Data Collection Capabilities Research Articles

Hotel digital intelligence capability: dimension exploration and scale development

Purpose This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale. Design/methodology/approach This study adopts qualitative and quantitative approaches to conduct an exploratory inquiry into the structural dimensions of hotel digital intelligence capability with the help of grounded theory. Based on this, several questionnaires were developed to test the measurement scale and verify its validity. Findings The results reveal that hotel digital intelligence capability comprises four dimensions: data collection and processing capability, customer service personalization capability, digital intelligence decision support capability and sustainable development capability. The measurement scale consists of four factors and 13 items, with reliability and validity tests demonstrating ideal levels. Originality/value This study not only provides a new perspective to understand hotel digital intelligence capability but also develops a corresponding measurement scale, laying a solid theoretical basis for hotel managers to scientifically evaluate this capability to achieve sustainable competitive advantage.

Design and Implementation of an Emergency Environmental Monitoring System

The collection and real-time transmission of emergency environmental information are crucial for rapidly assessing the on-site situation of sudden disasters and responding promptly. However, the acquisition of emergency environmental information, particularly its seamless transmission, faces significant challenges under complex terrain and limited ground communication. This paper utilizes sensors, line-of-sight communication with unmanned aerial vehicles (UAVs), and LoRa long-distance communication to establish an integrated emergency environmental monitoring system that combines real-time monitoring, UAV-mounted LoRa gateway relaying, and backend data analysis. This system achieves real-time acquisition, seamless transmission, storage management, and visualization of environmental emergency information. First, a portable emergency environmental monitoring device was developed to collect and transmit environmental factor data. Second, a UAV-mounted LoRa gateway was designed to extend the data transmission coverage, ensuring seamless communication. Finally, multiple field experiments were conducted to evaluate the system’s performance. The experimental results indicate that the system possesses reliable capabilities for emergency data collection and transmission in complex environments, providing new technical solutions and practical support for developing and applying emergency environmental monitoring systems.

Challenges and Countermeasures in Cross-Border Data Collection

In the context of globalization, cross-border data collection has become a crucial tool for countries combating transnational crime. China primarily employs international criminal judicial assistance to conduct cross-border data collection. While this approach respects national sovereignty, it faces practical challenges such as low efficiency and limited authority. In contrast, the United States and the European Union adopt a more flexible data controller model, achieving robust cross-border data collection capabilities. However, conflicts in data sovereignty and differences in legal systems hinder the direct application of these models in China. This paper employs a comparative research methodology to analyze the legislative frameworks and backgrounds of the U.S. and EU cross-border data collection approaches, identifying reasons these models are unsuitable for direct implementation in China. Building on insights from these legislative models, the paper proposes recommendations such as increasing the number of bilateral judicial assistance agreements and establishing a categorized data management system, aiming to provide a reference for China's future development in this field.

Climate Risk Preparedness

This study explores the readiness of takaful operators in Malaysia and investigates the challenges and opportunities associated with incorporating climate parameters into their existing risk management frameworks. Survey findings indicate a widespread recognition among respondents of the importance of climate risk assessment for takaful operations while revealing a neutral stance on the efficacy of current risk management strategies in addressing unforeseen climate-related risks. Critical impediments identified include internal data deficiencies, skills gaps within the workforce, and regulatory constraints concerning climate risk adaptation. In addition, respondents notably favour direct climate-specific metrics, such as flood models and rainfall intensity, for quantifying climate impacts. These results underscore the pressing need for Malaysian takaful operators to integrate climate risk parameters into their frameworks, reflecting an increasing acknowledgment of climate-related financial risks. Recommendations include enhancing data collection and analysis capabilities, bolstering workforce proficiency in climate risk assessment, and fostering partnerships to innovate risk management tools and practices. The study holds significant implications for policymakers and stakeholders seeking to fortify resilience and sustainability within the takaful industry amidst evolving climate dynamics.

