bluuebunny/crossref_metadata_2025_split huggingface dataset:
An income-distributional analysis of the rent control subsidy
Authors: Donner Herman , Kopsch Fredrik 
Published: September 2023

AbstractRent control measures are typically in place to assist low-income households and decrease segregation. Yet, there is little empirical research on the social impact of such policies and specifically the role of how rent-controlled apartments are allocated. This study analyzes the income-distributional effects of rent control with a novel dataset that includes characteristics of those who received rent-controlled apartments between 2011 and 2016 in central Stockholm, Sweden. Specifically, this paper provides analysis of the impact of allocating apartments through a centrally managed queue with apartments from both public and private landlords. To quantify the rent subsidy, we estimate hypothetical market rents by taking the owner-occupied market as a point of deviation. We find a positive relationship between the rent subsidy and time in que. Apartments in the fourth quartile of subsidy require on average 21 years in que, while those in the first quartile require 10 years on average. There is considerably heterogeneity in the level of rent subsidy, and tenant income. Even as allocating through queuing should benefit high-income households less than allocation based on landlord preferences, we find several regressive effects. Controlling for time in queue, we find that tenants in the fourth quartile of annual income receive monthly rent subsidies that are substantially higher than renters in the first quartile of income. Similarly, rental apartments in the fourth quartile of the subsidy have older tenants with substantially higher incomes compared to less subsidized apartments. The regressive effect is driven by high earners renting larger apartments with larger absolute subsidies and being able to wait longer in queue.
Micro-segregation and residential mobility: case study of two athenian apartment blocks
Authors: Myofa Nikolina 
Published: September 2023

AbstractThis article considers changes regarding the social physiognomy of apartment blocks in urban housing in the densely built neighbourhoods of Athens City Centre. The aim is to illuminate the changes that have been a main consequence of the residential mobility that began during the post-World War II period and continues today. Thus, I will provide evidence of two apartment blocks located in the Kypseli neighbourhood of the Municipality of Athens that I have thoroughly investigated. These blocks are among the 28 I have previously examined in a project about Athenian apartment blocks.
Stakeholder perceptions of the benefits and barriers of implementing environmental management systems in the Maldivian construction industry
Authors: Rasheed Aminath S. , Booth Colin A. , Horry Rosemary E. 
Published: September 2023

AbstractUnprecedented increases in both tourism and population growth have put extreme pressure on the Republic of the Maldives, which has resulted in significant changes to the islands as a result of construction activities. This study is the first to investigate perceptions of the benefits and barriers of implementing environmental management systems (EMS) in the Maldivian construction industry, and what effective measures and strategies exist to drive environmental management practices. A subjectivist stance and deductive approach have been taken with an online survey adopting a quantitative strategy with a range of construction professions across the Maldivian construction sector. Using a central tendency measure of weighted average, the respondents’ opinions suggest that the primary benefits of having an EMS in the Maldives are perceived as the enhancement of corporate image and environmental protection; while the major barriers are lack of legal enforcement and the requirement for company structures and policies to change to accommodate an EMS. The findings also suggest that those companies who establish a waste management plan and where authorities enforce legal environmental requirements are believed to be the most effective means to encourage more widespread adoption. Based on these findings, it is recommended that managers, need to increasingly promote the principles of environmental management and sustainable practices/behaviours amongst construction industry stakeholders. Furthermore, policy makers within the Maldives should be exploring the viability of establishing financial incentive schemes (particularly for SMEs), in addition to encouraging wider adoption of EMSs across the Maldivian industry sectors.
The impact of negative gearing on the investment decisions of housing investors: the case of Greater Sydney
Authors: Bangura Mustapha , Lee Chyi Lin 
Published: September 2023

