Additional Objective Research Articles

Tightening Blocks in Complementary Analyses of Observational Studies: Optimization Algorithm and Examples

An observational block design has I blocks matched for covariates and J individuals per block, but treatments were not randomly assigned to individuals within blocks, as would have been done in an experiment. Tightening an observational block design means selecting J ′ < J individuals from each block, and possibly I ′ ≤ I blocks, to construct a new observational block design that, in some way, addresses unmeasured biases from nonrandom treatment assignment. Tightening must preserve covariate balance while altering the design to achieve some additional objective. An optimization algorithm is introduced that achieves this while maintaining the block structure by finely balancing covariates across blocks and through optimal subset matching. An example is considered in detail, both to motivate and illustrate the tightening of an observational block design. Two tightened designs are built from a study of light daily alcohol consumption and its possible effects on HDL cholesterol. One tightened design adjusts for an outcome tentatively presuming it was unaffected by the treatment. The second tightened design uses a differential effect to remove bias from an unobserved general disposition that promotes several treatments. An R package tightenBlock implements the method, contains the data, and in that package the help-file for the function tighten reproduces the example.

Multi-objective optimization of inerter-based building mass dampers

The building mass damper (BMD) is a design concept in which a structure is substructured in such a way that the upper substructure behaves as a large-mass tuned mass damper for the lower one. Its correct tuning usually requires softening (partial isolating) the upper substructure, which limits its application for retrofitting. The recently proposed inerter-based building mass damper (IBMD) solves, reasonably, the softening dynamically through the use of an inerter. This implies an in parallel intervention, which drastically simplifies the practicability of the BMD for retrofitting. The present paper involves comprehensive numerical simulations for multi-objective optimization of an IBMD, accounting for inherent structural damping. In particular, the deformation of the upper substructure is an additional objective function, besides the deformation of the lower substructure as considered in a previous work. Results demonstrate that the IBMD outperforms various benchmark interventions, including dampers, stiffeners, softening devices, and inerters without dampers. Additionally, it was found that the inherent structural damping partially replaces the need for additional damping. Finally, it was also shown that emphasizing the optimization of the lower substructure deformation enhances the overall structural safety in most cases. Nevertheless, the multi-objective optimization increases the versatility of the design concept in the case of substructures with different allowable deformations.

Revolutionizing E-Commerce Logistics

The traditional approach to logistics path planning is hindered by lengthy procedures. In this study, we explore the multi-objective optimization of logistics management, considering the conventional path and time efficiency indices alongside shelf safety and stability as additional objective functions. Based on particle swarm optimization (PSO), we optimize objective functions for internal path planning, scheduling timeliness, and shelf safety and stability. We then determine optimal routes under varying order demands using PSO and ultimately optimize the final path using dynamic programming and spline function restrictions to meet actual demand. Empirical results indicate that the proposed solution method outperforms other calculation methods, such as genetic algorithm (GA) and simulated annealing (SA), demonstrating over 10% improvement in time and total distance consumption. Further practical application tests demonstrate that the model in this study has a beneficial impact on all five distinct types of orders through efficient deployment optimization.

AI Integration on Capacity Building among University Graduate in Tourism Industry

The study assessed integration of AI in capacity building among university graduate in Tourism Industry. The study was started by identify the application of AI in tourism industry and evaluate graduate capability on applying those AI applications related to tourism industry. An additional objective was to identify challenges and provide solution by examine under which circumstances the adoption of AI applications could enable tourism graduates to increase their performance and competency in job market. Furthermore, the study explored the contribution of AI awareness and AI tools optimization to improve graduate employability through Conceptual frame work derived by RBV theory and Goal setting theory. The researchers utilized descriptive research methodology which employed both qualitative and quantitative method. A sample of 55 students from the institute of tourism SUZA were provided by questionnaire to answer specified questions and interview was used to get data from experts in tourism industry. Subsequently, SPSS and Thematic analysis were used to analyze the responded data. The findings noted various tourism students using AI technology in different activities such as Google translator, Google map, gps track, self-service kiosks etc. Moreover, the results of the findings explored the adequacy of existing AI infrastructure at tourism institute is not good in terms of curriculum adaptation. However, the findings further revealed that, there were few number of students who have no idea about AI which result delaying factors during job application and less awareness of online information. Moreover, the findings indicated that high percentage of graduate students agreed that AI has great impact on studies that enhance graduate employability in today world.

