Speed Of Data Collection Research Articles

Backyard flock sampling and artificial intelligence: A dual strategy for early detection of Avian Influenza and Newcastle Disease

Avian Influenza (AI) and Newcastle Disease (ND) are significant avian diseases that pose substantial threats to the commercial poultry industry due to their high contagion rates and potential for severe outcomes. Recent shifts in the epidemiology and ecology of both viruses have led to devastating impacts on wild bird and poultry populations, as well as farms and communities worldwide. These challenges underscore the necessity for One Health approaches to pandemic prevention and preparedness, emphasizing the need for multisectoral collaboration among animal, environmental, and public health sectors. To mitigate future pandemic risks, it is essential to control the transmission of AI and ND viruses among domestic and wild animals as well as humans. This involves addressing the upstream drivers of outbreaks, ensuring rapid response mechanisms, and conducting thorough risk assessments for zoonotic diseases. Effective implementation and maintenance of prevention programs, surveillance, and outbreak responses require strong political commitment and sustainable funding. A robust combination of active and passive surveillance strategies should be applied to monitor and control them. In addition, the integration of modern technologies, such as artificial intelligence and big data analytics, can enhance both active and passive surveillance efforts by improving the accuracy and speed of data collection, analysis, and reporting. The use of backyard flocks as an early warning system for avian diseases represents a proactive approach to understanding and managing the risks associated with AI and ND through targeted surveillance and timely interventions. In response to these challenges, the EpirORNIS Project aimed to leverage these sentinel backyard flocks for early detection of AI and ND, thereby enhancing surveillance and control strategies.

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology.

Transformations in the Field of Accounting and Their Impact on Accounting Practices: An Analytical Study to Understand Insights and Challenges

This study explores accounting transformations and their impact on practices by understanding the insights and challenges they bring. It analyzes the relationship between technological advancement and traditional accounting methods, focusing on major technological shifts affecting financial data analysis, decision-making, adoption of modern tools, and associated ethical and security challenges. Using an analytical approach, the study uncovers complex effects on accounting practices, offering important insights for professionals, scholars, and policymakers to understand the dynamics between tradition and innovation in accounting. It also highlights the correlation between heavy metal concentrations in the Tigris River, temperature changes, and their broader implications for public health and climate change risks. The study concludes that technological transformations have fundamentally reshaped traditional accounting practices, improving the speed and accuracy of financial data collection and analysis. However, these changes have introduced new challenges related to ethics and security, emphasizing privacy and safety concerns. Moreover, the study underscores significant impacts of technological advancements on financial decision-making and environmental consequences, particularly the increased heavy metal concentrations in the Tigris River due to rising temperatures, affecting aquatic life and public health. This study provides valuable insights for professionals, scholars, and policymakers into the interplay between tradition, innovation in accounting, and the environmental implications for public health.

The Impact of Incentives on Data Collection for Online Surveys: Social Media Recruitment Study.

The use of targeted advertisements on social media platforms (eg, Facebook and Instagram) has become increasingly popular for recruiting participants for online survey research. Many of these surveys offer monetary incentives for survey completion in the form of gift cards; however, little is known about whether the incentive amount impacts the cost, speed, and quality of data collection. This experiment addresses this gap in the literature by examining how different incentives in paid advertising campaigns on Instagram for completing a 10-minute online survey influence the response rate, recruitment advertising cost, data quality, and length of data collection. This experiment tested three incentive conditions using three Instagram campaigns that were each allocated a US $1400 budget to spend over a maximum of 4 days; ads targeted users aged 15-24 years in three nonadjacent designated market areas of similar size to avoid overlapping audiences. Four ad creatives were designed for each campaign; all ads featured the same images and text, but the incentive amount varied: no incentive, US $5 gift card, and US $15 gift card. All ads had a clickable link that directed users to an eligibility screener and a 10-minute online survey, if eligible. Each campaign ran for either the full allotted time (4 days) or until there were 150 total survey completes, prior to data quality checks for fraud. The US $15 incentive condition resulted in the quickest and cheapest data collection, requiring 17 hours and ad spending of US $338.64 to achieve 142 survey completes. The US $5 condition took more than twice as long (39 hours) and cost US $864.33 in ad spending to achieve 148 survey completes. The no-incentive condition ran for 60 hours, spending nearly the full budget (US $1398.23), and achieved only 24 survey completes. The US $15 and US $5 incentive conditions had similar levels of fraudulent respondents, whereas the no-incentive condition had no fraudulent respondents. The completion rate for the US $15 and US $5 incentive conditions were 93.4% (155/166) and 89.8% (149/166), respectively, while the completion rate for the no-incentive condition was 43.6% (24/55). Overall, we found that a higher incentive resulted in quicker data collection, less money spent on ads, and higher response rates, despite some fraudulent cases that had to be dropped from the sample. However, when considering the total incentive amounts in addition to the ad spending, a US $5 incentive appeared to be the most cost-effective data collection option. Other costs associated with running a campaign for a longer period should also be considered. A longer experiment is warranted to determine whether fraud varies over time across conditions.

