Application Integration Research Articles

Advanced Fanout Embedded Bridge Packaging Technology for Chiplets Integration

The increasing amount of data from all sectors driven by digital transformation is raising a problem of operational and storing cost of the data. Meanwhile, the exponential cost leaps of silicon scaling, the unfordable increasing of Si die size over reticle limit and unsustainable computer energy exceeding the world’s energy production have created an inflection point for semiconductor industry. It has driven the development of More-Than-Moore to augment increased device and system performance. Chiplets integration provides more flexible mix-and-match systems to accelerate performance and power efficiency. It is driving the development of advanced packaging technology to enable heterogeneous chiplets integration with separate designs and different manufacturing process nodes within a single package for yield improvement, IP reuse, performance and cost optimization, as well time to market reduction. Several advanced packaging technologies have been developed and used widely to integrate multi-chips with fine line/space interconnections, such as 2.5D Si TSV interposer, Fanout RDL (re-distribution layer) and EMIB (Embedded Multi-die Interconnect Bridge). Different packaging technologies have different sweet spots to fulfill various applications depending on the device design and performance requirements. In ASE, we have developed and introduced FOCoS (FanOut Chip on Substrate) chip first (FOCoS-CF) and FOCoS chip last (FOCoS-CL) technologies by using RDL interconnect for chiplets integration. However, due to the inherent fanout RDL process limitation, it has hit the bottleneck to manufacture RDL with higher layer counts (> 6 layers) and finer Line/Space (L/S < 1um/1um) for the applications that require high density die to die connections, high input/output (I/O) counts and high-speed signal transmitting. In this paper, a new FOCoS technology named as FOCoS embedded Si bridge (FOCoS-B) has been developed for high density die to die interconnect with RDL L/S < 1um/1um chipets integration for HPC application. The FOCoS-B packaging technology enables the USR (Ultra Short Reach) interconnection between die to die by embedded a small Si die in fanout RDL interposer. The small Si die play an interconnection bridge role between chiplets with L/S 0.8um/0.8um. Two FOCoS-B test vehicles (TVs) will be introduced in this study. One TV named as TV-1 is integrated by two chiplets of 1 ASIC die and 1 HBM2e with 1 Si bridge die to form a fanout module, which it is subsequently assembled on a FCBGA package with body size of 40x30mm2. Another TV listed as TV-2 is composed of two identical fanout modules, which are assembled on one FCBGA substrate with body size of 78x70mm2 in MCM (multi-chip-module) arrangement. The fanout module for TV-2 is integrated by 1 ASIC and 4 HBM2e with 4 bridge dies. TV-2 will have total 10 chiplets (2 ASICs and 8 HBM2e with 8 Si bridge dies). The process flow for FOCoS-B will be illuminated. The process DOE and the impact on warpage will be discussed. Furthermore, the reliability validations on two TVs will also be demonstrated. Finally, the comparison on warpage among different FOCoS solutions (FOCoS-B, FOCoS-CL and FOCoS-CL) for chiplets integration have been presented. The results indicated that the material selections and property compatibilities among the multi-layer stacked structures (Si die, RDL, Molding compounds, Underfill) play critical roles in warpage control in FOCoS-B processes. The package warpage of FOCoS-B and FOCoS-CL has showed similar behaviors. FOCoS-CF showed slightly high package warpage than that of FOCoS-B. The results also revealed that FOCoS-B has provided a wide choice and flexibility for multi bridge dies integration successfully for HPC application.

SQL Injection Detection Using RNN Deep Learning Model

SQL injection attacks are a common type of cyber-attack that exploit vulnerabilities in web applications to access databases through malicious SQL queries. These attacks pose a serious threat to the security and integrity of web applications and their data. The existing methods for detecting SQL injection attacks are based on predefined rules that can be easily circumvented by sophisticated attackers. Therefore, there is a need for a more robust and effective method for detecting SQL injection attacks. In this research, we propose a novel method for detecting SQL injection attacks using recurrent neural networks (RNN), which are a type of deep learning model that can capture the syntax and semantic features of SQL queries. We train an RNN model on a dataset of benign and malicious SQL queries, and use it to classify queries as either benign or malicious. We evaluate our method on a benchmark dataset and compare it with the existing rule-based methods. Our experimental results show that our method achieved high accuracy and outperformed the rule-based methods for detecting SQL injection attacks. Our research contributes to the field of web application security by providing a new and effective solution for protecting web applications from SQL injection attacks using deep learning. Our method has both practical and theoretical implications, as it can be easily integrated into existing web application security frameworks to provide an additional layer of protection against SQL injection attacks, and it can also advance the understanding of how deep learning models can be applied to natural language processing tasks such as SQL query analysis.

