Ranking Of Factors Research Articles

Soil organic carbon and total nitrogen stocks related to land use and basic environmental properties − assessment of soil carbon sequestration potential in different ecosystems

Soil organic carbon (SOC) and total nitrogen (TN) stocks are crucial for the development of wild plants and crops. This paper examines the current SOC and TN stocks and their variability within land uses under different environments, assessing the relationships between SOC and TN stocks with basic environmental properties and quantifying the magnitude of SOC sequestration potential for a better land use management. We studied 991 soil profiles, from steppe to wet mountain-soils. The land use was essential in influencing soil organic C and total N stocking, with the forestland showing the significantly highest SOC stocks specifically in mountain soils, followed by grassland and cropland. Altitude, clay content, pH and plant available phosphorous and potassium were other influencers of SOC and TN stocks. The best predictive multiple linear regression model explained 68 % of the 0.5 m depth SOC stock variability for forest, 61 % for grassland and 37 % for cropland, while Random Forest model explained 70 %, 65 %, and 28 % for the same land uses. The obtained models rank factors contribution and may be useful in management. Lands having the highest C sequestration potential occurred within fine-textured soils, mainly in croplands. The most favorable soil depth for further C sequestration is below C-saturated topsoil and this could be achieved by deep-rooting crops and conservative technologies. Additionally, changing some low-fertile soils of cropland into forestland or grassland would improve SOC sequestration. These measures might contribute to sequester additional C amounts in soils, in order to bolster initiatives for climate-change mitigation and adaptation.

Building patient safety culture by using interprofessional simulation with nursing, paramedic and emergency telecommunication students: A mixed-methods research study

AimThe aim of this study was to investigate student attitudes toward interprofessional education and collaborative practice and their perceptions of simulation design elements after participating in an interprofessional simulation educational activity. BackgroundPatient safety continues to be an international healthcare priority. However, critical incidents to patients continue to persist due to poor communication between professionals and poor teamwork. DesignA mixed methods design. MethodThe study sample consisted of Bachelor of Nursing (n=81), Paramedic (n=38) and Emergency Telecommunication (n=11) students randomized into interprofessional groups. Following the interprofessional learning experience, students completed two standardized self-reporting instruments, Students Perceptions of Interprofessional Clinical Education, National League for Nursing Simulation Design Scale and open-ended questions of their perceptions on teamwork and collaboration, communication skills, transfer of care and patient safety. ResultsStudents in each program reported high scores for attitudes toward the importance of teamwork and collaborative practice. There were significant differences in scores between programs for the ranking factors ‘Teamwork and Team-Based Practice’ (BN=4.74SD 0.72, PM=4.66SD 0.81, ET=4.95SD 0.15 *; p = 0.00005) and ‘Roles & Responsibilities’ (BN=3.78SD 1.12, PM=3.55SD 0.92, ET=4.67SD 0.85*; p= 0.012). Themes included: communication and collaboration in teamwork; roles, responsibilities and leadership; patient-centred care and safety; and emotional dynamics and professional growth. ConclusionAn interprofessional simulation is an effective strategy to practice teamwork and collaboration and communication skills during transfer of care. Ensuring earlier education of roles and responsibilities during transfer of care needs review as do strategies for structured handover communication tools.

The Key Factors for Improving Returns Management in E-Commerce in Indonesia from Customers’ Perspectives—An Analytic Hierarchy Process Approach

The rapid growth of e-commerce has led to an increase in the number of product returns in supply chains, which is both environmentally and economically challenging. E-commerce companies need to effectively manage product returns, as this has a direct impact on their reputation and consumer experience. Reducing returns is key to maintaining sustainable practices for online product sales. A significant increase in e-commerce transactions is also evident in Indonesia, which is the fourth largest country in the world. Despite the very large size of the market, research on e-commerce in the business-to-customer (B2C) market in Indonesia is underrepresented in the literature. The purpose of this paper is to identify key factors from the customer perspective that influence product returns in reverse logistics in Indonesian e-commerce. The novelty of this study stems from the focus on the customer perspective on product returns in the B2C market when shopping online and the spatial scope. Due to the uncertainty inherent in multi-criteria decision making, the analytic hierarchy process (AHP) method was used to rank factors and potential solutions derived from a critical literature review. As a result, the study provides a ranking of factors and alternatives for managing e-commerce returns in Indonesia. The results show that among Indonesian e-commerce customers, product quality (QP) was rated the highest, while (Pu) was rated the lowest. In terms of the alternatives that are the most suitable for improving the customer experience of e-commerce product returns in Indonesia, a clear returns policy (CRP) scored the highest, while the merchandise catalog (Cat) was rated as the lowest priority.

