Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator (Short Paper)

Spatial heterogeneity is a typical and common form of spatial effect. Geographically weighted regression (GWR) and its extensions are important local modeling techniques for exploring spatial heterogeneity. However, when dealing with spatial data sampled at a micro-level but the geographical locations of them are only known at a higher level, GWR-based models encounter several problems, such as difficulty in establishing the bandwidth. Because data with this characteristic exhibit spatial hierarchical structures, such data can be suitably handled using hierarchical linear modeling (HLM). This model calibrates random effects for sample-level variables in each group to address spatial heterogeneity. However, it does not work when exploring spatial heterogeneity in some group-level variables when there is insufficient variance in each group. In this study, we therefore propose a hierarchical and geographically weighted regression (HGWR) model, together with a back-fitting maximum likelihood estimator, that can be applied to examine spatial heterogeneity in the regression relationships of data where observations nest into high-order groupings and share the same or very close coordinates within those groups. The HGWR model divides coefficients into three types: local fixed effects, global fixed effects, and random effects. Results of a simulation experiment show that HGWR distinguishes local fixed effects from others and also global effects from random effects. Spatial heterogeneity is reflected in the estimates of local fixed effects, along with the spatial hierarchical structure. Compared with GWR and HLM, HGWR produces estimates with the lowest deviations of coefficient estimates. Thus, the ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modeling tool for handling the increasingly common occurrence where data, in secure settings for example, remove the specific geographic identifiers of individuals and release their locations only at a group level.
Geographic Analysis of Trade-Offs Between Amenity and Supply Effects in New Office Buildings (Short Paper)

The supply of new office buildings in the neighborhood both positively and negatively affects rents. This study attempts to deepen the quantitative knowledge of this trade-off relationship and estimate the correlation between new supply and rent within a specific geographic area based on a hedonic model. Although the results exhibit biases, they indicate that supply effects become apparent after construction is completed, and that they vary geographically and are related to local market characteristics
How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper)

Geoforensic analyses are used to identify the location history of objects or people of interest. An effective method for location history identification is to use joint probability or suitability of trace materials. Species distribution models have been used to derive joint suitability distributions using suitable biotic trace evidence such as pollen. One of the key objectives for such analyses is to effectively reduce potential search space and search effort for investigators. This research presents a novel framework for modeling the habitat suitability of pollen identified at the plant species-level to generate joint suitability maps. We provide major limitations and challenges faced by current geolocation analyses based on species distribution models, including opportunities to improve the joint suitability analyses for search space reduction. A conditional probability approach for geolocation identification is also demonstrated for possible future applications in real-world forensic cases
Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy (Short Paper)

Rates of anxiety and depression are increasing due to financial stress caused by energy pricing with over half of UK homes unable to afford comfortable heating. UK Government policies to address this energy crisis have been implemented with limited evidence and substantial criticism. This paper applies the dynamic microsimulation MINOS, which utilises longitudinal Understanding Society data, to evidence change in mental well-being under the Energy Price Cap Guarantee and Energy Bill Support Scheme Policies. Results demonstrate an overall improvement in Short Form 12 Mental Component Score (SF12-MCS) both on aggregate and over data zone spatial areas for the Glasgow City region compared with a baseline of no policy intervention. This is work in progress and discussion highlights potential future work in other energy policy areas, such as Net Zero
Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper)

Recent pioneering works have shown the potential of a new deep-learning-backed paradigm for automated map generalization. However, this approach also puts a high demand on the availability of balanced and rich training sets. We present our design and progress of constructing an open training data set that can support relevant studies, collaborating with the Swiss Federal Office of Topography. The proposed data set will contain transitions of building and road generalization in Swiss maps at 1:25k, 1:50k, and 1:100k. By analyzing the generalization operators involved in these transitions, we also propose several challenges that can benefit from our proposed data set. Besides, we hope to also stimulate the production of further open data sets for deep-learning-backed map generalization. 2012 ACM Subject Classification Information systems → Geographic information systems; Information systems → Data mining
Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements (Short Paper)

