Urban Heat Island Intensity Prediction in the Context of Heat Waves: An Evaluation of Model Performance

Geo-spatial Information Science

OpenStreetMap (OSM) is a dataset in constant change and this dynamic needs to be better understood. Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide, we investigate the temporal dynamic of OSM data production, more specifically, the auto- and cross-correlation, temporal trend, and annual seasonality of these activities. Furthermore, we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1–4 weeks. Several insights could be obtained from our analyses, including that the contribution activities tend to grown linearly in a moderate intra-annual cycle. Also, the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric, i.e. the arithmetic average of recent previous observations. In particular, the …

arXiv preprint arXiv:2401.04218

We present a benchmark for assessing the capability of Large Language Models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations' spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated 14 questions focusing on well-known American cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, while the remaining seven targeted locations less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55.3% accuracy, followed by GPT-3.5 at 47.3%, and Llama-2 at 44.7%. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4's accuracy dropped to 32.9% on these tasks, compared to 85.7% on others. Despite these inaccuracies, the models identified the nearest cardinal direction in most cases, suggesting associative learning, embodying human-like misconceptions. We discuss the potential of text-based data representing geographic relationships directly to improve the spatial reasoning capabilities of LLMs.

ERDKUNDE

Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to cyclists. To obtain a nuanced understanding of the needs and demands of the wider population, especially vulnerable groups, and to address the challenge of reducing urban heat stress, we used an inter-and transdisciplinary approach. The needs of vulnerable groups, the public, and the city administration were identified through participatory methods and various tools, including interactive city walks. Solution approaches and adaptation measures to prevent heat stress were evaluated and integrated into the development of a heat-avoiding route service through a co-design process. The findings comprise the identification of perceived hotspots for heat (such as large public spaces in the city centre with low shading levels), the determination of commonly reported symptoms resulting from severe heat (eg, fatigue or lack of concentration), and the assessment of heat adaptation measures that were rated positively, including remaining in the shade and delaying errands. Additionally, we analysed and distinguished between individual and community adaptation strategies. Overall, many respondents did not accurately perceive the risk of heat stress in hot weather, despite severe limitations. As a result, the heat …

Proceedings of the OSM Science

Advances in Artificial Intelligence (AI) and, specifically, in Deep Learning (DL) have fostered geospatial analysis and remote sensing, culminating in the establishment of GeoAI [1, 2] and the solidification of research on methodologies and techniques for AI-assisted mapping [3-7]. Nevertheless, a particular challenge lies in the substantial demand for training examples in DL. Manual labelling of these examples is labour-intensive, consuming a considerable amount of time and financial resources. Alternatively, semi or automated labelling of data emerges as a prominent solution, as exemplified by the tool ohsome2label [8], which harnesses data from the OpenStreetMap [9] to label satellite images. However, moving from characterising object types (road, river, building) based on geometry to categorising them by attributes might result in an imbalanced class distribution in the utilised Machine Learning (ML) dataset.Such imbalances are common in numerous practical applications. Learning from skewed datasets can be particularly challenging and often requires non-conventional ML techniques. A comprehensive awareness of the issues associated with class imbalance, as well as strategies for mitigating them, is essential [10]. In the context of spatial data, the distribution of classes can vary from country to country and region to region, adding a new layer of complexity and exacerbating this issue. In this context, an analysis was conducted on the distribution of road types, defined by the values of the OSM" highway" tag, in diverse-profile nations. The aim was to evaluate the extent of class imbalance and to identify any consistent patterns in the …

Environmental Monitoring and Assessment

Spatially explicit information on carbon fluxes related to land use and land cover change (LULCC) is of value for the implementation of local climate change mitigation strategies. However, estimates of these carbon fluxes are often aggregated to larger areas. We estimated committed gross carbon fluxes related to LULCC in Baden-Württemberg, Germany, using different emission factors. In doing so, we compared four different data sources regarding their suitability for estimating the fluxes: (a) a land cover dataset derived from OpenStreetMap (OSMlanduse); (b) OSMlanduse with removal of sliver polygons (OSMlanduse cleaned), (c) OSMlanduse enhanced with a remote sensing time series analysis (OSMlanduse+); (d) the LULCC product of Landschaftsveränderungsdienst (LaVerDi) from the German Federal Agency of Cartography and Geodesy. We produced a high range of carbon flux estimates, mostly caused …

