Search

Unmanned Aerial Vehicle (UAV)-based Road Damage Detection (RDD) is important for daily maintenance and safety in cities, especially in terms of significantly reducing labor costs. However, current UAV-based RDD research still faces many challenges. For example, the damage with irregular size and direction, the masking of damage by the background, and the difficulty of distinguishing damage from the background significantly affect the ability of UAV to detect road damage in daily inspection. To solve these problems and improve the performance of UAV in real-time road damage detection, we design and propose three corresponding modules: a feature extraction module that flexibly adapts to shape and background; a module that fuses multiscale perception and adapts to shape and background; an efficient downsampling module. Based on these modules, we designed a multi-scale, adaptive road damage detection model with the ability to automatically remove background interference, called Dynamic Scale-Aware Fusion Detection Model (RT-DSAFDet). Experimental results on the UAV-PDD2023 public dataset show that our model RT-DSAFDet achieves a mAP50 of 54.2%, which is 11.1% higher than that of YOLOv10-m, an efficient variant of the latest real-time object detection model YOLOv10, while the amount of parameters is reduced to 1.8M and FLOPs to 4.6G, with a decrease by 88% and 93%, respectively. Furthermore, on the large generalized object detection public dataset MS COCO2017 also shows the superiority of our model with mAP50–95 is the same as YOLOv9-t, but with 0.5% higher mAP50, 10% less parameters volume, and 40% less FLOPs. [ABSTRACT FROM AUTHOR]

BackgroundPublic health policy evaluation is crucial for improving health outcomes, optimizing healthcare resource allocation, and ensuring fairness and transparency in decision-making. With the rise of big data, traditional evaluation methods face new challenges, requiring innovative approaches.MethodsThis article reviews the principles, scope, and limitations of traditional public health policy evaluation methods and explores the application of machine learning in evaluating public health policies. It analyzes the specific steps for applying machine learning and provides practical examples. The challenges discussed include model interpretability, data bias, the continuation of historical health inequities, and data privacy concerns, while proposing ways to better apply machine learning in the context of big data.ResultsMachine learning techniques hold promise in overcoming some limitations of traditional methods, offering more precise evaluations of public health policies. However, challenges such as lack of model interpretability, the perpetuation of health inequities, data bias, and privacy concerns remain significant.DiscussionTo address these challenges, the article suggests integrating data-driven and theory-driven approaches to improve model interpretability, developing multi-level data strategies to reduce bias and mitigate health inequities, ensuring data privacy through technical safeguards and legal frameworks, and employing validation and benchmarking strategies to enhance model robustness and reproducibility.

We study the effects of receiving immunization from COVID‐19 on households' economic insecurity. To provide causal estimates we use a fuzzy regression discontinuity design which takes advantage of the UK's immunization plan. The plan was primarily based on age, granting differential eligibility to proximate cohorts. Our estimated local average treatment effect indicates that the share of households who declared being economically insecure dropped by 41 percentage points among those who received the vaccine due to the eligibility criteria. Using a difference‐in‐discontinuity design we next document that immunization was more salient for women as well as for large households and those with children. Our results suggest that the mass immunization campaign against COVID‐19 had relevant short‐run economic effects, well beyond its expected impact on people's health. [ABSTRACT FROM AUTHOR]

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables’ cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.

Income inequality is a distributional phenomenon. This paper examines the impact of U.S. governor's party allegiance (Republican vs Democrat) on ethnic wage gap. A descriptive analysis of the distribution of yearly earnings of Whites and Blacks reveals a divergence in their respective shapes over time suggesting that aggregate analysis may mask important heterogeneous effects. This motivates a granular estimation of the comparative causal effect of governors' party affiliation on labor market outcomes. This paper uses a regression discontinuity design (RDD) based on marginal electoral victories and samples of quantiles groups by wage and hours worked. Overall, the distributional causal estimations show that the vast majority of subgroups of Black workers earnings are not affected by democrat governors' policies, suggesting the possible existence of structural factors in the labor markets that contribute to create and keep a wage trap and/or hour worked trap for most of the subgroups of Black workers. Democrat governors increase the number of hours worked of Black workers at the highest quartiles of earnings. A bivariate quantiles groups analysis shows that democrats decrease the total hours worked for Black workers who have the largest number of hours worked and earn the least. Black workers earnings more and working fewer hours than half of the sample see their number of hours worked increase under a democrat governor. [ABSTRACT FROM AUTHOR]

Why are women strongly underrepresented in top political positions? We analyze the effect of party leaders' gender on their ability to capitalize on political power during negotiations to form a new government after elections. We leverage the as-if random assignment of a bargaining advantage in close local elections in Spain through a regression discontinuity design and find that women are about 25 percentage points less likely than men to secure the mayor's position when they win elections by a narrow margin, even if their parties manage to join the governing coalition anyway. This article contributes to the understanding of the role of personal characteristics in the political process and has far-reaching implications for gender equality and the quality of democratic representation. [ABSTRACT FROM AUTHOR]

