Usage Patterns Research Articles

Reducing the Energy Consumption of Magnetic Resonance Imaging and Computed Tomography Scanners: Integrating Ecodesign and Sustainable Operations.

This review aims to provide valuable insights into how energy consumption in magnetic resonance imaging (MRI) and computed tomography (CT) scanners can be effectively monitored, managed, and reduced, thereby contributing to more sustainable medical imaging practices. Demand for advanced imaging technologies such as MRI and CT scanners continues to increase, and understanding the resultant impact on greenhouse gas emissions requires a thorough evaluation of their energy consumption. This review examines the energy monitoring and consumption characteristics of MRI and CT scanners, highlighting potential approaches for energy savings. An overview of MRI and CT principles, hardware components, and their associated energy consumption is provided. After addressing the technical aspects, the hardware and software requirements essential for accurate energy metering are detailed. Baseline measurements of energy consumption data are then provided as a foundation to understand current usage patterns and identify areas for improvement. Ongoing efforts to reduce energy consumption are categorized into 3 main strategies: operations, scanner design enhancements, and active scanning techniques, including accelerated MRI protocols. Ultimately, we emphasize that achieving sustainability in medical imaging requires collaboration across disciplines. By incorporating eco-friendly design in new imaging equipment, we can reduce the environmental impact, promote sustainability, and set a health care industry standard for a healthier planet.

User behavior and energy-saving potential of electric washing machines

With the intensification of the global energy crisis and the increase in environmental awareness, energy-saving problems related to household appliances have garnered widespread attention. Here, the usage patterns of electric washing machine users and their energy-saving potential was mainly explored, so as to improve the current situation that the influencing factors of existing research behaviors were not deep enough and the energy saving potential was not specific enough. A questionnaire survey was used to gather information on 20,840 users, including individual characteristics, energy-saving awareness, and usage behavior. The study analyzed the differences in users’ energy-saving awareness and behavior through a series of analysis methods, and evaluated the energy-saving and water-saving potential of electric washing machines. The results showed that user behavior such as washing mode, washing temperature, and the volume ratio of clothes significantly affected on the energy and water consumption of electric washing machines. Individual characteristics of users such as gender, age, educational background, and family income were strongly correlated with their awareness of and decisions made regarding energy conservation. Improving the energy efficiency of electric washing machines and optimizing user purchasing behavior could result in 38,787.54 GWh national energy savings potential, and 6.90 million tons of water-saving potential. This study will help manufacturers and government departments better understand consumers’ usage behavior regarding electric washing machines, which could allow them to modify their market strategies and bolster the promotion and education of energy efficiency labels for electric washing machines. This also could support the nation’s objectives for environmental preservation, water and energy conservation, and the sale of products with lesser energy efficiency.

Old and Middle English adverbs of degree in their wider West Germanic context

Abstract Research on adverbs of degree in Old and Middle English has been largely self-contained and has paid little attention to developments that were happening in the neighbouring West Germanic languages. While research on these other languages is less extensive, “Middle” Germanic has been identified as a period of convergence for the usage of adverbs of degree (Visser 2023). The present study analyses the usage patterns of seven adverbs in both Old and Middle English using data from different corpora: swīðe/swīthe, ful, miċle/muchel, sāre/sǭre, ġearwe/yāre, fela/fę̄le, and hearde/harde. It is found that their development differs strikingly from their Continental West Germanic counterparts, and they appear to preserve more primary usage patterns.

Development of an enhanced base unit generation framework for predicting demand in free‐floating micro‐mobility

AbstractAccurate demand forecasting has become increasingly necessary in the burgeoning field of free‐floating micro‐mobility systems. However, for model training, the service area must be divided into specific areal units, which often involves grid‐based methods. Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. Therefore, a novel base areal unit generation algorithm is proposed that employs a clustering approach to enhance the prediction accuracy in free‐floating micro‐mobility system demand. The method identifies suitable base areal units by merging smaller ones while considering the similarities in temporal usage patterns and distances between different areas, mitigating the impact of MAUP during model learning. The approach was evaluated using shared e‐scooter data from two cities, Kansas City and Minneapolis, and it was compared to the traditional grid method. The findings indicate that the proposed framework generally improves prediction performance within the newly defined areal units.