Proceedings of the OSM Science

1 GIScience Research Group, Heidelberg University, Heidelberg, Germany; francis. andorful@ uni-heidelberg. de, nir. fulman@ uni-heidelberg. de 2 HeiGIT-Heidelberg Institute for Geoinformation Technology, 69120 Heidelberg, Germany; sven. lautenbach@ uni-heidelberg. de, christina. ludwing@ uni-heidelberg. de, herfort@ uni-heidelberg. de, zipf@ uni-heidelberg. de

International Journal of Applied Earth Observation and Geoinformation

The disease transmitting mosquito Aedes Aegypti is an increasing global threat. It breeds in small artificial containers such as rainwater tanks and can be characterized by a short flight range. The resulting high spatial variability of abundance is challenging to model. Therefore, we tested an approach to map water tank density as a spatial proxy for urban Aedes Aegypti habitat suitability. Water tank density mapping was performed by a semi-supervised self-training approach based on open accessible satellite imagery for the city of Rio de Janeiro. We ran a negative binomial generalized linear regression model to evaluate the statistical significance of water tank density for modeling inner-urban Aedes Aegypti distribution measured by an entomological surveillance system between January 2019 and December 2021. Our proposed semi-supervised model outperformed a supervised model for water tank detection …

Nature Communications

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data …

This discussion paper originated as the concluding publication of one of the pilot projects of the "Climate Action Science" research initiative at Heidelberg Center for the Environment (HCE), focusing on the Rhine-Neckar district and the city of Heidelberg. The aim of the explorative project was to generate a first overview on greenhouse gas emission data in order to initiate climate action of various actors and to provide well-founded support by using accurate infor-mation. The focus during the pilot phase was on the collection, compilation and evaluation of the quality of heterogeneous data sets and methods for a greenhouse gas emission inventory, as well as on the information preparation and evaluation of different inventory and presentation options. These should in turn be adapted to the needs of different users and fields of applica-tion. The study focused on different German approaches to greenhouse gas accounting, espe-cially in Baden-Württemberg compared to other German states, and in detail on the City of Heidelberg compared to the surrounding municipalities in the Rhine-Neckar district. The over-arching goal is to use the results beyond the case study projected here as a stimulus and pre-liminary work for further projects and activities in the overall "Climate Action Science" project. Several difficulties were encountered in processing the emissions inventory and compiling var-ious data sets on emissions in the study area. Three basic situations were identified: 1. De-sired data is not available (measurements required), 2. Desired data is not freely accessible (stakeholder involvement), 3. Data generation via proxy data. In the pilot phase …

European Neuropsychopharmacology

The COVID-19 pandemic strongly impacted people's daily lives. However, it remains unknown how the pandemic situation affects daily-life experiences of individuals with preexisting severe mental illnesses (SMI). In this real-life longitudinal study, the acute onset of the COVID-19 pandemic in Germany did not cause the already low everyday well-being of patients with schizophrenia (SZ) or major depression (MDD) to decrease further. On the contrary, healthy participants’ well-being, anxiety, social isolation, and mobility worsened, especially in healthy individuals at risk for mental disorder, but remained above the levels seen in patients. Despite being stressful for healthy individuals at risk for mental disorder, the COVID-19 pandemic had little additional influence on daily-life well-being in psychiatric patients with SMI. This highlights the need for preventive action and targeted support of this vulnerable population.

arXiv preprint arXiv:2307.02574

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which …

Remote Sensing of Environment

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from …

ISPRS International Journal of Geo-Information

As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice.

