Programming Techniques Research Articles

High-performance Deterministic Concurrency Using Lingua Franca

Actor frameworks and similar reactive programming techniques are widely used for building concurrent systems. They promise to be efficient and scale well to a large number of cores or nodes in a distributed system. However, they also expose programmers to nondeterminism, which often makes implementations hard to understand, debug, and test. The recently proposed reactor model is a promising alternative that enables deterministic concurrency. In this article, we present an efficient, parallel implementation of reactors and demonstrate that the determinacy of reactors does not imply a loss in performance. To show this, we evaluate Lingua Franca (LF), a reactor-oriented coordination language. LF equips mainstream programming languages with a deterministic concurrency model that automatically takes advantage of opportunities to exploit parallelism. Our implementation of the Savina benchmark suite demonstrates that, in terms of execution time, the runtime performance of LF programs even exceeds popular and highly optimized actor frameworks. We compare against Akka and CAF, which LF outperforms by 1.86× and 1.42×, respectively.

Design of Primal Soft-margin SVM Leveraging Piecewise Linear Approximation based Linear Programming Technique

This research introduces a new approach for training soft-margin Support Vector Machines (SVMs) using the primal formulation. The method, called soft-margin Piecewise Linear Approximation based SVM (Soft-margin PLA-SVM), streamlining the optimization of soft-margin SVM hyperparameters in linear programming framework using well-known GUROBI Optimizer Solver. It eliminates the need for an initial hyperparameter guess and uses an adaptable initial search domain. The study uses the Wisconsin Breast Cancer Original dataset from UCI machine learning to validate the effectiveness of proposed soft-margin PLA-SVM. Comparative analysis shows that proposed PLA-SVM outperforms other classifiers in terms of training speed, accuracy, precision, and ROC-AUC scores. The scalability and computational efficiency of soft-margin PLA-SVM make it suitable for high-dimensional and large-scale datasets. The research demonstrates the effectiveness of the primal perspective in solving the soft-margin SVM design problem.

Prediction of the rubberized concrete behavior: A comparison of gene expression programming and response surface method

Abstract The use of rubber in concrete to partially substitute mineral aggregates is an effort to decrease the global amount of scrap tires. This study investigates the behavior of rubberized concrete (RC) with various replacement ratios (0–50%) by volume and replacement type (fine, coarse, and fine-coarse) using soft computing techniques. The uniaxial compressive strength (CS), elastic modulus (EM), and ductility (D) are measured, and the effect of rubber content and the rubber aggregate type on the properties of RC is investigated. Scanning electron microscopy and X-ray diffraction analyses are made to determine its microstructural and chemical composition. This article compares the efficiency of two RC models using a recently developed artificial intelligence technique, i.e., gene expression programming (GEP) and conventional technique, i.e., response surface method (RSM). Statistical models are developed to predict the CS, TS, EM, and D. The mathematical models are validated using determination coefficient (R 2) and adjusted coefficient (R 2adj), and they are found to be significant. Furthermore, both methods (i.e., RSM and GEP) are very well correlated with the experimental data. The GEP is found to be more effective at predicting the experimental test results for RC. The projected methods can be executed for any practical value of RC.

Lessons learned from replicating a study on information-retrieval-based test case prioritization

Replication studies help solidify and extend knowledge by evaluating previous studies’ findings. Software engineering literature showed that too few replications are conducted focusing on software artifacts without the involvement of humans. This study aims to replicate an artifact-based study on software testing to address the gap related to replications. In this investigation, we focus on (i) providing a step-by-step guide of the replication, reflecting on challenges when replicating artifact-based testing research and (ii) evaluating the replicated study concerning the validity and robustness of the findings. We replicate a test case prioritization technique proposed by Kwon et al. We replicated the original study using six software programs, four from the original study and two additional software programs. We automated the steps of the original study using a Jupyter notebook to support future replications. Various general factors facilitating replications are identified, such as (1) the importance of documentation; (2) the need for assistance from the original authors; (3) issues in the maintenance of open-source repositories (e.g., concerning needed software dependencies, versioning); and (4) availability of scripts. We also noted observations specific to the study and its context, such as insights from using different mutation tools and strategies for mutant generation. We conclude that the study by Kwon et al. is partially replicable for small software programs and could be automated to facilitate software practitioners, given the availability of required information. However, it is hard to implement the technique for large software programs with the current guidelines. Based on lessons learned, we suggest that the authors of original studies need to publish their data and experimental setup to support the external replications.

