Simulated Environment Research Articles

Insight into the long-term corrosion evolution of borated stainless steels in the wet storage environment for spent nuclear fuel

Long-term corrosion evolution of boron-containing stainless-steel (BSS) in simulated environment of spent nuclear fuel wet storage is investigated. Corrosion evolution goes through three phases of rising, stabilization and declining, which corresponds to five electrochemical processes. As B content increases, the initiation and stable growth of pits are promoted by decreased Crox3+ and increased inhomogeneity of passive film and enhanced micro-galvanic effect, resulting in reduced |Z|10mHz value and the duration of the second stage. Due to reduced grain sizes, the corrosion resistance of hot-rolled BSS is substantially improved with homogeneous passive film, higher Crox3+ in it and weaker micro-galvanic effect.

A Sustainable Approach to How Roadway Recognition Affects Drivers’ Speed Choice

Previous research has reported that driving on a familiar roadway can influence speed choice. However, the findings have not been extensively discussed in simulated environments, which are frequently used for assessments of driving behavior and traffic safety. This study assesses the effects of familiar roadways on drivers’ speed behavior in a driving simulator environment. During testing, 120 individuals drove through two blocks of four scenarios, each representing a real stretch of a mountainous Brazilian highway, with differences among the scenarios in advisory signs but with the same regulated speed. The participants could drive during the first, second, third, or fourth round, as established by random sorting. Afterwards, a Kruskal–Wallis Analysis of Variance (ANOVA) test was applied to search for significant differences in average speed between the rounds and scenarios. The results showed no significant differences in average speed (p-value < 0.05; α = 0.05); moreover, the drivers’ ability and time licensed were not necessarily correlated with average speed, supporting future research with repeated scenarios towards maximizing the sample’s utility for speed analysis in driving simulators.

A FDA-based multi-robot cooperation algorithm for multi-target searching in unknown environments

AbstractTarget search using a swarm of robots is a classic research topic that poses challenges, particularly in conducting multi-target searching in unknown environments. Key challenges include high communication cost among robots, unknown positions of obstacles, and the presence of multiple targets. To address these challenges, we propose a novel Robotic Flow Direction Algorithm (RFDA), building upon the modified Flow Direction Algorithm (FDA) to suit the characteristics of the robot’s motion. RFDA efficiently reduces the communication cost and navigates around unknown obstacles. The algorithm also accounts for scenarios involving isolated robots. The pipeline of the proposed RFDA method is outlined as follows: (1). Learning strategy: a neighborhood information based learning strategy is adopted to enhance the FDA’s position update formula. This allows swarm robots to systematically locate the target (the lowest height) in a stepwise manner. (2). Adaptive inertia weighting: An adaptive inertia weighting mechanism is employed to maintain diversity among robots during the search and avoid premature convergence. (3). Sink-filling process: The algorithm simulates the sink-filling process and moving to the aspect slope to escape from local optima. (4). Isolated robot scenario: The case of an isolated robot (a robot without neighbors) is considered. Global optimal information is only required when the robot is isolated or undergoing the sink-filling process, thereby reducing communication costs. We not only demonstrate the probabilistic completeness of RFDA but also validate its effectiveness by comparing it with six other competing algorithms in a simulated environment. Experiments cover various aspects such as target number, population size, and environment size. Our findings indicate that RFDA outperforms other methods in terms of the number of required iterations and the full success rate. The Friedman and Wilcoxon tests further demonstrate the superiority of RFDA.

Effects of the Human Presence among Robots in the ARIAC 2023 Industrial Automation Competition

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems.

Accelerated discovery of lead-free solder alloys with enhanced creep resistance via complementary machine learning strategy

Minor alloying is an effective method to improve the performance of lead-free solder alloys. In this study, we propose a complementary Machine Learning (ML) strategy for minor alloying to design solder alloys with enhanced creep resistance. Two ML models, leveraging compositional and knowledge-aware features, respectively, were constructed to predict the creep stress exponent of Sn–Ag–Cu (SAC)-based solder alloys. Five new solder alloys were designed and experimentally evaluated by screening the virtual sample space consisting of the critical elements, including Bi, In, and Ni, affecting the creep resistance of SAC solder alloys with the ML model. The creep resistance of the designed alloys was characterized using nanoindentation tests. Notably, the creep stress exponent of SAC387-3 wt % Bi-0.4 wt% Ni was determined to be 12.82 at room temperature, indicating a significant 33.9% decrease in creep strain rate compared to the SAC387 alloy. This study demonstrates the potential of the ML approach to design solder alloys with enhanced creep resistance.

