Ranking Research Articles

Development and cultural adaptation of text messages for promoting healthy eating among Amazonian schoolchildren.

To develop text messages for a mHealth intervention promoting healthy eating among Amazonian schoolchildren. Two rounds of expert panel analysis and formative research interviews. Western Brazilian Amazon, Cruzeiro do Sul, Acre, Brazil. Included 17 healthcare professionals and 40 caregivers of school-aged children enrolled in the Maternal and Child Health and Nutrition in Acre birth cohort study. Evaluation focused on messages' cultural appropriateness, comprehensibility, and relevance, along with caregivers' understanding and intention to adhere to the recommendations. Quantitative analysis employed the modified Cohen Kappa, the Scale-Content Validity Index Average (S-CVI-Ave), frequency calculations, and a ranking system. Fifty-five text messages were drafted. In the initial stage, eight messages did not achieve a sufficient score and were subsequently revised and reevaluated, ultimately gaining approval. The S-CVI-Ave for each domain was as follows: cultural appropriateness=0.97, comprehensibility=0.96, and relevance=0.97. Formative research indicated a 92% understanding rate and a 70% intention rate to adhere to the proposed suggestions. The final set of messages was successfully culturally adapted and will contribute to the translation and dissemination of Dietary Guidelines for Brazilian Population recommendations to Western Amazon families.

Holistic Assessment of Social, Environmental and Economic Impacts of Pipe Breaks: The Case Study of Vancouver

This paper presents a holistic assessment framework for the impacts of water distribution pipe breaks to promote environmentally sustainable and socially resilient cities. This framework considers social, environmental, and economic vulnerabilities as well as probabilities associated with pipe failure. The integration of these features provides a comprehensive approach to understanding infrastructure risks. Taking the city of Vancouver as a case study, the social vulnerability index (SVI) is obtained following the application of a cross-correlation matrix and principal component analysis (PCA) to identify the most influential among 33 selected variables from the 2021 census of the Canadian population. The Environmental Vulnerability Index (EVI) is evaluated by considering the park and floodplain areas. The Economic Vulnerability Index (ECI) is derived from the replacement cost of pipes. These indices offer valuable insights into the spatial distribution of vulnerabilities (consequences) across urban areas. Subsequently, the Consequence of Failure (COF) is computed by aggregating the three vulnerabilities with equal weights. Pipe probability of failure (POF) is evaluated by a Weibull model calibrated on real break data as a function of pipe age. This approach enables a dynamic evaluation of pipe deterioration over time. Risk is finally assessed by combining COF and POF for prioritizing pipe replacement and rehabilitation, with the final objective of mitigating the adverse impacts of infrastructure failure. The findings show the significant impact of ethnicity, socioeconomic indices, and education on the social vulnerability index. Moreover, the areas close to English Bay and Fraser River are more environmentally vulnerable. The pipes with high economic vulnerability are primarily concrete pipes, due to their expensive replacement costs. Finally, the risk framework resulting from the vulnerabilities and pipe break probabilities is used to rank the Vancouver City water distribution network pipes. This ranking system highlights critical areas requiring different levels of attention for infrastructure improvements. All the pipes and corresponding risks are illustrated in Vancouver maps, highlighting that the pipes associated with a very high level of risk are mostly in the south and north of Vancouver.

Development of a zoonotic influenza distribution assessment and ranking system (ZIDAR): Technical application in Nepal to support cross-sectoral risk-based surveillance

Development of a zoonotic influenza distribution assessment and ranking system (ZIDAR): Technical application in Nepal to support cross-sectoral risk-based surveillance

Using predictive models to identify kelp refuges in marine protected areas for management prioritization.

