A Critical Analysis of Quadratic Boost Based High-Gain Converters for Electric Vehicle Applications: A Review.

Conventional DC-DC boost converters have played a vital role in electric vehicle (EVs) powertrains by enabling the necessary voltage to increase to meet the needs of electric motors. However, recent developments in high-gain converters have introduced new possibilities with enhanced voltage amplification capabilities and efficiency. This study discusses and evaluates the state-of-the-art high-gain DC-DC converters for EV applications based on the Quadratic Boost Converter (QBC). Research into innovative topologies has increased in response to the increasing demand for efficient and high-performance power electronic converters in the rapidly expanding EV industry. Due to its ability to provide more significant voltage gains than conventional boost converters, the QBC has become a viable option for meeting the unique requirements of EV power systems. This survey focuses on the efficiency, power density, and overall performance parameters of QBC-based high-gain converters. The literature review provides a foundation for comprehending power electronics converters' trends, challenges, and opportunities. The acquired knowledge can enhance the design and optimization of high-gain converters based on the QBC, thereby fostering more sustainable and efficient power systems for the expanding electric mobility industry. In the future, the report suggests that investigating new high-gain converter design methodologies will reduce component stress and enhance the intact system efficiency.

Computed tomography-based machine learning for donor lung screening before transplantation.

BACKGROUND: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. METHODS: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. RESULTS: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant. CONCLUSIONS: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.

Exploring the potential of dietary factors and plant extracts as chemopreventive agents in oral squamous cell carcinoma treatment.

Oral cancer, particularly oral squamous cell carcinoma (OSCC), is a prevalent malignancy having a significant fatality rate worldwide. Despite advancements in conventional treatment modalities, the overall survival rate for OSCC remains low. Therefore, there is a critical need to explore alternative therapeutic approaches that can improve patient outcomes. This review focuses on the potential of dietary factors and plant extracts as chemopreventive agents in treating oral cancer. These compounds possess diverse biological functions encompassing a range of attributes, such as antioxidative, anti-inflammatory, and anticancer capabilities. By targeting multiple cellular pathways involved in carcinogenesis, they possess the capacity to hinder tumor growth and development, promote programmed cell death, and impede the progression of oral cancer. Signaling pathways targeted by natural compounds that have been included in this review include Akt/mTOR/NF-κB signaling, Hippo-Tafazzin signaling pathway, notch signaling pathway, mitochondrial pathway, and Sonic Hedgehog pathway.

Surface-associated bacterial assemblages on marine anthropogenic litter in the intertidal zone of the Arabian Sea, India.

Anthropogenic marine litter (mainly plastic pollution) is a serious concern globally. The interactions between terrestrial and marine ecosystems lead to the accumulation of marine litter in the intertidal zone. The biofilm-forming bacteria tend to colonize on surfaces of marine litter which are composed of diverse bacteria and are less studied. The present study investigated the bacterial community composition using both culturable and non-culturable (Next-generation sequencing (NGS)) approaches associated with the marine litter (polyethylene (PE), styrofoam (SF) and fabric (FB)) at three distinct locations (Alang, Diu and Sikka) of the Arabian Sea, Gujarat, India. Predominant bacteria observed using culturable and NGS techniques belonged to Proteobacteria phyla. Alphaproteobacteria class dominated on polyethylene and styrofoam surfaces in the culturable fraction among the sites while the Bacillus dominated fabric surfaces. In the metagenomics fraction, Gammaproteobacteria dominated the surfaces except for PE and SF surfaces from Sikka and Diu, respectively. The PE surface at Sikka was dominated by Fusobacteriia while SF surface from Diu was dominated by Alphaproteobacteria. Both culture-dependent and NGS approaches identified hydrocarbon-degrading bacteria as well as pathogenic bacteria on the surfaces. The outcome of the present study illustrates diverse bacterial assemblages which occur on marine litter and increases our understanding of the plastisphere community.

CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.

Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation. Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning , which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.

Dye degradation by early colonizing marine bacteria from the Arabian Sea, India.

Malachite green dye belongs to the triphenylmethane group and is a common environmental pollutant that threatens non-target organisms. We report the potential of the early colonizing marine bacterium Pseudomonas sp. ESPS40 isolated from the Arabian Sea, India, to decolorize malachite green (MG). The bacterium ESPS40 exhibited a higher ability for MG degradation (86-88%) at varying NaCl concentrations (1-3%). The highest MG degradation (~ 88%) was observed at 1% NaCl. The bacterial strain ESPS40 showed degradation up to 800 mg L-1 MG. Further, enzyme activities such as tyrosinase (63.48-526.52 U L-1) and laccase (3.62-28.20 U L-1) were also analyzed with varying concentrations (100 mg L-1-1000 mg L-1) of MG during the degradation process. The dye degradation was confirmed by Fourier transform infrared spectroscopy (FTIR) and high-performance liquid chromatography (HPLC). The outcome of the present study demonstrated Pseudomonas sp. ESPS40 as a potential strain for the efficient degradation of MG at higher concentrations. Thus, Pseudomonas sp. ESPS40 can be utilized as a potential candidate for the biodegradation of MG in wastewater treatment.

Sediment microbial community structure, enzymatic activities and functional gene abundance in the coastal hypersaline habitats.