TF-IDF combined rank factor Naive Bayesian algorithm for intelligent language classification recommendation systems

With the continuous improvement of smart language systems, a large amount of language text data has emerged. How to efficiently and accurately process these text data has become an important challenge. Therefore, a speech classification recommendation system based on improved Naive Bayesian algorithm is proposed. The system first adopts the traditional Bayesian algorithm to classify language texts. The Term Frequency-Inverse Document Frequency and rank factor are combined to increase the weight of feature languages. Then, the classified language texts are combined with the improved algorithm for language classification recommendation. Finally, performance testing and simulation applications are conducted on the system. From the results, in the Gutenberg corpus, the research algorithm had the highest accuracy and completeness, with 98.5 % and 91.6 %, respectively, and the lowest values were 92.6 % and 89.4 %. The average values were 95.5 % and 91.1 %, with an F1 value of about 92.6 %. In the Brown corpus, the average accuracy, completeness, and F1 value of the designed algorithm were 96.2 %, 91.2 %, and 93.2 %, respectively. When the number of online customers reached 1000, the response time of the designed Chinese system was 1.15 s, the classification recommendation accuracy was 95 %, and the system stability was about 83 % on average. The response time of the English system was 0.64 s, the classification recommendation accuracy was 96 %, and the system stability was about 90 % on average. It shows that the designed method can significantly enhance the operation accuracy of the classification recommendation system.

Effectiveness of Information and Communication Programmes for Children with Special Educational Needs in the Context of Speech Development

The work aims to determine the effectiveness of information and communication programs for special educational needs (SENs) in the context of speech development. The aim was achieved through the use of general theoretical methods of analysis, observations, and calculations of the ranking factor, efficiency coefficient, and Phillips correlation coefficient. It was established that the most effective approaches for the development of children's speech are the use of information technologies (2.5), social interaction (2.37), and group approach (2.25). Results: The results provided grounds for developing a speech and general development program for children aged 4 to 7. The program provided for the creation of mind maps, provision of joint reading, performance of educational songs, and development of speech in accordance with the specified topic. Training became possible due to the use of modern technologies, namely the applications MindMeister, Digital Inclusion, and speech synthesis from Google and YouTube. It was established that the developed program had a positive effect on the improvement of children’s phonetic speech compared to the results at the beginning of the study. Conclusions: The practical significance of the work implies the possibility of using the developed program for speech correction among a larger number of children. Prospects: Research prospects are related to elaborating speech development mechanisms depending on the degree of speech impairment.

Ranking factors affecting the decontamination efficacy of non-thermal plasma: The approach of dissipated power per plasma volume through machine learning modeling

Non-thermal plasma treatment can preserve food, but a meta-analysis assessing its efficacy has not been performed. This study retrieved the inactivation kinetics parameter log10D (n = 519), which varied largely among different plasma setups. Atmospheric pressure plasma jet, corona discharge, surface barrier discharge, dielectric barrier discharge, and inductively-coupled plasma had the highest efficacy with median D-values under 2 min. Dielectric barrier discharge, the most frequent setup (n = 160), was analyzed using dissipated power per plasma volume (W/cm3) as an integrated predictor of decontamination efficacy. Using conventional and machine learning approaches the most correlated parameters to the log10D were: dissipated power per plasma volume and matrix category, followed by microbial genus and pH. This study uses active learning to improve literature screening for data collection and various data analysis techniques for data treatment and ranking of the factors affecting non-thermal plasma decontamination. Industrial relevanceNon-thermal plasma decontamination shows potential but is also highly affected by the setup, and technical, biological, and food parameters. This study gives an overview of the decontamination performance that is to be expected for different matrix categories and setups, which could guide possible industrial applications. The ranking of the most important parameters to affect decontamination efficacy could be used for the optimization of these applications.

