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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.

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.

Cutaneous Rosai-Dorfman Disease (CRDD), a rare form of Rosai-Dorfman Disease (RDD), lacks established treatment guidelines. In this case report, we detailed a successful case of CRDD management in a 50-year-old woman with facial lesions. Our treatment included 5-aminolevulinic acid photodynamic therapy (ALA-PDT) and low-dose oral corticosteroids. Remarkable improvement was evident after six ALA-PDT courses, and a one-year follow-up confirmed sustained remission. This case highlights the potential of combining ALA-PDT with low-dose corticosteroids as a promising therapeutic strategy for CRDD. Further research is necessary to fully elucidate its mechanisms and effectiveness.

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.

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.

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.

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.

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.

The current geopolitical situation suggests an increasing possibility of radiological dispersal device attacks on sensitive targets. Consequently, understanding the transport of radiation over great distances in a short time can help first responders and decision makers in effectively managing emergencies. By utilizing open-source computational codes, intentional releases of radioactive material and their transmission from person to person can be simulated to provide first responders and decision makers with a rapid tool to facilitate their work. In this study, the HotSpot code was employed to simulate two releases of Cs-137 resulting from the detonation of a dirty bomb in a major city and near an aqueduct waterworks. Additionally, the STEM code was used to simulate radiation propagation from the initially affected individuals, drawing comparisons to the vectors of viral infections. This approach allowed to compare the outcomes of a scenario involving many individuals in an urban setting with another scenario having fewer individuals but posing the risk of contaminating critical infrastructure. The results showed that both scenarios had similar relatively mild health consequences for the population, despite their considerable differences and variations in the analyzed timelines. However, both scenarios present numerous challenges in emergency management. In the first case, the incident generates widespread panic and media frenzy. In the second case, the dissemination of radiation and potential public unawareness must be taken into account. Addressing these considerations needs the involvement of multiple stakeholders, including police, firefighters, healthcare professionals, journalists, politicians, and others, in emergency management efforts.

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.

Particulate air pollution in cities is caused by a variety of sources. One of the less-studied contributors is wind-induced particle resuspension. As the wind speed increases, particles are removed from surfaces. These particles cause an increase in the total concentration in the air. It is known that particles of 10-2.5 μm in size can be resuspended (PM10-2,5). Modern emission monitoring in cities also allows the monitoring of fine particles of 10, 2.5 and 1 μm in size. The size fractions can then be sorted into PM10-2,5, PM2,5-1 and PM1. When breathed in, particles of different sizes cause various serious health risks. This paper focuses on the identification of the resuspension process of different particle size fractions by a data processing method. Data measured by automatic emission monitoring are used. It is confirmed that the concentration increase can be dominated by the fraction PM10-2,5. However, a concentration increase of fractions PM2,5-1 and PM1 is also evident with increasing wind speed. Although the increase in the PM1 fraction is smaller than PM10-2,5, it is more severe due to the respiratory deposition dose. The resuspension of particles of different fractions has different behaviours during the year. PM10-2,5 particles are dominantly resuspended in the summer months. In winter, on the other hand, the proportion of PM2.5-1 and PM1 particles increases, which may be related to the heating season

Coverage of the population within the sampling frame is a very important quality characteristic of a study. However, a metrical evaluation of the coverage bias to approach the question of representativeness is usually not possible. Switzerland stands out in that the federal statistical office (SFSO) has legal access to population registers (person universe) and a full list of landline telephone numbers (phone number universe). However, these data are not available for research institutes, which must rely on commercially available number collections or RDD sampling frames. This paper wants to quantify the coverage bias of such alternative sampling frames by metric calculation of their congruence with the SFSO universes. The analysis shows that 85.0% of private phone numbers and 88.9% of the resident population of Switzerland that can be reached via landline by the SFSO definition (non-ALTELs) are included in our exemplarily analyzed commercially available phone number collection. The highest coverage bias is present in the 20-39 age group. The RDD frame covers 97.8% of private phone numbers and 99.8% of non-ALTEL persons. Hence, both available alternative sampling frames are useful for representative studies. Finally, the potential of use of the Swiss coverage results as benchmarks for other countries is discussed.

Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets. Originally, Map-Reduce mining algorithm-based frequent itemsets on Hadoop were resolved. For handling the big data, Hadoop comes into the picture but the implementation of Hadoop does not reach the expectations for the parallel algorithm of distributed data mining because of its high I/O results in the transactional disk. According to research, Spark has an in-memory computation technique that gives faster results than Hadoop. It was mainly acceptable for parallel algorithms for handling the data. The algorithm working on multiple datasets for finding the frequent itemset to get accurate results for computation time. In this paper, we propose on parallel apriori and FP-growth algorithm to finding the frequent itemset on multiple datasets to get the mining itemsets using the Apache SPARK framework. Our experiment results depend on the support value to get accurate results.

O RDD foi a principal medida do governo de São Paulo para combater o PCC. No entanto, essa organização criminosacontinua exercendo amplo controle sobre a população carcerária, revelando o fracasso do RDD para alcançar seuobjetivo. Contudo, o RDD produz efeitos simbólicos e práticos fundamentais para compreender a dinâmica prisional.