Special Sciences Research Articles

The concept map as a tool for analyzing museum objects

AbstractThis paper discusses using the concept map to study museum objects. The tool's original purpose, which was created in the 1970s by Joseph Novak, was to graphically represent the construction of knowledge of scientific topics by learners, particularly children. Concept maps start from the premise that new knowledge is always built on previous knowledge and allow the representation, organization, and relationship between concepts, which are seen as the primary elements of knowledge. Novak recognized that its potential is insufficiently explored and encouraged its application in different disciplines and fields that deal with concepts and their relationships. This incentive led the Museum of Astronomy and Related Sciences (Rio de Janeiro, Brazil) to adopt the concept map on an experimental basis to analyze objects in its collection. At first, the tool sought to emphasize the conceptual potential of museum objects and shed more light on their trajectory in time and space and the numerous possible connections with events, institutions, concepts, people, and other objects. In the initial experimental phase, the maps were included in papers presented at academic events and discussed with professionals dedicated to science and technology collections and other specialists in Museology and Information Science. In the second stage, maps began to be produced in greater numbers and on demand, focusing on objects selected for a temporary exhibition. Based on the assumption that museum objects are both unique and representative of a class of objects that share the same name and function, the maps constructed were based on Ingetraut Dahlberg's Theory of Concept, which distinguishes between general and individual objects and concepts.

Branch and Price for the Stochastic Traveling Salesman Problem with Generalized Latency

We consider an extension of the symmetric traveling salesman problem with generalized latency that explicitly models uncertainty. The stochastic traveling salesman problem with generalized latency (STSP-GL) aims to choose a subset of nodes of an undirected graph and determines a Hamiltonian tour among those nodes, minimizing an objective function that is a weighted combination of route design and passenger routing costs. These nodes are selected to ensure that a predefined percentage of uncertain passenger demand is served with a given probability. We formulate the STSP-GL as a stochastic program and propose a branch-and-price algorithm for solving its deterministic equivalent. We also develop a local search approach with which we improve the performance of the branch-and-price approach. We assess the efficiency of the proposed methods on a set of instances from the literature. We demonstrate that the proposed methods outperform a known benchmark, improving upper bounds by up to 85% and lower bounds by up to 55%. Finally, we show that solutions of the stochastic model are both more cost-effective and robust than those of the deterministic model. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the Bundesministerium für Bildung und Forschung [Grant 03ZU1105FA]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0417 .

Artificial intelligence literacy among university students—a comparative transnational survey

Artificial intelligence (AI) literacy is a crucial aspect of media and information literacy (MIL), regarded not only as a human right but also as a fundamental requirement for societal advancement and stability. This study aimed to provide a comprehensive, cross-border perspective on AI literacy levels by surveying 1,800 university students from four Asian and African nations. The findings revealed significant disparities in AI literacy levels based on nationality, scientific specialization, and academic degrees, while age and gender did not show notable impacts. Malaysian participants scored significantly higher on the AI literacy scale than individuals from other countries. The results indicated that various demographic and academic factors influenced respondents’ perceptions of AI and their inclination to utilize it. Nationality and academic degree were identified as the most influential factors, followed by scientific specialization, with age and gender exerting a lesser influence. The study highlights the necessity of focusing research efforts on the detailed dimensions of the AI literacy scale and examining the effects of previously untested intervening variables. Additionally, it advocates for assessing AI literacy levels across different societal segments and developing the appropriate measurements.

DEEP LEARNING APPROACH FOR ASPECT CATEGORY DETECTION: A BIBLIOMETRIC ANALYSIS

This article presents a quantitative and qualitative assessment of current research trends by conducting a bibliometric analysis of the sentiment analysis literature from 2020 to March 2024 using the Scopus database. Our focus is on the review of scientific documents, the arrangement of subject categories, the research trend in aspect category detection, the top 10 scholars that write the most number articles in aspect category detection and keyword trends. Our research shows that specialists in computer science, engineering, mathematics, medicine, decision science, material science, social sciences, business and management accounting, energy, and health are the most common topic groups in this industry. A study of keywords shows that terms like “BERT” and “deep learning” are frequently used together. This highlights the use of sophisticated models like BERT in this field and suggests a tendency to use innovative architecture to achieve better results. Conversely, although terms such as “sentiment analysis” and “aspect-based sentiment analysis” have modest frequency, their link strengths indicate a significant correlation with the main theme, emphasising the relationship between aspect category detection and sentiment analysis in research projects. we also provide the deep-learning technique used by the author for aspect category detection.