An Approach to Near-Optimal Continuous-Thrust Solution for Plane Constellation Deployment

Paving the way in satellite constellation deployment, this article presents the Comprehensive Program (GCP) for planning constellation deployment missions by introducing a near-optimal solution for continuous-thrust maneuvers. The GCP establishes time constraints, enabling appropriate time scheduling and eliminating reconfiguration phases. It considers various cases of satellite departure and arrival sequences, collision avoidance constraints, and time limitations. A near-optimal solution for the continuous-thrust trajectory of each satellite is introduced, relying on the Fourier series approximation of the thrust pointing angle. The Fourier series with constant coefficients replaces the thrust angle in the satellite’s orbital motion equations in polar coordinates. The primary advantage of this near-optimal approach lies in its simplicity in accommodating space perturbations, posing no challenges in problem-solving. Additionally, the approach is beneficial due to its straightforward implementation and simple mathematical computations. This approach does not require pre-determining the revolution number of the transfer trajectory, unlike shape-based methods. The proposed method’s flexibility allows it to adapt and optimize the trajectory without prior assumptions about the number of revolutions required for the mission. Analyses demonstrate the proposed algorithm’s versatility and precision across various scenarios involving different orbital eccentricities, thrust forces, and thrust pointing angles. The constant coefficients of the Fourier series are determined by defining a set of objective functions and minimizing them using the Multi-Objective Particle Swarm Optimization algorithm (MOPSO). The first and second objective functions ensure that the transfer orbit reaches the desired deployment point, while the third and fourth ones guarantee the tangential conditions between the transfer orbit and the final orbit. An additional objective function minimizes the trajectory time. As a perturbation of interest, the effect of Earth’s oblateness is considered.

Reconstructing the Zama (Mexico) discovery source to sink story, Part I; detrital zircon U–Pb and (U‐Th)/He provenance analysis and implications for sediment source terranes

AbstractThe Zama discovery was identified off the coast of Tabasco, Mexico, in the Sureste Basin of the Gulf of Mexico and is hosted in a three‐way closure in the Upper Miocene. This study conducted a detailed detrital zircon (DZ) U–Pb and (U‐Th)/He provenance analyses on samples from sandstone reservoirs in the Zama‐3 and Zama‐2ST1 wells. A total of 22 sandstone samples (11 from each well) were collected for DZ U–Pb and (U‐Th)/He dating from different reservoir zones, testing the hypothesis that different zones were whether originally derived from varied sedimentary source terranes and associated transport pathways to the Zama mini‐basin depositional site. Additional objectives include determination of maximum depositional ages, reconstruction of paleofluvial systems, and exploring the temporal evolution of the drainage region and hinterland tectonics. The DZ U–Pb age spectra from both Zama wells have remarkably homogenous DZ signatures with very similar DZ age modes and modal percentages, displaying dominant Permian/Chiapas Batholith (ca. 35%–45%), Mesoproterozoic/Oaxaquian (ca. 20%–35%), Early Palaeozoic/Acatlán (ca. 8%–20%), and Cenozoic magmatic arc (ca. 15%–25%) age modes, as well as some subsidiary (&lt;5%) early Proterozoic/Archean and Early Cretaceous DZ age components, linked to recycled lower Palaeozoic strata and the Guerrero Terrane and Alisitos arc, respectively. Despite differences in paleocurrent directions, deduced from image logs, there are no systematic differences in DZ spectra, indicating a consistent sediment provenance and no changes in source area. All Zama samples analysed in the study are characterized by abundant syn‐depositional Late Miocene DZ grains, clustering between 8.6 and 10.2 Ma, corroborating a Tortonian (Late Miocene) depositional age, and yield rapid sediment accumulation rates of ca. 200 m in &lt;1.4 Ma (13 m/Myr). Doubled zircon U–Pb and (U‐Th)/He age pairs are indicative of recycling of early Mesozoic rift strata and Paleogene and younger Chiapas basement. These new DZ U–Pb and (U‐Th)/He data have a nearly invariant Tortonian sediment provenance that is similar to the modern Grijalva River flowing generally northward out of the Chiapas highlands. The paleo‐Grijalva drainage, providing sediment to the Late Miocene Zama mini‐basin, was likely drastically larger than the present catchment as it involved 10 Ma plutonic sources that were subsequently downfaulted along the Pacific coast in the latest Miocene. Importantly, DZ U–Pb age components are consistent with Oaxaquia, early Acatlán, and Guerrero/Alisitos signatures and point to sourcing from the Chortis block during its tectonic eastward translation. Such a scenario would allow for a substantially larger Miocene paleo‐drainage that would have encompassed both Chiapas and portions of Chortis. The Miocene tectonic translation of Chortis and erosion of a large and tectonically active hinterland would also reconcile the dramatically larger Middle to Late Miocene sediment supply, funnelling a large influx of sediment into the southern Gulf of Mexico. Rapid Late Miocene sediment accumulation in the salt‐defined Zama mini‐basin must have involved sustained sediment flux via the paleo‐Grijalva drainage and was likely facilitated and focused by continued salt deflation due to sediment loading, as described in Part II. Thus, our work provides new scientific insights into one of the largest hydrocarbon discoveries in Mexico in recent years.