The quest for identifying feasible routes holds immense significance in the realm of transportation, spanning a diverse range of applications, from logistics and emergency systems to taxis and public transport services. This research area offers multifaceted benefits, including optimising traffic management, maximising traffic flow, and reducing carbon emissions and fuel consumption. Extensive studies have been conducted to address this critical issue, with a primary focus on finding the shortest paths, while some of them incorporate various traffic conditions such as waiting times at traffic lights and traffic speeds on road segments. In this study, we direct our attention towards historical data sets that encapsulate individuals’ route preferences, assuming they encompass all traffic conditions, real-time decisions and topological features. We acknowledge that the prevailing preferences during the recorded period serve as a guide for feasible routes. The study’s noteworthy contribution lies in our departure from analysing individual preferences and trajectory information, instead focusing solely on macro-level measurements of each road segment, such as traffic flow or traffic speed. These types of macro-level measurements are easier to collect compared to individual data sets. We propose an algorithm based on Q-learning, employing traffic measurements within a road network as positive attractive rewards for an agent. In short, observations from macro-level decisions will help us to determine optimal routes between any two points. Preliminary results demonstrate the agent’s ability to accurately identify the most feasible routes within a short training period.
Wave packet molecular dynamics–density functional theory method for non‐ideal plasma and warm dense matter simulations

A new wave packet molecular dynamics–density functional theory (WPMD‐DFT) method is proposed for atomistic simulations of non‐ideal plasma and warm dense matter. The method is based on the WPMD approach, where the electronic exchange and correlation effects are treated using an additional energy term taken from DFT. This term is calculated by integration over the mesh values of the wave packet density. The local density approximation is implemented so far. WPMD‐DFT is meant as a replacement for the anti‐symmetrized WPMD (AWPMD) method which is more time consuming and lacks electron correlation. In this paper, we compare the results obtained by WPMD‐DFT, WPMD, AWPMD, classical molecular dynamics, and path integral Monte Carlo methods for the internal energy of the hydrogen plasma in the temperature range 10–50 kK and electron number density from 1020 to 1024 cm−3. We also demonstrate the ability to handle the simultaneous dynamics of electrons and ions by calculating the electron–ion temperature relaxation. The scalability of the WPMD‐DFT method with the number of electrons is shown for implementations in central processing unit and graphical processing unit.
Developed an algorithm to Reduce Operational Complexity for Cyclotomic Fourier Transform

The paper presents an algorithm, difficulty arises in efficient computation of discrete Fourier transform over Galois field is measured. Method for reduction of additive complexity in Cyclotomic fast Fourier transform is proposed. The approach presented in proposed algorithm is initially sort even matrix then identify the identical terms present in the equations and replace them with a new variable. Comparitive analysis of Proposed algorithm with existing available methods is done and expérimental result shows that the proposed algorithm reduces operational complexity of Cyclotomic Fourier Transform.
Classification of Philippine rice grains using machine vision and artificial neural networks.

This research investigated the use of a machine vision system and multilayer neural networks for automatic identification of the sizes, shapes, and variety of samples of 52 rice grains belonging to five (5) varietal groups of rice in the Philippines. Several multilayer neural networks were developed for sizes, shapes and varietal types classification using thirteen (13) grain features extracted from each sample image. These grain features were the major axis length, minor axis length, area, perimeter, Feret diameter, shape factor, compactness, elongatedness, mean major axis length, rectangularity, equivalent rectangular length, equivalent rectangular width, and number of pixels. The Artificial Neural Networks classifiers developed were able to identify the grain sample sizes and shapes at overall average accuracies of 98.76 per cent and 96.67 percent, respectively. The individual rice varietal types of the lowland irrigated, lowland rainfed, saline prone, cool elevated, and upland rice groups were classified at average overall accuracy of 85.81, 94.58, 96.16, and 97.39 percent respectively. An average overall accuracy of about 70 percent was obtained when the sample images of the 52 varieties were included in the group classification. The study results have demonstrated the capability and potential of machine vision with a well trained multilayer neural network classifiers for sizes, shapes, and varietal types identification of rough rice grain samples grown in the diverse agroecological zones in the country. With the comparably high accuracy of classification obtained, a machine vision together with the developed neural network architectures could be used as a tool to achieve better and more objective rice quality evaluation at trading points within the rice marketing system.
Effect of leading edge geometry on the performance of plate fin heat sinks in forced convection at transitional Reynolds numbers