Enhancing Institutional Performance of Tutoring Centers in Kuala Lumpur: The Role of Document-Based Budget Management

Effective budget management is pivotal for any institution, acting as a linchpin for informed decision-making. Central to this study is the exploration of how document-based budget management applications can substantially augment the quality of institutional performance. Specifically, this research focuses on the Jalan Kebun Tutoring Center, which serves the children of Indonesian expatriates employed in Selangor, Malaysia. The chosen methodological approach for this study is applied research via a case study format. Data interpretation relies on comparative descriptive analysis. The research progression encompasses several stages: needs identification, application development, testing, results revision, and, finally, implementation. The findings underscore the escalating demand for a dedicated application to streamline financial accounting processes. This urgency is heightened by the expansive growth of institutions and the increasing demand from various stakeholders for transparent financial reporting. Notably, the pilot testing of the application affirmed its capacity to bolster speed, precision, and efficiency in disseminating crucial financial information to relevant parties. An essential recommendation stemming from this study is the potential integration of this financial application with academic processes. Such an integration could present a holistic view, offering comprehensive information that bridges both academic and financial spheres, ensuring a unified institutional management approach.

A Survey on Integration of Applications - Tools, Types & It’s Challenges

Application integration services allow a variety of apps inside a company to share processes and business data. enabling the transformation and orchestration of the data necessary for business activities by integrating a number of assumptions and cloud apps. The program that integrates and improves the data transfer between two different software applications is called application integration. Businesses frequently use this software to build a bridge between an older on-premises application and a new cloud-based one, allowing the two systems to coexist. This article will describe application integration, its software and varieties, and the many actions that may be taken to implement application integration strategies.

Remote support of the process of teaching German grammar using mobile applications at the university

The article is devoted to the study of issues related to the disclosure of the potential of mobile applications in the field of foreign language teaching in the context of the modernization of Russian education. The article presents arguments in favor of the use of mobile applications: improving the effectiveness of educational and cognitive activities, the formation of independent work skills, increasing the motivation of students to learn a foreign language, the formation of the ability to organize and independently plan the process of learning a language, evaluate educational activities. The article pays special attention to the potential of mobile applications in teaching grammar of a foreign language at different levels – from beginner to advanced. The author recommends using several different types of applications to achieve maximum results at the three highlighted stages of work on grammatical material. The first stage is the presentation of grammatical phenomena and the creation of basic knowledge based on the use of specific mobile applications. The second stage involves automating the use of grammatical skills through their practice in mobile applications. The third stage is the control of the assimilation of knowledge using mobile applications. The author describes an experiment in which mobile applications were introduced into the educational process. At the preparatory stage of the experiment, the learning potential of mobile applications was studied and input conditions were diagnosed. At the second stage of the study, a choice of mobile applications was made. The integration of mobile applications at the third stage was implemented in the form of remote support for teaching German grammar as part of a traditional lesson. The results of the experiment, obtained after repeated testing of the experimental group at the fourth stage, demonstrated the effectiveness of using mobile applications in teaching German grammar.

Harmonic Response of CFRP Tensegrity System in a Suspen-dome

Abstract Wind-induced excitation causes structure to vibrate which leads to instability. This paper focuses on the performance of CFRP tensegrity system in a suspen-dome subjected to wind load by assuming such load as harmonic. A comparison is made with the traditional steel tensegrity system, in order to justify the integrity of CFRP cable application in a suspen-dome system. A finite element software, namely ANSYS, was implemented for the simulation by analyzing a physical model of 4 m span and 0.4 m height. Results show that CFRP tensegrity system has similar performance as steel tensegrity system and can be used as a substitute for steel.

