International Data Group (Sweden)

This article aims to analyze the data visualizations from the report “Other forms of work”, developed by the PNAD Contínua, emphasizing how reproductive labor (Federici, 2019) is measured and represented in its findings. To this end, a framework was developed based on three axes: (1) Context and Representation; (2) Information Structure; and (3) Data Production. The analysis revealed that the visualizations presented do not convey a critical and argumentative narrative about the phenomenon. Therefore, a non-oppressive, inclusive, and feminist approach is essential in the creation, collection, organization, and dissemination of socially oriented data.

Withdrawing life-sustaining treatment (WLST) in terminally ill patients' presents significant ethical and emotional challenges for health care professionals. This cross-sectional study surveyed 436 health care providers at a teaching hospital in central Taiwan to explore the conflicts and stressors they face during WLST. Key ethical conflicts included unclear definitions of terminal illness, differing family attitudes, and ambiguous responsibility for extubation. Major stressors involved performing extubation, disclosing medical conditions, providing grief support, and navigating institutional procedures. Unmarried, younger, less experienced staff, particularly those without children or working in pediatric or surgical ICUs, were more susceptible to ethical stress ( P <.01). Nurses and respiratory therapists reported more conflict over extubation responsibility, while physicians struggled more with defining terminal illness ( P <.05). The findings highlight the need for clear guidelines, structured family meetings, multidisciplinary coordination, targeted education, emotional support systems, and broader public engagement with advance medical directives to reduce the ethical burden on health care professionals involved in WLST.

This paper presents a new method for discovering Connected Things in a universal way. The method is designed to be used in all kinds of networks, from the smallest sensor networks to the largest interconnected cloud networks. The method allows different technologies to identify entities in different ways. It allows for full or partial identification. It does not rely on collecting information into central repositories, whether such repositories reside on central nodes, or are mirrored on distributed nodes, such as in a distributed ledger of blockchain type or similar. Instead, the method presented relies on local governance of local information and interoperability between nodes across the Internet for finding entities.

Human image datasets used to develop and evaluate technology should represent the variation in human phenotypes, including skin tone. Datasets that include skin tone information frequently rely on manual skin tone ratings based on the Fitzpatrick Skin Type (FST) or the Monk Skin Tone (MST) scales in lieu of the actual measured skin tone of the image dataset subjects. However, perceived skin tone is subject to known perceptual errors and skin tone appearance in digital images can vary substantially depending on the capture camera and environment, confounding manual ratings. Surprisingly, the relationship between skin tone ratings and measured skin tone has not been explored. To close this research gap, we measured the relationship between skin tone ratings from existing scales (FST, MST) and skin tone values measured by a calibrated colorimeter. We also propose and assess a novel Colorimetric Skin Tone (CST) scale developed based on prior colorimetric measurements. Using experiments requiring humans to rate their own skin tone and the skin tone of subjects in images, we show that the new CST scale is more sensitive, consistent, and colorimetrically accurate. While skin tone ratings appeared to correct for some color variation across images, they introduced perceptual errors related to race and other factors. These perceptual errors must be considered before using manual skin-tone ratings in technology evaluations or for engineering decisions.

To quantify mortality trends among female individuals of childbearing age (15-49 years) across global regions from 1990 to 2021, systematically evaluating persistent geographic inequities in preventable deaths. We analyzed data from the GBD (Global Burden of Diseases, Injuries, and Risk Factors Study) 2021, focusing on five leading causes of maternal mortality: hemorrhage, hypertensive disorders, sepsis and infections, obstructed labor and uterine rupture, and abortion and miscarriage. We calculated estimated annual percent change (APC) in age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) for these conditions, stratified by age group and sociodemographic index, to examine temporal trends. Joinpoint regression identified trends and inflection points in age and sociodemographic index stratification. Spearman correlation analysis assessed the relationship between ASIRs and sociodemographic index levels. Globally, an estimated 102,854,299 new incident cases of the five major maternal disorders and 129,331 deaths were reported in 2021. Between 1990 and 2021, the ASIR declined annually by -1.4% (95% CI, -1.5% to -1.3%) (estimated APC), and the ASMR decreased even more substantially at -3.6% (95% CI, -3.7% to 3.4%) per year. Annual declining trends in ASIR and ASMR were observed across all leading causes of maternal mortality. However, regions such as Central Sub-Saharan Africa still reported persistently high rates. Epidemiologic curves revealed peak mortality in the age stratum of 20-24 years, with progressive attenuation to nadir levels in individuals aged 45-49 years. Although abortion or miscarriage became increasingly prevalent as a cause of death worldwide over three decades, maternal hemorrhage remained the leading cause of death, accounting for 35.8% of deaths globally. Higher sociodemographic index correlated with declining ASIR and ASMR trends overall. Notably, sepsis-related mortality increased in younger cohorts, and high-middle-sociodemographic index regions achieved the steepest ASMR and ASIR reductions from 1990 to 2006. Joinpoint regression identified inflection points in incidence and mortality trends in age- and sociodemographic index-stratified populations over time. There was a substantial global reduction in maternal mortality between 1990 and 2021; however, Central Sub-Saharan Africa persists as the most critical regional hotspot. Maternal hemorrhage was the leading cause of death. The Sociodemographic index serves as a robust predictor of mortality, necessitating precision-targeted interventions that prioritize geographic regions with both elevated risk and obstetric complications.

The advent of new video delivery technology has generated speculation about the future of traditional cable services. Some analysts predict a total collapse of the legacy cable model, while others regard the rapid growth of non-linear, Internet-based services merely as a new wrinkle in the already complicated media landscape. Simultaneously, both mainstream media and the industry trade press have reported that traditional cable operators wish to create innovative service offerings that take advantage of the delivery efficiency still provided by the traditional cable plant. Such "blended" services would incorporate the subscriber-fee revenues associated with traditional cable services while also expanding access to newer, more innovative programming platforms associated with broadband delivery, such as Voice-Over-IP Phone Services. However, to date, neither research nor industry has yet developed a viable conceptual framework with which to assimilate the growing range of innovative cable service offerings. Industry progress toward the realization of such blended service models has been both piecemeal and stochastic.

<title>Abstract</title> <bold>Background</bold> People with disabilities are a marginalized group, facing significant socio-economic and public health challenges in low- and middle-income countries like Nigeria. Understanding their characteristics is important for developing effective policies and interventions that promote inclusion and well-being. <bold>Methods</bold> This analysis drawn data from the 2018 Nigeria Demographic and Health Survey, a nationally representative survey that included a disability module. The analytical sample comprised 67,663 household members aged 15 and older. Disability was assessed using the Washington Group Short Set (WGSS) questions. Descriptive analysis was used to determine the prevalence of disability among household members, and a logistic regression model examined the associated socio-demographic factors. <bold>Results</bold> Nearly 12% of Nigerians aged 15 and older living in households have some level of disability, with 2% experiencing severe disabilities. The most prevalent types of disability were visual (8.8%) and mobility-related (3.7%). Disability prevalence varied by state and region, with Imo State reporting the highest rate (26.4%) and Kebbi State the lowest (2.6%). Factors such as those age 65+ (aOR; 23.79, 95% CI: 20.22–27.97), primary (aOR: 1.14, 95% CI: 1.03–1.27) and post-secondary education (aOR: 1.73, 95% CI: 1.59–1.99), lower wealth (first quintile aOR: 0.84, 95% CI: 0.72–0.99), residing in the Southeast (aOR: 1.35, 95% CI: 1.13–1.61) or South-South region (aOR: 1.71, 95% CI: 1.43–2.05), being widowed (aOR: 1.52, 95% CI: 1.36–1.71), living in female-headed households (aOR: 1.18, 95% CI: 1.06–1.33), and household size (aOR: 0.96, 95% CI: 0.95–0.98) were significantly associated with disability. <bold>Conclusion and Implications</bold> The study underscores the necessity of tailored policies and programs to meet the diverse needs of people with disabilities in Nigeria. Interventions should prioritize equitable access to education, healthcare, employment, and public spaces, specifically addressing vulnerable subgroups. To achieve inclusion and well-being, in line with the Sustainable Development Goals, it is essential to address socio-economic disparities, promote inclusive practices, and strengthen support systems.

Big data-driven predictive maintenance is becoming a fundamental component of IIoT systems to enable failure predication proactively and streamline the scheduling process. This work examines the intersection of machine learning, digital twin technology, and optimization techniques in the context of increasing predictive maintenance efficiency and effectiveness. Four algorithms were evaluated via live IIoT sensor reading inputs: Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). The performance outcome indicates that XGBoost achieved the highest in fault detection accuracy at 96.4%, followed by CNN at 94.8%, LSTM at 92.3%, and then Random Forest at 90.1%. A blockchain-based federated learning framework was also utilized to facilitate secure and decentralized predictive maintenance and minimize false alarms by 28% compared to conventional methods. Optimization methods such as Koopman observables and Dynamic Mode Decomposition with Control (DMDc) also enhanced system efficiency, reducing computing cost by 35%. Scalability issues with predictive maintenance in large-scale industries are confirmed as part of this study, as well as edge AI integration and reinforcement learning as probable future trends. These results form the basis of the significance of data-driven predictive maintenance in minimizing downtime, optimizing resource utilization, and facilitating cost-efficient industrial processes.