Data Capture Techniques Research Articles

Smart Supervision of Public Expenditure: A Review on Data Capture, Storage, Processing, and Interoperability with a Case Study from Colombia

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Automated Invoice Data Extraction: Advancements and Challenges in OCR-Based Approaches

The Invoice Recognition System (IRS) is an innovative system that is poised to revolutionize automatic data extraction from invoices. The IRS expertly translates handwritten and printed invoice data into efficient digital formats, utilizing cutting-edge data capture techniques and Optical Character Recognition (OCR) technology. The IRS, built for businesses with huge invoice volumes, aims to significantly enhance financial and administrative productivity by reducing errors and eliminating the need for manual data entry. The system makes advantage of OCR precision to ensure that information is thoroughly interpreted and extracted, enhancing data accuracy and processing speed. The IRS appears to be a useful tool for improving complex financial and administrative procedures, thanks to its adaptability to various invoicing forms. Keywords: invoice recognition system,OCR(optical character recognition),Automation,Invoices.

Pengaruh Layanan Bimbingan Kelompok terhadap Keberanian Bertanya Siswa di Kelas XI SMK Swasta Karya Utama Dolok Masihul Tahun Ajaran 2021-2022

This study aims to find out the influence of group mentoring services on the courage of asking students. The data capture technique consists of a lift. This research was carried out at Dolok Masihul Primary Teaching Year 2021-2022, which has a total of 10 students. The design of the study was pre-test and post-test group design. To obtain the data, the researchers used a closed lift and a concentration scale instrument of 45 particles, but after testing it turned out there were 8 invalid lifts, so the lifts used in this study were only 37. This study shows that the courage of asking students before giving group guidance services to students in class XI SMK Private Primary Works Dolok Masihul is in the middle category, this can be seen based on the average score of courage ask students before being given service of 85.3 then courage to ask students in grade XI SmK Private Main Works Dolek Masihul was in the medium category.

Feasibility of and reactivity to ecological momentary assessment (EMA) during electronic cigarette use initiation in adults who smoke daily

Background and objectivesThis pilot study tested the feasibility of tracking e-cigarette initiation over three months using a burst-of-measurement approach and examined reactivity to the ecological momentary assessment (EMA) data capture technique in this context. MethodsAdults who smoked daily and were interested in trying e-cigarette use received an e-cigarette starter kit and were randomized to complete EMA reports (n = 30) vs. not (n = 29). Participants (Mage=44.1 years [SD=12.6], 61.0 % male, 54.3 % White, 38.9 % Black/African American) smoked 13.0 cigarettes per day on average (SD=8.4) and reported mild-to-moderate nicotine dependence (MFTND=4.0 [SD=2.3]). Biochemical assessment and surveys were conducted at enrollment, e-cigarette initiation (1 week later), and 1-week, 1-month, and 3-month post e-cigarette initiation. ResultsA subset (17 %) of participants did not achieve the minimum 75 % EMA adherence at Week 1. The remaining participants showed excellent adherence with the EMA protocol, with a consistently high response rate to audibly prompted mini-surveys during screening (93 %) and follow-up weeks (93 %, 93 %, and 92 %, respectively). No consistent trend emerged in comparing the two randomized groups, either on variables relevant to the public health impact of e-cigarette initiation (i.e., cigarettes/day, exposure to smoke, exposure to nicotine, motivation to quit smoking), or in e-cigarette use (i.e., bouts/day, puffs/bout), with average Cohen's d values across timepoints ranging from d = 0.02 for exposure to smoke to d = 0.06 for cigarettes per day. ConclusionsConducting EMA with adults who smoke daily and are initiating e-cigarette use appears feasible, with some caveats. Reactivity to EMA during e-cigarette initiation appears to be small or non-significant.

Examples of laser scanning application in JSW SA mines

Laser scanning offers timely assessments of mine sites despite challenges posed by operational environments. This article explores advancements in 3D scanning systems, data capture and processing techniques, and their primary applications in underground mining, using JSW SA mines as an example. While laser scanning has matured for tasks such as change detection, clearance measurements, and structure mapping, opportunities for enhancement exist in areas such as lithology identification, surface parameter measurements, and autonomous navigation. Limited infrastructure for data transfer and processing remains a constraint; however, laser scanners are increasingly pivotal in mine automation due to their affordability, accuracy, and mobility. Until recently, laser scanning was considered more of a curiosity than a mainstream measuring method. In Poland, creating maps for land surveying based on point clouds generated by laser scanners remains uncommon. Nevertheless, technological advances over the past decade have made surveyors, designers, architects, and conservators of historical monuments increasingly inclined and capable of utilizing point clouds generated by stationary scanners, which offer evergreater measurement accuracy. This article outlines how laser scanning, as an innovative measurement technique, has been integrated into the mining industry, particularly for shaft monitoring purposes.

