Collection Of Information Research Articles

Rapid ecological data collection from 360‐degree imagery using visualisation and immersive sampling in the R pannotator package

Abstract With rapid environmental change occurring globally there is an urgent need for new field survey methods that enable fast and efficient collection of ecological information from impacted systems. Advances in 360‐degree (panospheric) camera technology now allow the collection of geospatially explicit visual records of ecological properties far more cheaply, quickly and efficiently than traditional field survey methods. However, generalisable workflows and software platforms to manage, extract, process and use ecological information from large panospheric image datasets are still lacking. Here we develop a flexible, integrative workflow for rapid acquisition, preparation and extraction of macroscopic data from 360‐degree imagery. We introduce the open‐source R package Panospheric Image Annotator in R (pannotator), which allows the user to visualise and annotate images and extract cropped geocoded sub‐images in a repeatable, systematic way using customisable drop‐down menus and help files. We demonstrate the workflow and pannotator package using panoramic images collected from a study area at Uluṟu‐Kata Tjuṯa National Park in central Australia, which was affected by severe drought and fire in 2018–2020. In this study, 180 images were captured using GoPro Max cameras and imported into the pannotator package for data annotation. We extracted data for three key ecological attributes (plant species distribution, understorey cover and tree health) and show how these data can be used to spatially reconstruct species richness and community structure, plant size class, mortality and burn history. Modern 360‐degree cameras and immersive sampling using the pannotator package now offer a transformative solution for efficiently capturing and extracting macroscopic ecological and biogeographical data from field surveys. More generally, the package may be used to visualise and extract data from any georeferenced panospheric imagery and generate cropped images with embedded geolocation data for use in downstream artificial intelligence/machine learning–based applications.

News Brief: Collection of information about social determinants of health.

News Brief: Collection of information about social determinants of health.

Design of a Multi-Node Data Acquisition System for Logging-While-Drilling Acoustic Logging Instruments Based on FPGA

The logging-while-drilling (LWD) acoustic logging instrument is pivotal in unconventional oil and gas exploration, and in providing real-time assessments of subsurface formations. The acquisition system, a core component of the LWD acoustic logging suite, is tasked with capturing, transmitting, and processing acoustic signals from the formation, which directly affects the accuracy and timeliness of the logging data. Recognizing the constraints of current LWD acquisition systems, including limited data collection capabilities and inadequate precision, this study introduces an FPGA-based multi-node data acquisition system for LWD acoustic logging. This system increases sampling density and data accuracy, leading to a more comprehensive collection of formation information. The multi-node acquisition system is composed primarily of a main control circuit board and several acquisition circuit boards, all connected via an RS485 bus. The Field-Programmable Gate Array (FPGA) is utilized to develop the acquisition circuit board’s firmware, offering adjustable control over parameters, such as the AD7380’s operational mode, sampling rate, and depth, facilitating real-time and concurrent acquisition and storage of formation acoustic signals. The main control board communicates with the acquisition boards via the RS485 bus, issuing commands to enable autonomous data collection and transfer from each board, thus enhancing the system’s reliability and scalability. Experimental results confirm the system’s capacity to efficiently capture waveform signals and upload them in real-time, underscoring its dependability and timeliness. The findings suggest that the system is capable of high-speed, real-time acquisition and processing of acoustic signals, offering robust technical support for the continued application of LWD acoustic logging instruments.

Dental and oral health assessments in the German National Cohort (NAKO)

BackgroundDespite considerable improvements in oral health in recent decades, caries and periodontitis are still widespread, ranking among the most prevalent diseases worldwide and requiring future research. The German National Cohort (NAKO Gesundheitsstudie, NAKO) is a large-scaled, multidisciplinary, nationwide, multi-centre, population-based, prospective cohort study with oral examinations that aims to provide a resource to study risk factors for major diseases. The aim of the present article is to provide the methodological background, to report on the data quality, and to present initial results of the oral examinations.MethodsDuring baseline examinations (2014–2019), a total of 205,184 persons aged 19–74 years has been examined in 18 study centres, including, among others, a dental interview, stimulated saliva sampling, and recording of the numbers of present teeth and prostheses (standard Level 1 program). As part of the Level 2 program that was offered to 20% randomly selected participants, each study centre selected one of three modules, one of them being the Level 2 oral examination. This extended program was carried out in a subgroup of 20,828 participants, including collection of detailed information on the dental and prosthetic status as well as on periodontal, cariological and functional aspects. To ensure reliability and reproducibility, study nurses were trained and calibrated by dental experts. In addition, a reliability study was conducted among 794 Level 1 and 359 Level 2 participants, reporting intra class correlation and kappa coefficients.ResultsIntra class correlation and kappa coefficients for observer agreement and reliability were consistently above 0.7, indicating good to excellent reliability of all dental measurements. For example, intra class correlation was 0.937 for the number of present teeth (Level 1), 0.740 for mean probing depth (PD) and 0.797 for active mouth opening. An initial inspection of the data showed that the median number of present teeth was 27, of which on average 6.9 teeth were healthy and caries-free. Average mean PD was 1.92 mm. An orthodontic treatment was reported by 35.5% of participants.DiscussionOverall, the dental study protocol was feasible and successfully integrated into the NAKO’s overall assessment program. However, rigorous support of the study centres by dental professionals was required to ensure high quality data. In summary, high-quality data collection within the NAKO pave the way for future investigation of potential risk factors for oral diseases and links between oral and systemic diseases and conditions.

