Location-specific Characteristics Research Articles

GEOTECHNICAL CONDITIONS FOR THE WIDENING OF THE EXISTING EMBANKMENTS ON THE GOLUBOVCI-BAR RAILWAY SECTION, THE SECTION THROUGH SKADAR LAKE

The reconstruction of the existing embankments along the Golubovci-Bar railway line, particularly through the section that traverses Lake Skadar, represents a significant engineering challenge due to the location's specific characteristics and ecological restrictions. This area is a protected region, which limited the implementation of certain design solutions. Embankment stability analysis using the finite element methodndicated that the existing embankment does not meet the global safety factor requirements for design earthquake according to Eurocode 8. The paper presents the engineering-geological conditions of the existing embankments, as well as recommendations for their expansion and stabilization.

AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact.

In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access R package that quantifies the impact of vector control interventions depending on mosquito species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50 Anopheles species, field data on mosquito and human behaviour, and estimates of vector control effectiveness. Using the input data, the package parameterizes a discrete-time, state transition model of the mosquito oviposition cycle and infers species-specific impacts of various interventions on vectorial capacity. In addition, it offers formatted outputs ready to use in downstream analyses and by other models of malaria transmission for accurate representation of the vector-specific components. Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. The AnophelesModel R package is available under a GPL-3.0 license at https://github.com/SwissTPH/AnophelesModel.

Cohort-Specific Differential Vulnerability to Capture of Mallards and Wood Ducks in Baited Swim-in Traps

Abstract Attaching leg bands to birds directly before fall hunting seasons is a primary component of monitoring waterfowl in North America. Although capture and recovery data are primarily used for estimating survival and harvest distribution, these data may be used to estimate age ratios and other demographic rates, especially if recapture data are available from subsequent trapping and release events. We estimated recapture rates of mallards Anas platyrhynchos and wood ducks Aix sponsa and used those rates to estimate differential capture vulnerability, which is vital to estimate true age ratios from banding data. Posterior estimates of mallard age and sex cohorts and location-specific recapture rates varied among capture locations from a mean of 0.004 (0.002–0.008 95% credible interval) to 0.547 (0.486–0.609). Ratios of recapture rates among cohorts also varied, meaning no single differential vulnerability estimate would be useable across the study area. Our estimates of differential capture vulnerability for mallards, using the ratio of recapture probabilities, averaged 2.64 for adult female to adult male and 5.42 for juvenile female to adult female, with significant variation. Wood duck cohort-specific recapture rates were similar across locations. Similar wood duck recapture rates resulted in similar estimates of differential vulnerability, 1.24 for the ratio of adult female to adult male and 1.30 for juvenile female to adult female. The wide range of recapture rate estimates we found for mallards suggests that location-specific characteristics may have a strong effect on capture probability. Differences in recapture rates and apparent survival likely resulting from emigration suggest that if recapture data are to be used in population modeling, location-specific information is needed. The ability to monitor multiple demographic parameters using a single scheme improves continued assessment of population status. We recommend increased collection of in-season recapture data by biologists during active banding operations. Banders should be aware of the potential value of live, in-season encounters in monitoring populations and modeling demographic rates.

Housing Choices of Young Adults in Sweden

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion).

Economic Analysis of Large-Scale Renewable Energy (RE) Source Investment Incorporating Power System Transmission Costs

Recently, the proportion of renewable energy (RE) sources in a power system has been increasing worldwide to reduce carbon emissions. To effectively accommodate renewable energy, there is a growing need to develop integrated planning strategies for both renewable energy sources and transmission lines in a power system, taking into account the location-specific characteristics of renewable energy. Economically viable and rapid system expansion plans are required to resolve the problem of delayed integration of renewable energy into existing power system, which arises due to inadequate transmission facilities. To address these problems, this paper presents an integrated economic evaluation method that considers the inherently uncertain output characteristics of renewable energy, contingent on location, the costs associated with installing transmission lines, and environmental benefits. The case study is conducted using a modified power system in Korea. In the case study, it is assumed that by integrating solar power generators in a low-demand area and wind power generators in a high-demand area, wind power plants have higher construction costs than solar power generators. However, the transmission costs for wind power plants are lower than those for solar power generators due to the regional characteristics of the power grid in the installation areas because renewable energy in high-demand areas typically requires less additional power infrastructure than in low-demand areas. The results of this study show that the net benefit for the wind power plant is estimated at USD 225.4 M, while the solar power plant yields a net benefit of USD 22.9 M in the example system. These results demonstrate the effectiveness of the proposed method, underscoring its potential to mitigate the challenges faced in the practical implementation of renewable energy projects.

