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Recently, Audio systems transform the configuration of conventional sound reinforcement and public address systems using audio over internet protocol (AoIP), whereby audio signals are transmitted and received based on internet protocol (IP). Currently, AoIP technologies are leading the audio market, and various technologies have been released. Audio networks and the control hierarchy over peer-to-peer (Anchor) technology based on AoIP transmit and receive audio signals over a wide bandwidth without an audio mixer. Audio system based on Anchor technology is constructed by connecting the on-site audio center (OAC), a device that can transmit and receive audio sources and output equipment over IP. Receiving OAC of the Anchor technology can receive and mix audio signals transmitted from different IPs; consequently, novel audio systems can be configured by replacing conventional audio mixers. However, the Anchor technology does not have an equalizer function for improving the quality of audio equipment. Therefore, tone distortion may occur owing to signal loss between equipment, poor audio-signal clarity, and howling due to audio deformation according to different architectural structures and environments. In this study, we implemented an audio effect device capable of tone control using the Audio Processor Core. Using Anchor technology, tone control was realized through an audio effect device in the receiving OAC. The output of the incoming OAC was received by the audio effect device, which adjusted the tone and then outputted it. Thus, the tone issues in Anchor technology were overcome by the receiving OAC and audio effect devices. In future, audio system configurations using Anchor technology could be the standard for audio equipment.

The local storage method for home cameras, which relies on inserting an SD card into the device to store data, offers a convenient and cost-effective solution, as there are no recurring expenses after purchasing the SD card. However, we recognize that this method comes with significant security challenges. In particular, the ease with which third parties can access the SD card makes it vulnerable to both physical and software tampering. As the acceptance rate of home camera footage as evidence in courts has increased, we have become increasingly aware of the critical nature of these security issues. Digital data from home cameras, unlike other types of physical evidence, can be more easily tampered with and altered. To ensure that such data is recognized as valid legal evidence, we must prove its integrity and demonstrate that it has not been tampered with. In response to these challenges, we are committed to strengthening the security measures for both the home camera device and its local storage. By doing so, we aim to ensure the integrity and reliability of the data, thereby enhancing the overall security and trustworthiness of home camera systems.

With the recent popularity and technological development of online streaming video, interactive digital narrative (IDN hereafter) videos became one of the main formats for users. The current study proposed that the level of interactivity of IDN videos influences users’ evaluation of the video. The concept of flow was introduced as a mediating variable between interactivity and the users’ evaluation. Further, the type of IDN videos, users’ familiarity with IDN videos and trust toward platforms were employed as moderating variables. Data from a survey verified the mediating role of flow, moderating role of users’ familiarity and trust toward platforms. the type of IDN videos, users’ familiarity with IDN videos and trust toward platforms. We have observed a significant moderating effect of users’ trust toward the platform on users’ evaluation resulting from flow experience. It is evident that the higher the level of users’ trust towards the platform, the less pronounced the impact of flow experience on users’ evaluation. Theoretical and managerial implications are discussed.

The proliferation of video streaming services has led to a need for flexible networking protocols. As a result, the Dynamic Adaptive Streaming over HTTP (MPEG-DASH) protocol has emerged as a dominant streaming protocol due to its ability to dynamically adjust playback bitrates according to the end-user's network conditions. In this paper, we propose a novel I/O scheduling scheme tailored for the storage of MPEG-DASHenabled video servers. Using the renowned rate-reservation (RR) algorithm and bulk-SCAN mechanism, our proposed scheme improves storage bandwidth utilization while ensuring seamless playback of streams with varying bitrates. In addition, we provide a mechanism for reclaiming the idle I/O time typically incurred while retrieving video segments from storage. Consequently, our scheme offers practical solutions for reducing the storage costs of MPEG-DASH video servers. With a simple cost model, we evaluate the performance enhancements achieved by our proposed I/O scheduling scheme.