AbstractTo date, no empirical study has examined the impact of negative gearing and other factors on residential investors’ decisions using quantitative analysis. We applied a structural vector autoregression framework to trace the response of residential investors in Greater Sydney to shocks in its key drivers over the period 1991–2018. We discovered a residential investors’ profile in which negative gearing is being used to cushion any net rental loss during periods of low yield while expecting capital growth over their holding period. This supports the hypotheses of the study which posit that capital gains and negative gearing have a positive and negative relationship, respectively, with the number of residential investors. Additionally, a negative relationship between mortgage lending rate and number of investors is found, indicating a rising lending rate will increase expenses and contribute to low yield. We also found population growth and increased housing supply could increase the number of residential investors. These results could be used by tax and housing policy makers to recalibrate tax laws relating to negative gearing, especially for residential investment. Residential investors could potentially use this information for more informed decision making, particularly during periods of low yields.
Bioinks Functionalized with Natural Extracts for 3D Printing
Authors: Larraza Izaskun , Santamaria-Echart Arantzazu , Fernandes Isabel , Barreiro Filomena , Arbelaiz Aitor , Eceiza Arantxa 
Published: September 2023

AbstractIn the search of materials valid for direct ink writing (DIW) 3D printing and with special interest for the biomedical and pharmaceutical applications, the development of bioactive inks for DIW is of great interest. For that purpose, in this work bioactive waterborne polyurethane–urea inks were prepared by addition of natural extracts (logwood, chestnut, and alder buckthorn) and cellulose nanofibers (CNF). The rheological behavior of the inks proved to be strongly dependent on the extract type and content, and the addition route used. Inks prepared by ex-situ incorporation of the extracts showed a strong gel-like behavior, as did inks prepared with chestnut and alder buckthorn extracts, which, in turn, hindered a continuous flow during the printing process, resulting in 3D printed parts with poor shape fidelity. On the other hand, inks prepared in-situ and with logwood extract showed more facility to flow and higher homogeneity, which translated in better printability and better shape fidelity, further enhanced for CNF containing inks. 3D printed composites showed reinforced mechanical behavior, as well as in materials with enhanced antibacterial behavior. Overall, the possibility to successfully prepare bioactive inks valid for 3D printing was proven.
Incremental permutation feature importance (iPFI): towards online explanations on data streams
Authors: Fumagalli Fabian , Muschalik Maximilian , Hüllermeier Eyke , Hammer Barbara 
Published: September 2023

AbstractExplainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.
The role of miR-433-3p in vascular calcification in type 2 diabetic patients: targeting WNT/β-Catenin and RANKL/RANK/OPG signaling pathways
Authors: Elshamy Amira M. , Hafez Yasser Mostafa , Safa Mohamed A. E. , Ibrahim Hoda A. , Khalfallah Mohamed , Rizk Fatma H. , Eltabaa Eman F. , Ghafar Muhammad T. Abdel , Atef Marwa Mohamed 
Published: September 2023

Abstract
Background
Vascular calcification (VC) is a major predictor of cardiovascular diseases that represent the principal cause of mortality among type-2 diabetic patients. Accumulating data suggest the vital role of some microRNAs on vascular calcification as an epigenetic regulator. Thus, we assessed herein, the role of serum miR-433-3p in vascular calcification in type-2 diabetic patients.

Methods
Twenty healthy subjects (control group) and forty diabetic patients (20 without VC and 20 with VC) were involved in the study. miR-433-3p gene expression was measured. Runx2, Dickkopf-1 (DKK1), β-catenin, Receptor activator of nuclear factor kappa-B ligand (RANKL), and osteoprotegerin (OPG) levels in serum were assessed by ELISA technique.

Results
Diabetes patients had significantly lower levels of miR-433-3p expression in comparison to the control group, with the lowest levels being found in diabetic patients with VC. Furthermore, Runx2, β-catenin, and RANKL levels were significantly increased with concomitant lower DKK1 and OPG levels detected in the two diabetic groups especially those with VC.