Adaptive thermal responses of captive Nile crocodiles (Crocodylus niloticus) in South Africa

The current study assessed the ambient temperatures, and those selected, by captive adult Nile crocodiles on a commercial farm in South Africa. Non-invasive data capture techniques were developed to ensure the crocodiles natural behaviours were not disrupted or altered. Thermal and climate data, over summer and winter seasons, were collected from local weather stations, an on-site Internet of Things system, and a Mavic 2 Enterprise Dual drone. The method developed in this paper transformed relative thermal maps (produced by a DJI Mavic 2 Enterprise Dual drone) into a predictive model in which temperatures were derived to within 2.6°C per pixel of a processed orthophoto. Crocodile thermal and behavioural data were extracted from the drone imagery and juxtaposed with climate and thermal data from the pen. The greatest number of crocodiles were counted during early morning winter flights and the lowest number during late afternoon summer flights. Material (concrete, water, nest, grass/sand) selection by crocodiles varied with season, time of day and daily climatic conditions. Crocodile back temperature (10.2–49.6°C, µ = 30.4°C) ranges fell within those of their positional/environmental (10.6–66.6°C, µ = 28.7°C) temperature range selections. Strong, positive, significant correlations were found between crocodile back temperatures and positional temperatures for both winter and summer seasons, highlighting ectothermy. Application of this methodology on a commercial crocodile farm facilitated the inspection of potential shortfalls of the pens design from a thermal perspective, as well as suggestions for improvements that would ameliorate crocodile thermal discomfort (relating to hyperthermia).

Hepatitis B vaccination status among health workers in Nigeria: a nationwide survey between January to June 2021

BackgroundHepatitis B vaccination is the most important preventive measure against Hepatitis B viral (HBV) infection. Vaccination against HBV infection among healthcare workers is important because of their daily exposure to patients’ body fluids and the possible risk of transmission to other patients. Hence, this study assessed the risk of hepatitis B infection, vaccination status and associated factors among healthcare workers in six geopolitical zones of Nigeria.MethodsA nationwide cross-sectional study was conducted between January and June 2021 using electronic data capture techniques to enroll 857 healthcare workers (HCWs) in regular contact with patients and their samples through a multi-stage sampling method.ResultsThe participants’ mean (SD) age was 38.7 (8.0) years, and 453 (52.9%) were female. Each of the six geopolitical zones in Nigeria had a fair representation, with a range of 15.3 to 17.7% of the study population. The majority (83.8%) of healthcare workers in Nigeria knew that they were at an increased risk of being infected by their work. Also, 72.2% knew that if infected, there was a high risk of liver cancer in later life. Many participants (642 [74.9%]) responded that they consistently applied standard precautions, such as hand washing, gloves, and face masks while attending to patients. Three hundred and sixty (42.0%) participants were fully vaccinated. Of the 857 respondents, 248 (28.9%) did not receive any dose of the hepatitis B vaccine. Factors that were associated with not being vaccinated included age less than 25 [adjusted odds ratio (AOR) 4.796, 95% CI 1.119 to 20.547, p = 0.035], being a nurse (AOR 2.346, 95% CI 1.446 to 3.808, p = 0.010), being a health attendant (AOR 9.225, 95% CI 4.532 to 18.778, p = 0.010), and being a healthcare worker from the Southeast (AOR 2.152, 95% CI 1.186 to 3.904, p = 0.012) in Nigeria.ConclusionThis study showed a high level of awareness of the risks associated with hepatitis B infection and suboptimal uptake of the hepatitis B vaccine among healthcare workers in Nigeria.