Правозащитная деятельность с использованием уголовно-правовых, криминалистических и оперативно-розыскных мер как актуальное направление междисциплинарных исследований

Due to the adoption of a new nomenclature of scientific specialties in specialty 5.1.4 — “Criminal Law Sciences”, research on human rights activities can now be carried out using an interdisciplinary approach, that is, using criminal law, criminal procedure, criminalistic, operational investigative and other knowledge. The article examines the main directions of interdisciplinary research in the field of human rights activities, such as: criminal law, forensic and operational investigative means of protecting businesses; nomination and verification of protective versions in criminal cases of certain categories, and others. There are 11 directions in total. Some methodological and, most importantly, ethical requirements for this kind of interdisciplinary research are analyzed. It is noted that, despite the lack of scientific literature, in recent years, leading Russian law universities have prepared and successfully implemented educational projects of the appropriate profile. The data on the level of interest of young lawyers, practitioners and trainees in obtaining relevant knowledge and conducting scientific research are presented. It is necessary to stimulate such developments, develop their methodology, and offer young scientists, including lawyers, practice-oriented human rights thesis topics that take into account personal professional strategies. It has been established that the increased demand of the population for information in the field of protection from defamation, mass inspections and criminal prosecution (especially business) determines the rapid growth of human rights content in social networks, messengers, profile sites and business magazines. However, this is mainly journalistic and educational content that does not have a proper scientific basis. In conclusion, conclusions are formulated about the high level of relevance and relevance of interdisciplinary knowledge of human rights issues. And this demand, of course, must be met by appropriate scientific research.

Административное право в системе публично-правовых наук

In the context of modern realities of the development of russian science, interdisciplinary research is relevant, located at the intersection of public law sciences and constituting more than 30 areas of scientific research. According to the author, studies related to digital transformation and the introduction of artificial intelligence are especially significant. In conclusion, it is summarized that within the framework of the scientific specialty 5.1.2. “Public legal (state legal) sciences”, the predetermining place belongs to constitutional law, because The Basic Law of the Russian Federation lays the fundamental foundations for a decent future for the country and the well-being of citizens, while administrative law occupies one of the leading places in the system of public law because regulates legal relations in the field of public administration for the implementation of state policy in all spheres of life of russian society.

On-Demand Meal Delivery: A Markov Model for Circulating Couriers

On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where [Formula: see text] is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference.

Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.

Do research universities specialize in disciplines where they hold a competitive advantage?

Enhancing the effectiveness and efficiency of national research systems is a top priority on the policy agendas of many countries. This study focuses on one aspect of the macroeconomic efficiency of research systems: whether research institutions specialize in scientific domains where they have a competitive advantage. To evaluate this, we developed a novel methodology. This methodology measures the scientific specialization indices of each organization in various research fields and assesses their relative research productivity. It then examines the correlation between these scores and between the resulting rankings. We applied this methodology to Italian universities. We found that a significant rank correlation between universities’ field specialization and their performance appears only in a few areas, and overall, the rankings are completely unrelated. Providing such data to research managers and policymakers can help inform strategies to enhance both micro- and macro-level efficiency.

Multiple Realizability and Disjunction for the Special Sciences

Multiple Realizability and Disjunction for the Special Sciences

Two-Step Quadratic Programming for Physically Meaningful Smoothing of Longitudinal Vehicle Trajectories

Longitudinal vehicle trajectories suffer from errors and noise because of detection and extraction techniques, challenging their applications. Existing smoothing methods either lack physical meaning or cannot ensure solution existence and uniqueness. To address this, we propose a two-step quadratic programming method that aligns smoothed speeds and higher-order derivatives with physical laws, drivers’ behaviors, and vehicle characteristics. Unlike the well-known smoothing splines method, which minimizes a weighted sum of discrepancy and roughness in a single quadratic programming problem, our method incorporates prior knowledge of position errors into two sequential quadratic programming problems. Step 1 solves half-smoothed positions by minimizing the discrepancy between them and raw positions, subject to physically meaningful bounds on speeds and higher-order derivatives of half-smoothed positions. Step 2 solves smoothed positions by minimizing the roughness while maintaining physically meaningful bounds and allowing the deviations from raw data of smoothed positions by at most those of the half-smoothed positions and prior position errors. The second step’s coefficient matrix is not positive definite, necessitating the matching of the first few smoothed positions with corresponding half-smoothed ones, with equality constraints equaling the highest order of derivatives. We establish the solution existence and uniqueness for both problems, ensuring their well-defined nature. Numerical experiments using Next Generation Simulation (NGSIM) data demonstrate that a third-order derivative constraint yields an efficient method and produces smoothed trajectories comparable with manually re-extracted ones, consistent with the minimum jerk principle for human movements. Comparisons with an existing approach and application to the Highway Drone data set further validate our method’s efficacy. Notably, our method is a postprocessing smoothing technique based on trajectory data and is not intended for systematic errors. Future work will extend this method to lateral vehicle trajectories and trajectory prediction and planning for both human-driven and automated vehicles. This approach also holds potential for broader smoothing problems with known average error in raw data. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: The authors extend their gratitude to the Pacific Southwest Region University Transportation Center and the University of California Institute of Transportation Studies (UC ITS) Statewide Transportation Research Program (STRP) for their valuable financial support.

