Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
Journal Citation Indicator0.400
Impact Factor1.305

Experimental Investigation of a Novel Extracting Water System from Air by Soil Cold Source

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Mathematical Problems in Engineering is a broad-based journal publishing results of rigorous engineering research across all disciplines, carried out using mathematical tools.

 Editor spotlight

Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Adjustable Piecewise Quartic Hermite Spline Curve with Parameters

In this paper, the quartic Hermite parametric interpolating spline curves are formed with the quartic Hermite basis functions with parameters, the parameter selections of the spline curves are investigated, and the criteria for the curve with the shortest arc length and the smoothest curve are given. When the interpolation conditions are set, the proposed spline curves not only achieve C1-continuity but also can realize shape control by choosing suitable parameters, which addressed the weakness of the classical cubic Hermite interpolating spline curves.

Research Article

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.

Research Article

Influence of Exchange Rate on Foreign Direct Investment Inflows: An Empirical Analysis Based on Co-Integration and Granger Causality Test

Although it is widely recognized that Foreign Direct Investment (FDI) inflows have a dominant effect on economic growth of host countries, the determinants of FDI inflows are still unclear. Especially, about the effect of exchange rate on FDI inflow, the results reached by scholars vary across countries or regions. It is of great practical and theoretical significance to explore the influencing effects of exchange rate on FDI inflow and identify the mechanisms that underlie them in close association with regional economic characters so as to help local government implement targeted government policies to achieve sustainable FDI inflow and sustainable economic growth. For this purpose, the influencing effects and the influencing mechanisms of the exchange rate on FDI inflows are investigated for Zhejiang province, China, over 1985–2019 by employing the co-integration tests, vector error correction models, Granger causality tests, and impulse response tests. Empirical results indicate that there are long-term stable and unidirectional causal relationship between the exchange rate and FDI inflow. Continuous appreciation of RMB against USD discourages FDI inflow. The mechanism which underlies the long-term relationship is the wealth effect, rather than the cost effect or the demand effect. By contrast, in the short run, neither the exchange rate nor the three influencing mechanism has a significant impact on FDI inflow. These results suggest policy recommendations for improving FDI by accumulating human capital and improving infrastructure. These findings are also applicable for other countries or regions with similar economic characters.

Research Article

Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network

For batch processes that are extensively applied in modern industry and characterized by nonlinearity and dynamics, quality prediction is significant to obtain high-quality products and maintain production safety. However, some quality variables and key performance indicators are difficult to measure online. In addition, the mechanism-based model for batch processes is usually tough to acquire due to the strong nonlinearity and dynamics, which makes quality prediction a challenge. With the accumulation of historical process data, data-driven methods for quality prediction gain increasing attention, among which convolutional neural network (CNN) is quite successful for its automatic feature extraction of nonlinear features from raw data. Considering that most CNN-based methods mainly take the variety of extracted features into account and ignore the redundancy between them, this paper introduces the minimal-redundancy-maximal-relevance algorithm to select features obtained by original CNN and further improves it with a feature selection layer to form the proposed method referred as mRMR-CNN. Then, a quality prediction model is established based on mRMR-CNN and the effectiveness of it is verified on the penicillin fermentation process, where the proposed method shows remarkable performance.

Research Article

The Adoption of Geographic Information Systems in the Public Sector of Saudi Arabia: A Conceptual Model

The development of fuzzy sets in geographic information systems (GIS) arose out of the need to handle uncertainty and the ability of soft computing technology to support fuzzy information processing. Fuzzy logic is an alternative logical foundation coming from artificial intelligence (AI) technology with several useful implications for spatial data handling. GIS has been found to have a crucial role in the performance of public sector organizations (PSO). However, the literature shows no universal model to support and shed light on GIS adoption, which lessens the chances for effective GIS adoption and usage. Therefore, a new model is needed for successful adoption and eventual enhanced organization’s performance. Thus, there is a need to investigate the factors that can bring about GIS adoption. Models for GIS adoption in literature are few and far between, and the few that exist are not applicable as they do not cover all the significant factors that can contribute to adoption success. Hence, this paper brought a GIS adoption model for PSOs to promote their performance. The model was developed through the extension of the Technology Acceptance Model (TAM) in addition to the DeLone and McLean’s Success Model. The study involved interviews with ten experts in ranking the extracted factors, and data was analyzed through thematic analysis. On the basis of the obtained analysis findings, the fundamental factors were found to be significant in their effects, and GIS adoption sufficiently related to the overall performance. Thus, the study contributes to the body of knowledge by filling the gap in the literature.

Research Article

An Efficient Branch-and-Bound Algorithm for Globally Solving Minimax Linear Fractional Programming Problem

This paper presents an efficient outer space branch-and-bound algorithm for globally solving a minimax linear fractional programming problem (MLFP), which has a wide range of applications in data envelopment analysis, engineering optimization, management optimization, and so on. In this algorithm, by introducing auxiliary variables, we first equivalently transform the problem (MLFP) into the problem (EP). By using a new linear relaxation technique, the problem (EP) is reduced to a sequence of linear relaxation problems over the outer space rectangle, which provides the valid lower bound for the optimal value of the problem (EP). Based on the outer space branch-and-bound search and the linear relaxation problem, an outer space branch-and-bound algorithm is constructed for globally solving the problem (MLFP). In addition, the convergence and complexity of the presented algorithm are given. Finally, numerical experimental results demonstrate the feasibility and efficiency of the proposed algorithm.

Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
Journal Citation Indicator0.400
Impact Factor1.305
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.