Rosai Dorfman disease is generally defined as a massive bilateral painless cervical lymphadenopathy accompanied with both fever and leukocytosis with neutrophilia. Additionally, it may possibly be associated with polyclonal hypergammaglobulinemia, reversal of CD4/CD8 ratio, the elevated erythrocyte sedimentation rate (ESR), microcytic anemia, and thrombocytosis. Rosai-Dorfman disease is known as a benign self-limiting disease, so no treatment is required in many cases, although it causes death in some cases by involving vital organs like kidney. The treatment is required when there is a life-threatening situation such as airway obstruction or involvement of vital organs such as kidney, liver, and lower respiratory tract. The required treatment choices include steroid therapy, chemotherapy, radiotherapy, and surgery. Surgical treatment is performed for bulk removal to resolve the obstruction caused by the mass as well as taking biopsy for the definite histopathologic diagnosis of disease. A 26-year-old man was referred to oral and maxillofacial surgery (OMFS) clinic of Taleghani hospital with chief complaints of pain and swelling of left submandibular space. According to the patient himself, the swelling had been started three months earlier. After rejecting dental source of the lesion, we decided to remove the mass by excisional biopsy concerning the patient’s discomfort. Histopathology report verified Rosai Dorfman disease as definite diagnosis of the mass.

Objectives Rosai-Dorfman disease (RDD) is one of 3 major types of histiocytosis, along with Erdheim-Chester disease and Langerhans cell histiocytosis. While historically, RDD was considered a benign self-limited condition, current data show MAPK/ERK pathway mutations in 30% to 50% of cases, indicative of a clonal process. Rosai-Dorfman disease was incorporated as a histiocytic neoplasm in the fifth edition of the World Health Organization classification of hematopoietic tumors and the International Consensus Classification. Methods We discuss the diagnosis of RDD using 2 illustrative cases, interpretative challenges, and a diagnostic algorithm. Results Rosai-Dorfman disease involves nodal and extranodal sites, including skin, sinuses, salivary gland, orbit, central nervous system, kidney, and bone. In a subset, RDD can coexist with other neoplasms (lymphomas, other histiocytosis) or autoimmune disease. Morphologically, RDD histiocytes are characterized by enlarged round to oval nuclei, distinct nucleoli, and voluminous cytoplasm with engulfment of inflammatory cells (emperipolesis). By immunohistochemistry, they express CD68, CD163 (majority), S100, OCT2, and cyclin D1. Appropriate use of ancillary studies is important to support the diagnosis of RDD while excluding other histiocytic neoplasms and reactive histiocytic proliferations. Conclusions Management of RDD is dependent on the extent of organ involvement and clinical symptoms. In patients who require therapy, next-generation sequencing is recommended to identify MAPK/ERK pathway mutations for targeted therapy. [ABSTRACT FROM AUTHOR]

Abstract With the construction of ecological civilization, central government's direct inspections have become the new norm of environmental protection supervision. Since 2016, the central government has been dispatching supervision teams, one batch after another, to conduct environmental protection supervision in the provinces. In this paper, the regression discontinuity designs were used to study the impact of central environmental protection supervision on the quality of air and water. Our results indicate that air quality was improved significantly during the supervision, while water quality was improved significantly during the rectification period after the supervision. A heterogeneity analysis shows that supervision had a significant impact on highly polluted areas. The results further suggest that for the continuous improvement of environmental quality, it is necessary to build a long‐term governance mechanism.

