Date Of Purchase Research Articles

Efficacy, adherence and persistence of various glucagon-like peptide-1 agonists: nationwide real-life data.

The management of type 2 diabetes mellitus has advanced in the last two decades since the introduction of glucagon-like peptide-1 receptor agonists (GLP-1RAs). However, multiple factors may interfere with achieving better glycaemic control. This study evaluated the differences between various GLP-1RAs in efficacy, adherence and persistence. We conducted a retrospective cohort study using the electronic medical database from Clalit Health Services. Adults with type 2 diabetes mellitus who purchased any GLP-1RA between 2009 and 2021 were included. The Index Date was defined as the date of the first purchase of any GLP-1RA. We evaluated the adherence, persistence and glycaemic control after GLP-1RAs initiation. Baseline glycaemic and post-treatment glycaemic controls were analysed. In total, 70 654 patients were included. The mean age was 11.7 ± 60.4, and 51% were females. A significant reduction in glycated haemoglobin (HbA1c) was observed in all patients who received GLP-1RAs. However, the percentage of changes in the HbA1c was higher among weekly GLP-1RA than daily initiators (14.6% vs. 10.2%, p < 0.001). The proportion of subjects with any decrease in HbA1c was higher among the once-weekly compared with the daily dose (82.4% vs. 74.7%) and mainly patients initiated semaglutide or dulaglutide, with 16.0% and 14.7% reduction. The frequency of good adherence (the proportion of days covered ≥80%) was significantly higher among the weekly group odds ratio = 1.25 (95% confidence interval 1.21-1.28). Good adherence was reported in older age, female gender, Jewish ethnicity and high socio-economic status (p < 0.001). Weekly GLP-1RAs initiators were more adherent, persistent to therapy and achieved better glycaemic control. Epidemiological variables might play a role in achieving this goal.

Machine learning-based stock picking using value investing and quality features

Value Investing stands as one of the most time-honored strategies for long-term equity investment in financial markets, specifically in the domain of stocks. The essence of this approach lies in the estimation of a company's "intrinsic value," which serves as an investor's most refined gage of the company's true worth. Once the investor arrives at an estimation of the intrinsic value for a given company, she proceeds to contemplate purchasing the company's stocks solely if the prevailing market price of the stocks significantly deviates below the estimated intrinsic value, thus presenting an enticing buying opportunity. This deviation, referred to as the "margin of safety," represents the disparity between the intrinsic value and the current market capitalization of the company. Within the scope of this endeavor, our objective is to automate the stock selection process for value investing across a vast spectrum of US companies. To accomplish this, we harness a combination of value-investing principles and quality features derived from historical financial reports and market capitalization data, thereby enabling the identification of favorable value-driven opportunities. Our methodology entails the utilization of an ensemble of classifiers, where the class is determined as a function of the margin of safety. Consequently, the model is trained to discern stocks that exhibit value characteristics warranting investment. Remarkably, our model attains a success rate surpassing 80%, effectively identifying stocks capable of yielding an annualized return of 15% within a three-year timeframe from the recommended stock purchase date provided by the model.

INFORMATION TECHNOLOGY PLATFORM FOR ARMS TRADE

Based on the postulate that the arms trade is generally a complex, specific and multifaceted field of activity, which includes the production, distribution, sale and use of various types of weapons and military equipment. In this context, the authors of the article proposed the development of an innovative information technology platform for accounting and analysis of arms trade processes, which takes into account the peculiarities of domestic legislation. It was noted that the use of blockchain technology on the information and technological platform of weapons sales is aimed at increasing security and tracking the full set of transactions, reducing risks and ensuring maximum transparency of processes. It is assumed that each stage of the life cycle of weapons, starting from design, production and ending with sale, will be recorded by blockchain methods and means. Information about the manufacturer, the date of manufacture, transportation, purchase and acceptance of the weapon by the customer will be recorded in the blocks. With the use of an information technology platform, the validity of licenses will be checked with each transaction, ensuring the legality of the sale and taking into account the unique characteristics of each weapon unit. The information technology platform will store information about transactions, including price, date and participants, smart contracts, etc. Development of an information technology platform that is conveniently integrated into the system of international standards and regulators, as well as meets numerous specific requirements and restrictions on arms trade. The authors of the article claim that the use of cryptography in blockchain technology provides reliable encryption and protection of confidential information, which is critical in a sensitive subject area such as the arms trade, and also contributes to a significant increase in the level of security, transparency and trust among participants in this specific industry. The conducted analysis confirms the expediency of using the potential of blockchain technology to optimize and increase the efficiency of the accounting of arms trade processes, as well as to ensure the reliability and safety of the functioning of the innovative information technology platform.

