Contemporary Methods Research Articles

Landslide extraction from aerial imagery considering context association characteristics

Accurate extraction of landslide information is crucial for timely disaster emergency response, yet this process faces significant challenges due to the interference of bare land and vegetation cover as well as the variability in landslide scales. Furthermore, the scarcity of comprehensive landslide datasets has hindered the in-depth exploration of deep learning techniques in this domain. Addressing these issues, this study introduces a novel framework for landslide extraction that leverages the characteristics of context association. In the encoding phase, a two-branch multiscale context feature extraction module (TMCFM) is established, which captures the contextual relationships across different scales through an attention mechanism while concurrently extracting context information within the same scale. To refine the interplay between features at different levels, self-attention is utilized, allowing for the effective fusion of context information at both ends of the same scale through feature fusion exploiting static and dynamic context feature modules (FSDC). Building upon this, this study develops a deeply supervised classifier (DSC) that enhances the network’s discriminative capabilities in the prediction phase via six auxiliary branches. Additionally, this study contributes a new aerial image dataset for landslide extraction created through meticulous visual interpretation. The proposed method is thoroughly compared with 17 contemporary deep learning methods, demonstrating an increase in the intersection-over-union (IoU) metric of 0.92%-16.94% over these methods. The robustness and superiority of the proposed approach are further validated through various discussions and analyses.

A study of different machine learning algorithms for state of charge estimation in lithium‐ion battery pack

AbstractForecasting the state of charge (SOC) using battery control systems is laborious because of their longevity and reliability. Since battery degradation is typically nonlinear, predicting SOC estimation with significantly less degradation is laborious. So, the estimation of SOC is an increasingly major problem in ensuring the effectiveness and safety of the battery. To overcome these issues in SOC estimation, we found many methods in the scientific literature, with differing degrees of precision and intricacy. The SOC of lithium‐ion batteries can now be precisely predicted using supervised learning approaches. Reliable assessment of the SOC of a battery ensures safe operation, extends battery lifespan, and optimizes system performance. This work compares and studies the performance, benefits, and drawbacks of five supervised learning techniques for SOC estimates. Different SOC estimate methods are discussed, including both conventional and contemporary methods. These consist of techniques using voltage and current measurements and more complex algorithms using electrochemical models, impedance spectroscopy, and machine learning methods, incorporating the use of artificial intelligence and machine learning for flexible SOC estimation. In the future, SOC estimates will be a crucial component of a larger ecosystem for energy management, allowing for the seamless integration of energy storage into smart grids and adopting more environmentally friendly energy habits. The five methods we compare are random forest RF, gradient boosting machines, extra tree regressor, XG Boost, and DT. In these five methods, we are going to investigate, review, and discuss the current algorithms and overcome them to select one of the most precise and accurate algorithms to predict the accurate estimation of lithium‐ion battery SOC.

The Roman siege system of Masada: a 3D computerized analysis of a conflict landscape

AbstractThe 1st-c. CE Roman siege system of Masada exhibits a high degree of preservation due to its remote location and the arid climate. However, unlike the thoroughly excavated Masada fortress, the siege system has not received due attention. This article is part of a research project aimed at advancing our understanding of the conflict landscape around Masada using contemporary archaeological methods. Following a comprehensive surface survey and photogrammetric 3D modelling, we show that the circumvallation wall stood to a height of 2–2.5 m and served several functions – as an obstacle, a means of psychological warfare, and a platform from which to mount counterattacks. Based on our measurements and workload estimations, we argue that the construction of the siege wall and the camps around Masada occurred fairly quickly.

Recommendations for the assessment of glomerular filtration rate in patients with cancer: A position statement and systematic review on behalf of the American Society of Onco-Nephrology (ASON).

