Surrogate Data Research Articles

Clinical and molecular predictors of very late recurrence in oestrogen receptor-positive breast cancer patients

BackgroundRisk of recurrence from primary ER+ breast cancer continues for at least 20 years. We aimed to identify clinical and molecular features associated with risk of recurrence after 10 years.MethodsER+ breast cancers from patients with and without recurrence were analysed with the BC360 NanoString Panel and an 87 gene targeted-exome panel. Frequency of clinical, pathologic and molecular characteristics was compared between cases (recurred between 10 and 20 years) and controls (no recurrence by 20 years) in the Very Late Recurrence (VLR) cohort. Analogous data from METABRIC were examined to confirm or refute findings.ResultsVLR cases had larger tumours and higher node positivity. Both VLR and METABRIC cases had higher clinical treatment score at 5 years (CTS5). There was a trend for fewer GATA3 mutations in cases in both VLR and METABRIC but no statistically significant differences in mutation frequency. Cell cycle and proliferation genes were strongly expressed in VLR cases. Immune-related genes and cell cycle inhibitors were highly expressed in controls. Neither of these changes were significant after correction for multiple testing.ConclusionsClinicopathologic features are prognostic beyond 10 years. Conversely, molecular features, such as copy number alterations, TP53 mutations and intrinsic subtype which have early prognostic significance, have little prognostic value after 10 years.

Data assimilation method and application of shear stress transport turbulence model for complex separation of internal shock boundary layer flow

Data assimilation method and application of shear stress transport turbulence model for complex separation of internal shock boundary layer flow

Development of Microcontroller Based Water Quality Monitoring and Water Level Control Device

Access to clean and safe water is not only a fundamental human right but also a vital requirement for electronics industries, chemicals industries, pharmaceuticals, food and beverage production, fisheries and aquatic farming. This project addresses the pressing issue of water quality monitoring and efficient water usage within the context of these industries. The study's identification of problem areas for water quality can lead to more efficient interventions and solutions to safeguard the environment, and prevent waterborne diseases. This paper presents the design and implementation of microcontroller-based system for monitoring water quality and water level in the thank, the water in the thank is refilled when it goes beyond a set threshold with the aid of an automatic switch control. The system is a hybrid of 3 subsystems: a Water Level Checker, a Water Quality Monitoring System and an Automatic Switch control system. The Water level checker consists of Ultrasonic sensors. The Water Quality Monitoring system consists of pH sensors, TDS (Total Dissolved Solids) sensors and Turbidity sensors. The continuous (Analog) data from the water quality sensors used would be outputted via an LCD (Liquid Crystal Display). The Automatic Switch control system consists of 5v Relay modules and a 12v Pump that would automatically switch ON/OFF depending on the level of water in the tank. The values of EC (Electrical Conductivity) and TDS spike drastically to the introduction of salt from as low as 138 -906 for TDS and 0.34 – 2.04 for EC, signifying increase in water capacity of electrical conductivity. Temperature rise in the afternoon also increases TDS values from 142 for clean water to 920 for the salty water while it ranges from 138 for clean water to 906 for salt water at both morning and evening time. Pumping aspects of the system are triggered when water level in the thank falls below 10% and trips off when water level goes above 95% as programmed in the control unit. The developed system marks a significant achievement in water management and automation technology. integration of quality monitoring sensors has enabled real-time evaluation of water parameters that holds potential for applications in environmental monitoring, ensuring safe drinking water, and safeguarding aquatic ecosystems. The automated switch control mechanism responds dynamically to water levels, enabling the system to trigger actions such as pump activation or filling processes based on predefined thresholds.

Abietane Diterpenoids from the Bark of Cryptomeria japonica and Their Antifungal Activities against Wood Decay Fungi.

Phytochemical investigation of the bark of Cryptomeria japonica led to the isolation of five new abietane diterpenoids, 5-epi-12-hydroxy-6-nor-5,6-secoabieta-8,11,13-trien-7,5-olide (1), 12-hydroxy-6β-methoxy-6,7-secoabieta-8,11,13-trien-7,6-olide (2), 6β,12-dihydroxy-7,8-secoabieta-8,11,13-trien-7,8-olide (4), 5,12-dihydroxy-7,8-secoabieta-8,11,13-trien-7,8-olide (5), and 5α,8-epoxy-12-hydroxy-7,8-secoabieta-8,11,13-trien-7-al (6), together with one known abietane diterpenoid, obtuanhydride (3). Their structures were elucidated by analysis of spectroscopic data and comparison with the spectral data of known analogs. At the concentration of 100 μg/mL, compounds 4, 5, and 6 inhibited antifungal activities against wood decay fungi activity by 18.7, 37.2, and 46.7%, respectively.