Predicting Human Activity and Behavior Patterns from Cell Phone Data Using Machine Learning

Across the globe, Smartphones enjoy a high ownership rate. A person’s day-to-day activities are incomplete without the use of smartphones. These sensor-rich devices are often repurposed to collect various forms of data from the user in the domains of 1) app usage, 2) overall phone activity, 3) location, 4) day and night-time activity, 5) communication, 6) social media behavior and so on. This information collected from sensor and log data taken from smartphones can be used to create predictive measures extending to an individual’s Big Five personality traits and examine them. Human activity recognition (HAR) as a field has always tried to improve its performance in the aspects of feature extraction and improve the accuracy of recognition and prediction by performing various machine learning techniques on the data received from accelerometer and gyroscope sensors and creating convolutional neural networks to recognize the multiple activities such as walking, jogging, walking up or down the stairs, etc. Although HAR is a recognized research field, there have been significant challenges such as a) different movements for different individuals, b) limited training data, and c) complexity of performed activities. The idea of this project is to create a HAR model with the help of Machine Learning and Deep Learning, which will be more efficient in terms of performance and accuracy than the conventional models and try to overcome the challenges. That will be applied to the Smartphone data to predict a smartphone user’s activity at one point of the day. The idea is to also get an analysis of the behavioral traits of the user in terms of online expenditure and predict the date of the next purchase. That will be done to send personalized information such as offers and announcements to the user catered according to their needs to enhance their personal experience.

TWO-DIMENSIONAL MODELING OF CAR RELIABILITY DURING WARRANTY PERIOD

The paper focuses on presenting the concept of two-dimensional modeling of passenger car reliability during the warranty period. The main objective of this paper is to detect the regularity in the intensity of the number of first failure reports during the warranty period. The two-dimensional distribution of the time and mileage of failure-free exploitation is estimated. The period from the date of purchase to the first warranty repair is analysed. The concept presented incorporates the existing state of knowledge on two-dimensional warranties, expanding it through the use of a nonparametric approach and probability smoothing with the use of P-splines. The estimation involved censored data, i.e., data on vehicles that were not submitted for warranty repair within the warranty limits of time and mileage. The originality of this paper entails the combination of a nonparametric approach with probability smoothing. The statistical analyses presented in the paper were carried out on a population of 1005 vehicles of two car brands sold and serviced in 2011-2021 at the Authorized Service Station (Dealership). There were sales, repair, and warranty claim databases.

Hospitalization and the Risk of Initiation of Antipsychotics in Persons With Parkinson's Disease.

The use of antipsychotics in persons with Parkinson's disease (PD) is common, although their use may aggravate the symptoms of PD. Clozapine and quetiapine are the only antipsychotics recommended in PD treatment guidelines. Information on factors associated with initiation of antipsychotics is needed. We investigated whether recent hospitalization is associated with initiation of antipsychotics in persons with PD, and whether discharge diagnoses differ between those who had antipsychotics initiated and those who did not. Nested case-control study in the nationwide register-based Finnish Study on Parkinson's disease (FINPARK). The FINPARK study includes 22,189 persons who received an incident, clinically verified PD diagnosed during 1996-2015 and were community-dwelling at the time of diagnosis. The cases were 5088 persons who had antipsychotics initiated after PD diagnosis, identified with 1-year washout. The controls were 5088 age-, sex-, and time from PD diagnosis-matched persons who did not use antipsychotics on the matching date (antipsychotic purchase date). Recent hospitalization was defined as discharge in the 2-week period preceding the matching date. Associations were investigated with conditional logistic regression. Quetiapine was the most commonly initiated antipsychotic (72.0% of cases), followed by risperidone (15.0%). Clozapine was initiated rarely (1.1%). Recent hospitalization associated strongly with antipsychotic initiation [61.2% of cases and 14.9% of controls, odds ratio (OR) 9.42, 95% CI 8.33-10.65], and longer hospitalizations were more common among cases. PD was the most common discharge diagnosis category (51.2% of hospitalized cases and 33.0% controls), followed by mental and behavioral disorders (9.3%) and dementia (9.0%) among cases. Antidementia and other psychotropic medication use were more common among cases. These results suggest that antipsychotics were initiated because of neuropsychiatric symptoms or aggravation of those symptoms. Antipsychotics should be prescribed after careful consideration to avoid adverse effects in persons with Parkinson's disease.

Application of Fuzzy C-Means Algorithm for Clustering Customers

Today's fierce business competition requires companies to focus on the needs desired by consumers.This makes companies have to think about how to manage customer data so that it can be utilized properly for the development of marketing strategies.And Grouping (cluster) customers based on their respective characteristics can be an alternative in solving these problems.In clustering (cluster) customers there are several data mining clustering methods that can be used, one of which is the Fuzzy C-Means (FCM) method. FCM is a clustering algorithm where one object can be a member of several clusters and FCM cluster boundaries are vague. The output of FCM is a row of cluster centers and several degrees of membership for each data point. In this clustering, customers will be divided into 4 customer clusters namely Golden, Silver, Bronze, and Iron with the variables used as a reference are the final purchase date, purchase frequency and total purchase. The data used is customer transaction data for the period September - December 2015. The total data is 709 transactions from 75 customers. After the data is processed with the Fuzzy C-Means method, the final results show that the iteration ends at the 30th iteration with a change in the objective function of 9.8. The resulting customer clusters are Golden: 27, Silver: 15, and Bronze: 33 with a cluster validity of 0.596277.

Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management.

The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services. Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed. This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author's future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices' maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.

Infrastructure Management System

Technology cannot be disregarded because it has raised the living standards and sparked revolutions in many facets of contemporary society. In every industry, technology has decreased labor and time requirements, and enhanced production efficiency. A certain technology's innovation eventually makes it a part of society and essential to people's daily lives. The management of infrastructure manually in any organization or institution is a tedious task. In order to make the management of infrastructure in courts automated, an application is developed that maintains every detail of all infrastructures and IT stocks in the courts. Users can view the purchase date, warranty period, and location of the infrastructure in the court. A unique QR code is generated for each piece of furniture and IT stock to access its details. The Infrastructure Management System is touted as a revolution in court management. This application saves time and workload. Moreover, notifications are generated if the service period is nearing, and it would call the service person regarding the repair and service. This management system is found to be effective and efficient.

IMPLEMENTASI K-MEDOIDS DAN MODEL WEIGHTED-LENGTH RECENCY FREQUENCY MONETARY (W-LRFM) UNTUK SEGMENTASI PELANGGAN DILENGKAPI GUI R

The k-medoids algorithm is a partition-based clustering algorithm that groups n objects as much as k clusters. The algorithm uses medoids as the center point (partition) of the cluster. Medoids are actual objects that are randomly selected as the most centered object in a cluster so that the k-medoids algorithm is robust against outliers. Grouping objects in cluster analysis based on similarities between objects. Measurement of similarity between objects can use the euclidean and manhattan distances. The use of distance in cluster analysis can affect cluster results. Validation of cluster results using internal validation, namely the silhouette index. The Weighted-Length Recency Frequency Monetary (W-LRFM) model is a model that applies the relative importance (weight) of the LRFM model according to the importance of each variable in the LRFM model. LRFM model is a model used for customer segmentation based on customer behavior which consists of variables length, recency, frequency, and monetary. The relative importance (weight) of the W-LFRM model uses the Analytics Hierarchical Process (AHP) method. The W-LRFM model is used to calculate the Customer Lifetime Value (CLV) of each cluster. The implementation of k-medoids and the W-LFRM model in this study are used for customer segmentation based on the length, recency frequency, and monetary variable. The formation of these variables is the result of transformation of customer behavior data such as transaction id, date of purchase, and a total amount of 41,073 rows into variable length, recency, frequency, and monetary as much as 5,108 rows. The criteria of the best cluster formed are k = 2 using the manhattan distance with the average of coefficient values = 0.62. The weights on the W-LRFM model produced based on the AHP method are 0.16, 0.29, 0.47, and 0.08 for the variable length, recency, frequency, and monetary. CLV formed from two clusters, namely 0.158 and 0.499. CLV in the second cluster is bigger so that the second cluster becomes the main priority in the marketing strategy. The second cluster has the characteristics 0.29, 0.47, and 0.08 for the variable length, recency, frequency, and monetary. The second cluster has the characteristics means a loyal customer group. The first cluster has characteristics means a potential customer group. This research is assisted by using Graphical User Interface (GUI) R to facilitate analysis

Do uninformed traders move prices? Evidence from the Bank of Japan's ETF purchasing program

AbstractUsing the Bank of Japan (BoJ) ETF purchasing program as an exogenous shock to stock demand, we find that stocks with a higher BoJ demand experience higher positive abnormal returns on BoJ ETF purchase dates, which only partially revert in the long term. Our findings support the hypothesis that stocks have a downward‐sloping demand curve, implying that uninformed traders can cause a permanent shift in price.

Date of birth and purchase price as foals or yearlings are associated with Thoroughbred flat race performance in the United Kingdom and Ireland.

BackgroundThoroughbred breeders aim to have foals born early in the season, but scientific evidence on the advantages for race performance is scarce and contradictory.MethodsThe association between date of birth and purchase price as foal/yearling, with race performance by the end of the second and third years of life of Thoroughbreds racing in flat races in the United Kingdom (UK) and Ireland (IRE) was assessed using negative binomial and zero‐inflated negative binomial models on the entire 2014–2015 UK/IRE foal crops (n = 28,282).ResultsIn total, 6666 and 9456 horses raced in UK/IRE flat racing by the end of their second and third years of life. Prize money and prize money per start decreased with each additional day beyond 1 January that the foal was born. Purchase price as foal and yearling was negatively associated with the number of races run, while it was positively associated with prize money and prize money per start by the end of the third year of life.ConclusionsFoals born early in the season had higher earnings by the end of their second and third years of life than foals born later. Differences were more marked among males than females. The most expensive horses sold as foals or yearlings ran fewer races but earned more prize money and prize money per start than less expensive horses. Results from this population‐based analyses may inform strategies and management practices aiming to maximise horses’ racing performance potential and increase financial returns.

Public procurement of antineoplastic agents used for treating breast cancer in Brazil between 2013 and 2019

BackgroundBreast cancer is the most common cancer among women in Brazil and the country’s public health care system is the main care provider. Timely treatment can increase the chance of cure, prevent metastasis and improve quality of life. Effective public procurement of antineoplastic agents can therefore improve access to drug therapy. This study investigates patterns in the procurement of selected antineoplastic agents used for treating breast cancer by public bodies and avoidable expenditure on these drugs between January 2013 and December 2019.MethodsWe selected antineoplastic agents used for adjuvant or preoperative chemotherapy listed in the 2018 Breast Cancer Diagnosis and Treatment Guidelines and included in category L of the WHO Anatomical Therapeutic Chemical classification system. We analyzed regular purchases of antineoplastic agents registered in the Integrated General Services Administration System (SIASG), considering purchased quantity, unit price, date of purchase and procuring entity. Prices were inflation-adjusted to July 2019 based on the National Consumer Price Index.ResultsA total of 10 antineoplastic agents were selected. Trastuzumab and tamoxifen accounted for the largest share of total spending and largest volume of purchases, respectively. The Ministry of Education was the largest purchaser in volume terms of all the drugs studied, except trastuzumab 440 mg, where the category “Other Institutions” accounted for most purchases, and vinorelbine 20 mg, where the Ministry of Health made most purchases. The category “Other Institutions” accounted for the largest share of total spending. Total avoidable expenditure was R$99,130,645. Prices paid for medicines and avoidable expenditure were highest in the Ministry of Defense.ConclusionsThe differences observed in the performance of different categories of buyers as to amounts purchased and prices practiced for antineoplastic agents could be reduced by employing strategies to expand the centralization of purchases, resulting in expanded access to breast cancer medicines in the public sector.

Influence of Car Configurator Webpage Data from Automotive Manufacturers on Car Sales by Means of Correlation and Forecasting

A methodology to prove the influence of car configurator webpage data for automotive manufacturers is developed across this research. Firstly, the correlation between online data and sales is measured. Afterward, car variant sales are predicted using a set of forecasting techniques divided into univariate and multivariate ones. Finally, weekly color mix sales based on these techniques are built and compared. Results show that users visit car configurator webpages 1 to 6 months before the purchase date. Additionally, car variants predictions and weekly color mix sales derived from multivariate techniques, i.e., using car configurator data as external input, provide improvement up to 25 points in the assessment metric.

Factors associated with duration of care in community direct-to-patient (DTP) pharmacies among Chinese cancer patients taking PD-1/PD-L1 inhibitors: Results from a real-world study.

e18654 Background: Several PD-1/PD-L1 inhibitors have been approved in China for multiple cancers. Community DTP pharmacies play an important role in both drug distribution and patient management. Using real-world patient management data, we explored the factors associated with duration of care in community DTP pharmacies among cancer patients using PD-1/PD-L1 in the country. Methods: This is a retrospective cohort study using real-world sales and follow-up data routinely collected from 79 Medbanks community DTP pharmacies between Jan 1, 2019 and Mar 31, 2021. Duration of follow-up (DoFU) was defined as the time interval between first PD-1/PD-L1 purchase and last follow-up contact. Duration of drug purchase (DoDP) was defined as the time interval between the first and the last PD-1/PD-L1 purchase date plus a buffer period which was determined based on the amount of last purchase. Covariates included: gender, age, enrollment in a patient access program (PAP), experience of adverse events (AE) during treatment, patient’s awareness of the disease, and follow-up response rate (FRR; &lt; 30%, 30-70%, &gt; 70%). Uni- and multivariate Cox regression models were conducted to assess the association of DoFU/DoDP with predicting variables among both total population and cancer specific sub-populations. Results: A total of 23,559 patients were evaluated (median age: 61 yrs; male: 73%). The three most common cancer types were lung cancer (51.90%), hepatobiliary cancer (19.69%) and esophageal cancer (9.98%). The median DoFU and DoDP were 6.3 month (95% CI: 6.23̃6.50) and 2.3 months (95% CI: 2.27̃2.33). In multivariate Cox regression model, factors associated with longer DoFU included enrollment in PAP (HR = 0.52, p &lt; 0.001), experience of AE during treatment (HR = 0.71, p &lt; 0.001), FRR 30-70% (HR = 0.66, p &lt; 0.001; ref: &lt; 30%) and FRR &gt; 70% (HR = 0.56, p &lt; 0.001; ref: &lt; 30%); factors associated with longer DoDP included enrollment in PAP (HR = 0.90, p &lt; 0.001), experience of AE during treatment (HR = 0.84, p &lt; 0.001) and FRR &gt; 70% (HR = 0.80, p &lt; 0.001; ref: &lt; 30%). The protective effects of PAP, AE and higher FRR on DoFU and DoDP were also observed in the three main cancer specific sub-populations. Conclusions: High quality patient management service provided in community DTP pharmacies is significantly associated with longer duration of care, which implies the important role of DTP pharmacies in the longitudinal care of cancer patients. A comprehensive service package should be developed and applied in community DTP pharmacies for cancer patients in order to better address their medical needs and financial strain.

Chronological cues and consumers’ preference for mere newness

Chronological cues are ubiquitous in retail settings, whether they come in the form of production, release, on-shelf, or purchase dates, etc. Yet, they remain relatively underexplored in the marketing literature. Could newness that arises from these chronological cues lead consumers to prefer options merely because they are newer, above and beyond any substantive benefits conferred or implied by that newness? We propose and find in a series of eight preregistered studies (n= 2,216) that consumers exhibit mere newness preference across many product domains—preferring chronologically newer options over older options with no substantive benefits to newness. We provide evidence that overgeneralization is one important driver of mere newness preference: Most consumers hold positive (negative) associations with chronological newness (oldness) in an implicit association test, and mere newness preference is reduced in domains in which the opposite association exists. Consequently, consumers are willing to pay a newness premium even for mere newness.

Implementation in restorative treatments in public health: a 10-year analysis of resin composite procurement in Brazil.

This study aims to assess the resin composite purchases in a public health system and understand the variables associated with its cost and with the decision-making process over a period of 10 years. Secondary data collection was performed using the Brazilian Healthcare Prices Database (BDHP) from 2010 to 2019. All conventional and bulk-fill composites were selected. Date of purchase, institution type and location, procurement modality, number of purchased items, manufacture, and unit prices were collected. A multiple linear regression model assessed the influence of price in the procurement variables, while a multinomial logistic regression compared purchase probabilities between the materials. In total, 18,138 observations were collected with 2,129,294 purchased units and a total cost of USD 43,504,260.00. Conventional composites appeared in 98.5% of the procurement process. Nanofilled conventional composites were the most purchased materials, with an increased probability of purchase over time, despite its higher prices. An increase in prices was predicted for bulk-fill materials when compared to conventional ones. The odds of purchasing this category increased by 3.14x for every price increase over the years. Sociodemographic and type of institution influences the prices and the probability of procurement in the healthcare system. Nanofilled and bulk-fill resin are increasingly included in clinical practice. These findings highlight possible modifications to the standard-of-care in restorative treatments and how the translation of knowledge may occur from the development of new materials to the clinical application considering the economic impact of these modifications.

Sürdürülebilir Kültür Turizmi: Kars Örneği

Sustaniable tourism is a destination or people’s lifestyle, cultural histirical structures, customs and traditions, etc. It offres a wide variety of options such as tourism, which has an important place in cultural tourism in the world, is aimed at protecting and using our values in the cultural tourism tourism heritage, regnizing and promoting the principles and trainings for global development. Kars province, on the hand, is one that can increase its cultural tourism potential with its vegetation of Kars province and to raise it for the future. The training program of qualitative research methods is applied in the training carried out and continuing on the trainings for education. Transportation facilities in Turkey, places where transportation facilities are available, transportation etc. The choice of food, the choice of accommodation facilities are completely preferred and not preferred by the sector tourists, as well as the preference of local products fort he purposes suitable for the profiles of the local and general preference, the date of purchase of the historical city center from the modern buildings, suitable for the target market, suitable for the profile of the target market, cultural it is prefered to be taken and on top of training for tourism, etc.

Integrity of personal radiation protective equipment (PRPE): a 3-year longitudinal follow-up study

Abstract Background Personal radiation protective equipment (PRPE) such as lead aprons, vests, skirts and thyroid shields minimize radiation exposure of operators using ionizing radiation. However, PRPE might be prone to cracks and tears in the attenuating layer of the garments which results in inadequate radiation protection and increased operator dose. Currently, there are no uniform guidelines regarding regular inspection and/or rejection criteria of PRPE. Purpose This study aims to investigate the prevalence, qualification and quantification of PRPE integrity during a longitudinal follow-up. Methods All PRPE of a large, general hospital was yearly evaluated in the period 2018–2020. The equipment was inspected on a tele-operated X-ray table and cracks and tears were qualified and quantified using an X-ray opaque ruler. Rejection criteria of Lambert &amp; McKeon were applied to accept or reject further use of the PRPE. In brief, all pieces, except for thyroid shields, with a total defect area larger than 670mm2 are rejected. For tears, specifically at the position of the gonads, an extra rejection criterion of 15mm2 is defined. For thyroid shields, the rejection criterion is set to 11mm2. Results In the 3-year follow-up period, a total of 915 PRPE were evaluated. 43.3% of PRPE showed tears and 29.3% needed to be rejected based on the Lambert &amp; McKeon criteria. More specifically, in 2018, 2019 and 2020, respectively, 17.4%, 16.9% and 21.2% were rejected. In the attached figure, typical examples of visualized defects are represented. The median tear area (interquartile range) was 12 (30) mm2. Remarkably, of 195 newly registered PRPE, 8.7% showed tears in the first year of use of which 88.2% needed to be rejected. In the latter case, the median tear area (interquartile range) was 40 (50) mm2. Information regarding purchase date and size is missing in respectively 45.5% and 50.7% of the cases. Additionally, 13.7% lacked information regarding lead equivalency. Conclusion PRPE are prone to tears and cracks. Up to 40% of PRPE showed tears and cracks resulting in nearly 20% rejections. Newly purchased PRPE are not guaranteed to remain free of cracks and tears in the first year of use. These results urge the importance for regular X-ray based integrity analysis of PRPE. Funding Acknowledgement Type of funding sources: None.

Field Research Report on Radioactive Cesium Contained in Persimmon Growing at Orchards of Fukushima Eight Years after the Nuclear Power Plant Accident

Parts of the experimentally processed persimmon on the collection date (purchase date) in September of this year exceeded 100 Bq per kg, which is the standard value of the radioactivity concentration of foods set by the Ministry of Health, Labor and Welfare, Japan. As such, measures have been taken to avoid shipping of Anpo-gaki from that area as radioactive substances released into the environment from the explosion of the nuclear power plant in March 2011 remain in the environment.