A catalogue of land-based adaptation and mitigation solutions to tackle climate change

Informing and engaging all actors in the land sector, including land-owners and managers, researchers, policy-makers and citizens, on the most effective sustainable land-based solutions and behavioural changes is a key strategy for achieving climate change adaptation and mitigation targets at the global as well as at EU and local level. One requisite to support actors in the land sector is to provide them publicly available, reliable and ready-to-use information related to the implementation of Land-based Adaptation and Mitigation Solutions (LAMS). Here we introduce a LAMS catalogue, a collection of meaningful quantitative and qualitative information on 60 solutions characterised according to a set of specifications (e.g., mitigation and adaptation potential, cost of implementation, suitability factors, synergies and trade-offs, drivers and barriers to the implementation). The catalogue offers a reliable, science-based tool useful for different users’ needs, including valuable references for deriving context-specific quantitative inputs to simulate and evaluate the performance of solutions over time using modelling tools, such as Integrated Assessment Models at any scale.

Tell me more: integrating LLMs in a cultural heritage website for advanced information exploration support

Abstract Cultural Heritage websites’ capability to satisfy diverse information needs is limited by their high-quality but constrained knowledge bases. Thus, we investigate their extension with external large language models (LLMs), enriching the provision of cultural content by leveraging LLMs’ continuous collection and integration of information from heterogeneous data sources. This extension raises important challenges in synchronizing the LLM’s behavior with the user’s browsing activity on the website to offer a unified interaction environment. To address these challenges, we propose a loosely coupled integration model that provides users with curated content and an assisted question-answering function to answer information needs that the system’s knowledge base fails to cover. Our model is agnostic to the LLM and synchronizes its behavior with the user’s browsing activity through implicit prompt engineering. We tested a baseline website without LLM integration, one with free-text interaction with the LLM, and another that combines free-text interaction with the suggestion of context-dependent questions. In a user study involving 44 participants, we found that the LLM-powered website has higher usability and that context-dependent question suggestions further enhance user experience, especially for people with low curiosity levels (according to Curiosity and Exploration Inventory-II - CEI-II) who are guided in formulating effective questions. This shows the potential of LLMs to enrich engagement with existing Cultural Heritage websites.

Human Resources of Labour Market in Russia

Today scales, structure and forms of population employment are changing, models of labour activities are modifying. Human resources imply carriers of physical, intellectual, creative, psychological potential, competences, experience, skills, motives and goals. The notion of ‘resources’ as applied to this social and economic category means opportunities, which are available at present and may be accessible in the future for state and regions to supply economy with workers having necessary professional and personal qualities. The goal of the article is to identify reserves of human resources for Russian labour market on the basis of analysis of the current situation on labour market, impact of digital technologies on population employment, requirements of business to workers’ competences and changes in labour regulation. Methodological base of the research was formed by a compound approach rests on collection and analysis of various information about the current situation of labour market. Informational base of the research consists of official data of state power of Russia, academic works by Russian and overseas authors, surveys of consulting companies, etc. The subject field of labour market is enriched by new investigations, which causes the growth in academic publications. Analysis of quantitative reserves of human resources shows retention of a rather unfavorable situation on labour market. The authors propose to develop professional, social, personality and psychological features of young people, pensioners and people approaching the pension age as a potential reserve of human resources. By using the sum total of human resource reserves providing their qualitative characteristics and pro-active state policy in the field of employment could help overcome negative deformation of Russian labour market.