Classification of noisy vehicles from unsupervised measurements

The NEMO-project (https://nemo-cities.eu/) aims to identify noisy and polluting road and rail vehicles, using remote sensing technology. Noise levels from individual road vehicles are measured from the roadside, in normal traffic. Road authorities may use these data to enforce noise limits, to limit access to Low Emission Zones or to influence driving behaviour. Whether a vehicle is a 'high noise emitter' is a complex question, as the noise level depends on vehicle type and condition, driving style, weather and location-specific characteristics. From a legal perspective, the question may be answered in relation to type approval noise limits, or in relation to local noise disturbance regulations. Within NEMO, a classification model is developed from a large dataset of unsupervised pass-by noise measurements, from different locations. The model labels noisy vehicles based on the noise measurements, technical vehicle data, driving conditions, and external factors. Several modeling and machine learning techniques were evaluated, to find the most accurate solution. This paper presents the results, and it looks forward to how the technological solution could be applied to enforce regulations, leading to a reduction of traffic noise annoyance.

Predicting geological interfaces using stacking ensemble learning with multi-scale features

Understanding the variation of geological interfaces plays a crucial role in the analysis and design of infrastructure systems. Generally, there are two classes of techniques for predicting geological interfaces, for example, interpolation/regression-based techniques and machine-learning-based techniques. In this paper, a Multi-scale Meta-learning Model (M3) methodology is proposed. The new methodology improves the current state-of-the-art techniques by fusing two levels of information: (i) generic characteristics of the sampling locations, for example, coordinates, and (ii) location-specific characteristics, for example, local-scale predictions. The implementation starts from using an array of classic interpolation/regression-based techniques as base learners to provide first-level predictions at a local scale. These predictions are then combined with generic characteristics to train a meta-learner following the stacking ensemble learning framework. In this manner, the location-specific information from the base learners can be simultaneously considered with the generic information in the training process. The variation of rockhead elevation is predicted using the M3 methodology and a comprehensive borehole dataset in Singapore. A detailed comparative study involving several existing methods is also carried out to rigorously validate the M3 methodology. The results show that the M3 methodology achieves 20% improvement in the model performance compared to existing methods, indicating its promising potential in geotechnical site characterization.

Non-Parametric Bayesian Covariate-Dependent Multivariate Functional Clustering: An Application to Time-Series Data for Multiple Air Pollutants

Abstract Air pollution is a major threat to public health. Understanding the spatial distribution of air pollution concentration is of great interest to government or local authorities, as it informs about target areas for implementing policies for air quality management. Cluster analysis has been popularly used to identify groups of locations with similar profiles of average levels of multiple air pollutants, efficiently summarising the spatial pattern. This study aimed to cluster locations based on the seasonal patterns of multiple air pollutants incorporating the location-specific characteristics such as socio-economic indicators. For this purpose, we proposed a novel non-parametric Bayesian sparse latent factor model for covariate-dependent multivariate functional clustering. Furthermore, we extend this model to conduct clustering with temporal dependency. The proposed methods are illustrated through a simulation study and applied to time-series data for daily mean concentrations of ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5) collected for 25 cities in Canada in 1986–2015.

DevianceNet: Learning to Predict Deviance from a Large-Scale Geo-Tagged Dataset

Understanding how a city’s physical appearance and environmental surroundings impact society traits, such as safety, is an essential issue in social artificial intelligence. To demonstrate the relationship, most existing studies utilize subjective human perceptual attributes, categorization only for a few violent crimes, and images taken from still shot images. These lead to difficulty in identifying location-specific characteristics for urban safety. In this work, to address this problem, we propose a large-scale dataset and a novel method by adopting a concept of “Deviance" which explains behaviors violating social norms, both formally (e.g. crime) and informally (e.g. civil complaints). We first collect a geo-tagged dataset consisting of incident report data for seven metropolitan cities, with corresponding sequential images around incident sites obtained from Google street view. We also design a convolutional neural network that learns spatio-temporal visual attributes of deviant streets. Experimental results show that our framework can reliably recognize real-world deviance in various cities. Furthermore, we analyze which visual attribute is important for deviance identification and severity estimation. We have released our dataset and source codes at our project page: https://deviance-project.github.io/DevianceNet/

