Analysis Algorithm Research Articles

Enhanced Subspace Iteration Technique for Probabilistic Modal Analysis of Statically Indeterminate Structures

In structural stochastic dynamic analysis, the consideration of the randomness in the physical parameters of the structure necessitates the establishment of numerous stochastic finite element models and the subsequent computation of their corresponding vibration modes. When the complete analysis is applied to calculate the vibration modes for each sample of the stochastic finite element model, a substantial computational expense is incurred. To enhance computational efficiency, this work presents an extended subspace iteration method aimed at rapidly determining the vibration modal parameters of statically indeterminate structures. The essence of this proposed method revolves around efficiently constructing reduced basis vectors during the subspace iteration process, utilizing flexibility disassembly perturbation and the Krylov subspace. This extended subspace iteration method proves particularly advantageous for the modal analysis of finite element models that incorporate a multitude of random variables. The proposed modal random analysis method has been validated using both a truss structure and a beam structure. The results demonstrate that the proposed method achieves substantial savings in computational time. Specifically, for the truss structure, the calculation time of the proposed method is approximately 1.2% and 65% of that required by the comprehensive analysis method and the combined approximation method, respectively. For the beam structure, on average, the computational time of the proposed method is roughly 2.1% of a full analysis and approximately 48.2% of the Ritz vector method’s time requirement. Compared to existing stochastic modal analysis algorithms, the proposed method offers improved computational accuracy and efficiency, particularly in scenarios involving high-discreteness random analyses.

Weed Classification and Crop Health Monitoring in Microclimatic Conditions Using Thermal Image Analysis and Deep Learning Algorithms

Weed Classification and Crop Health Monitoring in Microclimatic Conditions Using Thermal Image Analysis and Deep Learning Algorithms

Rapid bacterial identification through volatile organic compound analysis and deep learning.

The increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms. AlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively. This work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society.

Detecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi‐Source Event Sequences

AbstractUncovering causal relations from event sequences to guide decision‐making has become an essential task across various domains. Unfortunately, this task remains a challenge because real‐world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross‐source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high‐level events in multi‐source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high‐level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi‐granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study.

Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

Early detection of Alzheimer’s disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer’s Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors.

The weighted GS-PIA algorithm for cubic B-spline curve interpolations and convergence analysis

The weighted GS-PIA algorithm for cubic B-spline curve interpolations and convergence analysis

Large DNA deletions occur during DNA repair at 20-fold lower frequency for base editors and prime editors than for Cas9 nucleases.

When used to edit genomes, Cas9 nucleases produce targeted double-strand breaks in DNA. Subsequent DNA-repair pathways can induce large genomic deletions (larger than 100 bp), which constrains the applicability of genome editing. Here we show that Cas9-mediated double-strand breaks induce large deletions at varying frequencies in cancer cell lines, human embryonic stem cells and human primary T cells, and that most deletions are produced by two repair pathways: end resection and DNA-polymerase theta-mediated end joining. These findings required the optimization of long-range amplicon sequencing, the development of a k-mer alignment algorithm for the simultaneous analysis of large DNA deletions and small DNA alterations, and the use of CRISPR-interference screening. Despite leveraging mutated Cas9 nickases that produce single-strand breaks, base editors and prime editors also generated large deletions, yet at approximately 20-fold lower frequency than Cas9. We provide strategies for the mitigation of such deletions.

Volumetric Integrated Classification Index: An Integrated Voxel-Based Morphometry and Machine Learning Interpretable Biomarker for Post-Traumatic Stress Disorder.