Radiation therapy is increasingly becoming more targeted in how the doses are delivered. Online MR imaging, when combined with particle therapy such as proton or carbon ion therapy will offer a novel and exciting opportunity for radiotherapy. Integration of radiotherapy with MR imaging for carbon ion therapy is an active area of research, but its clinical implementation is still a way off. It is known that the employment of extremely high magnetic fields in particle therapy poses several challenges, in particular those related to treatment planning, workflow, dose delivery, and dosimetry. The objective of this study is to validate the results of simulation calculations of the interaction of a carbon ion beam in water by comparative analysis with existing literature data and examination of Bragg curves. This involves evaluating the initial percentage of ion fragmentation and elucidating the variations of dose rates along the propagation axis of a carbon ion beam for specified values of the Z-axis (lateral side). In addition, the study aims to investigate the beam deflection and the displacement of the dose profile curves with respect to the lateral axis in a magnetic field oriented perpendicular to the beam axis. A carbon ion beam was delivered on a voxelized phantom of rectangular geometry simulated by applying a GEANT4 simulation code and ROOT 6.24/08 for the analysis. The beam deflection was calculated for a 270 MeV/u beam energy for three different magnetic fields of 0.35, 1.5 and 2 T. While the rates of decrease and increase in dose, as well as lateral shifts of dose profiles were performed in the presence of 1.5 T magnetic field (MF). The results of the simulation exhibit a satisfactory correlation with the experimental data, demonstrating that the Bragg peak area remained stable under a 1.5 T magnetic field and for a beam energy of 270 MeV/u. Furthermore, 52.32% of the carbon ions were fragmented into lighter particles before reaching the designated area. The analysis encompassed Bragg curves at various off-axis positions, the Rate of Decreasing Dose (RDD), the Rate of Increasing Dose (RID), Lateral Dose-Profile, and the lateral deflection of the primary beam dose-profile along the X-axis. The findings revealed a 45.919% reduction in dose at the center of the treatment field and a lateral displacement of 4.57675 mm under the influence of a 1.5 T magnetic field. Additionally, the displacement variation at the Bragg peak area as a function of the magnetic field followed a linear relationship. The simulation model demonstrates satisfactory performance. When administering treatment with carbon ions under a 1.5 T magnetic field, it is imperative to consider a lateral displacement of 4.57675 mm of the Bragg peak. Additionally, the percentage reduction in dose along the lateral axis must be accounted for. Furthermore, the calibration curve obtained must be utilized to accurately determine the extent of carbon ion beam deviation within the Bragg peak region. • Simulation of a therapeutic beam of carbon ions and a voxelized phantom. • Transport of carbon ion beams in different perpendicular magnetic fields. • Bragg curves in different off-axis positions and the relative dose perturbation by the presence of a 1.5 T magnetic field. • The decreasing and increasing dose rates along the carbon ion beam propagation axis. • The calibration line as a function of magnetic field values at the Bragg peak area. [ABSTRACT FROM AUTHOR]

Among tandem-mass-spectrometry approaches, radical-directed dissociation (RDD) is uniquely sensitive to molecular structure because the location and types of cleavage observed are dictated by radical migration propensities. Although the underlying chemistry for many RDD fragmentation pathways has been previously explained, x n -H 2 O fragment ions that occur exclusively at serine and threonine residues, have not been examined in detail. Creation of this fragment type inherently requires two dissociation events, one to lose water and another to cleave the peptide backbone. Double dissociations are typically disfavored relative to pathways requiring a single cleavage, yet x n -H 2 O fragment ions are abundant in RDD spectra. To understand why this fragmentation pathway is favorable, we used a combination of computational chemistry and experiments on peptides with a variety of covalent modifications. Our results explore the energetics, location, and migration of the radical in each step of the mechanism, revealing that favorability can be attributed to the stability of the required radical intermediates and access to low-energy pathways connecting them. Ultimately, the abundant nature of x n -H 2 O ions and the selectivity associated with their exclusive generation at Ser/Thr provides high value sequence information in RDD experiments. [Display omitted] • Radical-mediated dissociation occurring exclusively at Ser/Thr residues. • Alpha radical attack produces an unusual N-amidyl radical intermediate. • X-H 2 O fragments are the only example of alpha-radical initiated backbone cleavage. [ABSTRACT FROM AUTHOR]

Plain Language summary Maternal health care (MHC) utilization is one of the Millennium Development Goals (SDG) of pursuit. Globally, most low-income countries like Uganda contribute greatly to pregnancy-related mortalities that are largely preventable through adequate utilization of essential maternal health care services. Though Uganda over time has registered some increase in maternal utilization, this has been attributed to a number of factors. This study intended to demonstrate whether the introduction of free primary education in Uganda led to increase in the utilization of maternal health services. To address this, we used Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS), Probit and Regression Discontinuity Design (RDD) models using Demographic Health Survey (DHS) data 2006 and 2011. The study found out that indeed the introduction of free primary education increased the utilization of MHC.

We explore a discontinuity in the incentives created by the Brazilian Tourism Regionalization Program. We employ a research discontinuity design, focussing on municipalities in the transitions between two classes. We observe significant effects of the Program in tourist cities, including an increase in the value-added of services and per capita GDP. Additionally, we find that the value-added in industrial activities increases, implying a spillover effect from the improvement in local tourism conditions for other economic activities. Our article documents how tourism strategies can have a beneficial effect on a city's entire economic structure. [ABSTRACT FROM AUTHOR]

Big data analysis using machine learning has become a challenging problem today. Classification problems become more challenging when class distribution is imbalanced. In this paper, we propose a distributed ensemble model with an intelligence technique based on Particle Swarm Optimization to overcome the imbalanced problem. For compensating the class imbalance, first SMOTE is used to balance the minority class samples, and then sampling based on Particle Swarm Optimization is applied. Here, to perform fast processing, the whole model is implemented using spark-cluster computing, which uses the underlying concept of parallel programming of spark RDD. Results of the proposed system have shown consistent improvements on several evaluation metrics and overall processing time. Evaluation of the proposed system has been done using different performance metrices also comparison between sequential and distributed ensemble models. Most of the existing techniques show different performances for different datasets, while the proposed method has shown better generalization property, which improves the data-model dependency issue. The proposed model has been evaluated using KDD-CUP'99 intrusion detection and insect sensor datasets. For the datasets, it shows better improvement over traditional sampling techniques. F-Measure value is 99% for KDD'cup dataset and 92% for insect dataset. [ABSTRACT FROM AUTHOR]