Tool and Workpiece Condition Classification Using Empirical Mode Decomposition (EMD) with Hilbert–Huang Transform (HHT) of Vibration Signals and Machine Learning Models

Existing studies have attempted to determine the tool chipping condition using the indirect method of data capture and intelligent analysis techniques considering machine parameters, and tool conditions using signal processing techniques. Due to the obstructive nature of the machining operation, however, it is daunting to use signal capturing to intelligently capture the condition of the tool as well as that of the workpiece. This study aimed to apply some advanced signal processing techniques to the vibration signals captured experimentally during machining operation for the decision making and analysis of tool and workpiece conditions. Vibration signals were captured during turning operations while using four (4) classes of tools, based on their flank wear. The signals were first pre-processed and decomposed using the Empirical Mode Decomposition (EMD) method. The Hilbert–Huang transform (HHT) was applied to the resulting IMFs obtained to compute the feature vectors used to classify the condition of the tool and workpiece. A total of 12 features, consisting of instantaneous properties such as instantaneous energy, instantaneous frequencies, and amplitudes, were obtained for data training and classification of tool conditions. To optimize the classification process, feature selection was performed using a genetic algorithm (GA) to reduce the number of features from 12 to 4 for data training and classification. The feature vectors were first trained for tool classification with a neural network scaled conjugate gradient (SCG) algorithm. The result showed that the model classification error was 0.102. Two other machine learning models, support vector machine (SVM) and K-Nearest Neighbors (KNN), were also implemented for classifying the tool conditions, from the feature vector, to determine the model that most accurately predicted the condition of the tool. To avoid bias and reduce misclassification errors, the k-fold cross-validation technique was applied with ‘k’ taken as 5 and 10. The computed feature vectors were used as inputs to train the machine learning model using both SVM and KNN models to classify the tool and workpiece condition during machining. The error loss of each model was evaluated and plotted to review the performance. The average overall error loss of 0.5031 was observed for the SVM model with 5-fold cross-validation, whereas the error loss of 0.0318 was observed for the KNN model with 5-fold cross-validation. The average overall error loss of 0.5009 was observed for the SVM model with 10-fold cross-validation when trained using the features selected by a genetic algorithm (GA), while the average overall error loss of 0.0343 was observed for the KNN model. The optimal performance of the SVM model was obtained when all features were used for the training, whereas the KNN model performed better when feature selection was implemented. The error losses of the models were evaluated to be less in KNN models, compared to SVM and SCG. The obtained results also showed that the developed KNN models performed 10 times better than the SVM model in predicting the tool condition from the captured vibration signal during the machining process.

The Effect of Using Endorphin Massage for Decreasing Pain at First Stage in Normal Labor

Labor pain is a subjective experience of physical sensations associated with uterine contractions, dilation, thinning cervix and fetal descent during labor. To resolve the pain of labor, it has been implemented in a nonpharmacological method which is endorphin massage, in understanding endorphin massage for pressing pain in the first stage, in an active phase of normal multiparous mothers' deliveries. This research aims to research endorphin massage's influence on suppressing pain during the active phase of normal labor of multiparous mothers. This study is quasi-experimental with a cross-sectional approach. The study population of all women giving birth administered the purposive sampling data capture techniques, and it was obtained 132 multiparous mothers with normal labor in the observation sheet. Data were examined by administering a T-test. The majority of the pain intensity in the first stage of active phase multiparous mothers with normal labor, before the breath relaxation, is severe, with up to 49% experiencing severe pain. The majority of the percentage of moderate pain in the first stage of active phase multiparous mothers with normal labor, after breath relaxation, is as high as 42%. Meanwhile, the pain intensity of the first stage in active phase multiparous mothers with normal labor, prior to endorphin massage, is as high as 73.3%. Pain intensity of the first stage in active phase multiparous mothers normal labor after endorphin massage, the majority of the percentage is moderate pain up to 53.3%. Statistical test results obtained p-value is 0.004 endorphin massage. In conclusion, there is an effect of breath relaxation on the intensity of pain during normal labor in multiparous mothers. Furthermore, researchers can do a combination of endorpine massage with other treatments to treat pain during the I active phase of labor and pain intensity checks can be done by testing cortisol levels.

Mobile mapping for buildings, assets and estates : Benefits, new workflows and new deliverables

This paper looks at the opportunities presented by mobile mapping technology used for measured building survey, comparing data capture techniques and selecting the right match for the client’s end use. Secondly, it discusses how to balance ‘black box’ automated processing with tried-and-tested quality workflows. Lastly, it investigates how hosting survey data on web-based platforms enables the creation of new deliverables and adds value through embedding data and enabling access to a wider audience of potential stakeholders.