PTSD is a complex mental health condition triggered by individuals' traumatic experiences, with long-term and broad impacts on sufferers' psychological health and quality of life. Despite decades of research providing partial understanding of the pathobiological aspects of PTSD, precise neurobiological markers and imaging indicators remain challenging to pinpoint. This study employed VBM analysis and machine learning algorithms to investigate structural brain changes in PTSD patients. Data were sourced ADNI-DoD database for PTSD cases and from the ADNI database for healthy controls. Various machine learning models, including SVM, RF, and LR, were utilized for classification. Additionally, the VICI was proposed to enhance model interpretability, incorporating SHAP analysis. The association between PTSD risk genes and VICI values was also explored through gene expression data analysis. Among the tested machine learning algorithms, RF emerged as the top performer, achieving high accuracy in classifying PTSD patients. Structural brain abnormalities in PTSD patients were predominantly observed in prefrontal areas compared to healthy controls. The proposed VICI demonstrated classification efficacy comparable to the optimized RF model, indicating its potential as a simplified diagnostic tool. Analysis of gene expression data revealed significant associations between PTSD risk genes and VICI values, implicating synaptic integrity and neural development regulation. This study reveals neuroimaging and genetic characteristics of PTSD, highlighting the potential of VBM analysis and machine learning models in diagnosis and prognosis. The VICI offers a promising approach to enhance model interpretability and guide clinical decision-making. These findings contribute to a better understanding of the pathophysiological mechanisms of PTSD and provide new avenues for future diagnosis and treatment.

Prognostic value of EMT-related genes and immune cell infiltration in thyroid carcinoma

BackgroundThe Epithelial–Mesenchymal Transition (EMT) is a very important process involved in cancer invasion and metastasis. Additionally, the Cathepsin K (CTSK) gene is closely related to the degradation of the extracellular matrix, which is a critical component of the EMT. The purpose of this study was to determine the relationships between EMT-related genes and immune cell infiltration and their prognostic value in Thyroid carcinoma (THCA). The effect of the CTSK gene on the aggressive biological features of THCA was assessed.MethodsWithin the framework of the present study, the THCA cohort was analyzed in detail based on data obtained from The TCGA database in the context of the EMT. The TCGA-THCA cohort was then divided into two groups, namely, high- and low-risk groups, based on the calculated EMT scores. Finally, based on the findings from the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, LASSO regression analysis, and Kaplan−Meier plotter, we selected five genes (CTSK, C3ORF80, FBLN2, PRELP and SRPX2) associated with patient prognosis. Furthermore, this study examined the presence of various immune cells within the THCA samples using three distinct algorithms, namely ssGSEA, xCell, and MCPcounter. Additional studies have been conducted to establish the roles of CTSK in THCA cell proliferation and migration using various assays, such as CCK8, colony formation, EdU proliferation, Transwell migration and wound healing assays. Additionally, the involvement of CTSK in the regulation of various EMT-related markers was confirmed using Western blot analysis.ResultsBased on EMT scores, TCGA-THCA patients were further divided into two groups, and the study revealed that patients in the high-risk group had a worse prognosis than those in the low-risk group. Among the five genes linked to the prognostic value of EMT (CTSK, C3ORF80, FBLN2, PRELP, and SRPX2), CTSK exhibited notably elevated expression in the high-risk cohort. This group also exhibited pronounced immune cell infiltration, with a marked correlation observed between CTSK expression and the levels of macrophages, MDSCs, and various T-cell subtypes. Furthermore, in vitro studies demonstrated that reducing CTSK expression led to significant reductions in THCA cell viability; clonogenic, proliferative, motility and migratory capacities; and the expression of key EMT-related proteins, including N-cadherin, vimentin, slug, and snail.ConclusionOur results suggest that the expression of CTSK, a gene associated with the EMT, may be associated with THCA onset and progression and thus may serve as a promising prognostic biomarker.

Electric Vehicle Sentiment Analysis Using Large Language Models

Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle (EV) brands. This provides companies with valuable insight into the public’s opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performance compared to traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming significant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimised BERT (RoBERTa), and a generalised autoregressive pre-training method (XLNet) using Lucid Motors and Tesla Motors YouTube datasets. Results evidenced that LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically when fine-tuned. Furthermore, our findings present the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with an F1 score of at least 92%.

