We aimed to evaluate the effectiveness of ensemble optimal interpolation (EnOI) in improving the analysis of significant wave height (SWH) within wave models using satellite-derived SWH data. Satellite observations revealed higher SWH in mid-latitude regions (30o to 60o in both hemispheres) due to stronger winds, whereas equatorial and coastal areas exhibited lower wave heights, attributed to calmer winds and land interactions. Root mean square error (RMSE) analysis of the control experiment without data assimilation revealed significant discrepancies in high-latitude areas, underscoring the need for enhanced analysis techniques. Data assimilation experiments demonstrated substantial RMSE reductions, particularly in high-latitude regions, underscoring the effectiveness of the technique in enhancing the quality of analysis fields. Sensitivity experiments with varying ensemble sizes showed modest global improvements in analysis fields with larger ensembles. Sensitivity experiments based on different decorrelation length scales demonstrated significant RMSE improvements at larger scales, particularly in the Southern Ocean and Northwest Pacific. However, some areas exhibited slight RMSE increases, suggesting the need for region-specific tuning of assimilation parameters. Reducing the observation error covariance improved analysis quality in certain regions, including the equator, but generally degraded it in others. Rescaling background error covariance (BEC) resulted in overall improvements in analysis fields, though sensitivity to regional variability persisted. These findings underscore the importance of data assimilation, parameter tuning, and BEC rescaling in enhancing the quality and reliability of wave analysis fields, emphasizing the necessity of region-specific adjustments to optimize assimilation performance. These insights are valuable for understanding ocean dynamics, improving navigation, and supporting coastal management practices.

This research paper delves into the effects of noni juice concentration, fermentation temperature, and incubation time on the physicochemical and sensory properties of noni juice-fortified yogurt. The study found that increasing the concentration of noni juice leads to higher acidity in the yogurt, resulting in a decrease in pH, total soluble solids content, and syneresis. The optimal concentration for achieving the desired physical and sensory qualities is 3%. Incubation time and temperature were also found to significantly influence the yogurt’s pH, acidity, total soluble solids content, and syneresis, with higher incubation time and temperature consistently producing higher-quality yogurt. The best incubation time and temperature for noni juice-fortified yogurt were determined to be 10 h at 42oC. Therefore, the research suggests that adding 3% Bestone noni juice and incubating for 10 h at 42oC using the starter culture powder Yogourmet can lead to the production of consistently high-quality noni juice-fortified yogurt, which is of significant relevance and importance to the dairy and fermentation industries.

In the current study, the epoxy material was mixed with 10%, and 30% weight percent carbon material as filler in different thicknesses (1 cm, 1.5 cm, and 2 cm). Transmission electron microscope (TEM) measurements showed the average size of the nano-carbon was 20 nm with a standard deviation of 5 nm. The morphology of samples was examined using scanning electron microscopy (SEM), which showed the flatness of the epoxy surface, and when the content of carbon increases, the connection between the epoxy array and carbon increases. The compression test indicates the effect of nano-size on enhancing the mechanical properties of the studied samples. To survey the shielding properties of the epoxy/carbon composites using gamma-rays emitted from Am-241, Ba-133, Cs-137, Co-60, and Eu-152 sources, which covered a wide range of energies from 0.059 up to 1.408 MeV, the gamma intensity was measured using the NaI (Tl) detector. The linear and mass attenuation coefficients were calculated by obtaining the area under each peak of the energy spectrum observed from Genie 2000 software in the presence and absence of the sample. The experimental results obtained were compared theoretically with XCOM software. The comparison examined the validity of experimental results where the relative division rate ranged between 0.02 and 2%. Also, the measurement of the relative division rate between linear attenuation coefficients of microand nano-composites was found to range from 0.9 to 21% The other shielding parameters are calculated at the same range of energy, such as a half-value layer (HVL), mean free path (MFP), tenth-value layer (TVL), effective atomic number (Zeff), and the buildup factors (EBF and EABF). The data revealed a consistent reduction in the particle size of the shielding material across various weight percentages, resulting in enhanced radiation shielding capabilities. The sample that contains 30% nano-carbon has the lowest values of TVL (29.4 cm) and HVL (8.85 cm); moreover, it has the highest value of the linear attenuation coefficient (LAC), which makes it the best in its ability to attenuate radiation.

