Implementation Opinions on the People's Government of Guizhou Province's Rural Revitalization Strategy. The aim of this paper is to try to establish a model according to the rural revitalization strategy proposed by the Guizhou government and extracts the main influencing factors from its analysis. Through the analysis of the data collected during the process of the rural revitalization, the paper undertakes a deep analysis of the evaluation and studies an optimized approach to the revitalization to provide a referenced review for other villages.Over the past few decades of rapid development, China has always attached great importance to the redevelopment of rural areas. Since the 19th National Congress of the Communist Party of China, the rural revitalization strategy proposed at the meeting has always received extensive attention. In 2019, the Chinese central government released a document on its rural revitalization strategy. In the document, Guizhou Province was listed as a key area to receive focus on pursuing rural revitalization. Meanwhile, the Guizhou government also formulated The  The Opinions of the Central Committee of the Communist Party of China and the State Council on Implementing the Strategy of Rural Revitalization has been repeatedly emphasized as the outline for rural construction, which has been “No. 1 Central Document” since the central government focused on issues concerning agriculture, countryside, and farmers in 2018. The Strategic Plan for Rural Revitalization (2018–2022) was reviewed and released by the central government and the Political Bureau of the CPC Central Committee T can be obtained from step 3. Normalized matrix X=(xij)m×n is based on the weight matrix and step 1 so as to achieve Q=(qij)m×n, and qij=xijwj.Build the weighted matrix with elements. The weight matrix [Q=(qij)m×n obtained in step 4 can calculate the optimal solution and the worst solution of each element.Calculate the total scores of representative villages. The element weighting matrix A+={maxAij|i=1,…, n}={ai1+, ai2+,…, ain+}; and the worst is A−={maxAij|i=1,…, n}={ai1−, ai2−,…, ain−}.The optimal solution is Then, calculate the gap between the totals and the optimized solution and the gap between the totals and the worst solution.Gap between the totals and the optimized solution is Gap between the totals and the worst solution is Ci=Di−/Di++Di−.So, the total score is C ≤ 1. When C=1, the village has the optimized solution. When C=0, the village has the worst solution. When C is closer to 1, the village is closer to the optimized solution and has the highest score.From the formulas above, 0 ≤ To ensure the consistency and reliability of the data, it is mainly from statistical yearbooks and statistics on government websites.Based on the National Rural Revitalization Strategic Plan (2018–2022) and other relevant research, the element system tries to focus on key areas and links, such as rural economic efficiency, sustainable agricultural and rural development, narrowing the urban-rural gap, and leading a healthy and happy rural life. The five levels: thriving industries, social civility and etiquette, prosperity, effective governance, and pleasant living environments are regarded as first-level elements . In termThriving industries are needed for high-quality rural development because whether the industry is prosperous or not will fundamentally determine the speed and efficiency of the rural development. Industrial prosperity has an overall impact on development. Industrial prosperity has a linkage effect and will have an overall impact on the high-quality development of rural areas. This paper will measure industrial prosperity from agricultural production efficiency, mechanization level, agricultural technology level, marketization of agricultural products, and agricultural structure.General Secretary Xi Jinping has pointed out that a pleasant living environment with clear waters and green mountains are as good as mountains of gold and silver, and the maintenance and governance of the rural ecological environment is important for high-quality rural development and can effectively offer villagers a sense of security and improve the environment. The rural environment can guarantee a livable environment and rural production. Rural ecology is mainly to promote the establishment of a livable environment and ensure rural production, and ecological livability is mainly measured from the ecological environment and the living environment.Social etiquette and civility can promote high-quality development in the countryside. Rural development cannot be separated from the promotion of social civility. It is rich in content because it involves historical culture and education. Rural style civilization is the software support for high-quality rural development, and high-quality rural development is inseparable from the construction of rural style civilization. Rural culture involves the development of rural public culture, cultural education construction, and the inheritance of excellent culture.Effective rural governance is an important part of achieving high-quality rural development, and is also an important factor in promoting rural modernization, and to a certain extent, it can ensure the healthy and orderly development of rural society. Therefore, democratic practice, grassroots legal system, social morality, and balanced development are selected for measurement.Prosperity is the fundamental goal and an important drive for high-quality development. In other words, it is important for development. The quality of life of rural residents is measured by their income, income gap with other areas, and living standards.Using SPSS25.0 to conduct KMO test and Bartlett test on the sample data, the results in pk and component score coefficient βi as seen in Tables The data is analyzed for the cofactors and then variance contribution A1, A2, A3, have a greater impact on the comprehensive score, and other factors related to rural culture, such as C1, C2, C3, have the least impact on it.According to Judging from the scores in Villages with more pleasant living environments get lower total scores, and most of them are ones with relatively backward industries and poor industrial development. It has something to do with their destructive economic development, or an implacable difference exists between their industry and environment. The results show that environment has a great impact on rural development. Generally, there is interaction between environment and growth. Factors such as complex geographical environment and land-use policy reveal the evolution of contemporary rural areas. Due to changing economic and social framework conditions, future demands on rural landscapes will also change . As for Picture 1 shows that the overall coefficient score of rural civility and etiquette is not the highest, but it is always in a relatively stable position. The influence of rural civility and etiquette on villages is relatively stable. Therefore, it can have a direct or indirect effect on the high-quality development of rural areas so that the villages with lower scores in rural civility and etiquette get lower total scores. Therefore, it is necessary to explore the methods of developing rural civility and etiquette in villages. The development of rural civility and etiquette in China is greatly influenced by national and local policies. Also, it cannot be separated from the development of local culture. Therefore, rural civility and etiquette also need to be led by the government and subject to laws. It is difficult to maintain local culture and develop new culture if the former can no longer be protected and the latter is half formed. Limited living space and farmers' inability to express and develop cultural aspects are partly responsible for the issue. Culture is the foundation of rural development because decadent culture tends to bring about backward villages. Adequate living space is essential for the prosperity of rural culture, but these spaces need to be carefully planned to accommodate and develop local cultural needs. Despite China's rapid urbanization and industrialization, most Chinese people still live in the vast countryside areas or are still registered as rural residents. Rural civility and etiquette cannot be developed without economic growth, without keeping alive local culture, and without good leadership and the improvement of laws and regulations.Through the analysis of pictures 1 and 2, effective governance can guarantee high-quality rural development. Although the score of effective rural governance is relatively stable, the overall score of each village is relatively low and villages with better industrial development have higher scores regarding governance. But, overall scores of each village are not ideal, which greatly affects their development. So, it is necessary to explore in-depth the approach towards effective rural governance. Effective governance is an important part of advancing the modernization of the Chinese socialist governance system. The rule of law is the fundamental way to rural governance in China. To cultivate and apply the rule of law within rural jurisdictions must meet practical needs. Efforts should also be made to reform rural governance and promote the modernization of the rural governance system and capacity. The key to high-quality rural development is effective governance, which needs proper handling of issues such as ethical relationships within the governance process. The modernization of rural governance in the new era is the foundation for the strategy of high-quality rural development and the modernization of national governance. Self-governance, rule of law, rule of virtue, and rule of technology are the theoretical ideas behind functional, rural governance in the new era. The combination of autonomy, rule of law, rule of virtue, and the rule of technology is the only way forward for rural governance in the new era. Having an autonomous body, the rule of law, the rules of virtue, and the rational choice of technological transformation is the main contradiction of Chinese socialism within the new era of rural governance and the best combination of good rural governance.Common prosperity will be an important factor as to whether villages can achieve high-quality development . AccordiSince 2019, the country has reformed land-use policies and implemented national land space planning, which has imposed stricter restrictions on land use. Meanwhile, the data collected in the third land survey carried out in the past two years is more accurate than that of ten years ago, so that the use of land for construction is relatively strict. The original land reserved for construction is also very limited, and it will be difficult to expand on this in future. Therefore, due to the control of the land at national level, many buildings of secondary and tertiary industries cannot be constructed, leading to difficulties in industrial development. Maybe in the future, urban-rural integration will be the way of rural revitalization.According to our team's on-the-spot investigation of the villages, many villages are still at the planning stage, and the plan is difficult to carry out due to limited government funds. For example, Xijiang Qianhu Miao Village in Leishan County, Qiandongnan Prefecture, Guizhou Province, is rated as a 4-A level tourist attraction, which shows it is highly rated and is a very successful case of rural revitalization. It attracts millions of tourists every year who come to visit and experience the cultural customs of the local ethnic minorities. It also drives local economic development. For villages, tourism is usually needed to drive local development, but in fact, only 2–3 million yuan is invested in rural revitalization in each local village each year. Such a small amount of capital investment is like a drop in the ocean for a village with several hundred households.Limited conditions allowing for development of the villages lead to difficulties in rural revitalization and construction. As Guizhou Province is located in the southwestern mountainous area of China, it naturally faces hardships and difficulties with regards to transportation. In particular, methods of transportation to and from many villages is small and inconvenient, making it difficult for the development of tertiary industry. With such limited condition, there are some difficulties occurring in rural spatial planning—procedural and distributive justice and value conflicts. In addition, the infrastructure of the village itself is also far from being perfect. According to our survey, although many villages are basically electricity covered, but the wires are overhead and not buried in the ground. Despite this, equipment allowing for running water is installed although the sewage treatment system is not perfect, and some waste water is discharged into natural water bodies directly. Although most roads have been developed and maintained, the roads are basically single lanes with limited parking. Fire safety aspects are not ideal and some living facilities are very old. Some villages have poor mobile phone signals and there are almost no facilities that could support the development of tertiary industry. According to the existing