PsyRiskMR: a comprehensive resource for identifying psychiatric disorders risk factors through Mendelian randomization

BackgroundPsychiatric disorders pose an enormous economic and emotional burden on individuals, their families and society. Given that the current analysis of the pathogenesis of psychiatric disorders remains challenging and time-consuming, elucidating the modifiable risk factors becomes crucial for the diagnosis and management of psychiatric disorders. However, inferring the causal risk factors in these disorders from disparate data sources is challenging due to constraints in data collection and analytical capabilities. MethodsBy leveraging the largest available genome-wide association studies (GWAS) summary statistics for ten psychiatric disorders and compiling an extensive set of risk factor datasets, including 71 psychiatric disorders-specific phenotypes, 3,935 brain imaging traits, and over 30 brain tissue/cell-specific xQTL datasets (covering 6 types of QTLs), we performed comprehensive Mendelian randomization (MR) analyses to explore the potential causal links between various exposures and psychiatric outcomes using genetic variants as instrumental variables. ResultsAfter Bonferroni correction for multiple testing, we identified multiple potential risk factors for psychiatric disorders (including phenotypic level and molecular level traits), and provided robust MR evidence supporting these associations utilizing rigorous sensitivity analyses and colocalization analyses. Furthermore. we have established the PsyRiskMR database (http://bioinfo.ahu.edu.cn/PsyRiskMR/), which serves as an interactive platform for showcasing and querying risk factors for psychiatric disorders. ConclusionsOur study offered a user-friendly PsyRiskMR database for the research community to browse, search, and download all MR results, potentially revealing new insights into the biological etiology of psychiatric disorders.

Integrated Multimedia Wireless Sensor Node for Comprehensive Lifelogging and Beyond: Design, Development and Applications

This paper introduces the Integrated Multimedia Wireless Sensor Node (IMWSN), a significant advancement in environmental monitoring and lifelogging within Multimedia Wireless Sensor Networks (MWSN). MWSNs, equipped with wearable sensors, are crucial for documenting personal life experiences. However, current MWSNs often lack the ability to fully integrate data across various sensor types, including environmental, visual, and medical sensors. The IMWSN addresses this gap by providing a comprehensive view of an individual's interactions and environment. The IMWSN is composed of multiple modules: a processor module that manages the overall system efficiently, a visual module designed to capture video footage of the surroundings, an environmental module that allows for real-time monitoring of environmental conditions, and a medical module dedicated to recording health-related data of individuals, a process often known as lifelogging. These components are encased in a custom-designed 3D-printed enclosure and powered by a durable 4500mAh mobile battery. System programming and monitoring are facilitated through the user-friendly Arduino IDE, making the experience accessible and customizable. Beyond its primary function in lifelogging, the IMWSN is remarkably versatile and suited for a range of applications. It can function as an action camera, assist in forest fire monitoring, support ambient assisted living environments, and monitor patients' health and daily activities rigorously. This adaptability makes the IMWSN a valuable and essential tool in fields that require extensive data collection and sophisticated analytical capabilities, highlighting its broad potential impact.

Identification of Corrosion Factors in Blast Furnace Gas Pipe Network with Corrosion Big Data Online Monitoring Technology

The production of blast furnace gas plays a crucial role in steel manufacturing, as the processing of raw materials can result in the generation of potentially harmful substances such as benzene, toluene, and xylene, known for their toxicity and carcinogenic properties. Moreover, blast furnace gas, primarily composed of harmful gases like SO2, reacts with moisture to form a highly acidic corrosive solution, hastening the corrosion of pipeline materials upon contact.To mitigate the accelerated corrosion of blast furnace gas pipelines due to adverse environmental conditions, this study developed a high-throughput sensor system. Leveraging big data technology and the Internet of Things (IoT), we achieved real-time corrosion monitoring and rapid analysis of influential factors. The research followed a threefold approach: Firstly, we conducted field tests using an advanced corrosion electrochemical sensing system alongside six environmental sensors. This method effectively addressed issues related to data continuity, stability, and comprehensiveness. Secondly, we identified the most effective machine learning models and delved deeper into the data using them, along with data analysis techniques such as Spearman correlation analysis and partial dependency analysis. This allowed for a thorough exploration and interpretation of the large-scale corrosion data collected. Subsequently, we performed simulated condensate corrosion tests to comprehensively evaluate how environmental fluctuations in various states affect pipeline material corrosion.The study findings reveal that temperature reduction is the primary factor driving increased pipeline corrosion. Temperature fluctuations impact water evaporation and condensation within the tube, exhibiting a high negative correlation with relative humidity (-0.92). Additionally, temperature fluctuations, combined with corrosive gases, result in significant coupling effects, leading to more severe corrosion. Furthermore, the study determined the dose-response relationship between relative humidity, temperature, and corrosion, identifying critical points. Corrosion intensifies when humidity exceeds 50%, peaking at 70%, while rising temperature initially slows down corrosion, peaking at 40°C. However, further temperature increases do not continue to mitigate corrosion. Conversely, the relationship between corrosive gas concentration and corrosion rate follows a linear pattern.These observations underscore the significant enhancement in proactive management of blast furnace gas piping corrosion through advanced sensor technology and comprehensive corrosion data analysis. The proposed data-driven corrosion failure analysis method offers several advantages: (i) Enhanced data continuity with high-frequency data collection capabilities at one-minute intervals, enabling real-time insight into corrosion conditions and subtle changes that traditional methods might overlook. (ii) Comprehensive environmental data collection, including temperature, humidity, and gas concentrations, facilitating multidimensional correlation analysis and a deeper understanding of corrosion phenomena. (iii) Leveraging IoT for timely collection, transmission, and analysis of pipeline corrosion data, thereby improving the timeliness of corrosion control measures. Figure 1

