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This study aims to achieve optimum flow separation control for a road vehicle using a reverse flow fan on rear side.

A full-length reverse flow fan array (fan’s air speed is 50% of the car’s speed) is attached throughout the width of the vehicle at rear edge corner.

The reverse flow fan array positioned at rear edge of car pushes the airflow against the car’s rear window. It creates the recirculation region and alters the pressure distribution. This reduces the lift coefficient by 150%, which becomes the downforce and reduces the drag coefficient by 22%. As the car speed increases, fan speed should also be increased for effective flow control.

This active flow control method for 3D Ahmed car body has been studied computationally at low speed (40 m/s).

This design increases the downforce, thus gives better cornering speed and stability, and decreases the drag which improves fuel efficiency. It can be used for effective flow control of cars (hatchback/sedan). The findings can be applied to the bluff bodies, road vehicles, UAV and helicopter fuselage for flow separation control.

The fan array is attached on car’s rear side, which blows air against the car’s rear window. It alters the pressure distribution and aerodynamics forces favorably. But the existing high-speed fan used in a sports cars sucks the air from bottom and pushes it rearward, which increases both the traction force and drag.

This study aims to develop a four-dimensional (4D) textile composite that self-forms upon thermal stimulation while eliminating thermomechanical programming steps by using fused deposition modeling (FDM) 3D printing technology, and tries to refine the product development path for this composite.

Polylactic acid (PLA) printing filaments were deposited on prestretched Lycra-knitted fabric using desktop-level FDM 3D printing technology to construct a three-layer structure of thermally responsive 4D textiles. Subsequently, the effects of different PLA thicknesses and Lycra knit fabric relative elongation on the permanent shape of thermally responsive 4D textiles were studied. Finally, a simulation program was written, and a case in this study demonstrates the usage of thermally responsive 4D textiles and the simulation program to design a wrist support product.

The constructed three-layer structure of PLA and Lycra knitted fabric can self-form under thermal stimulation. The material can also achieve reversible transformation between a permanent shape and multiple temporary shapes. Thinner PLA deposition and higher relative elongation of the Lycra-knitted fabric result in the greater curvature of the permanent shape of the thermally responsive 4D textile. The simulation program accurately predicted the permanent form of multiple basic shapes.

The proposed method enables 4D textiles to directly self-form upon thermal, which helps to improve the manufacturing efficiency of 4D textiles. The thermal responsiveness of the composite also contributes to building an intelligent human–material–environment interaction system.

A new backward punch shape was designed and used in the hydroforming process of double-layer Y-shaped tubes to achieve uniform wall thickness. This study focuses on the implementation and effectiveness of this novel punch shape.

A numerical simulation and experimental validation of the hydroforming process of double-layer Y-shaped tubes under various backward punch, replenishment ratios (left and right feed ratios) and internal pressure loading paths was performed using finite elements. During the hydroforming process, an analysis was made on the distribution of stress, strain and wall thickness in both the inner and outer layers of the Y-shaped conduit.

The novel backward punch parallel to the main tube has been found to improve the distribution of wall thickness in Y-shaped tubes. By controlling the feeding ratio and modifying the loading path of the internal pressure, it is possible to obtain the optimal forming part of the double-layer Y-shaped tube. The comparison between the simulation and experimental results of the double-layer Y-shaped tube formed under the optimal path indicates that the error is within 5% and the distribution of wall thickness is consistent.

A novel backward punch technique is employed to control the hydroforming process in a Y-shaped tube. A study on hydroforming of double-layer Y-shaped tubes with asymmetric features and challenging forming conditions is being suggested.

The authors contrast Beckett and Professional Sports Authenticator (PSA) baseball card valuations. Also, the authors contrast the Bill James statistics for winshares (WIN) and reference.com statistics for wins above replacement (WAR).

This study examines the impact of analytics on Topps 1957 baseball card values.