Conclusion
Collectively, the study documented that down-regulation of miR-433-3p may contribute to the development of VC through activating WNT/β-Catenin and RANKL/RANK/OPG signaling pathways.
Improvement of Emission Monitoring System Accuracy in Aims of Increasing Air Quality of Smart City
Authors: Holubčík Michal , Jandačka Jozef , Nicolanská Miriam 
Published: September 2023

AbstractEnvironmental pollution and air quality are increasingly important topics within the areas of Smart City and state regulations. The aim of the article is to present the progress in the design of the emission monitoring system intended for use in non-industry heating appliances in households. The article analyses potential opportunities in aims of improving the design of a monitoring system that was experimentally tested on small heat source. Geometry modifications, sensor placements and orientation were evaluated using Computational Fluid Dynamics in Ansys Fluent software 2022 R2 including both air and CO/CO2 dispersion models. The final geometry introduced a streamlined funnel along with a separation element, which distributed the air flow evenly between the sensors. The results showed more uniform air flow as the standard deviation of downstream velocity improved from ± 0,693 m/s to ± 0,297 m/s. In conclusion, these outcomes assist in the reduction of air pollution.
Foundations and knowledge clusters in TikTok (Douyin) research: evidence from bibliometric and topic modelling analyses
Authors: Rejeb Abderahman , Rejeb Karim , Appolloni Andrea , Treiblmaier Horst 
Published: September 2023

AbstractThe goal of this study is to comprehensively analyze the dynamics and structure of TikTok research since its initial development. The scholarly composition of articles dealing with TikTok was dissected via a bibliometric study based on a corpus of 542 journal articles from the Scopus database. The results show that TikTok research has flourished in recent years and also demonstrate that the authors’ collaboration networks are disjointed, indicating a lack of cooperation among TikTok researchers. Furthermore, the analysis reveals that research collaboration among academic institutions reflects the North-South divide, also highlighting a limited research collaboration between institutions in developed and developing countries. Based on the keyword co-occurrence network and topic modeling, TikTok research revolves mainly around five thematic areas, including public health, health communication and education, platform governance, body image, and its impact on children and students. Based on these findings, numerous suggestions for further research are offered. As far as the authors are aware, this is the first application of bibliometrics and topic modeling to assess the growth of TikTok research and reveal the intellectual base of this knowledge domain.
Explainable Enhanced Recurrent Neural Network for lie detection using voice stress analysis
Authors: Talaat Fatma M. 
Published: September 2023

AbstractLie detection is a crucial aspect of human interactions that affects everyone in their daily lives. Individuals often rely on various cues, such as verbal and nonverbal communication, particularly facial expressions, to determine if someone is truthful. While automated lie detection systems can assist in identifying these cues, current approaches are limited due to a lack of suitable datasets for testing their performance in real-world scenarios. Despite ongoing research efforts to develop effective and reliable lie detection methods, this remains a work in progress. The polygraph, voice stress analysis, and pupil dilation analysis are some of the methods currently used for this task. In this study, we propose a new detection algorithm based on an Enhanced Recurrent Neural Network (ERNN) with Explainable AI capabilities. The ERNN, based on long short-term memory (LSTM) architecture, was optimized using fuzzy logic to determine the hyperparameters. The LSTM model was then created and trained using a dataset of audio recordings from interviews with a randomly selected group. The proposed ERNN achieved an accuracy of 97.3%, which is statistically significant for the problem of voice stress analysis. These results suggest that it is possible to detect patterns in the voices of individuals experiencing stress in an explainable manner.
Mouth and oral disease classification using InceptionResNetV2 method
Authors: Rashid Javed , Qaisar Bilal Shabbir , Faheem Muhammad , Akram Arslan , Amin Riaz ul , Hamid Muhammad 
Published: September 2023

