Local Fitting Research Articles

Applying taper function models for black locust plantations in Greek post-mining areas

A key process in forest management planning is the estimation of tree volume and, more specifically, merchantable volume. The ability to predict the cumulative stem volume relative to any upper stem diameter on standing trees or stands is essential for forest inventories and the management of forest resources. In the 1980s, the Hellenic Public Power Corporation (HPPC) started the rehabilitation of lignite post-mining areas in Greece by planting mainly black locust (Robinia pseudoacacia, L.). Today, these plantations occupy an area of approximately 2570 ha, but the stem volume has not yet been estimated. Therefore, we aimed to estimate the over- and under-bark stem volume using taper function models for 30 destructively sampled trees. Of the nineteen calibrated fixed-effects models, Kozak’s (2004) equation performed best for both the over-bark and under-bark datasets, followed by Lee’s (2003) and Muhairwe’s (1999) equations. Two fixed effect models were compared with fitted coefficients from Poland and the United States confirming that the local model fits were better suited, as the foreign model coefficients caused an increase in root mean square error (RMSE) for stem diameter predictions of 13% and 218%, respectively. The addition of random effects on a single-stem basis for two coefficients of Kozak’s (2004) equation improved the model fit significantly at 86% of the over-bark fixed effect RMSE and 69% for the under-bark model. Integrated taper functions were found to slightly outperform three volume equations for predictions of single stem volume over and under bark. Ultimately it was shown that these models can be used to precisely predict stem diameters and total stem volume for the population average as well as for specific trees of the black locust plantations in the study area.

Augmented degree correction for bipartite networks with applications to recommender systems

In recommender systems, users rate items, and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system tries to estimate unobserved preferences by finding similarities across users and across items. In this work, we treat the observed ratings data as partially observed, dense, weighted, bipartite networks. For a class of systems without outside information, we adapt an approach developed for dense, weighted networks to account for unobserved edges and the bipartite nature of the problem. The approach begins with clustering both users and items into communities, and locally estimates the patterns of ratings within each subnetwork induced by restricting attention to one community of users and one community of items community. The local fitting procedure relies on estimating local sociability parameters for every user and item, and selecting the function that determines the degree correction contours which best models the underlying data. We compare the performance of our proposed approach to existing methods on a simulated data set, as well as on a data set of joke ratings, examining model performance in both cases at differing levels of sparsity. On the joke ratings data set, our proposed model performs better than existing alternatives in relatively sparse settings, though other approaches achieve better results when more data is available. Collectively, the results indicate that despite struggling to pick up subtler signals, the proposed approach’s recovery of large scale, coarse patterns may still be useful in practical settings where high sparsity is typical.

Strontium isotopes track female dispersal in Taï chimpanzees.

Chimpanzees (Pan troglodytes) are patrilocal, with males remaining in their natal community and females dispersing when they reach sexual maturity. However, the details of female chimpanzee dispersal, such as their possible origin, are difficult to assess, even in habituated communities. This study investigates the utility of 87Sr/86Sr analysis for (1) assessing Sr baseline differences between chimpanzee territories and (2) identifying the status (immigrant or natal) of females of unknown origin within the territories of five neighboring communities in Taï National Park (Côte d'Ivoire). To create a local Sr isoscape for the Taï Chimpanzee Project (TCP) study area, we sampled environmental samples from TCP-established territories (n = 35). To assess dispersal patterns, 34 tooth enamel samples (one per individual) were selected from the Taï chimpanzee skeletal collection. 87Sr/86Sr analysis was performed on all 69 samples at the W.M. Keck Lab. The theoretical density and overlap of chimpanzee communities as well as generalized linear mixed models (GLMMs) were used to test each question. 87Sr/86Sr ratios for natal male chimpanzees ranged from 0.71662 to 0.72187, which is well within the corresponding environmental baseline range of 0.70774-0.73460. The local Sr isoscapes fit was estimated with the root-mean-square error value, which was 0.0048 (22% of the whole 87Sr/86Sr data range). GLMMs identified significant differences in 87Sr/86Sr ratios between natal and unknown North community origin groups, suggesting that after 1980, females of unknown origin could be immigrants to North community (n = 7, z-ratio = -4.08, p = 0.0001, power = 0.94). This study indicates that 87Sr/86This study indicates that 87Sr/86Sr analysis can successfully identify immigrant females in skeletal collections obtained from wild chimpanzee communities, enabling the tracking of female dispersal patterns historically. There are, however, significant limitations within the scope of this study, such as (1) the absence of reliable maps for the TCP study area, (2) limited capacity for environmental sampling, (3) small sample sizes, and (4) tooth formation in wild chimpanzees.