research, if a village wants to develop the local economy through tourism and other tertiary industries, the requirements for good basic local conditions, infrastructure, and supporting elements are demanding. For some traditional villages in Qiandongnan Miao and Dong Autonomous Prefecture, it takes considerable effort just to improve the living environment.Analysis of the status quo of 10 ethnic minority villages in Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou Province reveals that there are limitations that seem so prevalent regarding rural revitalization.This paper forms a system for the evaluation of the rural development with 5 elements including social etiquette and civility, pleasant living environments, effective governance, prosperity, and thriving industries, and a total of 17 indicators. Based on statistics and data from the local government, a comprehensive evaluation of the rural revitalization in representative villages of Qiandongnan Miao and Dong Autonomous Prefecture is conducted through the factor analysis and TOPSIS method. The results show that the evaluation system and evaluation method can more scientifically measure the development of all the cities and villages. Therefore, the evaluation index system can generally be used. As a result, it can be promoted in other regions in order to provide feedback on the experiences for the rest of the country.This paper uses this evaluation system to evaluate all data, and it can be found that: First, rural industrial development has an important impact on the implementation of the high-quality rural development strategy; second, the development in economically prosperous villages is significantly better than those with relatively underdeveloped economies, which are home to lower income and lower production output. Total scores for Laidong Village, Ganxi Village, Pingzhai Village of Lantian Town, and Diliang Village are relatively low. Third, there are large differences in resources regarding agricultural technology and power supplies for agricultural production between all villages, so joint efforts are required from the government and the local people. Fourth, democratic practice, the implementation of grass root policies, moral governance, and the inheritance of excellent and valued culture will each affect the overall scores of high-quality rural developments and affect the process. Therefore, these factors should not be underestimated. Finally, the ecological environment, living environment, and agricultural structure are generally not ideal in the villages of Qiandongnan Miao and Dong Autonomous Prefecture, they have multilayered characteristics, apart from that, local residents also play an important role in rural revitalization, especially leaders and key people , so univ


Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Observations to Benefit, the Environment land cover image database, is created by citizen scientists worldwide who use their handheld cameras to take a set of six images per land cover site. These images have clutter due to man-made objects, and the pixel uncertainties result in incorrect labels. The problem of accurate labeling of these land cover images is addressed. An integrated architecture that combines Unet and DeepLabV3 for initial segmentation, followed by a weighted fusion model that combines the segmentation labels, is presented. The land cover images with labels are used for training the deep learning models. The fusion model combines the labels of five images taken from the north, south, east, west, and down directions to assign a unique label to the image sets. 2916 GLOBE images have been labeled with land cover classes using the integrated model with minimal human-in-the-loop annotation. The validation step shows that our architecture of labeling the images results in 90.97% label accuracy. Our fusion model can be used for labeling large databases of land cover classes from RGB images. Land cover refers to the biological material on the surface of the Earth; on the other hand, it describes how people utilize the land and socio-economic activity. It also shows how people use the landscape for development, agricultural, or mixed uses. Thus, the Land Use Land Cover (LULC) refers to classifying the human activities or elements covering the Earth using scientific and statistical methods.LULC classification methods are broadly categorized as pixel-based or object-based methods. Traditionally, the LULC was conducted manually, later moved to digital technology using the GIS technology, and recently, the Artificial Intelligence (AI) revolution has led to the emergence of new algorithms. A review of machine learning methods using feature extraction for supervised and unsupervised LULC classification is given in . The AI The images are preprocessed using a pretrained DeepLabV3 architecture for removing man-made objects with a mask. Four architectures: DeepLabV3, DeepLabV3+, UNet, and UNet+ are used for segmentation.The segmented directional images are combined in a decision block using a weighted fusion method to generate a label for the most common land cover class of the group of images.The same scene in an image of a land cover may have complex spatial and structural patterns. In contrast, different land-use types may have a similar reflectance spectrum and texture. It becomes challenging to obtain a precise classification of the land use type due to high intraclass heterogeneity, and low interclass diversity . We obseIn this paper, we present an integrated deep learning model for segmenting directional LULC images, combining the segmented output for generating unique labels to groups of images. The rest of the paper is organized as follows. This paper uses CNN-based UNet, Unet++, DeepLabV3, and DeepLabV3+ for semantic segmentation on the GLOBE Dataset.The Global Learning and Observations to Benefit the Environment (GLOBE) application allows the public who are called citizen scientists to make observations of the environment using their camera and post the images in the GLOBE database. It is an open dataset available to researchers, students, and the scientific community for performing their investigation on different domains, such as the atmosphere, biosphere, hydrosphere, and pedosphere. The visualization tool  includesThe series of six photos taken by observers are quality checked. The citizen scientists who capture the photos are kept anonymous. Many applications of the GLOBE land cover data are listed in . The phoSince GLOBE is an international science and education program that allows students, teachers, citizen scientists, and the public worldwide to participate in data collection from anywhere on the planet, the collaborators contribute by taking images in the four cardinal points and the directions up and down. These images are varied, from blurry to out of focus and with different sizes, so we created a simple database of RGB images, taken from the Globe Visualization tool, fully manually annotated and diverse to train the models for accurate Land cover semantic segmentation. Data pre-processing involves image resizing, normalization, and data augmentation. The dataset has different sizes, so image resizing is required. We resize the image to 512 × 512 and zero pad if necessary. Image pixel values are normalized between 0 and 1 by dividing each pixel by 255. A deep learning model with many parameters should be trained on a relatively large proportional dataset for good performance. Data augmentation is a successful approach to improving a model performance. Hence, it increases variation in the image dataset [The GLOBE database does not have ground truth. Hence, we annotated the images and created a ground truth for training and validation. The trained model can now be used without human in the loop for labeling new images. The images are annotated using 12 classes: Grass, Building, Tree, Road, Sky, Soil, Bare Rock, Sand, Sidewalk, Water, Gravel, Blue Mountain as shown in We work with CNN-based models due to their high performance in high-resolution images for per-pixel semantic labeling (or image classification) . SpecifiDeepLabV3 is a semantic segmentation model designed and open-sourced by Google that outperforms DeepLabV1 and DeepLabV2. In DeepLab models, input feature maps become smaller while traversing through the convolutional and pooling layers of the network by using Atrous convolutions and Atrous Spatial Pyramid Pooling (ASPP) modules ,22. Deepx, each model x belonging to class j. These models predict class label for x as x as j in the directional image and if ⑤ The last step is the final fusion step that combines the segmentation results of the four models. The postprocessing step builds a new classifier by taking a majority vote of the four segmentation models in the previous step. If we denote the segmentations models as LOBE see  because For training semantic segmentation models, we use the Adam optimizer with a learning rate of 0.001. The cost function is essential in the training and validation stages. However, there are many of them, and it is impossible to choose one of them as the best  for the All our models are trained for 1000 epochs, with a batch size of seven. The validation metrics are the mean intersection over unit and labeling accuracy. Training the four models with 600 images takes four days in Google Colab.A total of 600 images are used for training the networks, and 100 images for the validation for 13 land cover classes. The output labels are verified for accuracy with testing images from existing correctly labeled GLOBE images. The integrated model is used to assign LC labels to 2915 GLOBE images. In order to standardize the image acquisition and sharing of LULC images around the world, certain requirements can be imposed on the citizen scientists for acquisition and sharing of LULC images such as (a) use of certain models of cameras, (b) imaging distance, (c) imaging time during the day, and (d) weather conditions such as cloud cover during the day, so that the illumination is uniform for all the acquired images.This section presents and discusses the results of applying the proposed method for labeling and classifying the GLOBE images. To evaluate and analyze the performance of UNet, UNet++, DeepLabV3, DeepLabV3+, and the fusion method proposed, we use Intersection over Union (IoU), also known as the Jaccard index shown in In The IOU of our fusion model increased accuracy by 1.91% on average compared to the the best of the four individual models. However, if we look at the soil class, the IOU increased by 2.94%. This result demonstrates that our method performs better than the existing models for segmenting directional, rotated and translated land cover imagery.For the image sets in The confusion matrix with land cover labeling accuracies for the 13 classes using the fusion model is given in The GLOBE images are obtained from handheld cameras; hence, they have a lot of variability in each land cover class. The proposed fusion model has a validation mIOU of 90.97%. The method has difficulty discriminating between the buildings, road, sidewalk classes, and grass and tree classes.The images are complex, as they are high-resolution RGB images taken from urban and rural areas with a high amount of detail and man-made objects. The images need a lot of preprocessing to remove the noise and variability due to multiple classes and be assigned a land cover label. Our fusion architecture is scalable and can be expanded to a larger application to include more land cover classes. We have developed the fusion architecture in GoogleColab, which runs the four models in parallel using the Graphical Processing Unit (GPU). The current architecture uses four models, but the fusion can be conducted with three models. At least three models are necessary for majority voting. We obtained good results by fusing four models.Each of the architectures perform well for a particular land cover class, and not for the other. For example, Unet performs well for water and is useful for removing background objects, and DeepLabV3+ performs well for grass and sand classes. Hence, it is necessary to check the accuracies individually for each land cover class.A fusion method combining four deep learning architectures is used for labeling high resolution remote sensing imagery from the GLOBE database. DeepLabV3+ is used to remove man-made objects from the images. A weighted averaging is used in the decision block to combine the segmentation results of the four architectures for generating land cover labels with probabilities. The accuracy of labeling achieved is 90.97% for the validation dataset. The GLOBE database does not have ground truth, and our proposed method uses minimal human in the loop based annotation. Our method successfully labeled 2916 image groups. A total of 20% of the images are used for training, and 7% for validation. This work provides a tool for the labeling of land cover images acquired by the public from different parts of the world with different camera settings as well as random image captures from humans with no fixed rotation, translation, or scaling parameters. Despite the variability of the images in the GLOBE database, our labeling tool assigns labels with minimal human in the loop annotation.