Implementing a routine and standard approach for the automatic collection of socio-economic impact observations for impact-based forecasting and warning

Globally, there are different scientific methods being developed to support the provision of Impact-based Forecasts and Warnings. Impact data is critically valuable to many of these methods enabling the development of new hazard impact models and forecasting applications, and the evaluation of these different products. However, routine and consistent approaches to collect socio-economic impact information remain challenging, with traditional approaches being labour intensive, error prone and unsustainable. This paper describes the design of an automated workflow that can collect, standardise, and harmonise impact observations from a range of sources, routinely. The workflow is a 6-step process including web scraping, Natural Language Processing (NLP) and geoparsing. The workflow enables essential criteria (i.e., date, location, types and details of the societal impacts and the hazard(s) which led to them) to be captured faster than previous manual approaches. It also allows a wider range of hazard and impact types to be collected than was previously practical to do. Testing of the workflow indicates that it could benefit from further refinement of the NLP and geoparse modules to allow for the detection of finer scale impact locations. This in turn would allow for different scales of event detection to be developed and tested. Additionally, the applicability of the workflow for different types of impact data sources such as news and social media remains to be tested. Despite the need for further refinement, this workflow represents a step change in the speed and efficiency of impact data collection within the Met Office.

Impact of magnetic data collection prior to electroanatomic model creation on acute outcomes in de novo AF procedures

Abstract Background As ablation and electroanatomical mapping technology evolve, workflows change to maximize the impact on procedural efficacy, safety, and efficiency. In a magnetic-primary navigation mode, optimization of the magnetic-impedance field links (VoXels) must be considered to provide accurate environments for anatomic modeling, mapping, and lesion placement. To achieve this accuracy and optimize speed of data collection, many operators have adopted the strategy of collecting VoXels using an ablation catheter prior to introduction of a multipolar mapping catheter. However, the impact of this workflow on procedural metrics has not yet been quantified in a multicenter, international cohort. Objective Assess the impact of optimizing the magnetic navigation field using an ablation catheter prior to collecting left atrial (LA) data using a high-density diagnostic tool on acute outcomes in de novo atrial fibrillation (AF) ablation procedures. Methods Acute observational data from 199 de novo pulmonary vein isolation (PVI) cases was prospectively collected from 56 operators at 42 centers in Europe, the US, and Australia. Procedural metrics, modeling workflow, ablation technology used, and acute outcomes were collected. Results Of 199 de novo AF (62.3% PAF) ablation procedures, VoXels were collected with an ablation catheter prior to LA map collection with a multipolar catheter in 65/199 (32.7%) cases; the remainder collected with multipolar catheter first. An irrigated, contact force sensing RF ablation catheter was used in all procedures. Mean procedure duration was similar in procedures in which VoXels were collected prior to map collection (VC) compared to those that did not (nVC) (107.0±46.8 min and 115.4±59.2 min, respectively (p=0.28)). Mean fluoroscopy time was significantly reduced in VC cases compared to nVC cases (3.0±4.5 min and 5.3±5.6 min, respectively (p=0.002)). Total RF duration was significantly reduced in VC cases compared to nVC cases (904±444s and 1063±632s, respectively (p=0.04)). There was also a significant reduction in fluid delivered through the ablation catheter in VC cases, 369±159mL, compared to nVC cases, 520±243mL (p<0.001). Acute success rates were similar between groups: 98.5% for VC cases and 99.2% for nVC cases (p=0.64). Conclusion In this international cohort, cases where VoXels were collected prior to map and model collection had significantly reduced fluoroscopy times, RF durations, and fluid volumes delivered through the ablation catheter, potentially as a result of improved model quality. Despite the introduction of an additional step to the workflow, there was a trend toward shorter procedures when VoXels were collected prior to map and model collection, although it did not achieve statistical significance. Better quantification of the impact of this workflow on model quality, along with analysis of long-term patient outcomes, is warranted.Procedure Duration Comparison_VC vs. nVCFluoroscopy Comparison_VC vs. nVC