Kelp forests serve as the foundation for shallow marine ecosystems in many temperate areas of the world but are under threat from various stressors, including climate change. To better manage these ecosystems now and into the future, understanding the impacts of climate change and identifying potential refuges will help to prioritize management actions. In this study, we use a long-term dataset of observations of kelp percentage cover for two dominant canopy-forming species off the coast of Victoria, Australia: Ecklonia radiata and Phyllospora comosa. These observations were collected across three scuba sampling programs that extend from 1998 to 2019. We then associated those observations with habitat and environmental variables including depth, seafloor structure, wave climate, currents, temperature, and population connectivity in generalized additive mixed-effects models and used these models to develop predictive maps of kelp cover across the Victorian marine protected areas (MPAs). These models were also used to project kelp coverage into the future by replacing wave climate and temperature with future projections (2090, Representative Concentration Pathways [RCPs] 4.5 and 8.5). Once the spatial predictions were compiled, we calculated percent cover change from 1998 to 2019, stability over the same period, and future predicted change in percent cover (2019-2090) to understand the dynamics for each species across the MPAs. We also used the current percentage cover, stability, and future percentage cover to develop a ranking system for classifying the maps into very unlikely refugia, unlikely refugia, neutral, potential refugia, and likely refugia. A management framework was then developed to use those refugia ranking values to inform management actions, and we applied this framework across three case studies: one at the scale of the MPA network and two at the scale of individual MPAs, one where management decisions were the same for both species, and one where the actions were species-specific. This study shows how species distribution models, both contemporary and with future projections, can help to identify potential refugia areas that can be used to prioritize management decisions and future-proof restoration actions.

Evaluation of Graduate Outcomes in Architecture Accreditation Requirements from a Student Perspective

Architectural education encompasses a variety of methods, with the primary goal of maintaining and improving education quality. In this context, the concept of accreditation has emerged as a system that ensures the preservation of certain standards and keeping these standards up to date in higher education. In Türkiye, the Association for Accreditation of Architectural Education (MIAK-MAK) has established certain accreditation requirements for Architectural Bachelor's Programs in 2021. According to these requirements, the targeted graduate profile is defined under the title of "Education and Learning Characteristics" of the program. The subheading "Knowledge, Skills, and Competencies that Graduates Should Acquire" summarizes the knowledge and skill areas that students need to acquire through the courses included in the program's curriculum, which are categorized into five titles. This study aimed to statistically evaluate the importance of the areas created for the knowledge, skills, and competencies that the graduate should gain from the perspective of the active student. The study focuses on the students of the Department of Architecture at KTO Karatay University Faculty of Fine Arts and Design. In this context, a face-to-face survey was conducted with the students, and the survey results were evaluated using the Analytic Hierarchy Process method. By comparing each subheading with the others, a ranking system was created, and their importance levels were determined. The results of student evaluations indicated that especially issues related to life safety, structural systems, sustainability, and global architecture stood out prominently. Studies evaluating accreditation criteria from the student perspective are very limited in the literature. For this reason, as an important approach, this study points out the gap in the field for researchers working on accreditation. This research, which aims to provide a perspective from students, is expected to offer an alternative approach and provide a participatory view in educational research.

Optimizing Performance in Badminton Tournaments: The Relationship Between Timing, Quantity, and Quality Among Professional Players.

Background/Objectives: Top badminton players must carefully schedule tournaments to perform well and improve their rankings. This study examines how players plan their tournament participation and whether their scheduling strategies affect their rankings and performance, especially during Olympic and non-Olympic years. Methods: Data were extracted from the Badminton World Federation (BWF) ranking system for the top 50 men's and women's singles players from May 2014 to May 2019. A computer-based simulation model and cluster analysis were applied to analyze tournament participation patterns, recovery intervals, and performance trends. Statistical analyses were conducted to identify correlations between these variables and ranking outcomes. Results: Top-ranked players participated in more tournaments during Olympic years, with those competing in more than nine tournaments achieving better results. Players performing well in higher-graded tournaments tended to take longer recovery breaks, whereas lower-performing players entered more tournaments. Cluster analysis revealed two distinct performance groups, with the top 20 men's and top 12 women's singles players adopting different strategies compared to lower-ranked counterparts. Conclusions: Strategic scheduling of tournaments, including balancing participation frequency and recovery intervals, is vital for optimizing performance and achieving sustained ranking success. These findings emphasize the importance of high-graded tournament selection and tailored schedules for elite players.