The underdevelopment of the higher education system in Guatemala and the fragility of its science and technology (S&T) contexts have compelled a significant number of talented Guatemalan scientists to be trained, educated, and employed abroad. The relocation of such skilled human power to different countries and regions has resulted in a growing Guatemalan Scientific Diaspora (GSD). Until recently, the emigration of scientists from the Global South to scientifically advanced countries in the North was studied as it negatively impacted the countries of origin. However, technological upgrades and globalization have progressively shifted the paradigm in which such scientific diasporas interact and connect, thus enabling them to influence their home countries positively. Due to the lack of knowledge-based evidence and functioning connecting platforms, the value and potential of the GSD in their involvement in proposing solutions to complex socio-economic, environmental, and other challenges faced by Guatemalan society remain unknown. Moreover, the lack of interaction of relevant stakeholders  represents a pervasive obstacle to the untapped impact of the GSD in the country. This study outlines the Guatemalan scientific diasporas' networking as a mechanism for building research excellence and intellectual capital. This force could respond to the need to strengthen the national science capacities and meet the demands for knowledge production and access to broader sectors of society. This research applied qualitative methodology that, through the conduction of focus group discussions and semi-structured interviews with members of the Guatemalan scientific community and relevant key stakeholders, delved into the existence and articulation of the GSD and potential stages for their engagement with their country of origin. Findings highlight the importance of digital and technological pathways that might leverage the GSD's knowledge and experience, channeling skills, and international connections for better interaction with the Guatemalan society. Furthermore, the discussion addresses how technology might turn brain drain into brain circulation, enabling the articulation of the GSD as a viable opportunity to generate collaboration between scientists abroad and local actors, ultimately impacting the building and development of Guatemalan science and national research capacities. IOM empIn the case of Latin America, some countries have been adapting their policies regarding their diaspora to maximize the potential contribution to local development. In 2007, 94.0% of governments held policies and programs for their diaspora residing abroad, mainly in the United States of America OIM, . In the Notwithstanding, research on how the needs and perceptions of the scientific community  dialogue with stakeholders back in their home countries is lacking. To plan for any future policy engagement, this research aimed to outline the potential of the GSD network as a mechanism for building research excellence and intellectual capital while also delving into the existence and accumulation of the GSD and stages for their engagement with their country of origin. Furthermore, it aimed to address how technology might turn “brain drain” into “brain circulation” and enable the articulation of the GSD as a viable opportunity to generate collaboration between scientists abroad and local actors, ultimately impacting the building and development of the Guatemalan scientific capacities. This research sheds light on the current state of the mapping, characterization, and identification of the GSD for their engagement and interactions with their peers, relevant stakeholders, and broader sectors of the society back in their country of origin.The research was conducted using a qualitative methodology. Data was collected from two groups of participants: (i) The Scientific Community with emphasis on members of the GSD, and (ii) the perspective of a comprehensive group of Stakeholders with relevance to the GSD. To select the first group, a strict set of criteria was designed to guarantee a diversity of views and perspectives .In the case of the second group, a categorization of six perspectives was followed to include an integral approach . The preAs for the methods for data collection, participants were offered two alternatives (considering their time availability): (i) Semi-structured interviews and (ii) Participation in Focus Group Discussions. To fulfill the general objective of this study, literature review and desk research was carried out in complement to the collection of primary data. All interviews and focus group discussions recorded used different digital platforms and software . Semi-structured interviews averaged 45 min, whereas focus group discussions averaged 60 min. A total of 30 h of audio-visual material were recorded. All materials were transcribed into text files (by listening and directly transcribing), codified, and analyzed to determine patterns, trends, shared content, and contrasting views. The study was reviewed and approved by the Ethics Committee at the University of Technology of El Salvador (UTEC). All participants were asked to sign an electronic informed consent form before participating in the study. Identities of participants are not identifiable nor traceable; all transcripts were encoded, and recorded material was shared only among members of the research team.n = 491) are female. Seventy percent (n = 441) reported as country of residence Guatemala  living in 33 countries, including Guatemala. In relation to their country of residency and work of the GSD, both North America  and Europe  followed by Asia 1.1% (n = 7), Oceania 0.5%  and no reports from countries in the African continent . Demographic information gathered from these databases included: (a) gender , (b) last completed academic degree, (c) field of knowledge/expertise , and (d) geographical location of their place of residence. Descriptive statistics were conducted to determine percentages, means, and standard deviations from the gathered data. The information available from the two databases summed up 631 scientists, out of which 78.0%  by signing a Constitutive Act during the event's closing ceremony. The RedCTI Network has been in operation since its creation (over 15 years) with the support of the National Secretariat of Science and Technology (SENACYT). The SENACYT is the coordinating public institution. It is responsible for supporting and implementing the decisions that emanate from the CONCYT (for its acronym in Spanish)In 2005, the National Council of Science and Technology (CONCYT) brought together a group of Guatemalan scientists who worked on research activities and had their place of residence outside of the Guatemalan territory. This action aimed at “strengthening the science and technology capacities in Guatemala to ultimately propose solutions to the problems in the Guatemalan society”RedCTI was created to contribute to the preparation and implementation of scientific-technological development plans through science, technology, and innovation and propose viable alternative solutions to improve the population's quality of life. Moreover, one of its main goals is to link Guatemalan scientists working outside with those inside the country. As of February 2022, this network registered a total of 196 members, according to the database provided by SENACYT. The directory allows mapping the GSD according to their gender, field of knowledge, and country of residence. Applying a binary gender filter  in this network, 71.4% (n = 140) are male and 28.6% female (n = 56). According to their field of knowledge professionals reported in this network includes 23.5 (n = 46) in the Medical Sciences 31.6% (n = 62) in the Natural Sciences, 27.6% (n = 54) in Engineering and Technology, 11.2% (n = 22) in Social Sciences, and 6.1 % (n = 12) in Agricultural Sciences. In terms of the level of education among its members, 5.1% (n = 10) reported having completed a bachelor's degree (licentiate), 29.1% (n = 57) master's degree, while 65.8% (n = 129) hold doctoral degrees (Ph.D.). Interesting data refers to the reported country of residence 53.6% (n = 105) reported their country of residence as Guatemala, while 46.2% (n = 91) reside abroad. In this aspect, a more comprehensive concentration of GSD is found in the United States of America with 17.9% (n = 35), followed by Mexico at 5.6% (n = 11) and 4.6% in France (n = 9). Other destinations with less presence include Argentina, Puerto Rico, Hungary, Italy, Netherlands, Sweden, Japan, and Taiwan (The d Taiwan .n = 336) of its members indicated they have a residence in Guatemala. Of those reporting living abroad, 11.3% (n = 49) indicated living in European countries, while 7.4% (n = 32) in countries of North America. 2.1% (n = 9) reports living in South America, 0.7% (n = 3) in Asia and just 0.9% (n = 4) in Oceania . With regards to their area of knowledge, the proportion of self-reported disciplines were: 27.0% (n = 118) in Agricultural, Biology, Earth Sciences (incl. Space Sciences and Astronomy) as well as for Interdisciplinary, Humanities, Social and Economic Sciences, respectively; 19.3% (n = 84) in Astrophysics, Physics, Math, Engineering, Computer Sciences, and Communication, 14.0% (n = 61) in Veterinary, Livestock and Health Sciences and 12.0% (n = 54) in Chemistry and Food Sciences. In terms of the level of education, 34.8% (n = 148) hold academic degrees at the undergraduate level, while 66.0% (n = 287) hold academic degrees at the graduate level  . This international organization was co-founded by UNESCO and the World Academy of Sciences TWAS in 1989 OWSD, . OWSD opoctoral) .Interactions between the GSD and different stakeholders seem to be erratic, episodic, and promoted with fragmented initiatives that ultimately obtain partial and inconsistent results. Moreover, as the engagement of the GSD with their country of origin is still unexplored to a significant extent, the roles and responsibilities among key stakeholders in their interaction with GSD is still a pending issue.At the policy level, CONCYT is the governing structure in scientific and technological development in the country. It is responsible for the promotion and coordination of activities carried out by the National System of Science and Technology members. The Law for the Promotion of National Scientific and Technological Development , the National Chapter for Guatemala of the Organization of Women in Science for the Developing World , and the National Directory of Researchers -DNI- created by the Guatemala Nacional Secretariat of Science and Technology (SENACYT). The DNI is the most extensive database of researchers in the country; however, the GSD indicated they have heard about it in general terms, yet it does not include specific targets for Guatemalan scientists residing abroad. The DNI platform has filters, statistics, and promotion limitations to include more scientists living abroad. According to sustained communications with the technical personnel from SENACYT during the study, this platform will be improved through 2022. Changes include improving a more pleasant user experience, updating profiles, and promoting the site to increase the number of registered scientists.Participants see an opportunity to build a robust platform using the technology and the data available in the existing platforms , similarly to experiences of other countries from the region and through the application