Data Collection System Research Articles

Energy efficiency optimization analysis of a ground source heat pump system based on neural networks and genetic algorithms

This paper reports on the performance of a ground source heat pump (GSHP) system located in Shandong Province, China. The system operation data were monitored and collected by a data collection system. According to the analysis of the accumulated operational data, it was found that the GSHP system showed a relative higher COP in cooling season of 2023 than that of 2022 due to the change of supplying water temperature at ground-source side. Based on the analyzed data, a BP neural network model for energy consumption prediction was established. Furthermore, genetic algorithm (GA) was used to optimize the control strategy on the basis of the energy consumption prediction model. Comparison between the artificial experience control strategy and the one optimized by the genetic algorithm was conducted. The results show that the optimization strategy of the genetic algorithm is superior in terms of energy saving, particularly in the load rate higher than 50%, in which, the average energy-saving rate reaches 39.66%. Within the load rate range of 30–50%, the energy-saving rate could also reach 7.84%.

Sales strategies with a probabilistic business model of an insurance company: The role of updating and targeting

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The Impact of Agricultural Investments on the Economic Efficiency of Production Factors: An Empirical Study of the Wielkopolska Voivodeship

The functioning and development of each economic entity requires the implementation of investments. In the case of farms in Poland, which are characterised by structural problems and a lack of capital, it is essential to undertake investment activities of a modern nature. The main objective of this research was to assess agricultural investments made in farms, considering the example of the Wielkopolska Voivodship in Poland, from the point of view of their impacts on changes in the economic efficiency of production factors. The data source was unpublished individual data from the agricultural accounting “Polish FADN” results for all farms in the Wielkopolska Voivodship who had continuously participated in the data collection system from 2009 to 2021. The project’s complexity was determined by measuring the scale of investments, relating the sum of investment expenditures to the average annual value of fixed assets (reduced by land value). Farmers endowed with a greater asset base had the ability to invest more comprehensively (i.e., in their most desirable scale regarding the value of possessed fixed assets) in a manner primarily concerning the value of fixed assets, especially the agricultural area, as a primary agricultural production factor. In terms of the effects of differentiating the scale of investment outlays (which was the aim of the study), a better initial state allowed farms to increase their area significantly, with an average annual rate of change of 3%. On the other hand, a lack of investment led to the decapitalisation of assets (−6.7% per year), which also concerned land sales (−1.5% per year). These research results indicate that not only does the undertaking of investments, in general, impact the development possibilities of farms—and, therefore, improvements in the resource situation of farms—but also the scale of investment (with respect to the value of possessed fixed assets).

Modeling performance of data collection systems for high-energy physics

Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ.

An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes

The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as Agriware, is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor on-field parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation.

Community Service Apply Technology To Support A Green Economy In The Bait Qurani Islamic Boarding School In Bogor District

Bait Qurani was founded in 2008, under the leadership of Ustad Mahsum Salim. This Islamic boarding school is here to build an intelligent, Hafiz and Moral generation based on sincere intentions fi sabilillah. The results of a survey by a team of lecturers and students from Mercubuana University show that there are several problems that occur in this Islamic boarding school, such as large piles of organic and inorganic waste, the absence of a data collection system for students and a lack of knowledge about product sales. Based on the background above, the aim of holding this PKM activity is to provide learning about the importance of this waste problem which must be overcome, through several activities, where this worthless waste will become valuable, namely through technological processing using a composter, where this organic waste will be processed by fermentation to produce liquid fertilizer that can be sold. Likewise, inorganic waste can be recycled into valuable and useful items such as wallets, etc. To collect student data in Islamic boarding schools, how to sell using Smart Application digital marketing. It is hoped that this community service activity can solve problems in Islamic boarding schools, student data collection will be easier and will improve economic levels, so that the welfare of managers will increase.

