Application Domains Research Articles

A conceptual data modeling framework with four levels of abstraction for environmental information

Environmental data generated by observation infrastructures and models is widely heterogeneous in both structure and semantics. The design and implementation of an ad hoc data model for each new dataset is costly and creates barriers for data integration. On the other hand, designing a single data model that supports any kind of environmental data has shown to be a complex task, and the resulting tools do not provide the required efficiency. In this paper, a new data modeling framework is proposed that enables the reuse of generic structures among different application domains and specific applications. The framework considers four levels of abstraction for the data models. Levels 1 and 2 provide general data model structures for environmental data, based on those defined by the Observations and Measurements (O&M) standard of the Open Geospatial Consortium (OGC). Level 3 incorporates generic data models for different application areas, whereas specific application models are designed at Level 4, reusing structures of the previous levels. Various use cases were implemented to illustrate the capabilities of the framework. A performance evaluation using six datasets of three different use cases has shown that the query response times achieved over the structures of Level 4 are very good compared to both ad hoc models and to a direct implementation of O&M in a Sensor Observation Service (SOS) tool. A qualitative evaluation shows that the framework fulfills a collection of general requirements not supported by any other existing solution.

Application of Machine Learning Methods in Predicting Lysine-specific Histone Demethylase 1 (LSD1) Inhibitors

Background: Lysine-specific histone demethylase 1 (LSD1) is a well-known anti-cancer target for drug discovery. Novel reversible inhibitors of LSD1 are desirable to be developed. Objective: This study aimed to build reliable predictive models to evaluate the antineoplastic efficacy of compounds and reveal the structural foundation underlying the inhibitory activity of LSD1. Methods: Multiple artificial intelligence algorithms were utilized in the development of quantitative structure-activity relationship (QSAR) models. A dataset comprising 915 compounds with welldefined IC50 values against LSD1 was assembled for analysis. The structures of these compounds were described by different descriptor packages. Principal component analysis (PCA) was performed to explore the chemical space distribution of each dataset. Y-randomization test and applicability domain (AD) analysis were deployed to validate the reliability of models. Results: For regression models, a consensus model was constructed by integrating the predictions of four top-performing individual models (SVM_ECFP4, RFR_PyDescriptor, RFR_RDKIT, and TRANSNNI), resulting in enhanced predictive performance as compared to the individual models. The consensus model achieved a determination coefficient (r2, for the test set) value of 0.82. For classification models, the consensus model was derived from the amalgamation of three individual models (ASNN_RDKIT, ASNN_ECFP4, and RFR_ECFP4), and the overall prediction accuracy of the model was 0.96 for external validation. Conclusion: The reliable models could be used to identify highly active LSD1 inhibitors for the purpose of efficient virtual screening. In addition, the important molecular properties and structural fragments derived from this work can provide guidance for the structural optimization of novel LSD1 inhibitors.

Blockchain-enabled traceability systems for supply chain quality management: empirical insights from pharmaceutical manufacturers

PurposeThe emergence of blockchain technology has the potential to bring about transformative changes in various industries, with supply chain management being a prominent domain of application. This study investigates the strategic performance benefits of using blockchain-enabled traceability systems for improving supply chain quality management.Design/methodology/approachThe present study employed structural equation modelling to analyse data obtained from 200 practitioners working in Indian pharmaceutical companies.FindingsThe results reflect that blockchain-enabled traceability systems have a positive impact on multi-tier quality governance (MQG); supply chain process alignment and coordination (SPAC) and quality centric collaboration (QCC); all of which are the facets of supply chain quality management. Furthermore, when examining the impact of blockchain-enabled traceability systems on firm quality performance and economic performance, the mediation role of SPAC and QCC was confirmed, whereas the mediation role of MQG could not be established.Practical implicationsThe study’s empirical insights offer practical guidance for pharmaceutical manufacturers, regulators and other industry participants seeking to harness the potential of blockchain technology for creating resilient and transparent supply chains that uphold product quality and safety standards.Originality/valueThese findings underscore the significance of blockchain-enabled traceability systems in revolutionizing supply chain quality management practices to achieve superior strategic performance in the pharmaceutical sector.