Blood pressure abnormality detection and Interpretation utilizing Explainable Artificial Intelligence

ObjectiveHypertension is a pressing medical dysfunction that increases the risks of many fatal diseases. Early detection of hypertension can be crucial to lead a healthy life. Machine learning (ML) can be useful for the early prediction of a patient's likelihood of having a blood pressure abnormality and preventing it. Explainable artificial intelligence (XAI) is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML model. This research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using XAI. MethodsThis research utilized the "Blood Pressure Data for Disease Prediction" dataset from Kaggle. Data were collected from medical reports of random participants in 2019 based on the presence of blood pressure abnormality, chronic kidney disease, and adrenal and thyroid disorders. We have used several ML algorithms (extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), decision tree (DT), and Logistic regression (LR)) to predict blood pressure abnormality based on patient's data. Principal component analysis (PCA) and recursive feature elimination (RFE) algorithms are used as feature optimizers. The receiver operating characteristic (ROC) curve analysis and accuracy were the key outcome metrics. Through the experiment, we have also calculated various performance measurement techniques like precision, recall, specificity, F1 score, and kappa to pin down the model with the best performance. Moreover, we have implemented several XAI methods namely permutation feature importance (PFI), partial dependence plots (PDP), Shapley additive explanations (SHAP), and local interpretable model-agnostic explanations (LIME) for additional exploration of our best model. ResultsThe combination of RFE and XGBoost provides the most significant results. The results of the study show that the algorithm has an AUC of 0.95, indicating good discriminatory power in detecting abnormal BP. The accuracy, precision, recall, specificity, F1-score, and kappa gained are 91.50%, 88.64%, 92.65%, 92.27%, 90.83%, and 0.8 respectively. According to the results of the XAI experiment, the genetic pedigree coefficient and hemoglobin level in a patient contribute the most to blood pressure abnormality prediction. Adrenal and thyroid diseases, as well as chronic kidney illness, have an impact on the projections. Existing research backs up this conclusion. ConclusionWhile comparing to previous works on this dataset, the results of our research are superior and the use of XAI shed new light on our model's prediction. This study would provide new insight into blood pressure detection in the medical profession.

GALADRIEL: A facility for advancing engineering science relevant to rep-rated high energy density physics and inertial fusion energy experiments.

Many current and upcoming laser facilities used to study high-energy-density (HED) physics and inertial fusion energy (IFE) support operating at high rep-rates (HRRs) of ∼0.1-10Hz, yet many diagnostics, target-fielding strategies, and data storage methods cannot support this pace of operation. Therefore, established experimental paradigms must change for the community to progress toward rep-rated operation. To this end, we introduce the General Atomics LAboratory for Developing Rep-rated Instrumentation and Experiments with Lasers, or GALADRIEL, to serve as a test bed for developing and benchmarking the engineering science advancements required for HRR experiments. GALADRIEL was constructed from the ground up around a commercial 1TW (∼25 mJ in ∼25fs at 800nm) laser with diverse experimental applications in mind. Assembly of the basic framework of GALADRIEL concluded with commissioning shots generating ∼1-4MeV electrons via laser-wakefield acceleration (LWFA) using a nitrogen gas jet. Subsequent LWFA experiments operated at 1Hz, utilized instrument feedback for optimization, and stored all data in a custom-built NoSQL database system. From this database called MORIA, or the MOngodb Repository for Information Archiving, data are retrievable via individual files or en masse by query requests defined by the user. GALADRIEL focuses on outstanding questions in engineering science, including targetry, diagnostics, data handling, environmental and materials studies, analysis and machine learning algorithm development, and feedback control systems. GALADRIEL fills a niche presently missing in the US-based user-facility community by providing a flexible experimental platform to address problems in engineering science relevant to rep-rated HED and IFE experiments.

Long-term, automated stool monitoring using a novel smart toilet: A feasibility study.

Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess stool form according to the Bristol Stool Form Scale (BSFS). Our smart toilet integrates a conventional toilet bowl with an engineered portal to image feces in a predetermined region of the plumbing post-flush. The smart toilet was installed in a workplace bathroom and used by six healthy volunteers. Images were annotated by three experts. A computer vision method based on deep learning segmentation and mathematically defined hand-crafted features was developed to quantify morphological attributes of stool from images. 474 bowel movements images were recorded in total from six subjects over a mean period of 10 months. 3% of images were rated abnormal with stool consistency BSFS 2 and 4% were BSFS 6. Our image analysis algorithm leverages interpretable morphological features and achieves classification of abnormal stool form with 94% accuracy, 81% sensitivity and 95% specificity. Our study supports the feasibility and accuracy of long-term, non-invasive automated stool form monitoring with the novel smart toilet system which can eliminate the patient burden of tracking bowel forms.