Analyzing usage patterns from video data through deep learning: The case of an urban park

Rapid urbanization, urban density, and COVID-19 effects have highlighted the need for high-quality urban parks within walking distance. A high-quality urban park maximizes a neighborhood's spatial, safety, and social potential, which are key factors to the well-being of its residents. Most studies evaluating urban parks rely on questionnaires, observations, interviews, and post-occupancy methods. These traditional methods are limited regarding the spatial and temporal dimensions as well as the size of the sample under investigation. In this paper, we demonstrate a new approach to evaluating urban parks by focusing on individuals' activity patterns, using big data extracted from city cameras by utilizing deep learning and computer vision. Our case study is a small urban park, Katznelson Garden, located in Or Yehuda, Israel. The imagery data is analyzed in relation to the gender of the parks' users, along with spatial and temporal analysis. Thus, activities during different hours of the day, days of the week, and in various parts of the urban park are identified. The results of our study revealed that females' and males' activity patterns are different and depend on the hour of the day and the type of park characteristics. Moreover, we found that activity levels and patterns varied according to the day of the week. As many cities seek to design better urban parks tailored to their residents' needs, these study findings can contribute to planning decisions by paving the way to customizing the design of urban parks in accordance with the revealed behavior.

Intravenous immunoglobulin therapy: usage patterns and response to treatment in Qatar over ten years

BackgroundIVIg is a blood-derived antibody product initially designed as a replacement therapy in inborn errors of immunity (IEIs). However, over the last 50 years, IVIg has been used to treat a growing range of autoimmune, autoinflammatory, and secondary immunodeficiency disorders. The US FDA has licensed IVIg for use in the treatment of nine clinical indications; although, IVIg global usage extends to off-label indications with variable treatment responses. Data from Qatar on the use of IVIg is scarce; thus, hampering the formulation of local policies. This study aimed to examine the utilization patterns, clinical indications, and safety profile of IVIg usage in Qatar; a nation with a predominantly young population, and to investigate the response rates to short- and long-term IVIg treatment, as well as explore associations between age at first IVIg dose, clinical indication, and treatment response.MethodsA retrospective chart review was conducted of patients who received IVIg between March 2009, and March 2019, in Hamad General Hospital, Qatar. Demographics, immediate adverse effects of IVIg, and treatment response were collected. IVIg clinical indications were categorized into FDA- and/or EMA-approved, those supported by international guidelines; those approved as second-line therapy, and those with low or no supportive evidence.ResultsIVIg was used for 63 indications during the 10-years. The age of patients skewed towards a younger demographic (median (IQR) 24 (44-6) years); however, no significant differences in response to short- and long-term treatment between age groups were observed. Of the 841 patients, 62% received IVIg in concordance with international recommendations, while 14% bestowed the treatment for indications with low or no supportive evidence. Immediate IVIg adverse effects were documented in 4% of patients in all of the infusions received, with headaches being the most prevalent (1.8%). Variable treatment responses were observed, with the highest recovery reported in immune thrombocytopenic purpura (35%), followed by transverse myelitis (28%).ConclusionThis study provided crucial insights into IVIg utilization, safety, and treatment outcomes in Qatar’s young population. Despite variability in treatment responses and off-label use, adherence to international recommendations remained eminent. Further research is warranted to inform local guidelines and optimize IVIg therapy outcomes.