Considerable changes in particulate matter (PM) during COVID-19 lockdown in major cities around the World demand changes in exposure assessment studies of PM. The present study shows variations in respiratory deposition dose (RDD) of both fine (PM2.5) and coarse (PM10) particles before, during and after Covid-19 lockdown phases at three sites (with different pollution signatures) in Delhi—Alipur, Okhla and Pusa Road. Exposure assessment study showed mean PM2.5 RDD (± S.D.) (µg/min) for walk and sit mode during before lockdown (BL) as 2.41(± 1.20) and 0.84(± 0.42) for Alipur, 2.71(± 1.60) and 0.94(± 0.56) for Okhla, and 2.54(± 1.28) and 0.88(± 0.44) for Pusa road, which decreased drastically during Lockdown 1(L1) as 0.85(± 0.35) and 0.30(± 0.12) for Alipur, 0.83(± 0.33) and 0.29(± 0.11) for Okhla, and 0.68(± 0.28) and 0.23(± 0.10) for Pusa road, respectively. Mean PM10 RDD (± S.D.) (µg/min) for walk and sit mode during before lockdown (BL) as 3.90 (± 1.73) and 1.36 (± 0.60) for Alipur, 4.74 (± 2.04) and 1.65 (± 0.71) for Okhla, and 4.25 (± 1.69) and 1.48 (± 0.59) for Pusa Road, respectively which decreased drastically during Lockdown 1(L1) as 2.19 (± 0.95) and 0.76 (± 0.33) for Alipur, 1.73 (± 0.67) and 0.60 (± 0.23) for Okhla and, 1.45 (± 0.50) and 0.50 (± 0.17) for Pusa Road, respectively. Significant decrease in RDD concentrations (Both PM2.5 and PM10) than that of BL phase have been found during Lockdown 1(L1) phase and other successive lockdown and unlock phases—Lockdown 2(L2), Lockdown 3(L3), Lockdown 4(L4) and Unlock1 (UL1) phases. Changes in RDD values during lockdown phases were affected by lesser traffic emission, minimized industrial activities, biomass burning activities, precipitation activities, etc. Seasonal variations of RDD showed Delhites are found exposed to more fine and coarse particles' RDD (walk and sit modes) before and after lockdown, i.e. during normal days than during lockdown phases showing potential health effects. People in sit condition found less exposed to fine and coarse RDD comparison to those in walk condition both during normal and lockdown days. [ABSTRACT FROM AUTHOR]

Little evidence currently exists on the effects of COVID-19 lockdowns on air quality in low-income countries, where most air pollution-linked deaths occur. We offer the first study on the lockdown impacts in Vietnam, a poorer country with the worsening air pollution that has had a successful fight against early waves of the pandemic. We compile a new, rich database from various sources including satellite air pollution data for the past year from January 2020 to January 2021 that covers both the pre-pandemic and post-pandemic lockdown periods. Employing the Regression Discontinuity Design method, we find NO2 concentration to decrease by 24 per cent to 32 per cent 2 weeks after the COVID-19 lockdown. While these positive effects on air quality are comparable to those found in previous studies on stricter gasoline regulations or transition to cleaner energy sources, they dissipate after 10 weeks. Our findings are robust to different measures of air quality and model specifications. We also find that mobility restrictions are a potential channel for improved air quality. Finally, our back-of-the-envelope calculations suggest that 2 weeks after the lockdown, the economic gains from better air quality are at least 33.9 million US dollars. [ABSTRACT FROM AUTHOR]

The corporate social responsibility (CSR) report is an important carrier of non-financial information disclosure of enterprises and an important bridge of communication between enterprises and interested parties. Compulsory disclosure has promoted the improvement of CSR levels to some extent. While, for interested parties, their attention to various dimensions of CSR has significant differences, which leads to the heterogeneous impact of mandatory disclosure policy on its different dimensions. Through regression discontinuity design model (RDD), as well as using quasi-natural experiments of Chinese mandatory disclosure policies issued in 2008, we are going to get the following conclusions by analyzing the heterogeneous impact of mandatory disclosure on CSR with the environment (CER), social (SOC) and economic (ECO) three-dimension on the basis of verifying that mandatory disclosure policy has a positive impact on CSR. (1) The effects of mandatory disclosure on the three dimensions of CSR are heterogeneous, that is, the significant effects and directions are significantly different in the three dimensions. (2) The heterogeneity of mandatory disclosure on CSR is reflected in the changing trend, and there is no significant difference at the turning point of the trend. (3) The heterogeneity of the impact mechanism of mandatory disclosure on CSR is reflected in the different mediating variables of policy on different dimensions impact, that is, the mediating variables of CER and ECO are the environmental disclosure information and return on assets. (4) The impact of mandatory disclosure on different dimensions of CSR is heterogeneous when the nature of industries and property rights are different.

Traditional electron-transfer dissociation (ETD) experiments operate through a complex combination of hydrogen abundant and hydrogen deficient fragmentation pathways, yielding c and z ions, side-chain losses, and disulfide bond scission. Herein, a novel dissociation pathway is reported, yielding homolytic cleavage of carbon-iodine bonds via electronic excitation. This observation is very similar to photodissociation experiments where homolytic cleavage of carbon-iodine bonds has been utilized previously, but ETD activation can be performed without addition of a laser to the mass spectrometer. Both loss of iodine and loss of hydrogen iodide are observed, with the abundance of the latter product being greatly enhanced for some peptides after additional collisional activation. These observations suggest a novel ETD fragmentation pathway involving temporary storage of the electron in a charge-reduced arginine side chain. Subsequent collisional activation of the peptide radical produced by loss of HI yields spectra dominated by radical-directed dissociation, which can be usefully employed for identification of peptide isomers, including epimers. Graphical Abstract ᅟ.

The non-intrusive screening of shipping containers at national borders serves as a prominent and vital component in deterring and detecting the illicit transportation of radioactive and/or nuclear materials which could be used for malicious and highly damaging purposes. Screening systems for this purpose must be designed to efficiently detect and identify material that could be used to fabricate radiological dispersal or improvised nuclear explosive devices, while having minimal impact on the flow of cargo and also being affordable for widespread implementation. As part of current screening systems, shipping containers, offloaded from increasingly large cargo ships, are driven through radiation portal monitors comprising plastic scintillators for gamma detection and separate, typically 3He-based, neutron detectors. Such polyvinyl-toluene plastic-based scintillators enable screening systems to meet detection sensitivity standards owing to their economical manufacturing in large sizes, producing high-geometric-efficiency detectors. However, their poor energy resolution fundamentally limits the screening system to making binary “source” or “no source” decisions. To surpass the current capabilities, future generations of shipping container screening systems should be capable of rapid radionuclide identification, activity estimation and source localisation, without inhibiting container transportation. This review considers the physical properties of screening systems (including detector materials, sizes and positions) as well as the data collection and processing algorithms they employ to identify illicit radioactive or nuclear materials. The future aim is to surpass the current capabilities by developing advanced screening systems capable of characterising radioactive or nuclear materials that may be concealed within shipping containers.

To increase public transport use, especially for individuals with lower incomes, many cities worldwide have introduced subsidies for public transport systems. However, quantitative evidence of their effects on actual ridership remains scarce, especially in Global South countries. Using a quasi-experimental regression discontinuity design (RDD) in combination with administrative data for all personalized travel cards of public transport users during the years 2017–2019 in Bogotá, Colombia, the present paper assesses the causal effect of a transport subsidy focused on low-income individuals on the number of trips that individuals undertake. Our results show that the subsidy, equaling 32% of the regular fare, significantly and substantially increases the total number of monthly public transport trips. However, the results suggest that the size of the subsidy's effect on ridership has decreased over time, while also evidencing a more pronounced effect among economically active individuals compared to inactive ones. Overall, our results suggest that public transport subsidies for low-income individuals can be an effective way for increasing public transport use among this population segment, which may translate into improved well-being because of improved access to local labor markets and recreational activities. • Many Global South cities have introduced public transport subsidies for poor individuals whose efficiency has not been evaluated yet. • We estimate the causal effect of Bogotá's transport subsidy for low-income individuals on the number of trips that passengers undertake. • Using a quasi-experimental design we find that Bogotá's transport subsidy for the poorest significantly increases public transport use. • However, the effect in terms of public transport use has decreased steadily over time, raising important questions about its efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Revue Economique is the property of Fondation Nationale des Sciences Politiques and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we investigate whether the unpleasant mood of managers caused by air pollution leads to poorer decision‐making quality. Using a sample of 9,282 firm‐year observations from 2014 to 2017 in China, we show that (i) the mood of managers becomes more negative as air quality decreases; and (ii) there is a negative relation between air pollution and financial reporting quality. Furthermore, this association is stronger for firms with (i) weaker corporate governance; and (ii) top management teams with a lower average age, fewer females and a lower average educational level. Our results hold through various robustness tests. [ABSTRACT FROM AUTHOR]

This paper investigates what is the effect on Brazilian municipal public finances of electing women to the position of head of the local executive branch. By performing a Discontinuous Regression Design (RDD), the results indicated that there is no effect of elected women on municipal fiscal balance. However, the findings point towards personnel and social expenditures. As a mechanism, the paper identifies that there is a greater hiring of education and health professionals as a result of the election of Women Mayors. These results were robust to other specifications and placebo tests. The article brings contributions to the fiscal literature, especially the Brazilian one, as well as enhances research that studies gender differences and its various effects on the social environment. [ABSTRACT FROM AUTHOR]

The particulate matter (PM) emission from tire burning is the main concern of on-site road pavement practice. This study estimated total PM (TPM) emission during different road pavement activities and found that tire burning contributes 40.74% to TPM emission. The box model approach estimated the ground-level TPM for inhabitants living within one km from pavement activity. The modeled TPM was 104.19 µg m−3. The measured personal exposure to PM10 for workers near tire burning was 18,940.80 µg m−3. The particle number concentration (PNC) was also measured at the activity site. The accumulation and coarser mode PNC were 1.16E + 08 # m−3 and 2.91E + 06 # m−3 (during activity) and 6.59E + 07 # m−3 and 1.48E + 06 # m−3 (after activity), respectively. The estimated respiratory deposition doses (RDD) of PM10 for workers during activity were 799.81 µg min−1 (male) and 719.06 µg min−1 (female). Additionally, the estimated RDD of TPM for inhabitants residing away from the source during pavement activities were 0.79 µg min−1 (male) and 0.57 µg min−1 (female). Furthermore, the RDD of PNC were 1.46E + 06 # min−1 (male) and 1.31E + 06 # min−1 (female) during activity and 7.47E + 05# min−1 (male) and 6.72E + 05# min−1 (female) after activity. The results reveal that workers get exposed to an enormous PM concentration due to tire burning, which contributes to the haphazard increase in RDD near the source. The on-site PM exposure can be reduced by 1.69 times by avoiding tire burning during road pavement activity. [ABSTRACT FROM AUTHOR]

Background: Maternal health care is among the key indicators of population health and economic development. Therefore, the study attempted to explore female education and maternal healthcare utilization in Uganda. The study identified the causal effect of introduction of free education by exploiting the age as an instrument at the second stage model (BMC Health Serv Res. 2015. https://doi.org/10.1186/s12913-015-0943-8; Matern Child Health J. 2009;14:988–98). This instrument provided an exogenous source of variation in the years of schooling and allowed to implement a regression discontinuity design which accounted for heterogeneity in the cohort overtime. Methods: The study used the Ordinary Least Squares (OLS) to help predict years of schooling that were used in the second stage model in the Two Stage Least Squares (2SLS). The study further used the Regression Discontinuity Design (RDD) model with a running variable of birth years to observe its effect on education. To control for heterogeneity in regions in the second stage model, a fixed effects model was used. Results: Female education indeed had a positive impact on maternal health care utilization. It was further found out that age also influences maternal health care utilization. Conclusions: Therefore, as an effort to improve professional maternal health care utilisation, there is need to focus on education beyond primary level. Uganda Government should also ensure that there is an improvement in community infrastructure and security across all regions and locations. Plain Language summary: Maternal health care (MHC) utilization is one of the Millennium Development Goals (SDG) of pursuit. Globally, most low-income countries like Uganda contribute greatly to pregnancy-related mortalities that are largely preventable through adequate utilization of essential maternal health care services. Though Uganda over time has registered some increase in maternal utilization, this has been attributed to a number of factors. This study intended to demonstrate whether the introduction of free primary education in Uganda led to increase in the utilization of maternal health services. To address this, we used Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS), Probit and Regression Discontinuity Design (RDD) models using Demographic Health Survey (DHS) data 2006 and 2011. The study found out that indeed the introduction of free primary education increased the utilization of MHC. [ABSTRACT FROM AUTHOR]

At the end of 2019, the COVID-19 pandemic broke out globally and had a tremendous impact on tourism development in countries around the world. The rapid shift of tourism from "over-tourism" to "under-tourism", threatening the future of the global economy and society, has generated considerable interest from academia and the policy community, but the impact of COVID-19 on tourism variation remains untested by empirical evidence. Based on the daily Baidu Index of 247 prefecture-level cities in China from 2018 to 2021, this study assessed the treatment effect of COVID-19 on tourism and analyzed its dynamic characteristics using the regression-discontinuity-design (RDD) method combined with tourism network attention (TNA) data. The results show that after the outbreak of the COVID-19 pandemic, the level value of TNA dropped significantly by 2.12 (p < 0.10), and the difference value of TNA (TNA_diff) dropped significantly by 10.77 (p < 0.01), indicating that COVID-19 has a negative causal effect on tourism development, and its impact is more pronounced in major tourist source cities, with a coefficient of −14.91 (p < 0.01) corresponding to −4.57 (p < 0.01) for non-major tourist source cities when the dependent variable TNA_diff. The identification of dynamic effects further confirms that the negative impact of the pandemic on tourism network attention is fluctuating and persistent during the study period, with the two major "golden weeks" and peak season being the most severe. Compared to 2020, the TNA has generally shown an upward trend since 2021, indicating signs of a rebound in the vitality of resident tourism, which is conducive to the healthy development of the tourism market. [ABSTRACT FROM AUTHOR]

Rosai-Dorfman-Destombes (RDD) disease is a rare syndrome characterised by benign lymphoproliferative disorder with sinus histiocytosis that presents with massive lymphadenopathy. It occurs mainly in children and young adults. It can be associated with autoimmune diseases like systemic lupus erythematosus (SLE). We present a 15-year-old male case of RDD who was diagnosed with SLE with class IV lupus nephritis and central nervous system small vessel vasculitis. Sensorium improved and lymph node size decreased with steroids. Hence, SLE can present with nephrito-nephrotic syndrome and central nervous system (CNS) small vessel vasculitis without any other systemic involvement. Steroids can be helpful in the management of RDD. Therefore, we should consider the possibility of RDD in cases of SLE with generalised lymphadenopathy, even though the association is rare., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Kandari et al.)

One of the main steps in the data lifecycle is to publish it for data analysts to discover hidden patterns. But, data publishing may lead to unwanted disclosure of personal information and cause privacy problems. Data anonymization techniques preserve privacy models to prevent the disclosure of individuals' private information in published data. In this paper, a distributed in-memory method is proposed on the Apache Spark framework to preserve the ℓ-diversity privacy model. This method anonymizes large-scale data in a three-phase process, which includes, seed selection, data clustering for ℓ -diversity, and finalizing phase. In this method, a hierarchical kmeans-based data clustering algorithm has been designed for data anonymization. One of the major challenges of anonymization methods is to establish a better trade-off between data utility and privacy. Therefore, for calculating the distance between records and forming more cohesive ℓdiverse-clusters, the authors have designed two Manhattan-based and Euclidean-based distance functions to satisfy the requirements of the ℓ-diversity model. Given the 100-fold speed of the Spark compared to MapReduce, the proposed method is presented using in-memory RDD programming in Apache Spark, to address the runtime, scalability, and performance in large-scale data anonymization as it exists in the previous MapReduce-based algorithms. Our method provides general knowledge to use parallel in-memory computation of Spark in big data anonymization. In experiments, this method has obtained lower information loss and loses about 1% to 2% accuracy and FMeasure criteria; therefore, it establishes a better trade-off than the state-of-the-art MapReduce-based Mondrian methods [ABSTRACT FROM AUTHOR]

With the explosive increase of big data in industry and academic fields, it is important to apply large-scale data processing systems to analyze Big Data. Arguably, Spark is the state-of-the-art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed Dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce Spark programming model and computing system, discuss the pros and cons of Spark, and have an investigation and classification of various solving techniques in the literature. Moreover, we also introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Finally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark. [ABSTRACT FROM AUTHOR]

The effectiveness of class size reduction on student performance has been of great research interest and policy debate worldwide. Nevertheless, the evidence has been largely inconclusive partially due to potential bias arising from non-random placement of students and teachers in classrooms of different sizes. This study applied instrumental variable (IV) methods and used a regression discontinuity design (RDD) to conduct analyses of TIMSS data in 2003, 2007 and 2011. The purpose was to investigate over time the effects of class size on eighth grade students' cognitive and non-cognitive outcomes on five mathematics and science subjects in four European countries (i.e. Hungary, Lithuania, Romania and Slovenia). IV estimates indicate that in Romania in 2003 smaller classes had significant and positive effects on academic scores in mathematics, physics, chemistry and earth science and in 2007 on enjoyment to learn mathematics. In Lithuania, in 2011 small classes had significant and positive effects on enjoyment to learn biology and chemistry and learning chemistry well. In 2007 however, the effects were reversed on some of the biology related non-cognitive outcomes. Overall, the class size effects that were significant were also substantial in magnitude and typically much larger than the effects reported in Project STAR. [ABSTRACT FROM AUTHOR]

Abstract In this paper we are proposing an adaptive and real-time approach to resolve real-time financial data integration latency problems and semantic heterogeneity. Due to constraints that we have faced in some projects that requires real-time massive financial data integration and analysis, we decided to follow a new approach by combining a hybrid financial ontology, resilient distributed datasets and real-time discretized stream. We create a real-time data integration pipeline to avoid all problems of classic Extract-Transform-Load tools, which are data processing latency, functional miscomprehensions and metadata heterogeneity. This approach is considered as contribution to enhance reporting quality and availability in short time frames, the reason of the use of Apache Spark. We studied Extract-Transform-Load (ETL) concepts, data warehousing fundamentals, big data processing technics and oriented containers clustering architecture, in order to replace the classic data integration and analysis process by our new concept resilient distributed DataStream for online analytical process (RDD4OLAP) cubes which are consumed by using Spark SQL or Spark Core basics.