OpenStreetMap (OSM) is a well-known example of volunteered geographic information. It has evolved to one of the most used geographic databases. As data quality of OSM is heterogeneous both in space and across different thematic domains, data quality assessment is of high importance for potential users of OSM data. As use cases differ with respect to their requirements, it is not data quality per se that is of interest for the user but fitness for purpose. We investigate the fitness for purpose of OSM to derive land-use and land-cover labels for remote sensing-based classification models. Therefore, we evaluated OSM land-use and land-cover information by two approaches:(1) assessment of OSM fitness for purpose for samples in relation to intrinsic data quality indicators at the scale of individual OSM objects and (2) assessment of OSM-derived multi-labels at the scale of remote sensing patches (. 1. 22× 1. 22 km) in combination with deep learning approaches. The first approach was applied to 1000 randomly selected relevant OSM objects. The quality score for each OSM object in the samples was combined with a large set of intrinsic quality indicators (such as the experience of the mapper, the number of mappers in a region, and the number of edits made to the object) and auxiliary information about the location of the OSM object (such as the continent or the ecozone). Intrinsic indicators were derived by a newly developed tool

The Arctic is experiencing severe changes to its landscapes due to the thawing of permafrost influenced by the twofold increase of temperature across the Arctic due to global warming compared to the global average. This process, which affects the livelihoods of indigenous people, is also associated with the further release of greenhouse gases and also connected to ecological impacts on the arctic flora and fauna. These small-scale changes and disturbances to the land surface caused by permafrost thaw have been inadequately documented.To better understand and monitor land surface changes, the project" UndercoverEisAgenten" is using a combination of local knowledge, satellite remote sensing, and data from unmanned aerial vehicles (UAVs) to study permafrost thaw impacts in Northwest Canada. The high-resolution UAV data will serve as a baseline for further analysis of optical and radar remote sensing time series data. The project aims to achieve two main goals: 1) to demonstrate the value of using unmanned aerial vehicle (UAV) data in remote regions of the global north, and 2) to involve young citizen scientists from schools in Canada and Germany in the process. By involving students in the project, the project aims to not only expand the use of remote sensing in these regions, but also provides educational opportunities for the participating students. By using UAVs and satellite imagery, the project aims to develop a comprehensive archive of observable surface features that indicate the degree of permafrost degradation. This will be accomplished through the use of automatic image enhancement techniques, as well as classical …

AGILE: GIScience Series

Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

Filling data gaps in various global regions requires a robust approach that can accurately provide detection results from earth observation data. One of the challenges arises from significant heterogeneity in satellite images and variation in features and characteristics for specific ground objects like Wastewater Treatment Plants (WTPs). To overcome these challenges, we propose a novel multiscale multifeature hybrid model. This model leverages the power of deep learning-based object detection models, namely Yolov6, RTMDET, EfficientDET, and Domain Adaptation, to accurately and efficiently identify WTP locations worldwide. Our approach focuses on performance enhancements, including reduced false positives (FPs) and broad coverage. The strategies for achieving these improvements involve effective data processing approaches, model tuning, and adaptation. Moreover, we optimize training data features …

AGIT—J. Fur Angew. Geoinformatik

Das Monitoring des tauenden Permafrosts in der Arktis ist ein wesentlicher Schlüssel, um die globalen Klimafolgen abzuschätzen. Bürgerwissenschaftliche Ansätze können dabei einen entscheidenden Beitrag leisten. In einer Fallstudie kartieren Besuchende einer Ausstellung arktische Frostmusterböden in Satellitenbildern basierend auf dem sogenannten Micro-Mapping-Ansatz. Die Auswertung der erfassten Daten ergibt, dass die Kartierung von Frostmusterböden eine größere Herausforderung darstellt als bereits etablierte Aufgabenstellungen, wie die Gebäude-Erkennung. Eine Vereinfachung der Aufgabe (binäre Klassifizierung) erhöht die Übereinstimmung in der Permafrost-Kartierung. Die bürgerwissenschaftliche Datenauswertung zeigt großes Potenzial für die Permafrostforschung, muss jedoch weiter erprobt werden.