A structural after measurement approach to structural equation modeling.

In structural equation modeling (SEM), the measurement and structural parts of the model are usually estimated simultaneously. In this article, we revisit the long-standing idea that we should first estimate the measurement part, and then estimate the structural part. We call this the "structural-after-measurement" (SAM) approach to SEM. We describe a formal framework for the SAM approach under settings where the latent variables and their indicators are continuous. We review earlier SAM methods and establish how they are specific instances of the SAM framework. Decoupled estimation for the measurement and structural parts using SAM possesses three key advantages over simultaneous estimation in standard SEM. First, estimates are more robust against local model misspecifications. Second, estimation routines are less vulnerable to convergence issues in small samples. Third, estimates exhibit smaller finite sample biases under correctly specified models. We propose two variants of the SAM approach. "Local" SAM expresses the mean vector and variance-covariance matrix of the latent variables as a function of the observed summary statistics and the parameters of the measurement model. "Global" SAM holds the parameters of the measurement part fixed while estimating the parameters of the structural part. Our framework includes two-step corrected standard errors, and permits computing both local and global fit measures. Nonetheless, the SAM approach is an estimation strategy, and should not be regarded as a model-building tool. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluation of children's oral diagnosis and treatment using imaging examination using AI based Internet of Things.

Although cone beam computed tomography (CBCT) plays an important role in the diagnosis and treatment of oral diseases, its image segmentation method needs to be further improved, and there are still objections about the clinical application effect of general anesthesia (GA) on children's dental fear (CDF). This study aimed to investigate the application value of CBCT based on intelligent computer segmentation model in oral diagnosis and treatment of children in the context of biomedical signals, and to analyze the alleviating effect of GA on CDF. Based on the regional level set (CV) algorithm, the local binary fitting (LBF) model was introduced to optimize it, and the tooth CBCT image segmentation model CV-LBF was established to compare the segmentation accuracy (SA), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), over segmentation rate (OR), and under segmentation rate (UR) between these model and other algorithms. 82 children with CDF were divided into general anesthesia group (GAG) (n= 38) and controls (n= 44) according to the voluntary principle of their families. Children in GAG were treated with GA and controls with protective fixed intervention. Children's fear survey schedule-dental subscale (CFSS-DS) and Venham scores were counted before intervention in the two groups. CFSS-DS scores were recorded at 2 hours after intervention and after recovery in children in GAG. CFSS-DS and Venham scores were performed in all children 1 week after surgery. The results showed that the S⁢A value of CV-LBF algorithm was higher than that of region growing algorithm (P< 0.05). OR, UR, MSSD, and ASSD values of CV-LBF algorithm were evidently lower than those of other algorithms (P< 0.05). CFSS-DS scores were lower in GAG than in controls 2 hours after intervention and at return visits after 1 week of intervention (P< 0.001), and Venham scores were lower in GAG than in controls after intervention (P< 0.001). After intervention, the proportion of children with Venham grade 0, 1, 2, and 3 was obviously higher in GAG than in controls (P< 0.001), while the proportion of children with Venham grade 4 and 5 was clearly higher in controls than in GAG (P< 0.001). The results revealed that the computer intelligent segmentation model CV-LBF has potential application value in CBCT image segmentation of children's teeth, and GA can effectively alleviate anxiety of children with CDF and can be used as biomedical signals.

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

Research on Risk Propagation in Fresh E-commerce Supply Chain Based on Complex Networks

In recent years, the rapid growth of the fresh e-commerce industry has introduced several potential risk factors to the supply chain that necessitate preventative measures. This paper aims to study the risk propagation within the fresh e-commerce supply chain and propose strategies for risk control. Firstly, a dual local world fitness network model is constructed by combining the fitness model with the local world model. This model is then validated, showing consistency with the actual network. Building upon this, an improved SEIRS risk propagation model is developed, and the basic reproduction number is calculated to simulate the risk propagation within the fresh e-commerce supply chain. The results indicate that the degree of risk propagation is positively correlated with the initial infection rate, risk outbreak rate, and loss of immunization rate, while negatively correlated with the risk recovery rate and risk elimination rate.