A decision support system for comments-adjusted ranking of hotels

Increasing online reviews provide a powerful support tool to obtain a relevant hotel selection for tourists. Some reviews seem to have similar contents but their latent meanings are discrepant, due to user preferences. It has a negative effect on the recommendation result without a well processing. To solve this issue, this study develops a cloud-based ranking method to provide hotel recommendation lists for tourists. The method uses the distribution of historical user ratings to adjust original user ratings for minimizing the understanding bias among reviews, and utilizes sentiment analysis and cloud models to quantify text comments. After these, both of them are merged into a holistic score to generate a hotel recommendation list. Our method is validated through a case study of 87,735 users and 835,462 reviews from TripAdvisor.com. Further, our research findings suggest that potential hotel stayers should realize to lower their expectations of a good stay from online reviews, and trust more online reviews of couple and friend travelers.

Evaluating Physical Properties of Biochar-Modified Bitumen: An MCDM Approach Using TOPSIS and VIKOR

Bitumen is essential in road construction due to its binding properties, water resistance, and durability, ensuring stable and long-lasting pavements. This study explores the potential of using agricultural waste, specifically biochar, to enhance bituminous materials. Biochar was added to bitumen in increments of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15% by weight to make biochar modified bitumen (BMB). The results showed that biochar increased the softening point, viscosity, and flash and fire points, indicating improved thermal stability and deformation resistance. Conversely, ductility and penetration values decreased, resulting in a harder, less flexible bitumen with better wear and indentation resistance. The modified bitumen was prepared at 160-180°C using a high shear mixer at 4000 rpm for 30 minutes and tested per IS code standards. To identify the optimal biochar-bitumen mix, Multicriteria Decision Making (MCDM) tools such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR were used. These tools provided a comprehensive ranking system to determine the best-performing composition. The study demonstrates that biochar significantly enhances bitumen's properties, making it a viable option for more sustainable and efficient road construction applications.

Journal Fuse Conflator - A Scalable and Secure Approach

The primary objective of the proposed Journal Fuse Conflator is to aggregate and summarize relevant articles based on user- specified keywords or key phrases. Leveraging sophisticated classification and ranking systems, the platform prioritizes news stories based on relevance, significance, and user preferences. It allows users to customize their experience by selecting topics, regions, or categories such as politics, technology, entertainment, or sport and delivers tailored content while filtering out irrelevant stories. By incorporating real-time updates, the system ensures users stay informed about breaking news and ongoing developments.A Journal Fuse Conflator offers a solution by centralizing information from reputable outlets, blogs, social media feeds, and official websites, providing users with a comprehensive and efficient platform for news consumption. This paper introduces the Journal Fuse Conflator, a novel system designed to enhance the way news is aggregated, summarized, and personalized. Keywords: Journal Fuse Conflator, Aggregate, Summarize, Classification, Personalized, Real- time

Heart transplant centers with and without liver transplant programs: Analysis of Scientific Registry of Transplant Recipients metrics

We studied the relationship between heart transplant centers with and without liver transplant programs regarding volume, waitlist duration, waitlist survival, and 1-y posttransplant survival. Scientific Registry of Transplant Recipients data were acquired in July 2023 and represented transplant centers with adult organ transplant volumes in the United States over the past year. This system involved a five-tier ranking system from one to five, where tier one programs had the lowest rating and tier five programs had the highest rating. Among heart transplant centers, there were 37 (29.6%) heart-only centers and 88 (70.4%) heart-liver centers. Median heart transplant volume was greater in heart-liver centers (28 [interquartile range: 18-47]) relative to heart-only centers (10 [2-20]; P<0.001). Median heart waitlist duration rating was higher among heart-liver centers (3[2-4] versus 2 [2-3]; P=0.05). A higher waitlist duration rating was associated with greater annual transplant volume (P<0.001). Waitlist survival rating distributions were similar across heart-only and heart-liver centers (3 [2-4] versus 4 [2-4]; P=0.33). No significant association was observed between heart transplant volume and waitlist survival rating (P=0.52). Median posttransplant survival rating between the two transplant center types was also comparable (3 [2-4] versus 3 [2-4]; P=0.43). A higher posttransplant survival rating was associated with higher transplant volume (P<0.05). Heart transplant centers with concomitant liver transplant programs have a superior waitlist duration rating as well as higher overall transplant volumes when compared with heart-only transplant centers.