Engineering Proceedings

Stochastic simulation optimization has been proposed by several researchers to optimize construction operations. Traditionally, explicit averaging is used to estimate the objective functions of candidate solutions. This is carried out by calculating the average estimates of the objective functions obtained from a number of simulation replications. However, the computation effort increases as the number of replications and the size of the search space increase. The main objective of this paper is to study the benefits of using implicit averaging and common random numbers to improve the quality of the optimum solutions while reducing the computation time. The initial results of this study showed a 91% reduction in the computation time and 2.6% improvement in the quality of the optimum solutions.

Engineering Proceedings

This study established a rapid estimation method for the fatigue limit of laser welds of aluminum alloys in order to optimize welding conditions, an important factor in ensuring the safety and reliability of laser welding. This study focused on an estimation method of fatigue limit based on the dissipated energy. Dissipated energy is the temperature change caused by local plastic deformation. The stress amplitude at which the temperature change due to dissipated energy rapidly increased in the laser welds was defined as the estimated fatigue limit, and it was almost consistent with the fatigue limit of the joint.

Engineering Proceedings

Commercial solar cells can be embedded between textile layers by laminating to harvest energy in e-textile applications. However, techniques for connecting conductive textile wires to flexible and solid solar cells have not been studied in depth. Based on the literature and experimental validation, this study proposes a stitching connection technique for solar textiles. The feasibility of stitching was experimentally validated. First, the textile wires were joined to flexible solar cells by stitching with a sewing machine, and then embedded between fabric layers with TPU-lamination to simulate a real set-up in e-textile application. The machine washing durability of textile solar cell components with stitch-connected conductive textile wires was successfully verified.

Engineering Proceedings

The design and optimization of supersonic nozzles are of great interest in aerospace and propulsion system applications. Designing and maintaining a mechanically simple nozzle would be beneficial over the complex nozzles that are currently in use. A detailed simulation-based study was conducted to design a virtual nozzle that produces the same effect as a traditional nozzle while using simpler geometries and secondary air injection. Secondary air injection is widely used for fluidic thrust vectoring. Because such a nozzle is operated by varying the thickness of the boundary layer, it is possible to control the effective throat area, and hence, achieve a variety of exit pressures and Mach numbers by just varying the input momentum ratios (or pressure ratios). A similar concept was kept in mind, and various geometries, such as flat-plate geometry, divergent geometry and convergent–divergent geometry, were tested for different jet pressure ratios, numbers of jets, locations of the jets and geometric parameters of the virtual nozzle. The main objective of the work was to achieve an exit Mach number of 2 from a subsonic flow. The lengths of various geometries and the input pressure ratios were altered iteratively based on the findings obtained in each test case. All of the results attempted to acquire the required exit Mach number while accounting for numerous complexities in fluid flows, such as shock waves, vorticities, etc. Although the desired Mach number is not achieved, this study establishes a strong foundation and an idea that has the potential to revolutionize propulsion systems and create nozzles that are mechanically simple, weigh less and do …

Engineering Proceedings

Antimicrobial resistance (AMR) is a significant global concern that endangers human health. To overcome this resistance, β-lactames are used in combination with β -lactamase inhibitors to bypass the enzymatic action. The current study incorporates the techniques of machine learning to cluster the patterns of the proteins which may be antibiotic resistant. K-Means Clustering is applied along with PCA analysis, to verify and validate the model’s accuracy where Mean Clustering Analysis was used to validate the number of clusters formed. The result showed 3 clusters in Class A and 4 clusters in Class B representing various characteristics of these mutants.

Engineering Proceedings

This paper introduces a methodology for precise object orientation determination using principal component analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with axis-aligned bounding boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing oriented bounding boxes. the methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.

Engineering Proceedings

Using additive manufacturing (AM) techniques like SLM and EBM provides a valuable opportunity for manufacturing biomedical devices with precise porous structures that can mitigate adverse implant complications. Gyroid sheet network structures exhibit an excellent performance among porous structures due to their bioinspired morphology and mechanical properties. This study investigates the mechanical behavior of gyroid sheet networks with different morphological parameters suitable for biomedical implants. The results show that gyroid sheet networks with 1 to 2.5 mm unit cell sizes and porosities between 50% and 85% are ideal for biomedical implants. Additionally, porous implants made of gyroid sheet networks and mentioned morphologies can be produced using SLM with a layer thickness of 30 µm, spot size of 90 µm, and powder size of around 50 µm.