Target Localization and Grasping of Parallel Robots with Multi-Vision Based on Improved RANSAC Algorithm

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrades in vision technology, more and more tasks are being replaced with machine vision. At present, the main method of target recognition used in palletizers is the traditional SURF algorithm, but this method of grasping leads to low accuracy due to the influence of too many mis-matched points. Due to the accuracy of robot target localization with binocular-based vision being low, an improved random sampling consistency algorithm for performing complete parallel robot target localization and grasping under the guidance of multi-vision is proposed. Firstly, the improved RANSAC algorithm, based on the SURF algorithm, was created based on the SURF algorithm; next, the parallax gradient method was applied to iterate the matched point pairs several times to further optimize the data; then, the 3D reconstruction was completed using the improved algorithm via the program technique; finally, the obtained data were input into the robot arm, and the camera’s internal and external parameters were obtained using the calibration method so that the robot could accurately locate and grasp objects. The experiments show that the improved algorithm shows better recognition accuracy and grasping success with the multi-vision approach.

Identification in a binary choice panel data model with a predetermined covariate

We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter theta , whereas the distribution of unit-specific heterogeneity, as well as the feedback process that maps lagged outcomes into future covariate realizations, is left unrestricted. We provide a simple condition under which theta is never point-identified, no matter the number of time periods available. This condition is satisfied in most models, including the logit one. We also characterize the identified set of theta and show how to compute it using linear programming techniques. While theta is not generally point-identified, its identified set is informative in the examples we analyze numerically, suggesting that meaningful learning about theta may be possible even in short panels with feedback. As a complement, we report calculations of identified sets for an average partial effect and find informative sets in this case as well.

SLEQP: An open‐source package for nonlinear programming

AbstractWe present SLEQP, an open source C‐package consisting of an active‐set method capable of solving large‐scale nonlinear programming (NLP) problems based on successive linear programming and equality constrained quadratic programming techniques. The method includes a feasibility restoration phase and second‐order corrections (SOCs), achieving robust practical performance. It has also been adapted for the special cases of unconstrained optimization, box‐constrained problems, and nonlinear least squares problems and contains interfaces to both Python and MATLAB. To demonstrate the performance of the package, we perform a computational study based on the well‐known CUTest suite of NLP problems.

Optimizing bi-objective solid transportation problem using hierarchical order goal programming technique: a case study problem

Optimizing bi-objective solid transportation problem using hierarchical order goal programming technique: a case study problem

Strong SDP based bounds on the cutwidth of a graph

Given a linear ordering of the vertices of a graph, the cutwidth of a vertex v with respect to this ordering is the number of edges from any vertex before v (including v) to any vertex after v in this ordering. The cutwidth of an ordering is the maximum cutwidth of any vertex with respect to this ordering. We are interested in finding the cutwidth of a graph, that is, the minimum cutwidth over all orderings, which is an NP-hard problem. In order to approximate the cutwidth of a given graph, we present a semidefinite relaxation. We identify several classes of valid inequalities and equalities that we use to strengthen the semidefinite relaxation. These classes are on the one hand the well-known 3-dicycle equations and the triangle inequalities and on the other hand we obtain inequalities from the squared linear ordering polytope and via lifting the linear ordering polytope. The solution of the semidefinite program serves to obtain a lower bound and also to construct a feasible solution and thereby having an upper bound on the cutwidth.In order to evaluate the quality of our bounds, we perform numerical experiments on graphs of different sizes and densities. It turns out that we produce high quality bounds for graphs of medium size independent of their density in reasonable time. Compared to that, obtaining bounds for dense instances of the same quality is out of reach for solvers using integer linear programming techniques.

Socio-demographic and behavioral correlates of excess weight and its health consequences among older adults in India: Evidence from a cross-sectional study, 2017-18.