A Game-Theoretic Framework for Generic Second-Order Traffic Flow Models Using Mean Field Games and Adversarial Inverse Reinforcement Learning

A traffic system can be interpreted as a multiagent system, wherein vehicles choose the most efficient driving approaches guided by interconnected goals or strategies. This paper aims to develop a family of mean field games (MFG) for generic second-order traffic flow models (GSOM), in which cars control individual velocity to optimize their objective functions. GSOMs do not generally assume that cars optimize self-interested objectives, so such a game-theoretic reinterpretation offers insights into the agents’ underlying behaviors. In general, an MFG allows one to model individuals on a microscopic level as rational utility-optimizing agents while translating rich microscopic behaviors to macroscopic models. Building on the MFG framework, we devise a new class of second-order traffic flow MFGs (i.e., GSOM-MFG), which control cars’ acceleration to ensure smooth velocity change. A fixed-point algorithm with fictitious play technique is developed to solve GSOM-MFG numerically. In numerical examples, different traffic patterns are presented under different cost functions. For real-world validation, we further use an inverse reinforcement learning approach (IRL) to uncover the underlying cost function on the next-generation simulation (NGSIM) data set. We formulate the problem of inferring cost functions as a min-max game and use an apprenticeship learning algorithm to solve for cost function coefficients. The results show that our proposed GSOM-MFG is a generic framework that can accommodate various cost functions. The Aw Rascle and Zhang (ARZ) and Light-Whitham-Richards (LWR) fundamental diagrams in traffic flow models belong to our GSOM-MFG when costs are specified. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: X. Di is supported by the National Science Foundation [CAREER Award CMMI-1943998]. E. Iacomini is partially supported by the Italian Research Center on High-Performance Computing, Big Data and Quantum Computing (ICSC) funded by MUR Missione 4-Next Generation EU (NGEU) [Spoke 1 “FutureHPC & BigData”]. C. Segala and M. Herty thank the Deutsche Forschungsgemeinschaft (DFG) for financial support [Grants 320021702/GRK2326, 333849990/IRTG-2379, B04, B05, and B06 of 442047500/SFB1481, HE5386/18-1,19-2,22-1,23-1,25-1, ERS SFDdM035; Germany’s Excellence Strategy EXC-2023 Internet of Production 390621612; and Excellence Strategy of the Federal Government and the Länder]. Support through the EU DATAHYKING is also acknowledged. This work was also funded by the DFG [TRR 154, Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks, Projects C03 and C05, Project No. 239904186]. Moreover, E. Iacomini and C. Segala are members of the Indam GNCS (Italian National Group of Scientific Calculus).

Dalia Gargasaitė - bibliographer and book science specialist

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Hypercongestion, Autonomous Vehicles, and Urban Spatial Structure

This paper examines the effects of hypercongestion mitigation by perimeter control and the introduction of autonomous vehicles on the spatial structures of cities. By incorporating a bathtub model, we develop a land use model where hypercongestion occurs in the downtown area and interacts with land use. We show that hypercongestion mitigation by perimeter control decreases the commuting cost in the short run and results in a less dense urban spatial structure in the long run. Furthermore, we reveal that the impact of autonomous vehicles depends on the presence of hypercongestion. The introduction of autonomous vehicles may increase the commuting cost in the presence of hypercongestion and may cause a decrease in the suburban population; however, it may make cities spatially expand outward. This result contradicts that of the standard bottleneck model. When perimeter control is implemented, the introduction of autonomous vehicles decreases the commuting cost and results in a less dense urban spatial structure. These results show that hypercongestion is a key factor that can change urban spatial structures. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by ACT-X [Grant JPMJAX21AE], Fusion Oriented REsearch for disruptive Science and Technology [Grant JPMJFR215M], the Japan Society for the Promotion of Science [Grants 23K13422 and 22H01610], and the Council for Science, Technology and Innovation [Grant JPJ012187]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0519 .