AbstractDigital tools have greatly improved the detection and diagnosis of oral and dental disorders like cancer and gum disease. Lip or oral cavity cancer is more likely to develop in those with potentially malignant oral disorders. A potentially malignant disorder (PMD) and debilitating condition of the oral mucosa, oral submucous fibrosis (OSMF), can have devastating effects on one’s quality of life. Incorporating deep learning into diagnosing conditions affecting the mouth and oral cavity is challenging. Mouth and Oral Diseases Classification using InceptionResNetV2 Method was established in the current study to identify diseases such as gangivostomatitis (Gum), canker sores (CaS), cold sores (CoS), oral lichen planus (OLP), oral thrush (OT), mouth cancer (MC), and oral cancer (OC). The new collection, termed "Mouth and Oral Diseases" (MOD), comprises seven distinct categories of data. Compared to state-of-the-art approaches, the proposed InceptionResNetV2 model’s 99.51% accuracy is significantly higher.
Reirradiation versus systemic therapy versus combination therapy for recurrent high-grade glioma: a systematic review and meta-analysis of survival and toxicity
Authors: Marwah Ravi , Xing Daniel , Squire Timothy , Soon Yu Yang , Gan Hui K. , Ng Sweet Ping 
Published: September 2023

Abstract
Purpose
This review compares reirradiation (reRT), systemic therapy and combination therapy (reRT & systemic therapy) with regards to overall survival (OS), progression-free survival (PFS), adverse effects (AEs) and quality of life (QoL) in patients with recurrent high-grade glioma (rHGG).

Methods
A search was performed on PubMed, Scopus, Embase and CENTRAL. Studies reporting OS, PFS, AEs and/or QoL and encompassing the following groups were included; reirradiation vs systemic therapy, combination therapy vs systemic therapy, combination therapy vs reRT, and bevacizumab-based combination therapy vs reRT with/without non-bevacizumab-based systemic therapy. Meta-analyses were performed utilising a random effects model. Certainty of evidence was assessed using GRADE.

Results
Thirty-one studies (three randomised, twenty-eight non-randomised) comprising 2084 participants were included. In the combination therapy vs systemic therapy group, combination therapy improved PFS (HR 0.57 (95% CI 0.41–0.79); low certainty) and OS (HR 0.73 (95% CI 0.56–0.95); low certainty) and there was no difference in grade 3 + AEs (RR 1.03 (95% CI 0.57–1.86); very low certainty). In the combination therapy vs reRT group, combination therapy improved PFS (HR 0.52 (95% CI 0.38–0.72); low certainty) and OS (HR 0.69 (95% CI 0.52–0.93); low certainty). In the bevacizumab-based combination therapy vs reRT with/without non-bevacizumab-based systemic therapy group, adding bevacizumab improved PFS (HR 0.46 (95% CI 0.27–0.77); low certainty) and OS (HR 0.42 (95% CI 0.24–0.72; low certainty) and reduced radionecrosis (RR 0.17 (95% CI 0.06–0.48); low certainty).

Conclusions
Combination therapy may improve OS and PFS with acceptable toxicities in patients with rHGG compared to reRT or systemic therapy alone. Particularly, combining bevacizumab with reRT prophylactically reduces radionecrosis.
Registration: CRD42022291741.
Territorial vulnerability to natural hazards in Europe: a composite indicator analysis and relation to economic impacts
Authors: Navarro Daniel , Cantergiani Carolina , Abajo Beñat , Gomez de Salazar Izaskun , Feliu Efrén 
Published: September 2023