The most significant threat to the ecosystem is emerging pollutants, which are becoming worse each year and harming the planet severely and permanently. Many organic and inorganic contaminants are present and persistent due to various world events and population growth. As a result, there is a greater need for new technology and its application to address the problems caused by developing pollutants. Carbon composite nanomaterials have significant potential in the fight against numerous environmental contaminants due to their distinctive attributes. This review discusses the reports of customized carbon composite nanomaterials to meet the need for specific elimination of emerging contaminants. Physical and chemical features such as high surface area, conductivity (thermal and electrical), and vibroelectronic properties, size, shape, porosity, and composite nature are making these tailored materials of carbon-based nanomaterials an emerging and sustainable tool to remove persistent compounds like emerging contaminants in aqueous solution. Different composite materials are well discussed in this review, along with their adsorption efficiency of diverse emerging contaminants, including Bisphenol A, estradiol, metformin, etc. This review provides insight into the recent trends limited to 2017–2023. The limitations of carbon-based nanomaterials, such as regeneration and cost-effectiveness, have also been overcome in recent years by diverse modifications in the production process, which can be further improved to make these materials well suited for an extended group of emerging contaminants.

Because most spent nuclear fuel storage casks have been designed for low burnup fuel, a safety-significant high burnup dry storage cask must be developed for nuclear facilities in Korea to store the increasing high burnup and damaged fuels. More than 20% of fuels generated by PWRs comprise high burnup fuels. This study conducted a structural safety evaluation of the preliminary designs for a high burnup storage cask with 21 spent nuclear fuels and evaluated feasible loading conditions under normal, off-normal, and accident conditions. Two types of metal and concrete storage casks were used in the evaluation. Structural integrity was assessed by comparing load combinations and stress intensity limits under each condition. Evaluation results showed that the storage cask had secured structural integrity as it satisfied the stress intensity limit under normal, off-normal, and accident conditions. These results can be used as baseline data for the detailed design of high burnup storage casks.

As part of the research program “Freshwater Prokaryotic Organisms Research and Discovery,” freshwater samples were collected from the Nakdonggang River. After plating the samples on several culture media and incubating aerobically, approximately 900 bacterial strains were isolated and identified using 16S rRNA gene sequences. Among the bacterial isolates showing higher than 98.7% 16S rRNA gene sequence similarity with those of already confirmed bacterial species previously unreported in Korea, 29 strains were selected. These strains were phylogenetically diverse and belonged to 3 phyla, 6 classes, 13 orders, and 21 genera. At the genus level, these previously unreported species were found to be affiliated with Novosphingobium, Sphingomonas, Polymorphobacter, Croceibacterium, Devosia, Endobacterium, Agaricicola, Bradyrhizobium, Paracoccus, and Pseudotabrizicola of the class Alphaproteobacteria; Undibacterium, Azonexus, and Dechloromonas of the class Betaproteobacteria; Acinetobacter and Budvicia of the class Gammaproteobacteria; Streptomyces, Nocardioides, Mycobacterium, and Cellulomonas of the phylum Actinomycetota; Flavobacterium and Pedobacter of the phylum Bacteroidota. These species were further characterized by examining their Gram reaction, colony and cell morphologies, biochemical properties, and phylogenetic positions. Detailed descriptions of these 29 previously unreported species are provided.