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95 % CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95 % CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95 % CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance their predictive value in future rapid screening efforts.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Harmonizing QSAR Machine Learning‐Based Models and Docking Approaches for Identifying Novel Histone Deacetylase 2 Inhibitors

AbstractMachine learning (ML) algorithms have gained widespread application in constructing computational models for predicting the bioactivity and physicochemical properties of numerous compounds, notably HDAC inhibitors. In this work, 2801 unique compounds with confirmed bioassays on HDAC2 were collected and employed to train ML models to virtually screening and possibly design potential inhibitors for HDAC2. A meticulous 3‐step procedure was first proposed to ensure the hits are within the application domain of the screening models. Through virtual screening of 91 million compounds, 59 structures were suggested by the four highest predictability models as potential HDAC2 inhibitors. The bioactivity of these compounds is further confirmed through a validated docking protocol, wherein 57 out of the 59 active hits proposed by the four models exhibited superior affinity with HDAC2 compared to vorinostat, with docking scores ranging from −10.74 to −7.06 kcal/mol. Lead optimization strategies were then implemented on the top‐performing compound from the molecular docking scores, CID 122648337, enhancing its binding affinity and interactions within the HDAC2 active site. This optimization led to the creation of two novel lead compounds, demonstrating higher affinity to HDAC2 than the initial hits. The stability of lead compounds in the active site was confirmed via MD simulations.

Interfacial fatigue fracture of pressure sensitive adhesives

Pressure sensitive adhesives (PSAs) are viscoelastic polymers that can form fast and robust adhesion with various adherends under fingertip pressure. The rapidly expanding application domain of PSAs, such as healthcare, wearable electronics, and flexible displays, requires PSAs to sustain prolonged loads throughout their lifetime, calling for fundamental studies on their fatigue behaviors. However, fatigue of PSAs has remained poorly investigated. Here we study interfacial fatigue fracture of PSAs, focusing on the cyclic interfacial crack propagation due to the gradual rupture of noncovalent bonds between a PSA and an adherend. We fabricate a model PSA made of a hysteresis-free poly(butyl acrylate) bulk elastomer dip-coated with a viscoelastic poly(butyl acrylate-co-isobornyl acrylate) sticky surface, both crosslinked by poly(ethylene glycol) diacrylate. We adhere the fabricated PSA to a polyester strip to form a bilayer. The bilayer is covered by another polyester film as an inextensible backing layer. Using cyclic and monotonic peeling tests, we characterize the interfacial fatigue and fracture behaviors of the bilayer. From the experimental data, we obtain the interfacial fatigue threshold (4.6J/m2) under cyclic peeling, the slow crack threshold (33.9J/m2) under monotonic peeling, and the adhesion toughness (~ 400J/m2) at a finite peeling speed. We develop a modified Lake-Thomas model to describe the interfacial fatigue threshold due to noncovalent bond breaking. The theoretical prediction (2.6J/m2) agrees well with the experimental measurement (4.6J/m2). Finally, we discuss possible additional dissipation mechanisms involved in the larger slow crack threshold and much larger adhesion toughness. It is hoped that this study will provide new fundamental knowledge for fracture mechanics of PSAs, as well as guidance for future tough and durable PSAs.

Critical Evaluation of the 2016 SOSORT Clinical Guidelines on the Detection and Clinical Management of Adolescent Idiopathic Scoliosis Using the AGREE II Tool: A Chiropractic Perspective

ObjectiveThe objective of this study is to assess the methodological quality of the 2016 Society on Scoliosis Orthopaedic and Rehabilitation Treatment (SOSORT) guidelines for the detection and management of adolescent idiopathic scoliosis. MethodsA diverse group of experts, including practicing chiropractors, chiropractors with a Diplomate by the American Chiropractic Board of Radiology, chiropractors with PhD degrees, and chiropractors involved in teaching within chiropractic programs was invited to participate in the study. Experts independently evaluated the guidelines using the Appraisal of Guidelines for Research and Evaluation II tool. Individual scaled scores were calculated for each item and the 6 domains, with methodological quality considered good if the score was equal to or greater than 70%. ResultsThe 19 experts, including 6 clinicians, 4 chiropractic radiologists, 6 researchers, and 3 lecturers, represented Canada (n = 3), United states (n = 4), France (n = 2), Australia (n = 1), United Kingdom (n = 1), Switzerland (n = 1), Sweden (n = 3), Denmark (n = 2), China (n = 1), and Côte d'Ivoire (n = 1). Domain scaled scores ranged from 38% to 90%. The 4 items of the applicability domain scored between 26% and 45%. The experts made suggestions for improving the applicability of the guidelines’ recommendations. ConclusionThe methodological quality of the 2016 SOSORT clinical practice guidelines was deemed good. However, strategies should be devised to improve their applicability. This could be achieved by involving a more diverse representation of healthcare professions in the development of future guidelines.