Validity of vaccination information in the COVID-19 surveillance system in Japan: Implications for developing efficient and highly valid data collection systems in future pandemics

Validity of vaccination information in the COVID-19 surveillance system in Japan: Implications for developing efficient and highly valid data collection systems in future pandemics

The Application of Digital-Genomic Tools to Improve Productivity and Profitability in Smallholder Dairy Systems in sub-Saharan Africa

Abstract The dairy sector contributes 30% and 44% of the livestock-related agricultural GDP in Tanzania and Kenya, respectively. Despite this significant contribution, it is predominantly comprised smallholder farmers with few cows providing vital support in terms of food security, household income and employment for millions of farmers. Research aimed at enhancing productivity and sustainability in these smallholder dairy systems is crucial for rural development, increasing farm incomes, creating employment and supporting women and youth. However, a major challenge is the absence of a routine performance data collection system. Previous efforts to address this limitation often relied on methods from well-established dairy systems in developed countries which frequently failed to adapt effectively. Advances in mobile technology have prompted attempts to investigate alternative cost-effective and practical data recording systems in the smallholder dairy farming with a major focus on information and communications technology (ICT). Successful implementation of digital tools for data collection and synthesis to generate management information as feedback to farmers using ICT-based extension tools has led to increased productivity and household income. Additionally, the use of genomic information allows for genetic evaluation with minimal pedigree information and supports the initiation of breed improvement programs for increased productivity. Information © The Authors 2024

Factors Influencing the Implementation of Data-Driven Techniques for Students’ Mental Health

Data-driven methods are being implemented in many schools around the world to improve education. In this study, two schools were studied to investigate how they implemented datadriven methods for the monitoring and improvement of the well-being of their students. These schools were part of a Swedish national program where 15 schools participated to use data on both classroom, school, and system levels for school improvement. We identified five factors that influenced the implementations, namely data collection and analysis, frequency, anonymity, involving students, and organizational changes. We conclude that continuous and frequent data collection provided insights on students´ well-being that cannot be achieved without systematic data collection. Since this kind of data collection can be time-consuming, dedicated digital tools can be used to automate data collection and analysis. These tools can also provide a better basis for decision-making since it is easier to connect and visualize data. We also conclude that the European Union’s (EU) General Data Protection Regulation (GDPR) is important when using student data, and there is a need for national guidelines on how to use data securely and efficiently in schools.

New library technologies and trends in 2025

As libraries continue to evolve in the digital age, 2025 will see a strong focus on integrating cutting-edge technologies to enhance both operational efficiency and user experience. Key trends include the widespread adoption of artificial intelligence (AI) for personalized learning experiences and smarter cataloging systems, alongside augmented reality (AR) and virtual reality (VR) to create immersive educational environments. Blockchain will enhance transparency and security, particularly in digital rights management and transaction verification. Furthermore, the integration of robotics for inventory management and automation in library services will streamline daily operations, allowing staff to focus on more complex tasks. Libraries will also continue embracing open-source software to reduce costs and foster innovation, while Internet of Things (IoT) integration will enable real-time data collection and smart systems for resource management. These advancements will ensure that libraries are not just repositories of knowledge, but dynamic hubs for innovation and learning, offering accessible, secure, and personalized services to their communities. These trends reflect a broader shift towards making libraries more interactive, responsive, and sustainable, addressing the needs of diverse populations in increasingly digital and interconnected environments. As we move into 2025, these technologies will help libraries keep pace with the rapidly evolving digital landscape while maintaining their essential role in society.

Laryngopharyngeal Reflux in Otolaryngology-Head and Neck Surgery Clinic: An Epidemiological Survey.

