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Found 6674 publications
Unveiling the impact of service attributes and review scores on sentiment: A deep learning and feature engineering approach to UberEats reviews
Publication Name: International Journal of Engineering Business Management
Publication Date: 2025-01-01
Volume: 17
Issue: Unknown
Page Range: Unknown
Description:
This study investigates the impact of SERVQUAL dimensions (Assurance, Reliability, Tangibles, Empathy, and Responsiveness) and review scores on customer sentiment. We analyze a large dataset of 920,407 UberEats reviews from the Google Play Store, classifying sentiment based on star ratings and using a Long Short-Term Memory (LSTM) model to predict sentiment from review content. Using text mining and sentiment analysis, the study employs robust feature engineering techniques to extract and quantify SERVQUAL components from customer reviews. The LSTM model demonstrated high accuracy (89.64%) in predicting sentiment, validating the alignment between predicted and assigned sentiments. Our analysis reveals that all SERVQUAL dimensions and review scores have a positive and significant impact on overall sentiment. Specifically, the Ordinary Least Squares (OLS) regression results highlight Empathy as the most influential SERVQUAL component, followed by Responsiveness, Reliability, Tangibles, and Assurance. Furthermore, review score emerged as the strongest predictor of customer sentiment. These findings provide actionable insights for service providers aiming to enhance customer satisfaction by optimizing key SERVQUAL dimensions and addressing review score trends.
Open Access: Yes
Ethical labyrinth in the period of knowledge acquisition and sharing of knowledge management systems
Publication Name: Problems and Perspectives in Management
Publication Date: 2011-01-01
Volume: 9
Issue: 3
Page Range: 93-99
Description:
To create a knowledge management system within a company is a very demanding goal. Organizations have to satisfy a lot of demands and prerequisites to make an operable system which can support management and which can realize business success. Knowledge management is influenced by organizational culture, by leadership and by people's attitudes - and this can obstruct knowledge acquisition and sharing, or can facilitate business success. It means that the business's ethical questions have to be managed and problems in this area have to be solved. Most companies do not deal with ethical questions. If they do deal with them, ethical problems will be the center of attention only in the external relationships of companies. According to a famous management slogan, "first we have to make order inside the company" (Oakley & Krug, 1997). Internal order will bring along order in external relationships, too. This means that you should make order first in your internal processes, systems and human relationships from the point of view of ethical problems. This paper approaches ethical problems theoretically, which can help in coming up with the creation and actuation of a knowledge management system to continue empirical surveys. These questions will be created at the end of this theoretical paper. On the basis of these questions it will be conducted an empirical survey at some companies. © Andrea Bencsik, 2011.
Open Access: Yes
DOI: DOI not available
Industry 5.0 research: an approach using co-word analysis and BERTopic modeling
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
This study addresses a substantial knowledge gap by conducting a complete evaluation of the present landscape and future directions of Industry 5.0. It recognizes the need of synthesizing a wide body of relevant research in order to get a better understanding of the complex nature of Industry 5.0. Using a comprehensive and systematic approach, the current study performed a co-word analysis and BERTopic modeling on a carefully selected dataset of 933 journal articles, sourced from Scopus and originally published between 2016 and 2024. These techniques facilitated the identification and analysis of significant patterns and uncovered integration of technology progress with human-centered strategies in Industry 5.0 frameworks. The study reveals the significant impact of new technologies, such as artificial intelligence (AI), cyber-physical systems, and blockchain processes, on improving operational efficiency, security, and sustainability. The study highlights the significance of incorporating these technologies into industrial processes to promote settings that are creative, productive, and attentive to human requirements. The trend analysis uncovers dynamism within Industry 5.0 research, which is featured by a blend of technological innovation, sustainability, and ethical considerations. Combined, these shape a future where these components are deeply interconnected. The research has significant implications, as it provides theoretical advancements and practical recommendations that might impact future industrial policy and firm operations. This study addresses a significant need by offering valuable information on how to combine technical advancements with ethical and sustainable approaches. The goal is to improve productivity and promote the well-being of society. This work is groundbreaking inasmuch as it is one of the first extensive studies in this field. It establishes an important guide for future academic research and practical use in the changing landscape of Industry 5.0.
Open Access: Yes
Calibration Measurements and Computational Models of Sensors Used in Autonomous Vehicles
Publication Name: Periodica Polytechnica Transportation Engineering
Publication Date: 2023-01-01
Volume: 51
Issue: 3
Page Range: 230-241
Description:
An increasing number of vehicles are equipped with cameras. As perception sensors, they scan the surrounding area and supply the Advanced Driver Assistance Systems (ADAS) for building up an environmental model through the use of computer vision techniques. While they perform well under good weather conditions their efficiency is reduced by adverse environmental influences such as rain, fog and occlusion through dirt. As a consequence, the vision based ADAS obtains poor quality information, and the model also becomes faulty. This paper deals with methods to estimate information quality of cameras in order to warn the assistance system of possible wrong working conditions. In particular, situations of contamination or occlusion of the windshield or camera lens, as well as foggy weather are taken into account in this paper. In the issue of occlusion total, fractional and transparent effectuations have to be recognized and distinguished. Therefore, this paper proposes an approach based on edge analysis of consecutive frames and presents initial experimental results of the implementation. In the field of Fog Detection a method based on the Logarithmic Image Processing Model is described and the results are shown.