e24143 Background: Accurate assessment of glomerular filtration rate (GFR) is crucial to guiding drug eligibility, dose-adjusting systemic therapy, and minimizing the risks of both undertreatment and toxicity in patients with cancer. To date, there has been a lack of guidance to standardize approaches to GFR estimation in the cancer population based on the available evidence. We aim to present data on the first systematic review (SR) of GFR estimating equations in patients with cancer. Methods: We conducted a SR on behalf of the American Society of Onco-Nephrology (ASON) on the accuracy of equations to estimate GFR in adult patients with cancer, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We required that studies use a reference methodology as comparator involving measured GFR via urinary or plasma clearance of an exogenous filtration marker or carboplatin. Two search strategies focusing on studies using serum creatinine (Cr) and cystatin C (Cys) were developed. Risk of bias and quality assessment was performed for all studies. Results: A total of 39 studies (21,949 patients in total, 19,025 with solid cancers) were included. Studies assessing populations in Europe were most frequent (50%), followed by Asia (27%) and the remaining from Australia/New Zealand and North and South America. Risk of bias and quality assessment allocated 15 studies (38%) as low or very low quality, and 24 (62%) as low-moderate or moderate quality. The most commonly assessed GFR estimating equations included Cockroft-Gault (32 studies), Modification of Diet Renal Disease (MDRD) (24 studies), Chronic Kidney Disese Epidemiology Collaboration Study (CKD-EPI)Cr (22 studies), Wright (13 studies), CKD-EPICys (8 studies), CKD-EPICr-Cys (8 studies), and Jeliffe (8 studies). Given the importance of accurate and timely eGFR assessment and the evidence supporting the performance (i.e., accuracy) of the CKD-EPI incorporating both Cr and Cys, we suggest the use of this equation in patients with cancer (grade 2C). Where Cys is unavailable, CKD-EPICr is the next preferred equation given the supporting data for its performance and widespread availability. Since included studies were moderate quality or lower, the ASON rated the certainty of evidence as low. Also, additional data on clinical outcomes related to the selection of eGFR equations is needed. We suggest measurement of GFR via an exogenous filtration marker in patients with cancer for whom eGFR results in borderline eligibility for therapies or clinical trials (ungraded). Conclusions: Based on the results of this SR, we suggest use of contemporary methods for estimation of GFR (CKD-EPICr-Cys and CKD-EPICr-Cr) and avoidance of older and less accurate formulae such as Cockcroft-Gault.

A System to Classify Chronic Obstructive Pulmonary Disease using Pre-trained-Densenet201 with TSA

COPD (Chronic obstructive pulmonary disease), ranking as the 3rd most common cause of death worldwide, frequently goes undiagnosed. Yet, the detection of COPD in its early stages is challenging due to the limited presence or mild nature of initial symptoms. In this work, the DL (deep learning) model DenseNet201 is utilized for classifying COPD using the PFT (Pulmonary Function Test) images. Initially, the pre-processing is carried out using the MF (median filter). After the noise elimination process, automated feature extraction and classification is carried out using the Pre-trained-DenseNet201 with TSA (tunicate search algorithm). The presented model provided satisfactory outcomes, attaining the accuracy of 0.985 and an AUC value of 98.73. These results surpass those reported in prior studies utilizing the similar database. Furthermore, the presented approach exhibits superior performance compared to various contemporary methods trained concurrently. This study represents the inaugural application of the Pre-trained-DenseNet201-with TSA model to this specific dataset for the purpose of COPD identification.

EMERGING TRENDS IN CHUARCHITRARANGAL - TRADITIONAL MURAL ART OF KERALA

The south Indian state of Kerala is home to charismatic murals painted between 9th and 18thcentury CE and are proudly known as Chuarchitrarangal. These paintings adorn the walls of Shrikovil – the sanctum sanctorum of Hindu temples all over Kerala that narrates stories of God and Goddesses. Kerala murals have largest collection of secco, based on ancient Indian mythological stories.This study aims to identify, analyse and document the emerging trends in Kerala Mural Paintings. Also, it will make an effort to forecast the future trends. Primary data of renowned artists and secondary data of ancient murals will be referred for the study. 7 temples which hold the best condition murals will be included in the research. The medieval era (9th to 12th century CE) saw the height of this art. The entry of European art into academia in the 19th century, hampered the local art and Kerala murals began to face a significant decrease. Because of the realistic finish of European painting and the use of new convenient oil paints, local traditional art styles were no longer recognized and respected. The Kerala mural tradition had already faded into history by the end of the 1900s. Currently Kerala has a decent number of professional muralists who work extensively in conventional and contemporary methods. This art is being revived steadily. New generation artists are experimenting to keep up with the pace and preferences of new world. Getting inspired from various genres, creating novel styles yet keeping the conventional value intact has become a major trend in Kerala mural art.