A Fast-Pass, Desorption Electrospray Ionization Mass Spectrometry Strategy for Untargeted Metabolic Phenotyping.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) provides direct analytical readouts of small molecules that can be used to characterize the metabolic phenotypes of genetically engineered bacteria. In an effort to accelerate the time frame associated with the screening of mutant libraries, we have developed a high-throughput DESI-MSI analytical workflow implementing a single raster line-scan strategy that facilitates the collection of location-resolved molecular information from engineered strains on a subminute time scale. Evaluation of this "Fast-Pass" DESI-MSI phenotyping workflow on analytical standards demonstrated the capability of acquiring full metabolic profiling information with a throughput of ∼40 s per sample. This Fast-Pass strategy was implemented in the analysis of genetically edited Escherichia coli strains that have been engineered to produce various free-fatty acids (FFAs) for applications relevant to biofuels. Due to the untargeted nature of DESI-MSI, the investigation of these strains yielded molecular information for both global metabolites and targeted detection of accumulated bioproducts, allowing simultaneous readouts of strain-specific chemical profiles and comparative measurements of FFA production levels.

Job satisfaction in public entities

The objective of this article was to analyze job satisfaction in public entities; It was developed based on a systematic review methodology using the PRISMA method that has contributed to the selection of studies related to job satisfaction and the collection of transcendent information and relevant conceptual judgments regarding the topic addressed by reviewing varied and significant publications in scientific journals. A selection of 23 articles was made that met the inclusion criteria, and whose contributions supported the objective of this systematic review, which is why the satisfaction and participation of public officials are essential for the organizational effectiveness of the sector, to strengthen the commitment to increase quality levels in the public service and the motivation to achieve and essentially to build a modern public administration that responds to the demands of society.

Missing methods and epistemic injustice: a scoping review of qualitative research with AAC users

Users of Augmentative and Alternative Communication (AAC) have been historically left out of research. Within knowledge production, we see a prioritisation of the spoken voice in a hierarchy that at worst excludes, and at best subjugates, mediated voices. This scoping review sought studies involving AAC users as research participants, to identify practical lessons that could help inform data collection strategies to address this gap. We identified several common logistical and ethical considerations, organising them into three ­overarching themes that require additional attention from researchers working with AAC users, specifically: timescales, communication partners, and personal agency. In documenting that AAC-friendly methods are missing from the literature, we relocate the problem of AAC users’ exclusion from qualitative research from the inability of individuals with complex communication needs to participate in research, to individual and institutional inability or unwillingness to design and fund data collection practices that suit the needs of AAC users.

PEMANFAATAN DANA ALOKASI KHUSUS NON FISIK TERHADAP PENCAPAIAN KINERJA SPM KESEHATAN

Background: The successful implementation of health sector activities needs to be supported by non-physical Special Allocation Funds (DAK). Non-physical DAK for health is a fund to improve access and quality of health services in the region. Several service targets in North Musi Rawas Regency show that they have not yet reached 100%. This research aims to analyze the use of non-physical DAK on the achievement of health SPM performance at the North Musi Rawas District Health Service. Method: Qualitative research design with a descriptive approach. The research was carried out at 8 Community Health Centers and the North Musi Rawas District Health Service. Information collection was carried out from March to April 2024. Information was collected by in-depth interviews and document review. Result : The research results show that the majority of health workers do not have sufficient competence because they have never attended training. Health workers do not yet have clear duties and functions. All Community Health Centers have a budget for the diabetes mellitus program, but the elderly program budget is only available for 3 Community Health Centers. The available facilities and infrastructure are not yet complete. Health workers do not yet know the policies related to the Diabetes Mellitus (DM) program and the elderly. Program reporting is still manual. Health services for DM sufferers and the elderly have been carried out well. The SPM achievement of DM health services has met the target of 100%, but elderly services have not met the target. The total budget absorption coverage for DM services is 100%, while for the elderly it does not reach 100%. Conclusion : Health service factors, SPM services and non-physical DAK absorption are still not up to standard. Keywords: SPM, Utilization, Funds, Non-Physical DAK, Performance

Cohort profile: the BangladEsh Longitudinal Investigation of Emerging Vascular and nonvascular Events (BELIEVE) cohort study

PurposeBangladesh has experienced a rapid epidemiological transition from communicable to non-communicable diseases (NCDs) in recent decades. There is, however, limited evidence about multidimensional determinants of NCDs in this population. The...

Identification of energetically critical processes for cocoa production in Santander, Colombia

The intensification of industrial activities in post-harvest cocoa processes has generated a disproportionate increase in energy consumption. The objective of this work is to describe those energetically critical ones that are used in the cocoa production line in Santander so that further research can evaluate the implementa-tion of alternative or renewable energy systems. Through the collection of information and data manage-ment, it is seeking to promote the efficient use of energy based on the NTC ISO 500001. For this end, a description of the processes involved in the production of cocoa was made considering the technical data. Then, the energy consumed by the equipment used in the different processes was determined for 30 days, operating 24 hours a day, assuming power factors of 0.75 and 0.9. Finally, through analysis, calculations, and the application of the Pareto methodology, it was possible to identify that the process with the highest energy consumption is the cocoa refining step, since the equipment used in this process consumes more than 30% of energy from the total energy used for cocoa production.

