Model Input Data Research Articles

The Open Data Potential for the Geospatial Characterisation of Building Stock on an Urban Scale: Methodology and Implementation in a Case Study

Energy renovation in buildings is one of the major challenges for the decarbonisation of the building stock. To effectively prioritise decision making regarding the adoption of the most efficient solutions and strategies, it is imperative to develop agile methods to determine the energy performance of buildings on an urban scale, in order to evaluate the impact of these improvements. In this regard, the data collection for feeding building energy models plays a key role in the accuracy and reliability of this issue, and the significant increase in recent years of available data from open data sources offers great potential in this respect. Thus, this study focuses on proposing a systematised and automated method for obtaining information from open data sources so as to obtain the most relevant geometric and thermal characteristics of residential buildings on an urban scale. The criteria for selecting the parameters to be obtained are based on their potential use as input data in different energy demand models aimed at assessing the energy performance of the building stock in a given area and, eventually, to evaluate the potential for improvement and the mitigation of different strategies. Geometric characterisation relies on obtaining and processing open data from cadastres to extract envelope surfaces categorised by orientation through QGIS (Free and Open Source Geographic Information System). For thermal characterisation, an automated process assigns different parameter-based information obtained from cadastral data, such as the year of construction. Finally, the applicability of the method is demonstrated through its implementation in the case study of Bilbao (Spain). The obtained results show that, although additional data should be collected when a detailed analysis of a building or building cluster has to be carried out, the existing open data can provide a first approximation, providing a first global view of the building stock in a region. It demonstrates the usability of the proposed method as an effective way to obtain and process these relevant data.

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and automated methods using Short-Term Average to Long-Term Average algorithms. However, these approaches encounter significant performance challenges, especially in the presence of real-time background noise. To overcome this limitation, this study proposes a novel P-wave FAP detection method that employs the U-Net model and incorporates spectrogram transformation techniques for seismic signals. Seismic signals, similar to those encountered in South Korea, were generated using the stochastic model simulation program. Synthesized WGN (White Gaussian Noise) was added to replicate background noise. The resulting signals were transformed into 2D spectrogram images and used as input data for the U-Net model, ensuring precise P-wave FAP detection. In the experimental result, it demonstrated strong performance metrics, achieving an MSE of 0.0031 and an MAE of 0.0177, and an RMSE of 0.0195. Additionally, it exhibited precise FAP detection capabilities in image prediction. The developed U-Net-based model exhibited exceptional performance in accurately detecting P-wave FAP in seismic signals with varying amplitudes. Through the developed model, we aim to contribute to the advancement of microseismic monitoring technology used in various industrial fields.

The Cost-Effectiveness of Smoking Cessation Programs for Prevention of Wound Complications Following Total Ankle Arthroplasty: A Break-Even Analysis.

Tobacco use significantly increases the rate of wound complications in patients undergoing total ankle arthroplasty (TAA). Preoperative optimization through smoking cessation programs significantly minimizes the rate of infection and improves wound healing in arthroplasty procedures. Despite its utility, minimal research has examined the cost-effectiveness of preoperative smoking cessation programs to reduce the need for extracapsular irrigation and debridement (I&D) due to wound complications following TAA. The cost of an I&D procedure was obtained from our institution's purchasing records. Baseline wound complication rates among tobacco users who have undergone TAA and smoking cessation program cost were obtained from literature. A break-even economic analysis was performed to determine the absolute risk reduction (ARR) to economically justify the implementation of preoperative smoking cessation programs. Different smoking cessation program and I&D costs were tested to account for variations in each factor. ARR was then used to calculate the number needed to treat (NNT) to prevent a single I&D while remaining cost-effective. Smoking cessation programs were determined to be economically justified if it prevents 1 I&D surgery out of 8 TAAs among tobacco users (ARR = 12.66%) in the early postoperative period (<30 days). ARR was the same at the literature high (27.3%) and weighted literature average (13.3%) complication rates when using the cost of I&D surgery at our institution ($1757.13) and the literature value for a smoking cessation program ($222.45). Cost-effectiveness was maintained with higher I&D surgery costs and lower costs of smoking cessation treatment. Our model's input data suggest that the routine use of smoking cessation programs among tobacco users undergoing TAA is cost-effective for risk reduction of I&D surgery in the early postoperative period. This intervention was also found to be economically warranted with higher I&D costs and lower smoking cessation program costs than those found in the literature and at our institution.Level of Evidence: Level III, economic and decision analysis.