Data capture in the 80's

This paper argues that social and economic pressures plus the rapid development of electronic devices will, through the present decade. produce a significant shift from face to face interviewing at private addresses, towards centralised interviewing, postal surveys, telephone surveys, qualitative research and direct data capture via specialised event recorders. The advantages of postal and telephone surveys will come to be better appreciated, and improved techniques will be adopted in both. The use of increasingly sophisticated and cos: effective electronics equipment will at first extend and improve existing techniques of data capture, but by the end of this decade electronic developments will have brought about fundamental and permanent changes in the way data can be collected.Survey research data capture techniques are going to change dramatically in the next decade, but this change can be expected to come about as the result of any single development in facilities, fashion, or philosophy. In fact these techniques will come about gradually, as a result of the changing pattern of commercial incentives and disincentives that face the individual and corporate researcher. By far the biggest disincentive is going to be the increasing difficulty and cost of arranging traditional face-to-face interviews at the home of the respondent, plus a growing awareness of the technical disadvantages of this technique.

Assessing Time to Diagnosis, End-Organ Damage and Treatment Response in Young Newly Diagnosed Multiple Myeloma Patients

Introduction Multiple myeloma (MM) primarily affects older adults, with a median age of 69 years at time of diagnosis. Presenting symptoms of MM are often vague and nonspecific, such as fatigue or musculoskeletal pain, which can delay diagnosis. This may result in irreversible end-organ damage caused by MM, notably renal insufficiency and lytic bone lesions resulting in pathologic fractures, in addition to the reversible anemia and hypercalcemia that are hallmarks of the disease. Those younger than age 50 represent only 5% of patients living with MM. Though they remain in the minority, the incidence in young patients is rising. Data regarding initial presentation, cytogenetics, and rates of end-organ damage are poorly described, and most information arises from individual case reports or small series. In this study, we performed a retrospective analysis of newly diagnosed MM patients at the age of 51 or younger to characterize this distinct population. Methods Patients were identified using automated data capture techniques and manually reviewed. All treatment-eligible patients with MM presenting to UC Davis Medical Center between January 2014 to July 2021 were assessed, regardless of diagnosis date. The diagnosis of multiple myeloma was established according to the IMWG criteria, and patients < 51 years were included. Exclusion criteria included patients with non-histologically confirmed MM. Time of symptom onset was defined as the first medical evaluation with documentation of symptoms or radiographic/laboratory abnormalities which could be ascribed to MM as ascertained through chart review. Baseline diabetes, hypertension and coronary artery disease (comorbidities) were tabulated. Myeloma-related complications were defined as hypercalcemia (serum calcium >11 mg/dL), renal insufficiency (serum creatinine >2mg/dL), anemia (hemoglobin <10g/dL), and bone lesions (one or more osteolytic lesion on skeletal radiography, CT, PET/CT, or MRI) at time of diagnosis. Results 123 patients were included in the study, and their baseline demographics are described in Table 1. The median age was 46.0 years, with a range from 27.8 to 50.9 years; 19 (15%) were age <40. Hypertension was seen in 27 (22%) patients, 7 (5.7%) had diabetes while 93 (74%) had no comorbidities. The most common presenting symptom was musculoskeletal pain 79% of patients. Other presenting manifestations included anemia (13%), fatigue (12%), bacterial infections (8.9%), and weight loss (5.7%). The median time from symptom onset to diagnosis was 1.47 months, with a range of 0 to 49.7 months. Clinical characteristics at diagnosis are described in Table 2. Among the 79 patients with R-ISS staging, 18% had R-ISS Stage I, 70% had Stage II, and 13% had Stage III. Of the 105 patients with cytogenetics data, 19% were high-risk, del(17p) , t(4;14), and/or t(14;16), while 20% had a +1q. For myeloma-related complications, 14 (11%) had hypercalcemia, 16 (13%) renal insufficiency, and 41 (33%) anemia, 95 (77%) of patients had lytic lesions, and 62 (50%) patients presented with fractures. Of these, 31 had spinal compression fractures, 12 had extra-axial pathologic fractures, and 9 had both. Median time from diagnosis to treatment was 0.60 months, with a range of 0 to 23.93 months. The most common induction regimens, received by 64 (52%) of patients, were immunomodulatory drug and proteasome inhibitor combinations. This is followed by CyBorD (20 patients, 16%) and doublet or other therapy (39 patients, 32%). Autologous stem cell transplantation was utilized by 89 (71%) patients, with 19 (15%) receiving a second transplant. The median age at first transplant was 45.7 years, and 49.8 years at second transplant. Conclusion We describe one of the largest cohorts of MM patients diagnosed under the age of 51. While on average we did not observe a significant delay between symptom onset and diagnosis, there were some notable outliers. The presenting symptoms and rates of end-organ damage were similar to what has previously been described in older cohorts, though fractures were more common. Transplant was more frequently used than the general population. Additional study on this population is needed to better understand treatment patterns and barriers to their care. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Reconstructing room scales with a single sound for augmented reality displays