A Cross-sectional Study on Utilization of Herbal Medications for Anxiety in the Lebanese Population: Usage Patterns and Implications for Harmonizing Mental Health

Background Anxiety is a highly prevalent mental health issue worldwide, but it is also notably prevalent in Lebanon, where herbal medications and plants are commonly used as complementary or alternative treatments to manage anxiety. Lebanese citizens have undergone various disasters, starting with the economic crisis in 2019, the Beirut port explosion in 2020, and the COVID-19 pandemic, which led to several mental disorders such as anxiety, unipolar depression, and insomnia. Objective This study aimed to measure the prevalence and patterns of herbal medications and plant usage for anxiety among the Lebanese population, as well as to explore the factors influencing the decision to use herbal remedies, patient perspectives, and potential implications for mental health support. Methods A cross-sectional study was conducted to assess the prevalence of anxiety and the usage of herbal medications and plants among 501 Lebanese outpatients (General Population) aged 20 years and older at Lebanese community pharmacies within Beirut, Mount Lebanon, South, North, Beqaa. Data was collected through personal face-to-face and online structured interviews and surveys, capturing information on herbal medications and plants usage, sources of recommendations, patient perspectives, and perceived efficacy and safety of herbal medications and plants for anxiety. Results The study revealed that anxiety was a prevalent medical condition among Lebanese outpatients, with more than 50% reporting anxiety through various symptoms. Furthermore, 27.34% of Lebanese patients had consumed herbal medications, with a notable proportion advising others to use herbal remedies for anxiety. The sources of recommendations for herbal medication usage included mass media, pharmacists, and personal reading/internet. Additionally, a considerable percentage of patients expressed beliefs in the effectiveness and safety of herbal remedies for anxiety while also indicating a lack of awareness regarding potential side effects. Conclusion The findings of this study underscore the need for increased awareness, education, and regulatory oversight in Lebanese pharmacists regarding anxiety management using herbal medications and plant-based treatments. This includes public education campaigns, healthcare provider training, and regulatory oversight. Prioritizing patient counseling and further research is essential to assess the efficacy and safety of these treatments within the Lebanese context. This integrative approach, blending traditional practices with evidence-based mental health care, could drive policy changes and improve mental health outcomes in Lebanon.

Efficient Charging Prioritisation and Optimisation of Solar PV-Powered Portable Electronic Devices

Efficiently managing and prioritising the charging of portable electronic devices powered by solar photovoltaic sources in off-grid and resource-limited environments is a huge problem. Ensuring that critical devices maintain operational uptime, especially when energy resources are scarce and in instances where multiple devices compete for charging from the limited solar power available is crucial. This paper introduces an optimisation framework designed to prioritise the charging of portable electronic devices powered by solar photovoltaic sources. The approach aims to maximise operational uptime for critical loads before addressing less essential ones. By strategically allocating charging priorities based on comprehensive evaluations of battery capacities, usage patterns, and operational requirements, the optimisation process seeks to enhance overall efficiency and readiness of portable electronic devices in dynamic, austere and resource-constrained settings. The charging prioritisation problem was solved using MATLAB’s (version number 9.13.0.2193358 (R2022b)) OPTI toolbox in conjuction with the SCIP solver. A case study, involving three portable electronic devices—a cellphone, GPS and radio, demonstrated the model’s effectiveness in maximising satisfaction by aggregating device priorities over time. The model prioritised charging of the GPS due to its critical operational role, followed by the radio for its essential communication function, while the cellphone, with lower usage demands, was assigned the lowest priority. The model developed in this study is versatile and applicable to diverse demand profiles and any number of portable electronic devices. Furthermore, it can be customised to operate effectively in various geographic locations, irrespective of solar radiation levels.

Developing Strategies to Improve Textbook Design Using Synergy of Native and Learner Corpora.