In this paper, we deal with a game theoretic problem to explore interactions between evasive Artificial Intelligence (AI) malware and detectors in Internet of Things (IoT). Evasive AI malware is defined as malware having capability of eluding detection by exploiting artificial intelligence such as machine learning and deep leaning. Detectors are defined as IoT devices participating in detection of evasive AI malware in IoT. They can be separated into two groups such that one group of detectors can be armed with detection capability powered by AI, the other group cannot be armed with it. Evasive AI malware can take three strategies of Nonattack, Non-AI attack, AI attack. To cope with these strategies of evasive AI malware, detector can adopt three strategies of Non-defense, Non-AI defense, AI defense. We formulate a Bayesian game theoretic model with these strategies employed by evasive AI malware and detector. We derive pure strategy Bayesian Nash Equilibria in a single stage game from the formulated Bayesian game theoretic model. Our devised work is useful in the sense that it can be used as a basic game theoretic model for developing AI malware detection schemes.

Fiber to the Home (FTTH) technology is among the most advanced broadband services, delivering voice, data, and television through a single optical fiber directly to customer premises, ensuring high-speed and reliable connectivity. The study conducted on Nepal Telecom's FTTH networks involved direct measurements from the optical line terminal to the fiber access point and optical network unit, providing detailed insights into network performance. Using the OptiSystem software, the analysis revealed a link loss of 24.99 dB, a Qfactor of 12.98, and a minimum Bit Error Rate (BER) of 7.31E-39, all within standard limits, which underscores the robustness of the network. The study also identified that the highest contributors to signal loss were connector loss, fiber attenuation, and fusion splices, emphasizing the importance of minimizing these factors to maintain optimal network performance. Overall, these findings highlight the critical aspects of FTTH network design and maintenance, ensuring that service providers can deliver high-quality broadband services to customers.

Non-orthogonal multiple access (NOMA) is a technique that forms a NOMA group composed of two or more users and transmits the superimposed signals of all users in the group through a single beam. In case all users in a NOMA group fall within the main lobe, a high data rate is guaranteed. However, in case not all users in the group fall within the main lobe due to the narrow beam width, the sum data rate decreases, and the data rate disparity between users inside and outside the main lobe widens significantly, leading to reduced fairness. On the other hand, an excessively wide beam might reduce the channel gain which lowers the sum data rate. This paper discusses the effects of beam configuration on the throughput and fairness performances of the NOMA system in the millimeter wave channel environments with simulation results for various channel parameters including the number of antennas and beam directions.

The model developed in this study holds significant importance in predicting carbon emissions in maritime transport. By utilizing ship data and EEDI (Energy Efficiency Design Index) guidelines, the model presents a highly accurate prediction tool, providing a solid foundation for maximizing operational efficiency and effectively managing carbon emissions in ship operations. The model's accuracy was demonstrated by an R² score of 0.95 and a Mean Absolute Percentage Error (MAPE) of 1.4%. Through SHAP (SHapley Additive exPlanations) and Partial Dependence Plots (PDP), it was identified that Speed Over Ground and relative wind speed are the most significant variables, both showing a positive correlation with increased CO2 emissions. Additionally, environmental factors such as exceeding an average draft of 22(m), a Leeway over 5°, and a current angle exceeding 200° were found to increase emissions significantly. Specific ranges of wind and swell wave angles also notably affected emissions. Conversely, lower pitch, roll, and rudder angle were associated with reduced emissions, indicating that stable ship operation enhances efficiency.

Breast cancer remains a significant global health burden, necessitating accurate and timely detection for improved patient outcomes. Machine learning techniques have demonstrated remarkable potential in assisting breast cancer diagnosis by learning complex patterns from multi-modal patient data. This study comprehensively evaluates several popular machine learning models, including logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-nearest neighbors (KNN), XGBoost, and ensemble methods for breast cancer prediction using the Wisconsin Breast Cancer Dataset (WBCD). Through rigorous benchmarking across metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), we identify the naive Bayes classifier as the top-performing model, achieving an accuracy of 0.974, F1-score of 0.979, and highest AUC of 0.988. Other strong performers include logistic regression, random forests, and XGBoost, with AUC values exceeding 0.95. Our findings showcase the significant potential of machine learning, particularly the robust naive Bayes algorithm, to provide highly accurate and reliable breast cancer screening from fine needle aspirate (FNA) samples, ultimately enabling earlier intervention and optimized treatment strategies.

Integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data is transforming elderly care services, particularly in nursing homes. This study explores the impact of these technologies on the quality of care in nursing homes in Tongling City, China. Using a mixed-methods approach, data were collected from 298 elderly residents across 12 nursing homes through detailed surveys and interviews. The findings indicate that smart platforms and intelligent terminals significantly enhance service quality, with institutional conditions and social participation emerging as the most influential factors. Although the study’s regional focus may limit the generalizability of the findings, it introduces novel applications of AI in dietary management and IoT in personalized environmental monitoring, which contribute original insights to the broader field of smart elderly care. These results underscore the transformative potential of advanced technologies in improving elderly care and offer a model that can be adapted to similar contexts globally. Future research should focus on longitudinal studies to assess the long-term impact of these technologies and explore their applicability in diverse cultural and regional settings.

This paper addresses the implementation of an on-device AI-based metal detection system using a Magneto- Impedance Sensor. Performing calculations on the AI device itself is essential, especially for unmanned aerial vehicles such as drones, where communication capabilities may be limited. Consequently, a system capable of analyzing data directly on the device is required. We propose a lightweight gated recurrent unit (GRU) model that can be operated on a drone. Additionally, we have implemented a real-time detection system on a CPU embedded system. The signals obtained from the Magneto-Impedance Sensor are processed in real-time by a Raspberry Pi 4 Model B. During the experiment, the drone flew freely at an altitude ranging from 1 to 10 meters in an open area where metal objects were placed. A total of 20,000,000 sequences of experimental data were acquired, with the data split into training, validation, and test sets in an 8:1:1 ratio. The results of the experiment demonstrated an accuracy of 94.5% and an inference time of 9.8 milliseconds. This study indicates that the proposed system is potentially applicable to unmanned metal detection drones.

With the advent of serverless computing, cloud customers no longer needed to maintain and manage server environments directly. Instead, cloud service providers took on that role, managing and maintaining the server environment according to customer requests, a concept known as Function as a Service (FaaS). This service demonstrated improvements in operational costs and resource utilization over traditional cloud computing, offering various advantages such as enhanced scalability. However, a delay occurred in processing and returning results to user requests, a phenomenon referred to as the cold start problem. This paper proposed the Time Warming Allocation Engine (TWAE) to improve resource management and mitigate the cold start problem in Function as a Service. The proposed engine comprised a collection module, a learning module, a classification module, and an allocation module. Additionally, it utilized a list called Pre-Warming. Through this approach, it suggested directions for improving cold start issues and resource utilization according to different time periods.

ESG is currently a global topic, meaning environmental, social, and governance, which are three important measures of socially responsible management. It is also having a great influence on improving competitiveness in the global market and enhancing corporate image. In this study, ESG in Korea was analyzed through big data, and four central keywords of ESG management in China based on Chinese data were derived. These four keywords are environment, management, corporate event, and quality certification. In addition, we want to understand the ESG perspective of China by studying ESG cases in China. Through this, we will be able to compare and analyze the differences between ESG approaches and key points between Korea and China.

In a distributed environment, data fabric refers to the technology and architecture that provides data management, integration, and access in a consistent and unified manner. To build a data fabric, it is necessary to maintain data consistency, establish a data governance system, reduce structural differences between data sources, and provide a unified view. In this paper, we propose the Fabricator system, a technology that provides data management and access in a consistent and unified manner by building a metadata registry. Fabricator manages the addition and modification of metadata schemas and matching processes by designing a matching tool called MetaSB Manager that applies B+Tree. This allows real-time integration of various data sources in a distributed environment, maximizing the flexibility and usability of data.

Recently, along with digital transformation, technologies such as cloud computing, big data, and artificial intelligence have been actively introduced. In a situation where these technological changes are progressing rapidly, it is often difficult to manage processes efficiently using existing simple workflow management methods. Companies providing current cloud services are adopting virtualization technologies, including virtual machines (VMs) and containers, in their distributed system infrastructure for automated application deployment. Accordingly, this paper proposes a process-based orchestration system for integrated execution of corporate process-oriented workloads by integrating the potential of big data and machine learning technologies. This system consists of four layers as components for performing workload processes. Additionally, a common information model is applied to the data to efficiently integrate and manage the various formats and uses of data generated during the process creation stage. Moreover, a standard metadata protocol is introduced to ensure smooth exchange between data. This proposed system utilizes various types of data storage to store process data, metadata, and analysis models. This enables flexible management and efficient processing of data.