Methodology for calculating the intensity of passenger demands for transport services by passenger taxi cars

The article provides formulas that make it possible to calculate the intensity of the incoming and outgoing flow of passenger demands for transport services by passenger taxi cars in such large transport hubs of the Moscow agglomeration as the airports of the Moscow Aviation Hub, which are typical examples of a queuing system. The main differences between the incoming and outgoing flow are noted. As a result of the fact that the incoming flow is subject to elements of randomness, the article discusses a method for determining the distribution law of a random variable, which is the probability of passengers arriving pi(t) at the airport at time t before the scheduled departure time of the aircraft. Using the standard Pearson’s criterion χ2, the hypothesis about the expected distribution law is tested. The intensities of incoming and outgoing flow calculated according to the above formulas can be used as input data for simulation modeling of the queuing system for passengers at transport hubs by various modes of transport in order to optimize the transportation process and improve the quality of passenger service. Keywords transport hub, urban agglomeration, taxi transportation, queuing system, incoming flow, random variable distribution law, Pearson’s criterion χ2

Contribution of hydraulic modeling and hydrology to flood prevention and design of engineering structures: Case study of a bridge over the Oued Ourika – Morocco

Located downstream of the Ourika watershed near the commune of Chouiter, the bridge studied is built over the Oued Ourika at the level of the N9 (national road linking Marrakech to Ouarzazat). The flood event of November 2014, reaching a peak flow of 347 m3/s, destabilized the bridge’s overall structure. Hence, the need to rebuild a new bridge, taking into account the magnitude of these extreme events. Hydraulic modeling involves creating mathematical and computer-based representations of hydraulic systems. Its purpose is to simulate water flow behavior and map flood-prone areas. Before initiating any hydraulic simulation, it is imperative to conduct a hydrological study to provide the input data for the hydraulic model. Precipitation records, evaporation rates, soil permeability, historical peak flow data, and site topography constitute, on the one hand, the product of the hydrological study and, on the other, the inputs for the hydraulic model. The hydraulic model is based on Saint-Vant equations to solve the conservation of mass and momentum equations. The aim of this work is to determine the critical water level at the bridge for a 100-year flood in order to optimize the height of the new bridge.

A Hybrid AHP-TOPIS-Based Marine Economic Activities Evaluation Model for Marine Geospatial Data Infrastructure

Marine economic activities are related to the economics of marine resources and maritime sectors, particularly for Marine Geospatial Data Infrastructure (MGDI) and the blue economy. Malaysia has different resource-based economic activities within its maritime delineation zones (MDZs), characterized to be a multi-criteria decision-making (MCDM) problem. However, previous studies have not adequately examined these activities across the MDZs. This paper assesses Malaysia's marine economic activities in these zones through a novel and hybrid framework incorporating the Analytic Hierarchy Process (AHP) and the Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) models. AHP was used to evaluate the economic criterion of MGDI, while, the priorities obtained serve as part of the input data for the TOPSIS model that examined the trends in economic activities across the MDZs and rank them in order of preference. The results suggest that naval administration, sovereignty, and defense are highly prioritized among the five marine economic activities considered, with a value of 42%. Meanwhile, the Contiguous Zone was the highest-ranked MDZ for economic activities in Malaysian waters, with a computed values of 27.7%. This approach provides stakeholders with an evaluation model to assess marine economic activities in different MDZs, providing a robust framework for effective evaluations of MCDM, and marine operations.

Exploiting geographic open data to improve urban building energy simulations: The Padova city center case study

In recent years, building stock models have been developed by researchers to examine the aggregate performance of stacks of buildings within large areas, thereby giving rise to the concept of urban building energy models (UBEMs). The input data for such models consists of geometric and non-geometric attributes of the buildings, in addition to meteorological information. In this perspective, the acquisition of precise and comprehensive data poses a challenge, as the existing datasets often lack certain parameters or are not in a standardized format. This study aims to address the challenges by proposing a workflow for generating an input data frame tailored for incorporation into UBEMs. The data frame should include all the essential parameters of the buildings, and its constitution should be reflective of the real-world data. Moreover, the proposed workflow should remain consistent with databases available at national or regional levels. Acknowledging this non-uniformity in databases across regions, the methodology proposed in this study strategically considers various alternatives. For this reason, the proposed automatized workflow ensures flexibility and adaptability to changes in data availability. The workflow proposed in this study is a QGIS based geographical calculation method. The method can combine data from various sources into one shapefile that can be used to simulate the energy performance of buildings in urban areas.

