Identification Stage Research Articles

Physical Characteristics and Somotatype of Myanmar National Hockey Players

Introduction: Performance in Field Hockey depends on different factors. Physical characteristics along with the Body shape and size of the players are one of them. The present study was aimed to find out the physical characteristics as well as Somatotypes of Myanmar National Field Hockey players. Methods: Twenty national Field Hockey players were measured for their height, weight, and Somatotype during their visit to a neighbuor country for a friendship tour. Health-Carter method (1967) was followed to calculate the Somatotype components. Results: The average Age, Height, and Weight of the Myanmar Hockey players of the present study were 21.7 (±1.1) yr, 164.8 (±4.6) cm, and 56.5 (±5.1) kg respectively. An average Somatotype of 1.7(±0.5)-3.8(±0.9)-2.9(±0.8) was observed for the present study. Conclusions: Most of the players (55 % ) were with Ectomorphic mesomorph body type, whereas 30 % of players were with Mesomorphic ectomorph body type. Rest were with Balanced mesomorph, Endomorphic mesomorph, and Mesomorph ectomorph body type. Compared to other countries, Myanmar Hockey players were shorter than their international counterparts. Thus, the height of the players should be considered during the selection of players at an early stage of Talent identification for good performance in Field Hockey.

Tindak Tutur Ekspresif dalam Novel Ranah 3 Warna karya Ahmad Fuadi

Research on expressive speech acts in novels is an important study to learn and understand. This research intends to discuss the types and functions of expressive speech in the novel Ranah 3 Warna by Ahmad Fuadi. The study of this novel is a descriptive method of qualitative research. The data of this research is in the form of characters' speech in the novel which relies on expressive speech acts. Data was collected using documentation techniques with the assistance of corpus linguistik technology, namely the korpus nusantara. The data was analyzed using computational linguistics technique through the stages of identification, classification, interpretation, and conclusion. This research found: (1) types of expressive speech acts: congratulating and thanking, expressing pleasure and anger, apologizing, praising, criticizing, insinuating, complaining, and insulting, (2) functions of expressive speech acts: convivial, collaborative, conflictive, and competitive functions.

Exploiting the capacity of deep networks only at the training stage for nonlinear black-box system identification

To benefit from the modeling capacity of deep models in system identification without worrying about inference time, this study presents a novel training strategy that uses deep models only during the training stage. For this purpose, two separate models with different structures and goals are employed. The first one is a deep generative model aiming at modeling the distribution of system output(s), called the teacher model, and the second one is a shallow basis function model, named the student model, fed by system input(s) to predict the system output(s). That means these isolated paths must reach the same ultimate target. As deep models show a great performance in modeling highly nonlinear systems, aligning the representation space learned by these two models makes the student model inherit the teacher model’s approximation power. The proposed objective function consists of the objective of each student and teacher model, adding up with a distance penalty between the learned latent representations. The simulation results on three nonlinear benchmarks show a comparative performance with examined deep architectures applied on the same benchmarks. Algorithmic transparency and structure efficiency are also achieved as byproducts.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

A systematic literature review on machine learning and deep learning-based covid-19 detection frameworks using X-ray Images

Coronavirus is an endangered disease to kills more than millions of people, but it has also put tremendous pressure on the whole medical system. The initial stage of identification of COVID-19 is necessary to isolate the patients with positive cases in order to stop the disease from spreading. The amalgamation of imaging techniques and deep learning algorithms takes less time and leads to more accurate outcomes for COVID-19 detection. Deep learning techniques have been employed by scientists to identify coronavirus infection in lung images during the COVID-19 worldwide epidemic. In this review, a review of the Covid-19 detection framework based on machine learning and deep learning techniques using X-ray images is done. First, the review of existing Covid-19 detection models is done. For this purpose, a detailed literature survey is carried out on Covid-19 detection papers from 2019 to 2023. Following the literature survey, the pre-processing procedures, the segmentation process, and the classification techniques used for Covid-19 detection using deep learning, machine learning, and optimization algorithms are reviewed and categorized. After that, the dataset and the implementation tool which are utilized for Covid-19 detection works are analyzed and grouped. Finally, the performance metrics validation such as accuracy, recall, F1-score, NPV, precision, sensitivity, and specificity is carried out. The research gaps in the existing Covid-19 detection techniques are provided further as references to aid in future works.