Engineering Proceedings

This study aimed to investigate the influence of Al2O3 nanoparticle concentration and flow rate on the convective heat transfer coefficient in a heat sink. A testing apparatus was constructed to examine a microchannel heat sink coupled with an Al2O3 nanoparticle fluid. Temperature sensors were strategically placed at the microchannel heat sink’s entrance (T-in) and exit (T-out). Furthermore, a heating element (T-heater) was utilized to monitor the temperature of the nanoparticle fluid. This experimental setup allowed for precise temperature measurements in the system. Aluminum oxide (Al2O3) nanoparticles were thoroughly dispersed in water for 15 min using a magnetic stirrer, resulting in a uniform mixture with concentrations ranging from 0.2% to 1%. The experiments involved altering the flow rates within the range of 0.2 to 1.4 L per minute, enabling the monitoring of temperature changes (T). The heat transfer coefficient positively correlated with escalating concentrations of Al2O3 particles. Incorporating nanoparticles up to a concentration of 1% significantly enhanced the heat transfer coefficient by 17.29%. Additionally, a direct relationship was observed between the heat transfer coefficient and the increase in the flow rate of the Al2O3/water nanofluid. Specifically, when the flow rate was increased from 0.2 to 1.4 lpm, a significant enhancement in the heat transfer coefficient of 29.95% was achieved.

Engineering Proceedings

Background:Open Access CorrectionCorrection: Krishnan et al. Taguchi Analysis of Hardness and Strength of Hot Compressed Duplex Steel Using Gleeble Thermomechanical System. Eng. Proc. 2024, 61, 5

Engineering Proceedings

Reducing energy consumption and increasing energy efficiency have been especially topical issues recently, affecting all areas of energy consumption, including industrial sectors. Continuous annealing lines, as important industrial production facilities, operate with high energy consumption, which can be analyzed and optimized using predictive mathematical models. For the purpose of this paper, a mathematical model was developed to compare five variants of different excess combustion air operating with the same heat input and fuel consumption. The reference variant had an excess combustion air with a value of 1.279 and the steel strip temperature at the outlet of the heating chamber was 609.5 °C. In terms of energy savings, variant 1 can be considered as the optimal variant, which had an excess combustion air value of 1.15 and a steel strip temperature at the outlet of the heating chamber of 637.3 °C.

Engineering Proceedings

A detailed assessment of the effect of implementing energy saving measures (ESMs) in administrative buildings, is presented in the following work. The main objective is to study the impact of these measures on energy consumption and to refine the difference between actual and predicted energy costs. To achieve this goal, extensive research is conducted, covering 44 administrative buildings in different areas in Bulgaria. After implementing a total of 144 ESMs in these buildings, the effect of the measures was followed over a four-year period. The results of the research show a significant reduction in energy costs after the implementation of energy saving measures in administrative buildings. This has a double benefit—it optimizes the financial resources of the organizations that use the buildings, and it contributes to the reduction of greenhouse gas emissions.

Engineering Proceedings

This research explores the influence of air pressure on the surface roughness of ST-37 steel plates in the sandblasting process. Sandblasting is a common method in industry to enhance material surfaces. This study focuses on the effects of varying air pressure on surface roughness, crucial for achieving the desired quality. ST-37 steel plates, known for their strength and versatility, are used in various applications. The research involves a sandblasting process with different air pressures, analyzing surface roughness and contact area. The results indicate a direct correlation between increased air pressure, surface roughness, and contact area.