of technological tools. For example, Costa Rica has a scientific diaspora mapping tool on the web with information about current location, plans of return to the government, reasons to leave the country, and collaborations opportunitiesSome participants considered that through AI and machine learning, the process of developing algorithms and statistical models could be adapted to identify and analyze patterns in the GSD. Mainly as more researchers are familiar with social networks, informing systems with information about their country of origin, institutional affiliation, and fields of research. Conversely, various participants consider that AI or machine learning might not be applicable to identifying, mapping, and characterizing the GSD as this is not large enough to require such complex technology. In any case, participants agree that the current state of the GSD shows inefficient articulation, management, and identification of the potential of the GSD.Most stakeholders understand the concept of scientific diaspora. They frequently refer to it as a loss for the emigrating country caused by poor conditions in the emigrating country regarding resources, technology, education, and political and economic stability. For example, civil society stakeholders reflected on the loss of connection between the diaspora and their home country. However, stakeholders in the industry expressed their understanding of the diaspora with a positive sentiment, highlighting how it represents new opportunities for migrating scientists and new opportunities for collaborations between the industry and the GSD. While still understanding the concept of diaspora as a loss in human capital, stakeholders representing international partners also expressed it as a possibility to strengthen Guatemalan systems, as one of them shared: “I thought of seeds that sprout for science or the beginning of strengthening a system.” Stakeholders representing the Science and Technology (S&T) policy sector pointed out that the definition of diaspora implies elements of organization and similar objectives, which does not characterize the GSD now. Notably, stakeholders representing Foreign Policy institutions were not entirely familiar with the concept of scientific diasporas.In general, stakeholders indicated a lack of structured actions focused on mapping the GSD. Still, most could identify entities with the potential . Stakeholders representing international partners expressed a keen interest in mapping the GSD and understanding the landscape regarding scientific development in Guatemala. An initial report about scientific development in Guatemala was published by UNESCO titled “Survey of research and innovation in the Republic of Guatemala”  The lack of doctorate programs in the country, which prevents more robust platforms from linking with local researchers. Moreover, social and economic scientists consider it essential to highlight the financial contributions of science to connect with government stakeholders and the population. (b) Lack of time was a barrier linking side projects in Guatemala or participating in existing networks. One of the participants mentioned, “Carrying out research takes a long time in collaboration, 5–6 years”. (c) The lack of funds incentivizes locals to connect with the GSD and academic institutions, where teaching has more weight than research.The members of the GSD suggested the use of existing technology platforms to connect with different projects and locals in Guatemala. Most mentioned platforms were WhatsApp®, email, video conferencing platforms such as Google Meets, Microsoft Teams, Zoom, Blue Jeans; social networking platforms such as private groups and public αMeta (Facebook) pages. They mentioned that using the existing ones could prevent social media exhaustion. Some others proposed the creation of new platforms that could allow for “posting” projects/interests, so the GSD can easily connect and identify opportunities for collaboration with other Guatemalan scientists, professionals, and other national authorities. Using AI algorithms, data can be mined, joined, and categorized to identify matching interests and demand of skills.Stakeholders are aware of numerous initiatives in the academic and scientific communities that promote and enable collaboration with scientists of the GSD. They acknowledged that it is mostly by individual researchers' motivation and through their personal and professional network that a large part of the existing collaborations is created. The strengthening of existing institutional networks was strongly recommended to promote working relationships between scientific communities and the GSD. Likewise, some specific scholarship programs, such as the German and UK Academic Exchange Service , were named influential in providing scholarships for higher education and maintaining strong ties and ongoing communications with its recipients.Technology was mentioned as a valuable tool to facilitate communication and exchange between both parties. For example, using Zoom, Google Meets, or YouTube to host conferences and talks where researchers can present their work and highlight opportunities for collaboration. ResearchGate, Academia, LinkedIn, Twitter, and Facebook groups were also recommended as a tool that allows researchers to meet one another and share their profiles and expertise. However, some stakeholders expressed concern over the excess of existing social and professional platforms and proposed that a new platform would require powerful incentives for scientists to join.Additionally, many thought it was critical to involve institutions such as CONCYT, SENACYT, and professional associations to make it easier to establish such connections and offer opportunities such as scholarships, research funding, and workshops for specialized training. Representatives of higher education identified some opportunities for improvement in CONCYT. They concluded that CONCYT needs to “keep a record, be active, have more budget, be more linked to GSD and include social sciences in its agenda.”Interestingly, there are already some organized efforts between stakeholders and the GSD. Stakeholders in Civil Society indicated they already hold strong ties with the GSD, particularly involving scientific collaboration and support to students aspiring to receive specific scholarships or study in a foreign country. International partners also mentioned collaborating with the GSD, specifically on topics regarding education. They also expressed the desire to further support women in the GSD, noting a disparity in their available opportunities.Representatives from different groups in society also identified some barriers to connecting the GSD with local scientific communities. Stakeholders in S&T policy mentioned that one of the significant challenges is the lack of interest of the GSD in working in the country. A participant noted, “Many of those scientists who live, and work abroad consider their condition of skilled emigrants as an achievement, [they] do not look back, they see escaping from their country as an opportunity in their career development. They are no longer interested in what happens back in their country; they may be working on a fascinating topic. Still, suppose they are asked to take part in collaborations. In that case, they disregard the invitations”. This sentiment was echoed by stakeholders in Civil Society, who mentioned that, once students' goal of moving abroad was achieved, they showed little interest in keeping in contact and little commitment to being involved in supporting projects in Guatemala.Moreover, one of the higher education representatives said that “the main barrier that I see is mistrust and the few spaces for advocacy with the public sector”; there are many difficulties connecting with decision-makers and promoting initiatives. It is generally complicated for scientists to be heard in political spheres. One of them mentioned that connecting the GSD to the political sphere is very “ambitious,” yet believes that something more feasible is strengthening the GSD's connection with academic institutions to achieve better doctoral programs and research projects.The stage of Engagement refers to a systematic, constant, and sustained participation of the GSD in different schemes, mechanisms, actions, and policies through which they exert influence in their country of origin.Development must be driven by both the public [sector] and the private [sector]. A national policy to engage the GSD is necessary. Member of the GSDThe biggest problem is that at the national level, there is no policy to include scientists in the development process of our home country. Member of the GSDRegarding the actions developed by the Ministry of Foreign Affairs, the GSD has not identified any program that links diaspora scientists with the country, and even when the scientists have suggested collaboration options, they have not received any formal response. The GSD stated that some embassies are helpful for students who study abroad. The embassy of the United States of America in Guatemala and the embassy of the United Kingdom in Guatemala were identified as some of the most dynamic connections with their alumni. These connections might be more systematic as they offer prestigious and globally recognized scholarships such as the Fulbright and Chevening programs. The GSD also pointed out that another limitation is the lack of diplomatic missions in certain countries such as Hungary and Switzerland. Whereas, having a Guatemalan embassy near them, such as the case of the Guatemalan embassy in Korea, might provide more support and resources. Many other members of the scientific diaspora point out that the inefficiency in general bureaucratic processes translates into the inefficiency of the embassies in connecting with the GSD.Regarding the interactions with the government, the GSD generally showed dissatisfaction with the work of government institutions for linking them with projects in Guatemala. They indicated that the government should be more intentional and proactive in contacting Guatemalan scientists abroad since the GSD “.... has the knowledge and desire to contribute to the country, but there are no clear ways to do it”. The GSD indicated that the meetings held by these institutions are fruitless because no concrete conclusions have been reached. It was also mentioned that the Red CTI is not agile. The research of institutions such as the Institute for Nutrition of Central America and Panama (INCAP) is not transferred to decision-makers. The GSD proposes that scientists get involved in the science and technology committees of the Guatemalan Congress. The GSD identified the importance of linking with the Ministry of Economy with structured conferences developed in Guatemala to involve more scientists. One of the actions suggested to get more involvement from the public sector is to measure indicators of the current situation in the country to assess the importance of making science-based decisions. Suggestions are used to strengthen the link between the Ministry of Economy and CONCYT, showing the economic relevance of science in the country to gain more confidence in the industrial sector with academia. The diaspora thinks that SENACYT has developed good outreach work, especially promoting STEM careers for girls. Among the limitations observed in the existing programs is the bureaucracy of the administrative system, i.e., for obtaining and