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The purpose of this study is to generate knowledge about assessment methods in blended business education, which have become increasingly important to establish sustainable assessment practices that support knowledge acquisition for undergraduate students in business administration at a Colombian university.

For the analysis, a two groups comparison was performed using a nonequivalent control group design with a sample of 420 students. As this study wants to find insights to improve the knowledge on assessment topics in marketing research (MR) education, it was focused on the students from the business administration program. This study also uses individual scores from the state test as prior cognitive scores and the high school classification provided by the National Ministry of Education in Colombia (2012).

It was found that the variables that best predict performance on the MR course examinations were the mathematics skills and reading comprehension scores on the state test. The study also showed a better performance of female students on both assessment methods. There were no significant differences between the assessment methods or among the high school levels.

One of the limitations of this study is the limited number of items on the tests. Additionally, the authors recommend conducting an analysis of the differences between the testing items to provide a detailed explanation of students’ performance when comparing computer-based testing and paper-and-pencil testing.

Further design of teaching material and assessments online and offline, based on local and regional marketing problems, is suggested. As the current text and readings are more oriented to the English-speaking contexts, most of the problems presented are oriented to multinational companies and brands.

Insights into the skills required for future jobs provide valuable guidance (World Economic Forum, 2020). Essential skills for emerging roles, like data scientists, can find robust support within the MR course. To further enrich in-class and online exercises with Excel and SPSS, Colombian educators can leverage data sets obtained from sources like the national statistics office and international market intelligence databases available through the university’s library, including Passport and Statista. Engaging with authentic data sets provides students with a more profound understanding of practical applications in MR.

This approach facilitates the identification of key variables, such as assessment and cognitive abilities in math and reading, which predict students’ knowledge acquisition in MR. It not only offers insights into the relevant factors influencing learning in MR but also provides valuable feedback. Additionally, it suggests potential avenues for future research in this field.

The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

The authors consider whether the emotional states of happiness and fear causally affect test performance using a new experiment. The paper aims to discuss this issue.

Happiness and fear are induced with 360-degree videos shown in virtual reality before participants take a test consisting of mathematics scholastic aptitude tests (SAT) questions.

The results show that scores improve by 0.48 standard deviations under the happiness condition, and the effect is particularly large for women (0.75 s.d.). Inducing fear has no effect on test scores.

This is one of the first studies to employ virtual reality for emotion induction. It establishes that test scores can be improved by inducing an emotional state of happiness shortly before the test.

This research investigates how the value creation interests and activities of different stakeholder groups within one open source software (OSS) project influence the project's development over time.

The authors conducted a case study of OpenSimulator using textual and thematic analyses of the initial four years of OpenSimulator developer mailing list to identify each stakeholder group and guide our analysis of their interests and value creation activities over time.

The analysis revealed that while each stakeholder group was active within the OSS project's development, the different groups possessed complementary interests that enabled the project to evolve. In the formative period, entrepreneurs were interested in the software's strategic direction in the market, academics and SMEs in software functionality and large firms and hobbyists in software testing. Each group retained its primary interest in the maturing period with academics and SMEs separating into server- and client-side usability. The analysis shed light on how the different stakeholder groups overcame tensions amongst themselves and took specific actions to sustain the project.

The authors extend stakeholder theory by reconceptualizing the focal organization and its stakeholders for OSS projects. To date, OSS research has primarily focused on examining one project relative to its marketplace. Using stakeholder theory, we identified stakeholder groups within a single OSS project to demonstrate their distinct interests and how these interests influence their value creation activities over time. Collectively, these interests enable the project's long-term development.

Explore the development trend of chemically-improved soil in railway engineering.

In this paper, the technical standards home and abroad were analyzed. Laboratory test, field test and monitoring were carried out.

The performance design system of the chemically-improved soil should be established.

On the basis of the performance design, the test methods and standards for various properties of chemically-improved soil should be established to evaluate the improvement effect and control the engineering quality.

This paper introduces a novel quality measure, the percent-within-distribution, or PWD, for acceptance and payment in a quality control/quality assurance (QC/QA) performance specification (PS).

The new quality measure takes any sample size or distribution and uses a Bayesian updating process to re-estimate parameters of a design distribution as sample observations are fed through the algorithm. This methodology can be employed in a wide range of applications, but the authors demonstrate the use of the measure for a QC/QA PS with upper and lower bounds on 28-day compressive strength of in-place concrete for bridge decks.

