Recognition Of Position Research Articles

An improved transfer learning method for rotor unbalance position identification from simulated data to experimental data

In the study of dynamic balancing for flexible rotors operating at high speeds, determining the unbalanced position of the rotor has consistently posed significant challenges. Accurate identification of the unbalanced position enables low-speed dynamic balancing to serve as a viable alternative to high-speed methods, ultimately reducing costs and enhancing operational efficiency. Although deep learning techniques utilizing limited labeled data have shown promising results in identifying unbalanced positions, the challenge of gathering a sufficient amount of labeled data for effectively training diagnostic models remains substantial. To address this issue, we propose a Pre-Adaptive Transfer Learning (PATL) approach that employs sample reconstruction through frequency domain correlation analysis. This technique facilitates cross-domain deep transfer recognition of rotor unbalanced positions by transferring insights from simulated dynamic model data to experimental datasets. Compared to existing methodologies, the proposed approach demonstrates significant improvements in both the accuracy and generalization of unbalanced position identification. The novelty of this research lies in the introduction of pre-adaptive transfer learning, which effectively minimizes the disparity between experimental and simulation data, thereby enhancing the model's recognition capabilities. Experimental results indicate that the proposed method achieves effective balancing outcomes across various operating conditions.

Whispers of dedication; unraveling motivational narratives among midwives in reproductive healthcare: a qualitative exploration

Background: Family planning is an important component of reproductive health care. Midwives play a key role in bridging the gap between community and essential reproductive health services. However, our focus extends beyond their service and deeper into what motivates them in this important area of ​​healthcare. We seek to understand the motivations for their commitment, and the challenges they face in meeting the needs of their patients and communities. Methods: A qualitative study was conducted involving ten midwives with structured guide in a focus group discussion. The interviews were recorded, transcribed, coded and thematic analysis was done. Results: Four themes were derived. The theme “Pinnacle Aspirations" essentially presented the motivation for training as an expression of passionate desire for career advancement and self-interest. “Navigating the Terrain” derived the challenges faced by midwives and their expectations which were limited resources, strong beliefs, and misinformation within the community as well as family resistance hindered contraception intake. “Dismal work realities” portrays midwives’ expectations to be acknowledged in terms of higher salary, position and status in health care system. “Women Empowered” explored midwives’ readiness to raise awareness among women about their rights to choose spacing and appropriate methods available. Conclusions: The study revealed the motivations of midwives and explored the various challenges faced by them and their desire to be acknowledged and rewarded for their invaluable service either through formal recognition of position of authority, monetary incentive or financial support, to aid them in their professional endeavors.

온라인플랫폼 사업자의 자사우대 행위의 위법성에 대한 소고

In December 2023, the Fair Trade Commission announced that it would introduce the tentatively named ‘Platform Fair Competition Promotion Act’ to pre-designate ‘dominant online platforms’ and prohibit abuse of their market dominant position. The fundamental question is whether online platforms’ self-preferencing can be considered subject to regulation under competition law, and what are the standards for judging its illegality. These controversies include what is the basis for recognition of market dominant position in the online market under the current legal system and whether self-preferencing can be included as a regulatory type of abuse of market dominant position. The Monopoly Regulation and Fair Trade Act prohibits abusive practices of market dominant Position in the relevant market, resulting in the effect of restricting competition. But, it is not easy to determine whether a market dominant position exists or whether there is a competition restriction effect in online platform market. Despite this situation, the Fair Trade Commission is establishing legislative policies to preemptively determine dominant online platforms and prohibit their abusive practices. However, the limitations of the use of the SSNIP test in online platforms did not overcome in ‘the Naver shopping case’, and the theory of monopoly leveraging, which is intended to be applied to determine the effect of restricting competition, has virtually lost its value in the United States, and the related discussions in the EU are different from the judgment standards established in ‘the Posco case’ in Korea. Considering the characteristics of the online platform market, which has the possibility of infinite competition in a multi-sided market without regional restrictions due to technological development, and the efficiency enhancing effect that the various services provided by online platforms will bring, regulation of the online platforms must be cautious.

