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This paper proposed a filter for the mitigation of power harmonics based on an integration of filters namely double-tuned plus C-type filter (DTPC). The proposed DTPC filter is mainly aimed to filter the total demand distortion (TDD), as well as the total harmonic distortion (THD), based on IEEE-519 standards. The harmonic filter is presented within the framework of harmonic mitigation as a method of power quality control. Besides, a neural network estimation model to identify harmonic percentage in the power system is also proposed. Two modelling schemes are presented for the simulation of the harmonic filter, which are the load modelling and the source modelling, using the neural network technique. The load modelling is a scheme to predict the current distortion, while the source modelling is a scheme to predict the voltage distortion at the point of common coupling. These two methods may work as standalone tools at the customer's side; thus, it will not interfere with the online operation of the customer's power supply system. The load and source modelling are combined with the DTPC filter in mitigating both the THD and the TDD effects. As a result, the DTPC filter allows customers to maintain a THD and TDD percentage below 10%, and hence customers could meet the IEEE-519 standard.

Approximately 18% of pregnant women are colonized with Group B streptococcus (GBS), which are β-hemolytic, gram-positive bacteria. GBS conversion from the asymptomatic commensal in the vagina to an invasive pathogen predisposes the pregnant women to ascending intrauterine infection that tiggers preterm birth and initiate fetal and neonatal infections. Here, we review the prevalence of colonizing GBS serotypes and sequence types (STs) in different geographical regions. Maternal components including demographical and obstetric factors that increase the risk for GBS colonization during pregnancy are also further elucidated. The prevalence of colonizing GBS serotypes and sequence types (STs) are elucidated in this review in addition to the maternal components including demographical and obstetric factors that increase the risk for GBS colonization during pregnancy. Investigating the epidemiology is crucial for the development of new therapeutic and preventive measures to reduce the burden of invasive GBS disease worldwide including risk-factor based screening protocols.

Vaginal colonization with Group B streptococcus (GBS) or Streptococcus agalactiae can potentially cause ascending intrauterine infection among pregnant women, and hence it is known as one of the risk factors for preterm delivery. Ascending intrauterine infection may also cause the transmission of GBS to the fetus in utero and the newborn during delivery, leading to the development of early onset of neonatal infection. GBS are β-hemolytic, gram-positive bacteria that are opportunistic commensal of the gastrointestinal and urogenital tract of approximately 18% of pregnant women globally. Intrapartum antibiotic prophylaxis (IAP) only reduces the rate of early onset neonatal infection, but not the late onset neonatal infection. Thus, the development of GBS vaccine is thought to be important to decrease the rate of preterm delivery and neonatal infections particularly in low-and-middle income countries where IAP program is not feasible. Vaccination can also be cost-effective for the healthcare system when executed together with IAP program. The aim of the current review is to summarize the mechanisms on how the GBS virulence factors interact with host immune components in the gestational tissues, leading to cervicovaginal colonization and ascending intrauterine infection. The elucidation of these mechanisms is essential for expediting the development of vaccines and novel therapeutic measures targeting these GBS virulence factors that will hamper the vaginal colonization, ascending intrauterine infection and conceptus tissue invasion by GBS. These strategies are crucial to potentially reduce the rate of preterm delivery and subsequent serious complications in the newborn.

The present dataset investigates the factors that influence Malaysian Muslims' Islamic donation intentions. The research model was developed based on the integration of the theory of planned behavior (TPB) and social capital theory with six latent variables. A self-administrated survey of 400 Muslims with various demographic characteristics yielded the dataset in February 2024. The dataset was the basis for identifying factors that influence Muslims' Islamic donation intentions, thereby helping scholars and Islamic non-profit organisations understand how Malaysian Muslims participated in donation activities by interacting with religious commitment, institutional trust, attitude, subjective norms, and perceived behavioral control., (© 2024 The Author(s).)