Background and Objectives: Laryngopharyngeal reflux disease (LPRD) has been implicated in the etiology of many laryngeal disorders and is also closely related to the occurrence and progression of a variety of throat diseases, including chronic cough, leukoplakia of the larynx, dysphonia, and even laryngeal tumors. LPRD can significantly reduce the quality of life of patients and cause substantial medical and economic burdens to the society. Reflux Symptom Index (RSI), a self-administered 9-item outcomes instrument for laryngopharyngeal reflux (LPR), has been widely used to screen for LPRD, but there is a lack of feasibility studies on the use of RSI for LPRD screening and alternative diagnostic tools in otolaryngology-head and neck surgery (OHNS) clinics. To study the incidence, clinical characteristics, diagnostic status, and influencing factors of LPRD at OHNS clinics, RSI as an alternative diagnostic tool has also been studied. LPRD was defined as RSI > 13. Methods: Systematic collection of data by the RSI questionnaire was used to identify patients in the outpatient clinic suffering from LPRD; in addition, the personal history of participants was collected. Follow-up observation was carried out for 6 months for patients with suspected LPRD (RSI > 13), including whether they were treated with standardized anti-reflux therapy, and the questionnaire based on RSI was completed again after treatment. Results: The LPRD rate was 7.92% (94/1187) in this survey. The proportions of LPRD patients with smoking history (vs no smoking) and alcohol consumption history (vs. no alcohol consumption) were significantly higher (χ2 values: 7.025 and 4.562, and P values: .008 and .033; respectively). Smoking significantly increased the risk of LPRD (OR: 2.140, 95% CI: 1.058-4.331, P = .034). Among patients with LPRD positive, the incidence of "foreign body sensation in the throat" (symptom 8) score equal to 5 was the highest (19.15%). The severity of "excess mucus in the throat or postnasal drip" (symptom 3) contributed mostly to the total RSI score in patients with LPRD (r = .409, P < .001). ROC curve analysis showed that RSI ≥ 14 had a sensitivity of 72.9% and a specificity of 71.4% for the diagnosis of LPRD, with AUC = 0.797 (95% CI: 0.577-0.884, P < .001). Conclusions: The incidence of LPRD was high in patients we examined in OHNS clinics. We recommend that RSI can be used by otolaryngologists as a reliable tool for screening and diagnosing LPRD in OHNS clinics, which is beneficial for clinical practice.

Correction to: On the Use and Benefits of Electronic Data Collection Systems: A Tutorial on Countee

Correction to: On the Use and Benefits of Electronic Data Collection Systems: A Tutorial on Countee

Innovative Approaches in Pet Food Preference Methodology: Developing a Comprehensive Assessment Framework

This report offers a comprehensive guide on the utilization of a structured performa for assessing pet food preferences and owner perceptions. Developed by the Department of Livestock Product Technology at the College of Veterinary Science and Animal Husbandry, DUVASU, Mathura, the performa enables systematic data collection and robust analysis. This paper details the performa's components, its application in field studies, and the methodologies for interpreting the results, thereby enhancing the rigor and depth of pet food research. Through this guide, researchers can achieve more accurate insights into pet food preferences, leading to improved nutritional products and informed decision-making in the pet food industry.

Advanced Artificial Intelligence Drivers and IoT Network Systems for Data Collection and Network Transmission in the Industry 5.0 Era

The primary bases of Industry 5.0, the Internet of Things (IoT), artificial intelligence (AI), and big data (BD) may serve as a basis for applying the concepts of smart manufacturing, innovative goods and services, and predictive maintenance (PDM) into practice. Assessing the remaining useful life of components using failure data and state monitoring information constitutes the AI techniques for predictive maintenance (PDM), which are critical in Industry 5.0. However, it is sometimes challenging to identify the annotation of component usage, creating an open problem for obtaining accurately labeled information and understanding it. This problem needs to be rectified if Industry 5.0 progress is proceeded with. In order to solve the PDM job, this study presents a new data-driven decision system (DDS) based on AI that overcomes the earlier issue. It does by concentrating on an actual industrial use scenario that involves modern processing and monitoring machines. Specifically, the most fundamental components of the recommended DDS include cloud-based storage, data interpretation, predictive modeling, recognition of features using Principal Component Analysis (PCA), and data collecting. An efficient prediction model called the unique moth flame search-tuned light gradient boosting machine (MFS-LGBM) technique is presented in this work. By enabling the ongoing gathering of identified samples and offering a data analysis environment to assist the maintainer/operator, the suggested approach resolves the PDM task. The research findings showed that when compared to the previous approaches, the suggested approach accomplishes the greatest conceivable balance between comprehension, computational effort, and forecasting accuracy in terms of quality estimation. Based on the suggested methodology, the performance is examined in terms of RMSE (600.10), MAE (390.60), [Formula: see text] (0.790) and MAPE (25.70) measures. By incorporating it into the cloud structure, the suggested technique minimizes maintenance expenses and enhances production efficiency by improving machining processes, assisting maintainers, and delivering current operating risk alerts.