EXPLORING THE RELEVANCE OF SRI AUROBINDOS INTEGRAL EDUCATION PHILOSOPHY IN CONTEMPORARY EDUCATIONAL PRACTICES

In todays educational landscape, educators grapple with the challenge of nurturing students for holistic development and lifelong learning amidst a rapidly evolving world. The Integral Education Philosophy of Sri Aurobindo provides significant insights in line with contemporary teaching methods. Sri Aurobindos philosophy, which emphasises the harmonious development of physical, mental, emotional, and spiritual elements, promotes education to help people realise their full potential in order to promote planetary change, social harmony, and personal fulfilment. The current movement towards student-centred learning approaches is in line with core ideas including experiential learning, holistic growth, individualised teaching, multidisciplinary integration, ethical cultivation, and spiritual improvement. This qualitative study examines how Integral Education might be used in contemporary educational settings by examining primary and secondary sources. Results highlight Integral Educations connection with modern educational techniques and clarify its guiding principles.The study also highlights the advantages, difficulties, and real-world applications of incorporating integral education into a range of educational contexts. The implementation of Integral Education presents a number of problems, including resource allocation, curriculum revision, assessment techniques, teacher preparation, cultural sensitivity, socio-political context, and opposition to change, despite its promise of holistic growth and well-being. To overcome these obstacles, careful preparation, teamwork, and on-going assistance are needed. To sum up, Sri Aurobindos Integral Education Philosophy is a revolutionary approach to learning that gives insightful guidance for developing well-rounded people who can successfully navigate a complex and interrelated world in a variety of educational settings.

Statement of Removal

ABSTRACT Based on the qualitative data gathered from the existing literature pertaining to science curriculum with analytical and critical research designs, this article considers the implementation of the science Competence-Based Curriculum (CBC) in Sub-Saharan African (SSA) countries. The study findings reveal that even though the CBC has been introduced in different SSA countries to enhance the quality of education, the science curriculum is still implemented in traditional ways. Consequently, CBC is not effectively addressing socio-economic needs as intended, at both individual and national level. The authors are concerned with how CBC is being implemented in other SSA countries that recently have adopted this kind of curriculum and how it will be implemented in other SSA countries that are planning to embrace it. In this paper, the authors suggest ways the new ideas could be introduced and reflect on how CBC can be implemented in SSA countries that share socio-economic and environmental similarities. They finally strongly recommend the establishment of special ‘laboratory’ schools or science education centres as well as school-based communities of practice to enhance teachers’ content knowledge and nurture contemporary teaching methods for the successful implementation of the new ideas.

The Application of Contemporary Methods According to the Curriculum for the Course of the Albanian Language in Primary Education

The curriculum field "Languages and communication" for primary education enables students to develop simple skills, such as: the difference between literary and non-literary texts, the recognition of the basic language system forms (phonetics, morphology, syntax), the development of expression skills through different linguistic means.This curricular area in primary education, based on the Curricular Framework, contains the following courses: mother tongue, first foreign language (English language, German language). This field also prepares students for functional, clear and substantive communication. Given the importance of using modern teaching methods, we intend to treat this research paper from the point of view of the application of contemporary methods in the Albanian language course, which we consider necessary for the identification of activities that influence the implementation of the curriculum in the course of the Albanian language. In order to carry out the research, we use the qualitative and quantitative method, preparing a questionnaire for the teachers of the Albanian language at the level of primary and lower secondary education, more precisely of grades VI-IX, in the Municipality of Gjilan, Vitia, Kamenica and Ferizaj. We also do a partial observation during the lessons wiin the Albanian Language course. From the findings of the research, we consider that contemporary methods are applied in the teaching of the Albanian language subject in primary education.