managing funds, within the Red CTI meetings and decision-making without interaction with members, and lack of financial support for both research grants and follow-up of research activities.Converciencia. This event in the past has provided opportunities for the GSD to connect with peers (period 2005–2020). The initiatives of the OWSD Guatemala chapter (period 2020–2021) have also been mentioned as crucial examples of how to link the diaspora and generate results and actions. Universities and industries have approached the diaspora collaborations by having them in conferences that have allowed them to connect and outreach to the Guatemalan audience.They also mentioned the importance of Regarding the barriers to linking actions, scientists consider the lack of trust to give preference to their interests by the different sectors, the weak link between the academic and public sectors, the lack of public investment in research, development, and innovation, and the low value of science reflected in the workload and working conditions of most scientists residing in the country. A few scientists consider that top-down changes are complex and must be motivated from the bottom up, starting with transforming students' lives by researching and producing results that allow them to demonstrate the importance of science to the daily lives of the general population. One of the solutions AI could generate to support the actions for the GSD connections is to create a Guatemalan platform for the community of scientists. They proposed a tool that visualizes scientists' profiles and connects them directly with private institutions, government, or civil sector organizations interested in their academic contribution and professional skills.Converciencia, which members of the GSD identified as a valuable opportunity for connection. According to stakeholders in Higher Education, Converciencia was identified as a relevant mechanism for developing collaborations with the GSD. However, it shows at least two limitations (1) bringing scientists (including members of the GSD) who want to implement projects that do not correspond to the national context and (2) bringing scientists (including members of the GSD) who have prejudices about the knowledge of local scientists.Stakeholders described current ties with the GSD as insufficient and weak. For example, stakeholders in the industry mentioned having some relations with the GSD but acknowledged these ties are weak and limited. Higher Education critiqued the approach of When identifying the reason for the current lack of collaborations, stakeholders underlined a lack of structure as one crucial obstacle to engaging the GSD in Guatemala's sustainable development. Stakeholders expressed that there is a lack of institutional systems and policies that enable the engagement of the GSD in the country's development. Only a few stakeholders had any knowledge about actions by the Ministry of Foreign Affairs and diplomatic corps in supporting such collaboration, which was described as insufficient and sporadic, even though both were considered relevant agents. Namely, the Department of State and the UK embassy were explicitly identified as entities that offer or have offered support to establish collaboration networks with the GSD. Likewise, stakeholders considered that there had been little action involving the GSD in topics of development and policymaking and a lack of interest and investment in science overall. SENACYT was mentioned as one of the most active government institutions because it finances the salaries of former members of the GSD who return to the country, involves GSD members in their activities, and has its Red CTI network. CONCYT was also mentioned as a relevant actor, although the result of its actions was deemed unsatisfactory. Finally, stakeholders representing Foreign Policy institutions considered that the Central American Parliament (PARLACEN) has the potential to generate regional impact in establishing such connections. However, its only action in this area is the creation of a regional fund for science and technology, which is currently being developed. The involvement of scientific experts in the development of industry was acknowledged as necessary. Still, none of the participating stakeholders had any knowledge of specific mechanisms that enabled the involvement of the GSD. It was generally agreed that universities and individual researchers are the main drivers of scientific collaboration with the GSD, using their personal and professional contacts to establish networks and develop projects.A second significant obstacle to engaging the GSD in Guatemala's sustainable development was a lack of sufficient resources and incentives. For example, representatives of Higher Education identified two main barriers to connecting the GSD with local scientists: lack of time and lack of funding. It was mainly acknowledged that the scientific community deals with various limitations  that hinder the support they can potentially provide. Furthermore, stakeholders in Civil Society observe a lack of institutional support for research and scientific development in Guatemala, which has resulted in a lack of opportunities and incentives for the GSD to be involved in actions for development in Guatemala, either by returning to the country or collaborating from abroad. This effect was perceived by industry stakeholders as a lack of interest from the GSD to be involved in projects in Guatemala and a tendency to “forget” about their home country. As one member said, “We have [a relationship with the GSD], but it isn't stronger because some of them have forgotten about Guatemala.” However, they acknowledged giving more support and incentivizing scientific development.Stakeholders broadly commented on how the development of solutions should involve conjunct work between the public sector, private sector, and academia. However, some expressed concern over the lack of interest of government agencies in science and indicated that academia has the most substantial potential to lead the way in developing opportunities to involve the GSD in development projects. Still, all stakeholders could identify opportunities in which collaboration with the GSD would be fruitful. Particularly, stakeholders in S&T highlighted the importance of linking the GSD with the academic sector and the private and public sectors to develop such opportunities. In this way, the transfer of knowledge and technology could specifically target problems in Guatemalan society. Further, industry stakeholders reiterated their willingness to explore possibilities for collaboration with members of the GSD.Regarding possible actions to overcome current obstacles, the need for more communication was highlighted. Industry stakeholders suggested having more open communication of industry needs to help identify opportunities for collaboration. Likewise, they underscored the importance of scientists showcasing their work and communicating their findings in a understandably and attractively way to the industry sector. One S&T stakeholder pointed out that multiple cultural barriers must be surpassed within the Guatemalan context, which requires contextualization and translation of scientific information.attaché in [Guatemalan] embassies and consulates, [and also] to train MINEX [Ministry of Foreign Affairs] staff and other public officials in science diplomacy guidelines.” Some of them also recommended having at least a minimum plan of things that scientists could contribute; in this way, they would guide them on how to apply their knowledge in the country. Stakeholders also considered that governmental institutions need to be active in involving the GSD and that science policy needs to be developed—especially by linking it to economic policy and development to add incentives. Stakeholders in the S&T policy indicated that better inter-institutional organizations could create the connection of the GSD with projects in Guatemala. For example, they mention the communication between SEGEPLAN and SENACYT, where SEGEPLAN  should systematically communicate the names and detailed information scholarship/fellowships awardees, especially at the graduate level . Then SENACYT can approach and connect them. Participants also mentioned the relevance of science diplomacy (as the interface between science and foreign policy) by suggesting that “the “SENACYT's policy and actions combined with the Ministry of Foreign Affairs [should engage] the GSD [by] having a scientific To our knowledge, this study is the first to provide supportive evidence of the growing and existing community of scientists outside of Guatemala, namely the Guatemalan Scientific Diaspora (GSD). Members of such GSD shared their past experiences, attempts, and results of their efforts for engagement with Guatemala and their current interest in contributing to the country's development through specific actions that need to be coordinated between the distinct sectors: government, academia, and industry. The importance of mapping, characterizing, and understanding the GSD to have a strong capacity-building mechanism and networking between GSD and local actors also became apparent. Until today, the existence of a GSD has not been systematically identified, registered, or studied and remains an untapped resource for development in the country. Moreover, the GSD has made positive initiatives and efforts, although they lack a legal or operational framework. As an initial step, systematized baseline information is required to develop further the actions toward structuring the GSD, develop future policies aimed to engage them, and highlight their impact at multiple levels of the country of origin.This study highlighted the lack of knowledge of the existence of a GSD, their interaction with their national counterparts and local stakeholders, and the need to recognize it and develop a structure or plan for them to interact efficiently with Guatemala. Due to the lack of articulation, independent and few successful experiences were shared by the participants of this study, though they recognized the potential to develop more substantial collaborations.Numerous members of the scientific diaspora have also acknowledged their responsibility to seek opportunities to remain relevant and positively influence Guatemala. Although the ideal scenario would involve an organized and systematized collective action, individual initiatives are also valuable. A sense of responsibility was also repeatedly mentioned, as many GSDs have benefited from scholarships, fellowships, grants, and funding based on their nationality or country of origin.In turn, our participants sensed a more positive view of the GSD and the perception of the host country. Based on our results, the continuation of collaboration with and employment of Guatemalan researchers was perceived as positive and possible. As stated by the subjects interviewed, skilled Guatemalan scientists contribute to furthering research on relevant topics, advance technological developments, allow deeper labor market specialization, and have a robust understanding of evidence-based knowledge and its application to real-world problems. Moreover, it was identified that the GSD allows the representation of an additional aspect of Guatemala: that of highly skilled, hard-working professionals who are passionate about science, technology, and development and make valuable contributions in their field.Converciencia, which is not