Engineering Expertise Research Articles

Pengembangan Media Interactive Assisted Learning (IVAL) dalam Mata Kuliah Asesmen untuk Mahasiswa Tunagrahita Ringan

The development of an innovative interactive assisted learning (IVAL) model for mildly mentally retarded students is an effort to create solutions related to the need for digital-based media to stimulate mildly mentally retarded students in assisting the learning process, in this context, in early childhood learning assessment courses at the PG-PAUD STKIP Muhammadiyah Kuningan. In fact, it has not used digital media to help students with special needs due to limited innovation of digital-based media. Through the research and development process carried out, the results of this research creates an android application entitled "Interactive Assisted Learning (IVAL) which is suitable to be applied as a learning medium for students with special needs and mild mental retardation to stimulate students in receiving, processing and storing information in brain memory. The feasibility of IVAL media is proven from the validation results of software engineering experts 73.3%, media experts 80.8% and PAUD lecturer practitioners 71.4%. The evidence of effectiveness is proven from the results of trials in the PG-PAUD Study Program which shows that the ability of students with special needs mild mental retardation is up to 90.3% with a gain score of 0.70 in the medium category and in class of assessment subjects, it is increased to 90.1% with a gain score of 0.70 in the medium category.

Automated design and optimization of distributed filter circuits using reinforcement learning

Abstract Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are not only time-consuming and labor-intensive but also rely heavily on the expertise and experience of electronics engineers, making it difficult to adapt to rapidly changing design requirements. Additionally, these commercial tools struggle with precise adjustments when parameters are sensitive to numerical changes, resulting in limited optimization effectiveness. This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Thus, it significantly reduces the subjectivity and constraints associated with circuit design. The experimental findings demonstrate clear improvements in design efficiency and quality when comparing the proposed method with traditional engineer-driven methods. Furthermore, the proposed method achieves superior performance when designing complex or rapidly evolving DFCs, highlighting the substantial potential of RL in circuit design automation. In particular, compared to the existing DFC automation design method CircuitGNN, our method achieves an average performance improvement of 8.72%. Additionally, the execution efficiency of our method is 2000 times higher than CircuitGNN on the CPU and 241 times higher on the GPU.

Study on the relevant parameters of direct economic loss assessment of reinforced concrete structure buildings considering seismic fortification and use function

The main cost and decoration cost of the building are the key parameters in the evaluation of the direct economic loss of the building. In the Earthquake Site Work Part 4 : Direct Loss Assessment of Disasters ( GB / T 18208.4-2011 ), considering factors such as economic development level, structural type, and building use, based on expert experience and engineering cost data, a grading index was developed and a corresponding correction coefficient was given to more accurately and reasonably assess the loss of the main building and the loss of decoration. In view of the rapid development of China 's economy and the acceleration of urbanization in the past ten years, the original grading indicators and coefficients need to be adjusted to adapt to the reality. This paper focuses on the reinforced concrete structure with various functions and wide distribution. By collecting a large number of corresponding building main body and decoration cost data, and combining with expert experience data, this paper analyzes the influence of seismic fortification level on the main body cost, as well as the influence of economic development level, decoration grade, use function and other factors on the decoration cost. The research results of this paper aim to provide reference for the update and refinement of several key parameters in the specification.

Analysis of Multistorey RCC Building with Differents Types of Bracing by using STAAD Pro.

The primary objective of this research is to evaluate structural stability using appropriate techniques, including standard configurations and the correct cross-sections for columns and beams. This involves developing preferred requirements for various support conditions, load types, and load combinations. It is noted that many buildings in India have been damaged or demolished by earthquakes, resulting in the loss of precious lives. The ability of structures to withstand major earthquakes is significantly improved by understanding how buildings respond to seismic activity and leveraging the expertise of engineers. This study examines the effects of different bracing systems (X- bracing bracing, inverted v bracing, k bracing) on the seismic performance of a building located in seismic Zone IV in India using STAAD Pro software. The analysis reveals that all bracing systems significantly control the lateral displacement of the frame. Designing an earthquake-resistant structure presents numerous challenges, especially in earthquake-prone areas. This research highlights the critical role of bracing systems in improving the seismic resilience of high-rise buildings in India.

