Key Performance Indicators Research Articles

A Study on the Role of Digital Transformation in Enhancing Bank Performance

This study investigates the role of digital transformation in enhancing the performance of banks, focusing on how technological advancements have reshaped the banking sector. The research explores various dimensions of digital transformation, including the adoption of digital banking platforms, artificial intelligence (AI), blockchain technology, and big data analytics. By analyzing secondary data from financial reports, industry studies, and case studies of banks that have undergone digital transformation, the study aims to assess the impact of these technologies on operational efficiency, customer experience, and financial performance. Key performance indicators such as cost- to-income ratios, transaction processing times, customer satisfaction levels, and risk management effectiveness are evaluated to understand the overall benefits of digital transformation. The findings reveal that digital transformation significantly enhances bank performance by improving operational efficiency, increasing customer engagement, and driving revenue growth. Specifically, the adoption of digital platforms has streamlined processes, reduced costs, and improved the accuracy of transactions. AI and machine learning have enabled more personalized customer service and effective risk management, while blockchain technology has enhanced transaction security and transparency. The research concludes with recommendations for banks to optimize their digital strategies and overcome these challenges, and suggests areas for further research to explore emerging trends and technologies in the banking sector. Keywords: Digital Transformation, Bank Performance, Financial Technology (Fintech) Artificial Intelligence (AI), Financial Performance Metrics, Technology Adoption

Evaluation of the Effectiveness of Ship Guiding Services at Tanjung Perak Port Surabaya

The efficiency and effectiveness of vessel piloting services at ports play a crucial role in ensuring smooth maritime operations and enhancing maritime safety. This study aims to evaluate the effectiveness of vessel piloting services at Tanjung Perak Port, Surabaya, Indonesia. The evaluation encompasses various aspects of piloting services, including navigational guidance, maneuver assistance, and response to emergency situations. By employing qualitative methodologies, data will be collected from stakeholders, including port authorities, vessel operators, and piloting personnel. Key performance indicators such as response time, safety record, and customer satisfaction will be analyzed to assess the current state of piloting services. Additionally, potential areas for improvement will be identified to enhance the efficiency and effectiveness of vessel piloting services at Tanjung Perak Port. The findings of this study are expected to provide valuable insights for port authorities and stakeholders to optimize piloting services and ensure safe and efficient maritime operations at Tanjung Perak Port, Surabaya.

Hidden Markov models for predicting cell-level mobile networks performance degradation

The ubiquitous deployment of Fourth-generation (4G) and Fifth-generation (5G) networks, while expanding user connectivity over a larger geographic area, presents significant challenges for mobile network management. The surge in connected devices increase cell congestion, while user mobility, fading, and interference further deteriorate channel quality. The combined effect of these factors contribute to a gradual degradation in cell and network performance, straining the capabilities of Mobile Network Operators (MNOs) to maintain optimal service quality. The degradation progresses through distinct stages, and ultimately leading to cell outages. Existing cell degradation detection methods, such as key performance indicators (KPIs) monitoring and drive tests, rely on indicators such as signal strength (like reference signal received power (RSRP)), network measurements (like traffic volume and handover requests), and user complaints. However, these methods may not provide real-time insights, leading to delays in identifying and addressing performance issues. This paper proposes a Hidden Markov Model (HMM) approach for proactive cell degradation prediction using real-time time-series KPIs data collected from a telecom operator’s network management system. The NetMax system, a network management system specifically designed for ZTE networks, was used for KPIs data collection. RSRP, 3GPP-recommended signal strength metrics for degradation measure, defines hidden states, while traffic volume and handover requests represent the observable states. The HMM achieves an average prediction accuracy of 93.12%, F1 score of 91.81%, and precision of 92.82% with 23 observation lengths, demonstrating the models’s effectiveness in predicting mobile network’s cell degradation severity.