РАЗРАБОТКА МАТЕМАТИЧЕСКОЙ МОДЕЛИ КОЛЕБАТЕЛЬНЫХ ПРОЦЕССОВ ПРИ ТОКАРНОЙ ОБРАБОТКЕ МАТЕРИАЛОВ С ОБРАТНЫМ РАСПРЕДЕЛЕНИЕМ ТВЕРДОСТИ

For technological guarantee of the required quality indicators of the surface layer of metallic rotation body parts at finishing machining needs the correct assignment of technological modes. During finishing turning, vibrational processes have a significant (and often decisive) effect on the formation of surface roughness. It is necessary to choose such values of technological modes, which provide an acceptable level of vibration (previously it is advisable to solve this problem by modelling). Mathematical model was carried out based on the determination of the turning tool deformations under the influence of cutting forces. To determine the amplitudes of the cutter oscillations, the Mohr integral was used, and the amplitude-frequency characteristics were determined using the Duhamel integral. Cutting forces were determined taking into account the shape and nature of chip formation, the value of technological modes and processing conditions. As the material to be machined, the authors considered the cast iron with a layer hardened by diffusion alloying. Determination of input data for model was carried out directly during machining on the lathe and with the help of measurements of chip parameters. As cutting tools, we used standard cutters with mechanically fastened plates. The evaluation of the modelling results showed high convergence with the experimental results: the error in amplitude was 8.5 percent, in oscillation frequency - 12.3 percent; in general, the error did not exceed 15 percent. Thus, the developed model can be used for preliminary vibration assessment at the design stage of turning operations.

Modelling structural relations between financial, social-economic and healthcare factors

Relevance of the article topic consists in the fact that it is critical to analyse relations between financial, social-economic and healthcare determinants for the post-pandemic recovery of macroeconomic stability. Consideration of these relations can provide complex recovery strategies, which will contribute to economic, medical amd social resiliemce. The latter is extremely important when there are new global challenges to overcome. The manuscript aims at identifying structural and functional connections between social-economic determinants that influence the healthcare sector. To achieve this purpose, we used the following research methods: induction and deduction, analysis and synthesis, comparison and logical generalisation, table and diagram visualisation, observation and assessment, method of principal components, structural modelling. The research data are taken from the World Bank. Totally, the paper analyses statistical data from 15 European countries in 2000-2021. The modelling input data comprise 18 indexes (financial, social-economic, healthcare ones). The constructed structural model provided the following conclusion: if healthcare indexes increase by 1, social-economic development decreases by 0.014. One of the reasons for that is the negative mortality value. The most important aspect is an efficient reaction to epidemics and pandemics. Development of containment plans, including the healthcare scaling and inter-authority coordination is critical for effective crisis management. We should develop a stable economy and policies to provide an affordable and equal access to healthcare for all population groups. Only a complex approach in each separate country may drop mortality and minimise impacts on different aspects of society life. The obtained results can be applied by authorities of public health to improve efficient recovery strategies, raise healthcare standards, make proper decisions for social-economic development.

Predicting Flood Water Level Using Combined Hybrid Model of Rainfall-Runoff and AI-Based Models

Vietnam faces on significant human and property losses from floods almost every year. Therefore, the aim of this study is to provide timely and highly accurate flood prediction information using a developed hybrid model by combination of the rainfall-runoff model and AI-based model. We used Tank model as a rainfall-runoff model for peak flood discharge and parameter calibration was conducted by GA (genetic algorithm) and PS (pattern search). The sum of squared residuals (SSR) and weighted sum of squared residuals (WSSR) as objective functions were used for peak flood discharge evaluation. The simulated flood discharge was converted as flood water level by rating curve and Monte Carlo simulation was applied to estimate the confidence bounds for 95% confidence level. These estimates were then utilized as input data for the AI-based models to identify the strengths and weaknesses of each model and develop an optimal flood water level prediction model. Two AI-based models, deep neural network (DNN) and long short-term memory (LSTM), were used for flood water level prediction. The LSTM model demonstrated the best performance with a correlation coefficient (CC) of 0.98, normalized root mean square error (NRMSE) of 0.04, and Nash-Sutcliffe efficiency (NSE) of 0.98.