АВТОМАТИЗОВАНЕ ПРОЄКТУВАННЯ ЛОНЖЕРОНУ КРИЛА БЕЗПІЛОТНОГО ПОВІТРЯНОГО СУДНА

One of the most crucial objective in designing aircraft structures is to ensure their strength while meeting the requirement for minimal weight. In operation, the structure must withstand loads without failure, as well as be designed with the lowest possible weight and, in most cases, volume.The determined geometric model of the structure based on the minimum weight criteria, reduces the overall weight of the aircraft which leads to: reducing the power requirements of the engine, and thus the cost of the aircraft; decreasing fuel consumption, making the aircraft cheaper in operation.The weight of the wing is equal 30-50% of the aircraft empty weight. Depending on the structural and load-carrying scheme of the wing, the weight of the spars can range 7-30% of the wing`s weight. Wing spar geometric model determination has a significant impact on the weight of the aircraft and thus on its cost and operational expenses.The problems of the wing spar automated design of an unmanned aerial vehicle are considered. The concept of creating a wing spar of an unmanned aerial vehicle according to the minimum mass criterion is presented.This study determines relation between the minimum required volume (weight) of the wing spar and the number of stiffeners as well as relations between the cross-section geometric properties of the spar (thicknesses of the flanges and web, flanges width) along its span.This study proposes approach for determining geometrical model of the wing spar based on the minimum weight criteria, which can serve as a valuable reference for the design process of spars or similar beam elements. The determined geometrical model of the wing spar for unmanned aerial vehicle, derived from analogous aircrafts data, is applicable for preliminary design purposes in this class of aircraft.

Testing & Evaluation of Road Bounce PRO-Mobile Phone App Based Technology for Road Measurement

Road roughness plays a crucial role in assessing the overall condition of a road. It impacts various factors such as vehicle operating costs, speed, comfort, safety, fuel efficiency, and wear and tear. Every road user seeks a smooth and pleasant driving experience. The measurement of road roughness is a key task that influences maintenance decisions. According to the Indian Road Congress (IRC) standards, roughness measurements should be conducted every 6 months on highways to determine the necessary functional overlay requirements based on the roughness index. Roughness measurements using profile meters namely Rod & Level, Dipstick, Merlin etc. all are time consuming. The Road Roughness Measurement System (RTRRMS) is a system installed on vehicles to assess how the vehicle reacts to the pavement profile. In India, the Central Road Research Institute (CRRI) has developed a 5th Wheel Bump Indicator that is mounted on cars to measure roughness. The RTRRMS 5th Wheel Bump Indicator utilizes binary true/false information and range-based analog data generated by digital accelerometers commonly found in smartphones. This measurement system is facilitated through an application called "ROAD BOUNCE”. This paper aims to showcase the results obtained from the testing and assessment of an App-based roughness measurement technology in terms of its precision and user-friendliness. The evaluation was conducted across various vehicles and mobile phones, considering different factors such as speed, tire pressure, gradients, and more. Keywords:- Road Bounce, RTRRMS, Roughness Measurement, Cell Phone based Roughness.

A manifold learning perspective on surrogate modeling of nitrate concentration in the Kansas River

Abstract A non-linear surrogate model of nitrate concentration in the Kansas River (USA) is described. The model is an (almost) Piece-wise Linear response surface that provides a mean field approximation to the dynamics of the measured data for nitrate plus nitrite (target product) correlations to turbidity and chlorophyll-a concentrations (input variables). The method extends the United States Geological Survey’s linear procedures for surrogate data modeling allowing for better approximations for river systems exhibiting algal blooms due to nutrient-rich source waters. The model and visualization procedures illustrated in the Kansas River example should be generally applicable to many medium-size rivers in agricultural regions.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs < 0.01 min-1), weak positive (0.01 ≤ kobs ≤ 0.025 min-1), or positive (kobs > 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

Broadband angular spectrum differentiation using dielectric metasurfaces

Signal processing is of critical importance for various science and technology fields. Analog optical processing can provide an effective solution to perform large-scale and real-time data processing, superior to its digital counterparts, which have the disadvantages of low operation speed and large energy consumption. As an important branch of modern optics, Fourier optics exhibits great potential for analog optical image processing, for instance for edge detection. While these operations have been commonly explored to manipulate the spatial content of an image, mathematical operations that act directly over the angular spectrum of an image have not been pursued. Here, we demonstrate manipulation of the angular spectrum of an image, and in particular its differentiation, using dielectric metasurfaces operating across the whole visible spectrum. We experimentally show that this technique can be used to enhance desired portions of the angular spectrum of an image. Our approach can be extended to develop more general angular spectrum analog meta-processors, and may open opportunities for optical analog data processing and biological imaging.

Technology Focus: Geothermal Energy (March 2024)

As the world seeks transformative solutions to ensure a sustainable energy future, there is a growing interest in geothermal energy because of its versatility and evolving technologies that enable the application of low-enthalpy fluids. One area where the untapped potential of geothermal energy has been realized is in harnessing thermal energy from abandoned or inactive oil and gas wells. Such wells could be exploited for electricity generation or direct use through well repurposing. Three innovative papers elucidate the challenges, complexities, applicability, and key operational considerations associated with repurposing abandoned or idle oil and gas wells for geothermal energy. The first paper offers a comprehensive overview of 20 case studies from various countries, including actual and simulated cases. Downhole and surface parameters, including production scenarios, heat exchangers, circulation fluids, and power-generation efficiency, are meticulously examined. The investigation concludes with a positive outlook, highlighting the potential of producing geothermal energy from underused oil and gas wells. The second paper delves into the technical and economic feasibility of repurposing oil and gas wells specifically for community heating through geothermal energy. The multidisciplinary approach involves a thorough analysis of physical, technical, economic, and environmental aspects. The results showcase the potential benefits of repurposing low-temperature wells for community heating, emphasizing the cost-effectiveness and sustainability of this approach. The third paper introduces a geothermal assessment project conducted in an operational high-pressure/high-temperature field on the UK Continental Shelf. This innovative project uses a novel integrated work flow to assess the geothermal energy potential of offshore oil and gas assets and provides insights into CO2 emission reduction. The life of oil and gas wells can be extended through repurposing for geothermal energy production. The findings underscore the technical viability, economic feasibility, and environmental benefits of oil and gas well repurposing, providing a promising avenue for transforming existing oil and gas resources and infrastructure into contributors to a cleaner and greener energy future. Recommended additional reading at OnePetro: www.onepetro.org. SPE 213068 A Robust Screening Tool To Repurpose Hydrocarbon Wells to Geothermal Wells in Oklahoma by Esteban R. Ugarte, University of Oklahoma, et al. SPE 215554 A Feasibility Study of Geothermal Energy Extraction in the Baram Basin, Sarawak, Using Numerical Reservoir Simulation Based on Analog Data by M. Bataee, Curtin University Malaysia, et al. SPE 212075 Geothermal Energy in the Caspian Region: Study of Heat-Extraction Methods From Deep Wells by Dmitriy Abdrazakov, SLB

Persistence of Basal Ganglia Oscillatory Activity During Tremor Attenuation by Movement in Parkinson's Disease Patients.

One of the characteristics of parkinsonian tremor is that its amplitude decreases with movement. Current models suggest an interaction between basal ganglia (BG) and cerebello-thalamo-cortical circuits in parkinsonian tremor pathophysiology. We aimed to correlate central oscillation in the BG with electromyographic activity during re-emergent tremor in order to detect changes in BG oscillatory activity when tremor is attenuated by movement. We performed a prospective, observational study on consecutive parkinsonian patients who underwent deep brain stimulation surgery and presented re-emergent tremor. Coherence analysis between subthalamic nucleus/globus pallidus internus (STN/GPi) tremorous activity measured by microrecording (MER) and electromyogram (EMG) from flexor and extensor wrist muscles during rest, posture, and re-emergent tremor pause was performed during surgery. The statistical significance level of the MER-EMG coherence was determined using surrogate data analysis, and the directionality of information transfer between BG and muscle was performed using entropy transfer analysis. We analyzed 148 MERs with tremor-like activity from 6 patients which were evaluated against the simultaneous EMGs, resulting in 296 correlations. Of these, 26 presented a significant level of coherence at tremor frequency, throughout rest and posture, with a complete EMG stop in between. During the pause, all recordings showed sustained MER peaks at tremor frequency (±1.5 Hz). Information flows preferentially from BG to muscle during rest and posture, with a loss of directionality during the pause. Our results suggest that oscillatory activity in STN/GPi functionally linked to tremor sustains firing frequency during re-emergent tremor pause, thus suggesting no direct role of the BG circuit on tremor attenuation due to voluntary movements. © 2024 International Parkinson and Movement Disorder Society.

Detection and evaluation of anthropogenic impacts on natural forest ecosystems from long-term tree-ring observations

Anthropogenic interventions lead to various direct and indirect impacts on natural ecosystems that are often hindered by natural long-term variability, and thus their detection and evaluation remain challenging. Ecological systems are strongly affected by climate variations that typically exhibit long-term correlations capable of imitating or hindering external trends in finite-time observations, thus complicating their detection and correct attribution to either anthropogenic interventions or natural variability. Here we focus on the quantitative assessment of the alterations in the tree-ring width (TRW) of four tree species in response to the changes in the soil water regime following a drainage experiment in a dwarf-shrub type peatland forest. We consider the long-term effects of the intervention, focusing on two characteristic quantities: the durations of clusters when significant discrepancies with relevant controls could be observed in every single consecutive year, and relative trends in the data reflecting long-term, gradual changes in the ecosystem. By extrapolating pre-drainage TRW dynamics and adjusting for recent climate variations using a multivariate model, we simulate surrogate data series that act as additional controls for the post-drainage time period. By comparing the long-term dynamics of the observational TRW data series against both natural and surrogate controls over several decades following the drainage experiment, we evaluate long-term alterations and gradual trends in the tree growth dynamics and reassess the statistical significance of these effects, taking into account long-term correlations in the natural TRW variations. Our results also indicate pronounced alterations in the drought stress response characterized by significant negative trends in the tree growth dynamics following the 2010 heatwave and associated flash drought in the drained area, while no similar effect could be observed in the undrained area, indicating that the increased productivity of the forest ecosystem following the drainage likely comes at the cost of its reduced drought stress resilience.

Detection of long-range correlation causing multifractality in H time series of geomagnetic field over the Northern Hemisphere during quiet geomagnetic conditions

The generation of geomagnetic time series frequently involves intricate spatio-temporal dynamics, wherein nonlinearity and scaling emerge as pivotal mechanisms. The present work investigates the fractal properties of geomagnetic field, and its sources during quiet geomagnetic conditions, by analyzing the horizontal component data of 1 min resolution (H time series) from different latitude regions. Analysis using Auto Correlation Function (ACF), Power spectrum analysis (PSA), Rescaled range analysis (R/S) and Detrended Fluctuation Analysis (DFA) gave indications of the presence of long-range correlations, fractal properties and scaling behaviour of quiet period H time series data. The strong q dependence of generalized Hurst exponent h(q) and scaling exponent Ʈ(q), obtained from the Multifractal Detrended Fluctuation Analysis (MFDFA) method, confirmed the multifractal behaviour of quiet period H time series. The multifractal source analysis of the quiet period H time series with its shuffled and surrogate time series data revealed that, the multifractal characteristics are caused almost entirely by the long-range correlations between small and large fluctuations. The analysis of the latitudinal variations in the multifractal characteristics is also performed as a part of the work, by considering H time series belonging to various latitudes under almost same longitude. The observed decrease in the generalized Hurst exponent values with increase in latitude indicates the change in the multifractal characteristics of the time series with latitude. Also the work revealed that, the H time series from high latitude region shows anti-persistent behaviour, even when the H time series from observatories located at the low and mid latitude regions exhibiting persistent behaviour. The change in the persistence, anti-persistence behaviour can be related to the strength of Earth’s magnetic field.

Naturally Occurring Asterric Acid Analogs: Chemistry and Biology.

Asterric acid and its analogs belong to diphenyl ethers (DPEs) with multiple substitutions on A/B aromatic rings. This member of DPEs originates from the polyketide pathway and displays a wide range of biological effects. Though the structures of asterric acid analogs are not complex, there were only more than 50 asterric acid analogs found in nature from 1960 to 2023. In this review, the structures, bioactivities, and biosynthesis of asterric acid analogs are summarized. More importantly, the empirical rule about the shielding effect of B-ring on H-6 is suggested, and this provides a convenient and useful way to analyze the NMR spectral data of asterric acid analogs, based on which the chemical shift values of the A-ring in some asterric acid analogs are revised.

Collecting, assembling, ordering: Border politics and the invisible data work of asylum

This article proposes to understand the ‘invisible data work’ that asylum seekers must do to put together a ‘credible’ asylum application. While the intersections between asylum and work have typically been analysed in relation to access to employment and labour conditions, we attend to the work of collecting, assembling, and ordering different forms of analogue and digital data inherent to the asylum process. Building on feminist interdisciplinary debates on work and drawing on a selection of asylum appeals from Italy and the UK, we argue that seeking asylum entails extensive and continual invisible work that requires significant resources, effort, skills and time. Attending to these forms of invisible work is crucial to understanding the challenges of seeking asylum beyond the migration journey and the implications of performing ‘invisible data work’ unaided and unequipped. It also counters problematic depictions of asylum seekers as passive subjects who are ‘just waiting’ for a decision to be made. Finally, rendering the collection and assemblage of data as ‘invisible work’ rather than just ‘doings’ has political implications for understanding the resources, responsibilities and resistance to the border politics of making precarious subjects.

A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.

Generation WhatsApp: inter-brain synchrony during face-to-face and texting communication

Texting has become one of the most prevalent ways to interact socially, particularly among youth; however, the effects of text messaging on social brain functioning are unknown. Guided by the biobehavioral synchrony frame, this pre-registered study utilized hyperscanning EEG to evaluate interbrain synchrony during face-to-face versus texting interactions. Participants included 65 mother-adolescent dyads observed during face-to-face conversation compared to texting from different rooms. Results indicate that both face-to-face and texting communication elicit significant neural synchrony compared to surrogate data, demonstrating for the first time brain-to-brain synchrony during texting. Direct comparison between the two interactions highlighted 8 fronto-temporal interbrain links that were significantly stronger in the face-to-face interaction compared to texting. Our findings suggest that partners co-create a fronto-temporal network of inter-brain connections during live social exchanges. The degree of improvement in the partners' right-frontal-right-frontal connectivity from texting to the live social interaction correlated with greater behavioral synchrony, suggesting that this well-researched neural connection may be specific to face-to-face communication. Our findings suggest that while technology-based communication allows humans to synchronize from afar, face-to-face interactions remain the superior mode of communication for interpersonal connection. We conclude by discussing the potential benefits and drawbacks of the pervasive use of texting, particularly among youth.

Development of an IoT-Based System for River Siren Control and Height Detection Utilizing LoRa and Solar Cell Technology

We need a tool that can measure river water levels automatically based on IoT so that water level data can be received in real time. This tool uses an Arduino microcontroller which functions to convert analog data into digital by measuring water level using an ultrasonic sensor so that the data can be converted into digital water level data and displayed via the internet and a buzzer or siren as a danger warning when the water level rises high as well as water level data. can be monitored as a whole. Apart from that, this tool is equipped with LoRa communication. This LoRa can send signals as far as 15 km without obstructions, so even if there is no internet, this tool can still work and can be used in areas without electricity because it uses solar cells as a power supply. If the water reaches the maximum limit, the siren will sound and stop again when the water in the river is at normal level.

Electrical synaptic devices with a high recognition rate based on eco-friendly nanocomposites of a poly(methyl methacrylate) matrix embedded with graphene quantum dots for neuromorphic computing

Artificial synapse devices are currently the subjects of great attention as next-generation hardware for data processing to overcome the problem of data explosion due to the rapid advances in artificial intelligence and cloud computing technology. Nanocomposite-based devices enable unique applications and have several advantages that cannot be achieved in single material-based devices. This study presents binary electrical synapses with digital data storing and analog data processing through a nanocomposite-based active layer composed of poly(methyl methacrylate) (PMMA) with embedded chlorine-functionalized graphene quantum dots (fGQDs) on an indium tin oxide (ITO) substrate. The Al/PMMA-fGQD/ITO devices with an fGQD concentrations of 5 wt% exhibited excellent memory performance with RON/ROFF ratio of 103. Moreover, we demonstrated that our device can successfully emulate biological synaptic functions such as potentiation/depression, short-term/long-term memory, paired-pulse facilitation, learning experience, and spike-timing-dependent plasticity. Furthermore, on the basis of the synaptic behaviors of the devices, they achieved about a 90 % recognition capability when a learning algorithm was used in a single-layer neural network.

Aleppo Pixelated: An Urban Reading through Digitized Historical Maps and High-Resolution Orthomosaics Case Study of al-ʿAqaba and al-Jallūm Quarters

This article relies on a combination of digital and analog data to analyze the 2D urban development of al-ʿAqaba and Jallūm districts in the Old City of Aleppo. The dataset consists of vectorized historical maps of the city spanning various historical periods. The oldest map in the collection dates back to the 1900s. Additionally, there are high-resolution orthomosaics created from a 3D model obtained through Terrestrial Laser Scanning (TLS) and Aerial Photogrammetry techniques. Through the analysis and integration of these various data types, the article proposes an analog-digital workflow that tracks the alterations in the urban fabric of the designated study area. The analysis primarily examines the alterations in the city’s two-dimensional layout and the distribution of mass and void. Tracking the changes in the street network of the studied area is the main goal of this research, along with recognizing the spatial changes in the built environment. The article identified changes in both the open spaces and the street layout.