The stabilized nonconforming virtual element method for the Darcy–Stokes problem

A stabilized nonconforming virtual element method is designed in order to solve the Darcy–Stokes problem, which preserves a divergence-free approximation to the velocity. The same degrees of freedom as the usual Crouzeix–Raviart-type virtual element is used, but a different virtual element space is obtained by modifying the conforming Stokes virtual element with the H1-projection operator. The proposed stabilized scheme contains two jump penalty terms over edges. One is the penalty for jumps of velocity approximation and the other one is the penalty for jumps of its normal component. We analyze this method’s well-posedness and prove its uniform convergence in a discrete energy norm. Finally, we verify the validity of this stabilized scheme by some numerical experiments.

The mass- and energy-conserving relaxation virtual element method for the nonlinear Schrödinger equation

This paper develops a conservative relaxation virtual element method for the nonlinear Schrödinger equation on polygonal meshes. The advantage of this method is to build the virtual element space where the basis functions do not need to be explicitly defined for each local element, and the bilinear forms and nonlinear terms can be computed by using elementwise polynomial projections and pre-defined degrees of freedom. Furthermore, the constructed schemes ensure the conservation of both mass and energy in discrete senses. By using the Brouwer fixed point theorem, we prove the unique solvability of the fully discrete scheme. Finally, some numerical experiments are implemented to verify the theoretical results.

Implementing and evaluating simple resampling techniques in federated learning for imbalanced data

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative machine learning. However, the challenge of data imbalance, exacerbated by the non-IID nature of distributed datasets, significantly impacts model performance and fairness in FL systems. This paper investigates the implementation and evaluation of simple resampling techniques to address data imbalance within the FL framework. Using the MIMIC-III healthcare dataset, a simulated FL environment with ten virtual clients was created to test various resampling methods: SMOTE, random undersampling, and a hybrid approach. The study employed logistic regression models and evaluated performance using common and novel FL-specific metrics. Experimental results demonstrate that the hybrid resampling technique significantly outperforms other methods, improving the F1-score by 13.1% and reducing communication rounds by 25.3%. Statistical analyses, including repeated measures ANOVA and hierarchical linear modeling, confirm the robustness of these findings across varied client data distributions. This research provides a replicable framework for addressing data imbalance in FL, contributing to enhanced model fairness and efficiency in privacy-sensitive applications.

Study on corrosion behavior of Sb-Sn high-strength weathering steel under simulated industrial atmospheric environment

Study on corrosion behavior of Sb-Sn high-strength weathering steel under simulated industrial atmospheric environment

Optimization of Logistic Solutions for Incoming and Outgoing Trucks System Using a Simulated Cross-Docking Centre Environment

Optimization of Logistic Solutions for Incoming and Outgoing Trucks System Using a Simulated Cross-Docking Centre Environment

The Influence of Psychosocial Factors on the Popularity of Consumer Behavior

This essay delves into the profound impact of psychosocial factors on the emergence and diffusion of popularity, highlighting the intricate dynamics that govern consumer preferences. The herding effect, wherein individuals follow the majority, is a pivotal force shaping trends. Consumers are drawn to products or ideas with widespread acceptance, influenced by the collective consciousness of society. Aesthetics also wield significant influence, as visually and sensory pleasing attributes enhance a product's appeal, contributing to its popularity. Moreover, the compelling drive of social affiliation emerges as a powerful force shaping consumer choices. The inherent human need for belonging and acceptance influences product selection, amplified by social media platforms where individuals showcase preferences, creating virtual spaces for exchanging ideas and trends.In conclusion, the interplay of psychosocial factors, including the herding effect, aesthetic appeal, and social affiliation, profoundly shapes the landscape of consumer popularity. Recognizing and understanding these influences are crucial for businesses, marketers, and policymakers navigating the ever-evolving societal context of consumer behavior. By acknowledging the potency of these factors, stakeholders can strategically leverage them to anticipate and respond to shifts in consumer preferences, ultimately fostering successful engagement in the dynamic marketplace.