Advancing urban water-energy demand predictions with a rotor hopfield neural network model optimized by contracted thermal exchange optimizer

ABSTRACT The relationship between urban water and energy demand is crucial for resource efficiency, sustainability, and environmental conservation. Rapid urbanization, population growth, and climate change necessitate integrated models that capture the complex interdependencies, feedback loops, and trade-offs between water and energy systems. This research addresses this intricate relationship by developing a modified Rotor Hopfield Neural Network (RHNN) Model using input indicators like population data, GDP, water consumption, precipitation, electricity consumption, wastewater discharge, and industrial coal usage. The model is optimized using a modified metaheuristic, called Contracted Thermal Exchange Optimizer (CTEO), resulting in a comprehensive forecast of urban water-energy demand. The model’s superior efficiency is demonstrated by comparing it with other contemporary methods. Upon comparison with alternative approaches, it is clear that the RHNN/CTEO model surpasses them, showcasing a mean relative error of 1.47% for water usage and 2.60% for energy consumption. This leads to an overall average MRE of 2.03%. This research contributes to the existing body of knowledge by offering an advanced model for urban water-energy demand forecasting, providing valuable insights for policymakers, urban planners, and stakeholders in making informed decisions related to resource allocation, infrastructure development, and sustainable urban development.

A Study on Role of Social Media in Modern Recruitment – A Review on Benchmark one

Organizations ability to attract and retain top talent is directly impacted by their recruitment efforts, making it a crucial function. The evolution of recruitment practices is examined in this dissertation, with particular attention paid to the incorporation of digital technologies and the move toward data-driven decision-making. Modern approaches that make use of social media, job portals, and sophisticated applicant tracking systems are either supplementing or replacing traditional methods in increasing numbers. The study looks at how effective these contemporary methods are, evaluating how they affect candidate quality, candidate efficiency, and overall organizational performance. Important issues like preserving data privacy, guaranteeing diversity and inclusivity, and handling the large number of applications are also covered. This research offers recommendations for improving the hiring process in a competitive job market, highlights best practices, and offers insights into current trends and future directions in recruitment through a thorough literature review and empirical analysis.

Tacho-less spur gear condition monitoring at variable speed operation using the adaptive application of variational mode extraction

Effective condition monitoring of machine components contributes to a safer working environment for operators and assists in averting critical machinery shutdowns. In real-world industrial scenarios, fault detection must remain simplified, user-friendly and robust despite speed variations. The operational demands of machinery frequently result in speed fluctuations, leading to spectral smearing, thereby compromising the efficiency of the analysis methodology. Furthermore, the intricacies of parameter initialisation often increase the complexity of the analysis methods. This study introduces a fault diagnosis algorithm that requires minimal initialisation and operates independently of tacho pulses. The algorithm proposed in the study incorporates variational mode extraction with the maxima tracking algorithm for instantaneous frequency estimation. Hankel matrix-based selective spectral fusion is proposed to mitigate the impact of frequency tracking errors caused by transient noise. The results of spectral side-band-based fault severity analysis, conducted on an in-house spur gearbox test-bed with seeded tooth chips, underscore the superior performance of the proposed algorithm when compared to contemporary non-stationary analysis methods.

COMPUTER VISION TECHNOLOGIES FOR HUMAN POSE ESTIMATION IN EXERCISE: ACCURACY AND PRACTICALITY

Information technologies are increasingly being integrated into all aspects of human life. Over the past few years, the use of machine learning models for human pose detection has significantly increased. As the realms of technology and physical activity converge, understanding the potential of these innovations becomes imperative for refining exercise monitoring systems. The aim of the research - evaluate the accuracy and viability of employing modern computer vision technologies in the identification of human pose during physical exercises. The study employed a combination of machine learning methods, video analysis, a review of scientific literature, and methods from mathematical statistics. The precision evaluation of contemporary machine learning models was conducted on a prepared dataset, comprising annotated images featuring students executing a body balance test with the camera positioned directly towards the subjects. The obtained data showed that both MediaPipe and OpenPose models proficiently recognize key anatomical landmarks during the conducted test. The MediaPipe model demonstrates a lower percentage of deviation from manual annotation compared to OpenPose for most key points: the mean deviation exceeds the threshold for 11 out of 15 key points and 7 out of 18 key points, as defined by the OpenPose and MediaPipe models, respectively. The most significant deviations are noticeable in the detection of points corresponding to the foot and wrist. The derived conclusions underscore the models can address only a portion of the tasks set. Essentially, this raises scepticism regarding the practical application of contemporary machine learning methods for human pose estimation without additional refinement.