This study introduces and experimentally validates a novel approach that combines Instruction fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning to optimize the performance of Large Language Models (LLMs). These models have become revolutionary tools in natural language processing, showing remarkable performance across diverse application areas. However, optimizing their performance for specific domains necessitates fine-tuning of the base models (FMs), which is often limited by challenges such as data complexity and resource costs. The proposed approach aims to overcome these limitations by enhancing the performance of LLMs, particularly in the analysis precision and efficiency of national Research and Development (R&D) data. The study provides theoretical foundations and technical implementations of Instruction fine-tuning and LoRA fine-tuning. Through rigorous experimental validation, it is demonstrated that the proposed method significantly improves the precision and efficiency of data analysis, outperforming traditional fine-tuning methods. This enhancement is not only beneficial for national R&D data but also suggests potential applicability in various other data-centric domains, such as medical data analysis, financial forecasting, and educational assessments. The findings highlight the method's broad utility and significant contribution to advancing data analysis techniques in specialized knowledge domains, offering new possibilities for leveraging LLMs in complex and resource- intensive tasks. This research underscores the transformative potential of combining Instruction fine-tuning with LoRA fine-tuning to achieve superior performance in diverse applications, paving the way for more efficient and effective utilization of LLMs in both academic and industrial settings.

As part of the KOPIA Paraguay Center rice pilot villages project, conducted from July 1, 2021, to the end of June 2024, this study examined the average yield and value of paddy rice harvested between January and April 2023 in five local rice pilot villages in Paraguay: Santa Rosa, Eusebio Ayala, Coronel Bogado, Santa Maria, and Yaguaron. The CEA-5K-PUNTA variety was planted in late August 2022 and subsequently harvested from January to April 2023. The farmers in the pilot villages received information on common pests, diseases, and weeds, including instructions on timing and the number of applications required for their control. They also received technical guidance on water management and weeding methods. The study found that the average yield per hectare in the pilot villages for the CEA-5K PUNTA variety in the 2022/2023 growing season was 794.50 kg/10a, compared to 477.17 kg/10a for the conventional variety IRGA 424 in the 2019/2020 season, which preceded the project. This indicates a 66.5% increase in yield per hectare for the pilot village farmers compared to the pre-project period (2020/2021). The analysis of paddy rice production value in the pilot villages, using the Difference in Differences method, revealed a 65.1% increase. Moreover, a financial analysis was conducted based on the addition of agricultural inputs to the paddy fields during the period from the end of August 2022 to January-April 2023. The analysis showed that the total cost was US$597.25 per hectare, while the gross income was US$1,685.3 per hectare. As a result, the net profit per hectare amounted to US$1,088.05.

Ischemic stroke is a high mortality disease that causes irreversible damage. Chlorogenic acid is a polyphenolic substance with neuroprotective properties. Bcl-2 family proteins perform a critical role in apoptosis process. Bcl-2 and Bcl-xL are anti-apoptotic proteins that prevent cell death, and Bax and Bad are pro-apoptotic proteins that promote apoptosis. We investigated whether chlorogenic acid modulates Bcl-2 family proteins during focal cerebral ischemia. We made a rat model of ischemic stroke by performing middle cerebral artery occlusion (MCAO). Chlorogenic acid (30 mg/kg) or phosphate-buffered saline was treated via intraperitoneal injection 2 hr before MCAO. Neurological behavioral tests were performed 24 hr after MCAO damage and cortical tissues were collected. Reverse transcription-PCR, Western blot, and immunofluorescence staining were performed to observe changes in Bcl-2 family proteins expression. MCAO-damage induced neurobehavioral disorders and chlorogenic acid alleviate these deficits. Bcl-2 and Bcl-xL expressions were decreased and Bax and Bad expressions were increased in MCAO animals. However, chlorogenic acid treatment attenuated the decrease of Bcl-2 and Bcl-xL and the increase of Bad and Bax due to MCAO surgery. Moreover, chlorogenic acid treatment attenuated MCAO-induced upregulation of caspase-3. These findings suggest that chlorogenic acid exerts neuroprotective effects against MCAO damage by regulating Bcl-2 family proteins including Bcl-2, Bcl-xL, Bax, and Bad.