Enhancing Multi-Cloud Storage: A Hybrid Framework Using Interleaving and LT Codes

This article presents a novel framework for enhancing the integrity and availability of data stored on multi-cloud storage systems. The proposed technique leverages a combination of interleaving and Luby transform (LT) codes, specifically employing either ideal or robust soliton distribution during the LT encoding stage. The proposed technique is rigorously designed, incorporating data preparation facilitated by a cloud broker and culminating in the measurement of key performance indicators (KPIs). A central component of the framework is data processing, which involves interleaving followed by the LT encoding with soliton distribution selection. The performance of the proposed technique is evaluated through data integrity, data availability, and others. The results demonstrate that using the robust soliton distribution offers a pragmatic balance between computational efficiency and robust data integrity, particularly crucial in dynamic multi-cloud environments. Additionally, the proposed technique achieves a high level of data integrity of more than 0.97 and strong resilience with average data availability of 98%. However, further optimization is necessary to address storage overhead, retrieval time, and computational overhead. Despite these considerations, the framework remains significant, especially within the context of confidentiality, integrity, and availability for multi-cloud storage protection. The proposed technique underscores the potential of integrating interleaving and LT codes to revolutionize data storage and access within cloud environments, paving the way for future innovations in the field.

Artificial intelligence-driven enhanced CBR modeling of sandy soils considering broad grain size variability

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively.

Looking beyond disruption to build agile and resilient supply chains for the future

The events of recent years, ranging from geopolitical tensions and global health events to labour shortages and economic crises, have highlighted significant vulnerabilities within global supply chains and emphasised the need for agility and resilience. Recent studies referenced in this paper, including the ‘Global Business Optimism Insights’ report, underscore that while large businesses tend to prioritise strengthening their supply chains, smaller businesses are often more focused on customer base expansion, potentially risking operational robustness. This approach could jeopardise customer satisfaction due to failures in meeting on-time, in-full (OTIF) delivery metrics, leading to potential financial penalties and customer dissatisfaction. With that in mind, this paper aims to help supply chain organisations understand how they can obtain the agility and resilience needed to weather ongoing disruption and futureproof their operations. It provides recommendations on how this can be achieved, pointing to access to trusted, real-time data across the supply chain network as a critical component. The paper also identifies, however, that many supply chain businesses struggle to obtain this access to data for rapid and informed decision making. Research from InterSystems finds that this data challenge is further complicated by a lack of end-to-end supply chain visibility, which is often exacerbated by data silos created by disparate systems and manual processes. These visibility challenges negatively affect key performance indicators such as OTIF metrics and restrict an organisation’s ability to adapt to disruptions swiftly. The paper explores how supply chain organisations can overcome these difficulties, identifying investment in digital supply chain technologies such as decision intelligence platforms as a way in which they can bridge the real-time execution gap and enable supply chain leaders to optimise processes. These platforms integrate real-time data analytics, artificial intelligence (AI) and machine learning (ML) to enhance visibility, decision making and operational efficiency. The paper draws the conclusion that building resilient, future-proof supply chains is crucial for organisations to manage disruptions and capitalise on opportunities effectively. In the wake of ongoing disruption, the paper aims to demonstrate that by adopting the right technological solutions and embracing a cultural shift to data-driven operations, supply chains can transform to better withstand future challenges and move their organisation forward, thus ensuring sustained growth and enhanced customer loyalty.

The Impact of Medical Care Provision on Teacher Welfare and Performance in Public Secondary Schools: A Case Study of Sheema Municipality, Uganda

This study investigates the relationship between the provision of medical care for teachers and their overall welfare and performance in public secondary schools within Sheema Municipality, Uganda. Teacher welfare, particularly medical benefits, is increasingly recognized as a critical factor in enhancing job satisfaction and effectiveness. Using a mixed-methods approach, the research explores how access to quality healthcare affects key performance indicators, such as attendance, time management, and class participation. The theoretical framework for this study integrates Herzberg’s Motivator-Hygiene Theory, Expectancy-Value Theories, and Maslow’s Hierarchy of Needs to highlight the essential role of healthcare in addressing basic needs and improving job satisfaction. The research concluded that the majority 64.4% of the teachers were in disagreement that schools always provide first aid to teachers whereas a minority 13.7% were in agreement. Findings suggest that access to medical care can significantly reduce teacher absenteeism, boost morale, and enhance engagement in professional duties, thereby positively impacting student outcomes. The results underline the importance of targeted welfare interventions to support teachers’ health, thereby contributing to broader educational goals and community development. Keywords: Medical care, Teacher Welfare, Teacher performance, Secondary Schools, Uganda

Abstract P330: Next-Gen Hypertension Control: Integrating Clinical Pharmacists And Cutting-Edge EPIC Electronic Medical Record For Quality And Result Driven Approaches at Federally Qualified Health Centers