DIFFERENTIAL DIAGNOSIS OF TUBERCULOSIS AND NON-SPECIFIC PULMONARY INFILTRATS (Lecture)

Following the reformation of healthcare system and the dissolution of the specialty "Phthisiology," the extensive anti-tuberculosis efforts in Ukraine now fall under the purview of family doctors and specialists across various fields. According to the Law of Ukraine “On Overcoming Tuberculosis in Ukraine”, enacted on 14.07.2023 under No. 3269-IX, Paragraph 4 (Article 4), individuals afflicted with tuberculosis or latent tuberculosis infection are entitled to specialized medical care. This includes the treatment of all forms of tuberculosis and associated conditions across a spectrum of healthcare institutions. Compliance with this legal mandate necessitates that doctors of various specialties possess not only proficiency in contemporary tuberculosis treatment methods, which will be facilitated by the regional tuberculosis control center, but also competence in diagnosing tuberculosis in its various presentations. When health issues arise, patients typically seek assistance first from primary care specialists. The “Pulmonary infiltrate syndrome” is an X-ray syndrome characterized by the presence of shadows in the lungs exceeding 1 cm in diameter. There are numerous causes for such changes in the lungs. Given that the lungs are the most common site for tuberculosis, particularly in the form of infiltrative shadows, which may signify not only tuberculosis but also pneumonia, tumors, lung infarction, eosinophilic infiltrate, and other conditions, the question of their differential diagnosis becomes quite relevant. The increasing frequency and subtle clinical presentation of the “Pulmonary infiltrate syndrome” necessitate proficiency in the clinical aspects and diagnosis of its most common manifestations. The key differential diagnostic features that distinguish infiltrative pulmonary tuberculosis from other nonspecific infiltrates include contact with a tuberculosis patient or a history of tuberculosis, often subacute onset, heterogeneous infiltrative shadow in 1, 2, or 6 segments with a connection to the lung root, moderate leukocytosis, lymphopenia, elevated ESR, presence of acid-fast bacilli in sputum or bronchial lavage, specific endobronchitis, positive response to antitubercular therapy, and lack of improvement with other nonspecific treatment methods.

Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality.

Algorithms for the estimation of gaze direction from mobile and video-based eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil. Tracking these features using traditional computer vision techniques can be difficult due to partial occlusion and environmental reflections. Although recent efforts to use machine learning (ML) for pupil tracking have demonstrated superior results when evaluated using standard measures of segmentation performance, little is known of how these networks may affect the quality of the final gaze estimate. This work provides an objective assessment of the impact of several contemporary ML-based methods for eye feature tracking when the subsequent gaze estimate is produced using either feature-based or model-based methods. Metrics include the accuracy and precision of the gaze estimate, as well as drop-out rate.

Hypertension and left ventricular hypertrophy.

In the initial stage, left ventricular hypertrophy (LVH) is adaptive, but in time, it transforms to maladaptive LVH which is specific for the development of various phenotypes that cause heart failure, initially with preserved, but later with reduced left ventricular ejection fraction. Pathophysiological mechanisms, which are characteristic for remodeling procedure, are numerous and extremely complex, and should be subjected to further research with the aim of making a comprehensive overview of hypertensive heart disease (HHD) and discovering new options for preventing and treating HHD. The contemporary methods, such as cardiac magnetic resonance (CMR) and computed tomography (CT) provide very accurate morphological and functional information on HHD. The objective of this review article is to summarize the available scientific information in terms of prevalence, pathophysiology, diagnostics, prevention, contemporary therapeutic options, as well as to present potential therapeutic solutions based on the research of pathological mechanisms which are at the core of HHD.

The impact of transformational leadership on classroom interaction in UAE secondary schools

AbstractDepartment heads play a critical role in executing school plans, particularly in adopting contemporary instructional methods, integrating technology, assessing student progress, and maintaining high standards of classroom interactions. They facilitate essential interactions within the classroom, spanning teacher-student, student–student, and student-content interactions, aligning with transformational leadership practices. This study explores the influence of department heads' transformational leadership on classroom interaction, mediating teachers' teaching experience in enhancing leadership capacity. Using a straightforward random sampling procedure, 374 teachers from 226 privately owned secondary schools were selected. Descriptive statistics were employed to represent the extent of department heads' engagement in transformational leadership practices. Findings indicate that teachers' role in clarifying activities and assignments to encourage classroom involvement received the highest average rating, emphasizing the importance of diverse instructional approaches. The study reveals a significant, positive influence of teachers' years of experience as a moderating factor in the relationship between department heads' transformational leadership and classroom interaction. A positive correlation was observed between student–teacher interactions and department heads' use of transformational leadership practices, with teachers' experience levels shaping these relationships. Notably, the study suggests that teachers' experience partially affects this phenomenon. The research concludes with recommendations for policymakers and educators to leverage their pedagogical expertise in fortifying the impact of school leadership on heightened student participation within the classroom.