Revolution in University Ranking System

Revolution in University Ranking System

Biofidelity Assessment of the GHBMC M50-O Seated in a Honda Accord Seat in a Rear-Facing Configuration during a High-Speed Frontal Impact

&lt;div&gt;Vehicles equipped with automated driving systems (ADS) may have non-traditional seating configurations, such as rear-facing for front-row occupants. The objectives of this study are (1) to generate biomechanical corridors from kinematic data obtained from postmortem human subjects (PMHS) sled tests and (2) to assess the biofidelity of the Global Human Body Models Consortium (GHBMC) 50th male (M50-O) v6.0 seated in an upright (25-deg recline) Honda Accord seat with a fixed D-ring (FDR) in a 56 km/h rear-facing frontal impact. A phase optimization technique was applied to mass-normalized PMHS data for generating corridors. After replicating the experimental boundary conditions in the computational finite element (FE) environment, the performance of the rigidized FE seat model obtained was validated using LSTC Hybrid III FE model simulations and comparison with experiments. The most recent National Highway Traffic Safety Administration (NHTSA) Biofidelity Ranking System (BRS) method was used to assess the biofidelity of the GHBMC M50-O. The occupant response score for GHBMC was 2.00. The average normalized root mean squared deviation (NRMSD) for seat reaction loads in the GHBMC simulation was less than 10%. Peak T-spine accelerations (avg. BRS = 2.28) and anterior-to-posterior (AP) chest deflection (BRS = 2.61) were underestimated. No rib fractures were predicted in the GHBMC using the default failure strain criteria of 1.8%; however, fractures were predicted in the 3rd rib (both left and right sides) using an updated failure strain criteria of 0.52%. Ramping up the seat back, as indicated by pelvis Z-displacement, was underestimated using a coefficient of contact friction of 0.2 (BRS = 3.65) but improved using a coefficient of 0.1 (BRS = 1.44). Local strain hotspots were predicted at the pubic rami locations in the GHBMC, corresponding well with fracture sites in the PMHS.&lt;/div&gt;

Decision Support System For Determining Prospective Students' Department At Vocational School 1 Buer Using Simple Additive Weighting

Choosing a major is essential for students, especially junior high school students, who must choose the appropriate field. Vocational High School (SMK) is a formal education unit that organizes vocational education at the secondary education level as a continuation of junior high school. At SMK Negeri 1 Buer, the central determination system is carried out with a ranking system where the ranking system is carried out based on the order in which prospective students register for the primary major so that it cannot be determined according to the interests and talents of the students. The purpose of this major is so that students can be directed to receive lessons based on their abilities and skills. SMK Negeri 1 Buer has eight majors: Building Modeling and Information Design, Motorcycle Engineering and Business, Visual Communication Design, Light Vehicle Engineering, Welding Engineering, Industrial Electronics Engineering, Animation, and Graphic Engineering. Therefore, this decision support system can make it easier for schools to determine majors according to students' interests and talents. This study uses the Extreme Programming method as a system development method and Simple Additive Weighting for calculating the Decision Support System, using PHP as a programming language with the Codeigniter framework and MySQL database. With this SPK, SMKN 1 Buer can distribute students according to their interests and make it easier for students to choose majors according to their interests

Magnetic and electrical stimulation in complex rehabilitation for myelodysplasia in children: a clinical randomized study