JURIDICAL STUDIES OF THE LEGAL STATUS OF DIGITAL RUPIAH IN THE CONTEXT OF MODERNIZING FINANCIAL MARKET INFRASTRUCTURE

The rise of exchange rates other than the official state currency in the digital world raises its own problems. The development of the digital economy that is currently developing is supported by digital economic infrastructure, one of which is the official state medium of exchange. Therefore, this is a demand for the public's need for official state exchange instruments that can apply in the digital world. This was then answered by Bank Indonesia through the concept of Digital Rupiah and its derivatives in various Bank Indonesia Regulations. However, there are problems regarding the Digital Rupiah policy and the implementation of secure use of the Digital Rupiah sistem. This research was conducted using a normative juridical method, and the writing stage was carried out through a literature search which was carried out by examining secondary data including primary legal materials, literature, articles, opinions and teachings of experts and their implementation in laws and regulations. Based on the discussion, it can be concluded that policies related to Digital Rupiah as a modernization of financial market infrastructure are still contained in various regulations, including Law Number 4 of 2023, Bank Indonesia Regulation Number 23/11/PBI/2021, and Bank Indonesia Regulation Number 23/10 /PBI/2021. Digital Rupiah security arrangements in Indonesia refer to Bank for International Settlements (BIS) guidelines and include secure technology infrastructure, cloud-based cybersecurity services, risk management and compliance sistems, as well as application and infrastructure security integration through DevSecOps. Reliability certificates are regulated in Government Regulation Number 71 of 2019.

Machine Learning in CLL

Introduction: Chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are closely related diseases with similar characteristics, classified as mature B cell neoplasms. They involve the accumulation of monoclonal B lymphocytes and are considered as one disease with different manifestations. CLL is more prevalent and is usually detected through routine blood tests, while SLL is identified by primary lymph node involvement. Although many patients with CLL/SLL are asymptomatic, some may experience lymphoma-related symptoms. The diagnosis involves blood tests and immunophenotypic analysis. CLL prognosis varies, and staging systems help in determining the appropriate treatment regimen. Machine learning (ML) is being increasingly used in hematology to aid in diagnosis, treatment, and risk-stratification. This review explores the application of ML in the classification and diagnosis of CLL, discussing the performance and limitations of existing models, with the goal of encouraging further research and integration into clinical settings to improve patient care. Materials and Methods: A literature search was conducted using the PubMed/MEDLINE and EMBASE databases. EndNote and Rayyan software were used to eliminate duplicate entries and for screening the articles. In addition, the references of the identified articles were manually screened to identify additional relevant studies. Articles with primary data pertaining to the use of ML in CLL/SLL diagnosis and classification were included without language or time restrictions. Results: A total of 169 articles were identified and duplicates were removed using Endnote® and Rayyan® software, resulting in 149 articles eligible for screening. The included articles met specific criteria: they utilized ML methods, reported conclusions on the method's reliability or accuracy, and focused on CLL diagnosis and classification. Excluded articles were non-English, animal/in vitro studies, abstracts, and review articles. After screening, 14 studies met the inclusion criteria. Studies examining the potential of ML algorithms in enhancing the diagnosis of CLL used various data sources like blood smears, flow cytometry, genetic data, and histopathological images. The current evidence suggests that ML can effectively predict CLL diagnosis, aid in screening, identify potential biomarkers, and explore the underlying molecular mechanisms. Many of the ML models evaluated in this review demonstrate high accuracy, ranging from 83% to 100%. Implementing AI and ML in hematology can automate tasks involved in patient workup, risk assessment, and treatment, allowing hematologists to focus on critical aspects of patient care and research. Despite the promising potential, some important considerations should be acknowledged. First, many reviewed models had limited sample sizes from single centers, limiting their generalizability. It's crucial to develop models using larger, diverse datasets from multiple centers to improve generalizability. Second, there is a lack of prospective studies and research on the impact of ML models on patient outcomes. Future investigations should focus on prospectively assessing the effect of ML models on CLL diagnosis, prognosis, and patient outcomes. Third, the integration of ML applications into direct patient care raises ethical and medico-legal concerns, including liability, data privacy, and doctor-ML application interaction. An ethical framework specific to ML applications in healthcare is needed, and doctors should receive training on ML applications to understand their capabilities and limitations. Addressing these considerations can lead to successful implementation of ML applications in the care of CLL patients, improving diagnosis and patient outcomes. Conclusion: Our findings demonstrate the promising potential of ML algorithms in accurately predicting CLL diagnosis, conducting CLL screening, and identifying genetic biomarkers associated with CLL. Future research endeavors should prioritize the development of large, standardized datasets to effectively train ML models, conduct prospective evaluations to assess their performance, explore their impact on patient outcomes, and establish ethical frameworks to govern their utilization.