Innovative approach for predicting biogas production from large-scale anaerobic digester using long-short term memory (LSTM) coupled with genetic algorithm (GA)

An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD5, COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models.

RESEARCH OF TRANSPORT SUPPLY OF METALLURGICAL INDUSTRY BY RAILWAY TECHNOLOGICAL ROUTES

Purpose. In the conditions of a full-scale war, ensuring the stable functioning of the national economy is one of the strategic tasks. The functioning of the industrial complex, the availability of jobs and the payment of taxes to the state budget are, among other things, part of ensuring the country's defense capability. One of the main branches of Ukraine's economy is the metallurgical industry, so it needs the implementation of measures to ensure reliable and stable work even in conditions of military aggression, constant risks of air strikes and infrastructure damage. In accordance with this, the purpose of the study is to analyze and determine the structure of cargo flows of technological routes of transport service of metallurgical production (on the example of PJSC «Zaporizhstal»). The dynamics of iron ore (iron ore concentrate) and coking coal (coke) transportation volumes by means of transport were studied. Irregularity was investigated and descriptive statistics of daily volumes of transportation of iron ore (iron ore concentrate) and coking coal (coke) for metallurgical production were performed (on the example of PJSC «Zaporizhstal»). Methods. An analysis of the unevenness of the daily volume of goods received for the needs of a metallurgical enterprise (on the example of PJSC «Zaporizhstal») by technological routes of railway transport was carried out. Methods of mathematical statistics, probability theory, theory of transport processes and systems were used. As a result of the analysis of cargo flows arriving at the enterprise, it was established that the distribution of the daily volume of iron ore arriving at the Zaporizhzhia Live station is subject to symmetrical distribution laws with a relatively low variation of 18.4%. The distribution of the random value of the daily volume of coke intake also indicates a certain symmetry with a fairly high probability (59%), and is approximated by a normal distribution. Scientific novelty. As a result of the conducted research, it was established that the approximate density of the distribution of volumes of iron ore (salt-ore concentrate) and coking coal (coke) for metallurgical production (on the example of PJSC «Zaporizhstal» for 2022). Practical significance. The mathematical expectation and the level of correlation of the volumes of iron ore (iron ore concentrate) and coking coal (coke) supply to metallurgical production are established (on the example of PJSC «Zaporizhstal» for 2022). In the future, the obtained results are planned to be used as input data for a simulation model of the functioning of the transport support system of an industrial enterprise.

Another one bites the dust – Two street canyons studied with magnetic biomonitoring and OSPM modelling

Urban areas form a mosaic of microenvironments and structures, such as street canyons that are susceptible to elevated levels of traffic related pollutants. Street canyons are a relevant topic in air quality research and modelling since they are prone to reduced natural ventilation and when exposed to increased traffic emissions, can pose a serious risk to public health. We applied magnetic biomonitoring using moss bags to evaluate the quality of Operational Street Pollution Model (OSPM) and to study vertical distribution of modelled particulate matter (PM), magnetic PM and heavy metal pollution in a street canyon in Turku and Helsinki, Finland. Moss bags were attached on opposite sides of the streets at 3-, 6-, 9- and 12-m heights for 44 days in late autumn. Samples were analysed for mass-specific magnetic susceptibility (χ), hysteresis parameters and elemental components. High χ values and elemental concentrations of Fe, Al, Ti, Zn, Mn, Cu and Ba were found in both street canyons. Compared to measurements with airborne magnetic PM, the OSPM model underestimated the difference in PM concentrations between the opposite street canyon faces and overestimated the dilution of PM concentrations with altitude. In Turku the results between the OSPM model and magnetic measurements were incompatible. We suspect this is due to meteorological model input data from a distant weather station, street canyon features and a high slope angle contributing to non-uniform emissions in the street canyon. We demonstrate that the moss bag technique is a versatile tool to understand the small-scale variations of concentrations in a variety of complex urban environments.