The authors demonstrate the use of this new quality measure to illustrate how it addresses the shortcomings of the percent-within-limits (PWL), which is the current industry standard quality measure. The authors then use the PWD to develop initial pay factors through simulation regimes. The PWD is shown to function better than the PWL with realistic sample lots simulated to represent a variety of industry responses to a new QC/QA PS.

The analytical contribution of this work is the introduction of the new quality measure. However, the practical and managerial contributions of this work are of equal significance.

The primary objective of this study is characterizing the anisotropic mechanical properties of corrugated cardboard and simultaneously simulating its drop cushioning dynamic effects under various drop conditions.

Static and dynamic tests were conducted on corrugated cardboard to obtain adequate experimental data in different directions. An effective anisotropic constitutive model is introduced by developing the honeycomb materials model in ANSYS LS-Dyna, and an effective approach is established toward effectively determining the material parameters from the test data obtained. The model is validated by comparing simulation results with experimental data from five drop conditions, including bottom drop, front drop, side drop, 30° side drop and edge drop. Additionally, simulations are conducted to study the cushioning performance of the packaging by dropping the corrugated cardboard at different heights.

The study establishes a fast and effective approach to simulating the drop cushioning performance of corrugated cardboard under various drop conditions, which demonstrates good agreement with experimental data.

This approach is of value for packaging protection and provides guidance for stacking of packaging during transportation.

This study aims to describe and examine the effectiveness of the mathematics-based learning method, called Accountamatics (Accounting from Mathematics perspective). The Accountamatics method is rooted in the principles of double-entry bookkeeping and the rules of debits and credits, which were initially documented by Luca Pacioli, a Mathematics Professor, in a mathematics book.

This study uses a quasi-experimental design, spanning a duration of one semester, to examine its research objectives. The participants included 230 students from the Faculty of Economics and Business in Indonesia, who were accredited by AACSB. Control variables, including gender, majors and study backgrounds, were taken into consideration. The data was analyzed using a linear regression test, followed by a comparative t-test.

The study findings show a significant positive impact of the Accountamatics method on student performance when compared to traditional learning approaches. Furthermore, the analysis indicates that the control variables examined in the study did not have a significant effect on student performance. Thus, it can be inferred that the implementation of the Accountamatics method has a beneficial effect on the academic performance of college students.

The study findings provide valuable insights for faculty members in higher education institutions, highlighting the potential benefits of incorporating mathematics-based teaching in accounting as an alternative approach for business students. These findings contribute to the existing knowledge on innovative teaching methods that can enhance the study of accounting within the business curriculum.

Over the span of around 25 years, extensive research has been conducted to compare traditional teaching methods with innovative approaches. However, the results have not consistently shown that the innovative methods outperform their traditional counterparts. In response to this, the Accountamatics method has been developed, building upon the fundamental knowledge in accounting that has been used since 1494. Despite its historical significance, this method is sometimes regarded as limited to technical knowledge in the modern era.

The study intends to examine the generational differences between GenY and GenZ Indian generational cohorts on the study variables, i.e. core self-evaluations (CSE), team cohesion, organizational culture and team performance. Further, the present research aims to analyze the impact of CSE, team cohesion and organizational culture on team performance as antecedents.

The study has adopted a descriptive cross-sectional survey method where the data were collected from Indian working professionals who belonged to GenY and GenZ generational cohorts. Further, a total of 370 responses were received, and thereafter, the data were analyzed by employing significant statistical tests such as exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and an independent samples t-test.

The study results revealed that GenY and GenZ cohorts significantly differ on CSE, team cohesion and organizational culture. However, no significant difference was reported in team performance between these two generational cohorts. Also, the study results disclosed that CSE, team cohesion and organizational culture positively influence team performance by acting as its determinants.

The study reports differences between GenY and GenZ that would assist managers in effectively dealing with these generational cohorts and formulating human resource (HR) policies that can accommodate the needs of these two cohorts. Additionally, the study benefits managers by highlighting the importance of core-self evaluations, team cohesion and organizational culture to enhance team performance.

Existing research depicts that there lies a paucity of generational studies in the Indian context. The present study attempts to address this lacuna by putting pioneering efforts into this field. The main contribution of the study lies in empirically investigating the Indian generational cohorts (GenY and GenZ) in the organizations. Further, the study has also conceptualized and examined a team performance model by considering factors at three levels (individual, team and organization).

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.