Search Results (476)

This study evaluates the operational performance and carbon emissions of an air-source heat pump (ASHP) system based on a one-year field monitoring campaign conducted at a single-family detached house in Gongju, South Korea. The system, equipped with a 9 kW air-to-water ASHP, supplied both space heating (SH) and domestic hot water (DHW), achieving average coefficients of performance (COPs) of 2.27 for SH and 2.06 for DHW. To estimate nominal COPs, a bi-quadratic regression model was developed using manufacturer catalog data and compared against field measurements. The analysis revealed a significant performance decline during winter: a paired t-test using 7119 samples confirmed a statistically significant discrepancy under low-temperature conditions. Annual CO₂-equivalent (CO₂eq) emissions were also evaluated. Under current grid emission factors, the ASHP system emitted 1532 kgCO₂eq—approximately 8.6% more than a condensing gas boiler (1411 kgCO₂eq), primarily due to winter performance degradation and the relatively high carbon intensity of electricity. These findings underscore the importance of incorporating actual operating conditions, seasonal variability, and the national electricity emission factor when assessing ASHP performance and life cycle climate performance (LCCP). Full article

Two strings of equal length are called order-isomorphic if their relative orders are identical at every position. The classical order-preserving pattern matching (OPPM) problem finds all substrings in a text T that are order-isomorphic to a pattern P. However, measurement errors can cause data loss or inaccuracies, making exact pattern detection difficult and motivating the active study of approximate OPPM variants, such as 2-partition OPPM. In this paper, we extend the existing partition-based relaxation of order-isomorphism and define the 3-partition OPPM problem. The 3-partition OPPM problem is to find all substrings in a text T that can be divided into three segments such that each partitioned segment is order-isomorphic to the corresponding segment of a pattern P. We propose an efficient algorithm to solve the problem in $O(nm+m^{2}logm)$ time, where $n=\left|T\right|$ and $m=\left|P\right|$. We conduct experiments on various time series datasets, comparing the number of occurrences and the runtime efficiency among the OPPM, 2-partition OPPM, and proposed 3-partition OPPM algorithms. Our experimental evaluation shows that the proposed algorithm becomes increasingly cost-effective for longer patterns. Full article

Background/Objectives: Astrocytic redox-inflammatory signaling has been implicated in Parkinson’s disease (PD) pathology and may constrain hippocampal neurogenesis. We previously identified an astrocytic NOX4–MPO–OPN axis associated with impaired neurogenic capacity. Here, we tested whether a saffron-derived antioxidant (SDA; Crocus sativus extract) and Passiflora incarnata L. extract (PI) modulate this pathway in an MPTP-induced PD mouse model. Methods: Male C57BL/6J mice were randomized to Sham, MPTP, and treatment groups (n = 9/group for behavior; n = 4–5/group for histology/immunoblotting). SDA or PI (50 mg/kg/day, oral, 5 weeks) was administered, with resveratrol as a positive control. Behavioral, histological, and molecular analyses were performed by investigators blinded to group allocation where feasible. Results: SDA and PI were associated with reduced NOX4/MPO/OPN signals, mainly in GFAP-positive astrocytes, along with recovery of neurogenesis markers (Ki67, DCX, BrdU/NeuN) and synaptic markers (PSD95, synaptophysin), and improved motor performance. Mitochondrial and oxidative injury markers (TIM23, TOM20, OXPHOS subunits; 4-HNE) and apoptotic markers (Bax, cleaved caspase-3, Bcl-2) also shifted toward Sham levels. Given previous reports of Passiflora extracts’ sedative effects, we note that metabolic measures (body weight, food intake, and water intake) were similar across groups; however, specific tests for sedation or arousal were not conducted. Conclusions: These findings offer preclinical evidence that SDA and PI modulate redox-inflammatory and mitochondrial stress signatures and are associated with neurogenic, synaptic, and behavioral improvements in an acute MPTP model. Further validation in chronic/genetic PD models and pharmacokinetic/brain exposure studies will be necessary to confirm their translational potential. Full article

Geranium wilfordii Maxim. is a traditional medicinal plant rich in ellagitannins such as corilagin (CG) and geraniin (GR), which possess antioxidant and anti-inflammatory properties. However, accurate quantification of CG and GR in complex herbal matrices is hindered by co-eluting impurities and poor UV resolution. Here, we developed and validated a simple isocratic HPLC–UV method for their simultaneous determination in G. wilfordii extract. Separation was achieved on a Polaris 3 C18-A column (250 mm × 4.6 mm, 3 µm) using acetonitrile/0.2% formic acid in water (11:89, v/v) with UV detection at 270 nm. The method showed excellent linearity (25–300 µg/mL, R² > 0.995), precision (RSD < 2.7%), accuracy (recovery 99.5–101.2%), and low detection limits (<3 µg/mL). Previous approaches have relied on gradient HPLC or MS-based techniques, often requiring long run times, costly instrumentation, or additional purification (e.g., HSCCC). In contrast, this study demonstrates a validated isocratic method that enables baseline separation and simultaneous quantification of CG and GR in a single run. This robust and simplified analytical strategy provides a practical tool for routine quality control and phytochemical standardization, with potential applications across pharmaceutical, food, and cosmetic industries. Full article