Seismic zonation and default suites of ground-motion records for time-history analysis in the South Island of New Zealand

The rise of performance-based earthquake engineering, in combination with the complexity associated with selecting records for time-history analysis, demonstrates an expressed need for localized default suites of ground motion records for structural designers to use in the absence of site-specific studies. In the current research investigation, deaggregations of probabilistic seismic hazard models (National Seismic Hazard Model, Canterbury Seismic Hazard Model, and Kaikōura Seismic Hazard Model) and the location-specific seismological characteristics of expected ground motions were used to define eight seismic hazard zonations and accompanying suite profiles for the South Island of New Zealand to satisfy the requirements of the New Zealand structural design standard NZS1170.5 for response-history analyses. Specific records, including 21 from the recent Kaikōura, Darfield, and Christchurch earthquakes, were then selected from publicly-available databases and presented as default suites for use in time-history analyses in the absence of site-specific studies. This investigation encompasses seismic hazards corresponding to 500-year return periods, site classes C (shallow soils) and D (deep soils), and buildings with fundamental periods between 0.4 and 2.0 seconds.

Clustering and hotel room prices in Dubai

In this article, we study the influence of the room properties, hotel amenities, hotel location, and, more importantly, the characteristics of hotels in the surrounding area on the prices of hotel rooms. The effects of different determinants are estimated using the hedonic price model for a cross-section of 250 hotels in Dubai. In addition to the typical characteristics of hotels and hotel rooms such as hotel amenities, star rating, and room size, we include location-specific characteristics such as accessibility to public transportation, airport, and, more importantly, clustering variables to capture the effects of local competition and spillovers from surrounding hotels. Our results indicate significant and strong effects of accessibility to attractions, transportation, hotel’s star rating, and room size, as expected. Our estimations also indicate that local competition reduces the room price, and local quality spillover increases the room price, and both effects are predominantly limited to the hotel’s immediate surroundings. Our estimations indicate that having one more hotel in the immediate surroundings decreases the room price by about one percent, and an increase in the average quality of the hotels in the immediate surroundings by one star rating increases the room price by more than 20%.

A GIS-Based Approach to Estimate Electricity Requirements for Small-Scale Groundwater Irrigation

Access to modern energy services is a precondition to improving livelihoods and building resilience against climate change. Still, electricity reaches only about half of the population in Sub-Saharan Africa (SSA), while about 40% live under the poverty line. Heavily reliant on the agriculture sector and increasingly affected by prolonged droughts, small-scale irrigation could be instrumental for development and climate change adaptation in SSA countries. A bottom-up understanding of the demand for irrigation and associated energy services is essential for designing viable energy supply options in an effective manner. Using Uganda as a case study, the study introduces a GIS-based methodology for the estimation of groundwater irrigation requirements through which energy demand is derived. Results are generated for two scenarios: (a) a reference scenario and (b) a drought scenario. The most critical need is observed in the northern and southern regions of the country. The total annual irrigation demand is estimated to be ca. 90 thousand m3, with the highest demand observed in the months of December through February, with an average irrigation demand of 445 mm per month. The highest energy demand is observed in the northern part of the study area in January, reaching 48 kWh/ha. The average energy demand increases by 67% in the drought scenario. The study contributes to current gaps in the existing literature by providing a replicable methodological framework and data aimed at facilitating energy system planning through the consideration of location-specific characteristics at the nexus of energy–water–agriculture.

Seasonal variation in mortality and the role of temperature: a multi-country multi-city study.

Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature. We compiled daily data on all-cause, cardiovascular and respiratory mortality, temperature and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day of year. Meta-analysis was used to summarize location-specific estimates for each climate zone. We estimated the PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on the PTR across locations by using meta-regression models. Seasonality estimates and responses to temperature adjustment varied across locations. The unadjusted PTR for all-cause mortality was 1.05 [95% confidence interval (CI): 1.00-1.11] in the tropical zone and 1.23 (95% CI: 1.20-1.25) in the temperate zone; adjusting for temperature reduced the estimates to 1.02 (95% CI: 0.95-1.09) and 1.10 (95% CI: 1.07-1.12), respectively. Furthermore, the unadjusted PTR was positively associated with average mean temperature. This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature.