Intergenerational transfer of engineering expertise: knowledge continuity management in storm surge barrier engineering

PurposeEngineering knowledge continuity is crucial for the life cycle management of long-lived and complex assets, such as nuclear plants, locks and storm surge barriers. At the storm surge barriers in the Netherlands, engineering knowledge continuity is not yet fully assured, despite long-standing efforts. This study aims to explore the relationship between system characteristics, the organizational demarcation of maintenance and operation and the challenges in achieving engineering knowledge continuity and provides suggestions for improvement of theory and policy.Design/methodology/approachTen semi-structured interviews were conducted with professionals from various backgrounds in construction, engineering and asset management of the Dutch storm surge barriers, augmented with visits to barriers and barrier teams. A thematic analysis was used to identify and describe the challenges to engineering continuity, their origins and potential solutions. We reviewed knowledge management policy documents and asset management consultancy reports to validate the findings. Additionally, we engaged in frequent interactions with professionals at the barriers. We achieved saturation and validation once no new issues were raised during these discussions.FindingsThe thematic analysis developed multiple themes describing the challenges to engineering continuity, their origins and potential solutions. The key findings are that expert engineers are critically important to deal with redesigns induced by obsolescence. Moreover, due to barrier uniqueness, long redesign cycles and reliability requirements, conventional knowledge continuity tools are insufficient to enable new engineers to reach expert level. Finally, the thematic analysis shows that, in some cases, outsourcing should be reduced to facilitate internal learning.Originality/valueThe study introduces the application of the knowledge-based view of the firm and the concept of requisite knowledge redundancy to the long-term management of complex assets. It calls for more attention to long gaps in the use of unique knowledge and the effect on knowledge continuity.

Development of a laboratory complex for measuring the condensation of atmospheric water vapors

This article presents the results of an experimental study of multiphase flows focusing on the influence of convective condensation of atmospheric water vapour on heat and mass transfer. The primary emphasis is on developing a laboratory setup capable of directly measuring the condensation rate of atmospheric water vapour on a water surface. Such an approach provides the opportunity to obtain more precise and reliable data on convective condensation and its impact on the water balance under various conditions. The experimental results revealed a nonlinear relationship between the condensation rate and the moisture excess, which differs from the linear relationship described by Dalton's law. This deviation was thoroughly examined, and a linear relationship between downward molecular flows and the forces induced by differences in relative humidity concentrations in the convective layer was proposed. The estimated average values of the condensation rate obtained in the experiments were 0.017 L/m²·hour, which, according to estimations, accounts for the condensation flux of water from the atmosphere to the ocean, consistent with the precipitation-evaporation balance in the land-ocean water balance. This information helps to more accurately determine the role of convective condensation in the water balance, contributing to refining assessments of water balance components in various watersheds. The results of this study have practical significance for hydrologists, climatologists, and experts in thermohydraulics and energy engineering. The proposed approaches and methods for measuring convective condensation of atmospheric water vapour can be applicable in various engineering systems and technologies to enhance the efficiency and accuracy of water balance calculations and forecasts.

Trustworthy and collaborative traceability management: Experts’ feedback on a blockchain‐enabled framework

AbstractBlockchain technology has attracted significant attention in both academia and industry. Recently, the application of blockchain has been advocated in software engineering. The global software engineering paradigm exacerbates trust issues, as distributed and cross‐organizational teams need to share software artifacts. In such a context, there is a need for a decentralized yet reliable traceability knowledge base to keep track of what/how/when/by whom software artifacts were created or changed. This study presents a blockchain‐enabled framework for trustworthy and collaborative traceability management and identifies benefits, challenges, and potential improvements based on the feedback of software engineering experts. A qualitative approach was followed in this study through semistructured interviews with software engineering (SE) experts. Transcripts were analyzed by applying the content analysis technique. The results indicated the emergence of five categories, further grouped into three main categories: experts' perceptions, blockchain‐based software process improvement, and experts' recommendations. In addition, the findings suggested four archetypes of organizations that may be interested in blockchain technology: distributed organizations, organizations with contract‐based projects, organizations in regulated domains, and regulators who may push the use of this technology. Further efforts should be devoted to the integration of the proposal with tools used throughout the software development lifecycle and leveraging the potential of smart contracts in validating the implementation of requirements automatically.

