Search Results (10)

The COVID-19 pandemic has profoundly influenced urban planning and disease management in residential areas. Focusing on Beijing as a case study (3898 communities), this research develops a big data analytics framework integrating anonymized mobile phone signals (China Mobile), location-based services (AMAP.com), and municipal health records to quantify COVID-19 transmission dynamics. Using logistic regression, we analyzed 15 indicators across four dimensions: mobility behavior, host demographics, spatial characteristics, and facility accessibility. Our analysis reveals three key determinants: (1) Population aged 65 and above (OR = 62.8, p < 0.001) and (2) housing density (OR = 9.96, p = 0.026) significantly increase transmission risk, while (3) population density exhibits a paradoxical negative effect (β = −3.98, p < 0.001) attributable to targeted interventions in high-density zones. We further construct a validated risk prediction model (AUC = 0.7; 95.97% accuracy) enabling high-resolution spatial targeting of non-pharmaceutical interventions (NPIs). The framework provides urban planners with actionable strategies—including senior activity scheduling and ventilation retrofits—while advancing scalable methodologies for infectious disease management in global urban contexts. Full article

Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM${}_{2.5}$ exposure by combining residents’ mobility traces and PM${}_{2.5}$ concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM${}_{2.5}$ exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM${}_{2.5}$ concentration, we quantify individual PM${}_{2.5}$ exposure and find that the average PM${}_{2.5}$ exposure of residences in Shanghai is 60.37 ug·h·m${}^{-3}$ during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM${}_{2.5}$ exposure in Shanghai. Our PM${}_{2.5}$ exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development. Full article

The field of structural health monitoring (SHM) faces a fundamental challenge related to accessibility. While analytical and empirical models and laboratory tests can provide engineers with an estimate of a structure’s expected behavior under various loads, measurements of actual buildings require the installation and maintenance of sensors to collect observations. This is costly in terms of power and resources. MyShake, the free seismology smartphone app, aims to advance SHM by leveraging the presence of accelerometers in all smartphones and the wide usage of smartphones globally. MyShake records acceleration waveforms during earthquakes. Because phones are most typically located in buildings, a waveform recorded by MyShake contains response information from the structure in which the phone is located. This represents a free, potentially ubiquitous method of conducting critical structural measurements. In this work, we present preliminary findings that demonstrate the efficacy of smartphones for extracting the fundamental frequency of buildings, benchmarked against traditional accelerometers in a shake table test. Additionally, we present seven proof-of-concept examples of data collected by anonymous and privately owned smartphones running the MyShake app in real buildings, and assess the fundamental frequencies we measure. In all cases, the measured fundamental frequency is found to be reasonable and within an expected range in comparison with several commonly used empirical equations. For one irregularly shaped building, three separate measurements made over the course of four months fall within 7% of each other, validating the accuracy of MyShake measurements and illustrating how repeat observations can improve the robustness of the structural health catalog we aim to build. Full article

Adequate nutrition and the nutritional status of pregnant women are critical for the health of both the mother and the developing foetus. Research has shown a significant impact of nutrition on the child’s health and the future risk of developing chronic noncommunicable diseases (NCDs), such as obesity, diabetes, hypertension, and cardiovascular disease. There is currently no data on the level of nutritional knowledge of Czech pregnant women. This survey aimed to evaluate their level of nutritional knowledge and literacy. An analytical cross-sectional study was conducted in two healthcare facilities in Prague and Pilsen between April and June 2022. An anonymous self-administered paper-form questionnaire for assessing the level of nutritional knowledge (40 items) and the Likert scale for assessing nutrition literacy (5 items) were used. A total number of 401 women completed the questionnaire. An individual’s nutritional knowledge score was calculated and compared with demographic and anamnestic characteristics using statistical methods. The results showed that only 5% of women achieved an overall nutritional score of 80% or more. University education (p < 0.001), living in the capital city (p < 0.001), experiencing first pregnancy (p = 0.041), having normal weight and being overweight (p = 0.024), and having NCDs (p = 0.044) were statistically significantly associated with a higher nutritional knowledge score. The lowest knowledge scores were found in the areas of optimal energy intake, optimal weight gain, and the role of micronutrients in diet during pregnancy. In conclusion, the study shows limited nutrition knowledge of Czech pregnant women in some areas of nutrition. Increasing nutritional knowledge and nutrition literacy in Czech pregnant women is crucial for supporting their optimal course of pregnancy and the future health of their offspring. Full article

In wireless multimedia networks, the Internet of Things (IoT) and visual sensors are used to interpret and exchange vast data in the form of images. The digital images are subsequently delivered to cloud systems via a sink node, where they are interacted with by smart communication systems using physical devices. Visual sensors are becoming a more significant part of digital systems and can help us live in a more intelligent world. However, for IoT-based data analytics, optimizing communications overhead by balancing the usage of energy and bandwidth resources is a new research challenge. Furthermore, protecting the IoT network’s data from anonymous attackers is critical. As a result, utilizing machine learning, this study proposes a mobile edge computing model with a secured cloud (MEC-Seccloud) for a sustainable Internet of Health Things (IoHT), providing real-time quality of service (QoS) for big data analytics while maintaining the integrity of green technologies. We investigate a reinforcement learning optimization technique to enable sensor interaction by examining metaheuristic methods and optimally transferring health-related information with the interaction of mobile edges. Furthermore, two-phase encryptions are used to guarantee data concealment and to provide secured wireless connectivity with cloud networks. The proposed model has shown considerable performance for various network metrics compared with earlier studies. Full article

The influx of wearable sensor devices has influenced a new paradigm termed wearable health crowd-sensing (WHCS). WHCS enables wearable data collection through active sensing to provide health monitoring to users. Wearable sensing devices capture data and transmit it to the cloud for data processing and analytics. However, data sent to the cloud is vulnerable to on-path attacks. The bandwidth limitation issue is also another major problem during large data transfers. Moreover, the WHCS faces several anonymization issues. In light of this, this article presents a batch processing method to solve the identified issues in WHCS. The proposed batch processing method provides an aggregate authentication and verification approach to resolve bandwidth limitation issues in WHCS. The security of our scheme shows its resistance to forgery and replay attacks, as proved in the random oracle (ROM), while offering anonymity to users. Our performance analysis shows that the proposed scheme achieves a lower computational and communication cost with a reduction in the storage overhead compared to other existing schemes. Finally, the proposed method is more energy-efficient, demonstrating that it is suitable for the WHCS system. Full article

The heterogeneity of the formats and standards of clinical data, which includes both structured, semi-structured, and unstructured data, in addition to the sensitive information contained in them, require the definition of specific approaches that are able to implement methodologies that can permit the extraction of valuable information buried under such data. Although many challenges and issues that have not been fully addressed still exist when this information must be processed and used for further purposes, the most recent techniques based on machine learning and big data analytics can support the information extraction process for the secondary use of clinical data. In particular, these techniques can facilitate the transformation of heterogeneous data into a common standard format. Moreover, they can also be exploited to define anonymization or pseudonymization approaches, respecting the privacy requirements stated in the General Data Protection Regulation, Health Insurance Portability and Accountability Act and other national and regional laws. In fact, compliance with these laws requires that only de-identified clinical and personal data can be processed for secondary analyses, in particular when data is shared or exchanged across different institutions. This work proposes a modular architecture capable of collecting clinical data from heterogeneous sources and transforming them into useful data for secondary uses, such as research, governance, and medical education purposes. The proposed architecture is able to exploit appropriate modules and algorithms, carry out transformations (pseudonymization and standardization) required to use data for the second purposes, as well as provide efficient tools to facilitate the retrieval and analysis processes. Preliminary experimental tests show good accuracy in terms of quantitative evaluations. Full article

Background: Parkinson’s disease (PD) is a chronic neurodegenerative disease that implies a progressive and invalidating functional organic disorder, which continues to evolve till the end of life and causes different mental and physical alterations that influence the quality of life of those affected. Objective: To determine the relationship between motor and nonmotor symptoms and the quality of life of persons with PD. Methods: An analytic, descriptive, cross-sectional study was conducted with patients with different degrees of PD in the Albacete Health district. The estimated sample size required was 155 patients. The instruments used for data collection included a purpose-designed questionnaire and “Parkinson’s Disease Questionnaire” (PDQ-39), which measures eight dimensions and has a global index where a higher score indicates a worse quality of life. A descriptive and bivariate analysis was conducted (SPSS^® IBM 24.0). Ethical aspects: informed consent and anonymized data. Results: A strong correlation was found between the number of motor and nonmotor symptoms and global health-related quality of life and the domains mobility, activities of daily living, emotional well-being, cognitive status, and pain (p < 0.05). Receiving pharmacological treatment and taking more than four medicines per day was significantly associated with a worse quality of life (p < 0.05). Patients who had undergone surgical treatment did not show better global quality of life (p = 0.076). Conclusions: All nonmotor symptoms and polypharmacy were significantly associated with a worse global quality of life. Full article

Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention. Full article

Premarital Sexual Practice (PSP) among adolescents usually involves sexually risky behaviors, such as multiple sexual partners and inconsistent or non-condom use. These behaviors, in combination with other underlining factors, undermine the overall outcomes of Adolescent Sexual and Reproductive Health (ASRH). To assess the adolescents’ knowledge, attitudes and perception of risk and preventive behaviors towards PSP, a school-based analytical cross-sectional study was conducted among a sample of 423 students aged 15 through 19 years. A well-validated anonymous self-administered questionnaire was used for collecting the data, which were analyzed using mean (SD), frequency (%), t-test, ANOVA and multiple regression methods. Participants’ knowledge of risk and preventive behaviors was average, as only 53% of knowledge items were correctly answered. Being a female, of high-income status, in the second study year, perceived susceptibility and perceived severity were significant determinants of knowledge. All measures of perception except perceived self-efficacy were positive determinants of attitude. Being female, in the third study year and of high-income status were determinants of perception as measured by perceived self-efficacy. Therefore, our results suggest that tailored educational programs, with special emphasis on financially disadvantaged male adolescents, are needed to effectively increase adolescents’ knowledge, attitude and perception of risk and protective behaviors towards PSP. Full article