Visualization and monitoring dynamic water levels of steam generators based on deep learning

In a nuclear power system, the steam generator (SG) is the most important part of the second loop, and accurate monitoring of the internal water level of the SG has always been a challenge. In this study, a two-loop steam generator is mounted on a six-degrees-of-freedom (6-DoF) platform to fully imitate the marine condition of the SG, and the internal water level is visualized by opening a window on the SG. The heating power, period and angle of the 6-DoF platform are considered. By calibrating the relationship between vertical pixels and actual height of the water level, the recognition of water level position in the image is mapped to the actual water level height. 700 water level images were obtained for constructing the dataset. An enhanced deep learning recognition network (YOLO-v7-SE) is proposed to focus on the water level of SG, which is added a channel attention mechanism to the backbone network for enhancing the extraction of target features. Compared with the original YOLO-v7 network, the enhanced YOLO-v7-SE network enhanced the detection accuracy of water level recognition by 12.5% and the mean average accuracy (mAP) by 13.3%. In addition, as the heating power increases, the water level without feeding water drops faster, and that with feeding water keeps constant. The swing periods and angles have complex effects on the water level. In contrast, the traditional magnetic flap level gauge could not obtain the variation of the water level. This shows that the enhanced YOLO-v7-SE network can accuracy recognize the real-time water level change, and achieve fast dynamic response to the water level change, which is a reliable and powerful guarantee for the safety of the SG.

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

Eksistensi Ondofolo dalam Dinamika Pemerintahan Formal Kampung Yobeh

The research, located in Yobeh Village, Jayapura Regency, aims to identify the existence of Ondoafi in the dynamics of informal government in Yobeh Village, Jayapura Regency. This research uses descriptive qualitative method. Data collection is conducted by interview, literature study, and documentation as a complement. The data analysis technique uses data reduction, data display, and conclusion drawing/verification models. This research uses the concept of local strongman. The result of the research shows that community awareness and the existence of Ondofolo determine whether Ondofolo is valued, respected or there has been a change in viewing Ondofolo's leadership. The existence of Ondofolo of Yobe Village is still maintained through the recognition of Ondofolo's position and the recognition of Ondofolo as a customary leader by his followers or community. On the way, disorganisation of Ondofolo khose of Yobe village also occurs as a result of incoming social change, which causes changes due to external factors and internal factors, which become obstacles to the existence of Ondofolo.

Method for Dynamic Load Location Identification Based on FRF Decomposition

The location identification of dynamic load is an important part of load-identification technology. Traditional methods are mostly aimed at the identification of dynamic load’s amplitude and phase. A new method for dynamic load location identification is proposed in this paper. An amplitude ratio or a phase difference between structural dynamic response signals is only related to the frequency response function (FRF), which is a complex-valued function that implies the location information of the load to be identified. In this method, the amplitude and phase variables of the excitation can be eliminated with symbolic calculations, and the location variables can be extracted from the FRF in a complex-number field. An excitation location can be identified quickly with parameter optimization using a Genetic Algorithm (GA), avoiding the ill-posed problem caused by matrix inversion. Numerical simulations and experiments demonstrate that this method can realize the fast recognition of several excitation positions, and has a high recognition accuracy, a short calculation time and a strong anti-noise ability.

Flexible rotor unbalance fault location method based on transfer learning from simulation to experiment data

In the study of the high-speed dynamic balance of flexible rotors, rotor unbalance positioning is a challenging topic. Particularly for slender rotors, the axial position of the unbalance has an important influence on the high-speed dynamic balance. The unbalance at different axial positions is not the same or even opposite in different rotor mode vibration behaviors. If the unbalance position of a rotor can be identified, the actual unbalance of the rotor can be reduced from the root. This balance method has the same effect in each vibration mode of the rotor; hence, low-speed dynamic balance can be realized to replace high-speed dynamic balance, considerably saving on costs. Deep learning based on few labeled samples can achieve good results for the identification of unbalanced positions; however, there are infinite potential positions of unbalance in the actual rotor. It is difficult to collect sufficient labeled samples to train a reliable intelligent diagnostic model. Fortunately, a large number of rotor vibration datasets labeled with different unbalance positions are available using the rotor dynamic model, and the unbalance position data calculated using the dynamic model contain diagnostic knowledge related to the rotor unbalance position data measured in the rig. Hence, inspired by transfer learning, this study proposed a transfer learning method using dynamic model simulation and experiment data for flexible rotor unbalance fault location. Cross-domain deep transfer recognition of rotor unbalance position was realized.