As the size of data and models in machine learning training continues to grow, training on a single server is becoming increasingly challenging. Consequently, the importance of distributed machine learning, which distributes computational loads across multiple machines, is becoming more prominent. However, several unresolved issues remain regarding the performance enhancement of distributed machine learning, including communication overhead, inter-node synchronization challenges, data imbalance and bias, as well as resource management and scheduling. In this paper, we propose ParamHub, which utilizes orchestration to accelerate training speed. This system monitors the performance of each node after the first iteration and reallocates resources to slow nodes, thereby speeding up the training process. This approach ensures that resources are appropriately allocated to nodes in need, maximizing the overall efficiency of resource utilization and enabling all nodes to perform tasks uniformly, resulting in a faster training speed overall. Furthermore, this method enhances the system's scalability and flexibility, allowing for effective application in clusters of various sizes.

Genomic data plays a transformative role in medicine, biology, and forensic science, offering insights that drive advancements in clinical diagnosis, personalized medicine, and crime scene investigation. Despite its potential, the integration and analysis of diverse genomic datasets remain challenging due to compatibility issues and the specialized nature of existing tools. This paper presents the GenomeSync system, designed to overcome these limitations by utilizing the Hadoop framework for large-scale data handling and integration. GenomeSync enhances data accessibility and analysis through SQL-based search capabilities and machine learning techniques, facilitating the identification of genetic traits and the resolution of forensic cases. By preprocessing DNA profiles from crime scenes, the system calculates similarity scores to identify and aggregate related genomic data, enabling accurate prediction models and personalized treatment recommendations. GenomeSync offers greater flexibility and scalability, supporting complex analytical needs across industries. Its robust cloud-based infrastructure ensures data integrity and high performance, positioning GenomeSync as a crucial tool for reliable, data-driven decision-making in the genomic era.

We look at three use cases by business model types of Web3.0 social tokens shaped by ‘token eonomics (tokenomics).’ As the platform token, ‘Roll’ mints unique tokens to creators’ reputation and allows them to own the value they create. Creators incentivize their followers contributing to the community. Tokens issued on Roll have a fixed supply with 20% minted for creators and 80% distributed across three years. With ‘Roll Memberships,’ followers gain benefits across token-gated platforms and protocols while getting something in return from the creator. ‘Roll Staking’ allows creators to integrate their community into crypto-specific products like trading markets, enhancing the features being possible in a creator’s community. As the community token, ‘Whale’ creates WHALE token backed by non-fungible tokens (NFTs), so that it derives its value from NFTs kept in NFT art collection, ‘The Vault.’ ‘Hold-to-Play(H2P)’ rewards distributed to token holders owning a minimum threshold of tokens allow them to access to exclusive access to benefits like airdrops, tips, rewards, and exclusive information. Whale DAO open to members locking 1,000 tokens allows them to post a proposal twice a month and to vote in the senate. DAO-Voter role allows members locking 500 tokens to access the vote in the senate, but not to present proposals. As the personal token, ‘RAC’ distributes RAC tokens to his loyal supporters as a reward. These tokens are available for exclusive content access. RacOS makes it possible for RAC Patreon subscribers to claim RAC tokens each month corresponding with their membership tier.

The field of Generative AI(Artificial Intelligence) involves a technology that autonomously comprehends user intentions through commands and learns from provided data to generate new content, such as images or text. This capability, which allows autonomous creativity even with design keywords, is anticipated to play a significant role in the domain of visual communication design. This article delves into the tools of generative AI applicable to visual design and the methodology for design creation using these tools. Furthermore, it discusses how designers can interact visually with AI technology in the era of generative AI.

Looking at the recent game market, classic games released in the past are being re-released with highquality visuals, and users are generally satisfied. It can be said that the realization of realistic digital actors, which was not possible in the past, is now becoming a reality. Epic Games launched the MetaHuman Creator website in September 2021, allowing anyone to easily create realistic human characters. Since then, the number of animations created using MetaHumans has been increasing. As the characters become more realistic, the movement and expression animations expected by the audience must also be convincingly realized. Until recently, traditional methods were the primary approach for producing realistic character animations. For facial animation, Epic Games introduced an improved method on the Live Link app in 2023, which provides the highest quality among mobile-based techniques. In this context, this paper compares the results of animation produced using both keyframe facial capture and mobile-based capture. After creating an emotional expression animation with four sentences, the results were compared using Unreal Engine. While the facial capture method is more natural and easier to use, the precise and exaggerated expressions possible with the keyframe method cannot be overlooked, suggesting that a hybrid approach using both methods will likely continue for the foreseeable future.