Ethnobotanical study of medicinal plants used to manage human ailments in Lay Gaint District, South Gondar Zone, Amhara Region, Northwestern Ethiopia

The aim of this work is to document and record the use of medicinal plants to treat various ailments in the study area. A total of 84 informants, consisting of 54 men and 30 women, between 35 and 80 years of age were chosen. Additionally, 20 key informants were deliberately selected for their expertise. Data were collected through face-to-face interviews, group discussions, and guided field walks using semi-structured interview questions. Ethnobotanical data were analyzed using quantitative analytical tools such as preference ranking, direct matrix ranking, fidelity level and informant consensus factor. Forty-six medicinal plants belonging to 41 genera and 33 families were collected and identified. Brassicaceae, Ranunculaceae, and Polygonaceae had the highest abundance of medicinal plants used to treat various diseases in the study area. Most of the medicinal plants were herbs (43 %), followed by shrubs (35 %). The study revealed that leaves (45.7 %) were the most frequently used plant parts in the preparation of remedies. The most common mode of administration was oral ingestion (61 %), followed by dermal application (30 %). Zingiber officinale Roscoe and Allium sativum L.had the highest preference rank, whereas Croton macrostachyus Hochst. ex Delile was identified as the most popular medicinal plant due to its multipurpose use. while the value of fidelity level (FL) for medicinal plants in the study ranged from 34 % to 94 %. The study also identified several threats to medicinal plants in the study area, including charcoal production, overgrazing, and agricultural expansion. To prevent the eradication of these medicinal plants, the involvement of local communities in the management and conservation of plant resources is crucial.

NIH funding and journal selection: trends in dermatology publication models across tiers from 2021 to 2023.

This study examines the influence of National Institutes of Health (NIH) funding on the publication choices of dermatologists, particularly in terms of journal tiers and pay-to-publish (P2P) versus free-to-publish (F2P) models. Utilizing k-means clustering for journal ranking based on SCImago Journal Rank, h-index, and Impact Factor, journals were categorized into three tiers and 54,530 dermatology publications from 2021 to 2023 were analyzed. Authors were classified as Top NIH Funded or Non-Top NIH Funded according to Blue Ridge Institute for Medical Research rankings. The study finds significant differences in publication patterns, with Top NIH Funded researchers in Tier I journals demonstrating a balanced use of P2P and F2P models, while they preferred F2P models in Tier II and III journals. Non-Top NIH Funded authors, however, opted for P2P models more frequently across all tiers. These data suggest NIH funding allows researchers greater flexibility to publish in higher-tier journals despite publication fees, while prioritizing F2P models in lower-tier journals. Such a pattern indicates that funding status plays a critical role in strategic publication decisions, potentially impacting research visibility and subsequent funding. The study's dermatology focus limits broader applicability, warranting further research to explore additional factors like geographic location, author gender, and research design.

Personal and Environmental Contextual Factors in Socio-Medical Orthopedic Evaluation Reports

The personal and environmental contextual factors of the ICF describe not only physical and mental health but also (occupational) participation. So far, the extent to which these contextual factors are taken into account in the socio-medical evaluation has not been clarified. Contextual factors can guide allocation to disability pension or rehabilitative interventions in an individualized and targeted manner. The aim of this study was to determine the frequency of ICF contextual factors in socio-medical evaluation. A frequency analysis of contextual factors in socio-medical reports for musculoskeletal disorders was performed. Contextual factors mentioned several times were coded once in the report. In the frequency ranking, each environmental and personal factor was categorized as "frequent," "moderate," and "rare." 215 socio-medical reports with musculoskeletal diagnoses starting from 2017 were retrospectively analyzed. All socio medical reports were analyzed for the occurence of personal contextual and environmental factors. In particular, personal factors were identified, which provide information about general personal characteristics or the general state of health. Almost half of the environmental factors were rarely identified. Many ICF contextual factors are constantly recorded in the analyzed reports. The important influence of these factors on return to work is well known. Thus, the absence or low presence of the ICF contextual factors from the chapters Attitudes (i4), Basic Skills (i4) and Support & Relationships (e3), which are also contextual to the assessed person's experience of the world of work, was surprising. The relevance of the frequently and rarely identified contextual factors for the socio-medical evaluation of musculoskeletal disability pension applicants must be questioned.

