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This study aims to investigate the behavior of different types of stone columns, including the short and floating columns, as well as the ordinary and the geosynthetic encased stone columns (OSC and GESC). The effectiveness of the geosynthetic encasement and the impact of the installation using the lateral expansion method on the column performance is evaluated through a three-dimensional (3D) unit cell numerical analysis.

A full 3D numerical analysis is carried out using the explicit finite element code PLAXIS 3D to examine the installation influence on settlement reduction (ß), lateral displacement (U_x) and vertical displacement (U_z) relative to different values of lateral expansion of the column (0% to 15%).

The findings demonstrate the superior performance of GESC, particularly short columns outperforming floating counterparts. This enhanced performance is attributed to the combined effects of geosynthetic encasement and increased lateral expansion. Notably, these strategies contribute significantly to decreasing lateral displacement (U_x) at the column’s edge and reducing vertical displacement (U_z) under the rigid footing.

In contrast to previous studies that examined the installation effect of OSC contexts, this paper presents a comprehensive investigation into the effect of geosynthetic encasement and the installation effects using the lateral expansion method in very soft soil, using 3D numerical simulation. The study emphasizes the significance of the consideration of geosynthetic encasement and lateral expansion of the column during the design process to enhance column performance.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

Despite city authorities in Bangladesh being concerned about urban sustainability, they often face difficulties in addressing predominant urban challenges threatening urban sustainability, due to limited relevant literature. To reduce this gap, this study aims to address the predominant urban challenges and assess their severity levels in four city corporations of Bangladesh, e.g. Rajshahi, Sylhet, Barishal, and Gazipur.

Using a mixed-method approach, this study rigorously analyzed field-level data obtained from 1,200 residents across selected cities using diverse statistical techniques. The quantitative analysis included descriptive analysis, exploratory factor analysis, and chi-square tests, whereas qualitative insights were derived through thematic analysis.

The study uncovered nine predominant urban challenges under two crucial factors “Feeble Urban Management” and “Illicit Activities” that collectively explain 62.20% variance. “Feeble Urban Management” explains 44.17% variance, whereas “Illicit Activities” accounts for 18.13%. Within these challenges, uncontrolled urban sprawl, inadequate disaster management, congested roads, and shabby drainage and waste management pose significant threats to urban sustainability. Illicit activities, manifested by encroachment on water sources, grabbing roadside, destruction of natural properties, and activities undermining social security, compound the urban sustainability issue. Severity analysis reveals Sylhet (54.5%), Rajshahi (46.4%), and Barishal (31.2%) as highly impacted, whereas Gazipur exhibits moderate severity (66.7%).

The findings of this study reveal intrinsic insights into urban challenges in Bangladesh that will provide valuable guidance to city authorities, equipping them to implement integrated and effective initiatives and programs that overcome these predominant urban challenges, with a specific focus on Rajshahi, Sylhet, and Barishal city corporations.

This study examines the cultural history of the Api-Opi deity in Opi, Nsukka, Enugu State of Nigeria. The study sets out to examine the re-emergence of youthful worshippers of Api-Opi, despite the penetration of Christianity in the area.

The study employed ethnographic observation and field visits to the shrine of Api-Opi in Opi community of Enugu State, Nigeria. In addition, this study uncovers new information drawn from semi-structured interview questions undertaken in the study area between March and October of 2019.

Against certain claims on the impact of Christianity on Africa's traditional religions, the study found that the Api-Opi deity has withstood these post-colonial changes, growing its followership, particularly amongst the youths. It demonstrated the resilience of Igbo Traditional Worship System even in the midst of culture clash and religious iconoclasm advanced by Christianity in Igboland, Nigeria.

Evidence from this study helps debunk the notions of Eurocentric scholars who say African traditional religions are fetish, barbaric or primitive. It also shows how indigenous communities have protected and preserved their religious heritage despite the wave of modernization and other eternal influences. The study contributes to the increasing conversations about the role of traditional religion in the cultural resilience/revitalization of indigenous communities.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The purpose of this paper is to study the correlation between different topographies and the reaction of Ulva Linza fouling species.

In this research, topographies with a different method, such as hot embossing and hot pulling, were achieved, and biological analyses were done with macroalgae Ulva Linza cells. The effect of topography via local binding geometry (honeycomb size gradients) and Wenzel roughness on the settling of Ulva microorganisms was tested.

As a result, Ulva spores confirmed different reactions to a similar set of tapered microstructures that was in agreement with the results on distinct honeycombs. The local binding geometry and the Wenzel roughness factor “r” were dominant on settling of Ulva Linza spores.

The reaction of an organism at the interface of vehicles’ substrate is powerfully affected by surface topographies.

The best embedment occurred on structures with bigger sizes than Ulva Linza’s spores. The density of settled spores was proportional to Wenzel roughness and the spores favour to attach to “kink sites” positions.

Unfortunately, unpleasant aggregation of marine biofouling on marine vehicles’ surfaces, generate terrific difficulties in the relevant industry.

There was a sharp relationship between Wenzel roughness and settle of Ulva Linza spores. The local binding geometry and the Wenzel roughness factor “r” were dominant on settling of Ulva Linza spores.