The New Horizons Extended Mission Target: Arrokoth Search and Discovery

Following the Pluto flyby of the New Horizons spacecraft, the mission provided a unique opportunity to explore the Kuiper Belt in situ. The possibility existed to fly by a Kuiper Belt object (KBO), as well as to observe additional objects at distances closer than are feasible from Earth-orbit facilities. However, at the time of launch no KBOs were known about that were accessible by the spacecraft. In this paper we present the results of 10 yr of observations and three uniquely dedicated efforts—two ground-based using the Subaru Suprime Camera, the Magellan MegaCam and IMACS Cameras, and one with the Hubble Space Telescope—to find such KBOs for study. In this paper we overview the search criteria and strategies employed in our work and detail the analysis efforts to locate and track faint objects in the Galactic plane. We also present a summary of all of the KBOs that were discovered as part of our efforts and how spacecraft targetability was assessed, including a detailed description of our astrometric analysis, which included development of an extensive secondary calibration network. Overall, these efforts resulted in the discovery of 85 KBOs, including 11 that became objects for distant observation by New Horizons and (486958) Arrokoth, which became the first post-Pluto flyby destination.

Special Issue on Informatics Education: ChatGPT Performs Worse on USMLE-Style Ethics Questions Compared to Medical Knowledge Questions.

The main objective of this study is to evaluate the ability of the Large Language Model ChatGPT to accurately answer USMLE board style medical ethics questions compared to medical knowledge based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and to assess the variability of responses given by each version. Using AMBOSS, a third party USMLE Step Exam test prep service, we selected one group of 27 medical ethics questions and a second group of 27 medical knowledge questions matched on question difficulty for medical students. We ran 30 trials asking these questions on GPT-3.5 and GPT-4, and recorded the output. A random-effects linear probability regression model evaluated accuracy, and a Shannon entropy calculation evaluated response variation. Both versions of ChatGPT demonstrated a worse performance on medical ethics questions compared to medical knowledge questions. GPT-4 performed 18% points (P < 0.05) worse on medical ethics questions compared to medical knowledge questions and GPT-3.5 performed 7% points (P = 0.41) worse. GPT-4 outperformed GPT-3.5 by 22% points (P < 0.001) on medical ethics and 33% points (P < 0.001) on medical knowledge. GPT-4 also exhibited an overall lower Shannon entropy for medical ethics and medical knowledge questions (0.21 and 0.11, respectively) than GPT-3.5 (0.59 and 0.55) which indicates lower variability in response. Both versions of ChatGPT performed more poorly on medical ethics questions compared to medical knowledge questions. GPT-4 significantly outperformed GPT-3.5 on overall accuracy and exhibited a significantly lower response variability in answer choices. This underscores the need for ongoing assessment of ChatGPT versions for medical education. ChatGPT, Large Language Model, Artificial Intelligence, Medical Education, USMLE, Ethics.

Legal and admisnitrative-management conditions of the operation of polish spa resorts

The article shows that the spa, as a territorial part of the municipality, determines the status of the municipality, its leading spa function, to which most of the important matters are necessarily subordinated. This applies, for example, to land use plans, development plans, environmental protection, construction, business development of the entire municipality, not just the part on which the resort is located. The aim of the work is to try to answer the question of what legal, administrative and organizational solutions will stimulate the effective functioning and development of Polish spas, under conditions of strong competition in the European market for tourism and spa services. An additional objective of the study is to identify the factors stimulating competitiveness and quality of tourism and spa services of customers of Polish spas. The evaluation of laundry and economic-administrative solutions was limited to the assessment of economic instruments and legal regulations found in national and EU government documents, directives and legal acts. The evaluation of current and projected solutions and changes in legal regulations takes into account the short and long term.