Hybrid uncertain buckling analysis for engineering structures through machine learning method

Buckling analysis is fundamental yet essential for engineering structures in stability assessment, design optimization, structural design, and the like. Uncertainty as an inherent feature in engineering significantly affects structural buckling behaviour. A hybrid uncertain buckling analysis accounting aleatoric and epistemic uncertainties simultaneously is introduced. To determine the statistical features of the relevant bounds on the concerned structural response, an optimization strategy is developed based on a surrogate model. For surrogate model construction, an improved machine-learning technique is developed by possessing the global trend regression and local information fitting. A convex optimization program can be formulated to capture the global trend with optimal solutions, and subsequently, local information fitting improves the accuracy in handling unbiased datasets. The sampling method on random variables led the hybrid uncertain buckling analysis to be solved as a series of interval analyses on the established surrogate model. Essential statistical data regarding the limits of critical structural reactions can be assessed both effectively and efficiently. Additional features, such as information updates, further underscores the viability of the proposed scheme. Finally, the crucial buckling loads of two engineering structures facing hybrid uncertainty are thoroughly examined to highlight the potential benefits of the introduced method.

Finding fun in the fatigue: Exploring healthism among group fitness instructors

The concept of health is culturally contingent, and fitness practices provide a venue to gain insights into the construction of health. In this case study–based article, I focus on group fitness instructors’ narratives on healthiness and fitness rituals. By combining Foucault’s notion of the ‘microphysics of power’ and the socio-cultural phenomenon healthism, I employ a genealogical approach that reveals how healthism renders a discursive space for group fitness instructors (‘local fitness experts’) to navigate their understanding of health norms. A qualitative method consisting of 22 semi-structured interviews was used. Group fitness instructors teaching in Austria for a global group fitness distributor (Les Mills) were interviewed. The results show that eliminating risks to health is the highest imperative to the instructors, which is traced back to a dichotomy between ‘disciplined training’ and ‘fun training’, where fun training is seen as feminine and risky. Furthermore, instructors underline their health literacy through skeptical consumerism – choosing health for the sacrifice of fun or finding fun in the fatigue.

A new method for unmanned aerial vehicle path planning in complex environments

To solve the problems of UAV path planning, such as low search efficiency, uneven path, and inability to adapt to unknown environments, this paper proposes A double-layer optimization A* and dynamic window method for UAV path planning. Firstly, the neighboring node clip-off rule is defined to optimize the node expansion mode of the A* algorithm, and the obstacle coverage model is designed to dynamically adjust the heurizing function of the A* algorithm to improve the path search efficiency. Then, the Bresenham algorithm is adopted for collision detection and critical path nodes are extracted to significantly reduce the number of path turning points. Secondly, a new tracking index is proposed to optimize the evaluation function of the dynamic window method to make the local path fit the global path further. By detecting the dangerous distance, the dynamic adaptive method of evaluation function weight is designed to improve the fixed weight of the dynamic window method. Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the two algorithms is realized. Simulation results show that the proposed method can significantly improve the efficiency and smoothness of mobile robot path planning, enhance the real-time obstacle avoidance and adaptive ability of unknown environments, and better meet the requirements of complex planning tasks.

Schemes of local fitting for damage detection of bars using longitudinal operating deflection shapes measured by 3D laser scanning

Longitudinal operating deflection shapes (ODSs) of a bar can be measured by three-dimensional laser scanning. The difference between slopes of ODSs (SODSs) under damaged and intact statuses of the bar can be used to indicate and locate the damage. This study inspects ODSs in a multiscale manner, whereby multiscale ODSs (MODSs) and multiscale SODSs (MSODSs) are produced. Noise interference can be eliminated by increasing scale parameters to their satisfying values. In addition, two schemes of local fitting are applied to measured MODSs to generate locally fitted MSODSs under virtual intact statuses of the bars. By relying on differences between measured and locally fitted MSODSs, two individual damage indices (DIs) are established, namely the type-I DI and the type-II DI, in which damage-induced peaks can indicate and locate the damage. The capability of the two schemes of local fitting is theoretically verified and experimentally validated by detecting two-sided notches in bars.

The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population.

Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO=0.65) was higher in the recipient population than predicted had no augmentation occurred (HO=0.58) but less than what was predicted by our model (HO=0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype-environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.