Assessing attitudes toward research and plagiarism among medical students: a multi-site study

BackgroundResearch involves the systematic collection and analysis of data to enhance understanding of a particular phenomenon. Participation in medical research is crucial for advancing healthcare practices. However, there has been limited focus on understanding the factors that motivate medical students to engage in research. Additionally, in the era of e-learning, the easy accessibility of online resources has contributed to a widespread ‘copy-paste culture’ among digital-native students, which is recognized in academia as plagiarism. Existing studies suggest that a contributing factor to the increasing prevalence of plagiarism is students’ limited understanding of this act. The purpose of this study was to assess medical students’ attitudes toward research and plagiarism, and to evaluate the psychometric properties of the Attitudes Toward Research (ATR) and Attitudes Toward Plagiarism (ATP) questionnaires.MethodsThis was a multicenter study conducted among medical undergraduate and postgraduate students attending the three medical universities who were involved in research. Students’ attitudes toward research and plagiarism were assessed using the ATR and ATP questionnaires. The research instruments underwent translation and cultural adaptation in accordance with internationally accepted methodology. The psychometric properties of the ATR and ATP, including validity and reliability, were assessed. Confirmatory factor analysis was used to test the model’s fit to the data.ResultsThe ATR and ATP questionnaires were completed by 793 medical students who were involved in research (647 undergraduates and 146 PhD students). Cronbach’s alpha coefficients of 0.917 and 0.822 indicated excellent and good scale reliability for the ATR and ATP questionnaires, respectively. The five-and three- factor structures of ATR and ATP have been validated with maximum likelihood confirmatory analysis, and the results demonstrated an adequate level of model fit (TLI = 0.930, CFI = 0.942 and TLI = 0.924, CFI = 0.943, respectively). Medical students showed a high degree of positive attitudes toward research and favorable scores across all three domains of attitudes toward plagiarism. In multivariate regression models, age was found to be positively associated with favorable attitudes of research usefulness, positive attitudes, relevance to life subscales and total ATR scale (p < 0.001), while PhD study level was related to research anxiety (p < 0.001) and favorable attitudes across all three ATP domains (p < 0.001).ConclusionMedical students who were involved in research showed a high degree of favorable attitudes toward research and plagiarism. Adjusting medical school curricula to include research courses would broaden the students’ interest in scientific research and maximize their impact on the full preservation of research ethics and integrity.

Data-Driven Insights Into Post-Earthquake Reconnaissance Findings: 2023 Türkiye Earthquake Sequence

Post-earthquake reconnaissance stands as a vital endeavor, encompassing a systematic survey and data collection to gain insights into the aftermath of seismic events. Grappling with a multitude of multidimensional data poses inherent challenges in the proper interpretation and extraction of hidden information. This paper unfolds in two parts. Initially, we delineate the reconnaissance mission following the February 06 Türkiye earthquake sequence, elucidating the challenges, complexities, and nature of the amassed data. Subsequently, a paradigm shift toward automated machine learning (AutoML) is embraced to ascertain the optimal model, categorize observed damages, and unveil underlying patterns within the collected data. The most accurate machine learning model is then coupled with Shapley Additive Explanations (SHAP) to explicate observations, steering away from a black-box model. SHAP values discern and prioritize factors significantly contributing to various damage levels. The results reveal a test set accuracy of 0.75 and 0.95 for multi- and binary-class problems, respectively, employing both raw and composite features. Building-related features exert more control over light damage, while earthquake-related factors dominate severe damage. In critical damage scenarios, duration- and velocity-dependent intensity measures assume significance. Furthermore, findings indicate that if column and wall indices are below 0.07 and 0.08, respectively, they positively contribute to the likelihood of critical structural damage.

Development of an Integrated Surveillance System to Improve Preparedness for Arbovirus Outbreaks in a Dengue Endemic Setting: Descriptive Study.