Applied Research on the Impact of Big Data, Cloud Computing and Cloud Storage on Smart Manufacturing Supply Chain

The rapid advancements in big data, cloud computing, and cloud storage technologies are profoundly reshaping the operational models of smart manufacturing supply chains. The integration and application of these technologies not only drive the enhanced data collection and analysis capabilities across various supply chain segments but also significantly improve the accuracy of demand forecasting, the rationality of resource allocation, and the scientific nature of decision support. Consequently, the collaborative efficiency and dynamic response capabilities of supply chains have been substantially elevated, providing robust technological backing for enterprises in navigating the intricately volatile market environment. Nonetheless, while reaping the benefits of these technological advancements, challenges such as data security and privacy protection, system integration, and technical selection cannot be overlooked. By judiciously selecting technology platforms, reinforcing data security management, and continuously investing in technological advancements and talent cultivation, the holistic effectiveness of smart manufacturing supply chains will be further optimized.

Means and Methods of Collecting Indicators for Energy Supply Companies

This study provides a comprehensive overview of the various means and methods employed in gathering data, emphasizing the need for advanced technologies in the face of increasing energy demands and evolving regulatory environments. A thorough comparative analysis focuses on several key aspects, including technology comparison, data accuracy and reliability, real-time data collection capabilities, cost effectiveness, scalability, and flexibility, consumer interaction, and feedback mecha- nisms. Particular attention has been given to the security and confidentiality of data, as well as the environmental implications. The analysis extends to explore hardware and technological advancements in the industry, comparing traditional systems with modern automated and digital solutions, such as smart meters and integrated data management platforms.

Design and implementation of terminal power information collection and control system based on low-code platform

Abstract As the number of power consumption collection terminals continues to increase, the business needs for remote signaling and telemetry data collection through interface services continue to expand. Different professional collection front-end openness levels and open standards are not unified, and each professional terminal can only be managed in a decentralized manner. As a result, the current power consumption information data collection capabilities are scattered. In general, active data collection and sensing in enterprises must rely on various collection services to carry out applications. The lack of unified operation and management mechanisms and tools leads to difficulties in the development of interfaces and the management of terminal equipment. A set of power consumption information collection service integration systems developed based on a low-code platform is proposed. In view of the different communication protocols of power terminals and the need to develop different interfaces, etc., the command issuance and message encapsulation of different power collection protocols is carried out, to provide customized collection services and improve unified access and management capabilities of terminals.

Diet quality is associated with lower prevalence of menopausal symptoms: the ZOE PREDICT 3 study