The research aims to enhance the handling of modal verbs (MVs) in EFL textbooks by leveraging perspectives from corpora that include both native speakers' language data and the language data of individuals who are learning the language. To assess the authenticity of language in textbooks, an analysis between the native corpus and a collection of language data compiled from textbook is conducted. The research delves into the developmental patterns of MV usage among learners through a stratified analysis of student essays (Grades 7, 8, 9). Comparisons between learner patterns and a graded textbook series are made to ascertain potential correlations. A novel aspect of the examination explores how the salience and complexity of L2 forms and functions shape the impact of EFL textbooks on learner production. Findings reveal significant differences in the use of MVs between EFL textbooks and the British National Corpus (BNC). Moreover, analysis of student essays indicates a substantial influence of textbooks on learners' MV usage. The study proposes strategies to enhance EFL textbook design, advocating for authenticity and learner-centric approaches. Utilizing native and learner corpora facilitates targeted instruction, addressing common errors and challenges. The incorporation of authentic language examples from the native corpus is recommended to expose learners to real-world language use. This research underscores the significance of integrating native and learner corpora insights in EFL textbook design, ultimately fostering more effective language learning outcomes.

Exploring the Predictors of AI Chatbot Usage Intensity Among Students: Within- and Between-Person Relationships Using the Technology Acceptance Model

The current research investigated the factors associated with the intensity of AI chatbot usage among university students, applying the Technology Acceptance Model (TAM) and its extended version, TAM3. A daily diary study over five days was conducted among univserity students, distinguishing between inter-individual (between-person) and intra-individual (within-person) variations. Multilevel structural equation modeling (SEM) was used to analyze the data. In Study 1 (N = 72), results indicated that AI chatbot anxiety was associated with perceived ease of use (PEOU) and perceived usefulness (PU), which serially mediated the link with AI chatbot usage intensity. Study 2 (N = 153), supported these findings and further explored the roles of facilitating conditions and subjective norm as additional predictors of PEOU and PU. Results from both studies demonstrated that, at the between-person level, students with higher average levels of PEOU and PU reported more intensive AI chatbot usage. In Study 1, the relationship between PEOU and usage intensity was mediated through PU at the within-person level, while the mediation model was not supported in Study 2. Post-hoc comparisons highlighted much higher variability in PEOU and PU in Study 1 compared to Study 2. The results have practical implications for enhancing AI chatbot adoption in educational settings. Emphasizing user-friendly interfaces, reducing AI-related anxiety, providing robust technical support, and leveraging peer influence may enhance the usage intensity of AI chatbots. This study underscores the necessity of considering both stable individual differences and dynamic daily influences to better understand AI chatbot usage patterns among students.

A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction

While dockless bike-sharing systems are growing in popularity due to their convenience, indiscriminate parking creates disorder in cities. To address this problem, this paper proposes a novel prediction-based approach for planning dynamic electric fences. These fences can adapt to dynamic usage patterns and efficiently guide parking behavior. First, graded clustering and spatial point integration algorithms are used to determine the locations of electric fence candidates. Second, initial parking and scheduling simulations are developed to calculate the inventory of each candidate. Finally, a spatiotemporal graph neural network is utilized to predict inventory and generate real-time deployment plans for electric fences. Case studies are conducted in two regions in Shanghai, China. Compared to a non-electric fence scenario, extensive experiments show that our framework can satisfy 98.6% of the parking demand and reduce spatial entropy by 15% within a reasonable walking distance. The results contribute to improving urban orderliness and promoting the sustainability of bike-sharing systems.

The Impact of Social Media on Mental Health: A Comprehensive Review

Introduction: This comprehensive review explores the multifaceted relationship between social media use and mental health, highlighting its positive and negative effects. While social media fosters connectivity and mental health awareness, excessive use has been linked to adverse outcomes such as anxiety, depression, and cyberbullying. It is utilizing theoretical frameworks such as Social Comparison Theory and the personal Model.Objectives: This study examines how curated online interactions influence psychological well-being. Methods: A systematic scoping review was conducted, encompassing recent studies from 2019 onward. Results: Findings reveal that age, developmental stage, and usage patterns significantly moderate the effects of social media on mental health. Emerging concerns include Fear of Missing Out (FOMO), sleep disturbances, and low self-esteem, particularly among adolescents and young adults. Despite its challenges, social media presents opportunities for mental health support through peer networks and online interventions. Conclusions: This review underscores the need for tailored strategies, digital literacy programs, and policy regulations to mitigate risks and maximize social media’s potential for positive mental health outcomes. Future research should focus on longitudinal studies, cross-cultural analyses, and innovative digital interventions to address gaps in understanding the complexities of social media's impact on mental health.