a program nor a policy, but an event or recurrent activity (over 15 years of implementation with changes over time).Scientific diasporas are fundamental for science and research capacity development. They are also recognized as a solid force to encourage novel and fruitful collaborations abroad; therefore, governments, civil societies, other organizations, corporations, and academic groups are needed. In the case of Guatemala, no public policies or legislative actions, nor existing collaborative structures focused on creating possibilities of engagement with GSD were identified. In terms of Foreign Policy, attention is mainly centered on irregular and vulnerable forced emigration, particularly toward the United States of America. As for public policies in science and research, the single initiative identified as a consistent activity to connect GSD with their country was Converciencia or the International Guatemalan Scientists Network. Nevertheless, they complain about the lack of policies, bureaucracy, or non-existence of engagement programs. This is the best scenario. The other governmental institutions hadn't created any program or platform in most cases. The GSD has not found support in the Ministry of Foreign Affairs or Embassies, among other government institutions.Evidence suggests that the GSD cooperates with governmental institutions such as the National Secretary of Science in established programs such as Guatemalan universities and research centers have a fundamental role in developing new strategies to increase inclusiveness and actively engage the GSD by responding to contemporary science's global demands and needs as part of their programs. They need to expand local scientists' participation by informing them about existing programs and promoting and sharing authority in engaging them in all the research decision-making processes. The inclusion of GSD is an opportunity for creating models of governance of science centers based on the participation of local and abroad scientists' experiences as integral components alongside the ones who traditionally place the role of these centers as a provider of trained persons and basic knowledge.The GSD suggested strategies using artificial intelligence and machine learning to data-mine all the Guatemalan scientists' online professional information and publications. Members of the GSD also suggested creating a platform to enable better communication between the diaspora and the different key actors within the Guatemalan science, research, and innovation system. They indicated it is relevant to include actors and decision-makers from the government, higher education, research institutions, and the industry, relevant stakeholders representing international partners, non-governmental organizations, and civil organized social groups.International partners play a relevant role in the engagement of the GSD, mainly through alumni associations, such as the Fulbright and Chevening alumni networks, having an active connection amongst alumni. International partners also provide research grants, scholarships for postgraduate studies, and short courses in their countries of origin. Other international  are also relevant to the GSD. They are sources of grants, awards, and spaces for researchers and scientists to connect and engage with their peers not only from their countries of origin but also from other regions with similar needs and challenges i.e., the International Network for Advancing Science and Policy -INASP-, the Organization of Women in Science for the Developing World-OWSD-, the World Academy of Science TWAS, the InterAcademy Partnership IAP, to cite some.https://owsd.net/network/guatemala, https://redcti.senacyt.gob.gt/portal/index.php/investigadores/directoriocti.Publicly available datasets were analyzed in this study. This data can be found here: The studies involving human participants were reviewed and approved by the Ethics Committee from the University of Technology of El Salvador (UTEC). The patients/participants provided their written informed consent to participate in this study. All participants were asked to sign an electronic informed consent form prior to their participation.KB: conceptualization (lead), data curation (lead), project administration (lead), methodology (lead), resources (lead), validation (lead), and writing—original draft . CR-O and SA: conceptualization , data curation , supervision (lead), project administration , writing original draft , and visualization . NYOO, MA, AD, and GO-C: investigation (supporting), visualization (supporting), writing original draft , and writing—review and editing . SM: formal analysis , investigation (supporting), data curation , and resources . GM-B: critically analyzed the manuscript and suggested edits. NYOO: investigation , data curation , writing original draft , and visualization (supporting). All authors contributed to the article and approved the submitted version.This research received funding from the International Network for Advancing Science and Policy (INASP) through the grant INASP0652021.SA was employed by New Sun Road. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The used search words were {“Child Abuse”} AND {"Saudi Arabia"} AND {“awareness”}. The risk of bias (ROB) was assessed using the Risk of Bias Instrument for Cross-Sectional Surveys of Attitudes and Practices. Six studies were included in this review. Most studies had high ROB in recruiting the participants, designing the questionnaires, and stating the rate of response. The awareness regarding physical abuse seemed fair in most studies, but the awareness about shaken baby syndrome was poor. Also, there was a misconception about the parent's right to discipline their children through corporal punishment. Most participants did not perceive a need for establishing protective laws or programs. Public awareness about emotional abuse and neglect was lower than in cases of physical abuse. The overall knowledge about child abuse seems to be fair, but poor knowledge was observed in some forms, such as shaken baby syndrome. The public concepts about physical punishment and the need for protective laws and programs are also negative and require more efforts to alter them.Child abuse represents a serious problem worldwide. In Arab countries, the problem is complicated because abuse may be perceived as a method of discipline. This review aimed to describe awareness of the public in Saudi Arabia regarding child abuse. A search was performed in the databases of MEDLINE/PubMed, Scopus, Academic Search Complete (EBSCOhost), and Web of Science for articles published in English from the 1 Child abuse is a worldwide serious problem that can lead to life-long consequences and even mortality. Child maltreatment is defined as “all forms of physical and emotional ill-treatment, sexual abuse, neglect, and exploitation that results in actual or potential harm to the child’s health, development, or dignity” . AccordiChild abuse can present in different forms as physical abuse, psychological/ emotional abuse, sexual abuse, and neglect. The abuse may occur through an act of commission (such as hitting or humiliation by words) or an act of omission  .Several consequences can result from child abuse and can last for the life of the victim or cause death. These consequences may be physical and/or psychological. Physical consequences comprise neurologic deficits, developmental delays, cerebral palsy, or other forms of disability according to the nature and the site of the inflicted injuries. Psychological consequences include a higher risk of suffering depression and conduct disorder, as well as becoming dependent on substances. Abused children may also exhibit poor academic performance at school due to decreased cognitive function .Studies from Saudi Arabia reported various forms of child abuse -7. To reAs prevention is better than cure, an essential step to reduce the suffering of abused children is to increase public awareness about child maltreatment. When public awareness increases about this problem, abusive practices will become condemned by society, and the rate of abuse will decrease. In addition, a society with increased awareness can take active measures, such as reporting the abuse to the concerned authorities and aiding the child in getting help and support.Assessing the awareness of the public can help also in designing future awareness campaigns to address the misconceptions held by the public so that the public will recognize the abusive practices that they now consider normal and harmless and will hopefully cease to perform them in the future.Some studies were conducted to assess the awareness of parents, teachers, health professionals, and the public about child abuse -13. HoweTherefore, this systematic review aimed to describe the public awareness in Saudi Arabia regarding child abuse and to summarize the factors influencing awareness.MethodsMethodologyThis systematic review was conducted according to the principles of the Cochrane Handbook for Systematic Reviews of Interventions, version 6. The reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .The Research QuestionDoes the public in Saudi Arabia have sufficient awareness regarding child abuse?Research Aim and ObjectivesThis systematic review aimed at investigating the awareness of the public in Saudi Arabia about child abuse. The following objectives were addressed: (a) to assess the level of awareness of the public towards the different forms of abuse  and (b) to identify the factors influencing the level of awareness.Inclusion Criteria for Studiesst of January 2000 to the 14th of November 2022. We did not include studies before 2000 as it was expected that a marked difference in awareness level will be observed between older and more recent studies, owing to the enormous efforts for enhancing awareness that the Saudi government exerted through NFSP.This systematic review included cross-sectional survey studies that were published in English during the period from the 1The included studies were conducted on adult individuals of the public in Saudi Arabia. Studies that included questionnaires assessing awareness, perception, or beliefs about any form of child abuse were included. We excluded types of publications other than surveys , as well as conference abstracts/posters. We also excluded surveys that investigated the prevalence rates or risk factors of abuse rather than the awareness or knowledge about child abuse. The review excluded studies that addressed the awareness of professionals only as healthcare workers, medical/nursing/dental students, or teachers. These categories are expected to have necessarily higher levels of awareness owing to the inclusion of the subject of child abuse in their education and training.Search Strategyst of January 2000 to the 14th of November 2022. The search process was carried out between November 1, 2022, and November 14, 2022. The terms used for the search were {“Child Abuse”} AND {"Saudi Arabia"} AND {“awareness”}. The reference lists of studies that were obtained by the electronic search were scrutinized to identify relevant studies.We searched the electronic databases of MEDLINE/PubMed, Scopus, Academic Search Complete (EBSCOhost), and Web of Science for studies published in English during the period from the 1Selection of StudiesISJH, SAOA, and RMHA conducted the search process. The yielded results were screened through their titles and abstracts to remove duplicates as