With the global population aging, managing fall risk among older adults has become a critical public health concern. Older adults attending adult daycare centers in South Korea are vulnerable due to poorer physical function and higher walking aid usage compared to community-dwelling counterparts. However, existing studies have primarily targeted community-dwelling older adults and rarely integrated both psychological and physical factors comprehensively. This study aimed to bridge these gaps and enhance fall prevention strategies. A study was conducted with 78 older adults (mean age = 83.24 ± 6.50 years; 82.1% female) from adult daycare centers. Physical assessments and structured interviews were conducted to measure strength, balance, and proprioception. Structured interviews collected demographic data and fear of falling. Significant differences in walking aid use, strength, balance, and proprioception were observed between participants with and without a history of falls (p < 0.05). Logistic regression analysis revealed that knee extension strength (OR = 0.893, 95% CI = 0.819–0.973) and longer timed up and go test times (OR = 1.098, 95% CI = 1.012–1.192) were significant predictors of falls. The combined model achieved an AUC of 0.83 (95% CI = 0.73–0.92), demonstrating good discriminative validity for fall prediction. Given the high fall risk in this population, targeted programs emphasizing lower-limb strengthening and mobility enhancement are essential to reduce fall incidence. Full article

In recent years, the rapidly changing environment and climate have emphasized the need for sustainable development, particularly in the agricultural sector. Tractors are the most widely used machines in agriculture, making their energy efficiency crucial not only for environmental protection but also for reducing farming costs and enhancing economic sustainability. This study applies Yeo–Johnson data transformation to normalize the discretized data of 111 tractor models, enabling the classification of agricultural tractors based on energy efficiency. Tractors were categorized into five classes according to energy efficiency, and the upper limit of each class was used to quantify the rate of improvement in energy efficiency. Furthermore, a comparative analysis between the classification model from 2006 to 2010 and that from 2016 to 2020 demonstrated that the latter exhibits superior energy consumption efficiency. Specifically, the 2016–2020 model showed an improvement in energy efficiency ranging from approximately 20.57% to 54.86% across all power categories, with higher-rated power tractors achieving greater improvements. This comparison confirms that the energy efficiency of tractors in the latest classification model is further improved, reflecting the substantial technological advancements made over the past decade. Full article

Background/Objectives: Vaginitis is a prevalent inflammatory disorder of the vaginal mucosa, frequently arising from its anatomical proximity to the anorectal region and a microenvironment conducive to pathogen colonization and dysbiosis. This prospective, multicenter, randomized, third-party-blinded study assessed the efficacy and safety of a plasma vaginal cleanser (WOMEN CARE^®) employing plasma-activated water (PAW) as a non-pharmacological alternative to conventional antimicrobials for restoring vaginal homeostasis. Methods: Women aged ≥19 years with clinically suspected vaginitis were assigned to either the experimental group (WOMEN CARE^®) or the control group (standard pharmacotherapy). The primary endpoint was the proportion of participants exhibiting decreased Nugent scores between baseline and Visit 4. Results: Of 144 participants in the experimental group, 125 completed the study. The experimental group showed comparable outcomes to standard pharmacotherapy group across Nugent scores, vaginal pH, and symptoms severity, with pathogen suppression confirmed as non-inferior. Additionally, PAW exerted anti-HPV activity through a potential effect against new genotypic HPV infection. While the control group experienced antibiotic-associated adverse effects (e.g., headache, abdominal discomfort, nausea), no treatment-related adverse events occurred in the WOMEN CARE^® group. Conclusions: These results indicate that PAW vaginal cleansing provides an effective, safe, non-antibiotic approach for managing vaginitis and maintaining vaginal ecological balance. Full article