A commentary on transformative consumer research: Musings on its genesis, evolution, and opportunity for scientific specialization

A commentary on transformative consumer research: Musings on its genesis, evolution, and opportunity for scientific specialization

Destroying Phantom Jams with Connectivity and Automation: Nonlinear Dynamics and Control of Mixed Traffic

Connected automated vehicles (CAVs) have the potential to improve the efficiency of vehicular traffic. In this paper, we discuss how CAVs can positively impact the dynamic behavior of mixed traffic systems on highways through the lens of nonlinear dynamics theory. First, we show that human-driven traffic exhibits a bistability phenomenon, in which the same drivers can both drive smoothly or cause congestion, depending on perturbations like a braking of an individual driver. As such, bistability can lead to unexpected phantom traffic jams, which are undesired. By analyzing the corresponding nonlinear dynamical model, we explain the mechanism of bistability and identify which human driver parameters may cause it. Second, we study mixed traffic that includes both human drivers and CAVs, and we analyze how CAVs affect the nonlinear dynamic behavior. We show that a large-enough penetration of CAVs in the traffic flow can eliminate bistability, and we identify the controller parameters of CAVs that are able to do so. Ultimately, this helps to achieve stable and smooth mobility on highways. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by the University of Michigan’s Center for Connected and Automated Transportation [U.S. DOT Grant 69A3551747105]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0498 .

A Paradox of Telecommuting and Staggered Work Hours in the Bottleneck Model

We study the long- and short-term effects of telecommuting (TLC), staggered work hours (SWH), and their combined scheme on peak-period congestion and location patterns. In order to enable a unified comparison of the schemes’ long- and short-term effects, we develop a novel equilibrium analysis approach that consistently synthesizes the long-term equilibrium (location and percentage of telecommuting choice) and short-term equilibrium (preferred arrival time and departure time choice). By exploiting their special mathematical structures similar to optimal transport problems, we derive the closed-form solution to the long- and short-term equilibrium while explicitly considering their interaction. These closed-form solutions elucidate the discrepancies between the effects of each scheme and uncover a paradoxical finding: the introduction of SWH, in conjunction with TLC, may increase the total commuting costs compared with the scenario with only TLC, without yielding any improvement in worker utility. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), the 3rd period of SIP “Smart Infrastructure Management System” [Grant JPJ012187] (Funding agency: PublicWorks Research Institute, Japan). This work was also supported by Japan Society for the Promotion of Science (JSPS) KAKENHI [Grants JP20J21744, JP21H01448, JP24K00999, JP20K14843, and JP23K13418] and the Support Program for Urban Studies of the Obayashi Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0520 .

Inverse Optimization for Routing Problems

We propose a method for learning decision makers’ behavior in routing problems using inverse optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks second compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers’ decisions in routing problems. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the European Research Council [TRUST-949796].

Computer Vision―The Frontier of Modern Environmental Diagnostics: A Review

Computer vision (CV), in combination with various sensors and image analysis algorithms, is a frontier direction in diagnosing the state of the environment and its biogenic and abiogenic objects. The work generalizes scientific achievements and identifies scientific and technical problems in this area of research based on the conceptual system of analysis on the time axis: from implemented achievements as part of the past and present to original new solutions—the future. Our work gives an idea of three areas of application of CV in diagnosing the state of the environment: phenotype recognition in digital images, monitoring of living and abiogenic objects, and development of new methods for identifying pollution and its consequences. The advantages of CV, which can be attributed to scientific achievements in this field of research, are shown: an increase in the volume of analyzed samples, simultaneous analysis of several parameters of the object of observation, and leveling of subjective evaluation factors. The main CV problems currently solved are the accuracy of diagnostics and changing quality of the survey, identification of the object of analysis with minimal operator participation, simultaneous monitoring of objects of different quality, and development of software and hardware systems with CV. A promising direction for the future is to combine the capabilities of CV and artificial intelligence. Thus, the review can be useful for specialists in environmental sciences and scientists working in interdisciplinary fields.

Network social capital as a subject of research in education

The article contains the results of a comparative analysis of the main approaches, directions and methods for studying network social capital in the social sciences and humanities in order to identify the latest and promising scientific areas in specific scientific disciplines. The article notes the growing interest of scientists from various fields – philosophers, sociologists, political scientists, economists and lawyers, teachers and psychologists in the study of the phenomenon of network social capital as a complex and multifaceted category that generates a plurality of approaches and methods for studying its genesis, types, formation mechanisms and measurement parameters. To achieve this goal, multidisciplinary and integrated approaches to research, methods of comparative analysis and classification of information, bibliographic analysis are used. On the basis of the analysis, the essential methodological foundations of the study of network social capital are revealed, the relationship of the methods used (quantitative, qualitative and mixed) with the profile of the scientific discipline that studies network social capital is shown. It is indicated that each scientific discipline brings unique ideas and methods to the study of social capital, including qualitative and quantitative, mixed methods, network analysis, experiment, etc. The author reveals the urgent need for the integration of methodological approaches and research methods of network social capital as an independent field of research based on the synthesis of ideas, concepts and principles of polydisciplinarity and interdisciplinarity.