Decoding career success: A personality-based analysis of data science Professional based on ANFIS modeling

Career selection is one of the most important decisions every person faces in their life. Finding the right career path can be a complicated task, particularly in choosing careers with similarly required proficiencies. One of the critical factors affecting a person's career success is their personality, and taking account of this factor is of paramount importance. This study uses the NEO-FFI questionnaire to find personality patterns of software engineering and data science experts based on the Big Five personality traits: Neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Afterward, an ANFIS (Adaptive Network-Based Inference System) is conducted using the experts' personality data to match the participants of these fields with their corresponding choices. This study demonstrated that data scientists and software engineers score higher in conscientiousness and agreeableness, respectively. Also, data experts have higher scores in all traits overall. In the end, the ANFIS is tested with another similar dataset and the prediction accuracy of the model is measured.

Subject and Object of Forensic Electrical Engineering in Proceedings Electricity Theft

The purpose of this article is to summarize the results of electrical engineering expert research (in particular, performed at National Scientific Center «Hon. Prof. M. S. Bokarius Forensic Science Institute» of the Ministry of Justice of Ukraine) and a complex of general scientific and special methods (in particular, formal-logical, comparison, analysis and synthesis) to determine the place of examination on the facts of theft of electricity in the forensic electrical examination, to formulate the concept of the subject and object of such examination, to compile a list of its types .Prospects for the development of forensic electrotechnical expertise are outlined. The results of forensic electrotechnical researches in proceedings on the theft of electricity are summarized. Continuity of the general tasks of the forensic electrotechnical examination has been proven by these studies. On the basis of the considered separate trends of species genesis in forensic examination, an opinion is expressed about the further development of forensic electrical engineering. The structure of areas of forensic research on unaccounted electricity use was considered and their place in the system of forensic electrical technical examinations was determined. The principles of formation of typical lists of questions in forensic electrotechnical examination on the facts of electricity in accordance with the method of committing a criminal offense are laid down. According to provisions of the general theory of forensic examinations, the subject and object of the forensic electrical engineering on the facts of electricity theft are defined. Typical questions to forensic expert in proceedings about electricity theft due to the influence of electromagnetic radiation on the metering device are formulated. On the example of this list, the principles of forming typical lists of questions for other areas of re-search into the facts of electricity theft, depending on the method of committing the criminal offense, are considered.

Design and manufacture of a prototype dryer for fruits and vegetables using a convection system

The scarcity of funds for acquiring industrial-type equipment and devices for laboratory practices at technological universities is a recurring issue. This financial constraint is particularly evident in the educational program of engineering in Biofood Processes at the Technological University of Tecamachalco, where the absence of drying equipment for water reduction practices in fruits and vegetables is a pressing concern. To address this, we have developed a prototype of a convection dryer. The Mechatronics engineering research group has been at the forefront of developing this innovative prototype. Our team has harnessed the power of a PID control system, integrating a microcontroller, a potentiometer for temperature programming, and an-LED screen for real-time monitoring. This stateof-the-art design is a testament to our commitment to advancing laboratory equipment. If the temperature is reduced, the installed electrical resistances will be activated. If the upper limit of the programmed temperature is exceeded, an air extractor placed in the upper part of the prototype will be activated to reduce it as necessary.After rigorous testing, we conducted three water reduction tests on apples with the guidance of food engineering experts. The apples were subjected to a temperature of 60 °C for approximately two hours, a condition necessary to prevent any deterioration. In all three tests, we successfully reduced the water activity from 70% to 12%, a crucial step for preservation. With the successful completion of the tests, the prototype was transitioned to the Food Engineering laboratory, ready for practical use. This significant advancement in our ability to carry out laboratory practices offers a promising solution to the lack of industrial-type equipment in our educational program. We are optimistic about the potential impact of this prototype and look forward to seeing it in action.