This study analyses the effect that leading edge geometry has on the thermal performance of plate fin heat sinks in forced convection. The range of Reynolds numbers studied covers the so-called transitional flow regime, where laminar- turbulent transitional flow can exist even above critical Reynolds numbers. Plate fins with sharp (rectangular) and rounded leading edges are considered. 2D numerical analysis on forced convection over the fins is carried out in ANSYS Fluent using the Transition SST turbulence model to capture transitional effects. At lower Reynolds numbers, a fully laminar boundary layer exists along both the sharp and rounded fins, with the sharp fin exhibiting slightly lower heat transfer rates and higher flow resistance due to large flow separation at its leading edge. Above a critical Reynolds number, separation-induced transition occurs at the leading edge of the sharp fin, creating a turbulent boundary layer along the fin. This increases the average Nusselt number of the sharp fin by 58 - 186% relative to the rounded fin. Using a Quality Factor metric, defined as the ratio of heat transfer rate to pumping power across the fin, the sharp fin was found to have a 24.6 - 70.1% higher thermal efficiency than the rounded fin above the critical Reynolds number. These results suggest that in the transitional flow regime, a sharp leading edge increases the thermal performance of plate fin heat sinks by promoting turbulent forced convection along the fins.
Recent Trends To Detect Melanoma Using Dermoscopic Images: A Survey

Skin cancer poses a significant public health concern, with melanoma being one of its most prevalent forms. Early detection of melanoma plays a crucial role in effective treatment and patient outcomes. Automated screening systems have shown promise in accurately identifying melanoma at its early stages. In recent years, advanced deep learning algorithms have demonstrated impressive outcomes, in detecting melanoma using dermoscopic images. This research article provides an insights into the recent developments and achievements in using deep learning algorithms for melanoma detection using dermoscopic images.
Serveur Académique Lausannois SERVAL serval.unil.ch

Purpose— To develop and evaluate a practical method for the quantification of signal-to-noise ratio (SNR) on coronary magnetic resonance angiograms (MRA) acquired with parallel imaging. Materials and Methods— To quantify the spatially varying noise due to parallel imaging reconstruction, a new method has been implemented incorporating image data acquisition followed by a fast noise scan during which radiofrequency pulses, cardiac triggering and navigator gating are disabled. The performance of this method was evaluated in a phantom study where SNR measurements were compared to those of a reference standard (multiple repetitions). Subsequently, SNR of myocardium and posterior skeletal muscle was determined on i n vivo human coronary MRA. Results— In a phantom, the SNR measured using the proposed method deviated less than 10.1% from the reference method for small geometry factors (<=2). In-vivo , the noise scan for a 10 minutes coronary MRA acquisition was acquired in 30s. Higher signal and lower SNR, due to spatially varying noise, were found in myocardium compared to posterior skeletal muscle. Conclusion— SNR quantification based on a fast noise scan is a validated and easy-to-use method when applied to 3D coronary MRA obtained with parallel imaging as long as the geometry factor remains low.
Fuzzy Preprocessing of Viola-Jones Algorithm for Face Recognition in Thermal Images

This paper proposes to improve the Viola-Jones algorithm to identify facial features in thermal images using a fuzzy linguistic modifier. The purpose is to improve the image quality in the infrared plane and to facilitate the recognition stage, feature selection and emotion classification. The results obtained show that adding a preprocessing stage improves the capability of the Viola-Jones algorithm in identifying facial features in thermal images.
The Simultaneous Management of Business Model Innovation and Replication Processes: The Case of Airbnb’s “experiences”

Purpose This article aims to understand how organizations can simultaneously manage their business model innovation and replication processes. Methodology Using an exploratory qualitative approach, we analyze Airbnb’s business model innovation and replication processes when the company launched its new value proposition labeled “experiences”. Findings Our findings show that innovation and replication processes are designed, tested and implemented simultaneously. We underline the role of the platform in standardizing the key elements of the different stages of the processes. Through its digital platform, Airbnb can standardize its value creation and value capture processes to drive replication at scale. Contributions and principal implications Our paper contributes to strengthening a bridge between the literature on replication and business model innovation, to complement previous research on the Airbnb business model and to enrich the debate about the adaptation perspective in the replication literature. Our research has practical implications for entrepreneurs in the tourism and hospitality industry who would like to develop and replicate their business model using a digital platform.
In-service EFL Teachers’ Sociocultural-based TPACK Beliefs and Practices: Voice of Teachers and Students