Study on Financial Risk Prevention of NIO in the Context of Big Data

With the continuous advancement of global informatization, the way of enterprise financial management under the big data environment is undergoing profound changes. This paper takes NIO as the research object to explore how to prevent financial risks in the context of big data. Firstly, the definition of big data and its specific application in financial management are studied to reveal the supportive role of big data technology in the collection, analysis and decision-making of enterprise financial information. Second, this paper provides a detailed definition and categorization of financial risks to help identify potential financial problems. Through in-depth analysis of NIO's financial data, possible financial risk points are identified, and an effective risk early warning model is established by combining the methods of big data analysis, aiming to realize the early identification and prediction of financial risks. Finally, based on the results of the analysis, relevant financial risk prevention measures are proposed, in order to provide reference and reference for NIO and other similar high-tech enterprises in financial risk management. Financial risk management in the context of big data not only requires enterprises to have strong data analysis capabilities, but also needs to eliminate potential risk factors in the system design to enhance the enterprise risk prevention capabilities. The research in this paper is of great practical significance to promote the intelligence and scientization of enterprise financial management.

AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.

Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population. This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model. This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023. Data were collected using a mobile app (Gosufit) that measured depression, stress, anxiety, basal metabolic rate, oxygen saturation, heart rate, and average daily step count. Health coordinators recorded data on diabetes, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, percent body fat, and percent muscle. The presence of diabetes was the target variable, with various health indicators as predictors. Machine learning algorithms, including random forest, gradient boosting model, light gradient boosting model, extreme gradient boosting model, and k-nearest neighbors, were employed for analysis. The dataset was split into 70% training and 30% testing sets. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). Shapley additive explanations (SHAPs) were used for model interpretability. Significant predictors of diabetes included hypertension (χ²1=197.294; P<.001), hyperlipidemia (χ²1=47.671; P<.001), age (mean: diabetes group 72.66 years vs nondiabetes group 71.81 years), stress (mean: diabetes group 42.68 vs nondiabetes group 41.47; t3082=-2.858; P=.004), and heart rate (mean: diabetes group 75.05 beats/min vs nondiabetes group 73.14 beats/min; t3082=-7.948; P<.001). The extreme gradient boosting model (XGBM) demonstrated the best performance, with an accuracy of 84.88%, precision of 77.92%, recall of 66.91%, F1 score of 72.00, and AUC of 0.7957. The SHAP analysis of the top-performing XGBM revealed key predictors for diabetes: hypertension, age, percent body fat, heart rate, hyperlipidemia, basal metabolic rate, stress, and oxygen saturation. Hypertension strongly increased diabetes risk, while advanced age and elevated stress levels also showed significant associations. Hyperlipidemia and higher heart rates further heightened diabetes probability. These results highlight the importance and directional impact of specific features in predicting diabetes, providing valuable insights for risk stratification and targeted interventions. This study focused on modifiable risk factors, providing crucial data for establishing a system for the automated collection of health information and lifelog data from older adults using digital devices at service facilities.

Improving schedule accuracy of a reconfigurable assembly system

ABSTRACT Scheduling requires a large amount of information to produce reliable results. This information is not always available or detailed enough. In a reconfigurable system, many scenarios are possible, which makes it almost impossible to have detailed timing information for every combination of tasks, products, configurations, etc. To improve the availability of information, a methodology is proposed that extends the available input information collected from theoretical estimations and past production runs with data from on-demand emulation runs. Those emulations are conducted on a digital model connected to the real control logic, allowing the collection of information without interrupting the physical system. The configuration of the system for the current product is compared to the configurations in the data store to analyze the similarity, improve the reuse of data, and limit the number of emulations to generate an accurate schedule. The methodology is demonstrated on a reconfigurable near-industrial assembly system.