Perception and reconstruction of our 3D physical environment is an essential task with broad applications for Augmented Reality (AR) displays. For example, reconstructed geometries are commonly leveraged for displaying 3D objects at accurate positions. While camera-captured images are a frequently used data source for realistically reconstructing 3D physical surroundings, they are limited to line-of-sight environments, requiring time-consuming and repetitive data-capture techniques to capture a full 3D picture. For instance, current AR devices require users to scan through a whole room to obtain its geometric sizes. This optical process is tedious and inapplicable when the space is occluded or inaccessible. Audio waves propagate through space by bouncing from different surfaces, but are not 'occluded' by a single object such as a wall, unlike light. In this research, we aim to ask the question ‘can one hear the size of a room?’. To answer that, we propose an approach for inferring room geometries only from a single sound, which we define as an audio wave sequence played from a single loud speaker, leveraging deep learning for decoding implicitly-carried spatial information from a single speaker-and-microphone system. Through a series of experiments and studies, our work demonstrates our method's effectiveness at inferring a 3D environment's spatial layout. Our work introduces a robust building block in multi-modal layout reconstruction.

3D SURVEYING AND FORMAL ANALYSIS OF A STRUCTURAL BAY OF A GOTHIC APSE

Abstract. In recent years, the development of massive data capture techniques such as Terrestrial Laser Scanner (TLS) have made it possible to develop new procedures to evaluate architectural heritage with a high level of accuracy. The study proposes a workflow to identify formal anomalies in the main elements that form a structural bay of the apse of the Cathedral of Girona, studying arches, vaults, buttresses and pillars. The methodology is based on different two-dimensional and three-dimensional procedures, and the database obtained that could help in future research to relate the results with the different construction phases and being applicable to other constructions of similar typology.

The accuracy of several pose estimation methods for 3D joint centre localisation

Human movement researchers are often restricted to laboratory environments and data capture techniques that are time and/or resource intensive. Markerless pose estimation algorithms show great potential to facilitate large scale movement studies ‘in the wild’, i.e., outside of the constraints imposed by marker-based motion capture. However, the accuracy of such algorithms has not yet been fully evaluated. We computed 3D joint centre locations using several pre-trained deep-learning based pose estimation methods (OpenPose, AlphaPose, DeepLabCut) and compared to marker-based motion capture. Participants performed walking, running and jumping activities while marker-based motion capture data and multi-camera high speed images (200 Hz) were captured. The pose estimation algorithms were applied to 2D image data and 3D joint centre locations were reconstructed. Pose estimation derived joint centres demonstrated systematic differences at the hip and knee (~ 30–50 mm), most likely due to mislabeling of ground truth data in the training datasets. Where systematic differences were lower, e.g., the ankle, differences of 1–15 mm were observed depending on the activity. Markerless motion capture represents a highly promising emerging technology that could free movement scientists from laboratory environments but 3D joint centre locations are not yet consistently comparable to marker-based motion capture.

BIM-based Facility Management Models for Existing Buildings

Building Information Modeling (BIM) is mainly used for the design and construction of new buildings. However, one of the main challenges facing BIM adoption for existing or old buildings constructed without consideration for their modeling is obtaining raw and accurate building data and converting it to the BIM model. Based on this challenge, this study develops a framework that uses different data capture techniques for existing buildings, then transforms this captured data into 3D BIM models through which it can improve its management processes as facilities during their service life. Specifically, 3D laser scanning techniques were used to capture indoor building data, while drones equipped with high-definition cameras (UAV) were used to capture outdoor building data such as geometric dimensions, material shapes, surfaces and various shapes. Then the captured data was converted into a high-resolution 3D model. This has been verified the accuracy of this engineering model by converting the two-dimensional engineering plans to three-dimensional through the well-known engineering design tools. Moreover, the 3D model has been integrated with the Web Based Building Management System (BMS) platform. This research framework helps in developing engineering facility management processes and modern digital transformation processes that aim at speeding up facility management during their service period using modern technologies. This study aims to develop current knowledge in the construction industry and facility management by reviewing a comprehensive understanding of how to use modern technologies to obtain various information to develop BIM models for existing and non-modeled buildings in order to exploit and integrate them with advanced facility management systems and platforms, which constitutes a major progress in this field allowing to save a lot of effort and money in the future.