Engineering Proceedings

In this manuscript, we are reporting on the influence of MWNTs (multiwalled carbon nanotubes) on the structural, bonding, and surface morphological response on sulfur nanoparticles. Sulfur and multiwalled carbon nanotube (MWCNT) composites are formed using the solution casting method. The concentration of MWCNTs (0.01 and 0.05) and sulfur (0.99 and 0.95), respectively, was taken in weight ratios during fabrication of the composites. These fabricated composites have been characterized using XRD (X-ray diffraction), FESEM (field emission scanning electron microscopy), and FTIR (Fourier-transform infrared spectroscopy) techniques. XRD spectra reveal that the crystallite size distribution was in the range of ca. 55 nm to 78 nm, as well as enhanced crystallinity upon increasing the concentration of MWCNTs in sulfur composites. Dislocation density and strain have been found to be increased in composites showing increased augmentation of MWCNTs (i.e., S95% MWCNT5%), while FESEM images confirm the uniform distribution of MWCNTs in sulfur composites, along with round structures at the nanoscale range. FTIR spectra depicted the bending and stretching of C-H bands. Composites with a higher concentration of MWCNTs show slightly more stretching vibrations. This indicates the further delocalization of electrons, which reveals that as MWCNTs’ concentration is increased, electrical conductivity enhances, showing that MWCNTs could perform better in electrical industries. The further delocalization of electrons also expresses that free electron–hole pair formation is better in composites with a higher concentration of MWCNTs …

Engineering Proceedings

Electricity and water are vital resources, but the current management systems face challenges due to growing demand and regulatory complexities. To address this, we introduce a novel blockchain-based solution for managing electricity and water services. Our proposed system connects various entities through smart contracts on the blockchain, automates processes, and ensures transparency. Customers can easily view and pay bills, track consumption, and enjoy secure online transactions while preserving their privacy. The shared digital ledger enhances trust among entities and promotes transparency. Additionally, our system contributes to environmental sustainability by reducing paper usage, incentivizing energy-saving devices, and efficiently managing electricity and water consumption. Finally, a meta-analysis of the related work is conducted to highlight the importance of our solution.

Engineering Proceedings

Graphing the consumption of daily essentials like electricity and water is crucial for minimising waste and estimating per-user usage in light of the modern-day data acquisition rally for a better understanding of customer consumption and patterns. Traditional methods of electrical measurement require the involvement of a trained professional, while more advanced alternatives can be prohibitively expensive or offer limited customisation options. We address the cost factor, flexibility, and complexity issues by using a non-intrusive clamp current transformer around power lines to measure current, estimate power, and upload it to the cloud with proper statistical data. For domestic and industrial applications, the filtered and referenced outputs are read by a low-cost CPU (ultra-low power) equipped with Wi-Fi, an analog-to-digital converter, and Bluetooth capabilities, which then determines the apparent power with an accuracy of 0.37 to 0.8%. Nonlinearity varies from 0.2% to 0.3% as a function of increasing current; nonetheless, offsets are imperceptible under typical operating conditions. Safety in the event of a sudden, large change in the current profile is one of several factors that determine the current measuring limit, together with the rating of the current transformer utilised and other related filtering, reference, calibration, and coding criteria. Our goal is to make the power consumption statistics accessible on the move at little cost by simplifying the circuit and coding of traditional metres. It is smart in that no hard coding is required to send credentials across routers, and fault signals are detected and relayed in accordance with an algorithm. User …

Engineering Proceedings

Over the past years, online payments or cloud-based payments have significantly increased around the globe. Cloud-based payment systems (CBPS) are more involved in the payment process due to their convenience and flexibility. Although CBPS offers obvious benefits, their adoption rates among Australian users are comparatively lower compared to those in other countries. People are dissatisfied with current payment methods or are unaware of the advantages of CBPS. Using the technology acceptance model (TAM) with perceived ease of use and perceived usefulness, a qualitative research method was applied through semi-structured interviews to collect data from 20 consumers in Australia. The findings pointed out an appreciation for security features such as two-factor authentication and cutting-edge technologies. The banks were trusted by Australians, but a lack of education and additional fees on digital payment platforms were sources of concern. In the context of CBPS, it was observed that electronic devices were easy to use and proved to be useful. Service providers need to improve security measures and implement innovative technologies to enhance user privacy and prevent fraudulent activities. Overall, Australians expressed satisfaction with their banks; however, there are opportunities for enhancement, particularly in bolstering security measures and providing education on emerging payments options.