Correlating viscosity and die swell in 3D printing of polyphenylsulfone: A thermo-mechanical optimization modus operandi

Additive manufacturing (AM) of polyphenylsulfone (PPSU), an amorphous high-performance polymer (HPP), has shown promise in exhibiting structural integrity for many applications. With thermoplastic material extrusion 3D printing, parts consisting of layered melts exhibited voids, where the adhesion quality and void size depend on the processing parameters implemented during printing. However, the structure-process-property (SPP) relationships between the thermal properties and rheological and mechanical behavior of AM PPSU are poorly understood. In this study, a comprehensive analysis of the thermal, thermo-mechanical, and layer-by-layer build-up of PPSU is investigated and correlated with viscosity and extrudate die swell behavior with void fraction volume as confirmed by high-resolution x-ray computed tomography (XCT). Gas chromatography–mass spectrometry (GC–MS) pyrolysis confirmed PPSU composition and degradation at 480 °C, consistent with thermo-gravimetric analysis TGA. A 141% difference in viscosity was observed when processing temperatures increased from 350 °C to 400 °C. Likewise, extrudate die swell decreased with increasing temperatures, resulting in a 17% difference. Tensile and compressive properties confirmed that higher extruding temperatures resulted in lower porosity and higher mechanical strength. In general, higher extruding temperatures lower the viscosity to allow for stronger cohesion between layers; however, increasing the temperature too high results in a below unity extrudate die swell, reducing mechanical properties. Therefore, to optimize the mechanical integrity of 3D printed amorphous PPSU parts, an increase in the viscosity, near unity of the extrudate die swell, and tempered extrusion temperature are needed.

Integration of Synergetic IoT Applications with Heterogeneous Format Data for Interoperability Using IBM ACE

Integration of Synergetic IoT Applications with Heterogeneous Format Data for Interoperability Using IBM ACE

Investigating the effects of Coating systems on the degradation behavior of 3D-Printed pressure vessels

This study investigates the long-term performance of 3D-printed pressure vessels subjected to accelerated aging through water mass infusion. Thermoplastic composites were used to construct the pressure vessels using Material extrusion 3D printing, and subsequent submersion in an ocean water solution at elevated temperatures was employed to simulate accelerated aging. The effectiveness of various coating systems in minimizing the long-term degradation of these 3D-printed pressure vessels was also examined. Implosion experiments were conducted on both aged specimens with and without coating systems using a high-pressure tank facility. The results demonstrate that the application of polyurethane coating systems significantly slows the degradation process of 3D-printed vessels, with aged specimens exhibiting a reduced critical operational depth by 10.3 % compared to those without coatings. The pressure differential between unweathered specimens with and without coatings was 3.61 %, increasing to 23.3 % for weathered specimens. Coatings proved effective in reducing mass absorption and increasing critical collapse pressure. The analytical collapse pressure prediction closely aligns with experimental results, enabling performance generalization for other coating systems. This research contributes insights into the use of coating systems to enhance the longevity and performance of 3D-printed pressure vessels, highlighting their potential for mitigating degradation and improving structural integrity in long-term underwater applications.

FTG: Score-based black-box watermarking by fragile trigger generation for deep model integrity verification

Deep neural networks (DNNs) are widely used in real-world applications, thanks to their exceptional performance in image recognition. However, their vulnerability to attacks, such as Trojan and data poison, can compromise the integrity and stability of DNN applications. Therefore, it is crucial to verify the integrity of DNN models to ensure their security. Previous research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the watermark. To address this problem, we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation (FTG). The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking process. It generates different fragile samples as the trigger, based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark it. Different prediction probability masks can promote the generation of fragile samples in corresponding distribution types. The whole watermarking process does not affect the performance of the target classifier. When verifying the watermarking information, the FTG only needs to compare the prediction results of the model on the samples with the previous label. As a result, the required model parameter information is reduced, and the FTG only needs a few samples to detect slight modifications in the model. Experimental results demonstrate the effectiveness of our proposed method and show its superiority over related work. The FTG framework provides a robust solution for verifying the integrity of DNN models, and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.