Solving Sparse Linear Systems Faster than Matrix Multiplication

Can linear systems be solved faster than matrix multiplication? While there has been remarkable progress for the special cases of graph-structured linear systems, in the general setting, the bit complexity of solving an n × n linear system Ax = b is Õ ( n ω ), where ω is the matrix multiplication exponent. Improving on this has been an open problem even for sparse linear systems with poly( n ) condition number. In this paper, we present an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. This speedup holds for any input matrix A with o ( n ω−1 / log (κ( A ))) non-zeros, where κ( A ) is the condition number of A . Our algorithm can be viewed as an efficient, randomized implementation of the block Krylov method via recursive low displacement rank factorization. It is inspired by an algorithm of Eberly et al. for inverting matrices over finite fields. In our analysis of numerical stability, we develop matrix anti-concentration techniques to bound the smallest eigenvalue and the smallest gap in the eigenvalues of semi-random matrices.

ASSESSMENT OF FACTORS INFLUENCING STUDENTS’ DECISION AFTER SECONDARY SCHOOL: A STUDY ON THE DECLINING ENROLLMENT TREND IN TERTIARY EDUCATION

Tertiary education plays a crucial role in promoting economic growth and development. Investing in tertiary education can contribute to economic growth by providing a skilled workforce, fostering innovation, and advancing research and development (World Bank, 2021). The Department of Statistics Malaysia (DOSM) released a report in 2019 revealed that, of the 560,000 SPM candidates, 390,000 were interested in starting a job right away and just 170,000 were interested in continuing their education. These concerning findings drive this study to identify the factor and subfactor that impact the student’s decision not to pursue higher education. The factors considered are financial constraints, student attributes and social influence. The subfactors are family background, high fees, availability of scholarships, dissatisfaction with the offered course, academic performance, social media, peer pressure, and a lack of motivation. This paper utilises the Fuzzy AHP Method to identify and rank the main factor and subfactor influencing the respondents’ decision, which are students from SMK Sungai Layar, Sungai Petani. Results show that financial constraint is the most preferred factor, while student attribute is the second-best factor. Meanwhile, social influence is found to be the lowest ranked factor.

Developing a mobile exercise program for individuals with Spinal Cord Injury: Stakeholder perceptions of app features and implementation determinants

BackgroundIndividuals with Spinal Cord Injury (SCI) often experience physical deconditioning, leading to long-term health challenges. While regular exercise can offer substantial health benefits, adherence to exercise guidelines among individuals with SCI is hindered by barriers such as inaccessibility. Exercise programs using the mobile application (App) tailored to individual needs present a promising solution for promoting exercise adherence among individuals with SCI. ObjectiveThis study aimed to identify factors contributing to the successful implementation of an app-based home exercise program for individuals with SCI and gather user feedback on app preferences, functionality, and features. MethodsGuided by the Consolidated Framework for Implementation Research (CFIR), twenty-six clinicians completed an expert panel survey to rank factors influencing the implementation of an app-based intervention for increasing exercise adherence for individuals with SCI. CFIR-selected factors and app quality features obtained from the Mobile Application Rating Scale (MARS) framework were discussed in seven focus groups with 23 individuals with SCI, 6 caregivers, and 6 clinicians. ResultsThe expert survey identified adaptability, complexity, evidence strength/quality, relative advantage, knowledge/beliefs about the initiative, and execution as the key CFIR factors that affected the intervention's success. Major themes emerging from focus groups with individuals with SCI and caregivers included usability, instruction and guidelines, user-friendly interface, and clinician interaction. In contrast, clinicians mentioned themes such as the representation of the SCI population, time commitment, accessibility, and equipment. ConclusionsThe study highlights the significance of incorporating these determinants into future designs to develop app-based home exercise interventions for individuals with SCI.

Impact of the COVID-19 Pandemic on the Scientific Production of Physical Education Researchers: A Five-Year Follow-Up Study.