Impact of the superimposition reference area on intraoral scanning accuracy in a partially dentate maxilla

Statement of problemThe alignment of 3-dimensional (3D) files involves selecting a reference area before performing a local best fit alignment during the digital scan superimposition and is essential for comparing digital scans. Scan alignment relies on both reference area location and the alignment algorithm. However, a consensus on the impact of different reference areas on intraoral scanning accuracy is lacking. PurposeThe purpose of this in vitro study was to assess the impact of 3 superimposition reference areas on the accuracy of 3 intraoral scanners for a partially dentate maxilla. Material and methodsA Kennedy class II resin cast was scanned using 3 intraoral scanners (Primescan, TRIOS 3, and Emerald) outputting 30 digital scans (10 per scanner). Test scans from intraoral scanners were subsequently compared with a reference digital standard tessellation language file generated by a laboratory scanner with validated accuracy. The files were superimposed using best fit alignment for each intraoral scanner using 3 different superimposition reference areas (whole region of interest, palate, and all teeth). Accuracy was assessed by using a 3D analysis program (Geomagic Control X; 3D systems) for each scanner at 4 preselected areas. Test and reference scan differences were depicted on color maps and quantified via root mean square deviations. Differences were analyzed using regression analysis with the post hoc student t test and Bonferroni correction (α=.05). ResultsThe TRIOS 3 and Emerald produced positive deviations in the palatal color maps, whereas Primescan produced more uniform color maps, regardless of the superimposition strategy used. Primescan exhibited the best accuracy (trueness and precision) in both palatal and bounded edentulous areas, regardless of the superimposition reference area. The TRIOS 3 recorded the highest distal extension trueness (ranging from 42.9±7.7 µm to 65 ±19.5 µm), and Primescan achieved the highest precision (ranging from 28.5 ±9.8 µm to 48.9 ±16.9 µm), regardless of the superimposition area. Emerald demonstrated the highest teeth trueness (ranging from 31.6 ±6.8 µm to 69.6 ±11.5 µm), while Primescan produced the highest precision (ranging from 17.9 ±6.1 µm to 30.7 ±9.2 µm), regardless of the reference area used. ConclusionsThe chosen reference area for best fit alignment significantly influenced digital scan accuracy (P<.001). Primescan displayed the highest palatal and bounded edentulous area accuracy, with TRIOS 3 recording the highest distal extension trueness. Emerald recorded the highest teeth trueness and Primescan recorded the highest distal extension and tooth precision. All conclusions were independent of the superimposition strategy used.

Evolutionary accessibility of random and structured fitness landscapes

Biological evolution can be conceptualized as a search process in the space of gene sequences guided by the fitness landscape, a mapping that assigns a measure of reproductive value to each genotype. Here, we discuss probabilistic models of fitness landscapes with a focus on their evolutionary accessibility, where a path in a fitness landscape is said to be accessible if the fitness values encountered along the path increase monotonically. For uncorrelated (random) landscapes with independent and identically distributed fitness values, the probability of existence of accessible paths between genotypes at a distance linear in the sequence length L becomes nonzero at a nontrivial threshold value of the fitness difference between the initial and final genotypes, which can be explicitly computed for large classes of genotype graphs. The behaviour of uncorrelated random landscapes is contrasted with landscape models that display additional, biologically motivated structural features. In particular, landscapes defined by a tradeoff between adaptation to environmental extremes have been found to display a combinatorially large number of accessible paths to all local fitness maxima. We show that this property is characteristic of a broad class of models that satisfy a certain global constraint, and provide further examples from this class.

Adapting TeachABI to the local needs of Australian educators - a critical step for successful implementation.

Background The present study is the foundational project of TeachABI-Australia , which aims to develop and implement an accessible, nation-wide digital resource for educators to address their unmet acquired brain injury (ABI)-related professional learning needs. The aim of the present study was to identify the adaptations required to improve the suitability and acceptability of the TeachABI professional development module within the Australian education system from the perspectives of Australian educators. Methods The research design employed an integrated knowledge translation approach and followed the ADAPT Guidance for undertaking adaptability research. A purposive sample of eight educators eligible to teach primary school in Australia provided feedback on the module through a quantitative post-module feedback questionnaire and a qualitative semi-structured interview. Results Participants rated the acceptability of the module as 'Completely Acceptable ' (Mdn = 5, IQR = 1), and reported 'only Minor' changes were required (Mdn = 2, IQR = 0.25) to improve the suitability to the Australian context. Qualitative analysis of transcripts revealed three broad categories: (1) the usefulness of TeachABI , (2) the local fit of TeachABI , and (3) pathways for implementing TeachABI in the local setting. Recommended adaptations to the module collated from participant feedback included changes to language, expansion of content, and inclusion of Australian resources, legislation, and videos. Conclusions TeachABI is acceptable to Australian educators but requires modifications to tailor the resource to align with the unique schooling systems, needs, and culture of the local setting. The systematic methodological approach to adaptation outlined in this study will serve as a guide for future international iterations of TeachABI .

A community-based modified sport program for rural community-dwelling older adults: A pilot study.

