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Objective Thalassemia, an inherited hemoglobin synthesis disorder, imposes a significant health burden in Asian regions with high prevalence. Detailed patterns and trends of the disease across countries and territories within these regions remain underexplored. Our study focuses on the disease burden indices of thalassemia within the four GBD-defined Asian regions and the twenty-five included countries and territories. It provides insights into the gender-age distribution, temporal changes, and economic aspects of the thalassemia burden. Methods Data on thalassemia prevalence, incidence, mortality, and Disability-Adjusted Life Years (DALYs) were extracted from the Global Burden of Disease (GBD) 2021 study for South, East, Southeast, and high-income Asia regions, encompassing the relevant countries and territories from 1990 to 2021. The Average Annual Percent Change (AAPC) in age-standardized rates of thalassemia was determined to assess temporal trends. Age-gender cohort proportions were considered. The economic aspect of the disease burden and frontier analysis were evaluated using the GBD Socio-Demographic Index and Global Health Expenditure data. Results Southeast Asia exhibited notably high age-standardized mortality rate (ASMR), age-standardized prevalence rate (ASPR), and age-standardized DALYs rate among the four studied Asian regions in 2021. The East Asia region had recorded the highest age-standardized incidence rate (ASIR). A general decline in disease burden indices across the four regions from 1990 to 2021 was evident, with the exception of ASIR in Southeast Asia. The ASMR was highest among pediatric population under five years old, with a significant male preponderance. An unusual increase in ASMR was detected among females of childbearing age and the elderly within the studied region. Further analysis had identified six high-burden countries and territories, particularly those with low-middle Socio-Demographic Index (SDI) rankings and limited health expenditure. Conclusion Although the overall burden of thalassemia has decreased substantially, the disease burden was influenced by gender, age, geography, temporal trends, and economic factors in distinct manners. Based on the current SDI, many countries and regions still have greater improvement potential in the disease burden. There is a necessity for enhanced attention and resource allocation, particularly in low-middle and low SDI countries, with an emphasis on policies that promote early diagnosis and comprehensive care.

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

Chronic disseminated candidiasis (CDC) is an invasive fungal infection typically affecting patients with hematological diseases and severe neutropenia, associated with increased mortality. However, there is a global shortage of clinical evidence on CDC. We retrospectively analyzed clinical data from 49 CDC patients over the past decade. Clinical characteristics of primary hematological diseases, CDC diagnosis, treatment and response evaluations were included. Clinical factors associated with CDC remission and patients’ survival were analyzed. The majority of patients had hematological malignancies (n = 43, 87.8%), and 27 patients (55.1%) had persistent severe neutropenia for more than 10 days prior to CDC. CT scans revealed liver lesions in 44 patients, spleen lesions in 34 patients, and kidney lesions in 9 patients. Proven, probable and possible CDC was diagnosed in 5 (10.2%), 3 (6.1%) and 41 patients (83.7%), respectively, and treatment outcomes at 3 months included 5 complete response (CR, 10.2%), 34 partial response (PR, 69.4%) and 10 treatment failure (20.4%). Caspofungin treatment showed a trend towards improving CR/PR rate, while severe neutropenia > 20 days and proven diagnosis were significantly associated with 3-month treatment failure. Kaplan–Meier curve showed achieving CR/PR within 3 months did not significantly prolong OS compared to treatment failure patients (1197.6 days vs. 564.8 days, P  = 0.074). Additionally, no patient deaths were directly attributed to CDC infection. Age > 45 years old and malignancy non-remission were prognostic factors of overall survival (OS). Furthermore, a prediction model identified severe neutropenia > 20 days, proven/probable diagnosis and concomitant bacteremia as risk factors to effectively predict treatment failure. Also, patients with a risk score < 0.203 in the model exhibited more rapid treatment response. After CDC symptoms onset, lymphocyte levels remained consistently higher in treatment failure patients, while the neutrophil-to-lymphocyte ratio was persistently higher in CR/PR patients. Our findings recommend CT scans for diagnosis and caspofungin as first-line therapy while continuing scheduled chemotherapy or bone marrow transplantation. Notably, risk factors identified by the prediction model could be used to predict treatment response.

Infections caused by carbapenem-resistant (CRPA) are related to higher mortality. The objective of this study was to explore clinical outcomes of CRPA bacteremia, identify risk factors and also, compare the efficacy of traditional and novel antibiotic regimens. This retrospective study was conducted at a blood diseases hospital in China. The study included hematological patients who were diagnosed with CRPA bacteremia between January 2014 and August 2022. The primary endpoint was all-cause mortality at day 30. Secondary endpoints included 7-day and 30-day clinical cure. Multivariable Cox regression analysis was employed to identify mortality-related risk factors. 100 patients infected with CRPA bacteremia were included and 29 patients accepted allogenic-hematopoietic stem cell transplantation. 24 received ceftazidime-avibactam (CAZ-AVI)-based therapy and 76 received other traditional antibiotics. 30-day mortality was 21.0%. Multivariable cox regression analysis showed neutropenia >7 days after bloodstream infections (BSI) (P=0.030, HR: 4.068, 95%CI: 1.146~14.434), higher Pitt bacteremia score (P<0.001, HR:1.824, 95%CI: 1.322~2.517), higher Charlson comorbidity index (P=0.01, HR: 1.613, 95%CI: 1.124~2.315) and bacteremia due to multidrug-resistant (MDR-PA) (P=0.024, HR:3.086, 95%CI: 1.163~8.197) were identified as independent risk factors of 30-day mortality. After controlling for confounders, an additional multivariable cox regression analysis revealed definitive regimens containing CAZ-AVI were associated with lower mortality in CRPA bacteremia (P=0.016, HR: 0.150, 95%CI: 0.032~0.702), as well as in MDR-PA bacteremia (P=0.019, HR: 0.119, 95%CI: 0.020~0.709). For patients with hematological diseases and CRPA bacteremia, 30-day mortality rate was 21.0% (21/100). Neutropenia >7 days after BSI, higher Pitt bacteremia score, higher Charlson comorbidity index and bacteremia due to MDR-PA increased 30-day mortality. CAZ-AVI-based regimens were effective alternatives for bacteremia due to CRPA or MDR-PA.

Fine-grained occupancy information plays an essential role for various emerging applications in smart homes, such as personalized thermal comfort control and human behavior analysis. Existing occupancy sensors, such as passive infrared (PIR) sensors generally provide limited coarse information such as motion. However, the detection of fine-grained occupancy information such as stationary presence, posture, identification, and activity tracking can be enabled with the advance of sensor technologies. Among these, infrared sensing is a low-cost, device-free, and privacy-preserving choice that detects the fluctuation (PIR sensors) or the thermal profiles (thermopile array sensors) from objects' infrared radiation. This work focuses on developing data processing models towards fine-grained occupancy sensing using the synchronized low-energy electronically chopped PIR (SLEEPIR) sensor or the thermopile array sensors. The main contributions of this dissertation include: (1) creating and validating the mathematical model of the SLEEPIR sensor output towards stationary occupancy detection; (2) developing the SLEEPIR detection algorithm using statistical features and long-short term memory (LSTM) deep learning; (3) building machine learning framework for posture detection and activity tracking using thermopile array sensors; and (4) creating convolutional neural network (CNN) models for facing direction detection and identification using thermopile array sensors.

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.