Rapid population aging is expected to become one of the major demographic transitions in the twenty-first century due to the continued decline in fertility and rise in life expectancy. Such a rise in the aged population is associated with increasing non-communicable diseases. India has suffered from obesity epidemic, with morbid obesity affecting 5% of the population and continuing an upward trend in other developing countries. This study estimates the prevalence of excess weight among older adults in India, and examines the socio-demographic and behavioral factors and its health consequences. The study used data from the Longitudinal Ageing Study in India (LASI) wave 1 (2017-18). A total sample of 25,952 older adults (≥ 60 years) was selected for the study. Descriptive statistics, bivariate Chi-Square test, and logistic regression models were applied to accomplish the study objectives. Body mass index (BMI) has been computed for the study according to the classification of the World Health Organization, and "excess weight" refers to a score of BMI ≥ 25.0 kg/m2. Overall, 23% of older adults (≥ 60 years) were estimated with excess weight in India, which was higher among women irrespective of socioeconomic and health conditions. The higher levels of excess weight (than the national average of ≥22.7%) were observed among older adults in states like Haryana, Tamil Nadu, Telangana, Maharashtra, Gujarat, Manipur, Goa, Kerala, Karnataka, Himachal Pradesh, Punjab, Sikkim and some other states. After adjusting for selected covariates, the odds of excess weight were higher among females than males [OR: 2.21, 95% CI: 1.89, 2.60]. Similarly, the likelihood of excess weight was 2.18 times higher among older adults who were living in urban areas compared to their rural counterparts [OR: 2.18; 95% CI: 1.90, 2.49]. Higher level of education is significantly positively correlated with excess weight. Similarly, higher household wealth index was significantly positively correlated with excess weight [OR: 1.98, CI: 1.62, 2.41]. Having hypertension, diabetes and heart diseases were associated with excess weight among older adults. Regional variations were also observed in the prevalence of excess weight among older adults. The findings suggest that introducing measures that help to reduce the risk of non-communicable diseases, and campaigns to encourage physical activity, and community awareness may help reduce the high burden of excess weight and obesity among older Indians. The findings are important for identifying the at-risk sub-populations and for the better functioning of any public health programme and suitable intervention techniques to lower the prevalence and risk factors for excess weight in later life.

Applications of the Linear Quadratic Regulator Optimal Control With Completely and Partially Observed Markovian Switching

Abstract We study optimal control problems where the dynamic system evolves according to a linear stochastic differential equation with (a) multiplicative and additive white noise, (b) a mixture of white noise, and (c) white noise and colored noise. The drift rate and the diffusion matrix of the linear dynamic system depend on a continuous-time Markov chain with finite state space that is (I) partially observed and (II) completely observed. Using the results of Wonham filter theory, we reduce the partially observed problems to one with complete observation. We solve the control optimal problems explicitly with the help of dynamic programing technique, and three applications are presented to illustrate our theoretical results.

Auditing of hadoop log file for dynamic detection of threats using H-ISSM-MIM and convolutional neural network

Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% .

Scene clusters, causes, spatial patterns and strategies in the cultural landscape heritage of Tang Poetry Road in Eastern Zhejiang based on text mining

The burgeoning field of digital humanities has provided important modern technological means for text mining in literary works. Chinese classical poetry, as a treasure in the world’s artistic treasury, holds significant value in recognizing the heritage of world culture. In this study, taking the 1589 Tang poems from the Tang Poetry Road in Eastern Zhejiang as an example, we constructed a research framework that explores the aesthetics of classical Chinese poetry landscapes and spatial imagery at the urban agglomeration scale by utilizing geographic and analytical tools such as Python programming, Gephi co-occurrence semantic networks, and GIS kernel density analysis. The framework exhibits three key innovations: (1) a text processing approach that treats individual characters as semantic units in ancient poetry texts, (2) a combined approach of Python programming techniques and Gephi visualization tool for social network analysis, and (3) a study focusing on the integration of textual and spatial aspects of literary landscape heritage corridors at the urban cluster scale. The constructed framework greatly enhances the efficiency and accuracy of Tang poetry text mining, it enables the extraction of natural and cultural landscape spatial imagery along the Tang Poetry Road, the construction of scene depictions, the identification of key regions within the scenes, and the derivation of location-specific strategies. This study broadens the scope of exploring the cultural heritage value of Tang poetry literature and provides practical guidance for the development of cross-regional heritage corridors.