AbstractThis article presents an assessment of territorial vulnerability to natural hazards in Europe at the regional level (NUTS3). The novelty of the study lies in assessing vulnerability to natural hazards through a composite indicator analysis over a large extension (1395 territories in 32 different countries), and in analysing the relation between vulnerability and economic impacts of past disasters. For responding to the first goal, a principal component analysis (PCA) was performed over 25 indicators, previously grouped into susceptibility and coping capacity, and subsequently combined to obtain the final vulnerability. The main result is the spatial distribution of vulnerability to natural hazards across Europe through a normalised and comparative approach, which indicates that 288 out of 1395 regions presented a high or a very high level of vulnerability. They are concentrated in Eastern and Southern Europe, and in the Baltic Region, and the sum of their population lives in territories with high or very high vulnerability level, representing 20% of the total sample, i.e. 116 out of 528 million inhabitants. Regarding the methodology for analysing the relation between vulnerability and economic impacts, a spatial regression model has been used to combine hazard, exposure and vulnerability. The outcomes indicate a high level of agreement between vulnerability and the distribution of past economic impacts, which confirm that the indicator-based approach is a good proxy for assessing vulnerability to natural hazards. Knowing the distribution of vulnerability is of high relevance for targeting disaster risk management and climate change adaptation actions to the highest priority regions.
Influence of small-scale spatial variability of soil properties on yield formation of winter wheat
Authors: Groß Jonas , Gentsch Norman , Boy Jens , Heuermann Diana , Schweneker Dörte , Feuerstein Ulf , Brunner Johannes , von Wirén Nicolaus , Guggenberger Georg , Bauer Bernhard 
Published: September 2023

Abstract
Background
With the increasing development of sophisticated precision farming techniques, high-resolution application maps are frequently discussed as a key factor in increasing yield potential. However, yield potential maps based on multiple soil properties measurements are rarely part of current farming practices. Furthermore, small-scale differences in soil properties have not been taken into account.

Methods
To investigate the impact of soil property changes at high resolution on yield, a field trial has been divided into a sampling grid of 42 plots. The soil properties in each plot were determined at three soil depths. Grain yield and yield formation of winter wheat were analyzed at two sites.

Results
Multiple regression analyses of soil properties with yield measures showed that the soil contents of organic carbon, silt, and clay in the top and subsoil explained 45–46% of the variability in grain yield. However, an increasing clay content in the topsoil correlated positively with grain yield and tiller density. In contrast, a higher clay content in the subsoil led to a decrease in grain yield. A cluster analysis of soil texture was deployed to evaluate whether the soil´s small-scale differences caused crucial differences in yield formation. Significant differences in soil organic carbon, yield, and yield formation were observed among clusters in each soil depth.

Conclusion
These results show that small-scale lateral and vertical differences in soil properties can strongly impact crop yields and should be considered to improve site-specific cropping techniques further.
MERCI: a simple method and decision-support tool to estimate availability of nitrogen from a wide range of cover crops to the next cash crop
Authors: Constantin Julie , Minette Sébastien , Vericel Gregory , Jordan-Meille Lionel , Justes Eric 
Published: September 2023

Abstract
Background and aims
Cover crops can efficiently increase nitrogen (N) recycling in agroecosystems. By providing a green-manure effect for the next crop, they allow reduced mineral fertilisation. We developed a decision-support tool, called MERCI, to predict N available from cover crop residues over time, from a single measurement of fresh shoot biomass.

Methods
We coupled a large experimental database from France with a simulation experiment using the soil-crop model STICS. More than 25 000 measurements of 74 species of cover crops as a sole crop or bispecific mixtures were collected. Linear regression models, at the species, family or entire-database level depending on the data available, were built to predict dry biomass, N amount and C:N ratio. Dynamics of N mineralized and leaching from cover crop residues were predicted at 24 contrasting sites as a function of the biomass, carbon (C):N ratio and termination date.

Results
Correlations between fresh biomass, dry biomass and N amounts in experimental data were strong (r = 0.80-0.96), and predicted N amounts in fresh shoot biomass were relatively accurate. Percentages of N mineralized and leached simulated by STICS were explained mainly by the C:N ratio, site and number of months after termination, but to different degrees.

Conclusion
MERCI is an easy and robust decision-support tool for predicting N release in the field, and could thus be adopted by advisors and farmers to improve management of nutrient recycling in temperate arable cropping systems.