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This paper seeks to analyze and evaluate different types of semantic web retrieval systems, with respect to their ability to manage and retrieve semantic documents.

The authors provide a brief overview of knowledge modeling and semantic retrieval systems in order to identify their major problems. They classify a set of characteristics to evaluate the management of semantic documents. For doing the same the authors select 12 retrieval systems classified according to these features. The evaluation methodology followed in this work is the one that has been used in the Desmet project for the evaluation of qualitative characteristics.

A review of the literature has shown deficiencies in the current state of the semantic web to cope with known problems. Additionally, the way semantic retrieval systems are implemented shows discrepancies in their implementation. The authors analyze the presence of a set of functionalities in different types of semantic retrieval systems and find a low degree of implementation of important specifications and in the criteria to evaluate them. The results of this evaluation indicate that, at the moment, the semantic web is characterized by a lack of usability that is derived by the problems related to the management of semantic documents.

This proposal shows a simple way to compare requirements of semantic retrieval systems based in DESMET methodology qualitatively. The functionalities chosen to test the methodology are based on the problems as well as relevant criteria discussed in the literature. This work provides functionalities to design semantic retrieval systems in different scenarios.

The present investigation has been carried out in the framework of the numerical studies of the wall function influence on the calculation of wall shear stress. This shear stress is generated by the action of a normal plane impinging jet on a flat plate. The Navier‐Stokes, continuity and energy equations are solved for a two‐dimensional turbulent compressible flow by using the finite volume method. The standard k‐ε turbulence model is used as a closure model. To take into account the external shape influence of the nozzle, a quasi‐orthogonal curvilinear mesh is used. For a suitable calculation of the wall shear stress, the classical logarithmic wall law and its generalized form are used. The results of these calculations depend on the near wall grid. Therefore, this influence of near wall grid is studied more specifically here. A hybrid wall law is proposed, which results from the simultaneous use of the classical logarithmic wall law and the generalized one. This hybrid law gives suitable shear stress distributions with less dependence on the near wall grid. The calculations are validated through experimental data.

Sarcasm is often used in everyday speech and writing and is prevalent in online contexts. The purpose of this paper is to investigate the analogy between sarcasm comments from sentiment tools and the human coder.

Using the Verbal Irony Procedure, eight human coders were engaged to analyse comments collected from an online commercial page, and a dissimilarity analysis was conducted with sentiment tools. Three constants were tested, namely, polarity from sentiment tools, polarity rating by human coders; and sarcasm-level ratings by human coders.

Results found an inconsistent ratio between these three constants. Sentiment tools used did not have the capability or reliability to detect the subtle, contextualized meanings of sarcasm statements that human coders could detect. Further research is required to refine the sentiment tools to enhance their sensitivity and capability.

With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – for example, to incorporate sophisticated human sarcasm texts in their analytical systems. Sarcasm exists frequently in media, politics and human forms of communications in society. Therefore, more highly sophisticated sentiment tools with the abilities to detect human sarcasm would be vital in research and industry.

The findings suggest that presently, of the sentiment tools investigated, most are still unable to pick up subtle contexts within the text which can reverse or change the message that the writer intends to send to his/her receiver. Hence, the use of the relevant hashtags (e.g. #sarcasm; #irony) are of fundamental importance in detection tools. This would aid the evaluation of product reviews online for commercial usage.

The value of this study lies in its original, empirical findings on the inconsistencies between sentiment tools and human coders in sarcasm detection. The current study proves these inconsistencies are detected between human and sentiment tools in social media texts and points to the inadequacies of current sentiment tools. With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – to incorporate sophisticated human sarcasm texts in their analytical systems. The system can then be used as a reference for psychologists, media analysts, researchers and speech writers to detect cues in the inconsistencies in behaviour and language.

The main objective of this article is to propose an interpretive model that attempts to decipher a product's values in terms of functionality, usability and meaning. This model can support companies in better integrating these values in their product offering and in defining the most adequate innovation strategies that they can adopt.

The authors conducted an empirical analysis on more than 450 products from the Italian furniture industry. Moreover, using an interpretative model 50 product signs were mapped (materials, surfaces, colours, etc.) for each product. The obtained database was analyzed via the principal component analysis (PCA) statistical technique with the intent to identify dominant product languages. In fact, interpreting a product language as a set of product signs, the article describes an objective process able to identify dominant product languages as combinations of different product signs.

The interpretive model described in this article represents a first result in itself. In addition, by mapping the dynamics of dominant product languages, it has been demonstrated that they evolve differently in relation to several product typologies. In turn, the possibility of “brokering” dominant product languages from one product typology to another and from one industry to another has been verified.

First of all, this model can support companies in the identification of emerging trends and, consequently, allows them to develop product semantic forecasts. In addition, the analysis of dominant product languages over time can also allow a company to propose combinations of product signs typical of past periods. Finally, the identification of dominant product languages can also allow companies to analyze the state‐of‐the‐art of the industry and, consequently, identify different ways to propose innovation to the market.

Most of conducted researches related to product languages have shown primarily a qualitative‐based approach, in which the observations are made by a restricted set of design experts on a subset of representative products. In contrast with the current literature in this research field, this article describes an objective process that is able to identify dominant product languages.