This study investigates the conditions under which the compensation effect occurs in advertising, focusing on the influence of warmth messages on consumer perceptions and responses. By comparing single-ad and comparative ad exposure contexts, the research reveals how warmth messages affect perceived brand competence and the intention to like ads. High warmth messages, when viewed in a comparative ad setting, lead to lower perceived brand competence compared to a single-ad setting, emphasizing the need for strategic message placement in competitive environments. The study further explores how consumers' construal levels— whether considering near-future or distant-future purchase decisions—moderate these effects. The negative impact of high warmth messages on perceived competence is amplified in a comparative context at low construal levels, while high construal levels mitigate this negative impact. These results provide both theoretical and practical insights, highlighting the importance of ad context and construal level in advertising strategies.

This paper presents an alternative solution for applying MetaHuman facial animations using MediaPipe, providing a versatile option to the Live Link iPhone system. Our approach involves capturing facial expressions with various camera devices, including webcams, laptop cameras, and Android phones, processing the data for landmark detection, and applying these landmarks in Unreal Engine Blueprint to animate MetaHuman characters in real-time. Techniques such as the Eye Aspect Ratio (EAR) for blink detection and the One Euro Filter for data smoothing ensure accurate and responsive animations. Experimental results demonstrate that our system provides a cost-effective and flexible alternative for iPhone non-users, enhancing the accessibility of advanced facial capture technology for applications in digital media and interactive environments. This research offers a practical and adaptable method for real-time facial animation, with future improvements aimed at integrating more sophisticated emotion detection features.

In the context of exploring how Artificial Intelligence(AI) can revolutionize the entertainment industry, more and more film and television productions have begun to try to intervene AI technology in various aspects of content creation. However, despite the fact that AI can generate a large amount of textual content and dynamic visual effects, it still faces challenges in terms of plot expression and delivery. This thesis explores the strengths and weaknesses, innovations, and future developments of AI technology in plot production by analyzing existing film and television productions and production practices generated using AI technology. The study proves that as AI technology continues to improve, its use in short-form production will become more and more prevalent in the future, helping human creators become more efficient and even able to produce Short Dramas in full flow.

Digital transformation (DT) has gained global attention in various service industries, due to the pervasive nature and proliferation of recent digital technologies. Given that we live in an age of DT, the current research examines the factors influencing the older adults’ quality of life due to DT. Specifically, we examine whether the older adults’ digital skills (i.e., ability to use applications and self-efficacy in using digital devices) and motivational factors regarding DT (i.e., involvement in DT and need for cognition regarding DT) predict their quality of life due to DT. To answer the research question, we conducted a hierarchical multiple regression analysis using the elderly Korean adults aged 65 or older. The results indicate that the older adults’ ability to use applications, self-efficacy in using digital devices, involvement in DT, and need for cognition regarding DT are positively associated with quality of life due to DT. The findings provide important implications to improve the elderly’s quality of life due to DT.

This study examines the impact of fashion brand collaborations in sandbox games on consumer purchase intentions, focusing on brand coolness and self-avatar identification. Through online surveys of U.S. consumers aged 20-40, it finds that aesthetics, scarcity, and familiarity contribute to brand coolness, with only aesthetics directly impacting purchase intentions. Emotional engagement, self-expression, and perceived enjoyment enhance brand coolness, with emotional engagement being the most influential, and all except perceived enjoyment positively affect purchase intentions. Brand coolness from collaborations positively impacts purchase intentions, indicating that positive consumer attitudes drive behavior. Self-avatar identification moderates the relationships between familiarity and brand coolness, self-expression, and purchase intentions, and moderates the mediating effect of brand coolness. The study underscores the importance of self-avatar identification in shaping consumer behavior and calls for further research in diverse industries and new marketing forms.

With the rapid development of digital technology, digital human character design has brought richer visual experiences and creative expressions to stage art. This thesis focuses on its unique application in stage art, exploring design and performance optimization, immersive experiences, and multimedia integration. The study shows that digital human character design enhances stage art with immediacy, interactivity, and multimedia integration, while also driving innovation in traditional stage art expressions.