Open Access: Yes
DOI: 10.3311/PPtr.18453
The nexus between agricultural land use, urbanization, and greenhouse gas emissions: Novel implications from different stages of income levels
Publication Name: Atmospheric Pollution Research
Publication Date: 2023-09-01
Volume: 14
Issue: 9
Page Range: Unknown
Description:
The current study establishes theoretical and empirical linkages among urbanization, economic growth, land use, and greenhouse gas (GHG) emissions. The prime objective of this article is to draw novel conclusions and policies for the different income levels of countries regarding the urbanization and agriculture sector land on environmental pollution. Employing panel data of 50 countries for the period 1990 to 2019, this study uses the lasso regression and non-parametric regression panel data methods to investigate the impacts of land use (arable, permanent pastures, and cropland), urbanization growth, and economic progress on the pollution levels. After estimating a Lasso regression to find the best auto-regressive predictive specification, we used an auto-regressive partially linear regression where each of the drivers’ effects was modelled non-parametrically. The elasticity effect of the urban population on emissions is significantly positive and sizable. In addition, the effect distribution shows a non-negligible share of observations with an elasticity higher than one. Urban population growth is a serious threat to climate change, as it seems to increase sharply CO2 emissions (although with an elasticity pace smaller than one). The elasticity effect of GDP is significantly negative, which implies that the scale of production, by triggering efficiency, can have a positive effect on emissions reduction. The results argue that agglomeration negative effects put in place by larger urban population can partly explain this finding. Overall, the study argues that urbanization growth and economic activities lead to GHG emissions, whereas the study also discusses novel implications and the role of agricultural land use apropos Sustainable Development Goals (SDGs). The empirical findings allow us to draw novel conclusions and guidelines in line with SDGs. The agricultural reforms might include irrigation and farming techniques such as spin farming, solar tube wells, tunnel farming, technology use agreements, plant double helix, etc.
Open Access: Yes
Data-Driven Prediction of Kinematic Transmission Error and Tonal Noise Risk in EV Gearboxes Based on Manufacturing Tolerances
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-10-01
Volume: 15
Issue: 19
Page Range: Unknown
Description:
Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R2 ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R2 ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment.
Open Access: Yes
DOI: 10.3390/app151910460
Additive manufacturing of rubbers: A new frontier in polymer science
Publication Name: Rubber Materials Fundamentals Sustainability and Applications
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: 601-640
Description:
This chapter delves into the advanced realm of additive manufacturing (AM) applied to elastomers, focusing on their unique properties, historical significance, and modern technological applications. Elastomers, particularly natural rubber (NR), have been integral to manufacturing since the 16th century, exhibiting remarkable flexibility, elasticity, and resilience due to their viscoelastic polymer chains. As the field of AM progresses, new possibilities for elastomers in creating complex, detailed components have emerged. Techniques like VAT photopolymerization (VAT) and extrusion-based 3D printing have expanded the versatility of these materials in industries ranging from healthcare to automotive. The chapter explores the evolution of AM technologies, from the early developments in rapid prototyping to modern methods like fused deposition modeling (FDM), digital light processing (DLP), and direct ink writing (DIW). These advancements have unlocked new potentials for elastomers, enabling customized, precise part fabrication with reduced waste. Challenges such as high viscosity in photopolymer resins, adhesion issues, and material compatibility are discussed, alongside the strategies to overcome them, including the incorporation of fillers, cross-linking methods, and rheological control. Furthermore, the chapter highlights ongoing research in optimizing elastomer formulations for improved mechanical properties and the development of advanced elastomeric materials for specialized applications in additive manufacturing.
Open Access: Yes
Technological and antioxidant characteristics of milk curds coagulated with fresh fig latex under different coagulation conditions
Publication Name: Applied Food Research
Publication Date: 2026-06-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Plant-derived milk coagulants have received growing attention as natural alternatives to rennet, yet limited information is available regarding the technological and antioxidant characteristics of curds produced with Ficus carica latex. This study evaluated the milk-clotting ability of fresh Ficus carica latex and its influence on the physicochemical, textural, and antioxidant properties of milk curds produced under different coagulation conditions (latex dosage: 200–300 µL; pH: 5.8–6.2; temperature: 32–45 °C). Coagulation time ranged from 15 min (45 °C, pH 6.2, 300 µL) to 52 min (32 °C, pH 5.8, 300 µL). Fig latex did not markedly affect final pH (6.00–6.11), and the highest curd yield (15.0 %) was achieved with 300 µL latex at 45 °C and pH 5.8, while the rennet-induced sample yielded 15.9 % under standard conditions. Latex addition significantly increased TPC values (up to 374 mg GAE/kg), whereas 200 µL resulted in lower TPC levels (149 mg GAE/kg). Fig latex–induced curds exhibited distinct mechanical properties, with generally lower hardness, cohesiveness, and chewiness values than the rennet-induced curd. Therefore, Ficus carica latex may represent a potential plant-derived milk coagulant, particularly for applications in fresh or soft-type cheeses, and the results suggest that antioxidant-related properties of curds can be enhanced without additional enrichment steps.
Open Access: Yes
Review of Vehicle Motion Planning and Control Techniques to Reproduce Human-like Curve-Driving Behavior †
Publication Name: Engineering Proceedings
Publication Date: 2024-01-01
Volume: 79
Issue: 1
Page Range: Unknown
Description:
Among the many technological challenges of automated driving development, there is an increasing focus on the behavior of these systems. Behavior is usually associated with multiple layers of control. In this paper, we focus on motion planning and control, and how these layers can be tailored to produce different behavior. Our review aims to collect and judge the most used techniques in the field of path planning and control. It has been revealed that model predictive planning and control provides high flexibility, with the cost of high computational capacity. There are simpler algorithms, such as pure-pursuit and Stanley controllers, however, these have very few parameters, therefore, the number of possible behavior patterns is limited.
Open Access: Yes