Multi-level feature interaction for open knowledge base canonicalization

Open Information Extraction (OpenIE) aims to construct expansive open knowledge bases (OKBs) by extracting triples (noun phrase, relation phrase, noun phrase) from unstructured text. One critical problem in OKBs is the lack of canonicalization for noun phrases and relation phrases, leading to the storage of redundant and ambiguous facts. Consequently, open knowledge base canonicalization, which clusters synonymous phrases into the same group, has emerged as an active research area. Existing approaches either leverage fact triples or source context in isolation, or at best, interact them at the clustering level. However, these approaches lack explicit interaction or only loosely couple the two types of knowledge, resulting in the potential loss of valuable intermediate information. In this paper, we propose MuFIC, a novel unsupervised framework that interacts the fact triples and source context at the feature level to address these limitations. In order to capture and integrate fine-grained fact and context knowledge, we design three levels of feature interaction: low-level context-guided feature interaction, mid-level fact-guided feature interaction, and high-level gated fusion feature interaction. Furthermore, we introduce an additional objective function via contrastive learning to improve the quality of extracted features and reduce knowledge-specific noise. Finally, we design a bidirectional feedback mechanism to better guide the learning process of joint features by harnessing side information prototype learning, and to dynamically optimize side information based on the clustering results formed by joint features. Extensive experiments on three public benchmark datasets demonstrate the superiority of our proposed framework.

Encountering Bias: Examining Biases and Stereotypes in the Evaluation Process Among Expert Psychologists During Specialization Exams.

The study of cognitive biases in job interviews has garnered significant attention due to its far-reaching implications for the economy and society. However, little research has focused on the biases exhibited by expert psychologists serving on psychology specialization examination committees. As such, this study has conducted a comprehensive examination of biases within the specialization exam in Israel. One additional objective of the research is to assess the levels of distress experienced by examinees following the examination. Questionnaires were administered to 418 psychologists participating in the clinical and educational psychology specialization exams. The findings unveiled several noteworthy outcomes. Firstly, several biases were identified, including ethnic stereotypes, biases stemming from cognitive load, and more. Secondly, examinees who presented a cognitive-behavioral therapy (CBT) case experienced a higher failure rate. Thirdly, a positive association was found between exam failure and personal distress and this effect was stronger for educational examinees compared to clinical examinees. The most intriguing discovery was that all biases, without exception, occurred among clinical psychologists, whereas educational psychologists displayed no biases. This outcome contrasted with initial expectations. Consequently, the present study aims to expand the existing knowledge about psychological biases and stereotypes by elucidate the reasons behind this discrepancy between the two disciplines while considering the advantages and disadvantages associated with a sense of "expertise" in the realm of adult diagnostics.

Optimizing organizational agility through agile complexity tolerant leadership

The main objective of this paper is to explore how the use of agile complexity can enhance organizational agility and competitive advantage in Small and Medium Enterprises (SMEs) during digital transformation. Additional objectives include addressing knowledge gaps in the leadership attributes needed to drive organizational agility, evaluating the research focus and methods used, and exploring agile complexity leadership as a model to enhance organizational performance. The research method involves a literature review using Google Scholar from 2013 to 2023, using keywords such as complexity, complexity science, leadership complexity, agile, and agile leadership. After initially identifying 81 journal articles, 13 relevant journal articles and four books were selected by reviewing their abstracts to assess their correlation with the role of agile complexity leadership in enhancing organizational agility during digital transformation in SMEs through prism model analysis. The main findings from this observation include a significant knowledge gap regarding the leadership attributes needed to enhance organizational agility, research dominance based on quantitative methods with statistically non-representative samples, and a lack of attention to the concept of complexity leadership as an effective model for improving SME performance. Although this paper provides new insights into the relationship between organizational agility and leadership during digital transformation in SMEs.