The authors' examination of variables that influence Topps 1957 baseball card values yields similar results for mint and very good rated cards over the early period (1982), pre-strike (1989), post-strike (1998) and recent (2009) periods. In single variable and multiple regressions, Baseball Hall of Fame (HOF) membership and New York Yankee (NYY) nostalgia coefficient are significant at the 5% level or higher for mint and very good rated cards over all reported periods. The Brooklyn Dodger (BD) parameter is significant at the 5% level or higher in single variable regressions for all reported periods and for 1982 and 1989 for multiple regressions. Reflecting a lack of nostalgia, the New York Giant card coefficients are statistically insignificant over all periods. Also, the authors see a lack of negative bias for Black-player cards. The authors observe that Black-player card coefficients are positive and sometimes statistically significant. This indicates a positive relationship between Black-player cards and prices.

This is the first study to examine the impact of WINS and WAR analytics on baseball card values.

The study investigates the relationship between hospital environmental factors and the well-being of geriatric in-patients. It aims to identify the impact of architectural design on comfort, safety, privacy and stress levels experienced by elderly patients during their hospital stays.

Employing a mixed-methods approach, the research assesses the experiences of 100 geriatric in-patients across various hospital types through surveys, observational checklists and state anxiety measurements. The methodology involves examining architectural features, patient perceptions and correlations among environmental variables and patient experiences. Statistical analyses, including correlations and chi-square tests, were employed to discern associations between environmental variables and patient experiences.

The research identified key architectural features significantly impacting geriatric patients' experiences. Factors such as sturdy beds, furniture quantity, lighting conditions, proximity to facilities and ward occupancy levels were found to influence spatial, sensory and social comfort. Notably, proximity to facilities and control over the immediate environment were crucial for self-control and safety perceptions. Privacy, highly valued by patients, correlated with the presence of curtains and ward occupancy. Moreover, patient stress levels exhibited correlations with autonomy, privacy and ward occupancy.

This research offers significant insights into the criticality of specific architectural elements in enhancing comfort and reducing stress for geriatric in-patients. These findings hold substantial value for healthcare facility design, emphasizing the need to prioritize certain design aspects to promote the well-being of elderly patients during hospitalization.

The determination of mode-I fracture toughness of brittle structural materials by means of the notched Brazilian disc configuration is studied. Advantage is taken of a recently introduced analytical solution and, also, of data provided by an experimental protocol with notched marble specimens under diametral compression using the loading device suggested by International Society for Rock Mechanics (ISRM) and also the three-dimensional digital image correlation (3D-DIC) technique.

The analytical solution highlighted the role of geometrical factors, like, for example, the width of the notch, which are usually disregarded. The data of the experimental protocol were comparatively considered with those concerning the response of the specific material under uniaxial tensile load.

This combined study provided interesting data concerning some open issues, as it is the exact crack initiation point and the level of the critical load causing crack initiation. It was definitely indicated that the crack initiation point is not a priori known (even for notched specimens) and, also, that the maximum recorded load does not correspond by default to the critical load responsible for the onset of catastrophic macroscopic fracture.

It was suggested that the load considered critical one for the determination of mode-I fracture toughness K_IC is erroneous. At a load equal to about 70% of the maximum one, a process zone is formed (zone of non-reversible phenomena) around the notch's crown, designating termination of the validity of any linear elastic solution used to determine the normalized stress intensity factors (SIFs). Moreover, at a load level equal to about 95% of the macroscopically observed fracture load, crack propagation has already begun. Therefore, the experimental procedure must be monitored with additional equipment, providing an overview of the displacement field developed during loading.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor.

The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm.

The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S.

Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

The case revolves around a critical incident that took place at an Indian pharmaceutical company, in which various stakeholders had very different perspectives regarding the nature, causes and consequences of the incident. By illustrating the contrasting perceptions of the same event, the authors have shed light on the nature of perception and perceptual processes, including cognitive biases and errors in human judgement. The case provides insights into how these manifest in the organisational context and how managers could be made more aware of them to avoid errors in judgment and make choices that are more informed.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.