Thermo-optic VO2-based silicon waveguide mid-infrared router with asymmetric activation thresholds and large bi-stability: errata.

The authors report an error in the phrasing and citation of the reference to simulation model input data in [Opt. Express31(14), 23260202310.1364/OE.493895]. The original phrasing misplaced "heat capacity" after the in-text citation, where the intended phrase was "electrical conductivity," and heat capacity was intended to be cited with thermal conductivity as external measured data. In the reference itself, the source cited for thermal conductivity and heat capacity was errantly cited as H. Kizuka, et al., Jpn. J. Appl. Phys.54, 053201 (2015)10.7567/JJAP.54.053201. The JJAP paper shows data for both thermal properties of VO2; however, the data utilized for our model input parameters are found in [J. Miranda, et al., Phys. Rev. B 98, 075144 (2018)], including heat capacity data reproduced therein from [T. Kawakubo and T. Nakagawa, J. Phys. Soc. Jap. 19, 4 (1964)]. There are no effects on the simulated data nor conclusions of this article due to the error.

Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models

Abstract. The current generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits a surprisingly cold-biased ensemble-mean mid-20th century global-mean surface temperature anomaly, compared to the previous generation Phase 5 (CMIP5) and to the observed mid-century (1940–1970) temperature anomaly. Most CMIP6 models, 31 of 36 models in contrast to 17 of 27 CMIP5 models, are colder than the uncertainty range of the observed anomaly, indicating that the CMIP6 suppressed warming is not caused by a few cold models. However, no clear cause that sufficiently explains the tendency towards suppressed mid-20th century warming emerges. Whereas models that best match observations exclusively exhibit weaker aerosol forcing than that exhibited by colder models, there is not a clear relationship between mid-century temperatures and aerosol forcing. Likewise, no systematic differences emerge among other model aerosol representations, such as inclusion of aerosol–cloud interactions for ice clouds in the model or the type of aerosol model input data set used, nor variations in greenhouse gas forcing or climate sensitivity, that could explain the suppressed warming. This indicates the presence of another cause, or more likely a set of causes, of the suppressed warming in many CMIP6 models. Thus, the prospect of a strong constraint on present-day aerosol forcing based on the mid-century warming is weakened, even if it is encouraging that those models that do match the observed warming best all have relatively weak aerosol forcing.

Optimizing deep learning-based piled-up pulse height correction method for high radiation-field application

In high-radiation environments, measured pile-up pulses can lead to unavoidable issues such as total count loss and spectrum distortion. Additionally, the recording of large volumes of data within a short period makes real-time processing difficult. In this study, a deep learning-based pulse height estimation (PHE) method was optimized to perform pile-up signal correction in high-radiation fields. First, we adopted a previous deep-learning-based PHE method that allows for fast correction without being restricted to specific detectors. However, the peak-finding method was slightly modified to improve the count restoration rate. Moreover, the input data length of the deep learning model was optimized for convolutional neural networks (CNN) and deep neural networks (DNN) to achieve the maximum correction performance using minimal input data. A series of single pulses was experimentally obtained from a LaBr3 detector with a short decay time to prepare a dataset for training the deep learning models. The pile-up signals were generated by randomly synthesizing single pulses. Samples around their peaks were sliced using the peak-finding method and used as input data for the deep learning models. As a result of the optimization, the modified peak-finding method improved the count restoration rate compared to the previous method by effectively detecting the peaks of tail pile-up, and peak pile-up pulses. Furthermore, the input data length and region were optimized based on the performance evaluation of each deep learning model. Despite having a simpler architecture than the CNN model, the DNN model demonstrated excellent PHE performance. The results of this study showed the efficient and practical considerations necessary for applying pile-up signal correction in high-radiation fields.

