Intensive and Integrated Assistance in the Development of the Tourism Sector in Racitengah Village, Sidayu District, Gresik Regency (Infrastructure Modeling)

The purpose of this community service activity in the assisted villages is to create a mapping model for the tourist vehicle of Raci Tengah Village, Sidayu Gresik District. The resurrected village rides are game tourism vehicles for children to improve the economic activity of local residents. This modeling is expected to be able to provide an overview of the mapping of the tourism vehicle sector and also the infrastructure sector supporting game tourism for these children. Keywords: Tourism Rides, Facilities

PENERAPAN PEMBELAJARAN KOOPERATIF MODEL MIND MAPPING UNTUK MENINGKATKAN HASIL BELAJAR SISWA SDN 1 LANDUNGSARI DI MASA PANDEMI COVID-19

The learning outcomes of class VIB SDN Landungsari 1 student did not reach the specified Minimum Completeness Criteria (MCC). Apart from the effects of the post-covid-19 pandemic, the cause of the low student learning scores was the learning model. The learning model must be following the times. One of the learning models is the mind mapping model of cooperative learning which is an innovative learning model. The purpose of this study was to describe the application of the mind mapping model of cooperative learning to improve student learning outcomes. This research was conducted in 2 cycles using the class action research method (CAR) with steps were action planning, action implementation, evaluation, and reflection in each cycle. The learning outcomes of cycle 1 showed the mean of affective, psychomotor, and cognitive values were 79.80, 62.50, and 73.84, respectively. The learning outcomes of cycle 2 showed an increase compared to the learning outcomes in preliminary observation and cycle 1 with the average values ​​of affective, psychomotor, and cognitive were 98, 93.75, and 86.92, respectively. The results of this study indicated that the mind mapping model of cooperative learning could improve student learning outcomes.

Predictive Control-Based Completeness Analysis and Global Calibration of Robot Vision Features

This paper provides an in-depth study and analysis of robot vision features for predictive control and a global calibration of their feature completeness. The acquisition and use of the complete macrofeature set are studied in the context of a robot task by defining the complete macrofeature set at the level of the overall purpose and constraints of the robot vision servo task. The visual feature set that can fully characterize the macropurpose and constraints of a vision servo task is defined as the complete macrofeature set. Due to the complexity of the task, a part of the features of the complete macrofeature set is obtained directly from the image, and another part of the features is obtained from the image by inference. The task is guaranteed to be completely based on a robust calibration-free visual serving strategy based on interference observer that is proposed to complete the visual serving task with high performance. To address the problems of singular values, local minima, and insufficient robustness in the traditional scale-free vision servo algorithm, a new scale-free vision servo method is proposed to construct a dual closed-loop vision servo structure based on interference observer, which ensures the closed-loop stability of the system through the Q-filter-based interference observer, while estimating and eliminating the interference consisting of hand-eye mapping model uncertainty and controlled robot input interference. The equivalent interference consisting of hand-eye mapping model uncertainty, controlled robot input interference, and detection noise is estimated and eliminated to obtain an inner-loop structure that presents a nominal model externally, and then an outer-loop controller is designed according to the nominal model to achieve the best performance of the system dynamic performance and robustness to optimally perform the vision servo task.

Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Objective. Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity. Approach. Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification. Main results. The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values. Significance. The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models.

Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data

Background Understanding the impact of socio-economic inequality on health outcomes is arguably more relevant than ever before given the global repercussions of Covid-19. With limited resources, innovative methods to track disease, population needs, and current health and social service provision are essential. To best make use of currently available data, there is an increasing reliance on technology. One approach of interest is the implementation and integration of mapping software. This research aimed to determine the usability and acceptability of a methodology for mapping public health data using GIS technology. Methods Prototype multi-layered interactive maps were created demonstrating relationships between socio-economic and health data (vaccination and admission rates). A semi-structured interview schedule was developed, including a validated tool known as the System Usability Scale (SUS), which assessed the usability of the mapping model with five stakeholder (SH) groups. Fifteen interviews were conducted across the 5 SH and analysed using content analysis. A Kruskal-Wallis H test was performed to determine any statistically significant difference for the SUS scores across SH. The acceptability of the model was not affected by the individual use of smart technology among SHs. Results The mean score from the SUS for the prototype mapping models was 83.17 out of 100, indicating good usability. There was no statistically significant difference in the usability of the maps among SH (p = 0.094). Three major themes emerged with respective sub-themes from the interviews including: (1) Barriers to current use of data (2) Design strengths and improvements (3) Multiple benefits and usability of the mapping model. Conclusion Irrespective of variations in demographics or use of smart technology amongst interviewees, there was no significant difference in the usability of the model across the stakeholder groups. The average SUS score for a new system is 68. A score of 83.17 was calculated, indicative of a “good” system, as falling within the top 10% of scores. This study has provided a potential digital model for mapping public health data. Furthermore, it demonstrated the need for such a digital solution, as well as its usability and future utilisation avenues among SH.