Identification of oxidative stress-related hub genes for predicting prognosis in diffuse large B-cell lymphoma

BackgroundOxidative stress is a cellular characteristic that might induce the proliferation and differentiation of tumor cells and promote tumor progression in diffuse large B-cell lymphoma (DLBCL). MethodsThe DLBCL gene sequencing dataset, tumor mutation burden data, copy number variation data of Somatic cell mutation data in TCGA were downloaded for data training analysis, along with four DLBCL datasets in GEO for validation analysis. The known oxidative stress related genes (OSRGs) were collected from websites. The weighted gene co-expression network analysis (WGCNA) was conducted on the TCGA DLBCL dataset to obtain gene modules related to oxidative stress and intersected with the known OSRGs to obtain the hub genes, which were used to perform consensus clustering on the samples to obtain new phenotypes. Next, the prognosis related OSRGs were selected through regression analysis algorithms and key genes were identified. These genes were used to establish the prognostic risk model and predictive model, and to compare functional and pathway differences among different risk groups. ResultsThrough website search, we obtained 297 known OSRGs, and after intersecting with WGCNA results, we obtained 26 OSRGs. The TCGA-DLBC samples were clustered into 2 subtypes with these genes and there were significant differences in immune infiltration between subtypes. After regression analysis, we obtained a total of four key genes, BMI1, CDKN1A, NOX1, and SESN1. The risk prediction model established with these four genes as variables has accurate prognostic prediction ability. The key genes interact with 65 miRNAs, 57 TFs, 47 RBPs, and 62 drugs, respectively, and are closely related to immune infiltration of the disease. Among them, CDKN1A and SESN1 had the highest variability. ConclusionsThe key genes involved in oxidative stress could predict the prognosis of DLBCL and potentially become therapeutic targets.

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Spectral and temporal acoustic differences between English and Dutch vowels

This study aims to compare the acoustic characteristics of English and Dutch vowels and to model the perception of Dutch vowels by English speakers on the basis of crosslinguistic acoustic similarity. The first three formant frequencies and the duration of vowels were extracted from the output of 20 native English and 20 native Dutch speakers. A linear discriminant analysis (LDA) algorithm was trained on the productions of English speakers and the Dutch productions were later supplied to the model to provide a predicted proportion of how each nonnative vowel would be classified in terms of the speakers’ L1 categories. The results of LDA demonstrated that all but one Dutch vowel were classified as above-chance responses in terms of two or more English categories. Due to the large and complex vowel system of English, completely overlapping contrasts were absent, while there was a large number of partially overlapping contrasts, which are predicted to exhibit moderate-to-good discrimination. The results of crosslinguistic acoustic similarity are largely consistent with those involving other English listeners of Dutch, potentially highlighting the importance of acoustic cues in estimating listeners’ speech perception patterns.

The development of a method for increasing the reliability of the assessment of the state of the object

The object of the research is assessment of object with different degrees of embeddedness. The subject of the research is the process of assessing the state of objects using the apparatus of neuro-fuzzy expert systems, the apparatus of relational analysis and bio-inspired algorithms. The problem that is solved in the research is to increase the reliability of the assessment of the objects state, regardless of the number of attachments. The originality of the research is that: – possibility of increasing the reliability of the object state assessment due to the parallel use of two bio-inspired algorithms; – taking into account the degree of awareness of the object state, due to the application of correction coefficients for the degree of awareness; – construction of both object and relational models, which allows to increase the reliability of assessment of the objects state; – possibility of combining the results of the work of bio-inspired algorithms, which makes it possible to mutually verify the correctness of the work of each of the algorithms; – universality of solving the task of assessing the state of objects with different degrees due to the hierarchical nature of their description; – possibility of simultaneously searching for a solution in different directions; – adequacy of the obtained results. An example of the use of the proposed method is presented on the example of solving the task of determining the composition of an operational group of troops (forces) and elements of its operational construction. The specified example showed an increase in the reliability of the assessment of the objects state by an average of 20 % due to the use of additional improved procedures. It is advisable to use the proposed method to solve the problems of assessing the state of multidimensional objects in conditions of uncertainty and risks, which are characterized by high requirements for the reliability of the information obtained

