An agent-oriented approach to change propagation in software evolution

Software maintenance and evolution are inevitable activities since almost all software that is useful and successful stimulates user-generated requests for change and improvements. One of the most critical problems in software maintenance and evolution is to maintain consistency between software artefacts by propagating changes correctly. Although many approaches have been proposed, automated change propagation is still a significant technical challenge in software engineering. In this paper we present a novel, agent-oriented approach to deal with change propagation in evolving software systems that are developed using the Prometheus methodology. A metamodel with a set of the Object Constraint Language (OCL) rules forms the basis of the proposed framework. The underlying change propagation mechanism of our framework is based on the well-known Belief-Desire-Intention (BDI) agent architecture. Traceability information and design heuristics are also incorporated into the framework to facilitate the change propagation process

Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield

Automated Decompression Table for the Individual and Targeted Treatment of Disc Herniation

AbstractTraction therapy is a common and effective non-surgical treatment of low back pain caused by degenerated or herniated intervertebral disc or other disc deformities. While lying on specially designed treatment tables and fixated on the pelvis, axial traction is applied to the patient's spine to separate the vertebrae and release pressure on the disc. Targeted traction of specific segments instead of pulling the whole spine can increase the efficacy of the traction therapy and reduces side effects due to less application of traction force. This paper presents a design approach of a traction table, which allows the targeted and accurate repeatable treatment of any specific intervertebral disc. Furthermore the treatment of malformations like scoliosis is possible due to the special design of the traction table. The automated measuring of the patient's back on the traction table enables the accurate and effective resp. ergonomic treatment of the patient by the comparison of MRI images and the measured spine shape

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

Reduced NGF in gastric endothelial cells is one of the main causes of impaired angiogenesis in aging gastric mucosa

This study detected reduced nerve growth factor (NGF) expression within gastric endothelial cells in both elderly patients and aged rats. Reduced NGF correlated with impaired angiogenesis and delayed gastric ulcer healing in aged rats. The defects could be reversed by exogenous NGF via phosphoinositide-3 kinase/serine threonine kinase signaling protein, and mammalian target of rapamycin signaling, and was dependent on serum response factor. These data show that down-regulation of endothelial NGF expression in aging is a significant contributor to impaired gastric mucosal repair

Protein sequence database for pathogenic arenaviruses

BACKGROUND: Arenaviruses are a family of rodent-borne viruses that cause several hemorrhagic fevers. These diseases can be devastating and are often lethal. Herein, to aid in the design and development of diagnostics, treatments and vaccines for arenavirus infections, we have developed a database containing protein sequences from the seven pathogenic arenaviruses (Junin, Guanarito, Sabia, Machupo, Whitewater Arroyo, Lassa and LCMV). RESULTS: The database currently contains a non-redundant set of 333 protein sequences which were manually annotated. All entries were linked to NCBI and cited PubMed references. The database has a convenient query interface including BLAST search. Sequence variability analyses were also performed and the results are hosted in the database. CONCLUSION: The database is available at and can be used to aid in studies that require proteomic information from pathogenic arenaviruses

On the fixed-effects vector decomposition

This paper analyses the properties of the fixed-effects vector decomposition estimator, an emerging and popular technique for estimating time-invariant variables in panel data models with unit effects. This estimator was initially motivated on heuristic grounds, and advocated on the strength of favorable Monte Carlo results, but with no formal analysis. We show that the three-stage procedure of this decomposition is equivalent to a standard instrumental variables approach, for a specific set of instruments. The instrumental variables representation facilitates the present formal analysis which finds: (1) The estimator reproduces exactly classical fixed-effects estimates for time-varying variables. (2) The standard errors recommended for this estimator are too small for both time-varying and time-invariant variables. (3) The estimator is inconsistent when the time-invariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence are incorrect. (5) We recommend an alternative shrinkage estimator that has superior risk properties to the decomposition estimator, unless the endogeneity problem is known to be small or no relevant instruments exist