Penilaian Risiko menggunakan Metode Failure Mode and Effects Analysis (FMEA) terkait Kontaminasi Silang pada Area Pengemasan di Industri Farmasi “XYZ”

Cross-contamination occurs due to other previously produced products, other rooms or areas producing different products, and particles from machinery or equipment. Quality risk management can be a preventive measure in reducing the potential for cross-contamination that occurs in the pharmaceutical industry. One part of quality risk management is risk assessment. One of the most common and effective methods is the Failure Mode and Effect Analysis (FMEA) method. This risk assessment is carried out using the stages of risk identification, risk analysis, and risk evaluation using categorization based on the Risk Priority Number (RPN). Based on the risk assessment using the FMEA method regarding cross-contamination in the Packaging area in the "XYZ" pharmaceutical industry, it can be concluded that the results of observations and interviews with operators and Packaging Supervisors identified 19 risks which were categorized as low, medium, high, and very high. Based on the results of the risk assessment obtained, the risk of cross-contamination that needs to be controlled is those with an RPN value of 30 ≤ RPN ≤ 60 (medium); 75 ≤ RPN ≤ 100 (high); RPN ≥ 150 (very high). Therefore, the "XYZ" pharmaceutical industry needs to determine and implement risk control measures. By adequately controlling the potential risk of cross-contamination, it will ensure the safety and quality of the products produced.

Analysis of Opaque, Peel-Off and Flex Rejects in the Electro Plating Process Using the Six Sigma Dmaic Method

The modern manufacturing industry is faced with increasing pressure to achieve high levels of quality while remaining efficient in its production processes. One of the challenges faced by PT. Plating services in electroplating production have a high reject rate, which can cause increased costs and have a negative impact on the company's reputation. PT. Jasa Plating in the production process still has a high number of rejects, the rejects are dominated by opaque, peel-off and spots. PT. Plating services for each production contribute a high average of 2% defects per day, DPMO 20,000 and Sigma value 3.55. Quite a few rejections resulted in a decrease in turnover and increased production costs. The costs incurred due to these rejects are an average of Rp. 1,268,593 per day and an average of Rp. 35,698,907 per month as a result of data collection from January – September 2023. In this business process, this is very detrimental for the company. The aim of this research is to suppress blurry rejects, peel-offs and spots in the electro plating process at PT. Plating Services. The steps taken in conducting research include: identification stage, data collection and processing, analysis and recommendations for improvement, and conclusions. The method used in this research is the Six Sigma DMAIC (Define, Measure, Analysis, Improvement, Control) method. Through analysis, you can find several factors that cause high reject rates, including operational errors, discrepancies in the production process, or raw material quality problems. After implementing the improvement plan, then evaluate the results. This can include re- measuring the DPMO, analysis of the resulting products to see a decrease in reject rates, and comparisons between before and after implementing improvements. From the comparison results after the repairs were carried out, the data before the repairs were carried out, the NG amount was 1.99%, the DPMO was 6,646 and the Sigma value was 3.98. After improvements to NG 0.84%, DPMO 2,791 sigma value 4.28. Factors causing high levels of blurring, peel-off and spots, namely insufficient or excessive cleaning of the metal surface before the electroplating process, can cause problems such as peel-off. Contamination of the electroplating solution by foreign materials can cause blurring and flecks in the final layer. Machinery or equipment that is not properly calibrated, or experiences wear and tear, can produce products that do not meet standards. Variations in temperature, time, or electric current in the electroplating process can produce inconsistent coatings. Carefully monitor and control process-parameters to ensure stability.

Jaminan Informasi dan Keamanan yang Lebih Baik: Studi Kasus BPJS Kesehatan

In 2021, BPJS Kesehatan experienced a significant data leak incident, where the personal data of 279 million Indonesians was leaked and traded on hacker forums. This incident has a serious impact on individual privacy and the reputation of BPJS Kesehatan. This paper analyzes the incident using the security lifecycle method, which consists of the stages of identification, analysis/assessment, protection, and monitoring. The findings suggest that weaknesses in IT security systems and a lack of continuous monitoring contributed to the leaks. Recommendations include strengthening security protocols, staff training, and implementing real-time monitoring to prevent similar incidents in the future.