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines (GA-SVM), each identifying ten pertinent AlvaDesc descriptors. The models were constructed using the Dragonfly Algorithm combined with the Support Vector Regressor (DA-SVR), with the GA-SVM-based model emerging as the top performer. Rigorous statistical metrics validate its superior predictive accuracy (R2 = 0.98142, Q2LOO = 0.98142, RMSE = 1.12836, AARD = 0.78746). Furthermore, an external test set of ten compounds was employed for model validation and extrapolation, along with assessing the applicability domain, further underscoring the model's reliability. The selected descriptors (X0Av, VE1sign_B(e), ATSC1e, MATS6v, P_VSA_ppp_A, TDB01u, E1s, R2m+, N-067, SssO) collectively elucidate the key structural factors influencing surface tension in the studied drugs. The model provides excellent predictions and can be used to determine the surface tension of new psychoanaleptic drugs. Its outcomes will guide the design of novel medications with targeted surface tension properties.

Assessing basic/fundamental psychological need fulfillment: systematic mapping and review of existing scales to foster cumulative science.

Because the fulfillment of basic/fundamental psychological needs affects people's motivation and well-being, measuring the degree to which these needs are met is of interest to researchers across various domains. Although numerous self-assessment tools have been developed, no recent comprehensive reviews exist, hindering cumulative scientific progress. The present systematic review aimed to identify and analyze the main approaches to developing self-report scales for assessing basic/fundamental psychological need fulfillment. The objective is to inform readers interested in selecting instruments for their studies and those intending to develop new scales. Following PRISMA, we conducted a search of Scopus, Web of Science, PubMed, and ProQuest in August 2023. The following information was extracted from eligible studies: Scale name, abbreviation, theoretical basis, application domain, final scale construction, scale development and validation methodology, and citation count. Our search identified 32 primary studies, in which 31 original scales were developed and validated, and 89 secondary studies that aimed to modify these original scales. The predominant theoretical basis was Self-Determination Theory, although eight scales were based on alternative need typologies. The scales were either domain-general or specific to contexts such as work, education, or exercise/sports contexts. While most were designed to measure need satisfaction, some also addressed need support, frustration, and thwarting. Despite significant efforts in developing, adapting, and applying scales to measure need fulfillment, we found several issues resulting from diverse perspectives on conceptualizing psychological needs and need typologies, discordant approaches in developing and validating measures, and other inconsistencies that should be acknowledged and addressed in future research.

Promoting Synergies to Improve Manufacturing Efficiency in Industrial Material Processing: A Systematic Review of Industry 4.0 and AI

The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.

Machine learning approach for mapping the heat capacity of deep eutectic solvents for sustainable energy applications

This study introduces a breakthrough machine learning (ML) approach to predict the heat capacity of deep eutectic solvents (DESs), an emerging class of environmentally friendly solvents, for sustainable energy applications. Leveraging an extensive dataset of 2,696 data points across 55 DES mixtures with 389 unique compositions, the research significantly extends the scope of previous studies. The methodology employs group contribution-based critical properties to elucidate intricate molecular-level dynamics, enabling the models to decode complex interactions governing DES behavior. The multi-dimensional strategy encompasses conventional binary DESs, ternary DESs, and water + DES mixtures, providing a holistic understanding of these green solvents. The developed artificial neural network (ANN) exhibits exceptional performance, with an R2 value of 0.995 in training and 0.988 in testing, substantially surpassing existing models. Rigorous comparative analysis and applicability domain assessment confirm the robustness and superiority of this approach in predicting heat capacities for diverse DES compositions. Key findings include identifying the molecular factors influencing DES heat capacities, with critical volume, molecular weight, and critical temperature having the greatest impact. An open-source, user-friendly Excel tool based on the ANN model is developed to promote accessibility and practical application in energy and environmental sectors. This work revolutionizes the understanding and prediction of DES thermophysical properties, paving the way for their enhanced utilization in eco-friendly energy systems, thermal management, green materials development, and sustainable fuel processing.

Solid face sheets enable lattice metamaterials to withstand high-amplitude impulsive loading without yielding

Owing to their ability to provide tunable mechanical responses, lattice materials are frequently studied to elucidate their response to static and dynamic loads. However, these roles are typically in opposition: static loads must be supported sufficiently far away from the onset of buckling or yielding, whereas dynamic loads are typically ameliorated by crushing of the lattice, which provides excellent energy-absorption due to the large plastic deformation accompanying densification. In contrast, this work considers the octet truss as an exemplar topology, in a structural role where it must simultaneously support static loads while enduring high-amplitude impulsive loads. This study focuses on the ability to withstand impulsive loads without yielding, an essential prerequisite to enduring dual loading. Computational studies using the ALE3D hydrocode were performed to examine the response of the octet truss under a short temporal width impulse shape associated with laser-driven shocks. A key finding was that covering the lattice with a solid face sheet and treating this face sheet thickness as a design variable allows the Taylor-like pulse to be attenuated prior to entering the weaker lattice, at the cost of added mass up front. Experimental validation was accomplished by laser-driven shock testing, using octet trusses printed out of Ti-5Al-5V-5Mo-3Cr. The results show that for a given quantity of mass, the attenuation is maximized when as much mass as possible is moved into the face sheet, leaving a more slender lattice structure. The effect of placing mass in the face sheet rather than lattice beams dominates the effect of relative density, to the point where a low-mass structure with most of the mass concentrated in the face sheet can outperform a high-mass structure with most of the mass in the lattice. By further understanding the propagation of short pulse width waves within under-dense structures, this study expand the domain of applicability of such structures, including lattice materials, to challenging dual-loading regimes spanning decades of strain rates.

Defects-Rich Heterostructures Trigger Strong Polarization Coupling in Sulfides/Carbon Composites with Robust Electromagnetic Wave Absorption

Defects-rich heterointerfaces integrated with adjustable crystalline phases and atom vacancies, as well as veiled dielectric-responsive character, are instrumental in electromagnetic dissipation. Conventional methods, however, constrain their delicate constructions. Herein, an innovative alternative is proposed: carrageenan-assistant cations-regulated (CACR) strategy, which induces a series of sulfides nanoparticles rooted in situ on the surface of carbon matrix. This unique configuration originates from strategic vacancy formation energy of sulfides and strong sulfides-carbon support interaction, benefiting the delicate construction of defects-rich heterostructures in MxSy/carbon composites (M-CAs). Impressively, these generated sulfur vacancies are firstly found to strengthen electron accumulation/consumption ability at heterointerfaces and, simultaneously, induct local asymmetry of electronic structure to evoke large dipole moment, ultimately leading to polarization coupling, i.e., defect-type interfacial polarization. Such “Janus effect” (Janus effect means versatility, as in the Greek two-headed Janus) of interfacial sulfur vacancies is intuitively confirmed by both theoretical and experimental investigations for the first time. Consequently, the sulfur vacancies-rich heterostructured Co/Ni-CAs displays broad absorption bandwidth of 6.76 GHz at only 1.8 mm, compared to sulfur vacancies-free CAs without any dielectric response. Harnessing defects-rich heterostructures, this one-pot CACR strategy may steer the design and development of advanced nanomaterials, boosting functionality across diverse application domains beyond electromagnetic response.

Customization of 2D Atomic-Molecular Heterojunction with Manipulatable Charge-Transfer and Band Structure.

Manipulating the properties of 2Dmaterials through meticulously engineered artificial heterojunctions holds great promise for novel device applications. However, existing research on the crucial charge-transfer interactions and energy profile regulation is predominantly focused on 2D van der Waals structures formed via weak van der Waals forces, limiting regulatory efficiency at high costs. Herein, a refined atomic-molecular heterojunction strategy featuring strong covalent bonds between organic molecule and 2D violet phosphorus (VP) atomic crystal is developed, which enables enhanced charge-transfer dynamics and customizable band structure regulation at the molecular level. Both experimentally and theoretically, it is demonstrated that grafting efficiency, charge redistribution, and energy gap regulation critically depend on organic electronegativity, providing a low-cost yet high-efficiency regulatory effect on a large scale. As a proof of concept, the novel VP-molecular heterojunctions exhibit optimized performance in diverse application domains, presenting a general platform for future high-performance device applications.

Multi-relational graph contrastive learning with learnable graph augmentation

Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-dimensional representations, which has been successfully applied to various multi-relationship prediction tasks, such as information retrieval, question answering, and etc. Recently, contrastive learning has shown remarkable performance in multi-relational graph learning by data augmentation mechanisms to deal with highly sparse data. In this paper, we present a Multi-Relational Graph Contrastive Learning architecture (MRGCL) for multi-relational graph learning. More specifically, our MRGCL first proposes a Multi-relational Graph Hierarchical Attention Networks (MGHAN) to identify the importance between entities, which can learn the importance at different levels between entities for extracting the local graph dependency. Then, two graph augmented views with adaptive topology are automatically learned by the variant MGHAN, which can automatically adapt for different multi-relational graph datasets from diverse domains. Moreover, a subgraph contrastive loss is designed, which generates positives per anchor by calculating strongly connected subgraph embeddings of the anchor as the supervised signals. Comprehensive experiments on multi-relational datasets from three application domains indicate the superiority of our MRGCL over various state-of-the-art methods. Our datasets and source code are published at https://github.com/Legendary-L/MRGCL.

Critical insights into data curation and label noise for accurate prediction of aerobic biodegradability of organic chemicals.

The focus of this work is to enhance state-of-the-art Machine Learning (ML) models that can predict the aerobic biodegradability of organic chemicals through a data-centric approach. To do that, an already existing dataset that was previously used to train ML models was analyzed for mismatching chemical identifiers and data leakage between test and training set and the detected errors were corrected. Chemicals with high variance between study results were removed and an XGBoost was trained on the dataset. Despite extensive data curation, only marginal improvement was achieved in the classification model's performance. This was attributed to three potential reasons: (1) a significant number of data labels were noisy, (2) the features could not sufficiently represent the chemicals, and/or (3) the model struggled to learn and generalize effectively. All three potential reasons were examined and point (1) seemed to be the most decisive one that prevented the model from generating more accurate results. Removing data points with possibly noisy labels by performing label noise filtering using two other predictive models increased the classification model's balanced accuracy from 80.9% to 94.2%. The new classifier is therefore better than any previously developed classification model for ready biodegradation. The examination of the key characteristics (molecular weight of the substances, proportion of halogens present and distribution of degradation labels) and the applicability domain indicate that no/not a large share of difficult-to-learn substances has been removed in the label noise filtering, meaning that the final model is still very robust.

A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images.

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) tracer is the standard clinical technique to measure myocardial and vessel metabolism and viability and to investigate the metabolic syndrome associated with cardiovascular diseases. The quantitative analysis of PET images allows one to study the cardiovascular physiological processes, by extracting quantitative parameters from the analysis of the tracer kinetic. Here, we propose a new methodology to quantify and evaluate the evolution of glucose metabolism inside the myocardium and the large vascular structures over time. We merge and analyze PET and CT cardiac images, extracting different volumes of interest (VOI) and performing quantitative measurements. To validate it, we apply the methodology to merge images of the aorta vessel for patients affected by metabolic syndrome. The application of the proposed approach to the use case reveals a correlation between administered drugs and metabolic syndrome, measuring the glucose metabolic rate (MRGlu) in both the myocardium and aorta. The proposed methodology can be used to evaluate some cardiovascular risk indexes of diabetic patients, too. The proposed methodology can also be deployed to analyze other application domains.

Design and implementation of a scalable IoT-based real-time environmental monitoring and alarm system: an experimental study

Digital transformation is crucial for organizations to survive, be competitive, and grow in the modern age. IoT is the key to enabling this transformation by connecting devices and optimizing processes. This paper presents the design and implementation of a generic IoT-based real-time environmental monitoring and alarm system. The platform is validated by applying it to a manufacturing plant scenario, where various sensors simulate industrial conditions. Scalable message distribution systems such as MQTT and Apache Kafka facilitate reliable data transmission. A microservice architecture is constructed for the backend services to ensure uninterrupted and high throughput services in the application domain. Instead of deploying a real WSN, traffic generation services were chosen to minimize costs, provide greater control and flexibility, and facilitate faster, scalable testing in a controlled environment. The platform also features an integrated alarm system with an event definition module, which allows users to define custom action rules. This flexible, scalable, and resilient architecture can be used across a wide range of application domains that require digital transformation. The experimental study demonstrates the platform's capabilities and great potential for broader IoT applications.

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.