As convenience and anonymity, people with mental illness are increasingly willing to communicate and share information through social media platforms to receive emotional and spiritual support. The purpose of this paper is to identify the degree of depression based on people's behavioral patterns and discussion content on the Internet.

Based on the previous studies on depression, the severity of depression is divided into four categories: no significant depressive symptoms, mild MDD, moderate MDD and severe MDD, and defined each of them. Next, in order to automatically identify the severity, the authors proposed social media digital cues to identify the severity of depression, which include textual lexical features, depressive language features and social behavioral features. Finally, the authors evaluate a system that is developed based on social media digital cues in the experiment using social media data.

The social media digital cues including textual lexical features, depressive language features and social behavioral features (F1, F2 and F3) is the relatively best one to classify four different levels of depression.

This paper innovatively proposes a social media data-based framework (SMDF) to identify and predict different degrees of depression through social media digital cues and evaluates the accuracy of the detection through social media data, providing useful attempts for the identification and intervention of depression.

The study investigates supply chain leaders’ initiatives to support their partners in the early stages of the coronavirus disease-2019 (COVID-19) pandemic, identifying measures taken to increase supply chain resilience and their impact on the quality of supply chain relationships.

Two complementary phases are employed. First, an exploratory approach is adopted, with the method of discourse analysis being employed in the identification of the supplier crisis response strategies by S&P500’s top 30 firms. Second, two scenario-based experiments with 983 participants evaluated the impact of such strategies in two dimensions of supply chain relationships’ quality (supplier satisfaction and supplier commitment).

Phase one revealed five initiatives’ groups adopted: safety measures, innovative tools, information and knowledge sharing, supply chain finance and supply chain continuity. Phase two results indicate that supplier crisis response strategies have positive effects on both supplier satisfaction and commitment. Data also suggest that safety measures, innovative tools, and information and knowledge sharing strategies negatively impacted supplier satisfaction and commitment, when compared with strategies adopted by other buying firms competing for the same supplier. Supply chain continuity was negatively associated with both dimensions when other buying firms implemented innovative tools and information and knowledge sharing strategies with their suppliers, while supply chain finance yielded in no differences in comparison to strategies adopted by competing buying firms.

The authors offer a theoretical typology for supply chain resilience (i.e. natural and artificial), providing support for buying firms’ decisions regarding supplier crisis response strategies through the strengthening of artificial supply chain resilience to increase the likelihood of vulnerable key suppliers’ survival.

Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.

The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.

A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.

Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start.

A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters.

Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics.

Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

The purpose of this paper is to investigate the effect of strut size on the compressive response for selective laser-melted lattice structure with a body-centered cubic (BCC) unit cell.

Theoretical analysis and numerical simulation were used to predict the compressive stiffness and strength of the lattice structures with different struts, and compression testing was conducted to validate the predicted results. The effect of strut size on actual porosity was determined with the dry weighting method. Scanning electron microscopy was used to observe the fracture morphologies.

The actual porosities in all the specimens turned out to be a little lower than the values expected from design. The maximum deviation appears at the strut size of 1.25 mm. The theoretical analysis reveals that the junctions of BCC unit cells are the most loaded points, and the maximum compression resistance load is proportional to the strut size. The stress–strain curves and collapse modes predicted by numerical simulation are in good agreement with the theoretical calculation and experimental results. The compression stress increases monotonously in strut size of 0.50–2.00 mm. The fracture morphologies reflect a transition from a mixed to ductile fracture mechanism. The lattice structure shows a stable plastic deformation without a destructive fracture for the strut size of 2.00 mm.

The findings of this study can provide theoretical and experimental support for the choice of strut size under different stress conditions. In addition, they are conductive to in-depth study of the compressive properties for lattice structures with different geometrical dimensions fabricated by selective laser melting.

During the operation of a wet clutch, there are fluctuations in speed and torque, which have an impact on the stability of the clutch and the strength of the friction plate and the spline pair of the dual steel plate. The purpose of this study is to investigate the vibration characteristics of the wet clutch and the dynamic load characteristics of the spline pairs.

The spline pair model is established by the piecewise linear function method, and on this basis, dynamic equations considering the spline pair of dual steel plates and friction plates are established. Considering that the wet clutch has multiple spline pairs, an equivalent model of the number of teeth and the equivalent model of the tooth width were proposed, and the Runge-Kutta numerical method was used for the wet clutch for these two models.

The research results show that the equal tooth number model has greater meshing stiffness and smaller fluctuation than the constant tooth width model, which shows that increasing the meshing stiffness of the system is beneficial to reduce system fluctuation and improve system stability.

The friction plate has the system that multiple splines are independent of each other, which is relatively complicated. Therefore, an equivalent calculation is performed on multiple pairs of steel plates (friction plates) to simplify the calculation of the spline pairs.

This paper provides a theoretical basis for further dynamic characteristics analysis of wet clutch and reducing fluctuation of speed and torque.

Dynamic equation considering the spline pair of the dual steel plates and the friction plates is established to study the vibration characteristics of the wet clutch and the dynamic load characteristics of the spline pair, etc.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0078/