Enhancing rice crop health: a light weighted CNN-based disease detection system with mobile application integration

Enhancing rice crop health: a light weighted CNN-based disease detection system with mobile application integration

Personalized Design of Student Autonomy in STEM Curriculum

This paper explores the implementation of personalized design approaches to enhance student autonomy in STEM (Science, Technology, Engineering, and Mathematics) education. The current state of STEM curriculum in China is examined, highlighting existing challenges in its implementation. The role of autonomy in learning is discussed, emphasizing its definition, importance, and its correlation with effective STEM learning. The paper then delves into personalized design approaches, including the customization of learning paths, integration of real-world applications, utilization of educational technology, and the adoption of project-based learning. Barriers to implementing personalized design are identified, encompassing teacher training, resource challenges, and cultural considerations. The advantages and implications of personalized design are discussed, focusing on improved student engagement, performance, and alignment with educational goals. The paper concludes by emphasizing the transformative potential of personalized design in STEM education.

Framework for Project Sustainability for Power Installations Using Business Intelligence Approach: A Systematic Literature Review

The growing concerns about environmental sustainability and energy conservation have led to increased interest in optimizing electricity consumption and billing processes in various projects. This research article presents a comprehensive study on the development and application of Business Intelligence (BI) frameworks for enhancing project sustainability through data-driven energy management. Through the integration of BI tools and techniques, this research investigates the analysis of electricity consumption patterns, billing accuracy, and cost-effectiveness in diverse project contexts. The article emphasizes the significance of data preprocessing, statistical analysis, and predictive modelling in uncovering valuable insights to support informed decision-making. Additionally, the review examines the concept of project sustaibility, emphasizing its significance in achieving desired outcomes, meeting stakeholder expectations, and ensuring the project’s viability in an ever-changing environment. Traditional project management approaches often fail to adequately address sustainability concerns, leading to project failures or limited long-term impact. Hence, the review highlights the growing importance of leveraging BI-driven frameworks to enhance project sustainability in various sectors, including in Information and Communication Technology (ICT) domain. The Systematic literature review (SLR) method was used involving the scooping of 230 articles from over 8 global academic databases. With the use of exclusion criteria, only 61 articles were used in the study. The analysis of the articles shows that 57% were journal articles, 39% were conference proceedings, 2% were thesis/dissertations and 2% were generic. Within the scope of this literature review, key terms and keywords were identified to provide insights into the development of a novel BI-driven framework for project sustainability. Consequently, future research directions are identified to further explore the integration of renewable energy sources, AI and machine learning applications, and behaviour-based energy management strategies within BI frameworks for sustainable project outcomes. This review lays the foundation for future research endeavours in developing innovative BI-driven frameworks that foster sustainable practices and contribute to a greener and more resilient future across diverse industries and projects.

The Evolution and Global Significance of STEM Education in The 21st Century

Developing and changing technology brings with it different business opportunities. This change in the field of work also affects the education models in schools. With this study, he examines the importance and necessity of STEM (Science, Technology, Engineering and Mathematics) education and follows its development and impact in various countries. Emphasizing the importance of STEM for the 21st century workforce, this study offers a holistic approach with the integration of theory and practical application. This study also explores the harmonious relationship between STEM and 21st century skills and draws attention to the need for multifaceted competences in a rapidly changing world. In addition, the relationship between STEM education and multilingualism in our globalized age is discussed and the enhanced linguistic development that STEM can promote is emphasized. The research also provides an insight into Turkey's STEM journey, known as STEM, and reveals the country's initiatives, challenges and the road ahead. In conclusion, this research provides a comprehensive look at the role of STEM in equipping students with the skills, knowledge and adaptability necessary for the contemporary world.
 