With Australia's ageing population, especially in rural regions, promoting physical activity among older adults is crucial. To identify factors influencing participation of older adults (60 years or over) from a rural community in a modified sport program and assess satisfaction with a pilot modified sport program informed by these findings. Insights and ideas from two focus groups exploring community perspectives on a modified sport program informed the development of a six-week pilot modified sport program delivered with the local fitness centre. Post-program surveys assessed participant satisfaction, willingness to continue, and likelihood of recommending the program to others. Nine participants attended the focus groups, highlighting increased social connection, health management, and healthy ageing as key motivators. Joint and health concerns, cost, and time constraints emerged as main barriers. Preferences for the program included weekly sessions, alternating sports, indoor venues, and supporting modifications, but maintaining traditional rules. Thirteen participants participated in the pilot modified sport program. There was strong program attendance (86%). All participants (13/13) rated their satisfaction as very satisfied. They were highly likely to recommend the program to others (8.5/10) and were interested in continuing modified sport after the pilot (7.7/10). This study demonstrates for the first time that modified sport is of interest to older people in the rural setting, and that delivering such a program is feasible in a small community. Rural communities implementing modified sport should consider the availability of suitable facilities and equipment, access to an engaging facilitator and participant cost. Through a strong connection between the health service and community fitness centre, a modified sport program was developed and implemented, well-attended and enjoyed by participants in a rural community.

Exploratory Factor Analysis of the NEPSY-II Conceptual Template: Acting on Evidence

The present study examined the structure of the NEPSY-II within the norming sample using exploratory factor analysis. For the 3–4-year-old group, our results were conceptually uninterpretable. As a result, a unidimensional model was retained by default as a remedy to local fit issues. For the 7–12-year-old group, our analysis supported some aspects of the NEPSY-II conceptual domains in the form of a six-factor model that yielded the best fit to the data. While variance partitioning results indicate that the majority of NEPSY-II subtests at ages 7–12 contain adequate specificity to be interpreted in isolation, caution is suggested for interpreting the Social Perception subtests; in particular, given the inability to locate that latent dimension in either of the analyses conducted. Implications for the clinical interpretation of the instrument moving forward are discussed.

Vertically spliced tabletop light field cave display with extended depth content and separately optimized compound lens array.

Tabletop three-dimensional light field display is a kind of compelling display technology that can simultaneously provide stereoscopic vision for multiple viewers surrounding the lateral side of the device. However, if the flat panel light field display device is simply placed horizontally and displayed directly above, the visual frustum will be tilted and the 3D content outside the display panel will be invisible, the large oblique viewing angle will also lead to serious aberrations. In this paper, we demonstrate what we believe to be a new vertical spliced light field cave display system with an extended depth content. A separate optimization of different compound lens array attenuates the aberration from different oblique viewing angles, and a local heating fitting method is implemented to ensure the accuracy of fabrication process. The image coding method and the correction of the multiple viewpoints realize the correct construction of spliced voxels. In the experiment, a high-definition and precisely spliced 3D city terrain scene is demonstrated on the prototype with a correct oblique perspective in 100-degree horizontal viewing range. We envision that our research will provide more inspiration for future immersive large-scale glass-free virtual reality display technologies.

The digital (il)literacy of local and regional politicians and civil servants –An analysis based on recent data from Romania

In 2021/2022 a consortium of European universities delivered a scientific basis for a policy fighting fake news and hate speech with support of the Congress of Local and Regional Authorities of the Council of Europe, describing also the technical and legal basics of both phenomena. In this context a survey was performed with the delegates of the Congress and the book, which contained also the survey results, was presented to the Congress in its Spring Session 2022. In 2023 a broader survey was done in a selection of countries, which also returned 675 filled-in questionnaires from Romania. The part of the questionnaire dealing with possible remedies contained questions which remedies are considered technically and legally feasible by the participants. Confirming the results of the survey in 2021/2022 the results for Romania showed a severe lack of basic knowledge about how the internet works and its governance. In this paper we want to show the results for Romania in detail, discuss them and suggest possible training measures which seem necessary to make the local and regional politics and administration fit for the Digitalization.The analysis of data from other European countries showed that this phenomenon is not restricted to Romania but is widely recognizable all over Europe. It also confirms the results from the survey among the Congress delegates. Therefore, this paper may be based on the Romanian dataset, however it signifies a pan-European issue.The authors are grateful to Vice Dean Nicolae Urs (Babeș-Bolyai University Cluj-Napoca) and Catalin Vrabie (National University of Political Studies and Public Administration) for their support in spreading the survey among Romanian municipalities.

Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.