A Bivariate Mapping Model Identifies Major Covariation QTLs for Biomass Allocation Between Leaf and Stem Growth of Catalpa bungei

Biomass allocation plays a critical role in plant morphological formation and phenotypic plasticity, which greatly impact plant adaptability and competitiveness. While empirical studies on plant biomass allocation have focused on molecular biology and ecology approaches, detailed insight into the genetic basis of biomass allocation between leaf and stem growth is still lacking. Herein, we constructed a bivariate mapping model to identify covariation QTLs governing carbon (C) allocation between the leaves and stem as well as the covariation of traits within and between organs in a full-sib mapping population of C. bungei. A total of 123 covQTLs were detected for 23 trait pairs, including six leaf traits (leaf length, width, area, perimeter, length/width ratio and petiole length) and five stem traits (height, diameter at breast height, wood density, stemwood volume and stemwood biomass). The candidate genes were further identified in tissue-specific gene expression data, which provided insights into the genetic architecture underlying C allocation for traits or organs. The key QTLs related to growth and biomass allocation, which included UVH1, CLPT2, GAD/SPL, COG1 and MTERF4, were characterised and verified via gene function annotation and expression profiling. The integration of a bivariate Quantitative trait locus mapping model and gene expression profiling will enable the elucidation of genetic architecture underlying biomass allocation and covariation growth, in turn providing a theoretical basis for forest molecular marker-assisted breeding with specific C allocation strategies for adaptation to heterogeneous environments.

Extended Graph of the Fuzzy Topographic Topological Mapping Model

Fuzzy topological topographic mapping (FTTM) is a mathematical model which consists of a set of homeomorphic topological spaces designed to solve the neuro magnetic inverse problem. A sequence of FTTM, FTTMn, is an extension of FTTM that is arranged in a symmetrical form. The special characteristic of FTTM, namely the homeomorphisms between its components, allows the generation of new FTTM. The generated FTTMs can be represented as pseudo graphs. A graph of pseudo degree zero is a special type of graph where each of the FTTM components differs from the one adjacent to it. Previous researchers have investigated and conjectured the number of generated FTTM pseudo degree zero with respect to n number of components and k number of versions. In this paper, the conjecture is proven analytically for the first time using a newly developed grid-based method. Some definitions and properties of the novel grid-based method are introduced and developed along the way. The developed definitions and properties of the method are then assembled to prove the conjecture. The grid-based technique is simple yet offers some visualization features of the conjecture.

The Effectiveness of Vaccination Against The Spread of COVID-19 with SEIR Mathematical Modeling in Gowa District

The spread of disease in epidemic range, endemic range, as well as in the pandemic range that is spreading of the disease can be stopped with getting vaccinated. The vaccines that are effective and efficient can be the missile that stopped this Covid-19 pandemic. The aim of this research is to (1) know the mapping model of mathematics SEIR in distributing vaccines toward the spread of Covid-19 in Kabupaten Gowa, (2) know the analysis and model simulation of mathematics SEIR in distributing vaccine toward the spread of Covid-19 in Kabupaten Gowa, also (3) know the impact of distributing vaccine toward the spread of Covid-19 in Kabupaten Gowa. The method we used is the literature review, the collecting data obtained by an interview and documentation review. The research result discovered that the model of mathematics SEIR is used to describe the distribution of vaccines toward the spread of Covid-19 in Kabupaten Gowa. The analysis and simulation results model of mathematics SEIR showed that the higher vaccines effectiveness and the number of the population in Kabupaten Gowa that already had vaccinated is higher, then showed no more spreads of Covid-19 and the pandemic is over.