Paired associated SARS-CoV-2 spike variable positions: a network analysis approach to emerging variants.

Amino acids in variable positions of proteins may be correlated, with potential structural and functional implications. Here, we apply exact tests of independence in R × C contingency tables to examine noise-free associations between variable positions of the SARS-CoV-2 spike protein, using as a paradigm sequences from Greece deposited in GISAID (N = 6,683/1,078 full length) for the period 29 February 2020 to 26 April 2021 that essentially covers the first three pandemic waves. We examine the fate and complexity of these associations by network analysis, using associated positions (exact P ≤ 0.001 and Average Product Correction ≥ 2) as links and the corresponding positions as nodes. We found a temporal linear increase of positional differences and a gradual expansion of the number of position associations over time, represented by a temporally evolving intricate web, resulting in a non-random complex network of 69 nodes and 252 links. Overconnected nodes corresponded to the most adapted variant positions in the population, suggesting a direct relation between network degree and position functional importance. Modular analysis revealed 25 k-cliques comprising 3 to 11 nodes. At different k-clique resolutions, one to four communities were formed, capturing epistatic associations of circulating variants (Alpha, Beta, B.1.1.318), but also Delta, which dominated the evolutionary landscape later in the pandemic. Cliques of aminoacidic positional associations tended to occur in single sequences, enabling the recognition of epistatic positions in real-world virus populations. Our findings provide a novel way of understanding epistatic relationships in viral proteins with potential applications in the design of virus control procedures. IMPORTANCE Paired positional associations of adapted amino acids in virus proteins may provide new insights for understanding virus evolution and variant formation. We investigated potential intramolecular relationships between variable SARS-CoV-2 spike positions by exact tests of independence in R × C contingency tables, having applied Average Product Correction (APC) to eliminate background noise. Associated positions (exact P ≤ 0.001 and APC ≥ 2) formed a non-random, epistatic network of 25 cliques and 1-4 communities at different clique resolutions, revealing evolutionary ties between variable positions of circulating variants and a predictive potential of previously unknown network positions. Cliques of different sizes represented theoretical combinations of changing residues in sequence space, allowing the identification of significant aminoacidic combinations in single sequences of real-world populations. Our analytic approach that links network structural aspects to mutational aminoacidic combinations in the spike sequence population offers a novel way to understand virus epidemiology and evolution.

A frequency-spatial hybrid attention mechanism improved tool wear state recognition method guided by structure and process parameters

Real-time and accurate monitoring of tool wear status is critical for optimizing product quality and production costs. Deep learning has received much attention as an emerging data-driven technique in the field of tool wear monitoring because of its powerful feature extraction and nonlinear mapping capabilities. However, the model structure and features' poor interpretability limits the application of deep learning in practical machining. Herein, a frequency-spatial hybrid attention network is proposed, driven by tool structure and process parameters (FSHAN-SPD). The structure of FSHAN-SPD considers the recognition of critical frequencies and spatial positions simultaneously, and the receptive field of its spatial attention module can effectively distinguish the cutting stage of each tooth. Attention modules-weighted features are used for interpretability analysis. The effectiveness and flexibility of this method are verified in the PHM2010 dataset. A milling experiment illustrates a more detailed procedure. The experimental results are well interpretable in both frequency and spatial.

Industrial Robot Assembly Line Design Using Machine Vision

In order to further improve the functional requirements and performance indicators of the industrial robot assembly system and more accurately realize the measurement and recognition of the target position of the assembly line by the vision system, this article constructs a robot assembly line system based on obstacle detection and robot arm obstacle path planning based on machine vision technology and further improves the intelligence and accuracy of the assembly line system through the design and optimization of the system software module. Through the experimental verification of the positioning error based on the eye-to-hand binocular vision system and eye-in-hand monocular vision system, the system proposed in this article meets the design accuracy requirements of less than 0.1 mm in x/y direction and less than 1 mm in depth direction and verifies the feasibility and high accuracy of the system.