Systematic Review of Kansei Engineering Method Developments in the Design Field

Kansei engineering is a critical method for designing products that meet functionality, usability, and pleasurability, essential elements for business success. Despite its significance, there is limited understanding of how this method has evolved in recent years. This study aims to analyze the development of Kansei Engineering research from 2018 to 2022 using the Scopus database. The research methodology involved stages of identification, screening, filtering, and inclusion, resulting in 41 articles for detailed analysis out of an initial 215. The results indicate that 85% of Kansei Engineering research focuses on tangible products, with 83% categorized as type 1 studies, and 56% not integrating other methods. Additionally, 88% of the studies use only Kansei words, and 41% visualize design results as 3D images, with 95% not considering unique aspects. is dominance of tangible product design and the lack of integration with other methods suggest a need for diversification in research approaches. Furthermore, the high reliance on Kansei words and 3D visualizations points to a potential area for innovation and expansion in research techniques. This review highlights a significant research gap in Kansei Engineering studies, emphasizing the need for more diversified approaches. By identifying these gaps, the study provides a clear direction for future research, recommending that Kansei Engineering should explore beyond the predominant trends and consider integrating with other methods and unique aspects. This can enhance the method's application in industrial engineering and lead to more comprehensive and innovative product designs. Future research should aim to fill these gaps, ensuring that Kansei Engineering continues to evolve and contribute effectively to the field of product design and development.

Utilizing Sertoli Cell Transplantation as a Therapeutic Technique for the Management of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as, Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), are defined by aberrant protein accumulation, brain atrophy, and gradual decline of neuronal function. Despite the considerable endeavors devoted to discovering treatments for NDs in recent decades, the demand for efficient therapeutic agents persists. Sertoli cells (SCs) play a crucial role in providing a supportive structure and environment for the development of germ cells. SCs, whether transplanted as xenogeneic or allogeneic cells, present a viable choice for enhancing graft persistence via the release of immunomodulatory and trophic factors, including neurturin (NTN), platelet-derived growth factor, Fas (CD95) ligand (FasL), glial-derived neurotrophic factor, interleukin 1 (IL1), brain-derived neurotrophic factor, interleukin 6 (IL6), transforming growth factors, and vascular growth factor, that protect replaced cells and tissues from the immune system. However, there is currently no cohesive evidence regarding the neuroprotective influence of SCs transplantation on NDs. Therefore, the focus of this review is to assess the neuroprotective impact of stem cells on neurodegenerative diseases in preclinical settings and present cohesive information. A comprehensive search was conducted between 2000 and 2022 in Medline, Scopus, and the Web of Science. In the identification stage, after a comprehensive search across databases including Web of Science, Scopus, and PubMed/Medline, 103 papers were obtained. Our search yielded a total of nine relevant papers on the therapeutic effect of SCs transplantation on NDs. It was found that SCs transplantation exhibits a promising impact on enhancing neurological diseases' symptoms in rats. Our findings highlight the need for multiple standardized preclinical trials to be performed to find reliable information to confirm the utilization of SCs transplantation and the reduction of neurodegenerative disease symptoms.

Karakterisasi dan Identifikasi Rhizobakteri Asal Mangrove Tanjung Pilawang sebagai Biokontrol dan Agen Hayati Pertumbuhan Tanaman

Rhizobacteria are a type of microbe that has an important role in providing nutrients to plants. Rhizobacteria live in symbiosis with plant roots, where their activity has a significant influence on plant growth and development. Various microbes that have the potential to stimulate plant growth are thought to reside in mangrove root areas. The aim of this research is to determine the type of rhizobacteria of the Tanjung Pilawang mangrove, Gura Village, Tobelo District, North Halmahera Regency which is capable of inhibiting the growth of the fungus Collectotricum capsici and dissolving phosphate to be used as a biocontrol and biological agent for plant growth. The research methods used include the isolation stage, selection and characterization stages, and identification stages. The results of the research showed that 11 isolates of rhizobacteria were isolated from Tanjung Pilawang mangrove soil, of these 11 isolates there were two isolates that had the potential to inhibit the growth of the pathogenic fungus Colletotricum capsici and were able to dissolve phosphate, where each isolate was able to provide a Dissolution Index (IP) value. the highest is 2.85 mm and 1.91 mm. After identification, the two isolates were identified as Bacillus subtilis (RTM 10) and Pseudomonas sp (RTM 06), so that these two bacteria have potential as biocontrol and biological agents for plant growth.

Risks assessment to the health of the population in the area of emissions pollutants influence of carbon graphite production enterprises

Risk assessment of the impact of the enterprise's activities on the environment includes risk assessment of the impact of the activity on the health of the population. Risk as the probability of undesirable events is an integral component of any person's life. Risk characterization integrates data on the danger of the analyzed chemical substances, the amount of exposure, parameters of "dose-response" dependence, with the aim of quantitative and qualitative assessment of risk, identification and assessment of the comparative significance of existing problems for the health of the population. In scientific terms, hazard identification is the process of establishing a causal relationship between the action of a chemical substance and the development of adverse effects on human health. The main goal of the hazard identification stage is the selection of priority, indicator chemical substances, the study of which allows to reliably characterize the sources of occurrence and the level of risk of public health impairment. Assessment of dose-response relationships is a process of quantitative characterization of toxicological information and establishment of a relationship between the likely influencing dose (concentration) of a pollutant and the incidence of adverse events in the exposed population. Characteristics of "dose-response" dependence, which are most often used to assess carcinogenic risk, have been sufficiently studied in epidemiological studies. Among them is the factor of carcinogenic potential (ICS), which reflects the probability of the development of a harmful reaction when the dose (concentration) increases by 1 mg/kg or 1 mg/m3 per exposure level, associated with a certain probability of effect (the indicators of this group are used for establishment of reference, i.e., reference doses and concentrations). The individual carcinogenic risk characterizes the upper limit of possible carcinogenic risk during the period corresponding to the average life expectancy of a person (70 years). Today, in the world, the classification and assessment of the acceptable risk to the health of the population caused by the chemical influence of chemical substances that pollute the environment is quite wide. the system of acceptance criteria recommended by WHO is used. In accordance with these criteria, the first range of risk (individual lifetime risk equal to or less than 1·10-6, which also corresponds to an additional case of serious illness or death per 1 million exposed persons) characterizes such levels of risk as are perceived by all strata population as quite small, which do not differ from ordinary everyday risks (De minimas level). Such risks do not require additional measures to reduce them and their levels are subject to periodic control. The second range (greater than 1·10-6 and less than 1·10-4) corresponds to the zone of conditionally accepted (acceptable) risk. It is at this level that most of the foreign and recommended by international organizations hygienic standards for the population as a whole are established. Levels of suitable risk belong to constant control. The third range (greater than 1·10-4 and less than 1·10-3) is suitable for professionals and unsuitable for the general population. The emergence of such a level of risk requires the development and implementation of planned health measures. The fourth range (equal to or greater than 1·10-3) is unsuitable neither for the population nor for professionals. This range is designated as De manifestis Risk, and when it is reached, it is necessary to carry out emergency health measures to reduce the risk on the part of the city authorities. During the assessment of the risk to the health of the population of Zaporizhzhia from the emissions of PJSC "Ukrgrafit", priority pollutants were identified: carbon oxides, nitrogen oxides, sulfur oxides, substances in the form of suspended solid particles of undifferentiated composition. Hazard indices were determined for individual HQ substances (non-carcinogenic risks) and in the case of combined exposure to HI, individual carcinogenic risks (ICR) and combined exposure (CRa) were calculated. Assessment of long-term effects (at the level of average annual concentration) on human health of the investigated substances. allowing to assess the chronic inhalation effect of the occurrence of negative effects in the health of the exposed population during the lifetime for the assessment and characterization of multistage risk showed that the hazard indices (HQ) do not exceed the norms.