Introduction. Myelodysplasia (MD) in children is accompanied by disorders of the musculoskeletal system and pelvic functions, causing a decrease in activity and restriction of participation in society, leading to disability and contributing to social isolation. The rehabilitation potential, determined by the International Classification of Functioning, Disability and Health (ICF), is realized when the child is active and participates in the processes of life. The improvement of methods and technologies for the rehabilitation of children with MD remains relevant. Aim. To study the effect of medical rehabilitation in children with MD on the development of motor skills in all children and self-care in children over the age of 1 year. Materials and methods. The study included 117 children diagnosed with male and female MD between the ages of 6 months and 13 years, 11 months and 30 days (average age 6 years and 9 ± 6 months). The diagnosis of the functioning of the body was performed in the categorical profile of the ICF using a ranking system for the degree of disorders / limitations of functioning. The subjects are divided into three groups. Patients of group I received physical rehabilitation, ergotherapy, magnetic stimulation of the spinal cord (MS) and electromyostimulation (EMS); group II — physical rehabilitation, ergotherapy and MS; group III — standard physical therapy techniques, massage, ergotherapy, therapy with sinusoidal modulated currents (SMT-therapy) on the muscles of the lower extremities. Results and discussion. The effectiveness of consistent use of MS, EMS during physical exercises for the development of motor skills, classes with an occupational therapist on self-service skills has been demonstrated in children with MD, which is confirmed by positive dynamics in mobility and self-service skills assessed using functional scales and questionnaires. Medical rehabilitation increases muscle strength in children with MMD, and the combined use of MS with EMS during targeted exercises contributes to the effective development of motor functions. The development of motor skills and classes with an occupational therapist increase the level of independence in children with MD. Conclusion. Medical rehabilitation with the inclusion of MS and EMS during the performance of targeted exercises contributes to the better development of motor skills and improves self-care in children with MD.

A novel approach for integrating spatial data in gold prospectivity mapping using a modified TODIM method, a case study: Basiran-Mokhtaran - Eastern Iran

To address the significant challenges posed by varying methodologies across diverse environments, it is imperative to enhance innovative approaches for Mineral Prospectivity Mapping (MPM). A significant issue arises in greenfield regions, where determining the appropriate weights for various geospatial layers in MPM leads to a considerable difficulty. Given the scarcity of known deposits of similar types in these areas, relying on experienced geologists for layer weighting often results in biased outcomes that reflect the subjective views of the experts. This study utilized available geological, airborne geophysical, geochemical, and remote sensing datasets to develop reliable evidence layers for gold exploration, despite the fact that much of the study area is covered by alluvial deposits and has only a few known occurrences of gold and copper. The prediction-area (P-A) method was used to assess the weight of each geospatial layer, independent of the geologist's subjective evaluations. This data-driven approach quantifies the significance of each layer by analyzing the ratio of known deposits to their respective areas. Ultimately, these calculated weights were utilized to create a gold prospectivity map for the region through a modified TODIM and multi-index overlay (MIO) method. The TODIM method, a sophisticated pairwise comparison technique that accommodates preferences for either profit or loss, was refined through subtle adjustments to its calculation algorithm, enhancing its efficiency in managing a multitude of alternatives. In our approach, we employ a classification procedure rather than a ranking system to determine the final outputs of TODIM. To assess the efficacy of the modified TODIM, we utilized the index overlay method as a benchmark for performance evaluation.

A real-time energy and cost efficient vehicle route assignment neural recommender system

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m(m≤n) trips. This new assignment system has been deployed and integrated into the POLARIS.11POLARIS is an Argonne-based high-performance, open-source agent-based modeling framework for simulating large-scale transportation systems. Transportation System Simulation Tool for use in research conducted by the Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).