Metabolomics approach, in vitro and in vivo antioxidant activity assay provide insights into impact of multiple variations on the vine tea (Ampelopsis grossedentata)

Plucking position and harvest time influence the metabolites of vine tea which are closely related to the taste quality and antioxidant activity. In this study, 75 vine tea samples produced by three plucking positions and harvested in five months were analyzed by untargeted metabolomics, and in vitro and in vivo antioxidant activities of different vine teas were evaluated. 37 compounds were tentatively identified through various methods. Compared with the high-concentration results, VIP scores of the major components in the low-concentration model were much higher and were filtered as characteristic markers. Chemometrics analysis showed that plucking position had a stronger influence on vine tea quality than harvest time. 16 identified metabolites, including flavonoids, organic acids and catechins, were found to be responsible for the recognition of the plucking position. Vine tea infusion made of young green leaves exhibited higher scavenge radical activity in vitro, and tender tip leaves exhibited higher antioxidant activities in vivo zebrafish, which may be due to the presence of a higher concentration of flavonoids in their phytochemical profile. This research is anticipated to provide useful enlightenment for the effective utilization of vine tea resources.

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation.

In this study, methods for the mechanoluminescent (ML) visualization of crack propagation and mechanical behavior to evaluate adhesive joints are demonstrated and explained. The first step involved sample preparation; an air spray was used to apply ML paint to the surface of the adhesive joint specimens. The performance of the ML sensor was described to examine the measurement conditions. The results of ML sensing during a double cantilever beam (DCB) test and a lap-shear (LS) test are demonstrated as these are the most frequently and widely used methods for evaluating adhesives. Originally, it was difficult to directly quantify the crack tip and strain/stress distribution and concentration because the crack tip was too small, and the effects of the strain could not be observed. The mechanoluminescence, crack propagation, and mechanical behavior during mechanical testing can be visualized via the ML pattern during the adhesive evaluation. This allows for the recognition of the precise position of the crack tips and other mechanical behaviors related to structural failure.

Variation of conus medullaris location based on magnetic resonance imaging of the lumbar spine in Indonesia: A study at Dr Soetomo General Academic Hospital, Surabaya, Indonesia

Introduction: Recognition of conus medullaris position, contains lumbosacral plexuses which supply motor and sensory innervation to the whole lower limb, pelvic and perineal areas, is critical to avoid injury due to lumbar procedures, such as spinal anesthesia and lumbar punctures. This study aimed to investigate the position of conus medullaris among Indonesians through magnetic resonance imaging (MRI) of the lumbar spine at Dr. Soetomo General Academic Hospital in order to minimize the risk of conus medullaris trauma. Methods: A retrospective study was conducted to investigate the conus medullaris level and its correlation to gender and age Indonesian patients. The data were collected from lumbar MRI at Dr Soetomo General Academic Hospital during January 2020 and December 2021. The level variations of conus medullaris were recorded and determined as the highest, lowest and most common location descriptively. Results: A total of 257 patients (126 male and 131 female) were included. The highest level of conus medullaris was in T11-T12 vertebrae (five patients). There were 12 patients in T12 vertebrae, T12–L1 in 86 patients, L1 in 62 patients, L1–L2 in 76 patients, and L2 in 12 patients. The lowest level was in between L2 and L3 vertebrae in 4 patients. Gender and age had no association with conus medullaris level. Conclusion: The lowest level of conus medullaris was in between L2 and L3 vertebrae. The lumbar procedure such as punctures should be done below the L3 to avoid conus medullaris puncture trauma.