Demonstrating sorption analogy of lanthanides in environmental matrices for effective decision-making: The case of carbon-rich materials, clay minerals, and soils

Examining the effect of lanthanide-contaminated wastes, which have the potential to impact to other environmental compartments, requires conducting interaction studies with soils, as feasible first receptors of lanthanide leachates, and, if necessary, with sorbent materials, such as clay minerals and carbon-rich materials, which can serve as natural barriers and immobilisation agents used in remediation strategies. In this context, it is relevant to have available and reliable data on solid–liquid distribution coefficients (Kd) to understand the lanthanide sorption in these environmental matrices. Moreover, confirming lanthanide sorption analogies permits filling data gaps and data extrapolation among different contaminated scenarios, and thus facilitate to have available input data for decision-making related to the impact of a contaminated site. In this study, we demonstrate for the first time an analogous sorption of La, Sm, and Lu in carbon-rich materials (i.e., biochar and activated charcoal), clay minerals and soils, through laboratory batch experiments. The obtained sorption Kd values revealed similar sorption patterns among the three lanthanides for each matrix tested, even at different initial lanthanide concentrations. In all matrices, the maximum Kd values exceeded 104 L kg−1, with a significant decrease when testing high lanthanide concentrations. The analogy was first confirmed by examining the Kd correlations for the La-Sm, Lu-Sm, and La-Lu pairs within each matrix, for which strong linear correlations were obtained in all cases. Data compilations were built with own and literature data, and derived cumulative distribution functions revealed statistically equal lanthanide distributions and Kd best estimates. In addition to this, Kd variability decreased when grouping the data according to significant material properties. For the first time, Kd (Ln) best-estimates for different scenarios and materials were proposed as input data for risk assessment models.

Average monthly recharge, surface runoff, and actual evapotranspiration estimation using WetSpass-M model in Low Folded Zone, Iraq

Abstract The evaluation of the spatial and temporal distribution of groundwater recharge is required to develop the regional groundwater model for more precise simulations of various management scenarios. WetSpass-M (Water and Energy Transfer between Soil, Plants, and Atmosphere under Steady-State Conditions), which is a GIS-based spatially distributed water balance model, is deployed to evaluate monthly groundwater recharge, surface runoff, and actual evapotranspiration in the Low Folded Zone from 2000 to 2019. ArcGIS software prepares the essential relevant input data for the Wetspass-M model as grid maps. They include monthly climatological measurements (precipitation, temperature, and wind speed), land cover distribution, soil map, groundwater depth, topography, and slope. The mean annual groundwater recharge, evapotranspiration, and runoff were found to be 128, 131, and 72 mm, respectively. Accordingly, recharge accounts for 39% of the precipitation, while the rest, 40 and 21%, become evapotranspiration and surface runoff, respectively. WetSpass-M model results are meant to enable integrated groundwater modeling. The study of simulation data demonstrates that the WetSpass-M model accurately simulates the hydrological water budget components of the Low Folded Zone. In addition, a better understanding of the simulated spatial and temporal distribution of water balance components is beneficial for managing and planning the available water resources of the Low Folded Zone in Iraq, which faces water scarcity threats.

Superheater ash deposit ageing – Impact of melt fraction on morphology and chemistry

The initial melting behaviour of synthetic ash deposits, with a focus on how the amount of melt formed, affects the final deposit morphology, was studied. Binary and ternary reciprocal deposits were exposed to a temperature gradient with varying exposure time, steel temperature, and furnace temperature. Two different archetype deposit morphologies emerged. The final deposit morphology was identified to depend on the amount of melt formed during the experiment. A skeletal deposit morphology developed in systems with less than 30 wt-% melt. Short-term experiments proved that the formation of the skeletal structure is accelerated by the presence of melt, accumulating at contact points of distinct particles. If melt fractions exceeded 30 wt-%, a molten morphology developed, as the amount of melt present was sufficient to fill the pores within the deposit. For molten deposits of ternary reciprocal systems, enrichment of K in the formed melt and concurrent movement toward the steel was observed. The results confirmed a deposit ageing mechanism that has been proposed earlier, based on full-scale superheater deposit samples. The experiments showed that deposit melting and ageing behaviour are strongly connected, as the two identified morphologies differed in their ageing behaviour. The presented results improve the understanding of the initial melting behaviour of deposits and illustrate the connection between deposit morphology and deposit ageing. The results can be utilised as input data for deposition models and help in predicting the final deposit morphology and its associated ageing behaviour, aiding boiler operators and manufacturers dealing with deposit-related issues.

Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.

Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the "chaos game" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences. We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.