Sensing Fairness-Based Energy Efficiency Optimization for UAV Enabled Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is developing rapidly due to the advantages of bandwidth saving, hardware cost reduction and energy saving. This paper proposes a unmanned aerial vehicle (UAV) enabled ISAC (UAV-ISAC) system, where the UAV will sense ground users and forward sensed information to base station (BS). Radar mutual information (MI) is introduced to measure the UAV sensing performance. On the basis of sensing fairness for each user, a multi-objective resource optimization problem of maximizing both energy efficiency (EE) and minimum radar MI is studied by jointly optimizing user scheduling, transmit power and UAV trajectory. To solve the non-convex optimization problem, we decompose it into three sub-problems: user scheduling optimization, transmit power optimization, and UAV trajectory optimization. Each subproblem can be solved by successive convex approximation (SCA), fractional programming and relaxation technique. By iteratively optimizing the three sub-problems, we can obtain the suboptimal solution to original optimization problem. Simulation results show that the proposed optimization scheme can maximize the EE of UAV while ensuring sensing fairness for all users.

A multi-microcontroller-based hardware for deploying Tiny machine learning model

<span lang="EN-US">The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform.</span>

Cross-Sectional Study to Explore the Prevalence of Shoulder Impingement and Its Association with Training Volume Among Cricketers in Sport Academies of Faisalabad

Purpose: Cricketers are vulnerable to high-risk injuries. Throwing and striking are the two activities that put the most strain and effort at the shoulder joint and leads to shoulder impingement and other problems. To explore the prevalence of shoulder impingement and its association with training volume among cricketers in sport academies of Faisalabad.
 Methodology: It was cross-sectional study. Study was conducted from November 2018 to January 2019. Sample size of the study was 116. Convenient sampling technique was used to enrolled participants in study. Inclusion criteria of the study were the cricket players of age between 13 to 22 years, practice regularly in sport academies of Faisalabad. Players of Faisalabad cricket academies including, Saeed Ajmal International Cricket Academy, Eden cricket & Fitness center, Combined cricket club, National Cricket Club, Lyallpur Gymkhana cricket club and Prince Gymkhana Cricket Club, Faisalabad were interviewed and evaluated for the presence of shoulder impingement. Data was analyzed by using SPSS version 22. Data was presented in forms of tables and graphs.
 Findings: Out of 116 cricket players, 28.44% were spinners, 41.37% were fast bowlers and 30.17% were batsmen. Shoulder impingement was found to be present in 49(42%) of the cricketers in sport academies of Faisalabad. Chi- square test showed a statistically significant association between shoulder impingement and training volume (p<0.05).
 Recommendations: Shoulder impingement had a prevalence of 42% in cricketers of Faisalabad. A statistically significant association was found between shoulder impingement and training volume among cricketers of sport academies in Faisalabad. This study was informed by the BiomechanicalTheory, emphasizing the importance of considering mechanical stressors on the shoulder region in cricketers. To reduce the occurrence of shoulder pain among cricket players, practitioners should emphasize proper biomechanical techniques in training programs, with a focus on shoulder joint stability and injury prevention strategies. Policymakers should advocate for sports injury prevention programs that incorporate fundamental biomechanical concepts into training curricula, ensuring cricket players' long-term shoulder health and ensuring the sport's long-term viability.

The Role of Artificial Intelligence in Civil Engineering Applications and Programs

The integration of artificial intelligence (AI) techniques in civil engineering programs and drawing techniques has the potential to revolutionize the industry. This study explores the benefits, challenges, and strategies for the effective utilization of AI in civil engineering. The research investigates the current state of AI integration, examines its applications in structural analysis, design, documentation, and project management, and identifies the barriers hindering widespread adoption. The study highlights the importance of data-driven decision-making, enhanced design visualization, improved accuracy and efficiency, streamlined documentation and communication, and enhanced project management and control. By addressing the challenges and providing insights into AI techniques, this study aims to bridge the gap between theoretical potential and practical implementation, paving the way for more sustainable and intelligent civil engineering projects.