Memory conflicts and memory grey zones: War memory in Bosnia–Herzegovina between public memory disputes, literary narratives and personal experience

In Bosnia and Herzegovina, memories of the 1990s war remain hugely divided on political and institutional levels, constituting what we may think of as a mnemonic war. Interview-based qualitative research shows that people in Bosnia on the individual level tend to follow the dominant narrative of their own group, yet, when challenged on these viewpoints, may also admit that other narratives and different truths may exist. Indeed, this research seems to propose the existence of a memorial grey zone where more open understanding and recognition of other positions is possible. Thus, while memory politics and memory institutionalization are rigidly opposed, other types of memory mediation may challenge the ethnic divisions of the memory landscape, opening up a memorial grey zone. In this article, we study the individual reception of literary works written by Bosnian émigré writers, asking how readers interact with established yet fluid memory discourses in Bosnia. Using focus groups as an interviewing method, we explore how the texts are perceived and discussed by lay readers in the two political entities, the Bosniak–Croat Federation and Republika Srpska. We are particularly interested in how readers make sense of the memory accounts in the texts, and how this relates to personal experiences and official memory narratives within each of the two entities. We argue that the reading and discussions of literary war memories allow for complex negotiations between personal and official ‘group’ narratives, opening a memorial grey zone that transcends the sharp divisions dominating memory politics in Bosnia and creates space for alternative memory positions.

Spatiotemporal Modulation of Plasticity in Multi‐Terminal Tactile Synaptic Transistor

AbstractNeuromorphic system based on artificial synaptic devices is considered as a potential candidate to realize the in‐memory computing and parallel processing of data for overcoming the von Neumann bottleneck. However, to fully imitate the complicated functions of the biological neural networks at the hardware level is still a challenging task. In this work, a multi‐terminal MoS2 synaptic transistor is developed, which not only simulates various biological synaptic behaviors, including paired pulse facilitation (PPF), excitatory/inhibitory post‐synaptic current (EPSC/IPSC), spike‐rate‐dependent plasticity (SRDP), and spike‐timing‐dependent plasticity (STDP), but also can independently mimic the parallel signal processing and transmissions in biological multipolar neurons. By combining the multi‐terminal MoS2 synaptic transistor with the micro‐structured polydimethylsiloxane (PDMS) pressure sensors, an intelligent tactile recognition system is built up, which can realize the spatiotemporal recognition of touch position. Furthermore, with sensor selection, the spatiotemporal modulation of synaptic plasticity and the human learning and forgetting behaviors to the knowledge with different difficulty degrees can be mimicked. This work provides a novel interconnection scheme for simulating signal transmission and processing among neurons, showing broad application prospects of the multi‐terminal MoS2 synaptic transistor in intelligent human–computer interaction and bionic neuromorphic perception systems.

Association between issues in activities and psychosocial health status in the social positions of older residents' community-based activities: a cross-sectional study

Objectives When local governments and community nurses provide support for community-based activities led by older residents, it is imperative to reduce the burden of participants in leadership positions. This study aimed to identify the issues associated with the activities by social position, such as leadership, support, and regular participation, to discuss effective support for community-based activities and to examine the association between social position, relating issues, and psychosocial health.Methods Participants in community-based activities were recruited by the local government in Tokyo, Japan. Overall, 2,367 people from 155 activity groups from 40 municipalities responded. Social positions in the groups were defined as leaders who manage activities; supporters who support leaders; and regular members who do not have any specific role. The participants chose the issues of community-based activities from 10 items. Psychosocial health was measured by the WHO-5 well-being index (WHO-5) and Lubben social network scale-6 (LSNS-6). The relationship between recognition of issues and social positions were examined by the chi-square test. Interaction effects of social positions and issues (with or without) on the WHO-5 and LSNS-6 were investigated using a two-way analysis of covariance.Results The final sample comprised 2,096 respondents: 174 leaders, 296 supporters, and 1,626 regular members. There was a significant association between recognition of social positions and issues in the groups: respondents who reported no issues in the activities comprised 8.6% of the leaders, 27.7% of the supporters, and 53.6% of the regular members (P<0.001). There was a large difference in recognition between roles for issues related to group management, such as the lack of management members. There was no significant interaction between these issues and social positions in either WHO-5 or LSNS-6 (P=0.729, P=0.171, respectively). The main effect of the social positions was significant in both analyses (P<0.001). The leaders and supporters showed significantly higher WHO-5 and LSNS-6 scores than regular members.Conclusion The issues that the participants reported differed by social positions in activities. It may be effective to provide support according to the difficulty of sharing issues within the group. Regardless of whether or not the participants report issues, leaders and supporters had improved psychosocial health than regular members. The study concludes that organizational position in community-based activities may offer health benefits for older adults.