Seasonal variation in mortality and the role of temperature: a multi-country multi-city study

BACKGROUND AND AIM: Although seasonal variations in mortality have been recognised for a long time, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature. METHODS: We compiled daily data on all-cause, cardiovascular, and respiratory mortality, temperature, and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day-of-year. Random-effects multivariate meta-analysis was used to summarise location-specific estimates for each climate zone. We estimated PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on PTR across locations by using random-effects meta-regression models. RESULTS:Seasonality estimates and responses to temperature adjustment varied across locations. Unadjusted-PTR for all-cause mortality was 1.05 (95% confidence interval (CI): 1.00–1.11) in the tropical zone and 1.23 (95% CI: 1.20–1.25) in the temperate zone; adjusting for temperature reduced the PTR estimates to 1.02 (95% CI: 0.95–1.09) and 1.10 (95% CI: 1.07–1.12), respectively. Furthermore, unadjusted-PTR was positively associated with average annual mean temperature, and adjusting for temperature in PTR moved the estimate towards the null. CONCLUSIONS:This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature. On behalf of the MCC collaborative research network. KEYWORDS: Seasonality of mortality, temperature, a multi-country multi-city study

Do the Heterogeneous Determinants of Repayment Affect Differently across Borrowers of Diverse Credit Sources in Rural Assam? A Double Hurdle Approach

This study made an attempt to answer the question: Do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently? The study is based on data collected from 240 households from three districts in the lower Brahmaputra valley of Assam through a carefully designed primary survey. Besides, the study uses the double hurdle approach and the instrumental variable probit model to reduce possible selection bias. It observes better repayment performance among formal borrowers, followed by semiformal borrowers, while occupation wise it is prominent among organised employees. It has been found that in general, the household characteristics, loan characteristics and location-specific characteristics significantly affect repayment performance of borrowers. However, the nature of impact of the factors influencing repayment performance is remarkably different across credit sources. It ignores the role of traditional community-based organisations in rural Assam while analysing the determinants of repayment performance. The study also recommends for ensuring productive opportunities and efficient market linkages in rural areas of Assam. The study is based on an original data set that has specially been collected to examine question that—do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently in the lower Brahmaputra valley of Assam—which has not been studied before.

Shifting demand to sustainable fishing practices in Darwin's Archipelago: a discrete choice experiment application for Galapagos' certified Yellow-fin tuna

In spite of several policies to promote fisheries management in the Galapagos Marine Reserve (GMR), in the past there has been a collapse or near collapse of the two most important fisheries: the sea cucumber and the lobster fishery respectively. Currently, there is particular concern for the Galapagos Sailfin Grouper (Mycteroperca olfax), a regionally endemic species showing signs of severe decline and targeted by the white-fin fishery, a multi-species coastal fishery of high market value. Within this context, the Galapagos National Park Directorate (GNPD) and local NGOs have designed a policy consistent in a product certification scheme aiming to shift fishing efforts from depleted coastal fisheries to healthier pelagic stocks, whilst supporting artisanal fisher’s livelihoods. Using a discrete choice experiment and accompanying survey data, the goal of this research is to analyze the preferences and tradeoffs made by individuals across attributes when purchasing certified fish in the Galapagos. In particular, we account for pro-environmental attitudes within a WTP space model specification and estimate the expected price premiums that tourists would be willing to pay for yellowfin tuna in the area in light of specific characteristics of its local value chain. We find that tourists visiting the Galapagos hold pro environmental attitudes as measured by the New-ecological paradigm and that there is a considerable increase in consumer’s utility from a certification scheme. Tourists care about the level of bycatch associated with their consumption, food safety, quality, and whether is locally sourced, and would be willing to pay for these attributes. This evidence, driven by location specific characteristics of consumers and tourists, supports the design of market-based mechanisms for sustainable yellow fin tuna fisheries in the Galapagos

Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter

A modular pipeline for improving the constitutive modelling of composite materials is proposed.The method is leveraged here for the development of subject-specific spatially-varying brain white matter mechanical properties. For this application, white matter microstructural information is extracted from diffusion magnetic resonance imaging (dMRI) scans, and used to generate hundreds of representative volume elements (RVEs) with randomly distributed fibre properties. By automatically running finite element analyses on these RVEs, stress-strain curves corresponding to multiple RVE-specific loading cases are produced. A mesoscopic constitutive model homogenising the RVEs’ behaviour is then calibrated for each RVE, producing a library of calibrated parameters against each set of RVE microstructural characteristics. Finally, a machine learning layer is implemented to predict the constitutive model parameters directly from any new microstructure. The results show that the methodology can predict calibrated mesoscopic material properties with high accuracy. More generally, the overall framework allows for the efficient simulation of the spatially-varying mechanical behaviour of composite materials when experimentally measured location-specific fibre geometrical characteristics are provided.

Urban Sustainability Transitions in Two Frontrunner Cities: Insights from the Multi-level Perspective

ABSTRACT Research on urban sustainability has extended our understanding of what principles cities can apply to minimize their impacts on the global ecosystem. However, how urban sustainability transitions unfold and how location-specific characteristics in different territorial contexts impact transition processes have been previously addressed only to limited extent. Drawing on relevant transition theory (in this case the Multi-level Perspective), this paper examines the underlying drivers, opportunities, and barriers to sustainability transitions in cities. The analysis draws on two case studies from both the developed (Freiburg) and the developing world (Curitiba). We argue that cities have fundamentally different endowments and inherent characteristics; therefore, policy dealing with urban transitions should be responsive to the specific requirements of its application domain.

Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning.

Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images. Assigning landmarks is tedious and error prone. Predefined landmarks may in addition miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are interpretable and hence to mitigate the black-box nature of ML methods. To overcome the black-box nature of ML we apply complementary methods and investigate internal representations with saliency maps to reliably identify location specific characteristics in images of Nile tilapia populations. Analyzing fish images which were sampled from six Ethiopian lakes reveals that deep learning improves on a conventional morphometric analysis in predictive performance. A critical assessment of established saliency maps with a novel significance test reveals however that the improvement is aided by artifacts which have no biological interpretation. More interpretable results are obtained by a Bayesian approach which allows us to identify genuine Nile tilapia body features which differ in dependence of the animals habitat. We find that automatically inferred Nile tilapia body features corroborate and expand the results of a landmark based analysis that the anterior dorsum, the fish belly, the posterior dorsal region and the caudal fin show signs of adaptation to the fish habitat. We may thus conclude that Nile tilapia show habitat specific morphotypes and that a ML analysis allows inferring novel biological knowledge in a reproducible manner.

Mechano-driven regeneration predicts response variations in large animal model based on scaffold implantation site and individual mechano-sensitivity.

It is well founded that the mechanical environment may regulate bone regeneration in orthopedic applications. The purpose of this study is to investigate the mechanical contributions of the scaffold and the host to bone regeneration, in terms of subject specificity, implantation site and sensitivity to the mechanical environment. Using a computational approach to model mechano-driven regeneration, bone ingrowth in porous titanium scaffolds was simulated in the distal femur and proximal tibia of three goats and compared to experimental results. The results showed that bone ingrowth shifted from a homogeneous distribution pattern, when scaffolds were in contact with trabecular bone (max local ingrowth 12.47%), to a localized bone ingrowth when scaffolds were implanted in a diaphyseal location (max local ingrowth 20.64%). The bone formation dynamics revealed an apposition rate of 0.37±0.28%/day in the first three weeks after implantation, followed by limited increase in bone ingrowth until the end of the experiment (12weeks). According to in vivo data, we identified one animal whose sensitivity to mechanical stimulation was higher than the other two. Moreover, we found that the stimulus initiating bone formation was consistently higher in the femur than in the tibia for all the individuals. Overall, the dependence of the osteogenic response on the host biomechanics means that, from a mechanical perspective, the regenerative potential depends on both the scaffold and the host environment. Therefore, this work provides insights on how the mechanical conditions of both the recipient and the scaffold contribute to meet patient and location-specific characteristics.