Управление предпринимательскими сетями арктических территорий: методика проектирования сетевой архитектуры и ее реализация на Python

This study aims to develop a methodology for designing entrepreneurial networks based on assessing the potentials of actors, which are social and economic groups participating in entrepreneurial relations localized in the territories of the Russian Arctic. The objective is to ensure the reasoned participation of actors in network alliances. Methodological approaches and results of assessing the entrepreneurial potential of actors were previously presented, with the author developing approaches that account for the regional features of Arctic processes and allow for broad interpretation. To achieve this, the potential of various actors, or stakeholders operating in Arctic territories, is evaluated. This involves the use of indicators to integrate data in order to detail entrepreneurial properties. Among the indicators are involvement in creating business infrastructure, the scale of activity, innovative endeavors, professionalism of public organizations, and the efficiency of government administration in fostering a favorable investment climate. Other indicators include population engagement in innovation-driven processes (the degree of integration of local and scientific knowledge achieved by establishing formal and informal institutions and programs for interaction between the population and scientific communities; the degree of development of public institutions as subjects of the transfer of innovative solutions between participants in the territorial innovation system, etc.), the degree of monopolization of the territory's economy, the presence of large venture investors, and the venture policy of corporations indicating the stage of territory development. The article's objective is to lay theoretical foundations and methodological provisions for designing the architecture of Arctic network alliances. The focus is on ensuring maximum entrepreneurial potential, considering Arctic processes and their rapid recombination amid swift changes in the business environment. As a result, the author proposes a methodological approach to identifying actors in entrepreneurial networks and connections that support relationships between central actors. This identification is based on the target function of maximizing and restructuring entrepreneurial potential in the Arctic, whose calculation is automated through the use of the Python programming language. The article introduces the author's version of software for generating management information for network brokers. All data for assessment and design can be obtained from official open sources, ensuring the speed of data collection and processing. Research plans involve developing provisions of a methodology focused on the meso-level of the Russian Arctic macroregion for territorial, production, and socio-economic systems with significant potential for forming relations in the innovation economy.

Supplementing a Paper Questionnaire with Web and Two-way Short Message Service (SMS) Surveys

Abstract When deciding which modes to offer, researchers consider cost, known respondent contact information, and potential mode effects. For a short survey on employment, we evaluate the effect of adding one of two new electronic data collection modes to a mailed questionnaire. We sent a survey to principals who previously responded to the National Center for Education Statistics’ (NCES) National Teacher and Principal Survey (NTPS) asking about their current job status. This questionnaire, known as the Principal Follow-up Survey (PFS), has typically been administered as a short paper form that is mailed to NTPS respondents. In 2022, the PFS introduced two new modes of completion, and principals were randomly assigned to receive: (i) a paper form only; (ii) a paper form, as well as emails with a direct link to complete a web survey; or (iii) a paper form, as well as invitations by text message to complete an automated two-way short message service text survey by responding to texted “yes/no” questions. This article compares overall response rates and time-to-response by mode to determine respondent preferences for completing short surveys. Adding either electronic mode significantly increased response rates and decreased the number of days in which completed surveys were received, compared to offering only a paper questionnaire. Although email and text messages are both forms of electronic communication that may be accessible on a smartphone, the added text message survey resulted in higher response rates than the added web survey. This suggests that respondents interact differently with emails and text messages they receive and that offering an option to complete a survey by text message can increase the speed and efficiency of data collection for short surveys.