The paper aims to present an experimental and numerical investigation of the load–settlement behavior of soil reinforced by stone column, as well as to evaluate the plane strain unit cell model for the analysis of stone columns.

The numerical analysis was done using both axisymmetric and plane strain models. The elastic perfectly plastic behavior of Mohr–Coulomb was adopted for both soil and column material. The numerical results of this study were validated by the comparison with the in-situ measurements of a full-scale loading test on a stone column. This study also evaluated the effect of different parameters involved in the design of a stone column, including Young’s modulus of the column material, column diameter, spacing between the stone columns and Poisson’s ratio of the column material.

After the numerical simulation, the results from both axisymmetric and plane strain models are quite comparable. In addition, the numerical results revealed that the stone column with low spacing, a large diameter and a high Young’s modulus indicated better behavior against the settlement.

The axisymmetric unit cell model was used in many numerical studies on the behavior of stone columns. In the present work, a field load test on stone column was simulated using a plane strain unit cell model. This research adds that the plane strain unit cell model can be used to predict the settlement of reinforced soil with stone columns.

This pan-Canadian study examines the information practices of STEM-trained immigrant women to Canada as they navigate workfinding and workplace integration. Our study focuses on a population of highly skilled immigrant women from across Canada and uses an information practice lens to examine their lived experiences of migration and labour market integration. As highly trained STEM professionals in pursuit of employment, our participants have specific needs and challenges, and as we explore these, we consider the intersection of their information practices with government policies, settlement services and the hiring practices of STEM employers.

We conducted a qualitative study using in-depth interviews with 74 immigrant women across 13 Canadian provinces and territories to understand the nature of their engagement with employment-seeking in STEM sectors. This article reports the findings related to the settlement and information experiences of the immigrant women as they navigate new information landscapes.

As immigrants, as women and as STEM professionals, the experiences of the 74 participants reflect both marginality and privilege. The reality of their intersectional identities is that these women may not be well-served by broader settlement resources targeting newcomers, but neither are the specific conventions of networking and job-seeking in the STEM sectors in Canada fully apparent or accessible to them. The findings also point to the broader systemic and contextual factors that participants have to navigate and that shape in a major way their workfinding journeys.

The findings of this pan-Canadian study have theoretical and practical implications for policy and research. Through interviews with these STEM professionals, we highlight the barriers and challenges of an under-studied category of migrants (the highly skilled and “desirable” type of immigrants). We provide a critical discussion of their settlement experiences and expose the idiosyncrasies of a system that claims to value skilled talent while structurally making it very difficult to deliver on its promises to recruit and retain highly qualified personnel. Our findings point to specific aspects of these skilled professionals’ experiences, as well as the broader systemic and contextual factors that shape their workfinding journey.

The aim of this paper is to determine the scope and content for a holistic conservation and development strategies in the case of Gaziantep Rumkale. This approach seeks an answer to the question of “How can Rumkale archaeological heritage be preserved with their original values and passed on to future generations?”

The materials of the paper are based on field research and written and visual resource. The paper has been handled with a four-step method setup. The first stage is to examine the spatial and functional background of Rumkale and its immediate surroundings based on the historical development process. The second stage is to evaluate the upper- and lower-scale planning experiences of the heritage site. The third stage of the research methodology is strengths, weaknesses, opportunities, and threats (SWOT) analysis. The fourth stage is the creation of cultural heritage conservation-development strategies that define strategies for the protection and development of archaeological heritage in the context of sustainability.

The paper emphasizes the development of a holistic perspective that theoretically deals with the archaeological heritage sites of Rumkale together with the surrounding rural areas. In this context, the paper suggests ecological, spatial, socio-cultural, economic and institutional protection development strategies for the Rumkale archaeological site together with the surrounding rural areas for their delivery to future generations.

Although the area has an important cultural heritage value for different religions and cultures, it has not been subject to sufficient scientific research. The paper develops a holistic approach by considering Rumkale and its surroundings together.

Climate hazards in the form of cyclones are projected to become more intense under the pressures of future climate change. These changes represent a growing hazard to low lying coastal cities like Beira, Mozambique. In 2019, Beira experienced the devastating impact of Cyclone Idai. One of the many impacts resulting from this Cyclone was disrupted drinking water access. This investigation explores the distribution of Cyclone Idai’s impact on drinking water access via an environmental justice lens, exploring how preexisting water access characteristics may have predisposed households to the impacts of Cyclone Idai in Beria.

Relying on household survey data collected in Beira, the investigation applied a decision tree algorithm to investigate how drinking water disruption was distributed across the household survey sample using these preexisting vulnerabilities.

The investigation found that households that mainly relied upon piped water sources and experienced inconsistent access to water in the year prior to Cyclone Idai were more likely to experience disrupted drinking water access immediately after Cyclone Idai. The results indicate that residents in formal areas of Beira, largely reliant upon piped water supply, experienced higher rates of disrupted drinking water access following Cyclone Idai.

These findings question a commonly held assumption that informal areas are more vulnerable to climate hazards, like cyclones, than formal areas of a city. The findings support the inclusion of informal settlements in the design of climate change adaptation strategies.