Data Set and Benchmark (MedGPTEval) to Evaluate Responses From Large Language Models in Medicine: Evaluation Development and Validation.

Large language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs' responses create substantial risks, potentially threatening patients' physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. We developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. First, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. Dr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. MedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

Development of 3D gingival in vitro models using primary gingival cells.

Since March 2013, animal testing for toxicity evaluation of cosmetic ingredients is banned in Europe. This directive applies to all personal care ingredients including oral ingredients. Gingival in vitro 3D models are commercially available. However, it is essential to develop "in house model" to modulate several parameters to study oral diseases, determine the toxicity of ingredients, test biocompatibility, and evaluate different formulations of cosmetic ingredients. Our expertise in tissue engineering allowed us to reconstruct human oral tissues from normal human gingival cells (fibroblasts and keratinocytes). Indeed, isolation from surgical leftover was performed to culture these gingival cells. These cells keep their endogenous capacity to proliferate allowing reconstruction of equivalent tissue close to in vivo tissue. Reconstruction of gingival epithelium, chorion equivalent, and the combination of these two tissues (full thickness) using primary gingival cells displayed all characteristics of an in vivo gingival model.

Analysis of AP1000 Small-Break Loss-of-Coolant Accident Using Reactor Transient Simulator

The Westinghouse Electric Company’s Advanced Passive Reactor (AP1000) is characterized by the incorporation of passive safety systems (PSSs) designed to ensure core cooling during transient events. The assessment of PSSs requires evaluation of their performance through a combination of experiments and simulations employing various thermal-hydraulic codes. In addition, detailed evaluation of PSSs for a specific reactor system transient analysis such as loss-of-coolant-accident analysis supports understanding representative integral effects test facility development and the further evolution model development and assessment process. Developing a reactor system code is a complex and time-consuming process that requires significant engineering expertise and effort. It can take several months to even years to complete in the early stages of reactor system design and analysis. However, this process can be expedited through the use of transient simulator models for similar reactor systems, which can be used for lesson learning and training purposes. This study uses the Personal Computer Transient Analyzer (PCTRAN) code. The main advantage of PCTRAN is its ease of use and ability to run faster than real time. This study presents the results obtained for a small-break loss-of-coolant accident (SBLOCA) for two breaks using the full version (licensed) of PCTRAN. The purpose of this investigation is to evaluate the overall system behavior during the postulated SBLOCA event as well as assess the capability of the PCTRAN code to reproduce the system response during transient events. The obtained results were compared with the Westinghouse NOTRUMP system code. The PCTRAN code proved to be reliable in predicting the qualitative behavior of the system in both transient cases. As for the system response, it was found that it is contingent on the activation time of the PSSs. The differences in reactor coolant system pressure between the two codes were attributed to the critical flow model and simplification of mass and energy balance. Despite PCTRAN’s limitations, it can still provide a reasonable prediction of various reactor parameters such as pressure, mass flow rate, and void fraction during a SBLOCA scenario. It is worth noting that PCTRAN currently employs a bulk approach similar to that of the Modular Accident Analysis Program (MAAP) and MELCOR codes. However, the upcoming version of PCTRAN will include an artificial intelligence–based detection and accident prevention system, as well as different models for different reactor components. Consequently, PCTRAN has the potential to be upgraded to match the system thermal-hydraulic codes of the U.S. Nuclear Regulatory Commission and become more widely used in cybersecurity to safeguard nuclear power plants from cyberattacks.