Engineering Proceedings

Steam methane reforming (SMR) approaches are highly recognised and pivotal in industrial H2 production, contributing over 40% to global hydrogen production. The prime objective of this study is to optimise the significant parameters involved in the SMR process to achieve the utmost conversion of CH4 to H2. To attain this, a sophisticated one-dimensional unsteady-state heterogeneous plug flow reactor (PFR) model was methodically constructed and simulated using the Aspen HYSYS V11 software. The study comprises an exhaustive comparison of seven diverse sets of catalysts, primarily categorised based on the different weight percentages of Ni in Ni/Al2O3 catalysts, along with various promoters incorporated to enhance the conversion rate in the SMR process. This comprehensive evaluation identifies the most operative catalyst configuration for optimising CH4 conversion. The results obtained through the simulations revealed that CH4 conversion intensifies with an increase in temperature, while it weakens with higher pressures within the catalyst set considered for the study. The analysis yielded promising conclusions by comparing the simulated CH4 conversion percentages at various temperatures with data from the existing literature. The maximum absolute error encountered was only 3.72%, signifying the accuracy and reliability of the developed model. Moreover, the Mean Absolute Error (MAE) calculated was a low 1.42%, suggesting the robustness of the proposed approach. The findings lay the foundation for future innovations and improvements in the field, ultimately fostering more efficient and sustainable hydrogen generation …

Engineering Proceedings

This research delineates the synthesis and subsequent application of a hydrogel nanocomposite enriched with titanium dioxide (TiO2) nanoparticles as an adsorbent for pollutants and an antibacterial agent. The nanocomposite was prepared using a hydrothermal method, facilitating the efficient incorporation of TiO2 nanoparticles. Physicochemical characterizations revealed the nanocomposite’s augmented adsorption capabilities, specifically for pollutants such as Congo red dye (CR), Amoxilline drug (AMX), and Chlorophenol (CPH). Notably, the study demonstrated that the nanocomposite could be completely regenerated and desorbed in water, attesting to its potential for recyclability. The antibacterial potential of the nanocomposite was also investigated, demonstrating significant efficacy against Gram-negative bacteria (E. coli and Klebsiella spp.) compared to Gram-positive strains. The findings of this study emphasize the potential applicability of the hydrogel nanocomposite as an efficient, reusable agent for pollutant removal and antibacterial activity, providing pertinent insights for environmental remediation and biomedical applications.

Engineering Proceedings

This paper investigates the utilisation of smart interactive products by millennial consumers in the fashion industry and how their perceptions and experiences influence the adoption of such products. To achieve this, it employs a generational perspective. It utilises Midgley and Dowling’s theory of predisposition to innovate as its theoretical framework, providing a comprehensive exploration of consumers’ experiences with these products. To bridge the gap in understanding consumers’ limited adoption of smart textile (ST) products, this research employs Interpretive Phenomenological Analysis (IPA). This methodological choice is driven by uncovering how real-life experiences impact consumer behaviour in this context. Expanding on previous work, the research comprised two separate qualitative studies utilising Interpretive Phenomenological Analysis (IPA). Participants interact with specific interactive smart textiles, namely, the Levi’s Jacket by Google. Participant recruitment utilised the snowballing method, which was adapted due to the constraints imposed by the COVID-19 pandemic.

Engineering Proceedings

Multi-walled carbon nanotubes (MWCNTs) combined with clay have shown potential as effective adsorbents for dye removal. This study aims to characterize MWCNT/clay nanocomposites and analyze their removal efficiency for methylene blue (MB) dye under various conditions. The nanocomposites were characterized using techniques such as FESEM, TEM, EDX, TGA, and XRD. The removal efficiency was studied concerning different weights, concentrations, temperatures, pH levels, and comparative amounts of CNT in the composites. The findings revealed distinct properties and behaviors of the nanocomposites, with removal efficiency significantly influenced by weight, MB dye concentration, temperature, and pH. A higher CNT content in the composite corresponded to better removal results. The study demonstrates the potential of MWCNT/clay nanocomposites in wastewater treatment, with insights into optimal conditions for dye removal. The investigation adds valuable knowledge to the field and indicates promising directions for future research.