Foam detection in a stirred tank using deep learning neural networks

A deep learning method based on the Convolutional Neural Network (CNN) U-Net architecture has been explored as an image analysis tool for the detection and automated quantification of foam generated in a stirred tank. Multiple datasets of different sizes and with different types of foam were obtained experimentally and processed with data augmentation techniques in order to train the deep learning networks. The impact of adding attention and residual gates to the U-Net model to improve its performance, as well as the size of the dataset used to train the model have been assessed. The main factor influencing the accuracy of the models to detect the foam in the stirred tank is the size and quality of the dataset use to train the U-Net models; it must be sufficiently large and varied in terms of foam type/structure. The addition of attention gates and residual blocks to the U-Net model slightly improves the accuracy of foam detection, particularly for images that contain additional objects or obstructions, however the training time is 30 % longer than the standard U-Net model. In all cases, this image analysis method based on the U-Net model largely out performs conventional image analysis, which was not able to automatically differentiate between foam, additional objects in the tank and bubbles in the liquid. Two methods for the measurement of foam quantity (maximum foam height and foam volume) have also been developed based on the foam regions detected by the models. This is important for the application of the tool, which is to be used to understand the impact of operating conditions on foam formation in lab-/pilot-scale stirred tanks and then develop scale-up guidelines for foam control in industrial tanks.

Unraveling the binding problem in working memory: insights from the hierarchical binding model.

The binding problem is a crucial issue in the study of working memory (WM) and remains a central topic of debate among various WM models. Over the past decade, we have explored feature binding within WM, guided by the Hierarchical Binding Model (HBM). This model suggests that WM binding occurs in two stages: an initial implicit binding involving rapid, coarse feature processing, followed by explicit binding where focused attention refines these features via a reentry process. We found that implicit binding is closely related to the attentional processing of features during the perceptual stage. Basic features that can be rapidly and coarsely processed in parallel through spread attention are involuntarily extracted into WM along with the target features, forming a rough bound representation. For explicit binding, we examined the role of attention in retaining explicit binding in WM, emphasizing the unique role of reentry in the HBM. Our findings indicate that WM binding requires additional object attention through the reentry process. These results demonstrate that both implicit and explicit bindings are integral to WM and that the HBM is effective in elucidating the binding mechanisms within WM.

Two-stage coevolutionary constrained multi-objective optimization algorithm for solving optimal power flow problems with wind power and FACTS devices

As a large amount of wind energy is integrated into the grid, the randomness it brings poses a challenge to modern power systems. The application of Flexible AC Transmission Systems (FACTS) in the grid is becoming more and more common, and it is necessary to consider how to choose suitable equipment in the appropriate locations. In this paper, a multi-objective optimal power flow (MOOPF) model with wind farms and FACTS devices is established. The Weibull probability density function is used to establish the wind speed model, and the cost problem brought by wind power is considered. The locations and ratings of thyristor-controlled series compensators, thyristor-controlled phase shifters, and static VAR compensators are added to the system as control variables. In addition, the constraints on the prohibited operating areas of thermal power generators and the valve point effect are also considered. Coevolutionary constrained multi-objective optimization algorithm (CCMO) is an advanced technology, and this paper improves it and names it two-stage coevolutionary constrained multi-objective optimization algorithm (TSCCMO). The proposed algorithm uses the constraint violation value as an additional objective function in the sub-population environmental selection process, and integrates a neighborhood selection strategy into the mating selection process. The population evolution process is divided into two stages, in the first stage the two populations cooperate weakly, and in the second stage the two populations will have strong cooperation. TSCCMO is used to solve this complex constrained MOOPF problem, and its results are compared and analyzed with CCMO, NSGA–II–CDP, C3M, and PPS. The comprehensive performance of TSCCMO is the best among the 6 cases.

Inclusive Education Learning Process in Islamic Religious Education Subjects for Children with Special Needs

Inclusive education is a fundamental right that provides children with disabilities access to education alongside their peers. This study examines the implementation of inclusive learning within Islamic Religious Education (PAI) at the Creative Islamic Elementary School (SDIK) Makkah in Padang City. Employing a descriptive qualitative approach, data were collected through observations, interviews, and documentation from school principals, PAI teachers, and special guidance teachers (GPK). The findings reveal that the lesson planning in PAI classes is inclusive, covering all students, including those with special needs (ABK), with additional objectives tailored by GPK according to ABK abilities. In the learning implementation phase, GPKs actively support ABK, prioritizing their participation. Evaluations are customized to ABK’s unique capabilities and learning characteristics, differing from standard assessments. Key supportive factors include knowledgeable principals, dedicated GPKs, and adequate infrastructure, while challenges arise from fluctuating ABK moods, teachers’ instructional dilemmas, and limited parental engagement due to busy schedules. This research contributes to the body of knowledge on inclusive education by providing insights into effective practices and obstacles, offering a valuable reference for future studies on inclusive learning in PAI subjects.

An Algorithm for Ship Detection in Complex Observation Scenarios Based on Mooring Buoys

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms.

Automatic grouping of wind turbine types via multi-objective formulation for nonuniform wind farm layout optimization using an analytical wake model

This study introduces a novel methodology that aims to minimize the number of different wind turbine types used in nonuniform wind farms. This approach involves a trade-off to tackle the logistical challenges associated with installing, operating, and maintaining such wind farms. To accomplish this, a Gaussian analytical wake model is used in conjunction with evolutionary algorithms (NSGA-II and SMS-EMOA) for multi-objective optimization. A multi-objective problem is formulated, regarding wind farm capacity factor (CF) and levelized cost of energy (LCOE), and addressed using two distinct approaches. In the first approach, proposed here, the number of different wind turbine types is limited and treated as an additional objective. In contrast, the second approach allows for flexibility in choosing any number of different turbine types. Comparing the results obtained, it was observed that the methodology presented here yielded a reduced number of different turbine types without compromising CF and LCOE objectives. A multi-criteria decision maker was applied to extract the final non-dominated solutions from the obtained Pareto set. Finally, economic indicators show the profitability of these solutions.

Mice integrate conspecific and contextual information in forming social episodic-like memories under spontaneous recognition task conditions

The ability to remember unique past events (episodic memory) may be an evolutionarily conserved function, with accumulating evidence of episodic-(like) memory processing in rodents. In humans, it likely contributes to successful complex social networking. Rodents, arguably the most used laboratory models, are also rather social animals. However, many behavioural paradigms are devoid of sociality, and commonly-used social spontaneous recognition tasks (SRTs) are open to non-episodic strategies based upon familiarity. We address this gap by developing new SRT variants. Here, in object-in-context SRTs, we asked if context could be specified by the presence/absence of either a conspecific (experiment 1) or an additional local object (experiment 2). We show that mice readily used the conspecific as contextual information to distinguish unique episodes in memory. In contrast, no coherent behavioural response emerged when an additional object was used as a potential context specifier. Further, in a new social conspecific-in-context SRT (experiment 3) where environment-based change was the context specifier, mice preferably explored a more recently-seen familiar conspecific associated with contextual mismatch, over a less recently-seen familiar conspecific presented in the same context. The results argue that, in incidental SRT conditions, mice readily incorporate conspecific cue information into episodic-like memory. Thus, the tasks offer different ways to assess and further understand the mechanisms at work in social episodic-like memory processing.

Testing a novel haptic tram master controller technology via virtual reality: feasibility and user acceptance considerations

PurposeVirtual reality (VR) has been explored as a training and testing environment in a range of work contexts, and increasingly so in transport. There is, however, a lack of research exploring the role of VR in the training of tram drivers, and in providing an environment in which advances in tram technology can be tested safely. This study aimed to test a novel haptic tram master controller within a tram-based Virtual environment (VE).Design/methodology/approachThe master controller is the primary mechanism for operating a tram, and its effective manipulation can significantly influence the comfort and well-being of passengers, as well as the overall safety of the tram system. Here, the authors tested a haptically enhanced master controller that provides additional sensory information with 16 tram drivers. The feasibility and user acceptance of the novel technology were determined through surveys.FindingsThe results indicate that the haptic master controller is seen as beneficial to the drivers suggesting that it could enhance their driving and demonstrate good acceptance. The VE has provided a potential training environment that was accepted by the drivers and did not cause adverse effects (e.g. sickness).Research limitations/implicationsAlthough this study involved actual tram drivers from a local tram company, the authors acknowledge that the sample size was small, and additional research is needed to broaden perspectives and gather more user feedback. Furthermore, while this study focused on subjective feedback to gauge user acceptance of the new haptic technology, the authors agree that future evaluations should incorporate additional objective measures.Practical implicationsThe insights gained from this VE-based research can contribute to future training scenarios and inform the development of technology used in real-world tram operations.Originality/valueThrough this investigation, the authors showed the broader possibilities of haptics in enhancing the functionality and user experience of various technological devices, while also contributing to the advancement of tram systems for safer and more efficient urban mobility.