Robotics Autonomous Systems (RAS) in Food Laboratories are the Future of Food Processing Industries: A Review

Recent advancements in robotics have witnessed significant progress in both industrial and mobile robotics, paving the way for a new era of automation. However, a paradigm shift is underway in robotics research, focusing on enhancing the interaction between humans and robots, termed as service robotics. This emerging field aims to cater to a wide array of human social needs by bridging the gap between man and machine. Traditionally, laboratory automation has been constrained by the rigid control mechanisms of computer-driven robots. Despite their utility, particularly in liquid handling tasks, many laboratory procedures remain only partially automated. Nonetheless, by breaking down laboratory processes into discrete unit operations and integrating them, overarching analysis schemes can be accomplished. The future of laboratory automation necessitates interdisciplinary skills, requiring scientists to blend biological knowledge with engineering expertise to fully exploit its potential. Simultaneously, a new wave of robotic innovations is permeating various sectors, from robot lawn mowers to autonomous vehicles, alongside smarter robots in manufacturing environments. This progression underscores the increasing reliance on automation, with future research endeavours expected to pivot towards leveraging laboratory automation to tackle novel scientific challenges. This abstract underscore the necessity for future scientists to acquire a comprehensive skill set that integrates both biological knowledge and engineering expertise. It highlights the trend towards greater automation in laboratory environments, representing the merging of scientific and engineering fields to tackle new research challenges.

Engineering focusing on cancer

While cancer research and care have benefited from revolutionary advances in the ability to manipulate and study living systems, the field is limited by a lack of synergy to leverage the power of engineering approaches. Cancer engineering is an emerging subfield of biomedical engineering that unifies engineering and cancer biology to better understand, diagnose, and treat cancer. We highlight cancer engineering's unique challenges, the importance of creating dedicated centers and departments that enable translational collaboration, and educational approaches to arm a new generation of scientists with engineering expertise and a fundamental understanding of cancer biology to transform clinical cancer care.

Conceptual Design of Offshore Jacket Substructures Using Machine Learning

The offshore wind sector is mature and has led to standardized design methods for offshore substructures. The conceptual design phase is critical for efficiency and cost-effectiveness and forms the basis for further design iterations. As turbine capacity increases, so does the complexity of offshore substructures, making design more challenging. However, the design process still relies on the expertise of the design engineers. These engineers rely heavily on their experience and intuition when designing, which can lead to biases due to limited information. To address this problem, Machine Learning (ML) techniques offer a promising way to improve the accuracy and efficiency of the conceptual design of offshore substructures. The current study is limited to the conceptual design of jacket substructures and was conducted on a self-developed global dataset of real jackets. The ML-based approach proposed in this study is capable of learning from existing data, recognizing intricate relationships between design variables, and potentially providing more accurate estimates for the initial conceptual design of offshore jacket substructures.

The Role of Teacher in Industry 5.0, Technology, and Social Capital in for Vocational High School Graduates in School-to-Work Transitions

The large number of graduates who lack the skills needed for the workplace makes it difficult for them to find employment. This study aims to analyze the influence of teachers, technology, and social capital in preparing vocational school graduates for their first jobs. Participants came from five districts in Yogyakarta, including state and private VHS with engineering expertise, including 25 teachers and 99 students. The research steps involve conducting literature reviews, focus group discussions, and data collection via questionnaires. The results show that: (1) the stability of choosing a career that is in line with their education for graduates who study further is better than graduates who work or are entrepreneurs; (2) the measurement model showed valid and reliable results: outer loading (0.702 -0.967), AVE ³ 0.5, Cronbach's alpha ³ 0.7, Fornell-Larcker criterion, and HTMT ratio. The structural model using the SRMR is 0.078 (good fit); (3) With p-value 0.000 (sig 0.05), there is direct significant influence between TCR - TGR (f2 0.897), TCR - SCR (f2 0.638) with large effect, and TCR - STWT (f2 0.310), TGR - STWT (f2 0.189), SCR - STWT (f2 0.267) with medium effect and the indirect influence (TCR - STWT), which is mediated by TGR and SCR. The role of the teacher (TCR) has a significant impact on optimizing the role of technology (TGR) and social capital (SCR) in the STWT effectivity. The role of teachers, technology, and social capital all have significant and medium effects on the STWT smoothness. This is in line with the concept of Industry 5.0, specifically integration. Education in the Industry 5.0 era is dependent not just on the contributors' roles but also on integration.