Tweeting the academic resistance in Turkey and Hungary: from cultural crisis to defending the nation

ABSTRACT This paper explores the reemergence of universities as spaces for the contestation of government policies in an era of illiberalism, rising nationalism and populism. We focus on high-profile contestations occurring at two public universities in Turkey and Hungary: Boğaziçi University and the University of Theatre and Film Arts, respectively. At both universities, attacks on democratic governance and liberal-democratic values have resulted in academic resistance that have played out on physical campuses and in online spaces. We conceptualise a virtual space as a space of contestation and analyse social media data to identify the dominant narratives of governments and their supporters on one side and resistance supporters on the other that gives the way to two major narratives: while right-wing imaginaries and pro-government actors frame the liberal and pro-freedom inclinations of universities as a ‘threat’ to the nation, students and faculty portray the government as the true threat to their rights and freedoms and knowledge production within what was once a ‘safe space’ in universities, thus framing themselves as the ‘true guardians’ of their institutions. Our analysis shows how universities have become contemporary flashpoints in and upon which broader political contestations over national culture play out.

Personalized rehabilitation approach for reaching movement using reinforcement learning

Musculoskeletal disorders challenge significantly the performance of many daily life activities, thus impacting the quality of life. The efficiency of the traditional physical therapy programs is limited by ecological parameters such as intervention duration and frequency, number of caregivers, geographic accessibility, as well as by subjective factors such as patient’s motivation and perseverance in training. The implementation of VR rehabilitation systems may address these limitations, but the technology still needs to be improved and clinically validated. Furthermore, current applications generally lack flexibility and personalization. A VR rehabilitation game simulation is developed, which focuses on the upper-limb movement of reaching, an essential movement involved in numerous daily life activities. Its novelty consists in the integration of a machine learning algorithm, enabling highly adaptive and patient-customized therapeutic intervention. An immersive VR system for the rehabilitation of reaching movement using a bubble popping game is proposed. In the virtual space, the patient is presented with bubbles appearing at different locations and is asked to reach the bubble with the injured limb and pop it. The implementation of a Q-learning algorithm enables the game to adjust the location of the next bubble according to the performance of the patient, represented by his kinematic characteristics. Two test cases simulate the performance of the patient during a training program of 10 days/sessions, in order to validate the effectiveness of the algorithm, demonstrated by the spatial and temporal distribution of the bubbles in each evolving scenario. The results show that the algorithm learns the patient’s capabilities and successfully adapts to them, following the reward policy dictated by the therapist; moreover, the algorithm is highly responsive to kinematic features’ variation, while demanding a reasonable number of iterations. A novel approach for upper limb rehabilitation is presented, making use of immersive VR and reinforcement learning. The simulation suggests that the algorithm offers adaptive capabilities and high flexibility, needed in the comprehensive personalization of a rehabilitation process. Future work will demonstrate the concept in clinical trials.

Convolutional Neural Network-Based Digital Diagnostic Tool for the Identification of Psychosomatic Illnesses

This paper appraises convolutional neural network (CNN) models’ capabilities in emotion detection from facial expressions, seeking to aid the diagnosis of psychosomatic illnesses, typically made in clinical setups. Using the FER-2013 dataset, two CNN models were designed to detect six emotions with 64% accuracy—although not evenly distributed; they demonstrated higher effectiveness in identifying “happy” and “surprise.” The assessment was performed through several performance metrics—accuracy, precision, recall, and F1-scores—besides further validation with an additional simulated clinical environment for practicality checks. Despite showing promising levels for future use, this investigation highlights the need for extensive validation studies in clinical settings. This research underscores AI’s potential value as an adjunct to traditional diagnostic approaches while focusing on wider scope (broader datasets) plus focus (multimodal integration) areas to be considered among recommendations in forthcoming studies. This study underscores the importance of CNN models in developing psychosomatic diagnostics and promoting future development based on ethics and patient care.

Digital identities in the metaverse: Privacy, security, and user authentication in virtual financial systems

The advent of the metaverse has ushered in a new era of digital interaction, commerce, and finance. As users increasingly immerse themselves in virtual spaces, the need for robust digital identities becomes paramount. This study investigates the intricate interplay of privacy, security, and user authentication within the context of digital identities in the metaverse, with a particular focus on virtual financial systems. Employing a cross-case analysis method within the qualitative research paradigm, the study extensively examines real-world instances to extract valuable insights, identify challenges, and propose potential solutions in navigating this dynamic landscape. This approach allows for a comprehensive examination of overarching patterns and trends across case studies, revealing persistent challenges in digital identity management. The study delves into the complexities of pseudonymity in virtual engagement, emphasizing the crucial balance required for user trust and accountability. Furthermore, it uncovers commonalities in successful implementations, spotlighting the transformative role of blockchain technology, exemplified by IBM Blockchain World Wire, and the growing trend of biometric authentication, as seen in the FIDO use case. The successful cases examined demonstrate that a user-friendly experience and scalability are integral to the adoption of advanced security technologies. Industry leaders should take note of the global trends in digital transactions, emphasizing efficiency, transparency and cross-border security. Additionally, the study offers forward-looking insights with implications for future research and practice, advocating for continued exploration of the nuanced dynamics between user privacy, trust-building mechanisms and accountability in the ever-evolving metaverse landscape.

Internationalisation at home: Creating an equitable virtual space for language learning

ABSTRACT This study investigates the impact of telecollaborative practices grounded in internationalisation at home (IaH) on foreign or additional language learning and intercultural understanding. Two higher education institutions (HEIs) from Taiwan and Aotearoa New Zealand (NZ) collaboratively re-innovate a virtual learning space, originally designed for third-year university students, to facilitate equitable access to intercultural communication experiences for first-year university language learners. Using a qualitative approach, data were collected through student e-portfolios, social networking sites, and reflections to evaluate the effectiveness of the telecollaborative learning design in achieving its goals. Findings demonstrated that the telecollaborative practices not only afforded students opportunities to practice with peers from native-language countries but also prompted them to strengthen their sense of belonging and identity the more their intercultural knowledge broadened and deepened. Future research is suggested to explore the potential impact of IaH initiatives on a broader range of tertiary students.

From the simulation lab to the operating room: simulation performance predicts intraoperative performance in robotic gastrojejunostomy.

Simulation and video-based assessment (VBA) offer residents the opportunity to develop operative skills while ensuring patient safety. This study aims to determine whether simulation training can predict residents' operative performance, focusing on the gastrojejunal (GJ) anastomosis during robotic pancreatoduodenectomy. Twenty-seven general surgery residents completed simulated robotic GJ drills and subsequently performed GJs in the operating room (OR). Both simulated and intraoperative performances were video recorded and retrospectively assessed by two blinded graders using the Objective Structural Assessment of Technical Skills (OSATS) scale, time to completion, and occurrence of errors. Intraoperative GJ OSATS scores were compared in cases with and without Clinically Relevant Delayed Gastric Emptying (CRDGE). Statistical analysis was performed using Spearman's rho, Chi-square, and Kruskal-Wallis tests. For simulated GJs, the median OSATS score was 29 (IQR 27-33), time to completion was 30min (IQR 27-35), and 11 cases had at least one error. Intraoperative GJs had a median OSATS of 30 (IQR 27-31), time to completion of 41min (IQR 36-51), and errors occurred in nine cases.The OSATS score on the simulated GJs demonstrated a significant positive correlation to the OSATS score on the operative GJs (r = 0.74; p < 0.001) and less time to completion (r = - 0.68; p < 0.001). A shorter simulated GJ completion time significantly correlated with a higher intraoperative OSATS score (r = - 0.52; p < 0.01). Residents with at least one error in the simulated GJs had lower OSATS scores and higher times intraoperatively. Those cases with CRDGE had significantly lower intraoperative OSATS scores than those without CRDGE. Performance on a simulated robotic GJ environment is a robust predictor of OR GJ performance, demonstrating predictive validity. VBA of residents' operative GJ performance is associated with the presentation of CRDGE. Simulation-based training may be crucial to optimizing surgical outcomes before operating on patients.

Efficient State-Specific Natural Orbital Based Equation of Motion Coupled Cluster Method for Core-Ionization Energies: Theory, Implementation, and Benchmark.

We have implemented a reduced-cost partial triples correction scheme to the equation of motion coupled cluster method for core-ionization energy based on state-specific natural orbitals. The second-order Algebraic Diagrammatic Construction (ADC) method is used to generate the state-specific natural orbital, which provides quicker convergence of the core-IP value with respect to the size of the virtual space than that observed in standard MP2-based natural orbitals. The error due to truncation of the virtual orbital can be reduced by using a perturbative correction. The accuracy of the method can be controlled by a single threshold, and there is a black box to use. The inclusion of the partial triples correction in the natural orbital based EOM-CCSD method greatly improves the agreement of the results with the experiment. The efficiency of the present implementation is demonstrated by calculating the core-ionization energy of a molecule containing 60 atoms and more than 2000 basis functions.

In-situ forming Cu-based metal-organic framework in the presence of chitosan-Fe3O4 nanohybrids: A pH-sensitive carrier for controlled release of doxorubicin

In recent years, stimuli-responsive drug delivery systems based on pH, particularly those developed using bio-derived nanocomposite systems, have gained significant attention. In this work, a novel magnetic carrier was designed based on biopolymeric chitosan and metal-organic framework (MOF) for pH-controlling the release of anticancer drugs. To end this, an in-situ green method was performed to form Cu-based MOF in the presence of a magnetic polysaccharide synthesized by precipitation method toward the construction of CS/Fe3O4/Cu-MOF nanocomposite. The nanocomposite was immersed in an aqueous solution of a model anticancer drug, doxorubicin (DOX), and a higher loading capacity (90.1 ± 0.5 %) was achieved. The in-vitro drug release study showed low release rates in simulated physiological environments (pH 7.4, 37 °C, lower than about 20 %), but higher release rates in tumor tissue conditions (pH 4.5, 41 °C, higher than about 60 %) over 96 h, allowing for sustained and extended delivery of DOX. Additionally, the MTT assay demonstrated that the blank and DOX-loaded CS/Fe3O4/Cu-MOF had good cytocompatibility (over 80 % cell viability) and considerable cytotoxicity (lower than 40 % at 16 μg/mL) toward breast cancer (MCF-7) cell line, respectively. These results indicated that the synthesized nanocomposite with suitable pH-sensitivity has potential as a targeted anticancer agent.

Criminal Law Regulation and Conviction Standards of Cyber Violence: Taking the Crime of Cyber Insult and Defamation as an Example

With the advent of the Internet era, interpersonal communication in real space has gradually spread to cyberspace. Whether the legal regulation of real space should follow and restrict the behavior norms of virtual space has become an important issue discussed by the academic community. Although civil law and other laws have certain regulations on the cyberspace, it is not enough to rely solely on general laws such as civil law to limit the escalating problem of online violence in cyberspace. Therefore, discussing the intervention of criminal law has become of utmost importance. Due to the ease of making comments in virtual spaces, as well as the large number of people and a mix of good and bad, the phenomenon of insult, defamation, and wanton abuse is the most widespread. Therefore, online insult and defamation has become a typical manifestation of online violence. The provisions of China's criminal law on the crime of online insult and defamation are still relatively broad and lack specificity, which brings certain difficulties to judicial practice when facing serious consequences of online insult and defamation. By exploring the criminal law regulations and conviction standards for online violence, using case analysis and comparative research methods, the criminal law rules for online insult and defamation can be further refined, effectively punishing and timely preventing online insult and defamation, purifying the online environment, safeguarding the rights and interests of netizens, and promoting the further development of criminal law in virtual space.