The COVID-19 pandemic caused significant changes in society's dynamics, particularly affecting the landscape of education. Research in several areas may have been affected during periods of social restrictions. This study analyzed the curricula of 558 researchers across 27 graduate programs in physical education in Brazil to investigate the potential impact of the COVID-19 pandemic on scientific publications. Researchers' production from 2018 to 2022 underwent a comprehensive analysis, considering the total number of publications, Qualis rank, and journal impact factor. Data were analyzed using chi-squared and Kruskal-Wallis tests. Significance was set at 5%. Overall, the analyzed researchers published a total of 17,932 manuscripts from 2018 to 2022. During the COVID-19 pandemic, there was a decline of 16.4% in the number of articles published (p = 0.001). This decline was similar between men and women (p = 0.603) and was associated with a worsening in Qualis rank (p = 0.001). The number of studies published in journals with impact factors was also affected (p = 0.001). The findings suggest a potential impact of the COVID-19 pandemic on the scientific production of Brazilian researchers in the field of physical education. Funding agencies should consider the challenges associated with the COVID-19 pandemic before evaluating researchers and programs.

Factors leading to lapses in professional behaviour of Gynae residents in Pakistan: a study reflecting through the lenses of patients and family, consultants and residents

IntroductionProfessional behaviour is the first manifestation of professionalism. In teaching hospitals, the residents can be considered vulnerable to lapses in professional behaviour when they fail to meet the set standards of professionalism. Residents of some specialties are more at risk of lapses in professional behaviour due to the demanding nature of work. Research focusing on the behaviour of residents in the field of Gynae and the underlying factors contributing to such behaviour is notably lacking in the literature. Additionally, there is a gap in understanding the perspectives of patients from Pakistan on this matter, as it remains unexplored thus far, which constitutes the central focus of this study.An increase in complaints lodged against Gynae resident’s professional behaviour in Pakistan Citizen Portal (PCP) was observed. Therefore, an exploratory qualitative study was conducted to investigate the factors and rationales contributing to the lapses in resident’s professional behaviour.The study collected the viewpoints of three stakeholder groups: patients and their families, consultants and residents. The study was conducted in three phases. First, the document analysis of written complaints was conducted, followed by face-to-face interviews (11 per group) conducted by trained researchers from an independent 3rd party. Finally, the interview data was transcribed, coded and analysed.In total 15 themes were identified from the interviews with 3 stakeholders, which were then categorized and resulted in 6 overlapping themes. The most prevalent lapse reported by all 3 stakeholders was poor verbal behaviour of residents.ConclusionThe highly ranked factors contributing to triggering the situation were associated with workplace challenges, well-being of residents, limited resources, patients and family characteristics, patients’ expectations, lack of administrative and paramedic support, cultural factors and challenges specific to Gynae specialty.Another intriguing and emerging theme was related to the characteristics of patients and attendants which helped in understanding the causes and implications of conflicting environments. The value of competency also emphasized that can be accomplished by training and mentoring systems. The thorough examination of these factors by key stakeholders aided in accurately analysing the issue, its causes, and possible solutions. The study's findings will assist higher authorities in implementing corrective actions and offering evidence-based guidance to policymakers to improve healthcare system.

The critical success factors of smart port digitalization development in the post-COVID-19 era

The international outbreak of the new coronavirus (COVID-19) at the end of 2019 has caused an indelible impact on the global economy. However, it has grown against the trend in unmanned distribution and online consumption. In recent years, the maritime industry has gradually introduced artificial intelligence, blockchain, the Internet of Things, and big data. Through literature review and expert interviews, this research figures out the development status of smart ports at home and abroad and the digital development barriers of ports and then uses the Fuzzy Delphi and the Fuzzy Analytic Hierarchy Process method to identify three dimensions and thirteen key factors target companies. The survey targets shipping companies, shipping agencies, and port management companies. The paper conducted the questionnaires to identify the critical success factors of port digitization. It was found that the “digital solutions” dimension had the highest weight value among the three dimensions and the top five critical factors in the overall ranking were based on the order of “carbon emission management,” “enhancing port safety”, “technology standardization,” “digital asset management,” and “optimization of the supply chain.” The result of this study can help port operators and shipping companies identify critical success factors of smart ports and adopt port digital development strategies for operation efficiency improvement and climate change mitigation.