The Russian vector of Japanese intelligence activities (1874–1945)

The article is devoted to the activities of the Japanese intelligence community in 1874– 1945. The main work, first, against the Russian Empire, and later against the Soviet Union, was carried out by the army and navy intelligence agencies of the empire. In some cases, diplomatic missions and military gendarmerie were also engaged in secret intelligence against our country following the instructions of the supreme command of the Japanese army and navy. The beginning of Japan’s intelligence activities in Russia dates back to 1874–1875, when residents from the army and navy were sent to Saint Petersburg and Vladivostok. Before the start of the 1904–1905 campaign, Japanese intelligence managed to organize channels for obtaining reliable information about the state of the Tsarist navy and army, which largely ensured the success of its armed forces in this war. After the “Course of the National Defense of the Empire” was adopted in 1907, Japanese army intelligence focused on intelligence work against Russia, while naval intelligence began to collect information mainly focusing on the United States and Great Britain. In the 1920s, the interest of the army intelligence agencies also switched to the armed forces and military industry of Western Europe and the United States, but, after the occupation of Manchuria in 1931–1932, army intelligence focused on the Soviet direction again. Due to the tightening of the administrative, counterintelligence, and border protection regimes in the USSR in the second half of the 1930s, the collection of information by means of radio intelligence and cryptoanalysis received significant development in the activities of the Japanese intelligence community. Largely due to information from army and naval intelligence, the Japanese leadership abandoned its plans to attack the USSR in the autumn of 1941 and was informed in the spring and summer of 1945 about the upcoming war with the Soviet Union. Despite Japan’s defeat in World War II, in the mid-1950s, it resumed the activities of its intelligence community.

On the issue of the use of artificial intelligence technologies by financial market entities and ensuring the security of their personal and corporate data

The current study analyzes the issues of the accelerated development of artificial intelligence (AI) in the Russian Federation, the use of artificial intelligence technologies by market participants, including financial ones, and ensuring the security of their personal and corporate data, in the context of the rapid progress of AI technologies, contributing to the efficiency of technological solutions through the use of various methods and algorithms. Development in this area is supported by government agencies, scientific institutions, government organizations, and leading companies, which portends a significant expansion of the market by 2030. Artificial intelligence offers many opportunities, but it also has negative aspects. One such aspect is the collection of personal information of users, which is necessary in order to maintain, improve the performance of AI. This can happen either within the law or through illegal actions by digital criminals. Collecting all sorts of data allows algorithms to learn, adapt to society's needs, offer personalized recommendations, improve app interactions, much more. But misuse of these practices leads to serious privacy threats. The responsibility for protecting the information received lies with organizations, including financial ones, which must comply with security measures and proper protection of personal information. However, users themselves are sometimes insufficiently vigilant and cautious in handling their data, thereby increasing the risk of confidential information leakage. Cases of increasing digital crimes confirm that AI creates favorable conditions for various manipulations by cybercriminals, which negatively affects the economic security of the country and its citizens. The article discusses the current problems associated with the introduction of artificial intelligence in all spheres of human life, provides statistics on the number of court cases related to artificial intelligence, concludes that it is necessary to introduce a multi-level set of measures, including not only the general regulation of artificial intelligence, providing basic principles and approaches, but there are also self-regulation mechanisms that will allow market participants to adapt to rapidly changing conditions.

Novelties in Russian criminal law on liability for personal data breaches

This article presents the results of the first critical legal analysis of a novel provision in Russian criminal law that penalizes anyone who engages in the illegal use, transfer, collection, or storage of computer information containing personal data, as well as in the creation and management of information assets designed for the unauthorized storage and distribution of such data. The major flaws and contradictions in Article 272 of the Criminal Code of the Russian Federation were exposed, and potential solutions of certain problems associated with its enforcement were proposed. Theoretical recommendations were developed to strengthen the legislative measures targeting breaches of computer information containing personal data of minors, special categories of personal data, or biometric personal data. Some challenges that may arise when categorizing acts involving the cross-border transfer of computer information containing personal data and the cross-border flow of computer information carriers holding personal data were identified. The findings contribute to improving the criminal law framework for safeguarding personal data and advancing the Russian criminal law doctrine on liability for cybercrimes.

Dynamic-Based Cluster Routing Algorithm for Data Transmission in WSN

Wireless Sensor Network has been used for collection of information from the non-approachable areas.WSN use sensor nodes for sensing the information from the environment. Sensor nodes have been powered using energy source that is battery connection. Battery source of WSN nodes can’t be charged from any external device so that energy efficient utilization of the nodes for sensing and transmitting information is the major concern. Various approaches had been introduced in the past researches that provide better energy efficient protocol for enhancement of network lifetime and data transmission. In this paper various approaches have been implemented for WSN. Clustering based approaches have been utilized and assumed to be efficient approaches for data collection and data transmission over the network. In this paper we have reviewed DEEC, LEACH, EIP-LEACH and comparison has been done with proposed approach based on various parameter i.e Network lifetime. Experimental results of above defined approaches have been demonstrated.