Applying machine learning classifiers to Sentinel-2 imagery for early identification of cotton fields to advance boll weevil eradication

Early identification of cotton fields is critical for boll weevil eradication programs to establish field traps for monitoring weevil populations. However, most studies on crop type mapping have focused on late- or post-season crop classification. This study proposed and evaluated a field-level classification method that integrated field boundary data and early-season Sentinel-2 imagery to identify cotton fields 20–60 days after planting. Three cloud-free images were selected from May and June 2020, covering a 110-square-km study area in Texas. To achieve field-level classification, the boundaries of all 320 fields in the study area were used, and 62 of them were selected as training fields. Three machine learning classifiers - random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) - were applied to the four 10-m bands for pixel-based classification and to segmented images by mean shift segmentation for object-based classification to determine the optimal classifier. To assess the impact of additional bands and vegetation indices (VIs) on classification accuracy, the six 20-m bands were combined with the 10-m bands to create 10-band images with 10-m resolution, and six vegetation indices (VIs) were derived and combined with the images. Recursive feature selection was used to identify optimal band combinations and principal component (PC) analysis was applied to reduce the dimensionality of the images. Based on accuracy measures derived from 2160 error matrices for all classification maps, RF provided the highest accuracy with faster processing times for the two May dates, while SVM and KNN exhibited slightly higher accuracy for June 10. The integration of field boundary data significantly improved classification accuracy for all classification maps. The overall accuracy (OA) of the optimal RF classification maps for the 4-band images increased from 71.4 %, 76.3 %, and 82.6 % to 85.8 %, 90.2 %, and 92.6 % (a net increase of 14.4 %, 13.9 %, and 10.0 %) for May 1, May 6, and June 10, respectively. Moreover, by including the six 20-m bands, the OA further increased by 4.8 %, 2.9 %, and 3.0 % for the respective dates. Similarly, the F-score for cotton increased from 0.801, 0.845, and 0.882 to 0.936, 0.947, and 0.976 for the three respective dates. In contrast, the inclusion of VIs had minimal effect on classification accuracy. The optimal bands and the PCs provided better classification results and were used for image classification to reduce and optimize input feature to the classifiers. These results demonstrate that combining RF with 10-band Sentinel-2 imagery and field boundary data is an effective method for early identification of cotton fields. This study provides practical techniques for boll weevil eradication program managers to identify cotton fields over large geographic areas during early growth stages.

Long-term optimal coordination of hydro-wind-thermal energy generation using stochastic dynamic programming

ABSTRACT Clean alternative energy and a greater focus on climate change aim to increase the integration of Renewable Energy Sources (RES) into power system networks. As a relatively inexpensive renewable energy, wind energy is integrated into the electrical network to reduce its operating costs. A long-term optimal scheduling model for hydro-wind-thermal in a hybrid generation system is established to find the minimum cost trajectory of energy generation at each period under various constraints. Based on the proposed model and different types of power plants, the original complex problem decomposed into hydro-wind-thermal subproblems. The stochastic Dynamic programming technique (SDP) is employed to solve the complete optimization. In this research, the SDP technique is preferred. This technique handles multistage decision processes by splitting problems down into sequential stages. Because it can incorporate nonlinear and stochastic features into a dynamic programming problem, it has been successful in this hybrid system. A penalty factor was added to the model to reduce outflow variations. As can be seen from the results, outflows are very high during peak demand periods and very low during high inflows. Furthermore, the cost decreases as demand increases, from 40,082.26 $/ GWh in May when demand is 10,275 Gwh to 16,536.32 $/ GWh in January when demand is 17,503 Gwh .

Knowledge representation of mathematical optimization problems and constructs for modeling

We introduce two new knowledge bases that we have developed to categorize mixed-integer linear programming (MILP) problems and standardize the element definitions of MILP models. MILP is a commonly used mathematical programming technique for modeling and solving real-life scheduling, routing, planning, resource allocation, and timetabling optimization problems providing optimized business solutions for industry sectors such as manufacturing, agriculture, defense, healthcare, medicine, energy, finance, and transportation among others. Despite the numerous real-life combinatorial optimization problems (COPs) found and solved, and many yet to be discovered and formulated, the number of types of constraints (the building blocks of a MILP) is relatively small. In the search for a uniform categorization of MILP problems and a machine-readable knowledge representation structure for MILP models, we have developed an optimization modeling tree (OMT) and a MILP model ontology. The two knowledge structures can serve as a standardized, uniform representation in understanding MILP problems and developing MILP models for combinatorial business optimization problems. While there are several algebraic modeling languages (AMLs) for developing and solving MILP models, the semantic correctness of such models cannot be guaranteed using the syntactic grammar of such AMLs. The MILP model ontology will act as the main resource for semantic validation of MILP models through ontology instantiations and axiom assertions.