well as the types of publications to be excluded and non-relevant studies. For the identified potentially relevant articles, their full text was retrieved and assessed for eligibility. We checked the search results as well the selection process and the causes for the exclusion of studies.Data ExtractionAMMA, MMSA, AAH, SAMA, and DAHA extracted the data from the included studies into a standardized data extraction sheet. The extracted data included: (a) the study characteristics ; (b) the respondents’ characteristics ; and (c) the questionnaire used: open/closed questions, the methods of devising and distributing the questionnaire, response rate, and the focus of the questions . We revised the collected data regarding their consistency and clarity.Measured OutcomesThe primary outcomes included the level of awareness of the participants regarding child abuse and the most frequent areas of defective knowledge or misconceptions. The secondary outcomes included the factors that affect the awareness level of the public.Assessment of the Risk of Bias in the Included StudiesAMMA, MMSA, AAH, SAMA, and DAHA assessed the risk of bias (ROB) for the included studies using the “Risk of Bias Instrument for Cross-Sectional Surveys of Attitudes and Practices” developed by the CLARITY Group at McMaster University in conjunction with Evidence Partners. The tool consists of five questions, and each is given a score of “definitely yes” (low ROB), “probably yes”, “probably no” (higher ROB), and “definitely no” (high ROB). The questions assess whether the source population is representative of the population of interest, the adequacy of the response rate, the amount of missing data, the clinical sensibility of the survey, and the reliability and validity of the survey instrument.ResultsResults of Literature Search and Study SelectionThe PRISMA 2020 flowchart that summarizes the results of the literature search and study selection can be seen in Figure The search of the electronic databases yielded 64 records from databases. After removing the duplicates, 28 records were screened for the titles and abstracts, out of which 26 records were non-relevant to the research question or the type of publication and were excluded. The full text of the remaining two studies was examined for eligibility ,16. In aBasic Characteristics of the Included StudiesFour studies were conducted in Riyadh, which is the capital of Saudi Arabia ,17-19, wAs regards the focus of the included studies, different types of abuse were assessed in the studies by Alqurashi et al.  and AlreSummary of the Included StudiesAlanazi  distribuIn their study, Al Dosari et al.  recruiteAlqurashi et al.  aimed toAlreshidi et al.  assessedAlreshidi et al.  exploredAlOmran et al.  investigAssessment of the Risk of Bias in the Included StudiesThe ROB of the included studies was assessed using the “Risk of Bias Instrument for Cross-Sectional Surveys of Attitudes and Practices” developed by the CLARITY Group at McMaster University in conjunction with Evidence Partners Table .The source of the population was representative of the population of interest only in the study by Al Dosari et al. , who recAs regards the response rate, Al Dosari et al.  was the The clinical sensibility of the survey referred to “the formal assessment of comprehensiveness, clarity, and face validity of the questionnaire in a similar population.” The studies by Alanazi  and AlOmThe evidence of reliability and validity was available only for the questionnaire in the study by Al Dosari et al. , though DiscussionSummary of Main ResultsThis systematic review was undertaken to describe the awareness of the public in Saudi Arabia regarding child abuse and to summarize the factors influencing awareness. Six studies were retrieved that fulfilled the eligibility criteria set for this review. The studies showed considerable heterogeneity in the included questions, so pooling of the data was not feasible.As regards awareness about physical abuse, five studies were found ,17-20. APrevious studies in Saudi Arabia regarding the barriers to reporting child abuse have identified these misconceptions about the parents' rights in hitting their children, which stems from a mistaken interpretation of Islamic teachings. Interestingly, slapping a child was condemned as abuse by most parents in the studies by Al Dosari et al. , AlqurasShaking of the children was the principal issue in the study by AlOmran et al. , and it Awareness about sexual child abuse was assessed by Alreshidi et al. . They foAwareness about emotional abuse was assessed by Alqurashi et al.  and AlreAs regards awareness about child neglect, three studies were found ,19,20. AThe factors that affect public awareness about child abuse were investigated in some studies ,16,19, wOverall Completeness, Applicability, and Quality of the EvidenceThe included studies showed a high ROB in several domains, which could impact the results and the derived implications. There is a need to initiate studies to assess public awareness and adhere to the best methodological quality measures as using the random sampling technique, recruiting respondents from multiple centers or areas, using validated questionnaires, and reporting the response rate. Noticeably, four out of the included six studies were conducted in Riyadh, the capital of Saudi Arabia ,17-19. OAgreements and Disagreements With Other Studies or ReviewsTo the best of the authors’ knowledge, no previous systematic reviews assessed public awareness about child abuse. However, some of the discussed concepts in the included studies were mentioned in narrative reviews that discussed barriers against reporting child abuse in Saudi Arabia , such asImportance of Awareness CampaignsAwareness campaigns have demonstrated their effectiveness in elevating the level of knowledge of the public. In Saudi Arabia, Temsah et al.  conducteThe results of this systematic review provide insights into the level of public awareness about child abuse. The public awareness was fair to good, particularly in the cases of physical and sexual abuse, but the practices of emotional abuse and neglect were less recognized. Also, the shaken baby syndrome was not known to the public as a form of abuse, and the potential of developing serious injuries was not perceived by most respondents. This warrants the designing of awareness campaigns that emphasize these neglected aspects of child abuse to improve public awareness about them.The misconceptions held by the public that were summarized in this review should be overcome to ensure the cooperation of the public with the established programs and to create a sound culture of child’s rights and safety in Saudi society. In addition, the results of this review can guide future research in this area as the review highlighted the pitfalls and limitations in the currently available studies.


Capital structure is an integral part of the corporate finance that sources the funds to finance growth and operations. Managers always have to maintain value of the firm to be higher than the cost of capital in order to maximize the shareholders wealth. Empirical studies have used sources of finance like debt and equity as variables of capital structure. A choice between debt and equity finance analyzes the firm's ability to perform under the financially constrained environment to attain the sustainable growth. Therefore, it gives rise to a dire need to estimate the cost of capital precisely. We examined the capital structure of top ten market capitalization of the stock markets included in MSCI Emerging index with the use of artificial neural networks, support vector regression, and linear regression in forecasting methods. The capital structure is measured as the proportion of total debt over total equity . Other financial ratios such as profitability, liquidity, solvent, and turnover ratios were considered as drivers of the capital structure. Applying logistic and hyperbolic tangent activation functions, it was concluded that ANN has a great potential of replacing other traditional forecasting models with the nonstationary data. This research contributes with a new dimension for estimation through different activation functions. There is a possibility of ANN dominance as compared to the other models applied for predictability in financial markets. Artificial intelligent systems (AI) and machine learning (ML) have transformed the decision-making from human with an aim to improve quality of business and investments . The dynOver the past few years, a progressive revolution in technology took a root in creating artificial intelligence system. With the COVID-19, AI growth is accelerated and aligned towards reorganization of the industries. The concept and applications of AI is subject of interest for the business and academicians. Neural networks consist of multiple simple processors arranged in a communicative network, each of which is programmed to perform one identical, elementary processing task . ANNs arNeural networks are considered to be promising tools for forecasting financial time series. The factors such as selection of input variables, architecture of the network, and quantity of training data have an impact on accuracy of the model. ANNs models are highly multicomputational and interconnected units where in each input unit is allocated with sets of weights, bias adjustment, and transfer function to transforms the bias and sum of the weighted inputs to determine the output value from the input unit .Classification, clustering, predicting, forecasting, and pattern recognition problems are the basic features of ANN . AIS domThe main motivation of the research is due to the explosive growth in the ML and AI tools in the advancement of the finance literature. In the multiple studies performed based on ANN, it was concluded that artificial intelligence had outperformed economic, statistical methods, and other general forecasting models.The large number of datasets used in the study assist in predictions through training algorithms with high dimension and increase in the number of variables as compared to the traditional structures.The main purpose of using MSCI index from emerging markets is due to availability of many growth opportunities, free trade agreements, increased investments, and the capital flow. Nature of capital as prior to financial crisis was characterized by low debt levels, while after the crisis, higher debt levels are adopted. The rationale is to set a model in academic research, business managers, policy makers, and investors for real-time implementation and adjustment of capital structure based on predictions to maximize the firm's value .Additionally, MSCI Emerging Markets Index (MSCI EMI) has accounted since 2000 for a Sharpe ratio of 0.39 and an annualized net return of 7.78%, which has outperformed the MSCI World and MSCI ACWI indexes. Within the MSCI EMI, China stands for a 35% of the country weights mean, while Brazil just 4% and Saudi Arabia less than 4%. Besides Brazil and Saudi Arabia show small weights within MSCI EMI, but in their index regions, they outstand with greater than 60% and 30%. So, despite country weights in one index, opportunities from companies of these countries cannot be misled as shown in the weights in other indexes .This research addresses for a company's market capitalization criteria instead of an industry type selection. If all financial ratios were not available on any company, then it was selected the next