Exploring the impact of SEO-based ranking factors for voice queries through machine learning

The use of voice search is proliferating and expected to grow into the foreseeable future; this is why websites increasingly optimize their content associated with voice-based search to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs research. Moreover, many predictions about its future excite the subject and require systematic investigation. This research aims to analyze important features that contribute to the SEO of webpages. Therefore, there is a need to examine various ranking factors that improve the ranking of the webpages for voice search queries on the Search Engine Results Page (SERP). This study consists of two phases. The first phase comprises systematic data acquisition and identifying important SEO-based ranking factors. The second phase includes a longitudinal case study to evaluate the impact and significance of identified factors. To achieve this goal, we conduct experiments on methodical combinations of features through machine learning algorithms such as Support Vector Machine, Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbors, Decision Trees and Random Forest. Comparing results for multiple feature designs evaluates the contributing nature of specific features in SEO-based optimization for ranking. Results suggest the importance of the newly identified feature set (FF) outperforms baselines (EF and EFN) by a significant margin. A longitudinal case study on a blog over four months confirms that optimizing these features improves page ranking; therefore, webmasters must optimize these features while preparing the webpage.

The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining.

A 5-year survival rate is a predictor for the assessment of oral cancer prognosis. The purpose of this study is to analyze oral cancer data to discover and rank the prognostic factors associated with oral cancer 5-year survival using the association rule mining (ARM) technique. This study is a retrospective analysis of 897 oral cancer patients from a regional cancer center between 2011 and 2017. The 5-year survival rate was assessed. The multivariable Cox proportional hazards analysis was performed to determine prognostic factors. ARM was applied to clinicopathologic and treatment modalities data to identify and rank the prognostic factors associated with oral cancer 5-year survival. The 5-year overall survival rate was 35.1%. Multivariable Cox proportional hazards analysis showed that tumor (T) stage, lymph node metastasis, surgical margin, extranodal extension, recurrence, and distant metastasis of tumor were significantly associated with overall survival rate (p < 0.05). The top associated death within 5 years rule was positive extranodal extension, followed by positive perineural and lymphovascular invasion, with confidence levels of 0.808, 0.808, and 0.804, respectively. This study has shown that extranodal extension, and perineural and lymphovascular invasion were the top ranking and major deadly prognostic factors affecting the 5-year survival of oral cancer.

Latent structure and reliability

There are numerous reliability coefficients and α is the most popular. It is known that different coefficients can be appropriate in specific conditions and that α should not be used indiscriminately. However, some coefficients and conditions, particularly regarding the latent structure, lacked attention in previous research. In four Monte Carlo simulations, this study compared α, λ2, maximized λ4, λ4 based on locally optimal splits, μ2, Gilmer-Feldt, Kaiser-Caffrey α, Heise-Bohrnstedt Ω, Joreskog's ρ, ωtotal, algebraic greatest lower bound, greatest lower bound based on minimum rank factor analysis in every condition and also hierarchical ω and asymptotic ω hierarchical in multidimensional conditions. Findings suggest each of these coefficients can be useful at least in some conditions. Most differences in performance were observed in congeneric conditions and conditions with up to moderate loadings. Some coefficients were found to be more useful than previously considered. Results are discussed in the context of existing theory and previous Monte Carlo studies.

Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery.

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR) parameterized by multilayer perceptrons (MLPs), which can continuously represent data beyond meshgrid with powerful representation abilities. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization, and utilize MLPs to paramterize factor functions of the tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.

Finite odometer factors of rank one group actions

In this paper, we give explicit conditions characterizing the Følner rank one group actions that factor onto a finite odometer; those that factor onto an arbitrary, but specified odometer, and those that factor onto an unspecified odometer. We also give explicit conditions describing the Følner rank one actions that are conjugate to a specific odometer, and those that are conjugate to some odometer. These conditions are based on cutting and stacking procedures used to generate the action, and generalize results given by Foreman, Gao, Hill, Silva and Weiss for rank one Z-actions.