Urban vegetation plays a role as a sink, but accurately estimating carbon storage requires cultivar-specific allometric equations due to variations in growth patterns. This study develops and compares carbon storage models for cultivars of Hibiscus syriacus L.: ‘Wonhwa’ and ‘Chilbo’, ranked first and second in preference in South Korea and most widely planted in urban areas, to address the lack of specific data for these popular varieties. We destructively sampled 106 trees from experimental nurseries in Korea, measuring growth parameters, partitioned biomass, and component-specific carbon content. A non-linear regression equation modeled the relationship between root collar diameter (RCD) and total carbon storage. RCD proved the most effective predictor, resulting in high-performance power-function models (R² = 0.99) for both cultivars: ‘Wonhwa’ (CS = 0.02RCD^2.41) and ‘Chilbo’ (CS = 0.01RCD^2.38). An extra sum-of-squares F-test confirmed a statistically significant difference between the models (p < 0.001). Notably, both cultivars exhibited a branch-dominant allocation pattern (accounting for approximately 50–51% of total biomass), which contrasts significantly with the stem-dominant pattern typically observed in forest-grown trees. The observed inter-cultivar differences indicate that using a single species-level equation can yield inaccurate carbon estimates. Consequently, we recommend that urban managers apply these cultivar-specific equations rather than generic species-level models to minimize estimation uncertainty and support precise carbon inventory management. Full article

Solar power forecasting is important for energy management and grid stability, yet many deep learning studies use a large set of meteorological and time-based variables because of the belief that more inputs improve model performance. In practice, a large feature set can introduce redundancy, increase computational effort, and reduce clarity in model interpretation. This study examines whether dependable forecasting can be achieved using only the most influential variables, presenting a minimal feature deep learning approach for short term prediction of solar power. The objective is to evaluate a Transformer model that uses only two key variables, solar irradiance and soil temperature at a depth of ten centimetres. These variables were identified through feature importance analysis. A real world solar power dataset was used for model development, and performance was compared with RNN, GRU, LSTM, and Transformer models that use the full set of meteorological inputs. The minimal feature Transformer reached a Mean Absolute Error of 1.1325, which is very close to the result of the multivariate Transformer that uses all available inputs. This outcome shows that essential temporal patterns in solar power generation can be captured using only the strongest predictors, supporting the usefulness of reducing the size of the input space. The findings indicate that selective feature reduction can maintain strong predictive performance while lowering complexity, improving clarity, and reducing data requirements. Future work may explore the adaptability of this minimal feature strategy across different regions and environmental conditions. Full article

The accurate monitoring of metal ions is essential for applications that include environmental protection, food safety, and biomedical diagnostics. These areas depend on highly sensitive and selective methods for detecting both toxic and biologically relevant ions. Electrochemical sensors have emerged as promising devices due to their excellent sensitivity, cost-effectiveness, and ease of use. Within these sensor systems, ionophores, either synthetic or natural ligands that exhibit selective ion binding, are fundamental in boosting analytical performance. This review outlines the current progress of ionophore-based electrochemical sensors for metal-ion analysis, emphasizing material selection, architectural strategies, and practical applications. Key classes of ionophores, such as crown ethers, calixarenes, Schiff bases, porphyrins, and oxime derivatives, are discussed with an emphasis on their recognition mechanisms. We also examine strategies for incorporating ionophores into diverse electrochemical sensor configurations and explore recent advances in technologies, such as all-solid-state sensor construction and the development of portable analytical devices. This review bridges the chemistry of ionophores with sensor engineering and serves as a resource for the rational development of advanced platforms for metal-ion sensing. Full article

Rochia nilotica is a tropical Pacific gastropod inhabiting shallow coral reef habitats and supporting important marine resources in Pacific island nations. In this study, we analyzed specimens collected from Weno Island, Chuuk Atoll, Federation States of Micronesia (FSM), using an integrative approach that combined morphological characteristics, molecular phylogenetics (COX1 and 16S rRNA), and complete mitochondrial genome analysis. While the Chuuk population exhibited morphological features consistent with R. nilotica, molecular data revealed substantial genetic divergence. Phylogenetic analyses based on the complete mitochondrial genome (17,664 bp) clustered the Chuuk specimen with Rochia virgata. Phylogenies inferred from concatenated COX1 and 16S rRNA gene sequences yielded congruent topologies, placing the Chuuk lineage within the Rochia clade but clearly separated from other R. nilotica populations in New Caledonia and Mo’orea Island, French Polynesia. This genetic divergence is likely driven by the long-term geographic isolation of Chuuk Atoll. The lagoon’s fringing reefs descend rapidly into waters exceeding 4000 m, which may act as a barrier to restricting larval dispersal. Combined with the extremely short planktonic larval duration of R. nilotica (approximately four days), such environmental isolation may promote the formation of a distinct gene pool. Despite morphological uniformity, the observed genetic divergence suggests that the Chuuk population may represent a cryptic species. Our study provides a complete mitochondrial genome and offers robust phylogenetic framework that provides an understanding of species boundaries within Rochia. These findings underscore the importance of integrating genomic and morphological data for accurate species identification and have implications for conservation and sustainable aquaculture practices in geographically isolated reef ecosystems. Full article