Scaled codon usage similarity index: A comprehensive resource for crop plants

Over the past three decades species-specific codon usage bias has been used to optimize heterologous gene expression in the target host. However, synthesizing codon optimized gene for multiple species is not achievable due to the prohibitive expense of DNA synthesis. To address this challenge, grouping species with similar codon usage can reduce the need for species-specific codon optimised gene synthesis. We introduced Scaled Codon Usage Similarity (SCUS) index to standardize species similarity assessments based on codon usage profiles. By analysing the SCUS index of 77 plant nuclear genomes from 13 families, we identified codon usage patterns and similarities. We developed an online SCUS index database and a Consensus Relative Synonymous Codon Usage (CRSCU) calculator, available at https://pcud.plantcodon.info. The CRSCU calculator helps determine the most suitable codon usage pattern among two or more species. The SCUS index and CRSCU calculator will facilitate the development of multi-species expression systems, enabling the efficient expression of a single synthetic gene across various crop species. This innovation paves the way for cost-effective and efficient heterologous gene expression across diverse crop species

Revolutionizing library systems with advanced automation: A blueprint for efficiency in academic resource management

The advent of advanced automation technologies has the potential to revolutionize library systems, particularly within academic institutions, where effective resource management is essential. This paper outlines a comprehensive blueprint for integrating automation into library operations to enhance efficiency, accessibility, and user engagement. Key technologies discussed include Radio Frequency Identification (RFID), Artificial Intelligence (AI), and Machine Learning (ML), which streamline processes such as cataloging, circulation, and inventory management. The implementation of RFID facilitates self-service checkouts and real-time inventory tracking, significantly reducing the time spent on manual tasks, thereby allowing library staff to concentrate on enhancing user experiences and curating resources strategically. Furthermore, AI-driven chatbots provide personalized recommendations, improving user satisfaction and ensuring that patrons receive tailored support. This blueprint emphasizes the role of automated digital repositories and intelligent content indexing systems, which accelerate cataloging and ensure timely access to scholarly materials. By automating information retrieval processes, libraries can reduce wait times and improve the overall accessibility of resources. Data analytics are also pivotal, enabling libraries to monitor usage patterns, optimize resource allocation, and forecast future demands, ensuring alignment with academic priorities. Moreover, the paper addresses challenges related to automation implementation, including financial constraints, data privacy concerns, and staff training requirements, proposing solutions such as gradual implementation and interdepartmental collaboration. Ultimately, the transition to advanced automation not only enhances operational efficiency but also boosts academic productivity by making resources more accessible and user-centered. This strategic move toward a digital-first library environment signifies a profound transformation in academic libraries, promoting an agile approach to resource management. As institutions increasingly adopt these technologies, they position themselves at the forefront of educational innovation, effectively integrating academic resources into the digital learning landscape.

Digital Dependencies: A Descriptive and Comparative Analysis of Technological Habits and Health Implications among College Students

Abstract—During recent years most of us started to use the smart technologies and electronic gadgets even to fulfill every day-to-day life activities. This will help the human society in a great deal especially in the situation like COVID 2019. During the past five years especially the technology usage grows to a greater extent due to this COVID situation. Even now it extends to maintain the same level of technology usage. It is the right time to decide whether we have to continuously use the smart technologies or not, and it is friutful to discuss whether it has create any health implications to the human society. This research study tries to analyze this fact using the sample students’ data. The results show that there is a little health impact issue due to the usage of technology and a few number of recommendations is given to avoid health implications due to the usage of technology. Index Terms—device usage pattern, health implication, time spent for technology, mobile phones, smart devices