Решение обратной задачи кинематики для последовательных роботов- манипуляторов на основе нечетких численных методов

Nowadays the introduction of robotic systems is one of the most common forms of the technological operations automation in various spheres of human activity. Among the robotic systems a special place is occupied by sequential multi-link robotic manipulators (SRM). SRM have become widespread due to relatively small dimensions and high maneuverability, which makes their use indispensable to solve various tasks. In practice, the effectiveness of the functioning of the SRM can be influenced by various types of external environment fuzzy factors. Among the external factors there is a group affecting the ability to determine the exact target position. Such factors often affect technical vision systems. This problem is especially relevant for special purpose mobile robots operating in aggressive environmental conditions. A situation similar to the described one also occurs when a medical robot manipulator is used for minimally invasive surgery, when the role of the control and monitoring system is assumed by an operator. In this regard, the organization of effective control taking into account influence of the external fuzzy factors, that prevent the correct recognition of the target position of the SRM instrument, is an urgent problem. The authors consider the solution of the inverse kinematics problem for SRM based on the use of fuzzy numerical methods, taking into account the possible occurrence of singular configurations in the process of solving.

PROMOSI JABATAN DAN ROLLING JABATAN/MUTASI DALAM PEMBERDAYAAN SDM DI MA AL AMIRIYYAH BLOKAGUNG BANYUWANGI

High work performance will have an impact on high work results. Advanced educational institutions will be meaningless without the welfare of their employees. If employees feel more and more welfare from the results of their hard work, then employees do not hesitate to continually increase their sense of ownership and high responsibility for the sake of educational institutions and for their welfare. Therefore promotion and rolling positions/mutations in empowering human resources in educational institutions are significant to support one of the roles in the success of an organization/educational institution. This study uses a descriptive qualitative research method using observation, interviews/interviews, and documentation as data collection techniques. The data sources of this research inc 1) person, namely, (a) the head of the madrasa, (b) waka of the curriculum, and (b) supervisor. 2) place, namely, at MA Al-Amiriyyah Blokagung Banyuwangi. Data analysis techniques, drawing conclusions or verification. This study also checked the validity of the data by triangulation of sources, methods, and investigations. The results of his research: (1) the implementation of promotions and rolling positions at MA AL Amiriyyah includes several basic foundations based on experience, skills, and a combination of experience and skills, as well as using a merit system for the implementation of rolling positions, (2) the contribution of promotions and position rolling includes developing the vision and mission of the madrasa itself as well as producing certain jobs/angels so that the institution can provide recognition of positions and greater service rewards to employees whose performance is increasingly high. (3) evaluation of job promotions and rolling positions, namely with supervision from the institution as well as holding trainings and workshops for teachers and employees as well as seeing RKM for one year.

Improving the Degree of Teacher Professionalism Through Professional Education

Teachers' enthusiasm for taking educator certification is still high. This enthusiasm grew because of the recognition of positions as professional educators after passing certification. This article is based on the results of observations of implementing the teacher professional development competency model through PPG Jabatan. This research aims to obtain an overview of the implications of PPG in the Department for teachers' degrees in mastering professional knowledge competencies, professional learning practices, and professional development. This study employs qualitative research methodologies, utilizing the library as the primary information source and additional literature relevant to the paper's thematic focus. Research findings show that PPG demands participants' and managers' ability, sincerity, readiness, and ability. By analyzing the findings of this research, it can be concluded that the Teacher Professional Education (PPG) program can significantly increase teacher professionalism through an in-depth qualitative approach. PPG participants increase their pedagogical knowledge and skills and experience significant growth in self-understanding and reflection abilities. Conclusion PPG increases the mastery of professional knowledge, professional learning practices, and professional development ranging from advanced, decent, and capable to advanced degrees.