontroller executes the user's application program, which is a client of the Jacdac services on the bus. Our evaluation shows that Jacdac supports a service-based abstraction for sensors/actuators at low cost and reasonable performance, with many benefits for prototyping: ease of use via the automated discovery of devices and their capabilities, substitution of same-service devices for each other, as well as high-level programming, monitoring, and debugging. We also report on the experience of bringing Jacdac to commercial availability via third-party manufacturers.

Agile Establishment of ERP System Roll Out in Maintaining Production Sustainability during the PSC Transition &amp; Liquidation Phase Case Study a Site Acquisition in the Oil &amp; Gas Industry

In the dynamic landscape of the Oil &amp; Gas Industry, Information Technology (IT) emerges not only as an enabler but as the central driving force reshaping business operational paradigms. This paper delves into the transformative power of Enterprise Resource Planning (ERP) systems, with a spotlight on the agile approach unveils the critical role of the Software as a Service (SaaS) Systems establishment of the Applications, and Products in Data Processing (SAP), in providing a means to mitigate the business process gaps anticipates the indefinite time of Gazettal issuance, streamlining operations, and accelerate business deployment to maintain the oil production sustainability during liquidation and transition phase for a new Production Sharing Contract (PSC) in the case study of a site acquisition in Oil &amp; Gas Industry. The primary data collection is gathered through Focus Group Discussions (FGDs) with key stakeholders, including the Company’s Management, Subholding Upstream, and Holding’s task force team consisting of SAP Consultants, and Business Process Owners (Subject Matter Experts) who are well-versed in the business requirements and current surrounding issues. Combining the literature review for secondary data collection for broader context and insights into best practices and lessons learned in similar cases. The data analysis focuses on the External Strategic Factors Analysis Summary (EFAS) is conducted using PESTLE, Porter’s Five Forces, and the Competitor Analysis to gain a competitive landscape, while the Internal Strategic Factors Analysis Summary (IFAS) with the VRIO framework, assessing a firm’s internal resources and capabilities to maintain a competitive edge over the long term, summarizes the key external &amp; internal factors for the SWOT analysis resulting the alternative strategies. The ultimate best alternative strategy utilizes the Quantitative Strategic Planning Matrix (QSPM) method by analyzing attractiveness scores to the factors &amp; enhances the probability of selecting the best strategic options. Through real-world case studies on a site acquisition and Oil &amp; Gas Industry insights, the paper underscores how SAP SaaS Roll-Out with an established Shared Service Centre as a support organization could revolutionize the Oil &amp; Gas sector, paving the way for enhanced efficiency, sustainability, and strategic decision-making would benefit in the Liquidation and Transition phase of a PSC. Ultimately, this research illuminates the indispensable nature of the SAP applications, as the linchpin driving the industry’s progression towards a more agile, competitive, and ready-to-use landscape as it opens further feasibility study of SAP SaaS “packaged-solution” deployment for the benefit of next block acquisition.

VIRTUAL POTHOLE DETECTION SYSTEM

The intersection of artificial intelligence and vehicular technology has birthed a realm of innovation, revolutionizing our engagement with the automotive sphere. This survey delves into the realm of virtual pothole detection systems, a pioneering endeavor aimed at mitigating road hazards through technological intervention. As society gravitates towards a digitalized landscape, the imperative for enhanced road safety looms large. Virtual pothole detection systems represent a concerted effort to address this pressing concern, leveraging cutting-edge advancements in sensor technology, data analytics, and vehicular communication protocols. This paper scrutinizes the evolution, methodologies, and efficacy of virtual pothole detection systems, offering insights into their operational paradigms and prospects. Amidst the backdrop of burgeoning urbanization and escalating vehicular traffic, the significance of such systems cannot be overstated. By elucidating the intricacies of virtual pothole detection systems, this survey endeavors to catalyze further research and innovation in the pursuit of safer, more resilient road networks Keywords: Virtual Pothole Detection System (VPDS), Sensor Technology, Data Analytics, Vehicular Communication, Road Safety, Pothole Detection, Accident Prevention, Road Maintenance, Artificial Intelligence.

On the prediction of the induced damage by the start-up sequence of Francis turbines: On operational resilience framework

The number of transient operations in hydraulic machinery connected to power grid, notably start-ups and shut-downs, has observed a substantial increase in recent decades, primarily driven by the global shift towards intermittent renewable energy sources. Simultaneously, advancements in MW power converter technology and its cost-effectiveness have altered the operational paradigms of hydropower plants. These transformations offer novel opportunities for asset management within the industry. This study introduces a new method aimed at forecasting stress during start-up procedures, leveraging steady-state measurements acquired from a reduced scale model of the hydraulic turbine. Through a dedicated modular telemetry measurement system and the integration of strain gauges onto the reduced-scale runner blades, a tool is devised to predict dynamic loads characteristic of these transient operations. Utilizing steady-state measurements and a Voronoi cell tessellation, the transient predicted stress signal is constructed by concatenating the information from each encountered cell, thus providing an estimated projection of the stress likely to occur during the sequence. The proposed methodology demonstrates a robust forecast of the stresses on the runner blades during steady-state and transient operations of Francis turbines. Its immediate value lies in the ability to correctly estimate the lifetime of the runner as well as the possible integration into an optimization framework for stress reduction, thereby potentially extending the operational lifespan of the unit, from the runner structure perspectives.

The Effectiveness of ChatGPT in Translating Chunky Construction Texts in Chinese Political Discourse

From the theoretical perspective of Chinese-English temporal-spatial cognitive differences, this study employs a self-constructed bilingual parallel corpus of political discourse totaling one million words. It extracts chunky construction texts with latent agents in the source language (Chinese) and corresponding texts of target language translations (English) for a comparative experiment between ChatGPT translations and those of human translators. Driven by the “Human-AI Interaction Model”, three different prompts were set up to conduct three rounds of translation testing with ChatGPT. The study discovers: 1) ChatGPT has limitations in actively understanding the implicit elements of the source Chinese texts that are driven by a strong spatial preference, which are also reflected in the target language English translations; 2) The “Human-AI Interaction Model” can guide and train ChatGPT effectively through continuous optimization of prompts, enhancing AI abilities in understanding the source language and achieving effective target language translation; 3) Currently, ChatGPT cannot replace human translators in translating chunky discourses of Chinese, but the collaborative interaction between human translators and AI is highly effective. By optimizing the design of prompt instructions, this research offers cognitive reference and operational paradigms to improve ChatGPT’s recognition and cognitive reconstruction capabilities of Chinese-English temporal-spatial differences, providing insights for future research, translation practice, and teaching in related areas.