Evaluation of the Sragi Secondary Channel of the Kaliwadas Irrigation Area, Pekalongan Regency

The Sragi secondary channel is located in the Kaliwadas irrigation area, Pekalongan Regency has a length of 7,587 m. The position of the channel is in a rice field area which is used to optimize irrigation to support agricultural business productivity. The existing condition of the channel in the form of native soil causes frequent leaks. Technical handling is needed to repair irrigation buildings in the form of a study on the existing condition of the Sragi secondary channel through field surveys and checking physical conditions. Furthermore, identification of the level of damage to irrigation channels is carried out. From the problems that arise at the identification stage, recommendations are then given in the form of proposals for improvements to buildings and irrigation channels. The results of the field survey in the form of network tracing provide data that the level of physical and functional damage to the Sragi secondary channel infrastructure reaches >40%. The condition is categorized as very bad with the classification of severely damaged which requires repair or replacement. Most of the damage in the form of spots or localized occurred on the existing lining of masonry and precast concrete starting from Hm 50+00 to Hm75+68. The recommendations for improvement are in the form of dismantling the existing precast concrete lining according to the point of damage. It is recommended to repair the lining using K-300 precast concrete so that the structure of the irrigation channel is safe against the risk of deformation.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm−1 to 0.233 mm−1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm−1 before to 0.205 mm−1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

TRAINING TO BUILD PERSONAL BRANDING ON LINKEDIN SOCIAL MEDIA TO HANDLE EDUCATED UNEMPLOYMENT RATES AT HIGHER COLLEGE LEVEL

The number of unemployed in Indonesia according to the Central Statistics Agency as of August 2023 at the tertiary level has increased. Technology is developing rapidly and the emergence of new jobs, a combination of macro trends and technology adoption is causing the disappearance of a number of types of jobs. Students as one of the keys to the nation's success are required to have a high level of productivity, especially for those who are starting a career. The aim of this Community Service activity in the form of training is to build students' personal branding with Linkedin to prepare them for careers using Linkedin social media to anticipate the number of educated unemployed at the tertiary level. The method used is Participatory Action Research through the stages of problem identification, implementation and evaluation. Participants in this training numbered 90 students. The results show that the participants gained new knowledge and skills to prepare for a career and this is one solution in facing the challenges of technological disruption by using social media Linkedin.

Mixing Codes in Comments on Youtube Video Posts Debate for Presidential and Vice Presidential Candidates for the 2024 Election

This February, Indonesia will hold a democratic party to elect members of parliament and the president. Of course, the political and national atmosphere became warm with various political ripples and upheavals occurring, including political campaign activities, both political party campaigns and campaigns for Presidential and Vice Presidential Candidates. Various attributes and activities of political campaign speeches are interesting to study from various aspects, including the aspect of language use. This research aims to identify forms of code mixing found in KompasTV YouTube comments. This research method uses a descriptive method with a qualitative approach. The data for this research are comments from internet users (netizens) that researchers observed through screenshots from the comments column on the KompasTV YouTube channel. The data comes from the comments column of the KompasTV YouTube channel which contains elements of code mixing. Data collection techniques use listening, note-taking and documentation techniques. Data were analyzed using the Mills and Huberman model through the stages of data identification, data reduction, data analysis, and data presentation. Based on the research results, it was concluded that the code mixing that many netizens do in the comments column occurs at the word level. This is very possible because comments in word form are simpler, more practical and "to the point".

Qualitative Exploration of Speech Pathologists' Experiences and Priorities for Aphasia Service Design: Initial Stage of an Experience-Based Co-Design Project to Improve Aphasia Services.

Stroke survivors with aphasia (impaired language/communication) have poor outcomes and gaps in the clinical implementation of best practice contribute to this. Little is known, however, about speech pathologist perspectives on the touchpoints (key moments shaping experiences) in the clinical care pathway that have the greatest impact on service delivery nor how this varies by geographical location. We explored the experiences of speech pathologists who provide aphasia services to establish priorities for improvement and design. This is the initial experience gathering and priority identification stage of an experience-based co-design (EBCD) project. Speech pathologists were recruited from 21 geographically diverse Hospital and Health Services in Queensland, Australia. Speech pathologists working in acute, rehabilitation and community services shared positive and negative experiences of delivering aphasia care in interviews and focus groups. Experiential data were analysed using qualitative thematic analysis to determine touchpoints. Priorities for service design were identified using an adapted nominal group technique. Speech pathologists (n = 62) participated in 16 focus groups and nine interviews and shared 132 experiences of delivering aphasia care. Providing care in teams with poor awareness of the impacts of aphasia was identified as a key challenge, as poor patient-provider communication was perceived to increase risk of adverse outcomes for patients. Speech pathologists identified areas for improvement related to their own professional needs (e.g., greater access to clinical supervision); collaborative health care (e.g., better coordination and interdisciplinary care to increase therapy time); and the service context and environment (e.g., psychological services able to support diverse communication needs). Speech pathologist delivery of aphasia services could be improved through increased access to clinical supervision, opportunities for peer debriefing and interdisciplinary care. Priorities for service design varied by geographical location and included: education to support care transitions (remote areas), improved referral pathways and service linkage (regional areas) and dedicated aphasia staffing (metropolitan areas). A consumer advisory committee comprising people with aphasia (n = 3, authors K.M., K.D. and B.A.), their significant others (n = 2, authors J.D. and P.M.), and a Cultural Capability Officer (author G.B.) guided this research. The team: (1) reviewed participant information; (2) co-designed surveys and workshop resources; (3) copresented research outcomes and contributed to publications. Research questions and study design (e.g., analysis methods and assessment measures) were developed by the research team (authors L.A., V.J.P., D.A.C. and S.J.W.).

Kernel Block Based Morphing Identification and Elimination In Server

The primary stage of duplicated region identification for investigating duplicate and insert image forgeries does the image block match. Because of evolution of highly sophisticated tools, numerous image modifications have been made. When images are difficult to duplicate and transfer, forgeries of those parts may happen. The images are examined closely in the area where they were forged. By using the suggested Gaussian RBF kernel PCA, the segment of the image that is copied and pasted will be known. One of the most significant issues in this step is the high processing time required to locate related locations. This research proposes a digital block-based picture water mark embedding strategy this uses singular value decomposition (SVD), a mathematical technique. There is already embedding a water mark across the entire image using traditional SVD watermarking. The suggested method involves breaking up the partitioning original image into blocks, after which the water mark existindividually inserted into the singular value of each block. Additionally, the employ the temporal complexity calculate the recommended algorithm's performance. The result of the experiment and thecalculation analysisdemonstrate that two layer matching is possible more traditional approaches Similar to lexicographic sorting, illustrate how two-layer matching can be accomplished. . High key points are awarded for the dimensionality of the feature vector representation in the picture matching. The suggested approach eliminates compression, identifies picture features using blurring, and contaminates the image with noise. The most straightforward way to identify image forgeries using editing technology or morphing is through computational efficiency. Numerous tests have shown that the system is more efficient than the one that is already in use. It is helpful for recording historical time intervals with patterns resembling those of a query time range. To obtain an accurate prediction, the system determines the location, MAC address, and IP address of the morphing uploader.

MSME Tax Socialization for Pakuncen Village Communities

In the process of realizing optimal taxpayer compliance in paying taxes, of course the basic factor is the taxpayer's own awareness. Because in fact Taxpayer awareness in paying taxes is the main key to the success of government programs in increasing state income through taxes. Tax outreach for Micro, Small and Medium Enterprises (MSMEs) is very important to help business actors understand their tax obligations. The MSME Tax socialization activity is carried out. The method used in this community service is carried out in several stages. The first stage is identification to find out priority issues that will be resolved through this service. The identification stage was carried out by interviewing MSMEs in Pakuncen Village. The results of this activity introduced MSME Tax to mothers who have businesses so that residents in Pakuncen Village are aware of the importance of taxation, especially for mothers who are working hard. From this socialization there were several participants who felt enthusiastic about the introduction of this taxation because according to the participants they had never before known about the MSME Tax. This is very helpful and motivates local residents to be more active in entrepreneurship so that they can increase their tax obligations because previously socialization has been carried out, in the future there will be more sustainable assistance.

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network

Objective. Aiming for the research on the brain–computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples. Approach. In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model. Main results. The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a. Significance. The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.