Many aspects of English as a foreign or second language (EFL/ESL) teachers and students’ technological pedagogical content knowledge (TPACK) have been extensively studied. However, it has been relatively sparse regarding TPACK research on in-service EFL teachers’ online development programs reflected from the lens of a sociocultural approach. This mixed-methods design surveyed 125 in-service EFL teachers’ (53 males and 72 females, 39-47 years) and 375 students (177 males and 198 females, 17-19.4 years) to rate their in-service teachers’ beliefs sociocultural-based TPACK during their two semesters online professional development program. In-depth interviews with 25 teachers and 50 students were then conducted to draw their in-service teachers’ practices of sociocultural-based TPACK during online teaching practicum as part of their professional development program funded by the Indonesian Ministry of Education. The findings indicated that the in-service teachers had a high level of pedagogical, technological content knowledge but their sociocultural TPACK practices were restricted school-based cultural interactions because of their low understanding of cultural concepts during the online professional development program. Teachers’ low understanding of sociocultural conceptions, limited experiences, and access to wider cultural communities became the main barriers for the implementation of sociocultural-based TPACK. Successful practice, challenges, and future prospects regarding the implementation of sociocultural-based TPACK were also discussed in the study.
UvA-DARE ( Digital Academic Repository ) Overview of the TREC 2012 contextual suggestion track

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Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis

ABSTRACT Rifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC0–24) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC0–24 values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n = 1,000). The geometric mean AUC0–24 value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with an r2 value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of −0.4%. This study showed that the rifampin AUC0–24 in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.
Emotion Regulation and Executive Functions in Children 1 Exploring the Relationship Between Emotion Regulation and Executive Functions in Children

The present research addresses how emotion regulation and executive functions may be related in 7and 8-year-old children, allowing us to probe the question of whether these two constructs may have shared, rather than specialized, cognitive underpinnings. To do this, we explored whether specific temperamental traits (i.e. internalizing and externalizing) relate to different types of cognitive control (i.e. inhibitory and proactive control) through individual differences. Temperament was measured through a parent-report survey and inhibitory and proactive control were measured through two cognitive tasks standardly used for measuring those constructs, the Antisaccade task and the AX-continuous performance task (respectively). We did not find evidence that specific externalizing and internalizing temperamental traits reliably predict cognitive control. In post hoc analyses, we found that a combined measure of externalizing and internalizing measures (i.e. Total Problem Behavior) significantly predicted worse inhibitory control, while controlling for age and gender, suggesting that worse emotion regulation is related to worse inhibitory control. From these results, we were able to conclude that there may indeed be shared cognitive mechanisms between emotion regulation and certain executive functions, such as inhibitory control, though we cannot make any specific claims about what those mechanisms may be, given that emotion regulation does not appear to be related to proactive control.
Retenção de água em solos arenosos da Bacia Sedimentar do Tucano, Bahia.

O conhecimento das relacoes entre a agua e o solo e de fundamental importância para uma exploracao agricola, pois sendo o solo um reservatorio natural de agua, e essencial a avaliacao da sua capacidade de armazenamento para que haja um manejo eficiente de ambos. O objetivo do estudo foi avaliar a retencao e distribuicao de agua no solo, a partir da curva de retencao de agua em solos arenosos da bacia sedimentar do Tucano, no municipio de Gloria, Bahia. Em amostras de horizontes superficial A e subsuperficial C ou Bw de perfis de solos, foram determinadas em laboratorio capacidade de campo, ponto de murcha permanente e curva de retencao da agua. Foram tambem submetidas amostras as tensoes de coluna de agua de 40, 60, 80 e 100 cm, em tres repeticoes de cada horizonte, atraves da mesa de tensao. Em todas as areas estudadas, uma das caracteristicas mais marcantes dos resultados esta correlacionada com a distribuicao e arranjamento dos graos, bem como os conteudos de materiais finos nos perfis de solos. As curvas de retencao de agua dos Latossolos e Neossolos Quartzarenicos evidenciam que praticamente toda a agua e retida nos potenciais maiores (baixas tensoes), com ocorrencia de queda brusca da umidade, a partir da capacidade de campo.