Classification of Drug Usage Patterns and Identification of Diseases in the Provision of Drug Types Using the K-Nearest Neighbors Method

The increasing complexity of healthcare systems highlights the need for data-driven approaches to optimize drug usage patterns and improve disease management. This study employs the K-Nearest Neighbors (KNN) algorithm to analyze correlations between prescribed medications and associated diseases, utilizing a dataset comprising attributes such as patient demographics, drug types, dosages, and treatment frequencies. The results reveal significant trends, including the predominance of "Drug_D" due to its versatility across multiple conditions such as hypertension, diabetes, and cardiovascular diseases. The study also highlights the prevalence of chronic conditions like hypertension and respiratory disorders, underscoring the importance of preventive healthcare and resource allocation. Simplified dosage regimens, predominantly "Once_Daily," were found to enhance patient adherence, aligning with global best practices in chronic disease management. The analysis further emphasizes targeted prescribing practices, with specific drugs strongly correlated to particular diseases, such as "Drug_A" for hypertension and "Drug_B" for respiratory disorders. However, the broad usage of certain medications raises concerns about potential over-reliance, necessitating regular monitoring. These findings demonstrate the value of machine learning in improving healthcare decision-making, enhancing operational efficiency, and supporting evidence-based practices. Future research should expand the dataset to include genetic and lifestyle factors to further refine predictive accuracy and contribute to the advancement of personalized medicine. This study underscores the transformative potential of integrating data mining techniques into healthcare systems to achieve better patient outcomes and more effective resource management.

Understanding the home range characteristics of the first naturally bred pair of crested ibis(Nipponia nippon) released into the natural habitat

BackgroundThe crested ibis, a species that relies on wetland ecosystems for survival, was once found throughout East Asia but has declined to near extinction in Korea, Russia, and Japan, except China. Artificial propagation of seven individuals found in Yangxian, Shaanxi Province, China has resulted in a stable population. Furthermore, South Korea and Japan are working on restoring populations through donations from China. Artificial propagation began in 2008, and in 2019, 40 individuals born between 2014 and 2018 were released into the natural habitat for the first time. We conducted this study to analyze the habitat environment, home range, and habitat usage patterns of a 2016-born male and a 2017-born female who attempted to reproduce naturally for the first time.ResultsAfter forming a breeding pair on April 3, 2020, the pair made two breeding attempts, built a nest in Pinus densiflora, and succeeded in hatching the chicks, but failed to raise them. The home range analysis showed that the area was 1.777–2.425 km² for MCP 100%, and 0.347–2.085 km² for 95% KDE. Meanwhile, the core habitat ranged from 0.007 to 0.296 km² (KDE 50%), indicating differences depending on the time of year and the individual being studied. Breeding pairs were estimated to spend over 50% of their recorded occurrences within 50 m during nesting for incubation, resting, and other activities. They mainly used in paddy fields, but from April to June, when onions and garlic were growing, they searched for food in fields, cemeteries, reservoirs, and other areas.ConclusionBreeding pairs have increasingly become more active near the nest, and Changnyeong-gun, where they were released, has large agricultural land suitable for crested ibis habitat. However, there is a problem that during the breeding season from April to June, most paddy fields are maintained as garlic and onion fields, which are then converted back for rice cultivation from May to June through double-cropping. Accordingly, for stable laying and rearing, it is necessary to contemplate how to maintain rice paddies, which serve as feeding grounds in the core habitats.