Kreasi Seni Sebagai Daya Tarik Wisata Budaya di Padepokan Bagong Kussudiarja Yogyakarta

Yogyakarta is a city of education and cultural city, as well as tourist areas, are still waking life order of the society and various art and cultural activities are still maintained their authenticity. Art creations that there in hermitage is one of the cultural heritage, both in the form of classical dance, dance creations, Ballet, theatre, art and others which are the legacy of the Bagong Kussudiardja. The research method used is descriptive qualitative research with data collection techniques derived from primary data is information obtained from primary sources and secondary data is data capture techniques through observation, interview, documentation and literature. Some of the art creations in Bagong Kussudiardja is one of the artists to create and store a variety of works that made some of the Bagong Kussudiardja one of the Cultural Attractions in Yogyakarta. Art creations are in hermitage : jagongan wagen, estuary, angjasana, edukreasi, fine art, among the classical dance, sedulur Ballet, dance, dances and other activities is a form of art and cultural works with the aim that the some of the Bagong Kussudiadrja is one of the cultural heritage in Yogyakarta and Indonesia

Applying the FFP Approach to Wider Land Management Functions

The initial focus of implementing the Fit-for-Purpose Land Administration (FFPLA) methodology was to address the significant, global security of tenure divide. We argue that this land tenure methodology is proving successful in scaling up the provision of security of tenure for developing countries. The increasing adoption of the FFPLA methodology has also opened opportunities and provided flexibility for the innovative use of emerging technologies to accelerate the global roll out of security of tenure, such as the use of autonomous drones and machine learning techniques applied to image analysis. Despite wider adoption of participatory approaches to the recording of land tenure, similar FFP solutions for the other components of land administration services (land value, land use and land development) and land management functions are still evolving. This article therefore explores how the FFP approach can be applied to this wider set of land administration services and land management functions. A case study methodology, using three case studies, is used to determine if the case study approaches meet the FFP criteria. The focus is on the urban environment, drawing mostly from experiences and case studies in the Urban, Disaster Risk Management, Resilience &amp; Land Global Practice of the World Bank. These opportunities for the wider application of the FFP approach and associated principles are being triggered by the innovative use of emerging new data capture technology developments. The paper examines the innovative use of these emerging technologies to identify a common set of data capture techniques and geospatial data that can be shared across a range of urban land administration and management activities. Finally, the paper discusses how individual land projects could be integrated into a more holistic land administration and management program approach and deliver a significant set of socio-economic benefits more quickly. It is found that the FFP approach can be more widely adopted across land administration and land management and in many cases can share a common set of geospatial data. The authors argue that the wider adoption and integration of these new, innovative FFP urban management approaches will require a significant cultural, professional, and institutional change from all stakeholders. Future work will explore more deeply these institutional weaknesses, which will provide a basis for guidance to the World Bank and similar institutions.

A perspective on the use of ecological momentary assessment and intervention to promote stroke recovery and rehabilitation

ABSTRACT Motivated by recent advances in technologies, ecological momentary assessment (EMA), and ecological momentary intervention (EMI) have seen a rise in behavioral medicine research that in real-time, informs the context for the behavior and prompts interventions to change that behavior in the natural setting when necessary. However, EMA and EMI have yet to be fully embraced in the field of stroke rehabilitation. Our objective is to provide a theoretically based perspective for the combined and synergistic use of EMA and EMI to promote person-centered, recovery-based durable changes in functional movement behaviors of stroke survivors. Research abounds for non-stroke populations with emerging evidence for the benefits of using real-time data capture techniques (i.e. EMA) coupled with EMI to better customize the content and timing of interventions to the inherent fluctuations in state and context that encompass the target behavior. We review existing EMA and EMI literature broadly in behavioral medicine and psychological science to identify how real-time repeated sampling technology has been used in the context of stroke rehabilitation and to delineate the pros and cons of this approach in general with non-stroke populations. We propose a coupled EMA and EMI strategy be used in conjunction with existing stroke recovery and rehabilitation practices. There is tremendous potential to effectively personalize recovery-promoting interventions to achieve durable behavior change, and importantly, shift the focus of rehabilitation practice from the health-care provider and clinical environment to the individual and their lived experience in the home and community.

Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device.

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.