 Gold, which is one of the important investment instruments, is followed by many investors around the world due to its impact on the markets. For this reason, the estimation of gold prices and the determination of the affecting factors is one of the important problems. In this study, the percentage change in gold prices and its causes are estimated with hidden Markov models. Gold, dollar, interest and inflation rate data between 2013-2023 were used. Percentage change in gold prices is defined as observations and 18 possible hidden state are obtained from dollar, interest and inflation rates that affect gold prices. The forward algorithm is used to solve the first fundamental problem of the hidden Markov model. Observation sequence probabilities in 2-month periods were calculated with this algorithm. In this period, the probability of the percentage change in gold prices to be greater than 0.5 was found to be 23.6%. Hidden states that best explain the observation sequences are examined with the Viterbi algorithm. It has been observed that high accuracy rates are obtained by comparing the results with real data.

Two Decades of Online Teaching: Trends, Challenges, and Future Directions.

Over the past two decades, significant advancements in educational technology have revolutionized the field of chemical education. This publication presents our contributions using web-based tools that have been developed over this period, enabling chemistry students to engage with exercises that closely mirror real-world scenarios encountered in their future careers. These freely available online resources have facilitated the seamless integration of theoretical knowledge and practical applications, fostering critical thinking and problem-solving skills.

The Application of STEAM Education in CLIL--A Case Study of Halloween Jack-O'-Lantern

In recent years, there has been a promotion and emphasis on CLIL (Content and Language Integrated Learning) and STEAM (Science, Technology, Engineering, Arts, and Mathematics) education. The integration of interdisciplinary concepts and applications in English classrooms has brought new value and significance to English teaching. This study aims to investigate the students’ perceptions by STEAM-Integrated CLIL instruction into English Classroom, a Halloween Jack-O-Lantern as an example. The participations were eight freshman students from different department in a university in central of Taiwan. Through a survey, the research aims to analyze different aspects of STEAM and assess the effectiveness of English learning outcomes. The results indicated that the students were satisfied with the application of STEAM education in CLIL instruction in English classroom.

A comparative evaluation of machine learning algorithms for network anomaly detection

In this era of digital transformation, the importance of network anomaly detection has been amplified to safeguard the security and integrity of various vital applications. These applications include the protection of critical infrastructures, prevention of cyber-attacks, and upkeep of network performance, among others. Our study presents a thorough evaluation of an array of machine learning algorithms for the proficient detection of network anomalies. These algorithms include, but are not limited to, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). We illustrate an in-depth comparison between these chosen algorithms, analyzing their performance metrics, strengths, and weaknesses, with a specific focus on their practical applicability and influence on network security methodologies. The investigation into these machine learning algorithms exhibits potential advantages and constraints of employing such methodologies for network anomaly detection. We further shed light on the determinants that affect their performance in diverse scenarios. Based on the exhaustive analysis, we provide suggestive guidelines for choosing the most suitable algorithm depending on specific use cases or requirements. This study thus serves as a comprehensive guide to understanding the role and impact of machine learning in the critical field of network anomaly detection.

Stepping Toward Business Excellence: Building an Integrated and Efficient Enterprise Architecture System

In today's digital era, businesses face increasingly complex challenges. To achieve business excellence, organizations need to build integrated and efficient systems. Enterprise architecture (EA) is a comprehensive approach to designing and managing information technology and business structures in organizations. This journal aims to investigate the importance of building an integrated and efficient EA in achieving business excellence. We use a research methodology approach to analyze case studies and results related to EA implementation in several organizations. Our findings show that an integrated and efficient EA can provide benefits such as improved operational efficiency, cost savings, increased flexibility, and better decision-making. In this journal, we describe the methods used in building EAs, the results of EA implementation in organizations, and a discussion of the implications and resulting benefits. We conclude that building an integrated and efficient EA is an important step for organizations that want to achieve business excellence. An integrated and efficient EA in achieving business excellence. An integrated EA involves a comprehensive planning and design process, integration of systems and applications, effective data and information management, and necessary organizational changes. An integrated and efficient EA implementation can provide a number of benefits to an organization, including improved operational efficiency, reduced costs, increased visibility and accessibility of information, and better decision-making. However, the challenges and complexities associated with EA implementation need to be addressed through a holistic approach, stakeholder engagement, effective communication, and proactive change management.