Mitigating Alert Fatigue in Cloud Monitoring Systems: A Machine Learning Perspective

Next generation networks will be largely based on monitoring and telemetry tools that are essential for maintaining optimal performance, ensuring security, managing costs, and performing fault detection and resolution. An integral part of the overall monitoring strategy is alerting, which provides administrators with the necessary information to proactively or reactively manage and optimize network services. However, when monitoring systems generate an excessive number of alerts, many of which may not be actionable or may not represent critical issues, the phenomenon of alert fatigue occurs. Alert fatigue refers to a situation where the volume and the speed of the continuous influx of alerts becomes so overwhelming that the network administrators become desensitized and do not respond to them. To this end, and inspired by recent trends in network automation, where human intervention tends to be minimized, we introduce an alert fatigue mitigation mechanism in monitoring focusing on cloud computing infrastructures. In particular, a composite machine learning methodology is proposed in order to select which alerts will be hidden and which ones will be presented to the administrators. Additionally, to personalize the results, the proposed approach considers the level of users’ experience along with the alert features to further optimize the accuracy of the alert filtering mechanism. The research has been conducted in a realistic environment of a leading monitoring enterprise, Netdata, which provided two datasets for testing our approach. Furthermore, the attained results of the filtering mechanism were evaluated by expert engineers of the company that verified the output of the proposed framework. Specifically, the outcomes confirm that our proposed methodology mitigates the alert fatigue problem with an accuracy that surpass 90% in most cases.

Toward Sustainable Urban Mobility: A Multidimensional Ontology-Based Framework for Assessment and Consensus Decision-Making Using DS-AHP

Urban mobility is a critical aspect of sustainable urban development, with significant environmental, social, and economic implications. Assessing the sustainability of urban mobility systems in order to create more carbon neutral, liveable, healthier, and sustainable cities and neighborhoods for the future requires a multidimensional approach that integrates diverse factors. However, the lack of a unified assessment framework poses challenges in comparing and evaluating different urban mobility projects. This article proposes an ontology for assessing the sustainability of urban mobility systems. This ontology is based on a multidimensional approach that integrates knowledge from experts in transportation engineering, urban planning, environmental science, and social sciences to incorporate existing sustainability indicators and frameworks, as well as domain-specific knowledge. A consensus approach based on Dempster–Shäfer (DS) and Analytic Hierarchy Process (AHP) methods is proposed to account for uncertainties and to allow for the consideration of preferences and ill judgment. Through a case study in Romania, the authors demonstrated the applicability of the proposal to provide a comprehensive and flexible framework for assessing urban mobility sustainability. The proposed ontology provides a valuable tool for policymakers, urban planners, and transportation engineers to make informed decisions towards sustainable urban mobility, and the sensitivity analysis is carried out to demonstrate the robustness of the proposed framework. It has potential for iterative validation and feedback from domain experts, and can serve as a foundation for future research.

HUM-CARD: A human crowded annotated real dataset

The growth of data-driven approaches typical of Machine Learning leads to an ever-increasing need for large quantities of labeled data. Unfortunately, these attributions are often made automatically and/or crudely, thus destroying the very concept of “ground truth” they are supposed to represent. To address this problem, we introduce HUM-CARD, a dataset of human trajectories in crowded contexts manually annotated by nine experts in engineering and psychology, totaling approximately 5000 hours. Our multidisciplinary labeling process has enabled the creation of a well-structured ontology, accounting for both individual and contextual factors influencing human movement dynamics in shared environments. Preliminary and descriptive analyzes are presented, highlighting the potential benefits of this dataset and its methodology in various research challenges.