The role of diet in the prevention and management of menopausal symptoms is poorly understood1,2. Digital health platforms have the potential to optimise collection of population-level menopause symptom data particularly in a rapidly changing environment2,3. This study explored the prevalence of menopause symptoms utilising data collection capabilities of a mobile app in a large population of peri- and postmenopausal women and to investigate novel associations between diet quality and symptoms.Baseline data on diet, 20 menopause symptoms (i.e. 3 vasomotor, 3 sexual, 4 psychological and 10 somatic) and other characteristics of 70,412 participants of the ZOE PREDICT 3 app-based study in UK (n = 27,932 peri- and n = 42,480 postmenopausal) were analysed (NCT04735835). Dietary assessment was performed through an app-based food frequency questionnaire (PREDICTFFQ)4 and diet quality assessed using the Healthy Eating Index-2020 (HEI, score 0-100)5.The burden of menopausal symptomatology was high; with 99.8% and 92.7% of peri- and postmenopausal participants reporting 1 or more symptoms respectively and 66.0% and 41.2% of peri- and postmenopausal participants experiencing >12 symptoms, respectively (P<0.001). Mean ( ± SD) symptom number was greater in the peri- versus postmenopausal group; 13.5 ± 3.8, vs. 10.5 ± 5.5 (P<0.001). HEI (mean ± SD) was slightly lower in the peri- versus postmenopausal group; 73.9 ± 9.7 vs. 76.6 ± 8.9 respectively (P<0.001). Logistic regression analyses showed that a 20 unit increase in HEI was associated with reduction of 6%-37% and 12%-43% in likelihood of symptom presence (dependent on the symptom) in peri- and postmenopausal participants, respectively (P<0.05). ORs remained significant in adjusted models (for age, BMI, use of hormone replacement therapy, total energy intake and home environment (urban, rural) showing reductions of 7%-19% and 6-21%, respectively (P<0.05). Logistic regression analyses revealed that a similar increase in HEI (20 units) was associated with 17% and 19% reductions in the number of symptoms reported by peri- and postmenopausal women after multiple adjustments (P<0.05).This large observational study confirms the high prevalence of menopausal symptoms among peri- and postmenopausal women and provides new evidence between adherence to healthy eating and reduced likelihood of menopausal symptoms, increasing the potential for symptom reduction by specific dietary approaches. Further studies should examine this relationship in longitudinal and dietary intervention settings.

Research on the Impact of Big Data and Enterprise Digitalization on the E-commerce Industry and Coping Strategies

Abstract: Big data, enterprise digitalization, and e-commerce all have one thing in common: they were born from the Internet. Since the onset of the 21st century, the global Internet industry has experienced rapid growth, with e-commerce emerging as a key player. Participants in e-commerce engage with both the global online marketplace and the physical market, utilizing modern communication networks and information technology to conduct business activities through e-commerce platforms or service platforms. E-commerce embodies characteristics such as market globalization, rapid transactions, virtualization, and cost-effectiveness, making it highly favored by enterprises. Presently, e-commerce relies heavily on robust databases, as well as the data collection, processing, and analysis capabilities of big data. This reliance compels traditional enterprises to undergo digital transformation. This study delves into the impact of big data and enterprise digitalization on the e-commerce industry, exploring the mechanisms underlying their influence on cross-border e-commerce. By examining these dynamics, the study aims to provide insights into the evolving landscape of e-commerce and the transformative potential of big data and digitalization in shaping its future trajectory.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Harnessing the Power of VR and AR Technologies in Internet Celebrity Marketing and User Interaction

The integration of Virtual Reality (VR) and Augmented Reality (AR) technologies into internet celebrity marketing offers unprecedented opportunities for enhancing user engagement, personalizing user experiences, and creating interactive content. This paper explores how VR and AR can revolutionize user interaction by providing immersive experiences and valuable data collection capabilities. By examining various applications, including virtual events, AR advertisements, and branded experiences, we highlight the significant business opportunities these technologies present for internet celebrities and brands. The ability to monetize VR and AR content, combined with insights gained from data analysis, drives innovative marketing strategies and fosters deeper connections with audiences. Through collaborations between internet celebrities, brands, and tech companies, the potential of VR and AR to transform digital marketing and engagement is immense, offering exciting new avenues for growth and success.

In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning

Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing.

Ultrasensitive and multiplexed Gastric cancer biomarkers detection with an integrated electrochemical immunosensing platform

Developing immunosensing platforms capable of simultaneously detecting multiple cancer markers is crucial for clinical diagnosis and biomedical research. Here, we introduce a novel dual-mode electrochemical biosensing assay platform capable of detecting two gastric cancer biomarkers: pepsinogen I (PG I) and pepsinogen II (PG II). Methylene blue (MB) and Prussian blue (PB) were used as dual signal sources to label PG I and PG II, respectively. The platform integrates an ARM STM32F411 microcontroller and an AD5941 analog front-end, which not only facilitates cyclic voltammetry (CV) and differential pulse voltammetry (DPV) with efficacy comparable to commercial electrochemical workstations but also offers data collection and synchronous analysis capabilities, allowing simultaneous output of PG I and PGR (PG I/PG II) values. Equipped with an interactive screen for operational control and result display, the immunosensing platform provides linear detection ranges for PG I (5 pg/mL–100 ng/mL) and PG II (50 pg/mL–200 ng/mL), enabling rapid detection within 5 min. It demonstrates excellent sensitivity and selectivity when comparing serum samples from healthy individuals and gastric cancer patients. The dual-marker detection platform significantly enhances early diagnosis and screening of gastric cancer, offering substantial improvements over single-marker assays. Furthermore, this platform shows potential for detecting multiple biomarkers in various diseases, highlighting its utility for biomedical applications.