DNA demethylases function in conjunction with DNA methyltransferases to modulate genomic DNA methylation levels in plants. The Arabidopsis genome contains four DNA demethylase genes, DEMETER (DME), REPRESSOR OF SILENCING 1 (ROS1) also known as DEMETER-LIKE 1 (DML1), DML2, and DML3. While ROS1, DML2, and DML3 were shown to function in disease response in somatic tissues, DME has been thought to function only in reproductive tissues to maintain the maternal-specific expression pattern of a subset of imprinted genes. Here we used promoter:β-glucuronidase (GUS) fusion constructs to show that DME is constitutively expressed throughout the plant, and that ROS1, DML2, and DML3 have tissue-specific expression patterns. Loss-of-function mutations in DME cause seed abortion and therefore viable DME mutants are not available for gene function analysis. We knocked down DME expression in a triple ros1 dml2 dml3 (rdd) mutant background using green tissue-specific expression of a hairpin RNA transgene (RNAi), generating a viable ‘quadruple’ demethylase mutant line. We show that this rdd DME RNAi line has enhanced disease susceptibility to Fusarium oxysporum infection compared to the rdd triple mutant. Furthermore, several defence-related genes, previously shown to be repressed in rdd, were further repressed in the rdd DME RNAi plants. DNA methylation analysis of two of these genes revealed increased differential promoter DNA methylation in rdd DME RNAi plants compared to WT, beyond the difference observed in the parental rdd plants. These results indicate that DME contributes to DNA demethylase activity and disease response in somatic tissues.

The non-intrusive screening of shipping containers at national borders serves as a prominent and vital component in deterring and detecting the illicit transportation of radioactive and/or nuclear materials which could be used for malicious and highly damaging purposes. Screening systems for this purpose must be designed to efficiently detect and identify material that could be used to fabricate radiological dispersal or improvised nuclear explosive devices, while having minimal impact on the flow of cargo and also being affordable for widespread implementation. As part of current screening systems, shipping containers, offloaded from increasingly large cargo ships, are driven through radiation portal monitors comprising plastic scintillators for gamma detection and separate, typically 3He-based, neutron detectors. Such polyvinyl-toluene plastic-based scintillators enable screening systems to meet detection sensitivity standards owing to their economical manufacturing in large sizes, producing high-geometric-efficiency detectors. However, their poor energy resolution fundamentally limits the screening system to making binary "source" or "no source" decisions. To surpass the current capabilities, future generations of shipping container screening systems should be capable of rapid radionuclide identification, activity estimation and source localisation, without inhibiting container transportation. This review considers the physical properties of screening systems (including detector materials, sizes and positions) as well as the data collection and processing algorithms they employ to identify illicit radioactive or nuclear materials. The future aim is to surpass the current capabilities by developing advanced screening systems capable of characterising radioactive or nuclear materials that may be concealed within shipping containers. [ABSTRACT FROM AUTHOR]

Now-a-days, traffic congestion is one of the troubles in metro cities. It is mainly due to escalate in vehicle, population etc. Due to the raise in vehicle, the road is incapable of handling the traffic. Consequently it is very important to revamp the traffic management system. The advanced traffic management system intention is to ameliorate the safety and efficiency of the transportation system. Advanced traffic management system permit opportunities for new methods of evaluations and continuing assessment is provided. A few scenarios were analysed in the traffic issues and better performance is acquired in this research. The traffic map in New York City is taken as a database model and to discover the shortest path an advanced Dijkstra's algorithm is applied. The advanced form of Hadoop known as Apache spark is used. Spark is the open standard and it is easy to program as it has tons of high level operators which can be flexible in-memory data processing that enables batch, real-time, and advanced analytics on the Apache Hadoop platform. R-tool is an open source programming language and software environment for statistical computing is also employed. By using time forecasting algorithm, for each scenarios the vehicle speed, count, collusion, time, etc. is calculated and the Map Analysis is done with good performance by using R-tool. The overall performance of the system that enhances the traffic control efficiency is 96.23% [ABSTRACT FROM AUTHOR]

It is known that the largest developmental changes in the hippocampus take place during the prenatal period and during the first two years of postnatal life. Few studies have been conducted to address the normal developmental trajectory of the hippocampus during childhood. In this study shape analysis was applied to study the normal developing hippocampus in a group of 103 typically developing 6- to 10-year-old preadolescent children. The individual brain was normalized to a template, and then the hippocampus was manually segmented and further divided into the head, body, and tail sub-regions. Three different methods were applied for hippocampal shape analysis: radial distance mapping, surface-based template registration using the robust point matching (RPM) algorithm, and volume-based template registration using the Demons algorithm. All three methods show that the older children have bilateral expanded head segments compared to the younger children. The results analyzed based on radial distance to the centerline were consistent with those analyzed using template-based registration methods. In analyses stratified by sex, it was found that the age-associated anatomical changes were similar in boys and girls, but the age-association was strongest in girls. Total hippocampal volume and sub-regional volumes analyzed using manual segmentation did not show a significant age-association. Our results suggest that shape analysis is sensitive to detect sub-regional differences that are not revealed in volumetric analysis. The three methods presented in this study may be applied in future studies to investigate the normal developmental trajectory of the hippocampus in children. They may be further applied to detect early deviations from the normal developmental trajectory in young children for evaluating susceptibility for psychopathological disorders involving hippocampus.