Dengue fever, transmitted by Aedes aegypti and Aedes albopictus mosquitoes, poses a significant public health challenge in tropical and subtropical regions. Dengue surveillance involves monitoring the incidence, distribution, and trends of infections through systematic data collection, analysis, interpretation, and dissemination. It supports public health decision-making, guiding interventions like vector control, vaccination campaigns, and public education. Herein, we report the development of a surveillance system already in use to support public health managers against dengue transmission in Foz do Iguaçu, a dengue-endemic Brazilian city located in the Triple Border with Argentina and Paraguay. We present data encompassing the fieldwork organization of more than 100 health agents; epidemiological and entomological data were gathered from November 2022 to April 2024, totalizing 18 months of data collection. By registering health agents, we were able to provide support for those facing issues to fill their daily milestone of inspecting 16 traps per working day. We filtered dengue transmission in the city by patient age, gender, and reporting units, as well as according to dengue virus serotype. The entomological indices presented a strong seasonal pattern, as expected. Several longtime established routines in Foz do Iguaçu have been directly impacted by the adoption of Vigilância Integrada com Tecnologia (VITEC). The implementation of VITEC has enabled more efficient and accurate diagnostics of local transmission risk, leading to a better understanding of operational activity patterns and risks. Lately, local public health managers can easily identify hot spots of dengue transmission and optimize interventions toward those highly sensitive areas.

Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

Integrating Earth observation, biophysical, and survey data to evaluate the ecological impacts of a common land protection and restoration intervention in Rajasthan, India

Approximately, one-quarter of India’s land area comprises common-pool resources, e.g., pasture land, forests, and water bodies, upon which the livelihoods of over 300 million rural people depend. Despite their importance, these resources are subjected to encroachment and degradation, raising important questions about what can be done to promote their protection and restoration. Since 2001, the Foundation for Ecological Security (FES) has worked with over 7000 villages to secure their rights to the commons, while facilitating collective action and access to finance to promote their restoration. To evaluate the impact of FES’s intervention model, we compare prioritized common land areas in 24 intervention and 24 control villages located in two districts in the state of Rajasthan—Bhilwara and Pratapgarh. We employ Propensity Score Matching (PSM) to control for program placement bias while contrasting changes in key ecological indicators over time derived through remote sensing. Based on data collected through village surveys (n = 48), household surveys (n = 911), systematic biophysical data collection (n = 480), and remote sensing, we find evidence that the rollout of FES’s intervention model (a) was relatively greater in the intervention villages but below expectation; and (b) facilitated significant increases in tree cover and tree and shrub diversity, while significantly reducing encroachment, particularly in the intervention villages intervened before 2011. We further find that the institutional strengthening dimension of FES’s work is a plausible mechanism that gave rise to these effects.

The effect of comorbidity on 28-day perioperative mortality rate among non-cardiac surgical patients in Northwest Ethiopia: a prospective cohort study using propensity score matching

BackgroundNon-communicable diseases account for nearly three-fourths of global deaths impacting public health and development. Coexisting diseases can complicate the management of surgical diseases, increasing the risk of perioperative mortality. Despite this, few studies have examined the burden of comorbidities among surgical patients and their effect on perioperative outcomes in low-income countries. In this study, we assessed the impact of comorbidity burden on 28-day perioperative mortality using a prospective data set.MethodsThis was a facility-based prospective cohort study. Adult patients aged ≥ 18 who underwent non-cardiac surgery were included. Patients were followed for 28 days following surgery. Perioperative data were collected using an electronic data collection system from June 01, 2019, to June 30, 2021, at Tibebe Ghion Specialized Hospital, Northwest, Ethiopia. A propensity score-matched analysis was employed to assess the effect of comorbidity on the 28-day perioperative mortality rate.ResultsOf the 3030 patients included in this study, 715 (23.59%) had at least one comorbidity. Based on the prevalence rate, the top four comorbid conditions observed were hypertension (0.050), cancer (0.036), diabetes mellitus (0.021), and human immunodeficiency virus (0.021). The 28-day perioperative mortality rate was shown to be significantly higher among patients with comorbidity, where 45 (6.29%) of the 715 patients with comorbidity died compared to 49 (2.12%) of the 2315 patients with no comorbidity (p-value < 0.0001). After propensity score matching potential confounders, patients who have comorbidity had a 2.52% (average treatment effect on treated (ATT) = 0.0252) higher risk of perioperative death at 28 days after surgery compared with patients who did not have comorbidity.ConclusionThis study found a moderate prevalence of comorbid illnesses among non-cardiac surgical patients, with comorbidity increasing the risk of 28-day perioperative mortality. Preoperative screening and optimization are highly recommended for patients with comorbid illness to decrease perioperative mortality rate.