Optimising the review of electronic monitoring information for management of commercial fisheries

Electronic monitoring (EM) systems incorporating cameras and other devices can collect a broad range of data to support fisheries management. We reviewed the data collection capabilities of EM and considered approaches to increasing efficiency, including cost effectiveness, of EM review. EM can provide information on catch, effort, catch handling, bycatch mitigation, fishing gear and operational data, which are relevant for fisheries management including by Regional Fisheries Management Organisations (RFMOs). Methods to increase efficiency and decrease costs of EM review apply from the programme design phase, through data collection and review. At review, costs may be reduced by sampling imagery optimally to meet monitoring objectives. Considering RFMOs as users of EM-collected information, we applied EMoptim, an open-source simulation model developed in R that estimates the amount of EM review necessary to meet one or more user-specified monitoring objectives. EMoptim uses stratification to increase review efficiency and incorporates a function to explore review costs against the monitoring objectives set. We evaluated the amount of EM review needed to estimate catch with specified precision, using fishery data available from the Western and Central Pacific Fisheries Commission. Model outputs show that EM review requirements increase as catch frequency decreases, dispersion of catch events increases, and when more precise catch estimates are required. Geographical stratification reduced the amount of review required for more commonly caught species and when catch events were focused in a limited area. Optimising review rates across multiple monitoring objectives was most effective for more commonly caught species. We highlight opportunities for future use and development of this prototype modelling package.

Emerging Paradigms in Fetal Heart Rate Monitoring: Evaluating the Efficacy and Application of Innovative Textile-Based Wearables.

This comprehensive review offers a thorough examination of fetal heart rate (fHR) monitoring methods, which are an essential component of prenatal care for assessing fetal health and identifying possible problems early on. It examines the clinical uses, accuracy, and limitations of both modern and traditional monitoring techniques, such as electrocardiography (ECG), ballistocardiography (BCG), phonocardiography (PCG), and cardiotocography (CTG), in a variety of obstetric scenarios. A particular focus is on the most recent developments in textile-based wearables for fHR monitoring. These innovative devices mark a substantial advancement in the field and are noteworthy for their continuous data collection capability and ergonomic design. The review delves into the obstacles that arise when incorporating these wearables into clinical practice. These challenges include problems with signal quality, user compliance, and data interpretation. Additionally, it looks at how these technologies could improve fetal health surveillance by providing expectant mothers with more individualized and non-intrusive options, which could change the prenatal monitoring landscape.

Multidimensional time series motif group discovery based on matrix profile

With the continuous advancements in sensor technology and the increasing capabilities for data collection and storage, the acquisition of time series data across diverse domains has become significantly easier. Consequently, there is a growing demand for identifying potential motifs within multidimensional time series. The introduction of the Matrix Profile (MP) structure and the mSTOMP algorithm enables the detection of multidimensional motifs in large-scale time series datasets. However, the Matrix Profile (MP) does not provide information regarding the frequency of occurrence of these motifs. As a result, it is challenging to determine whether a motif appears frequently or to identify the specific time periods during which it typically occurs, thereby limiting further analysis of the discovered motifs. To address this limitation, we proposed Index Link Motif Group Discovery (ILMGD) algorithm, which uses index linking to rapidly merge and group multidimensional motifs. Based on the results of the ILMGD algorithm, we can determine the frequency and temporal positions of motifs, facilitating deeper analysis. Our proposed method requires minimal additional parameters and reduces the need for extensive manual intervention. We validate the effectiveness of our algorithm on synthetic datasets and demonstrate its applicability on three real-world datasets, highlighting how it enables a comprehensive understanding of the discovered motifs.

Illuminating IT Landscapes: A Comparative Analysis of Discovery in ServiceNow and BMC Helix

Abstract: This research paper presents a comprehensive comparison of the Discovery applications offered by ServiceNow and BMC Helix. The study examines key features, discovery mechanisms, data collection capabilities, integration with CMDB, automation features, and analytics tools of both solutions. By evaluating their strengths and weaknesses in discovering and mapping IT assets and relationships, this paper aims to provide insights for organizations seeking to enhance their visibility into complex IT environments and improve overall IT service management.