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To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

The purpose of this paper is to examine feature fatigue with products and how to deal with it.

The paper looks at feature fatigue and how products perform on the market. It then suggests the right and wrong ways of defeating fatigue.

The paper reveals the problem of too many features in a product leading to fatigue. However, it suggests that, with increasingly demanding consumers and ever‐shortening product life cycles, firms should attempt to defeat feature fatigue not by reducing features, but by improving product designs that reduce the fatigue. Otherwise their products can disappoint consumers and perform poorly on the market, opening an opportunity for competitors.

This paper presents useful information on ways to deal with customer fatigue with product features.

The purpose of this paper is to present a comparative study of selected existing library automation software available in India.

The literature search was carried out to collect the data regarding library automation, library software. Survey, questionnaire and personal visit to local library software developer, method were used. The questionnaire was designed on the basis of software application, operating system used, hardware requirement and modules provided. These questionnaires were sent to all software developers. On receiving of the ten questionnaires, data were analyzed and tabulated. The last step was report preparation and drawing conclusions.

It is observed that total ten software provided acquisition, cataloguing, online public access catalog (OPAC), circulation and serial control facility. Only five library softwares provide online facility. Out of ten library software; LibSys is the highly rated one having 37 software features followed by SOUL software having 36 points followed by 35 points of Wolk Soft Tech software. SLIM 21 and SLM+ + has 34 points. It is necessary to improve the quality of software for providing effective services. In circulation, 18 features are added. Out of 18 features, SOUL has all the 18 features and is highly ranked followed by Libman, SLIM 21, SLIM+ +and Wolk Soft tech have 17 point each, respectively. Libtech has 16 circulation features. In the OPAC module, 20 features have been studied. Out of 20 features, Libman has all the 20 features and is highly rated followed by SOUL software, which includes 19 features. Out of 20 features, LibSys has 18 software features. Out of 11 features, LibSys, SLIM 21 and SOUL has all 11 features followed by DELPLUS, Libman and Libtech having ten points each. In serial control, six features are added. Out of six features, I‐Lib, Libman, LibSys, Libtech, SLIM 21, SLIM++, Softlib and SOUL have all the six features followed by four features in DELPLUS, and Wolk Soft tech. The credibility of library software packages is evaluated on the basis of their salient features and the facilities available in the software. On the basis of the salient features, and the facilities provided, the software is ranked by giving points to each facility as per features. SOUL software scored first rank (90 points) among all ten software followed by Libman (84 points) and LibSys (83 points).

In total, 72 library automation software packages have been developed in India. But for this study, ten selected software packages have been chosen on the basis of their usability and implementation to libraries.

After this study, one can enable to evaluate the library automation software packages and ranks them according to their features so that they can implement the particular software for library automation to their own library.

The paper provides a comprehensive picture of features, facilities available and services rendered by various library automation software packages in India. None of the research has been done till now on evaluation of these ten library software in India.

Addresses the need for a unified product information model and presents a new representation scheme for mechanical component modelling using shells as its principal geometric primitives for modelling form features. The representation scheme was implemented using the ACIS geometric modeller and C++ on a SUN SPARC/10 station. The advantage of using shells is that both surface and volume information of a form feature can be derived from a shell. Different levels of product data representation can be integrated into a single model. Therefore, it allows the user to model the geometry effectively and form features of a mechanical part on one system.

The purpose of this article is to examine the hypothesis that ‘classified schedules of features, with specific/generic relationships, are of value in feature‐card indexing.’ The data in support of the hypothesis has been drawn from experience gained in the construction and use of a feature‐card index designed for anaesthetics literature. This index has already been described in detail elsewhere; but to make this article comprehensible it is necessary after outlining the problem of the information explosion in medicine, and in anaesthesia in particular, to give a brief account of the development and structure of this feature‐card index.

We designed our own hardware and drew up our own word list in a purely empirical way, unhampered by theory. We do not think of our word list as a language, but as a list of descriptive features, a list of only about 450 words, and this we find to be ample. It seems that there is a limit to which the feature system can subdivide a field, and beyond this limit trouble arises from cross‐fertilization. The limit, however, is more than adequate for most purposes. If greater subdivision is required it seems preferable to split the larger field into several smaller ones, and to provide a separate set of features for each of these. The general tendency for beginners is to overdo the degree of subdivision. This should be resisted in the interests of efficiency. The essence of the feature system is that it is a method for narrowing down the search; each feature that is used in retrieval divides the field by a factor which may range from about five to twenty or more, so that the convergence is very rapid. In our system we regard the author, source, form, language and country of origin as features. Proper names are also features, so that the system is self‐contained, without the necessity of separate author or title indexes.

Manufacturer specifications for the resolution of an additive manufacturing (AM) machine can be ten times smaller (more optimistic) than the actual size of manufacturable features. Existing methods used to establish a manufacturable design rule-set are conservative piecewise-constant approximations. This paper aims to evaluate the effectiveness of a first-order model for producing improved design rule-sets for feature manufacturability, accounting for process variation.

A framework is presented which uses an interpolation method and a statistical model to estimate the minimum size for a wide range of features from a set of iterative experiments.

For an SLS process, using this approach improves the accuracy and reliability of minimum feature size estimates for a wider variety of features than assessed by most existing test artifacts.

More research is needed to provide better interpolation models, broaden applicability and account for additional geometric and process parameters which significantly impact the results. This research focuses on manufacturability and does not address dimensional accuracy of the features produced.

An application to the design of thin channels in a prosthetic hand shows the utility of the results in a real-world scenario.

This study is among the first to investigate statistical variation of “pass/fail” features in AM process characterization, propose a means of estimating minimum feature sizes for shapes not directly tested and incorporate a more efficient iterative experimental protocol.