Effect of Probiotic Supplements on the Oral Microbiota-A Narrative Review.

Data from systematic reviews and meta-analyses show that probiotics positively impact clinical parameters of oral diseases such as gingivitis, dental caries, and periodontitis. However, the working mechanism of probiotics is not fully understood, but is hypothesized to be mediated by direct and indirect interactions with the oral microbiota and the human host. In the present narrative review, we focused on the microbiological effect of probiotic supplements based on data retrieved from randomized clinical trials (RCTs). In addition, we assessed to what extent contemporary molecular methods have been employed in clinical trials in the field of oral probiotics. Multiple RCTs have been performed studying the potential effect of probiotics on gingivitis, dental caries, and periodontitis, as evaluated by microbial endpoints. In general, results are conflicting, with some studies reporting a positive effect, whereas others are not able to record any effect. Major differences in terms of study designs and sample size, as well as delivery route, frequency, and duration of probiotic consumption, hamper comparison across studies. In addition, most RCTs have been performed with a limited sample size using relatively simple methods for microbial identification, such as culturing, qPCR, and DNA-DNA checkerboard, while high-throughput methods such as 16S sequencing have only been employed in a few studies. Currently, state-of-the-art molecular methods such as metagenomics, metatranscriptomics, and metaproteomics have not yet been used in RCTs in the field of probiotics. The present narrative review revealed that the effect of probiotic supplements on the oral microbiota remains largely uncovered. One important reason is that most RCTs are performed without studying the microbiological effect. To facilitate future systematic reviews and meta-analyses, an internationally agreed core outcome set for the reporting of microbial endpoints in clinical trials would be desirable. Such a standardized collection of outcomes would most likely improve the quality of probiotic research in the oral context.

SSAformer: Spatial–Spectral Aggregation Transformer for Hyperspectral Image Super-Resolution

The hyperspectral image (HSI) distinguishes itself in material identification through its exceptional spectral resolution. However, its spatial resolution is constrained by hardware limitations, prompting the evolution of HSI super-resolution (SR) techniques. Single HSI SR endeavors to reconstruct high-spatial-resolution HSI from low-spatial-resolution inputs, and recent progress in deep learning-based algorithms has significantly advanced the quality of reconstructed images. However, convolutional methods struggle to extract comprehensive spatial and spectral features. Transformer-based models have yet to harness long-range dependencies across both dimensions fully, thus inadequately integrating spatial and spectral data. To solve the above problem, in this paper, we propose a new HSI SR method, SSAformer, which merges the strengths of CNNs and Transformers. It introduces specially designed attention mechanisms for HSI, including spatial and spectral attention modules, and overcomes the previous challenges in extracting and amalgamating spatial and spectral information. Evaluations on benchmark datasets show that SSAformer surpasses contemporary methods in enhancing spatial details and preserving spectral accuracy, underscoring its potential to expand HSI’s utility in various domains, such as environmental monitoring and remote sensing.

Democratising local values and priorities in regional landscape planning: a Danish strategy-making case

This article analyses and discusses how to develop contemporary methods to conduct democratic and sustainable planning in rural landscapes based on place-making, collaborative planning and strategy-making. The analysis is based on a Danish regional strategy-making case, the Nørreådalen river valley (NRV), which is used to explore how to democratically incorporate local values and establish sustainable development priorities. The NRV case highlights the significance of cultivating a shared frame of reference to foster inclusive and informed landscape strategies, which underscores the importance of understanding diverse values and their holders in landscape governance. Collaborative strategy-making may serve as a powerful method for addressing complex challenges and fostering sustainable development in rural landscapes provided it is tailored to the specific contextual considerations and nurtures a comprehensive understanding of shared landscape values.