Operational Tasks Research Articles

Digital Transformation in Tourism: The Role of E-Booking Systems

Abstract The tourism industry has undergone significant transformation with the rise of digital technologies, particularly online booking systems. E-booking platforms have revolutionised how customers plan and book their travel, offering convenience, cost-efficiency, and the ability to access real-time information through smartphones and other digital devices. These systems not only streamline administrative and operational tasks for businesses but also provide valuable insights by analysing customer data to tailor services and predict trends. This study explores the critical role of e-booking in the digital era of tourism, employing cluster analysis to group countries by their adoption and performance of e-booking services. The findings highlight the growing global reliance on e-booking platforms and their potential to enhance the customer experience and optimise business operations.

Motivational dysregulation with melanocortin 4 receptor haploinsufficiency

Abstract Obesity, by any standard, is a global health crisis. Both genetic and dietary contributions to the development and maintenance of obesity were integral factors of our experimental design. As mutations of the melanocortin 4 receptors (MC4R) are the leading monogenetic cause of obesity, MC4R haploinsufficient rats were fed a range of dietary fat (0–12 %) in a longitudinal design. Physiological and motivational assessments were performed using a locomotor task, a 5-choice sucrose preference task, an operant task with fixed and progressive ratios, as well as a distraction operant task. Dendritic spine morphology of medium spiny neurons (MSNs) of the nucleus accumbens (NAc), cells with ample D1 and D2 receptors, was also assessed. The percentage of lipid deposits in the liver of each rat was also analyzed using the Area Fraction Fractionator probe for stereological measurements. MC4R haploinsufficiency resulted in a phenotypic resemblance for adult-onset obesity that was exacerbated by the consumption of a high-fat diet. Results from the operant tasks indicate that motivational deficits due to MC4R haploinsufficiency were apparent prior to the onset of obesity and exacerbated by dietary fat consumption after obesity was well established. Moreover, MSN morphology shifted to longer spines with smaller head diameters for the MC4R+/− animals under the high-fat diet, suggesting a potential mechanism for the dysregulation of motivation to work for food. Increasing our knowledge of the neural circuitry/mechanisms responsible for the rewarding properties of food has significant implications for understanding energy balance and the development of obesity.

Robot hybrid inverse dynamics model compensation method based on the BLL residual prediction algorithm

Abstract The inverse dynamics model of an industrial robot can predict and control the robot’s motion and torque output, improving its motion accuracy, efficiency, and adaptability. However, the existing inverse rigid body dynamics models still have some unmodelled residuals, and their calculation results differ significantly from the actual industrial robot conditions. The bootstrap aggregating (bagging) algorithm is combined with a long short-term memory network, the linear layer is introduced as the network optimization layer, and a compensation method of hybrid inverse dynamics model for robots based on the BLL residual prediction algorithm is proposed to meet the above needs. The BLL residual prediction algorithm framework is presented. Based on the rigid body inverse dynamics of the Newton–Euler method, the BLL residual prediction network is used to perform error compensation on the inverse dynamics model of the Franka robot. The experimental results show that the hybrid inverse dynamics model based on the BLL residual prediction algorithm can reduce the average residuals of the robot joint torque from 0.5651 N·m to 0.1096 N·m, which improves the accuracy of the inverse dynamics model compared with those of the rigid body inverse dynamics model. This study lays the foundation for performing more accurate operation tasks using industrial robots.

Building integrated assessment model for IoT technology deployment in the Industry 4.0

Internet of Things (IoT) platforms have become the building blocks of any automated system but they are more important in case of industrial systems where sensitive data are captured and handled by the information system. Therefore, it is imperative to deploy the right IoT platform to perform the computational and operational tasks in a better way. During the last few years, an array of IoT technologies/platforms with different capabilities and features were introduced in the markets. This abrupt rise created selection and decision-making issues particularly for the network engineers, designers, and industrial managers due to a lack of technical understanding and skill in this area. Therefore, we present an integrated assessment model focusing on evaluating and ranking IoT platforms in the industrial environment. It encompasses multiple methods such as the proposed model leverages a well-known data collection technique such as Delphi for data collection related to the criteria features. It adopts the Analytic Hierarchy Process (AHP) for giving weights to the criteria features. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been applied for the evaluation of the top twenty (20) Industrial IoT(IIoT) platform alternatives according to the proposed criteria. It selects the most rational choice of IoT platform that can be employed in the Industry 4.0 setting. The proposed integrated assessment model produces the most accurate and consistent outcomes. Hence, it is believed that it can be used as a guideline by different stakeholders like researchers, developers, network engineers, and policymakers for the assessment and deployment of IoT platforms in the industrial environment. It is believed that it is the first kind of multi-methods integrated assessment mode for the assessment, decision-making, and prioritization of IoT technologies in the industry 4.0 domain.

Robotic Warehousing Operations: A Learn-Then-Optimize Approach to Large-Scale Neighborhood Search

The rapid deployment of robotics technologies requires dedicated optimization algorithms to manage large fleets of autonomous agents. This paper supports robotic parts-to-picker operations in warehousing by optimizing order–workstation assignments, item–pod assignments, and the schedule of order fulfillment at workstations. The model maximizes throughput, managing human workload at the workstations and congestion in the facility. We solve it via large-scale neighborhood search with a novel learn-then-optimize approach to subproblem generation. The algorithm relies on an off-line machine learning procedure to predict objective improvements based on subproblem features and an online optimization model to generate a new subproblem at each iteration. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches. In particular, our solution enhances the utilization of robotic fleets by coordinating robotic tasks for human operators to pick multiple items at once and by coordinating robotic routes to avoid congestion in the facility. Funding: This research was partially funded by Amazon.com Services LLC under award number 2D-12552417. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2024.0033 .

Психологическая работа с сотрудниками органов внутренних дел Российской Федерации, выполняющими служебные обязанности в особых условиях

The problem of psychological work with employees of the internal affairs bodies of the Russian Federation at all stages of their official duties in special conditions is considered. The goal is to develop and test methodological recommendations for conducting psychological work with employees of the internal affairs bodies of the Russian Federation performing official duties in special conditions. Materials and methods. The total sample of the study consisted of 244 people aged 21 to 55 years from among psychologists of the Ministry of Internal Affairs of the Russian Federation, the National Guard and the Ministry of Defense. Psychological conversations were conducted with psychologists from internal affairs bodies and other law enforcement agencies. A survey of psychologists from the internal affairs bodies of the Russian Federation was conducted to identify the main problems of psychological work with employees of the internal affairs bodies of the Russian Federation who performed official duties in special conditions. The methodological recommendations developed as a result of the study underwent expert evaluation. The expert group was formed from among the most experienced psychologists of the internal affairs bodies of the Russian Federation. Results and discussion. According to the survey results, it was revealed that: 96% of the psychologists surveyed have experience working with employees after they have completed official tasks in special conditions; 53% of psychologists surveyed have experience working with employees who have been injured, traumatized, disfigured and 43% with members of their families; significantly fewer psychologists have experience working with family members of employees who died in the line of duty (10%); only 5% of the surveyed psychologists have experience in providing emergency psychological assistance to employees in the process of performing operational and official tasks in special conditions. Also, based on the results of the survey, the most effective methods of psychological assistance to employees of the internal affairs bodies of the Russian Federation were determined during and after the performance of their official duties in special conditions, as well as the most effective methods of providing psychological assistance to members of their families.

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns.

Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller's cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.

Impact of prior experience and gender on cognitive load during initial drone use

The purpose of this demonstration study was to determine the effect of prior experiences and gender on cognitive demands at the initial educational technology experience. The task of drone operation was selected as a novel educational technological tool and videogame use was determined to be a tangential prior experience. Through the framework of embodied cognition, the NASA Task Load Index was used to measure cognitive demand. Practicing middle- and high-school science teachers were recruited to participate in this study. Participants operated drones at three instances over two weeks. An additional challenging task was also presented. A mixed ANOVA suggested a significant interaction between videogame use and time: F(2, 28) = 3.77, p<\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<$$\\end{document} 0.05. A significant interaction between gender and time was also observed: F(2, 29) = 3.72, p<\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p<$$\\end{document} 0.05. For both prior experience and gender, the differences were observed at the first instance of drone operation and not at subsequent trials. Results suggest prior experience and gender may only affect cognitive load at the initial experience and leveling of cognitive load occurs with continued practice. These findings can support an educator’s planning for drone-based activities and help to mitigate the stigma of gender differences regarding technology use. Additionally, these findings support the need to examine and enhance related and transferable skills, such as hand–eye coordination and spatial thinking, to lessen cognitive load at the initial educational experience.

Vahagn: VisuAl Haptic Attention Gate Net for slip detection.

Slip detection is crucial for achieving stable grasping and subsequent operational tasks. A grasp action is a continuous process that requires information from multiple sources. The success of a specific grasping maneuver is contingent upon the confluence of two factors: the spatial accuracy of the contact and the stability of the continuous process. In this paper, for the task of perceiving grasping results using visual-haptic information, we propose a new method for slip detection, which synergizes visual and haptic information from spatial-temporal dual dimensions. Specifically, the method takes as input a sequence of visual images from a first-person perspective and a sequence of haptic images from a gripper. Then, it extracts time-dependent features of the whole process and spatial features matching the importance of different parts with different attention mechanisms. Inspired by neurological studies, during the information fusion process, we adjusted temporal and spatial information from vision and haptic through a combination of two-step fusion and gate units. To validate the effectiveness of method, we compared it with traditional CNN net and models with attention. It is anticipated that our method achieves a classification accuracy of 93.59%, which is higher than that of previous works. Attention visualization is further presented to support the validity.

Study of Collaborative Space in Rebar Tying Robotic Systems

Abstract In the process of rebar tying operations conducted by multiple robots, the flexibility and operability of the robot’s end effector directly influence the overall performance of the rebar tying system. This reflects the crucial role of the coordinated working capability of multiple robots in the trajectory planning of the rebar tying system. Utilizing an eight degrees of freedom rebar tying system composed of two collaborative robots and a truss structure, the D-H (Denavit-Hartenberg) dynamic model for each robotic arm is established. The working area of the system is ascertained via Monte Carlo methods. Flexibility and operability are adopted as metrics to evaluate the system’s performance, subsequently leading to the formulation of a mathematical model for assessing the system’s flexibility. MATLAB is employed to delineate the robotic working range and simulate analysis of flexibility and operability when operating on the rebar tying plane, yielding an intuitive distribution of system flexibility. By analysing the distribution of flexibility and operability across the working plane at varying robot spacing intervals, an optimal robot movement distance can be determined. This ensures that during operational tasks, the rebar tying system not only possesses a larger total working area but also maintains a high level of flexibility and operability within the collaborative region, thereby fulfilling the objective of enhancing workspace utilization and improving rebar tying efficiency.

Article: Reflections on the New Rules of Origin of Mercosur*

This article analyses to what extent the new Mercosur’s Rules of Origin contribute to facilitating trade from the perspective of commercial operators. It reflects on whether this compilation and updates help simplify the tasks of commercial operators. For this purpose, specific characteristics of some chapters of the New Rules of Origin (NRO) will be addressed, briefly detailing their structure and analysing the wording and impact of certain articles considered crucial for an operator to declare a product as originating

DIANA: An underwater analog space mission

The DIANA mission represents an underwater analog space mission designed to simulate and study the impact of long-duration spaceflight and extraterrestrial habitation on crew performance, psychosocial dynamics, and technological systems. The mission utilized an underwater habitat, the Hydronaut H03 DeepLab, to mimic the isolated, confined, and extreme (ICE) environment of space. Over eight days, a six-member crew lived and worked in underwater (3) and water surface (3) habitats, performing scientific experiments and operational tasks. The mission schedule encompassed a variety of activities such as drone exploration, extravehicular activities (EVAs), soil sampling, and media interactions, culminating in a simulated departure from the lunar surface. Data collection methods included continuous biomedical monitoring, cognitive task assessments, and sociomapping to analyze team communication and cooperation. This paper provides an overview of the mission architecture and outcomes, offering valuable insights into the challenges of future human space exploration and informing improvements in crew selection, training, and support systems.

Dynamic Analysis of Metamorphic Mechanisms with Impact Effects During Configuration Transformation

Metamorphic mechanisms have attracted considerable attention owing to their capability to switch their topology to adapt to different operational tasks. One feature of topological change is the re-contact of different bodies, which inevitably causes collisions affecting operation accuracy and service life. Consequently, in this study, a collision incidence matrix was introduced to describe the topology of a system involved in collisions, and a method for reducing the closed-loop system to an open-loop system was proposed. The complex movement of the metamorphic mechanism in a changing topology was classified into two different running stages of the source metamorphic mechanism. Based on the relative coordinate method, dynamic modeling of the source metamorphic mechanism considering the impact effects was conducted. Combining the classical collision theory and Newton–Euler equation, the generated impact impulse and the motion after collision were determined. Subsequently, a dynamic analytical method for the full configuration of metamorphic mechanisms was proposed to reflect the changes in the topological structure in the dynamic model. Finally, two typical metamorphic mechanisms used in packaging and spinning were considered as examples to verify the correctness and effectiveness of the proposed method, and their impact characteristics during configuration transformation were analyzed. The proposed analytical method of internal impact for a variable topology process provides effective theoretical guidance for the stability analysis of configuration transformation and structural design aimed at minimizing impacts.

Differential kinematics of a single-point-suspended manipulator with center-of-mass shift compensation

The single cable-suspended manipulator is suitable for special occasions such as aerial operation tasks of unmanned aerial vehicles (UAVs) and deep-well search and rescue. However, due to the lack of complete constraints at the base, the manipulator will have errors in end position due to the center-of-mass offset during the motion. In this paper, the model of CoM shifting is established, and the Jacobian matrix is improved based on this model, to realize the differential kinematics solution for the single cable-suspended manipulator. In addition, by introducing the constraint of CoM shift in the Jacobian matrix, it makes it possible to synchronize the planning of the motion of the end and the center of mass. This can effectively avoid the wobbling of the manipulator in the presence of elasticity or instability at the suspension point. Both simulation and prototype experiments effectively verify the effectiveness of the proposed method. Using the method of this paper, the average error of the trajectories in the z-axis and x-axis can be reduced from 27.0 ± 2.6 mm to 5.6 ± 3.4 mm, and 43.0 ± 64.2 mm to 3.3 ± 4.8 mm, respectively.

Features of the use of throws in the section demonstration of applied technology hand-to-hand combat

Actuality. A characteristic feature of hand-to-hand combat is the technique demonstration section. Applied hand-to-hand combat fully corresponds to the specifics of solving operational tasks without and with the use of weapons by representatives of various military and law enforcement agencies. It is known that in certain types of martial arts, throwing is a means of achieving an advantage over an opponent, so these technical elements are often used to solve competitive or operational-service tasks. Athletes from hand-to-hand combat during competitive activities, as well as law enforcement officers during the performance of operative tasks quite often use throwing equipment. But, despite the similarity of technical performance, the use of throwing technique in the application section of hand-to-hand combat has its own characteristics. It is clear that the characteristics of hand- to-hand combat require new approaches to the construction of the training process of the combat direction, which differ from the classical types of martial arts. Research results. Competitive activity from the applied section of hand-to-hand combat fully corresponds to the specifics of solving operational-service tasks without and with the use of weapons by representatives of various power structures. Throwing technique is one of the main tools for solving a competitive task. The use of the throwing technique depends on the type of attack and has its own characteristics and patterns for effective execution. The most versatile throwing action is the rear leg throw. In certain types of threats: with a knife or a gun - the use of throwing techniques is impractical. Catching and reaching create conditions for the use of a large number of options for various throws, most of which are performed with the help of the body. Conclusions. It has been established that in hand-to-hand combat, throws are an important component of the technical arsenal for solving competitive tasks, both in the duel section and in the technique demonstration section, but the features of hand-to-hand combat do not create an opportunity to immediately throw the opponent, it is necessary to carry out preliminary offensive or defensive actions with a simultaneous reduction of the distance and by capturing or covering individual parts of the opponent's body or clothing. The main feature of the performance is maintaining balance in the rack position for further technical actions. The use of throws from certain classification groups depends on the type of attack. Threats in the form of captures and captures have the greatest variability for the use of throws, and a universal and effective means of using counterattacking actions (throws) from various types of attack from a practical point of view is the back step. In case of a threat with a knife or a gun, taking into account the specifics of this type of attack, the use of throwing techniques to repel the attack is impractical.

Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization

In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently.

Empowering Women's Business Groups through Microfinance in Village Enterprises

Background: Effective management of microfinance institutions is essential for rural economic development. Specific Background: This study examines BUMDesa Bersama “Gempol Sejahtera,” focusing on its application of microfinance principles in financial management. Knowledge Gap: There is limited research on how structured bureaucracies affect the success of microfinance programs in community organizations. Aims: The research evaluates the impact of organizational structure on microfinance initiatives and resource management. Results: Findings show that a clear bureaucratic structure enhances efficiency and role clarity, facilitating successful program implementation. Clear Standard Operating Procedures (SOPs) and task fragmentation are critical for optimal execution. Novelty: This study offers new insights into the link between organizational structure and microfinance effectiveness in community enterprises, enriching the literature on rural empowerment. Implications: The results underscore the importance of strong organizational frameworks to improve resource management and program responsiveness, thereby promoting economic empowerment, especially among women. Highlights : Organizational Efficiency: A clear bureaucratic structure enhances operational effectiveness and role clarity. Critical Procedures: Implementing Standard Operating Procedures (SOPs) is essential for program success. Economic Empowerment: Strong frameworks promote responsiveness and resource management, benefiting community initiatives. Keywords: microfinance, organizational structure, community empowerment, financial management, bureaucratic efficiency

Flight Arrival Scheduling via Large Language Model

The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.