Non-destructive and rapid identification of yeasts by nearinfrared spectroscopy and machine learning

This study aimed to apply near-infrared (NIR) spectroscopy combined with machine learning techniques to identify yeast strains rapidly and practically, comparing the results with traditional molecular identification methods. Yeasts were isolated from the digestive tracts of aquatic mining insects collected in the extreme north of the Western Amazon (Roraima), Brazil, and preserved through cryopreservation and mineral oil methods. Molecular identification involved PCR amplification and sequencing of ribosomal DNA regions. NIR spectroscopy, coupled with multivariate analysis and machine learning algorithms such as principal component analysis (PCA), hierarchical cluster analysis (HCA), k-nearest neighbor (KNN), and soft independent modeling by class analogy (SIMCA), was used to analyze and classify the yeast samples, accurately identified yeast strains at the genus and species levels, achieving 100% accuracy in both the calibration and validation sets. The results indicate that this method provides a rapid, non-destructive, and environmentally friendly alternative to traditional molecular techniques, making it suitable for real-time, in situ analysis with minimal sample manipulation.

Multi-Search Strategy-based Improved Water Flow Optimizer Algorithm for Cluster Analysis

Clustering is a popular technique that has proven its capability in diverse fields like data analytics, business intelligence, social mining, image recognition, document clustering and bioinformatics. This technique determines the valuable information from a pre-defined set of data and groups similar information into the same cluster. In literature, many algorithms have been presented based on several clustering approaches. It is observed that partitional clustering algorithms are widely popular due to there simplicity and easy implementation such as k-means, k-medoids, and k-harmonic means. However, traditional algorithms suffer from several limitations like being trapped in local optima and depending on the initial solution quality. Heuristic algorithms are also proposed to alleviate the problems of traditional clustering algorithms. But, sometimes, these algorithms also get stuck in local minima and exhibit a lack of balance between search mechanisms. Hence, this work presents an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. In the proposed IWFO algorithm, the initial solution is generated based on the logistic chaotic map instead of random initialization, and in turn, an optimal quality solution is generated. The search mechanism of the WFO algorithm is enhanced based on an improved search mechanism which is the combination of non-linear functions and the previous best solution. Further, the local optima issue is alleviated using a multi-start search mechanism. The efficacy of the proposed IWFO algorithm is evaluated using twelve benchmark clustering datasets and results are compared with seventeen clustering algorithms. The simulation results are assessed using intra-cluster distance (intra), standard deviation (SD), rank, accuracy rate (AR) and detection rate (DR) parameters. Further, a statistical test is also conducted to validate the efficacy of the proposed IWFO algorithm. The results showed that proposed IWFO algorithms obtain superior quality results on most of the datasets.

Editorial

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six accepted papers involving 17 authors from six different countries (Argentina, Brazil, Ecuador, Hong Kong, India, Saudi Arabia). Álex dos Santos Moura, Fábio Gomes Rocha, and Michel S. Soares from Brazil propose in their research a recommendation tool based on information retrieval to assist developers in choosing the suitable microservices pattern to solve a given problem. In a collaborative research effort between Ecuador and Argentina, Gonzalo P. Espinel-Mena, José L. Carrillo-Medina, Eddie E. Galarza, and Mario Matias Urbieta discuss a systematic mapping of configuration management activities in software product line. Edward Kai Fung Dang, Robert Wing Pong Luk, and Qing Li from Hong Kong contribute to the forum for negative results with their study on word bigrams for pseudo-relevance feedback in information retrieval. Prateek Thakral and Yugal Kumar from India present in their work an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. Samit Bhanja, Banani Ghose, and Abhishek Das from India highlight their findings on multi-step-ahead time series forecasting using a deep learning and fuzzy time series-based error correction method. And last but not least, Abdullilah A. Alotaibi and Salman A. AlQahtani from Saudi Arabia present in their research an intelligent distributed channel selection framework with hybrid mode selection for interference mitigation in D2D based 5G networks.  Enjoy Reading! Best regards,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria