Maps Approach Research Articles

How to use efficiently airborne criteria pollutants and radon-222 in source apportionment: A self-organizing maps approach

Pollutant source apportionment represents one of the fundamental activities in environmental science. Several efficient chemometric tools are available to the scope, mostly based on multivariate techniques and usually applied to aerosol chemical speciation data. In the present work, an alternative source profiling method is proposed, based on the self-organizing maps (SOM) algorithm. Moreover, the dataset used includes typical criteria pollutants and physical parameters related to airborne particulate matter widely used as a complement of aerosol source apportionment and largely available at a higher time resolution than bulk aerosol samplings, allowing the information on the dynamic behavior of the local airshed to be extended. In this work, data was collected at a coastal location in NW Italy, between January and July 2012. Hourly concentrations of typical gaseous pollutants (SO2, NO, NO2, benzene, toluene, (m-p)-xylene, o-xylene), black-carbon and particle number concentrations by an optical particle sizer (OPS) were collected. The dataset was integrated with radon-222 activity concentration and meteorological parameters to enrich and refine the information obtained by SOM computation as well as to improve the air pollution source localization. Despite the lower specificity of criteria pollutants, the approach developed was capable of revealing distinct pollution sources such as the urban background traffic, the coal-fired power plant active at the time of the study, and the harbor, in agreement with previous PM-based source apportionment studies carried out locally, while enlightening peculiar dynamical conditions detectable at the sub-daily time scale. The application of the SOM algorithm, with the integration of meteorological parameters and atmospheric radon, proved to be very efficient in unveiling the air pollution sources.

Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

In the fast-paced fashion world, unique designs are like early birds, grabbing attention as online shopping surges. Fabric texture plays an immense role in selecting the perfect design. Indian Traditional textile motifs are pivotal, showing rich cultural origins and attracting worldwide art fanatics. Yet, technology-driven abstract forms are posing a challenge for them. The decline of handmade artistic ability due to computerization is concerning. Crafting new designs associated with the latest trends is time- consuming and requires diligence. In this work an interactive CBIR (content-based image retrieval) system is presented. It utilizes deep features from InceptionV3 and InceptionResNetV2 models to match query designs with a database of traditional Indian textiles. Its performance is tested with Caltech-101, Corel-1K state-of-the-art datasets, and Indian Textiles datasets and the results are shown to be finer than the existing approaches. The similarity-based fine-grained saliency maps (SBFGSM) approach is employed to visualize the importance of features. Our approach combines deep feature fusion with PCA dimensionality reduction and speeds up search using a clustering approach. Relevance feedback is employed to refine the retrievals. This tool is expected to benefit designers by accelerating the design cycles by bridging the gap between human creativity and A.I. assistance.

Plithogenic Sociogram based Plithogenic Cognitive Maps Approach in Sustainable Industries

The theory of Plithogeny is primarily attribute based. Plithogenic Sociogram (PS) and Plithogenic cognitive maps (PCM) are distinct decision-making approaches developed to deal with attributes. This paper proposes an integrated decision-making model combining the approaches of PS with PCM and this sets the beginning of new genre of PCM. The development of this model is applied in investigating the association between the factors pertinent to the promotion of sustainable industries. This work also compares the working of the proposed integrated model of PCM with PS and the independent working of PCM model. The results are more promising to the proposed integrated approach and this paper strongly emphasises the efficacy of this hybrid approach. The blended model of PCM with PS is efficient in handling complex decision circumstances and this approach shall be extended to other kinds of Plithogenic representations.

Statistical Analysis of the Surface Roughness on Aircraft Icing

A statistical analysis of the surface roughness is performed on experimentally obtained ice shapes on an asymmetrical airfoil at Rec≈2⋅106. The ice shapes were generated in the Icing Wind Tunnel of the Technical University of Braunschweig under Appendix C and O conditions of the EASA airplane certification standards as part of the ICE GENESIS project. The photogrammetry method is used for the digitization of the experimental ice shapes, while statistical parameters such as the mean ice shape and the local root mean square (RMS) of the ice geometry are extracted using a traditional surface projection method, as well as a self-organizing maps approach. Results show the evolution of the statistical parameters over time and the influence of the freestream static temperature on these parameters. A comparison between the experimental values of the local RMS of the ice geometry and a correlation for roughness prediction is presented, showing a good match with the original formulation of the correlation for cases under Appendix C conditions while having a good match with Appendix O conditions when a temperature correction factor is applied to the formulation. Additionally, results show an almost linear growth of roughness over the whole accretion time.

Health Monitoring and Diagnosis for Geo-Distributed Edge Ecosystem in Smart City

With the increasing number of Internet of things (IoT) devices being deployed and used in daily life, the load on computational devices has grown exponentially. This situation is more prevalent in smart cities where such devices are used for autonomous control and monitoring. Smart cities have different kinds of applications that are aided through IoT devices that collect data, send it to computational processing and storage devices, and get back decisions or actuate the actions based on the input data. There has been a stringent requirement to reduce the end-to-end delay in this process owing to the remote deployment of cloud data centres. This eventually led to the revolution of edge computing, wherein nano-micro-processing devices can be deployed closer to the premises of the smart application and process the data generated with a lower turnaround time. However, due to the limited computational power and storage, controlling the workload diverted to the edge devices has been challenging. The workload scheduling policies and task allocation schemes often fail to consider the run time health of the edge devices due to a lack of proper monitoring infrastructure. Thus, in this paper, we proposed a health monitoring and diagnosis framework for geo-distributed edge clusters processing big data generated by smart city applications. This framework is built over the Map-Reduce approach for distributed processing of big data on edge clusters deployed across the smart city. Within this framework, SmartMonit (a monitoring agent) is deployed that collects the health statistics of edge devices and predicts the potential failures using an artificial neural network-based self-organising maps approach. The proposed framework is deployed over different clusters to test the efficacy concerning failure detection.

Projections of Large-Scale Atmospheric Circulation Patterns and Associated Temperature and Precipitation over the Pacific Northwest Using CMIP6 Models

Abstract Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500-hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to that in the current climate, especially in winter, whereas in summer the models project a general reduction in the magnitude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, and in summer the largest temperature increases are projected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above-average precipitation in the current climate are projected to become significantly drier by the end of the century. Significance Statement This paper uses a novel method to analyze projections of large-scale atmospheric circulation over the Pacific Northwest of North America, reducing the uncertainty of changes to the circulation patterns over the region under a high-emissions scenario of global warming.

Deep and fast label-free Dynamic Organellar Mapping

The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we enhance the performance of DOMs through data-independent acquisition (DIA) mass spectrometry. DIA-DOMs achieve twice the depth of our previous workflow in the same mass spectrometry runtime, and substantially improve profiling precision and reproducibility. We leverage this gain to establish flexible map formats scaling from high-throughput analyses to extra-deep coverage. Furthermore, we introduce DOM-ABC, a powerful and user-friendly open-source software tool for analyzing profiling data. We apply DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which identifies a subset of Golgi proteins that cycle through endosomes. An imaging time-course reveals different cycling patterns and confirms the quantitative predictive power of our translocation analysis. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.

Attributing the driving mechanisms of the 2015–2017 drought in the Western Cape (South Africa) using self-organising maps

The Southwestern Cape (SWC) region in South Africa experienced a severe rainfall deficit between 2015–2017. The resulting drought caused the City of Cape Town to almost run out of water during the summer of 2017–2018. Using the self-organising maps approach, we identify and classify the synoptic circulation states over Southern Africa known to influence the local climate in the SWC into three groups (dry, intermediate, and wet circulation types) using large ensembles of climate model simulations with anthropogenic forcing and natural forcing. We then assessed the influence of anthropogenic climate change on the likelihood of these circulation types and associated rainfall amounts over the SWC during the drought. Our findings suggest that during the drought, the frequency of dry (wet) circulation types increases (decreases) across all models under anthropogenic forcing relative to the natural forcing. While there was no clear direction in the associated rainfall change in the dry circulation types, rainfall decreased across most models in wet nodes. All models agree that anthropogenic climate change has increased the likelihood of dry circulation types (median probability ratio (PR): 0.93–0.96) and decreased that of wet circulation types (median PR: 1.01 and 1.12), indicating a shift towards lesser (more) wet (dry) synoptic circulation states and associated rainfall during the drought. The long-term climatology also depicts similar patterns indicating the drought may result from long-term changes in the frequency of wet circulations and their associated rainfall. This study further explains the anthropogenic influence on the dynamic (synoptic circulation states) and thermodynamic (rainfall) factors that influenced the SWC 2015–2017 drought.

Characterizing spatial patterns of satellite-derived chlorophyll-a in the Bohai and Yellow Seas of China using self-organizing maps (SOM) approach

Dynamic of chlorophyll-a (Chl-a) concentration is essential information to understand the status and trends of marine ecosystems. In this study, a Self-Organizing Map (SOM) was applied to delineate space-in-time patterns of Chl-a from satellite dataset during 2002–2022 over the Bohai and Yellow Seas of China (BYS). Six typical Chl-a spatial patterns were discerned through a 2 × 3 nodes SOM, while temporal evolutions of dominant spatial patterns were analyzed. The Chl-a spatial patterns were characterized by different concentrations and gradients, and obviously changed over time. The Chl-a spatial patterns and their temporal evolutions were mainly shaped by joint effects of nutrient level, light availability, water column stability, and other factors. Our findings provide first glimpse of space-in-time Chl-a dynamics in the BYS, and complement to the traditional time-in-space Chl-a pattern studies. The accurate identification and classification of the Chl-a spatial patterns are of great significance to marine regionalization and management.

Examining the Effectiveness of Thinking Maps Usage by Analysing Students' Achievement in Mathematics Subject

Teachers play a critical role in ensuring their pupils succeed in their studies. Educators must select acceptable teaching methods to ensure an effective learning experience. By applying the t-test statistical analysis, this research aimed to evaluate the efficiency of the thinking maps approach with traditional ways in increasing student achievement for a number base topic. This study involved 120 Form 4 pupils from 4 schools in Pahang. This research implemented a quantitative approach (quasi-experimental method). The research featured an experimental group (n = 60) and a control group (n = 60). As research tools, a set of pre-test and post-test inquiries were used. The application of thinking maps improved student achievement in mathematics subjects. There was a substantial difference in mean scores between the control and experimental groups, where the mean score of the experimental group seemed to be higher than that of the control group. Additionally, mean scores before and after the teaching and learning sessions showed a positive increase in both categories. Nevertheless, compared to the control group, the experimental group that utilised thinking maps showed a higher ascent in the mean score. The findings of this study can assist teachers to develop a thinking map as one of the teaching approaches for improving pupils' knowledge of a topic and, therefore, indirectly improving pupil academic achievement.

Understanding expectations from and capabilities of climate-economy models for measuring the impact of crises on sustainability

The multi-faceted emergencies of our time (recession, pandemics, and international conflict) can disrupt progress towards sustainable development goals (SDGs). While climate-economy models can show pathways out of these disruptions, numerous stakeholder-informed enhancements are required to navigate through the interconnected SDG landscape. We introduce a novel Fuzzy Cognitive Maps (FCMs) approach that integrates Monte Carlo simulations, aiming to capture and understand the differences among modellers' and experts' perceptions of the impacts of such crises on broader sustainability, considering models’ existing/planned capacity and uncertainty over perceived magnitude and propagation of impacts across SDGs. Our application shows that expert stakeholders appeared more concerned over the implications of these crises for sustainable development than modellers, but expectations were considerably constrained by model capabilities. SDGs 8 (growth), 7 (energy-sector sustainability), and 13 (climate action) were robustly found as most prone to examined shocks. Our proposed Monte Carlo Fuzzy Cognitive Mapping (MCFCM) approach also highlighted potentially important propagation effects as well as gaps in integrated assessment model capabilities to holistically study the SDG spectrum.

Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach

Crowdfunding as an alternative source of finance to banking is the subject of numerous research works. This research proposes the guidelines of a Decision Support System (DSS) in the area of crowdfunding based on a Fuzzy Cognitive Maps (FCM) approach. A framework consisting of three phases (ten steps) has been proposed to design the guidelines of a DSS in crowdfunding. The first phase focusses in the construction of the FCM. FCM-based scenarios have been built in the second phase. Finally, in the third phase the guidelines of the DSS in crowdfunding have been defined. Results show that DSS using FCM-based scenarios could be useful tools to promoters, funders and managers of crowdfunding projects.

SAPEVO-H² a Multi-Criteria Systematic Based on a Hierarchical Structure: Decision-Making Analysis for Assessing Anti-RPAS Strategies in Sensing Environments

Regarding high-level and complex decision-making scenarios, the study presents an extensive approach to the Simple Aggregation of Preferences Expressed by Ordinal Vectors-Multi Decision Making method (SAPEVO-M). In this context, the modeling proposal, named SAPEVO-Hybrid and Hierarchical (SAPEVO-H²), the objective of this study, based on the concepts of multi-criteria analysis, provides the evaluation of alternatives under the light of multiple criteria and perceptions, enabling the integration of the objectives of a problem, which are transcribed into attributes and structured in a hierarchical model, analyzing qualitative and quantitative data through ordinal and cardinal entries, respectively. As a case study, a decision analysis concerning the defense strategies against anti-Remotely Piloted Aircraft Systems (RPAS) strategies for the Brazilian Navy is carried out. Using the technique of the causal maps approach based on Strategic Options Development and Analysis (SODA) methodology, the problematic situation is structured for numerical implementation, demonstrating the performance of objectives and elements of a hierarchical structure. As a result, rankings concerning objectives and anti-RPAS technologies, based on the treatment of subjective information, are presented. In the end, the main contribution of the study and its limitations are discussed, along with the conclusions and some proposals for future studies.

An overview of energy management using fuzzy cognitive maps

Soft computing, especially fuzzy cognitive maps (FCMs), has become increasingly applicable to energy management and policy-making. In recent decades, there has been a worldwide effort to minimize energy consumption and manage energy flow in private and public buildings. We present a critical overview of today’s applications of FCM-based methods in the energy domain. We analyzed FCM methods related to energy planning, efficiency, sustainability, transition, forecasting, energy policy, and scenario analysis. We highlight FCM's applicability in the energy domain, especially its contribution to the academic and research communities. Specific drawbacks and limitations were identified while using FCM methods on several challenging applications, primarily when learning algorithms are used. A new approach addressing these issues is provided and defined as the advanced fuzzy cognitive maps (AFCM) approach. These drawbacks are considered when providing future research challenges of FCMs for building energy management and efficiency. Finally, research gaps are identified, and we suggest solutions, especially AFCM is advanced fuzzy cognitive maps.

Developing an industry 4.0 readiness model using fuzzy cognitive maps approach

Industry 4.0, or the fourth industrial revolution, is a new paradigm in manufacturing digitalization, which provides various opportunities for enterprises. Industry 4.0 readiness models are worthy methods to aid manufacturing organizations in tracking the development of their businesses and operations. Nevertheless, there are different Industry 4.0 readiness models; no work has yet analyzed Industry 4.0 readiness degree and causal effects relationships using fuzzy cognitive maps. This paper proposes an Industry 4.0 readiness model that consists of readiness requirements obtained from the literature and validated through a mixed-method approach, including literature reviews and questionnaires. To validate the proposed Industry 4.0 readiness model, the exploratory factor analysis and confirmatory factor analysis methods are used. Fuzzy Cognitive Map is utilized to assess readiness, identify relevant concepts to improve readiness degree, implement Industry 4.0, and analyze causal relationships among concepts and dimensions. Through this model and the FCM method, managers can recognize relevant concepts and predict complicated cause-effect relationships among concepts in two states of static and dynamic analyses to increase readiness degree. The paper concludes by emphasizing managerial implications for successful applications in practice as well as future research suggestions on developing the Industry 4.0 readiness model.

The utility of the emBODY tool as a novel method of studying complex phenomena-related emotions

Bodily sensations are one of the major building blocks of emotional experience. However, people differ in their ability to recognise and name their emotions, especially those in response to complex phenomena such as climate change or the COVID-19 pandemic. Therefore, we investigated whether the bodily sensation maps (BSMs) approach can be employed to study emotions related to phenomena that are likely to evoke various, and perhaps even conflicting, emotions in people. Using a unique topographical self-report method—the previously established emBODY tool, 548 participants marked where in the body they feel sensations (activations and deactivations) when they experience distinct emotions (e.g. happiness) and when they think about different phenomena, namely climate change, COVID-19 pandemic, war, nature, friends, and summer holidays. We revealed maps of bodily sensations associated with different emotions and phenomena. Importantly, each phenomenon was related to a statistically unique BSM, suggesting that participants were able to differentiate between feelings associated with distinct phenomena. Yet, we also found that BSMs of phenomena showed some similarity with maps of emotions. Together, these findings indicate that the emBODY tool might be useful in uncovering the range of emotions individuals experience towards complex phenomena.

Improved Salsa20 Stream Cipher Diffusion Based on Random Chaotic Maps

To enhance stream ciphers, numerous studies have concentrated on the randomness, unpredictable nature, and complexity of keystream. Numerous stream algorithms have been put forth. Most of them require a significant amount of computational power. Salsa20 is a high-performance stream encryption solution that works on computers with fewer resources and uses a secure method that is faster than AES. They suggest Salsa20 for encryption in common cryptographic applications. Users who value speed over certainty should utilize the Salsa20 family of reduced-round ciphers, such as the (8,12) round cipher. It uses a 256-bit key and a hash algorithm. A successful fusion makes use of both the Salsa20 algorithm's and the random maps' advantages to improve the Salsa20 algorithm's shortcomings by increasing its unpredictability. Particularly now that Salsa20/7 has been hacked and Salsa20/12 is no longer as secure as it previously was. As a result, Salsa20 needs to achieve a high level of diffusion to withstand known attacks. Right now, salsa20 and its shortened versions rank among the fastest ciphers. This study presents a novel lightweight approach to construct a strong keystream that is sufficiently random to avoid being predicted by adversaries, achieve good diffusion, and withstand known assaults. A NIST test found that the performance of the (Salsa20-chaotic maps) approach in terms of data integrity and secrecy is nearly 0.3131 higher than that of the Salsa20 .

EFFECTS OF SOCIO-SCIENTIFIC ISSUES BASED ON THINKING MAPS APPROACH ON FUTURE THINKING OF SECONDARY SCHOOL STUDENTS

This study was conducted to examine the effects of the socio-scientific issue (SSI) approach assisted by the future thinking map (FTM) on five constructs of future thinking, namely i) understanding the current situation, ii) identifying the trends, iii) analyzing the relevant drivers, iv) synthesizing the possibilities or needs of the future and v) choosing with the justification of the desired future. The future-thinking test instrument was developed to measure the level of future thinking. A quasi-experimental pre-test and post-test control group design was employed. A total of 255 form four students (age 16) from three randomly selected rural secondary schools in Tawau District, Malaysia were assigned to i) SSI-FTM (n = 85), ii) SSI (n = 85), and iii) conventional (CV, n = 85) teaching and learning (TL) strategies. The results of the MANCOVA analysis showed that there was a statistically significant effect across all three groups of TL strategies. The ANCOVA analysis showed that there was a statistically significant effect of the SSI-FTM TL strategies compared to the SSI and CV TL strategies on the five future thinking constructs. The quasi-experimental study proves that the SSI-FTM TL strategy is effective in nurturing future thinking among form four students in science lessons. Keywords: future thinking, future thinking map, secondary schools, socio-scientific issue

Energy Enhancement of WSN with Deep Learning based SOM Scheduling Algorithm

Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.