Smart Temperature Dependent Cooling of Solar panel Using Arduino

This project involves the integration of various hardware components, including an Arduino board, a temperature sensor, an LCD display, a relay module, a CPU fan, and an ESP8266NodeMCU. The goal is to create a system that can monitor temperature, display information on an LCD screen, a relay to control fan, a CPU fan, and upload collected data to a web server for remote access and analysis. The temperature sensor is connected to the Arduino board to measure the ambient temperature. The LCD display provides real-time temperature readings and system status information. The relay module enables the Arduino to control an external device based on predefined temperature thresholds. Additionally, a CPU fan is integrated to manage the temperature of a system component. This enhances the overall system stability and efficiency. The ESP8266 Node MCU, acting as a Wi-Fi Module, connects the system to the internet. It gathers temperature data from the Arduino and periodically uploads it to a designated web server via HTTP or other suitable protocols. This enables users to monitor temperature trends and system performance remotely, providing insights into environmental conditions and operational effectiveness. The integration of these components involves programming the Arduino and Node MCU using relevant libraries and languages such as c / c++ and Arduino IDE. The Arduino code manages data collection, LCD display, relay control, and fan speed regulation, ensuring seamless coordination between the components. The Node MCU code establishes a connection to the Wi-Fi network, formats and transmits data to the web server, and handles any necessary security considerations. Keywords: CPU Fan, Arduino, solar panels, Templates, Journals.

Development of Capacity Estimation Models for Multi-lane Roads under Heterogeneous Traffic Conditions

The objective of this study is to develop capacity models to estimate the traffic carrying capacity of multi-lane roads with heterogeneous traffic flows. Since capacity is an important parameter in traffic engineering studies, it is vital that it is accurately estimated. Most of the current capacity estimation methodologies available are developed based on homogeneous traffic flows and hence not conducive for use in heterogeneous traffic conditions observed in Sri Lanka. Therefore, this study is done to fulfill the requirement of a capacity estimation model for this purpose. Fifty midblock sections were surveyed using manual flow data collection methods and a novel Google Application Program Interface (API) based method for speed data collection. Based on the collected data, capacity values were developed following the fundamentals of traffic flow. Regression models were built to estimate four-lane and six-lane capacity values based on roadway characteristics such as effective lane width, access point density, median type, and built environment condition. Separate models were developed because the impact these characteristics have on four-lane and six-lane roads are different. The models showed good fit with R2 values of 0.81 and 0.86 for four-lane and six-lane roads, respectively. The base capacity value of a four-lane urban road was estimated to be 2044 pcu/h/l and for a six-lane sub-urban road section 2108 pcu/h/l. The outcomes of this study can be used to develop capacity guidelines in countries with heterogeneous traffic conditions for capacity estimation.

The advantages and comparison of resistive touch screens and capacitive touch screens

ATouch screens are an indispensable component in electronic products nowadays, and resistive and capacitive touch screens are the most common and widely used touch screens in touch screens. However, there is still a lack of intuitive literature to explain the differences and advantages between capacitive and resistive touch screens. This article provides a partial overview of the principles and structures of resistive and capacitive touch screen technologies and finally analyzes their respective advantages and disadvantages. The resistive touch screen is mainly divided into four-wire resistive touch screens and five-wire resistive touch screens. Its advantage over capacitive touch screens is that the process is more mature, and the product cost is lower. However, capacitive touch screens, due to their different working principles from resistive touch screens, can achieve multi-point touch, which is a significant feature. In capacitive touch screens, mutual capacitive projection capacitive touch screens have become the main favored technology in the touch screen industry due to their low power consumption, stable signal, and fast data collection speed. They are also an essential direction for the future development of touch screens.