Performance Evaluation of Battery Swapping Stations for EVs: A Multi-Method Simulation Approach

This study presents an optimisation framework for operating a battery swapping station (BSS) to enhance efficiency and sustainability in electric vehicle (EV) infrastructure. A hybrid modelling approach combines agent-based discrete event simulation and linear programming to model the dynamic behaviour of batteries and operational processes within the BSS. The model considers factors such as charging speed, battery degradation, grid power constraints, customer behaviour, and range anxiety. The agent-based model simulates the interaction between vehicles, batteries, and the station, capturing the stochastic nature of EVs’ arrivals and battery demand with the discrete event simulation. The linear programming component optimises battery state transitions to minimise degradation and ensure that the demand is met while respecting the power limits of the grid. Different battery types are considered based on vehicle category, each with specific capacity and usage patterns, reflecting real-world market conditions. The results demonstrate that the proposed optimisation framework can effectively manage the complex operational needs of a BSS. The proposed framework effectively balances service quality with resource efficiency by employing a strategic mix of charging modes and inventory management, reducing operational and degradation costs. This approach supports a more sustainable EV infrastructure, highlighting BSS as a viable solution to enhance the efficiency and sustainability of EV operations. Furthermore, the analysis highlights the critical role of power limits in determining charging strategies and their impact on operational efficiency. The findings suggest that with optimised operations, BSS can play a critical role in accelerating the adoption of EVs by offering a faster, more reliable, and sustainable alternative.

Dental Practitioners’ Knowledge and Attitudes Toward the Etiology, Diagnosis, and Treatment of Peri-Implantitis

The objective of this study was to assess the level of knowledge and attitude about the etiology, diagnosis, and management of peri-implantitis among dental practitioners. Methods: An online cross-sectional study on 303 dentists in Saudi Arabia was conducted. A closed-ended survey consisting of 28 questions was designed. Three sections were created, i.e., (1) participants’ characteristics; (2) knowledge of peri-implantitis etiology, risk factors, diagnosis, and complications; and (3) the use of antibiotics to manage peri-implantitis and methods used for diagnosis and treatment. The data were analyzed using mean and percentages. Results: A total of 303 dentists participated in this study with a 78.8% response rate, comprising 80% general dentists and 20% specialists. The majority of dentists demonstrated knowledge about peri-implantitis, with 85.5% identifying bacterial plaque as an etiologic factor and 83.8% recognizing it as an inflammatory reaction. Regarding risk factors, 88.1% identified smoking and 86.1% recognized periodontitis as contributors to peri-implant disease development. In terms of clinical decision making, 63% of participants considered implant mobility as a definitive indication for implant removal, and more than half reported using crater-like bone defects surrounding implants as a diagnostic criterion for peri-implantitis. For antimicrobial therapy, amoxicillin alone (30%) was the most commonly prescribed antibiotic, followed by the combination of amoxicillin with metronidazole (29%). Despite these treatment approaches, it is important to note that currently there are no established, predictable protocols for treating any phase of peri-implant disease. Conclusion: While participating dentists demonstrated a strong understanding of peri-implantitis etiology and risk factors, particularly regarding bacterial plaque and smoking as major contributors, there was considerable variation in antibiotic selection and usage patterns. These findings suggest the need for standardized guidelines and further research to establish evidence-based protocols for managing peri-implant diseases.

Branching out: Material Flow Analysis of Cascading Utilization in Domestic and Imported Wood Products for Strategic Carbon Reduction in South Korea

This study employs a material flow analysis (MFA) to examine the usage patterns of domestic and imported wood resources in South Korea, analyzing the significant differences in utilization, and their impacts on carbon storage. We found a contrast between manufacturing and industrial uses of domestic and imported wood. Imported wood products with high economic value and longevity are primarily used in sustainable construction and manufacturing. In contrast, domestic wood is primarily utilized in lower-value wood-based boards and energy production, with a shorter lifespan and lower carbon storage potential. This study outlined the economic and environmental value chains by wood origin, highlighting their industrial linkages and utilization pathways. Consequently, tailored strategies are essential to promote sustainable wood resource use and advance climate mitigation. Recommendations have been provided for policy instruments to enhance wood resource management sustainability and climate change mitigation.