Operational Modal Analysis of hydroelectric turbines using an order based likelihood approach

Information about mental health systems is essential for mental health planning to reduce the burden of neuropsychiatric disorders. Unfortunately, many low- and middle-income countries lack systematic information on their mental health systems. The objectives, scope, structure, and contents of mental health assessment and monitoring instruments commonly used in high-income countries may not be appropriate for use in middle- and low-income countries. The World Health Organization (WHO) has recently developed the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS), a comprehensive assessment tool for mental health systems designed for middle- and low-income countries. WHO-AIMS was developed through an iterative process that included input from in-country and international experts on the clarity, content, validity, and feasibility of the instrument, as well as a pilot trial. The resulting instrument, WHO-AIMS 2.2, consists of six domains: policy and legislative framework, mental health services, mental health in primary care, human resources, public information and links with other sectors, and monitoring and research. These domains address the ten recommendations of the World Health Report 2001 through 28 facets and 155 items. All six domains need to be assessed to form a basic, yet broad, picture of a mental health system, with a focus on health sector activities. WHO-AIMS provides essential information for mental health policy and service delivery. Countries will be able to develop information-based mental health policy and plans with clear baseline information and targets. Moreover, they will be able to monitor progress in implementing reform policies, providing community services, and involving consumers, families, and other stakeholders in mental health promotion, prevention, care and rehabilitation. This article provides an overview of the rationale, development process, and potential uses and benefits of WHO-AIMS.

IntroductionAyushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals.MethodsThe process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches–an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions.ResultsThe mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5).DiscussionCost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

We conducted an electronic, self-administered cross-sectional survey of all ODOs in Canada from November 2021 to January 2022. We targeted key knowledge holders familiar with the data collection processes within each Canadian ODO known to Canadian Blood Services. Categorical item responses are presented as numbers and proportions. We achieved a 100% response rate from 10 Canadian ODOs. Most data were collected by organ donation coordinators. Only 2 of 10 ODOs reported using scripts explaining why sociodemographic data are being collected or incorporated training in cultural sensitivity for any given variable. A lack of cultural sensitivity training was endorsed by 50% of respondents as a barrier to the collection of sociodemographic variables by ODOs, whereas 40% of respondents identified a lack of training in sociodemographic variable collection as a significant barrier. Few programs routinely collect sufficient data to examine health inequities with an intersectional lens. Most data collection occurs midway through the ODO interaction, creating a missed opportunity to better understand differences in social identities of patients who register their intention to donate in advance or who decline the donation. National standardization of equity-relevant data collection definitions and processes of the collection is needed.

Real-time data collection and analysis processes have been designed and an initial capability is demonstrated for the July, 1994 JTMDSN test data. The JTMDSN is a one-year, distributed interactive simulation (DIS) demonstration sponsored by the Defense Modeling and Simulation Office (DMSO). The JTMDSN is built on past TACCSF and RESA DIS accomplishments such as their War Breaker support, to make two more major contributions to the DIS community. First Army, Navy, Air Force, and National Systems interfaced via tactical data links using signal Protocol Data Units (PDUs). Second, the data was collected online from PDUs and reduced to records of key events which could be used to display mission performance and network performance measures in real-time. This paper focuses on this data collection and analysis capability. The JTMDSN DIS Gateway, including the data collection process, was designed and developed by Martin Marietta Corporation. Data analysis processes are being developed by BDM Engineering Services Company. >

BackgroundA national study, ‘Costing of healthcare services in India’ (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection.MethodsAn exploratory survey with 3 components–an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale.ResultsMean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays.ConclusionsMicro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies.

This paper aims to advance the field of data anonymization within the context of Internet of Things (IoT), an environment where data collected may contain sensitive information about users. Specifically, we propose a privacy-preserving data publishing alternative that extends the privacy requirement to the data collection phase. Because our proposal offers privacy-preserving conditions in both the data collecting and publishing, it is suitable for scenarios where a central node collects personal data supplied by a set of devices, typically associated with individuals, without these having to assume trust in the collector. In particular, to limit the risk of individuals' re-identification, the probabilistic k-anonymity property is satisfied during the data collection process and the k-anonymity property is satisfied by the data set derived from the anonymization process. To carry out the anonymous sending of personal data during the collection process, we introduce the delocalized-record chain, a new mechanism of anonymous communication aimed at multi-user environments to collaboratively protect information, which by not requiring third-party intermediaries makes it especially suitable for private IoT networks (besides public IoT networks).

Management of an inventory data within an agency requires an interactive management system to provide ease and informative data presentation. Data processing using an interactive system makes it easy for users to understand and interact with data, especially if the agency concerned is an agency that is in charge of long-term data storage, complex data processing, data collection and data distribution processes, controlling inventory availability, and data collection conducted every day. Data storage and conventional data processing has several weaknesses such as high error rate in data processing, the system is not interactive so that it troubles and impedes the inventory data collection process by users, and can produce data ambiguity that can cause misinformation. By using the inventory control model Just-In-Time (JIT) Inventory method that allows the compilation of data collection and inventory stock requests as needed so that it can easily distribute and minimize wasted stock, the processing, storage, and data collection of a particular data inventory can be controlled well and can provide clearer information. As a result, implementing inventory control methods into a web application can provide web applications that have better functionality in processing, storing, and collecting data.

State medical regulatory boards (SMBs) share a common mandate to safeguard patient safety through the effective oversight of licensed medical professionals. Across all U.S. jurisdictions in 2020 SMBs executed more than 7,100 adverse regulatory actions related to 3,342 licensed physicians.1 Yet, to date, SMBs have had no objective methods for determining that the regulatory actions they impose are, in fact, effective at rehabilitating licensees and preventing reoccurrences of similar conduct.This is concerning given the relatively high instance of repeat offenses or “recidivism.” Review of the FSMB’s SMB regulatory action data during the ten-year period from 2011–2020 finds that, on average, 45% of physicians disciplined nationally were first-time recipients of regulatory action.1 More than half of physicians (55%) disciplined during the same 10-year period had previously received at least one public regulatory action. It is unclear how many actions may have arisen from the same instance of misconduct, since the FSMB’s count of actions includes reciprocal actions.In 2019, in what is believed to be the first study of its kind, the North Carolina Medical Board (NCMB) undertook a comprehensive analysis of its disciplinary data. Its goals included gaining insight into the efficacy of its regulatory interventions, with an eye towards reducing recidivism. This work was supported by a generous grant from the FSMB Foundation. NCMB chose to focus its study on cases involving inappropriate opioid prescribing, which is an active enforcement area for North Carolina and most medical regulatory boards.What the project team discovered, ultimately, is that successful data analysis requires a level of forethought, planning and data infrastructure investment that few SMBs — including NCMB — have completed. As a result, the project was unable to complete its objectives. But the process of data collection and analysis during the study period positioned NCMB for more meaningful analysis of enforcement data in the future.We offer a summary of our experience here with the intention of providing lessons learned that might prove valuable for the broader state medical board community. The concept of “data-driven regulation” is an aspiration for many medical regulatory bodies, based on the idea that a better understanding of data can help boards better gauge the efficacy of their interventions, but the implementation of data-driven strategies may be easier said than done.NCMB engaged the expertise of Blaze Advisors, a health care consulting firm specializing in the strategic, tactical and technology services necessary to improve outcomes based in Wilmington, North Carolina, to conduct the data analysis for its study. Blaze looked at 39,286 NCMB licensees (MDs and DOs) with one or more prescribing cases during calendar years 2014–2018, using data exported from NCMB’s database.The firm identified 462 individuals (1.2 %), whose conduct resulted in 538 enforcement cases and 600 total actions. Data analyzed included cases closed with no action, as well as those that resulted in either private or public action. A “case” may be based on a single report of inappropriate prescribing or it may be based on multiple reports from different sources (e.g., patients or family members, pharmacy staff, other prescribers, etc.) that are consolidated into a single enforcement case. Research was initiated in March 2019 and results were reported to NCMB in August 2019.Study objectives included: Create a profile of licensees at risk of prescribing issuesDetermine which regulatory actions are effective in reducing recidivismIdentify gaps in education and trainingEvaluate the disciplinary process to ensure evidence-based regulationThe recidivism project was not able to fulfill these objectives, beyond creating a basic demographic profile of licensees with one or more regulatory actions related to prescribing. Factors that limited analysis included missing data fields, mismatched data, large numbers of free text fields, and duplicated entries in NCMB’s exported data.On further analysis it was determined that NCMB’s method for entering case information during the period studied, 2014–2018, did not allow researchers to easily identify enforcement cases that specifically involved opioids. The reason for this is that most information about each enforcement case is captured in written reports, summaries and notes that cannot be queried or used to generate data reports. These issues limited researchers’ ability to complete fine-detail profiling and meaningful analysis of the efficacy of regulatory actions at addressing deficiencies with opioid prescribing.The larger issue, however, and the ultimate reason the project could not complete desired study objectives, is that NCMB did not build its database with the idea of exporting quality data in mind. Rather, the database was built simply as a means to an end — an administrative tool to allow medical board staff to store and process licensing and enforcement data. It should be noted that the same is likely true for many, if not most, other SMBs, suggesting that other regulatory bodies would face similar challenges if attempting to analyze their data.Fortuitously, NCMB was in the process of planning its transition to a new database vendor when it received the results of its recidivism study from Blaze Advisors. The timing enabled NCMB to incorporate several suggestions to improve data collection into the planning and implementation of its new database system. Principles of sound data collection are explored in the Discussion section of this article.As noted, Blaze Advisors identified 462 licensees (374 men and 88 women) with one or more prescribing cases. Blaze determined that 406 (88%) licensees had a single prescribing case during the studied five-year period. Forty (8.7%) licensees had two prescribing cases between 2014–2018 and 12 (3.6%) of licensees had three cases. Less than 1% of the sample (4 individuals) had four prescribing cases during the period studied.Demographic analysis of the data identified the most common traits of licensees with one or more prescribing cases as a male prescriber over the age of 50. Some 81% of all prescribing cases analyzed involved male physicians (males make up 67% of the total licensed physician population in North Carolina) and just over 65% of all cases involved prescribers aged 50 or older. The highest number of prescribing cases was observed in U.S.-born white male physicians between the ages of 65–79. It must be noted that these data do not necessarily represent an accurate profile of licensees most at risk of engaging in substandard prescribing, but more likely simply reflect the current demographics of the medical profession in North Carolina. NCMB has no plans to use demographic information gathered through this analysis to target specific licensee groups within the state.Our analysis found that prescribing cases were nine times more likely among licensees in urban practice settings than among licensees in rural areas. This was especially striking because the population of the state of North Carolina is distributed at a rate of roughly 2:1 in urban vs. rural areas. The study was not able to establish any direct causal relationship to provide insight as to why prescribing cases were so much more prevalent in urban areas during the studied period.The project had limited ability to assess the impact of regulatory actions, in part because of the relatively small number of actions that occurred during the study period and in part because NCMB’s data did not capture enough information to facilitate study of this topic.It is not enough to simply want to practice data-driven regulation or aspire to evidence-based remediation and discipline. SMBs must also have the data infrastructure to harness the information needed to ask the right questions and devise the right strategies to achieve these aims.To effectively mine enforcement data for insights that can drive improvements to a state medical board regulatory program, it is critical to first assess whether the SMB is set up to capture the right range and depth of information needed to allow meaningful analysis. NCMB’s recidivism project was invaluable because it confirmed that the agency was not yet prepared to engage in the sort of meaningful data-analysis that is necessary to practice evidence-based regulation. NCMB offers this observation to any other SMBs that may be interested in data-driven regulation: In short, it is easier said than done, and substantial planning and investment in a board’s data collection strategy will likely be needed.In September 2021, following its initial study, NCMB engaged Blaze Advisers to collaboratively devise a comprehensive data strategy for the agency. This strategy will be customized to NCMB’s specific needs, goals, and data maturity level.2 The strategy encompasses the following principles:Data is an organizational asset. An agency must understand that its data is not just a byproduct of business activities but an asset with independent value and, potentially, a transformational tool that can support organizational and stakeholder objectives. Embracing this principle is a critical first step for any organization interested in using data to guide and inform regulatory or business activities.Quality data starts at data entry.The process of generating meaningful, usable data starts with ensuring that each raw data point is captured in a standard way. This cannot be emphasized enough. To facilitate data analysis, entered data must be standard, consistent, validated and trusted. Organizations must design software systems and processes to ensure this, beginning with identifying what data points must be captured. Use of free text fields should be kept to the bare minimum and use of pre-populated dropdown menus or checklists should be used wherever possible to ensure accuracy and consistency.These basic foundations are essential and should result in the ability for NCMB or other regulatory organizations to generate meaningful information that can be trusted and used for further inquiry and dimensional analysis. These activities are the basis for data-informed knowledge, not knowledge based on hunch or anecdotal experience, and subsequently leveraged in prescriptive actions to produce or drive toward a desired outcome.Staff training is key.Software and processes are only as good as the people using them. In order to generate quality data, staff who enter raw data must understand the value of accuracy and consistency and should know that their work is helping to create a valuable resource — the organization’s data asset — for their employer. Some may be familiar with the saying, “garbage in, garbage out.” It applies here. Staff who are not properly trained or who are unaware of the value of their work are more likely to make mistakes or introduce inconsistencies to the data.Patience yields results.Designing and implementing a system to collect data and training staff to use and maintain it is just the beginning of becoming a data-driven organization. It takes time to collect enough data to enable the types of analysis that help identify patterns, trends, outliers and other useful information. For example, most new licensee-reported data comes to NCMB through its annual renewal process. Since licensees renew annually during their birth month, it takes a full year to gather updated information from the entire active licensee population. It will take several years to collect enough data to facilitate some types of research.Therefore, embracing data-driven regulation takes time and a commitment to the agency’s unique data governance needs, as well as internal stakeholder involvement.By adopting the principles stated above, NCMB hopes to develop a blueprint that will set the organization up to conduct meaningful analysis of enforcement activity in the future. An additional goal for this initiative is to build a rich and robust culture around reporting and thinking about data collection processes at all levels of the organization.Having gained valuable insights from our 2019 Recidivism Project, NCMB remains optimistic about the potential of data-driven regulation and looks forward to enhancing its ability to harness enforcement data in the service of more effective regulation.

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

Modern deepwater marine 3D surveys require advanced infield systems in both data collection and data processing, if imaging and turnaround targets are to be met. In this paper we present the case history of a marine 3D reconnaissance survey, which presented challenges for both data collection and data processing. The challenges stemmed from the deep and complex waterbottom which caused issues with streamer geometry and also major multiple contamination within the seismic data.

A review of the study processes and protocols afterward by the researchers themselves is scarce. The present study aimed to evaluate the study design and the process of data collection of the Maastricht Intensive Care COVID (MaastrICCht) cohort during the COVID-19 pandemic. This evaluation provides information about the quality of the predefined questions and contributes to transparency in science. Critical appraisal of studies using data from the MaastrICCht cohort. Evaluation of the process of study design and data collection during the COVID-19 pandemic, focusing on the research process and results. From March 2020 to April 2023, all patients diagnosed with COVID-19 admitted to the ICU at Maastricht University Medical Center + (n = 544) were included in the MaastrICCht cohort. In total, 37 studies were carried out until April 2024. Fifteen studies addressed 11 of the 13 predetermined research questions, whereas 22 additional studies were performed based on the initial research questions described in the design. Furthermore, 10 studies were conducted with other researchers in national and international collaboration as a response to new arising questions based on evidence that appeared relevant during the pandemic. Our critical appraisal indicated that using a study protocol enabled many publications and (inter)national collaborations, although formulating pertinent research questions in the context of a novel disease appeared daunting. Despite this, most questions were successfully addressed, whereas few were resolved by other researchers or lost importance due to the expanding body of knowledge.

Sustainable urban transformation practices play a critical role in implementing the economic, environmental, and social dimensions of sustainable development in cities. Sustainability indices are important parameters for achieving sustainable development goals. The use of these indices supports the making of strategic decisions for the future of cities, such as in urban transformation practices. Indicators and data are defined as vital tools for evaluating the success of sustainability policies and monitoring the economic, environmental, and social performance of cities. Establishing more comprehensive and accessible data collection systems at the local level and effectively sharing this data is crucial for developing policies based on these indices. This study aims to emphasize the critical importance of sustainable urban transformation practices in relation to Sustainable Development Goals (SDGs) by showcasing the experiences gained during the development of a Sustainability Index for Bağcılar, a densely urbanized district in Istanbul. Bağcılar offers an important area of study in terms of sustainability with its dynamics such as rapid population growth, dense construction and socio-economic differences. Planning urban transformation projects within the framework of sustainability principles is a strategic necessity for Istanbul and similar metropolitan cities. By establishing a system that tracks regular, reliable, and internationally standardized data, it will be possible to create measurable, reportable, and comparable targets for sustainable urban transformation practices. However, in developing countries like Turkey, deficiencies in data collection and analysis processes make it difficult to achieve sustainability goals. This study suggests that improving data collection processes and increasing transparency are fundamental steps to ensuring sustainable urban transformation.

Modern unmanned aerial vehicles (UAVs) are increasingly integrated with sensor networks, which significantly expands the possibilities for data collection, transmission, and real-time processing. This integration is critically important for various sectors, including environmental monitoring, smart city infrastructure management, agriculture, and military operations. UAVs provide mobility and access to remote and hard-to-reach locations, enabling efficient monitoring in conditions where conventional networks are unavailable or ineffective. However, numerous technical challenges arise along with these advantages. They concern, in particular, the optimization of drone flight routes to ensure maximum sensor network coverage, the minimization of energy consumption, and the resolution of data security problems, including cyber threats. Another key aspect is flight duration, which depends on UAV battery capacity, as well as energy-saving approaches for sensor nodes, such as the use of alternative energy sources like solar panels. This study presents a dynamic interaction model of UAVs and a sensor network, examining the data collection process, the transmission of these data to a central server, and how increasing the number of sensor nodes affects the total mission time. A stochastic model is proposed to account for environmental heterogeneities such as data transmission delays caused by interference or variations in connection speed. An analysis is carried out regarding the impact of these factors on data collection efficiency and on the optimization of flight routes, particularly through the use of dynamic programming and heuristic methods.

A functional and reliable Health Information System (HIS) is vital for data-based decision-making in public health. Here we describe the assessment of data processes and general HIS principles by adapting a global approach to three cities. The assessments supported the data strategy of the CARDIO4cities initiative in each city aiming to improve urban population health by increasing the use of cardiovascular disease (CVD) data to inform decision-making. We aimed to explore data collection processes and entities, data availability and quality as well as data ownership and sharing regarding a set of identified key performance indicators (KPIs). KPIs were based on a global theory of change (ToC) and a global evaluation and indicator framework and were tailored to each location. By first assessing existing sources and processes regarding data, recommendations for changes and improvements are sure to build on current circumstances. To map the data, existing data collection, analysis and storage processes were investigated. A flow chart was created to visualize the data pathways and challenges for each system and findings were compared across cities to document differences and similarities. Data quality and interoperability of various separate systems were the most prominent challenges for all HISs. The observed dvata quality issues originated from incorrect, missing and incomplete data and were connected to the misunderstanding of indicators, incomplete data input forms or the lack of a systematic data routine in the workflow. Harmonization of the HISs to ensure interoperability can facilitate data collection and analysis of health data and can provide a solid basis for health management decision-making. Based on the presented HIS cases, we recommend to examine, map and verify current processes when conducting a HIS assessment, to visualize findings and to gauge the interest of government entities to ensure political support.

Unmanned aerial vehicles (UAVs) have recently been used in many applications from surveillance to communication. UAVs can also assist the process of data collection from ground Internet of Things (IoT) devices thanks to the low deployment cost and flexibility. Since the energy and flight time of UAVs is limited, the trajectory planning for the UAVs during this data collection process is vital. While there are several studies that look at this problem with varying objectives, there is still a need for finding the optimal UAV path for data collection from mobile IoT devices with both the delay and secure collection of data in mind as the main concern. In this work-in-progress paper, we study this problem where a UAV aims to minimize the average or maximum delay of the collected data from ground IoT devices within its flight duration while also staying away from potential eavesdroppers on its path. We model the problem using Integer Linear Programming (ILP) and present results for different scenarios. Our next goal is to develop a reinforcement learning based solution that can provide results that are close to optimal ILP based results but also applicable to real-life scenarios.