Abstract Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities...

Comparative insight into negative electrode performance in lead-acid and lead-carbon batteries under high-load and partial state-of-charge cycling profiles
Abstract The resulting cycle performances from our previous studies are compared for operation in 50% depth-of-discharge (DoD) and 17.5% DoD at 50% state-of-charge (SoC) cycling profiles, representing two working modes: high load and partial state-of-charge, respectively. This comparative analysis examines test cells configured with negative electrode as the capacity-limiting component. This comparative analysis, based on prior independent studies, highlights the distinct degradation mechanisms influencing negative electrode sulfation, morphology, and electrochemical behavior between the two cycling regimes. While the 50% DoD profile creates more overall sulfation due to frequent discharge to half of rated capacity, it produces greater evenness in PbSO4 distribution across the electrode cross-sectional area. In contrast, the...

Tunnel-phase α-, β-, and γ-MnO2 for salt removal in hybrid capacitive deionization
Abstract Urchin-like MnO2 polymorphs (α-, β-, and γ-) were successfully synthesized via a hydrothermal method with precise parameter optimization using a factorial design. This study provides the first comprehensive exploration of how tunnel-phase structures influence desalination performance in hybrid capacitive deionization. Among these polymorphs, α-MnO2 exhibited the most uniform urchin-like morphology with larger [2 × 2] tunnel structures, facilitating optimal ion transport and enhanced electrochemical properties. Electrochemical evaluations revealed that α-MnO2 achieved the highest specific capacitance of 148 F/g at 5 mV/s, significantly outperforming β-MnO2 and γ-MnO2. For desalination applications, α-MnO2 demonstrated a record-high NaCl adsorption capacity of 24.4 mg/g at 1.4 V, surpassing the performance of γ-MnO2 (14.5 mg/g) and β- MnO2 (5.5 mg/g). These findings underscore the transformative potential of...

Green synthesis of ω-hydroxydodecanoic acid by engineering C. viswanathii with Cas13d
Abstract ω-Hydroxydodecanoic acid (HDA) is a precursor for producing Nylon 12 but its chemical synthesis is environmentally unfriendly and bacterial production titers are only in the range of mg L−1. Here we developed a novel HDA production platform by engineering the yeast Candida viswanathii that converts dodecane into dodecanedioic acid (DDA), with HDA as a metabolic intermediate. We established the RNA-guided Cas13d system for programmable knockdown of various genes involved in HDA conversion to DDA and proved that repressing these genes enhanced HDA production. Fine-tuning the repression module achieved multiplexed inhibition and increased the HDA titer. Genomic integration of the gene repression...

Surface plasmon-enhanced visible and solar-driven nitric oxide photo-oxidation on Pd-decorated ZnSn(OH)6 perovskite
Abstract This research investigates the enhancement of photocatalytic efficiency for nitric oxide (NO) oxidation under solar irradiation by exploiting the surface plasmon resonance (SPR) effect of palladium nanoparticles (Pd NPs). The SPR effect of Pd NPs extended the light absorption range from 357 nm to 432 nm, facilitated charge separation, and reduced electron-hole recombination, directly enhancing photocatalytic efficiency. Pd NPs were successfully deposited onto ZnSn(OH)6 (ZHS) cubic structures, narrowing the band gap from 3.54 eV to 2.83 eV and significantly boosting photocatalytic activity. The Pd-ZHS composite achieved an NO oxidation efficiency of 59 % under visible light and 77.9 % under solar light, with minimal NO2 production and excellent stability even after five cycles. Notably, the kinetic...

Enhanced photocurrent and responsivity of PbS quantum Dot/ZnO nanoparticle films with amine passivation
Abstract This study investigated a combination of PbS quantum dots (QDs) and ZnO nanoparticles (NPs) layers in photodiodes for photodetection. Oxygen vacancies in ZnO NPs have been known to be recombination trap sites, hindering carrier transportation. We used various amines to passivate the oxygen vacancy of ZnO NPs. It is found that ethanolamine (EA) is the most effective in reducing the surface oxygen vacancies of ZnO, exhibiting a five-fold increase in electron mobility, enhancing PbS QD photodiode responsivity to 278.8 A/W and achieving an external quantum efficiency (EQE) of 36,700% under bias, and increasing the detectivity ∼ 15.5 folds to 8.14 × 10¹² Jones compared with the pure ZnO device. This demonstrates the...

Assessment of radioactivity in concrete grades M300 and M400 with fly ash addition and dose evaluation using the CEN room model
Abstract When fly ash is used in construction materials, it can increase exposure to gamma radiation and radon. This study aimed to evaluate the radioactive concentrations and exposure doses in concrete samples of grades M300 and M400 with added fly ash. We utilized an HPGe spectrometer and the RESRAD-BUILD simulation program for this purpose. The results indicated that the average activities of 226Ra and 232Th were below the reference values, while the average activity of 40K exceeded the UNSCEAR limit. The findings for M300 and M400 were 1.5 and 1.6 times higher, respectively, than the global average for indoor absorbed gamma dose. However, the annual...

Graphitic carbon nitride/carbon coating on negative plate extends lead-acid batteries cycle life
Abstract Lead acid batteries operate more efficiently by increasing hydrogen evolution overpotential. Battery performance is enhanced by adding graphitic carbon nitride (g-C3N4) and carbon black (CB). The CB-g-C3N4 composite material inhibited hydrogen gas evolution by increasing the overpotential of the reaction. The bilayer configuration of g-C3N4 stabilized nitrogen defect sites in a planar arrangement, introducing an additional energy barrier. The 25% and 50% g-C3N4 exhibited elevated overpotential relative to pure carbon material, as elucidated by density functional theory. The research revealed a high rate-partial state of charge (HR-PSoC) cycle life of 58,000 and 47,000 cycles for 25% g-C3N4 and 50% g-C3N4, respectively, at 50% depth of discharge. These findings present an innovative enhancement for lead-acid batteries, specifically for Idle-Stop-Go (ISG) cars, resulting...

A new insight into high-performance NiS2@g-C3N4 anode for lithium-ion batteries – a DFT calculation and ex-situ XPS approach
Abstract Various transition metal sulfides have been considered alternatives for graphite in the anode of lithium-ion batteries. However, poor cycling property caused by polysulfide dissolution and structure devastation hinders their practical applications. In this work, the NiS2 has been coated by a g-C3N4 layer using a facile gas-solid state reaction for a durable-cycling-performance anode for lithium-ion batteries. The composite anode delivered a specific capacity of 972.4 mAh·g−1 after 500 cycles. This improvement was regarded as a result of enhanced charge transfer by the local internal electric field formation and the key role of g-C3N4 in polysulfide hosting and solid electrolyte interphase stabilization, clarifying via ultra-violet electron spectroscopy, density functional theory calculations,...

HGCT: Enhancing temporal knowledge graph reasoning through extrapolated historical fact extraction
Abstract Extrapolation on Temporal Knowledge Graphs (TKGs) poses a critical obstacle, garnering significant attention in the academic sphere due to its far-reaching implications across various domains and areas of study. Predicting upcoming events through the analysis of historical data involves a complex task that requires the integration of structural patterns from historical graph data and temporal dynamics, which has been the focus of various recent research efforts. However, existing methods face significant limitations. Many approaches fail to effectively differentiate the importance of historical knowledge, leading to suboptimal message passing. Others struggle to capture both local and global temporal dependencies simultaneously,...