Acceleration of data collection and analysis using asynchronous programming tools in Web Scraping

This article discusses the importance and relevance of using web scraping technologies to effectively collect a significant amount of information in various fields. The potential of using asynchronous tools for fast and productive data retrieval from large-scale web resources has been studied. The article analyzes in detail the possibilities of using asynchronous tools in the context of web scraping, considering their advantages compared to synchronous approaches. Special attention is paid to the use of the Requests libraries, which provide tools for the standard linear approach, and Asyncio, Aiohttp, which help implement the asynchronous approach. After that, the article conducts a comparative analysis of their performance in the scenario of data collection from the website. The development process using the Python programming language is described in detail, and code is presented that illustrates each stage of the execution of the synchronous and asynchronous algorithms in combination with the libraries presented. The authors of the article consider asynchronous web scraping as a powerful tool for creating fast and efficient data collection mechanisms that can be used to train models and analyze large volumes of information. The article discusses the importance of further development of this method in order to ensure high speed of data collection and improve their applicability in various areas. It demonstrates the practical advantages of asynchronous web scraping, and also indicates the prospects for improving this method to improve the collection and processing of information on a scale that goes beyond standard methods. Further research may consider aspects of automating and expanding the capabilities of asynchronous web scraping, as well as the impact of this approach on the development of other areas of information technology. Taking these aspects into account will contribute to the further evolution and optimization of web scraping technologies for a wide range of applications.

Acquisition of electrical power using synchronous generators at the Universidad Politécnica De Victoria

The lack of use of renewable resources, in terms of wind energy, at the Polytechnic University of Victoria has been unnoticed. Specifically, the implementation of wind power systems leads to the formulation of a hypothesis that proposes the use of wind currents at levels lower than 5 meters above ground level between the buildings of the university to provide electrical power through 400 Watts wind generators. The methodology includes the acquisition of official plans of the university, delimitation of the study areas, collection of wind speed data, the analysis of the information obtained and installation of the wind generator that allows carrying out its operation in practice. The main result will be the use of available renewable resources to generate clean energy, reduce dependence on hydrocarbons, thus contributing to energy sustainability.

A pragmatic methodology to extract anesthetic and physiological data from the electronic health record.

Traditional manual methods of extracting anesthetic and physiological data from the electronic health record rely upon visual transcription by a human analyst that can be labor-intensive and prone to error. Technical complexity, relative inexperience in computer coding, and decreased access to data warehouses can deter investigators from obtaining valuable electronic health record data for research studies, especially in under-resourced settings. We therefore aimed to develop, pilot, and demonstrate the effectiveness and utility of a pragmatic data extraction methodology. Expired sevoflurane concentration data from the electronic health record transcribed by eye was compared to an intermediate preprocessing method in which the entire anesthetic flowsheet narrative report was selected, copy-pasted, and processed using only Microsoft Word and Excel software to generate a comma-delimited (.csv) file. A step-by-step presentation of this method is presented. Concordance rates, Pearson correlation coefficients, and scatterplots with lines of best fit were used to compare the two methods of data extraction. A total of 1132 datapoints across eight subjects were analyzed, accounting for 18.9 h of anesthesia time. There was a high concordance rate of data extracted using the two methods (median concordance rate 100% range [96%, 100%]). The median time required to complete manual data extraction was significantly longer compared to the time required using the intermediate method (240 IQR [199, 482.5] seconds vs 92.5 IQR [69, 99] seconds, p = .01) and was linearly associated with the number of datapoints (rmanual = .97, p < .0001), whereas time required to complete data extraction using the intermediate approach was independent of the number of datapoints (rintermediate = -.02, p = .99). We describe a pragmatic data extraction methodology that does not require additional software or coding skills intended to enhance the ease, speed, and accuracy of data collection that could assist in clinician investigator-initiated research and quality/process improvement projects.

The Design and Simulation of Engine Rotating Speed Modulation of Circuit

Since the amplitude and frequency of engine rotating speed signals are changed with the engine rotating speed, data cannot be directly collected due to the high-frequency noise. In this work, an engine rotating speed modulation of the circuit was designed. It was used to retain the complete information of positive and negative periods in signals and convert rotating speed signals to rectangular wave signals with certain amplitude at the same frequency, which could be directly collected. Further, in order to investigate the applicability of our design, a simulation test on the developed circuit based on Multisim was performed. This indicates that the raw signal with high-frequency interference, different amplitudes, and frequencies could be accurately and efficiently converted to regular rectangular wave signals. The design is characterized by a compact structure, low difficulty in device selection, high stability, and low power consumption, which is of extreme significance for accurate rotating speed data collection.

Robust plastic waste classification using wavelet transform multi-resolution analysis and convolutional neural networks

Mid-infrared spectroscopy (MIR) using photon up-conversion provides advantages over near-infrared spectroscopy (NIR) for plastic waste recycling, including comparable data collection speed and the ability to detect black plastics. However, high-speed MIR spectra suffer from the presence of significant noise. While convolutional neural networks (CNNs) have been utilized for accurate classification of noisy MIR spectra, the analysis of extracted features by the CNN has received less attention. In this study, we analyzed features extracted by a CNN from high-speed MIR spectra collected at 200 spectra per second. Visualizing salient features through the Grad-CAM method revealed that, although the CNN model achieved 100% accuracy, the predictions were not reliable or robust, as the model is susceptible to noise interference. To address this limitation, we propose a wavelet transform-based multi-resolution analysis (MRA) as a preprocessing method for noisy MIR spectra. We show that MRA reconstruction effectively captures features related to characteristic IR peaks, enabling the CNN model to extract informative features from noisy MIR spectra and significantly improves the prediction fidelity and robustness.

Evaluation of handheld Raman spectrometers for the detection of intact explosives

The detection of intact explosives in the field provides a unique challenge for investigators, considering the sensitive and dangerous nature of these samples. Handheld Raman instruments have grown in popularity for the analysis of unknown samples in the field, combining speed of data collection and reliability with a size that allows for the instruments to be field portable. Handheld Raman instruments are used commonly in the field, and yet there is very little research on the detection capabilities of these instruments, specifically for explosive compounds. The present study aimed to evaluate the detection capabilities of two handheld Raman spectrometers, the Rigaku ResQ-CQL and the Field Forensics HandyRam™, using explosives analytical standards, including 2,4,6-trinitrotoluene (TNT), nitromethane (NM), ammonium nitrate (AN) and smokeless powder components such as diphenylamine (DPA), ethyl centralite (EC), and methyl centralite (MC). The spectrometers were evaluated on their sensitivity, the repeatability of the data, and the performance of the internal library when available. In addition, an interference study with glass and plastic containers was also performed. Finally, authentic intact explosive samples, including TNT flakes, a mixture of ammonium nitrate and fuel oil (ANFO), smokeless powder and nitromethane were analyzed to evaluate the developed method and test the detection capabilities of the spectrometers with authentic samples. Spectra were reproducible for all the analytes across both instruments, with regards to the peak location and the intensity. Spectra obtained with the Rigaku ResQ-CQL displayed better resolution for all analytes, including the authentic samples. In addition, its wider scan range allowed for the detection of more detailed peaks below 400 cm−1. Identifying the detection capabilities of these handheld instruments can therefore help guide investigators on how to best utilize them in the field.

Comparison of response patterns in national surveys among Finnish adult population in 2020 and 2022

Abstract Background Self-administered survey questionnaires are an important data collection tool in public health research and epidemiology. These questionnaires can be completed electronically, which allows larger sample size and faster speed of data collection while reduced cost. Yet, a web survey might be a less feasible for older individuals. We evaluated the response patterns in the two recent surveys in 2020 and 2022. Methods We analysed the data from FinSote 2020 (n = 31,817) and Healthy Finland 2022 (n = 30,307) surveys in Finland. In both, invitees (aged 20+) were contacted four times via postal letter. A concurrent mixed-mode approach allowed for the invited individuals to complete either a web questionnaire or an identical paper questionnaire. In both surveys, the oldest age group (75+) received a paper questionnaire three times and those aged 20-54 years received it only once. In 2022, the survey design was changed so that a paper questionnaire was sent twice (instead of three times) for those aged 55-74 years compared to 2020. Results The overall response rate was 47 % both in 2022 and 2020. Persons aged 75 + rarely responded to web questionnaire, less than 10 % answered in web. Instead, among persons aged 20-54 years, around 80 % completed the web survey. The response mode for the web survey increased significantly from 32% to 46% for two years period. The largest increase in the proportion which completed the web survey was found in respondents aged 55-74 years (from 19 % to 49 %). In them, however, the response rate declined from 60 % to 58 %. Conclusions In mixed-mode surveys, push-to-web approach can shift respondents to a web survey without greatly impacting response rate. However, without offering a paper questionnaire, older people are likely to be missing resulting potentially biased estimates. Key messages • Completing survey in web has increased recent years. This may be partly due to COVID-19 pandemic that led to a shift towards use of digital apps and online services. • The move to the web-based surveys save the costs without impacting response rates.

Developing electric bus driving cycles with significant road gradient changes: A case study in Hong Kong

Battery electric buses have been increasingly being deployed to replace traditional diesel buses in providing urban public transport services. Accounted for over 30% of daily passenger trips, the franchised bus system in Hong Kong only retained a few battery electric buses after its trial programme. Under the unique bus driving environment in Hong Kong, more evaluations are anticipated for further deployment of electric buses. Driving cycle is a widely adopted platform for the assessment of vehicle fuel economy, energy consumption, emissions and driving range. Therefore, it is necessary to have a purposely developed driving cycle for battery electric buses. A comprehensive review of bus driving cycles developed elsewhere shows that the impacts of road gradients are seldom considered. Therefore, in this study, the only remaining battery electric bus route with significant gradient changes was selected for speed data collection and synthesis for a set of driving cycles. Results shown that driving characteristics of this route were comparable to urban bus cycles developed in other cities, but were slightly different from bus cycles developed for other conditions. It was also observed that battery electric buses appeared to be less responsive to drivers’ acceleration activities than that of a supercapacitor bus.

Evaluating state-of-the-art 3D scanning methods for stem-level biodiversity inventories in forests

Monitoring biodiversity in forests is crucial for their management and preservation, especially in light of increasing climatic disturbances. However, traditional methods of surveying forest biodiversity, such as the inventory of tree-related microhabitats (TreMs), are costly and time-consuming. For many years, terrestrial laser scanning (TLS) was the main method for producing highly accurate 3D models of forests. However, with recent advancements in 3D scanning technologies, there are now numerous alternatives available on the market. The aim of this study was to evaluate the performance of four different 3D data acquisition methods, i.e. close-range photogrammetry (CRP), fish-eye photogrammetry (FEP), mobile laser scanning (MLS), and mixed reality depth camera (MRDC), in terms of accuracy and ability to measure biodiversity (TreMs) at tree-stem level, in comparison to TLS. Analysis was performed based on geometric accuracy and point neighbourhood relevance. CRP was the most accurate alternative to TLS for TreM measurement with a median error of 1.5 cm, while FEP provided a good balance between accuracy (median error 1.4 cm) and speed of data collection. Although MLS showed promising results (median error 1.6 cm), noise in the point cloud limited its ability to identify TreMs. MRDC, on the other hand, had lower quality (median error 3.6 cm) and lower point density, making it unsuitable for TreM segmentation. Nevertheless, the study demonstrated the feasibility of augmenting the real world with virtual content at single-tree-stem level using mixed reality technology. Overall, the 3D scanning technologies presented hold great promise for recording the evolution of biodiversity at stem level.