Production of Xylitol and Ethanol from Agricultural Wastes and Biotransformation of Phenylacetylcarbinol in Deep Eutectic Solvent

Converting agricultural biomass wastes into bio-chemicals can significantly decrease greenhouse gas emissions and foster global initiatives towards mitigating climate change. This study examined the co-production of xylitol and ethanol from xylose and glucose-rich hydrolysates of corn cob (CC), sugarcane bagasse (SCB), and rice straw (RS) without prior detoxification, using C. magnoliae (C. mag), C. tropicalis (C. trop), and C. guilliermondii (C. guil). A score ranking system based on weighted yields and productivity assessed the best raw material and yeast strain combination. The study revealed that C. mag cultivated on RS hemicellulosic and CC cellulosic media exhibited statistically significant (p ≤ 0.05) superiority in xylitol (272 ± 5) and ethanol 273 ± 3, production. The single-phase emulsion system using frozen-thawed whole cells of CC—C. mag, CC—C. trop, and RS—C. guil was utilized for phenylacetylcarbinol (PAC) biotransformation. Although similar PAC concentration within 14.4–14.7 mM was obtained, the statistically significant higher (p ≤ 0.05) volumetric pyruvate decarboxylase (PDC) activity from C. mag at 360 min was observed by 28.3 ± 1.51%. Consequently, further utilization of CC—C. mag in a two-phase emulsion system (Pi buffer: vegetable oil (Vg. oil) and Pi buffer: deep eutectic solvents (DES)) revealed that Pi buffer: DES medium preserved volumetric PDC activity (54.0 ± 1.2%) statistically significant higher (p ≤ 0.05) than the Pi buffer: Vg. oil system (34.3 ± 1.3%), with no statistically significant difference (p &gt; 0.05) in [PAC]. These findings outlined the sustainable pioneering approach for the co-production of chemicals and reusing the residual yeast cells for PAC biotransformation in the Pi buffer: DES system.

Measured or communicated?

AbstractThis research paper explores the intricate relationship between university rankings, mass democracy, and postmodernity, elucidating the societal shift in evaluating academic excellence. As higher education experiences mass democratization, rankings emerge as vital tools in the new information market, providing accessible metrics for an increasingly diverse audience. The concept of “medialization” underscores the impact of rankings on shaping public perception, as journalists, lacking deep understanding of academic intricacies, turn to rankings for clarity. Reactivity within universities to achieve higher ranking placements underscores the internal influence of external media-driven evaluations. The paper argues that postmodern relativism challenges traditional meritocracy, disrupting the established communicative status of universities. Amidst ongoing debates about the legitimacy of rankings, the research recommends embracing a communicative meritocracy as a strategic response, emphasizing the importance of universities actively shaping their narrative. The emergence of AI, exemplified by chatGPT, is identified as a potential transformative force, offering personalized information and challenging traditional ranking methodologies. The research concludes with a call for continuous self-reflection within ranking systems and universities amidst a changing higher education landscape.

A Matching Policy to Address ESG and Non-ESG Risks Impacted by a Relocation Policy in China’s Chemical Industry

China’s chemical industry has faced severe environmental, social, and governance (ESG) issues, such as high safety and environmental accidents and risks. To address these issues and promote industrial upgrading, China’s central government has issued a national relocation and improvement policy targeting its chemical industry. However, its countrywide policy implementation may also lead to other ESG risks during the relocation of chemical enterprises, namely industrial transfer. The typical ESG risks that appear to occur in developed eastern region provinces include a one-size-fits-all solution and unemployment, while less developed central and western region provinces may encounter pollution transfer, carbon leakage, environmental injustice, and health disparities. These ESG risks might overlap with other economic and financial (non-ESG) risks, like stranded assets, industry hollowing-out, and debt sustainability issue. These ESG and non-ESG risks could result from potential mismatches between chemical enterprises and chemical parks, categorized as mismatching errors explained by social-ecological systems, behavioral economics, and information economics. To better manage these risks, we propose an ESG matching policy comprising a national standardized ESG scoring and ranking system, a deferred acceptance mechanism, and a score announcement instrument. Such a policy innovation aims at achieving fair and efficient chemical enterprise–chemical park pairs, which would help manage both ESG and non-ESG risks and provide a just transition toolkit for China and other developing countries.

A Leaf Chlorophyll Content Estimation Method for Populus deltoides (Populus deltoides Marshall) Using Ensembled Feature Selection Framework and Unmanned Aerial Vehicle Hyperspectral Data

Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R2 of 0.848, RMSE of 1.454 μg/cm2 and MAE of 1.121 μg/cm2. Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks.