Data Collection Mechanism Research Articles

Employing Social Participants for Timely Data Collection Using Pub/sub Solutions in Dynamic IoT Systems

In Mobile Crowd Sensing (MCS), the platform publishes sensing tasks with the requirements of location, time and data attributes and hires participants to perform sensing tasks, thus it can collect and process mass of sensing data to construct various applications and publish them to service requesters, which is a low cost and effective method for large-scale data processing. However, most of the existing studies do not consider the value of tasks with time-discount property and insufficient participation, which results in delayed task completion and low task completed ratio. In this paper, we propose data collection mechanisms in offline and online scenario considering both of these factors to collect data timely and increase the benefit of the platform effectively. We propose a multi-tiered spreading task structure, in which participants act as agents to recruit their social neighbors to perform tasks, and the social neighbors can be recruited as new agents to spread tasks so that there is a sufficient number of participants in the platform to be selected to perform tasks. In winner and agent selection of online mechanism, we change the allocation of budget in multi-stage sampling accepting process based on real-time task completed ratio. We proved that the proposed mechanisms achieve computational efficiency, truthfulness, individual rationality and budget feasibility, and after extensive experiments, we proved the proposed mechanisms are superior to previous strategies.

Employing Technology to Enable Remote Research Charrettes as a Method for Engaging Industry and Uncovering Best Practices: A Novel Approach for a Post-COVID-19 World

Methods to collect data in construction engineering and management (CEM) research are evolving, informed by recent technological advancements. One such method is research charrettes that allow effective interactions and knowledge sharing between expert industry practitioners and academic researchers, all colocated in a single venue, enabling rich data collection and live communication. A pivot point in technological evolution occurred with the COVID-19 pandemic, forcing a global shift to remote work. Hence, planned in-person research charrettes had to shift to remote sessions, relying on virtual conferencing platforms and online data collection mechanisms. Technology-enabled charrettes have allowed the authors to collect significantly richer data sets and ensure a more diverse representation of participants, while saving tremendous amounts of time. With the continuing emergence of technological applications, the world might not go back to functioning fully in person. The authors believe remote research charrettes (RRCs) will still be used in a post-COVID-19 world because of their superior performance. This paper builds on a previous publication that described traditional research charrettes as a method to enhance CEM research a decade ago; it offers a significantly updated and improved RRC method based on the knowledge gained from transitioning a dozen in-person charrettes into RRCs. It also presents performance comparisons between RRCs and traditional charrettes by quantifying metrics indicating how RRCs are more time-efficient and cost-saving, harness more participants from more diverse locations, and enable the collection of richer data sets and four times more industry comments and expert feedback. This paper also provides guidance on the integration of technology with traditional research charrettes, hence contributing to the CEM body of knowledge.

8.Q. Workshop: WHO’s activities to strengthen the evidence of public health and social measures

Abstract Public health and social measures (PHSM) are key to reducing the spread of infectious diseases like COVID-19, especially in the early stages of an outbreak. PHSM include non-pharmaceutical actions that individuals, communities, and governments take to reduce person-to-person contact and/or make them safer. PHSM reduce the pressure on the health care system to allow for the continuation of essential services and buy time for the development and dissemination of treatments and vaccines. While a combination of PHSM has proven to be effective in reducing transmission during the COVID-19 pandemic, the evidence on the relative effectiveness and broader health, social and economic impact of individual interventions is still scarce. However, PHSM packages such as lockdowns can have severe unintended consequences for individuals and societies including economic hardship, decreased mental health and wellbeing and exacerbated social and health inequity; therefore, precision in PHSM decisions and implementation is needed. This requires a strengthened evidence base as well as tools that support countries in making balanced decisions about PHSM with the best possible cost-benefit ratio. WHO works with multisectoral partners to achieve effective and context- specific PHSM implementation while maximizing the benefits of PHSM and keeping their health, social and economic burden to a minimum and justifiable. In the WHO Regional Office for Europe, PHSM are a key pillar in the COVID-19 response. The office provides, amongst others, a PHSM monitoring dashboard to track the severity of implementation across all 53 Member States as well as a calibration tool and related capacity building to guide countries in their implementation decisions as the pandemic situation evolves. The WHO has further launched a global initiative in 2021 to measure the effectiveness and impact of PHSM and improve precision in future PHSM decisions and policies. The initiative aims at providing robust data and research evidence on PHSM through a global conceptual model and research agenda, a central research monitoring system and harmonized data collection mechanisms during health emergencies. It further seeks to integrate PHSM into emergency preparedness and response assessments and yield a decision-making tool to facilitate evidence-informed and context-specific PHSM implementation. This workshop will provide an overview of WHO's activities in advancing PHSM research and implementation for better decision-making during future emergencies. Three presentations by the WHO Secretariat at the regional and global levels will focus on the strategic approach and the main deliverables of the respective initiatives so far. The presentation by the University of Munich will provide in-depth insights into the conceptual model on PHSM. The workshop will actively solicit feedback from participants on planned activities and results gathered to-date. Key messages • The lack of evidence on PHSM effectiveness and impact hampered an evidence-driven implementation approach during the COVID-19 pandemic. • WHO works to achieve effective and context- specific PHSM implementation to maximize the benefits of PHSM while keeping their health, social and economic burden to a minimum and justifiable.

IMPLEMENTASI PENDATAAN PENERIMA PROGRAM KELUARGA HARAPAN (STUDI KASUS DI DESA SUKA MAKMUR)

The Family Hope Program is one of the government programs that seeks to develop a social protection system for the poor in Indonesia. This program provides assistance in the form of cash to Very Poor Households (RTSM) provided that they must comply with the existing requirements. The initial data collection was carried out in determining the recipients of the Family Hope Program (PKH) assistance by following the data collection mechanism from the Central Statistics Agency (BPS), but after that the data collection was carried out by proposing DTKS where this proposal was carried out by the village or poor communities who volunteered to proposed to the Social Service which already has responsibility for the DTKS proposal. The purpose of this study was to see how the process of implementing the data collection for the Family Hope Program (PKH) in Suka Makmur Village. The research method is descriptive qualitative to get a clear picture of the implementation of data collection for the Family Hope Program (PKH). Data collection techniques with observational studies, interviews, and documentation. Based on the results of the study, it can be seen how the data collection process has been carried out and can also be seen how the data updating, verification and distribution of PKH funds to RTSM who receive the benefits can be seen. After conducting the research, it can be concluded that the process of implementing the data collection for the Family Hope Program has been going well even though there are still shortcomings in its implementation.

The Practice of Financial Data Collection in the “Talitihan/Gantangan” Tradition in Wedding Receptions in Gantar District According to Islamic Law

This article presents the findings of a study on the financial data gathering practiced by the gantangan people in rural Gantar District when hosting wedding festivities. This research was conducted utilizing a qualitative research method with a descriptive phenomenological approach that use life experience as a tool to better understand socio-culture by gathering data through observations, interviews, and recording, which is then evaluated to reach conclusions. The financial data collection mechanism has both advantages and disadvantages. An advantage is our willingness to help our fellow humans, while the disadvantages are what cause some individuals to complain about this talitihan/gantangan tradition, i.e. when the individual in question is not present but must still pay. The findings of the study indicate that people of Babakan Plasah Village, Gantar District, collect financial data on the talitihan/gantangan tradition as part of the celebrations' execution. No particular record exists for this record. The use of this custom is consistent with Islamic law, as the underlying principle promotes a spirit of assistance in conformity with Islamic principles.

Understanding Subordinate Animal Welfare Legislation in Australia: Assembling the Regulations and Codes of Practice.

Simple SummaryIn Australia, animal welfare protection is a state and territory responsibility. Having eight separate state and territories results in eight separate animal welfare legal frameworks, all which contain a primary piece of legislation and secondary or subordinate forms of legislation. These subordinate forms are known as regulations and codes of practice and are lower-ranking laws that are given powers under the primary legislation. Subordinate laws contain crucial details that govern everyday human–animal interactions and industry practices, for example companion animals used for breeding. There has been no review of the animal welfare-focused subordinate laws in Australia. This study has assembled each animal welfare regulation and code of practice in force in Australia as a reference for practitioners working in specific animal-related areas to easily identify relevant documents pertaining to welfare and understand the nature of their responsibilities in terms of compliance with these documents. A total of 201 pieces of subordinate legislation were identified and presented through the following utility categories of animals: companion, production, wild/exotic, entertainment. The benefits of housing the information identified from this study on an online database for animal industries to use are discussed.The state-based approach to regulating animal welfare in Australia is thought to create national dis-uniformity in that each state and territory legislates and operates inconsistently. The animal welfare legal framework in each of the eight Australian jurisdictions is made up of a primary statute and subordinate legislation, where subordinate animal welfare legislation, in the forms of regulations and codes of practices, are lower-ranking laws that are given power under the jurisdiction’s specific animal welfare statute. Since a review of animal welfare statutes identified broad patterns between the jurisdictions, this study is intended to be complementary by collating the subordinate legislation to provide a more comprehensive understanding of animal welfare laws in Australia. Using targeted search strategies stemming from the eight enabling animal welfare statutes, this study identified 201 pieces of subordinate legislation in force between 28 March 2022 and 5 April 2022. The scope of subordinate legislation is depicted through the following utility categories of animals: companion, production, wild/exotic, entertainment. Whilst subordinate legislation differed between the jurisdictions, it was common for similar welfare concerns or topic areas to be protected in higher-order legislation (statutes or regulations). Additionally, many jurisdictions were found to have similar shortcomings, all which likely could be managed through a mechanism of national data collection.

5G Edge Computing Access Node Selection Algorithm Based on Energy Efficiency and Delay

In order to address the dissipation of energy efficiency in 5G edge computing, and the problem of network delay, better improve the quality of user service, considering the data aggregation delay while improving the network energy efficiency. The author proposes a 5G edge computing access node selection algorithm based on energy efficiency and delay; through the energy efficiency and delay balanced data collection mechanism (EEDBDG), a new dynamic tree is used to organize the network topology, eliminating the hot zone problem; nodes dynamically choose routes and take turns acting as the root of the tree, which collects data and communicates directly with the base station. At the same time, three data collection strategies are proposed for different latency and energy efficiency requirements: delay optimal algorithm (EEDBDG-D), energy efficiency optimal algorithm (EEDBDG-E), and energy efficiency delay balance algorithm (EEDBDG-M). Experimental results show that, when the communication radius of nodes is limited, EEDBDG balances the energy consumption of nodes, prolongs the network life time, and shows outstanding performance in energy saving and time saving. Compared with GSEN, in the best case, the network lifetime of EEDBDG-E is increased by 72%, and the convergence delay of EEDBDG-D is reduced by 74%. Conclusion. The algorithm can effectively reduce the energy dissipation and delay of edge computing.

Occupational safety of janitors in Ethiopian University during COVID-19 pandemic: Results from observational study.

IntroductionJanitors are at high risk of COVID-19 infection, as they are among the frontline workers for the prevention and control of COVID-19. Poor occupational safety practices could contribute to loss of lives of janitors and the general public. However, there are no detailed investigations on occupational safety practices of janitors involved in different settings, such as universities where there are crowds of people. In addition, although observation is recognized as a better tool to investigate occupational safety practices, previous studies mainly employed self-administered questionnaires and/or face-to-face interviews as data collection mechanisms. Therefore, this study aimed to assess occupational safety practices to prevent COVID-19 transmission and associated factors among Ethiopian University janitors using an observation tool and a self-administered questionnaire.MethodsAn institutional-based cross-sectional study was conducted among 410 janitors of Bule Hora University (Ethiopia) from November to December, 2021. A multivariable logistic regression model was used to identify the independent factors associated with occupational safety practices.ResultsOccupational safety practices for COVID-19 were good only among 53.9% of the janitors. Training on COVID-19 prevention measures (AOR = 2.62; 95% CI: 1.57–4.37), availability of policy and protocol in the work place (AOR = 5.46; 95% CI: 3.57–8.36), and availability of soap/bleach (AOR = 2.71; 95% CI: 1.64–4.46) were found to significantly increase the likelihood of occupational safety of the janitors.ConclusionA significant proportion of the janitors had poor occupational safety practices. Therefore, an adequate supply of PPE and regular training and awareness creation on COVID-19 should be strengthened. Close follow-up and regular supervision of safety procedures should also be conducted as controlling strategies.

Ngaaminya (find, be able to see): summary of key findings from the Which Way? project.

Ngaaminya (find, be able to see): summary of key findings from the Which Way? project.

Use of real-world data and clinical registries to identify new uses of existing vascular endografts: combined use of GORE EXCLUDER Iliac Branch Endoprosthesis and GORE VIABAHN VBX Balloon Expandable Endoprosthesis

ObjectiveTo assess the feasibility of collecting, examining and reporting observational, real-world evidence regarding the novel use of the GORE EXCLUDER Iliac Branch Endoprosthesis (IBE) in conjunction with the GORE VIABAHN...

A comprehensive and systematic review of the IoT-based medical management systems: Applications, techniques, trends and open issues

• Analyzing the main strategies for IoT-dependent medical management systems; • Offering a Systematic Literature Review (SLR) and checking out the available methodologies to use IoT-based medical management systems; • Discussing critical IoT issues in the discussed approaches; • Presenting the category of the methods explored and emphasizing their primary attributes. The Internet of Things (IoT) is critical in a variety of modern city and society management domains, including intelligent medical management. The intelligent IoT with unlimited networking capacities for medical big data analysis strengthens the interaction across technology and healthcare society in smart cities today. ​IoT enables more convenient ways to remotely and efficiently provide medical services for the patients and monitor the health situation of patients by the doctors, physicians, and nurses over the Internet. Because of the lack of comprehensive and up-to-date review in this domain, the present investigation intends to offer a thorough examination of the role of IoT in medical management systems, discuss the available issues, and answer many empowering technologies and insinuations to numerous usages. Research schemes have been proposed for a 3-cluster categorization (patient data collection, exchange, storage, remote monitoring, and security mechanisms). These terms potentially affect the present and upcoming IoT-based medical system research. The results obtained from the literature review showed that improving security, cost, service time, and efficiency have been the most important goal of researchers in the field of management of medical systems based on IoT. Besides, the findings revealed that IoT is beneficial for governments to boost the condition of society and business relations in terms of well-being; however, IoT needs modern safety infrastructure.

Evaluating adaptive differential privacy model

Differential privacy is a statistical disclosure control that is gaining popularity in recent years due to easy application for the data collection mechanisms. Many variants of differential privacy are being developed for specific use cases and environments. One of them is adaptive differential privacy that modulates the generated noise in such a way, that the retrieved result is affected according to the risk profile of the asked query and the risk-accuracy tradeoff required for the queried database. This paper intends to evaluate the adaptive differential privacy using VIOLAS Framework and through assessing how the security characteristics satisfied by the adaptive differential privacy mitigate the risk of selected inference attacks.

Effects of the Practice of Federalism in Nigeria on its International Image

Nigeria is a heterogeneous State whose many ethnic groups were outlandishly merged by Britain purely for administrative purposes during the colonial era and without regard for their characteristic diversities. The amalgamation gave rise to the adoption of federal system of government by Nigeria in 1954 as well as the subsequent evolution of federal character principle, all with the aim of managing the diversity for the sake of national survival. Regardless of the efforts, the practice of federalism in the country has continued to be plagued by various challenges. How these challenges affect the international image of Nigeria was the major task of this study. The study was anchored on the Group Theory while the qualitative mechanism of data collection and analysis was applied in this study. Among other things, the study found out that among the challenges confronting the practice of federalism in the country was the ineffective application of the federal character principle. It also found out that the international image of Nigeria suffers unthinkable damages as a result of the challenges associated with the practice of federalism. In view of the findings, the study therefore recommended the need for the political class to show commitment to the practice of true federalism by way of promoting national, rather than ethnic and divisive interests. It equally recommended that the National Assembly must amend the constitution to request any president who disrespects federal character principle in allocation of benefits to resign his or her position.

The role of NGO administrative data in understanding the impact of COVID-19 on survivors of domestic abuse

COVID-19 quickly changed the context of domestic abuse in England. Within weeks of the first COVID-19 related death, the country was in lockdown. A quick response was essential for understanding the needs of survivors. With limited time to establish new data collection mechanisms, the role of administrative data was central in shaping the response by the Women’s Aid Federation of England. This article explores the opportunities and challenges of using administrative data to understand and respond to the impact of COVID-19 on survivors of domestic abuse in England, using analysis by Women’s Aid of administrative data as a case study. The article discusses the challenges, such as the complexity of analysing a longitudinal administrative dataset, and the need for increased skills and capacity within the NGO research environment. We also reflect on ethical considerations in light of the context of frontline workers responding to the pandemic, the opportunities for collaboration with other sector partners and academics and the benefits of being able to undertake reactive analysis to inform policy. The article concludes that our access to administrative data bolstered our ability to respond expediently to the pandemic, and achieve the long-term benefits of the partnerships that we built during this time.

Mechanism design for data reliability improvement through network-based reasoning model

The importance of business intelligence is increasing every day and many corporations are using data analysis and its results in the decision-making process. Although collecting reliable data is a key prerequisite for the effectiveness of business intelligence, the existing research has focused on improving the reliability of data that is already collected. This research points out the limitations of previous research related to data reliability and presents a new theoretical model that can secure highly reliable data to enhance the business intelligence effect. We designed a mechanism that introduces the concept of the power of influence by using the network effect based reasoning model and applying the two-step flow theory of social exchange theory and information. More than 2 million pieces of real users’ preference data was collected and verified by applying them to a recommendation system. More specifically, I made them recognize what the influence of the users’ preference data input behavior on each individual would be. Also, the limitation on the user input data with the low existing reliability was overcome by applying the recommendation system based on the network effect, which weights the preference data of the users having high influence. As a result, we have shown that the data collection mechanisms based on influence are more efficient in terms of data collection and data analysis.

A machine learned go-around prediction model using pilot-in-the-loop simulations

Go-around manoeuvers are challenging for the management of arriving air traffic due to their complexity and unpredictability. In the present work, we propose to augment the air traffic control (ATC) environment with a predictive tool that assists Air traffic controllers (ATCOs) in the detection and prediction of the likelihood of go-around events. First, a safe and cost-effective flight approach data collection mechanism is introduced via a pilot-operated flight simulator under varying visibility conditions. Then, data are processed and a go-around probability assignment mechanism is introduced. Finally, a go-around probability prediction model is proposed. The proposed model updates the go-around probability for each new flight record, namely every second, and converges by detecting either a go-around or a successful landing. Experiments are conducted for two airports, namely Philadelphia International Airport (PHL) and Van Nuys Airport (KVNY), which feature different go-around procedures and are operated by different types of aircraft. Findings demonstrate that the trajectories of predicted go-around probabilities follow closely the computed probabilities regardless of the dissimilarities in the trajectory patterns. Furthermore, results suggest that the likelihood of go-around exceeds 93% when the go-around is initiated, for almost 50% of the flights in the test set. Moreover, the proposed model is able to detect the successful landing of aircraft. In fact, the system indicates low chances of go-around when the aircraft landed. For instance, the likelihood of a go-around at the touchdown is below 35% and 10% for KPHL and KVNY, respectively. The findings underscore the ability of the proposed model in providing accurate and timely go-around probabilities. This will help the ATCO to be prepared for re-sequencing arrival flights and clearing the go-around path, prior to the actual initiation of a go-around.

Coverage with evidence development: where are we now?

CMS' coverage with evidence development (CED) policy allows Medicare beneficiaries to access promising therapies and services while additional data are collected. CED program characteristics are mostly unreported, and qualities associated with retirement of CED data collection requirements are unknown. We aimed to review and systematically describe CED program history and components and report programmatic elements correlated with retirement of CED data collection requirements, while identifying areas for policy improvement. Systematic review. We extracted CED information from the CMS website, ClinicalTrials.gov, PubMed, internet searches, and communication with CMS. There were 27 CED determinations from 2005 to 2022 in 8 therapeutic areas, with the most for cardiovascular diseases (8/27; 30%). Duration of CED programs (range, 1-16 years) and the number of related registries and clinical trials (range, 0-34) were widely variable. Only 4 CEDs have had data collection requirements with continued National Coverage Determination (NCD); 3 relate to cardiovascular therapies, and all have some public availability of findings resulting from CED-related data collection mechanisms. There were 2 instances of NCD revocation and deferral to local coverage decisions. Changes in the CED program through improving program predictability and transparency with regard to outstanding questions, roles of relevant stakeholders, and requirements for reporting and reevaluation would strengthen the program's effectiveness. Ultimately, these improvements would provide incentives for stakeholder participation in data collection to achieve the goal of increasing access to beneficial therapies and improving clinical outcomes.

Communication practice in village data collection

This study analyses two communication practices for rural data collection in Indonesia: top-down, carried out by the state, and bottom-up, initiated by collective intellectuals. This research is to reveal how to communicate rural data collection actions. The differences in data manifest the practice of communicating rural data collection actions; and Doxa, habitus, and symbolic violence that is ‘hidden’ in the procedures and mechanisms of data collection run by the state. The study area is Tegallalang Village, Gianyar Regency, Bali. Quantitative data in Prodeskel from the Ministry of Home Affairs and Precision Village Data (DDP) with a Drone Participatory Mapping approach were obtained independently by researchers. The research used Mixed Methods Research. Qualitative data were obtained through in-depth interviews using the Nvivo R1 application analysis. Knife analysis using Pierre Bourdieu and Nick Couldry. The study results found two differences in the practice of rural data collection, namely; first, the difference in data collection actors. The state represents Prodeskel, and collective intellectuals represent DDP; second, the difference in data is due to differences in the practice of communication actions (procedures and mechanisms) of data collection. Prodeskel data with a top-down approach produces low-accuracy data and vice versa for DDP. This research also reveals the opus operatum of communication actions in the form of Doxa, habitus, and symbolic violence in data collection of the country’s countryside and digital technology to build a space for communication and citizen participation which is the key to the birth of DDP.

Citizen Science and the Sustainable Development Goals: Building Social and Technical Capacity through Data Collection in the Upper Blue Nile Basin, Ethiopia

Engaging youth and women in data-scarce, least developed countries (LDCs) is gaining attention in the Sustainable Development Goal (SDG) arena, as is using citizen science as a multi-faceted mechanism for data collection, engendering personal empowerment and agency. Involving these populations in citizen science is a powerful synergy that simultaneously addresses the Leave-No-One-Behind promise in the United Nations’ 2030 Agenda, yet most citizen science takes place in the Global North, and attention to LDCs is needed. This article highlights a four-year, four-location, hydrology-focused, interdisciplinary citizen science initiative (CSI) in the Upper Blue Nile Basin, Ethiopia. Through a systematic evaluation, we explore scientific applications of the hydrologic data, as well as the social dimensions in the CSI, towards building a social and technical capacity that supports the SDGs at the local and international scale. In the CSI, Ethiopian high school students received training from local university faculty and graduate students, collecting river stage and groundwater level measurements, and farmers conducted soil resistivity measurements using a novel sensor technology developed for the study area. We found the datasets to be ample for use to locally validate regional groundwater models and seasonal forecasts on soil moisture and streamflow. We conducted written interviews with the students, which revealed their ability to perceive benefits of engagement in the CSI, as well as recognize their increased individual technical capacity. An analysis of the hydrological data demonstrates the readiness of the datasets to be used for evaluating water-related interventions that facilitate the SDGs, broadly, by building synergies between individuals and institutions. As such, we map how both the hydrologic data and experiences of the citizen scientists support the SDGs at the Goal and Target-level, while forging new social and technical pathways.

SISTEM INFORMASI PENDATAAN NARAPIDANA PADA LAPAS MENGGUNAKAN WEB

Every institution has important data that must be maintained its authenticity and integrity for the smooth performance of the institution. Both primary and secondary data. Because data collection is the most important part in any job. Meanwhile, in Kraksaan Prison, the data collection mechanism used is still inefficient because it inputs data twice. Namely with a notebook related to the identity of the inmate, and re-sorted into Microsoft Excel. Therefore, this study intends to facilitate prisons in tabulating data, the formulation of the problem in this study is how to make an information system application for data collection of prisoners in prisons using the web, the research method used in this study is a qualitative method. While the system development method in this study uses the waterfall method by conducting observations, interviews, and literature studies. The result of this research is the creation of an information system application for data collection of prisoners in prisons using the web, which aims to facilitate correctional officers in tabulating data. With the implementation of this data collection information system, it is expected to be able to handle problems in the agency, namely facilitating data collection and printing reports.