lower market capitalization company. In that sense, the research intends to assess the capital structure forecasting capability of AI models of the highest market capitalization companies in emerging markets with no distinction of the industry type .To identify the best-fit model out of the three models namely ANN, SVR, and LRPredictive performance of the capital structure in the financial marketsThis study contributes to application of artificial intelligence tools with an objective:Debt and equity are the two important variables of capital structure that provide the information to investors in the organization for supporting investment decisions. Research study by Pao  revealedAccording to the research work –19 contiAccording to Zahedi , ANNs trApplications of ANN were classified into three categories like financial analysis prediction, classification of pattern, and optimization and control . SophistWith the growing demand and transformation of the financial markets and the systems globally, the machine learning tools are extensively used, leading to extensive use of ANN technology in finance and accounting and literature for bankruptcy predictions, rating of the bond, forecasting in the stock markets, foreign exchange, evaluation of credit, and fraud detection . ResearcThe artificial neural network (ANN) technology identifies and reproduces a phenomenon or approximates a function without making the parameters explicit. It does not require any assumptions further like noisy, inconsistent, missing, and distribution of data. A wide and extensive literature is available on ANN applications. The high potential of ANN is identified through its highspeed processing model by academicians, researchers, and policy makers reflecting and considered as the superior performance of ANN compared to other econometric tools –28.Four different technical indicators are used in the neural network architecture that include raw data, day of the week, prices of stocks are forecasted using the network that consists of input and output layer with the hidden layer between them , 30. BacRecently, few studies have used artificial neural network (ANN) nonlinear models to resolve the forecasting problems  and focuFinancial economists predict increase in leverage with the fixed costs, nondebt tax shields, size of the firm, and investment opportunities . AccordiExtant literature is available on application of ANNs in classification and prediction problems in accounting and finance. ANN prediction in financial markets demonstrates the different techniques of the artificial intelligence tools that include technical and like fundamental analysis. A unique study on capital structure of G-7 countries except US related to factors similar to influence the capital structure of US firms by Rajan and Zingales  finds thAccording to A. V. Devadoss, T. Ligori , financiOn investigation of the best modeling of time series using Neural Network with four different approaches, i.e., raw data, raw data with a time index, detrending, and differencing for modeling, Qi and Zhang  concludeHeo et al.  examinedANNs are more appropriate where the inputs are missing or nonlinear as compared to traditional statistical methods . ANN model is a better choice for providing solutions involving complex practical problems because of their multidimensional analytical features as compared to other regression models. Factors such as number of observations, forecasting horizon, linear, or nonlinear form and data period effect the performance of ANN  is China's biggest oil and gas producer and distributor and one of the largest oil and gas companies in the world. services .Saudi Telecom Company offers landline and fixed infrastructure, mobile and data services. Saudi electric company provides on the generation, transmission and distribution of electric power. Saudi Arabian Mining Company is responsible for all the mining activities.Support vector regression and linear regression are widely used to predict. In our study, we give emphasis to build an ANN-based model.ANNs are based on the principle of neuron connecting and sending signals with an attempt to simulate the learning process of the human brain by using complex algorithms. Every connection has a weight attached either with a positive or negative value associated. Positive weights activate the neuron while negative weights inhibit it. Generally setting up of ANN is six-step procedures. The data input the financial ratios used are defined and presented to the ANN as a pattern indicated with the desired outcome or target. Data are categorized as 80% of it as the training set or validation and 20% as the testing set from the sample data taken. The validation set used will assist in predictive ability and recommends when to stop the training of the ANN. As neural network consists of an input layer, an output layer, and one more intervening layers also known as hidden layers. These hidden layers capture the nonlinear relationship between variables. Each layer consists of multiple neurons that are connected to neuron in adjacent layers. With these neurons in multiple layers, these networks can capture complex phenomenon. This process of training set helps in computation of output with minimizing errors in forecast due to iterations. The algorithms used will adjust the model parameters connecting weights and nodes. The output of each processing unit is put forward through each layer if the network .In this study, the main variable for analysis is Capital Structure (CS) measured as the proportion of Total Debt against Total Equity is considered to be as dependent variable. Independent variables are categorized as profitability, liquidity, solvency, and turnover ratios. These ratios are considered to be the drivers of capital structure (15 ratios are taken).R version 4.0.5.The following section is used to set a detailed context on normalization, error metrics using ANN model as an example.The normalization step helps to make sure that large values do not overcome features with small values. In that sense, the original financial ratio matrix is transformed to a matrix of range . The following expression represents the normalized value:After normalization, data are split into training (80%) and testing (20%) data. Different ANN structures were estimated based on number of hidden layers and number of nodes. The Logistic (sigmoid) and Hyperbolic Tangent (tanh) activation functions were applied as transfer functions from the weighted-sum inputs to determine the output.f represents the activation function.The following expression shows the logistic function:However, the tanh function is shown in the following expression:The performance comparison among algorithms is analyzed through the root mean squared error (RMSE) and mean absolute error (MAE) metrics. Expression  shows thAnd, the following expression shows the MAE metric:The results obtained using different models are compared in this section using the error metrics defined in the previous section. The three models are now predicted for different regions and their RMSE, RSE, RAE, and MAE are evaluated. For ANN models, different combinations of layers and nodes are experimented along two functions. Hence, the discussion of results is made on two folds, first to discuss on the best suitable structure of ANN model and then compare it with rest of the two models.Tables 3As per the ANN structures shown in As per algorithm, SVR has outperformed in Weg with RMSE value of 0.0464, while Vale and Petroleo Braileiro had LR with 0.0610 and LR of 0.0486, respectively.With reference to Based on the ANN structures through activation functions of logistics and tanh for all the layers and all nodes, the best model fit is tanh function indicates that ANN can predict better in nonlinear values.The ANN structures algorithm metrics indicate 0.0328 for LR in Saudi Telecom as per RMSE followed by ANN and SVR with 0.0807 and 0.1274, respectively, as reflected in Based on the ANN structures through activation functions of logistics and tanh for all the layers and all nodes, the best model fit indicates ANN can predict better in nonlinear values.Empirical results of ANN structures are validated in the tables in terms of Algorithm metric errors using recognized statistical tools RMSE and MAE. Results indicate the capital structure prediction performance based on sole ANN structure. All ANN structures fitted well as one layer-one node structure with the same activation function and assists to transfer the inputs combination to the capital structure forecast output, thus considering ANN as an ideal promoting tool.Equity index of Brazil, such as Vale, Petroleo, and Weg, were well fitted by the tanh function, which revealed best ANN network performance than logistic function.When activation functions were applied for industry wise comparison in oil and gas companies of China, both logistic and tanh function performed well indicating that ANN is an ideal model for predictive performance.Capital structure pattern of all three oil and gas companies, the Petroleo Brasileiro (Brazil) and Petroleum Chemical (China) and Petro China showed an average capital structure ratio, Petroleo Brasileiro and Petrochemical China were fitted by the logistic and tanh functions, respectively. The Saudi Arabian companies showed the best ANN structure based on the same activation function. Their average capital structures ratios were 24.55% (Telecom), 16.2% (Electricity), and 21.63% (Mining). It is concluded that when companies taken for the study are from same country then ideally same activation function could be fitted irrespective of their size of capital structure.In Brazil and China, the sample consists of other industry group hence same activation function will not be applicable. The comparative analysis applied among the ANN estimated structures revealed that common features as those of country, industry, or capital structure level that could justify a sole activation function in forecasting the capital structure of companies belonging to emerging stock markets. Thus, based on the research work, it can be concluded that for the nonlinear data, ANN models are considered to be the best as compared to the other models applied in the study. In the sample taken for the study, LR models were identified as the best model for the linear data. When the data are nonlinear, ANN predictions were the most suitable models. Hence, common features as those of country, industry, or capital structure level may also indicate similar results.With the application of algorithm, ANN model is considered to be the best-fit model in predictive performance as revealed and validate through low RMSE. The activation function of both logistic and tanh indicates low RMSE and support ANN structures. LR and SVR outperformed for the linear data. This research work supports the earlier studies that consider ANN network as superior in comparison with another model.This study emphasized on the prediction of capital structure through input variables as financial ratios and algorithm error metrics applied in ANN, SVR, and LR models. The study was limited for predictive performance only. Although financial ratios were used as inputs, the relationship among the ratios is not measured and is not included. Authors plan to extend this work to evaluate and predict the capital structure in future research work with individual applications of ratios in terms of profitability, liquidity, solvency, and turnover ratios .