Moss in container seedling nurseries competes with seedlings for water and nutrients while blocking light, thereby inhibiting growth. This study aimed to address this issue by evaluating the moss control efficacy of 11 chemical compounds, including terpinyl acetate (TA), limonene, and Hinoki essential oil (HEO). The plate experiment results led to the selection of 6 substances (TA, limonene, HEO, pine leaf extract, baking soda, pelargonic acid) that stably controlled both Polytrichum commune Hedw. and Marchantia. Polymorpha L. When TA, limonene, and HEO were combined with surfactants, moss control rates increased and showed stable performance. In the container seedling experiment, TA, limonene, and HEO demonstrated high moss control effects while exhibiting low growth inhibition. When these three substances were combined with surfactants, the electrolyte leakage index (ELI) decreased, indicating minimal cell membrane damage. Additionally, TA treatment maintained stable soil physicochemical properties with no significant changes in pH or nutrient levels. Microscopic analysis of moss cells showed cell wall deformation and expansion of intercellular spaces in the three substance treatment groups. Future verification of long-term effectiveness, expansion of application targets, and assessment of economic feasibility could lead to the development of eco-friendly moss removal agents for improving container seedling quality. Full article

Dynamic covalent bonds within polymer materials have been the subject of ongoing research. These bonds impart polymers, particularly thermosets, with capabilities for self-healing and reprocessing. Concurrently, three-dimensional (3D) printing techniques have undergone rapid advancement and widespread adoption. Since polymers are among the primary materials used in 3D printing, networks featuring dynamic covalent bonds have emerged as a prominent research area. This review outlines approaches for incorporating dynamic covalent bonds into polymers suitable for 3D printing and examines representative studies that leverage these chemistries in material design. Polymers produced using these strategies demonstrate both self-healing and reprocessability, primarily via bond-exchange (metathesis) reactions. In addition, we discuss how the type and amount of dynamic bonds in the network affect the resulting material properties, with particular emphasis on their mechanical, physical, and thermal performance. In particular, the introduction of dynamic covalent bonds seems to significantly improve the degree of anisotropy, which has been the limitation of 3D printing techniques. Finally, we compile recent applications for objects printed from polymers that include dynamic covalent bonds. Full article

The decarbonization of shipping and shipbuilding is a critical challenge under the Inter-national Maritime Organization’s (IMO) 2030 greenhouse gas (GHG) reduction target and 2050 net-zero strategy, requiring effective coordination between policy and technology. This study investigates how Japan, China, and Korea respond to these regulatory pressures by systematically analyzing their policy–technology linkages. A four-stage design was applied, combining qualitative case studies, policy–technology mapping, theoretical interpretation, and comparative analysis, to trace how national strategies shape eco-friendly transitions. Japan employs an innovation-led, institution-convergent model in which technological demonstrations drive institutional adaptation and diffusion, China follows a policy-designated, execution-oriented model where state-led interventions accelerate commercialization, and Korea adopts a coordination-based, cyclical model balancing public demonstrations, financial support, and international standardization to reduce transition costs. These findings demonstrate that sequencing between policy–technology linkage is context-dependent, shaped by technological maturity, economic feasibility and infrastructure, institutional predictability, and socio-environmental acceptance. The study contributes a cyclic co-evolutionary perspective that moves beyond technological or institutional determinism, reconceptualizes regulation as enabling infra-structure, and identifies implications for global standard-setting and industrial competitiveness. The insights inform practical strategies for major shipbuilding nations to reduce costs while sustaining competitiveness under the IMO’s decarbonization framework. Full article

Ports serve as key nodes in eco-friendly and digital logistics networks, and the volume of cargo handled continues to increase in response to growing international trade. However, the increased workload within limited spaces heightens the risk of safety accidents, and the number of casualties in port stevedoring operations has continued to rise. As the era of transition toward eco-friendly and digital paradigms unfolds, the adoption of new technologies in ports presents a strategic opportunity to enhance safety management. As of 13 May 2025, the study conducted a text-mining analysis based on research abstracts related to the keyword “New technology and port safety,” in the context of internal and external environmental changes. Specifically, a total of 639 research abstracts were collected, but 138 abstracts, which were unrelated to port safety, were excluded, and 501 abstracts from the Clarivate Web of Science database were analyzed, focusing on 2676 words that appeared at least twice. The study applied Term Frequency (TF) analysis, TF–Inverse Document Frequency analysis, Semantic Network Analysis, and Topic Modeling. The results indicate that Internet of Things emerged as a core solution for strengthening port safety management. However, challenges remain, including the prevention of security breaches, high infrastructure implementation costs, and limitations in battery life. Full article