The current Metaverse phenomenon, a collective virtual shared space, has drawn attention to Metaverse marketing in the fashion industry. Metaverse fashion marketing refers to the promotion and sale of fashion products and experiences within this virtual environment, which simulates real-world experiences. This study conducted an online survey to identify research problems empirically. The study subjects were surveyed by domestic male and female consumers aged 35.69 on average, and the authors conducted an online survey, reminiscent of the fashion brand's virtual reality store presented in the questionnaire. Three hundred copies of the collected response data were analyzed using the SPSS 28.0 program. As a result of the study, it was confirmed that consumer experience factors in the fashion brand's Metaverse virtual reality store environment significantly impacted the intention to visit the actual store. As a result of the study, it was found that consumers' perceived presence in the fashion brand Metaverse virtual reality store had a significant effect on entertainment, esthetic, educational, and escapism experiences. Consumers' perceived social presence influenced entertainment, esthetic, educational, and escapism experiences but did not affect educational experiences. It was confirmed that the consumer experience factors in a fashion brand's Metaverse virtual reality store environment had a significant effect on the actual store visit intention. Through the results of this study, we contributed to the related research stream by empirically analyzing the impact of various dimensions of the Metaverse fashion experience, which needed to be improved so far, on consumers' actual store visit intention.

The purpose of this study is to investigate the structural relationship between authenticity, club trust, club attitude, and club loyalty of e-sports professional clubs. To achieve the purpose of the study, data were collected from 260 e-sports fans. Sampling was performed using the convenience sampling method, and the completion of the questionnaire was made to respond with the self-administration. Among the collected data, 255 copies were used by adopting the final analysis data, excluding 5 copies of the data judged to be difficult to use. Data processing was performed by using SPSS 27.0. In order to verify the centralized validity and discriminant validity of the measurement items, a confirmatory factor analysis was performed using AMOS 23.0, and the hypothesis was verified using structural equation model analysis. The results were as follows. First, it was found that among the sub-factors of the authenticity of e-sports clubs, truth and effort had a significant effect on club trust. Second, it was found that club trust had a significant effect on club attitude. Third, it was found that club trust had a significant effect on club loyalty. Fourth, it was found that club attitude had a significant effect on club loyalty. The results of this study show that if e-sports clubs operate sincerely, fans' trust in the club increases. In addition, it can be seen that the higher the trust, the more positive the fans' attitude toward the club is, affecting the loyalty of the fans.

This paper explores the factors influencing the degree of freedom in open-world games, taking <The Legend of Zelda: Tears of the Kingdom> as a case study, it is carefully analyzed in comparison to similar well-known games such as <Genshin>,<Elden Ring>, and <Far Cry>. It also analyzes how the player skill system and its synergy with the combination of interactive elements can effectively enhance the freedom of the game. The results show that the diversity of player skill systems not only significantly enhances the in-game strategy and depth of exploration, but also the rich combination of interactive elements further enhances players' tactical flexibility. This paper also points out that simply expanding the map size while neglecting the content richness and balance of quest design can have negative impacts on the game. This study aims to provide game developers with insights that emphasize the application of skill diversity and interactive elements to improve players' gameplay freedom and overall experience.

One of the most time consuming issues in a city development is the identification of suitable areas for urban infrastructures and proper land uses. Suitability analysis is the process and procedures to find the best available land in given area -that is, the ability of a system to select the needs of users in land use. This paper studied the usage of Geographic Information System technique and methods for the selection of the most appropriate sites for public park in the city of Gwangju. GIS was used as a standard technique to find the best available sites for development in urban areas. For this cause, digital elevation model and spatial data were used to produce different thematic layers by using software Idrisi. Criteria for finding the suitable site for park development were decided to evaluate the land and the followings 4 criteria were selected: on land with less than 3 degrees in slope, outside a 200m buffer around lakes, on land currently designated as forests, and 20ha or greater in size. To meet and measure each criterion, distance and context operators were applied to reclassify the importance of certain weight and Boolean images were generated to meet the criteria. These weights and maps has been combined using ArcGIS tools and the final map was prepared showing the most suitable sites. We may assist city planners and government officials in future development of public facilities including parks and related land use plans at urban level and act as to ensure proper land use planning and management of the urban areas.