Salt marsh vegetation, mudflat and salt production are common features in worldwide coastal areas; however, their influence on microbial community composition and structure has been poorly studied and rarely compared. In the present study, microbial community composition (phospholipid fatty acid (PLFA) profiling and 16S rRNA gene sequencing (bacterial and archaeal)) and structure, enzymatic activities and abundance of functional genes in the sediments of salt ponds (crystallizer, condenser and reservoir), mudflat and vegetated mudflat were determined. Enzyme activities (ß-glucosidase, urease and alkaline phosphatase) were considerably decreased in saltpan sediments because of elevated salinity while sediment of vegetated mudflat sediments showed the highest enzyme activities. Concentrations of total microbial biomarker PLFAs (total bacterial, Gram-positive, Gram-negative, fungal and actinomycetes) were the highest in vegetated mudflat sediments and the lowest in crystallizer sediments. Nonmetric-multidimensional scaling (NMDS) analysis of PLFA data revealed that the microbial community of crystallizer, mudflat and vegetated mudflat was significantly different from each other as well as different from condenser and reservoir. The most predominant phyla within the classified bacterial fractions were Proteobacteria followed by Firmicutes, Bacteroidetes and Planctomycetes, while Euryarchaeota and Crenarchaeota phyla dominated the classified archaeal fraction. Cyanobacterial genotypes were the most dominant in the condenser. Mudflat and vegetated mudflat supported a greater abundance of Bacteroidetes and Actinobacteria, respectively. The results of the present study suggest that salt ponds had significantly decreased the microbial and enzyme activities in comparison to mudflat and vegetated mudflat sediments due to very high salinity, ionic concentrations and devoid of vegetation. The present study expands our understanding of microbial resource utilization and adaptations of microorganisms in a hypersaline environment.

Identity effects in social media.

Identity cues appear ubiquitously alongside content in social media today. Some also suggest universal identification, with names and other cues, as a useful deterrent to harmful behaviours online. Unfortunately, we know little about the effects of identity cues on opinions and online behaviours. Here we used a large-scale longitudinal field experiment to estimate the extent to which identity cues affect how people form opinions about and interact with content online. We randomly assigned content produced on a social news aggregation website to 'identified' and 'anonymous' conditions to estimate the causal effect of identity cues on how viewers vote and reply to content. The effects of identity cues were significant and heterogeneous, accounting for between 28% and 61% of the variation in voting associated with commenters' production, reputation and reciprocity. Our results also showed that identity cues cause people to vote on content faster (consistent with heuristic processing) and to vote according to content producers' reputations, production history and reciprocal votes with content viewers. These results provide evidence that rich-get-richer dynamics and inequality in social content evaluation are mediated by identity cues. They also provide insights into the evolution of status in online communities. From a practical perspective, we show via simulation that social platforms may improve content quality by including votes on anonymized content as a ranking signal.

Re-do cytoreductive surgery with hyperthermic intra-peritoneal chemotherapy (HIPEC): Risk factors and complications.

ABSTRACT: An effective multi-modal treatment option for patients with peritoneal surface metastatic malignancies has progressed and developed over the decades as cytoreductive surgery (CRS), and hyperthermic intra-peritoneal chemotherapy (HIPEC) delivers highly concentrated, heated chemotherapy drugs directly to the abdomen during surgery. Peritoneal metastasis and high staging abdominal malignancies were considered incurable and end up with the palliation only; the CRS+HIPEC combination approach increases the median survival rate and gives a better quality of life to these patients. It is a complicated surgery which poses a high rate of complications and challenges which are difficult to manage and requires a multi-disciplinary approach. The aim of this study is to elaborate the perioperative possible physiological changes, risk factors, and related complications after re-do HIPEC.

An appraisal of early stage biofilm-forming bacterial community assemblage and diversity in the Arabian Sea, India.

The community composition and distribution of early-stage (24 h) biofilm-forming bacteria on two different surfaces (glass slide and polystyrene plastic slide) at three different locations (Diu, Alang and Sikka) were studied using a culture-dependent and next-generation sequencing (NGS) approach in the Arabian Sea, Gujarat, India. The most dominant phyla observed using the NGS approach were the Proteobacteria among the sampling sites. Gammaproteobacteria class dominated both the surfaces among the sites and accounted for 46.7% to 89.2% of total abundance. The culture-dependent analysis showed Proteobacteria and Firmicutes as the dominant phyla on the surfaces within the sampling sites. During the initial colonization, hydrocarbon-degrading bacterial strains have also attached to the surfaces. The outcome of this study would be of great importance for targeting the early stage biofilm-forming and hydrocarbon-degrading bacterial isolates may help to degrade plastic in the marine environment.

Ultrasensitive room-temperature piezoresistive transduction in graphene-based nanoelectromechanical systems.

The low mass and high quality factors that nanomechanical resonators exhibit lead to exceptional sensitivity in the frequency domain. This is especially appealing for the design of ultrasensitive force and mass sensors. The sensitivity of a nanomechanical mass and force sensor depends on its mass and quality factor; a low resonator mass and a higher quality factor reduce both the minimum resolvable mass and force. Graphene, a single atomic layer thick membrane is an ideal candidate for nanoelectromechanical resonators due to its extremely low mass and high stiffness. Here, we show that by employing the intrinsic piezoresistivity of graphene to transduce its motion in nanoelectromechanical systems, we approach a force resolution of 16.3 ± 0.8 aN/Hz(1/2) and a minimum detectable mass of 1.41 ± 0.02 zeptograms (10(-21) g) at ambient temperature. Quality factors of the driven response of the order of 10(3) at pressures ∼10(-6) Torr on several devices are also observed. Moreover, we demonstrate this at ambient temperature on chemical-vapor-deposition-grown graphene to allow for scale-up, thus demonstrating its potential for applications requiring exquisite force and mass resolution such as mass spectroscopy and magnetic resonance force microscopy.