Hypertension management in Federally Qualified Health Centers (FQHCs) requires innovative and comprehensive strategies beyond traditional methods. This study explores a collaborative approach by integrating clinical pharmacists into the healthcare team and utilizing the EPIC Electronic Medical Record (EMR) system. The model addresses medical and non-medical health determinants, offering comprehensive treatment for underserved communities regardless of their ability to pay. We hypothesized that integrating clinical pharmacists would improve and sustain blood pressure control, resulting in more patients achieving their target health outcomes. As part of multidisciplinary teams, clinical pharmacists identify, track, and analyze patient data, manage medication therapies under collaborative practice agreements, and coordinate with other healthcare providers to ensure continuous care. By leveraging EPIC EMR’s data management and patient monitoring capabilities, clinical pharmacists use real-time data to tailor medication regimens, ensure timely interventions, and conduct follow-ups every 2 to 4 weeks via telemedicine or in-clinic visits until patients reach their goals. This approach enhances patient engagement and medication adherence, which is crucial for adequate blood pressure control and cost management. It also supports personalized care addressing medical needs, lifestyle, and socioeconomic factors affecting patient health. The study, initiated in January 2024, involves adult patients with hypertension at one of the largest FQHC in Houston, TX. Key performance indicators include the percentage of patients achieving target blood pressure levels, medication adherence rates, and the frequency of follow-up visits. Preliminary data from quarter 1, 2024, shows that out of 327 patients, 58% have had at least 2 consultations with a clinical pharmacist. Out of those patients, 46% reached blood pressure control below 140/90 mmHg, while 27% showed improvement toward their goal. This quality and result-driven approach elevates hypertension management standards, reduces health disparities, and enhances healthcare delivery within FQHCs. Clinical pharmacists have proven to be indispensable members of the healthcare team. This session aims to provide practical insights and strategies for healthcare professionals to implement similar models, paving the way for the future of hypertension care.

Automated generation of digital models for manufacturing systems: The event-centric process mining approach

Digital and simulation models support the design and management of complex systems. However, system modelling is a time-demanding and knowledge-intensive activity. Moreover, modern manufacturing systems are subjected to frequent changes in production plans and subsequent reconfigurations. Therefore, the quick regeneration of the digital models is necessary to align digital twins and cyber-physical systems. This paper proposes a novel event-centric process mining paradigm, a process discovery algorithm, and a set of Key Performance Indicators for the fast and automated generation of digital models and their benchmarking. The discovery algorithm is based on the Event Relationship Graph of the conceptual model of the physical line. The algorithm is tested in four realistic systems of increasing complexity to verify the accuracy in modelling multi-product systems with re-entrant flows and random reworks in the presence of the assembly, disassembly, and split processes beyond the processing operations, and multi-operation workstations. The Event Relationship Graphs of the four systems are presented through the equivalent Petri nets models. The proposed approach is suitable for systems where the sensor positions are known and meaningful, like manufacturing systems, and it is effective for the quick automated generation of digital models for the activities of production planning and control as it requires a few seconds of computation time and a few hours of system observation.

Cultivating a community from collaboration: The secret to a strong vertical video strategy and system

The University of Toronto Mississauga (UTM) central social media team has developed an overarching strategy and system over the course of three years, from 2020 to 2023, to help improve their vertical video production while building a community of practice with other internal campus content creators. This case study will discuss the benefits for campus content collaborators. It identifies three types of social video collaborations employed by UTM communications staff to meet the unique requirements and capabilities of different teams. Pulling from the UTM social media team's findings, this paper shares a formula for success used to create vertical videos that improve key performance indicators while building capacity among practitioners. By highlighting strategic practical tips to utilise when creating higher education video content, the authors aim to illuminate the steps involved for those wishing to set up a similar system and strategy. This case study concludes with a reflection on the lessons learned and pitfalls to avoid when collaborating with other campus content creators.

Prediction of key performance indicators of multi-effect vacuum membrane distillation systems integrated with PVT and Organic Rankin Cycle

The study presented a numerical simulation conducted using MATLAB® to analyze the performance of a poly-generation system. This system comprises solar collectors, a Photovoltaic-Thermal (PVT) system, an Organic Rankine Cycle (ORC), and a multi-effects vacuum membrane distillation (V-MEMD) system. The study investigates the impact of changes in operating conditions of the water stream on the performance of the V-MEMD system. The optimal performance values observed include a water flux of up to 13 kg/ m2·h, a GOR of 5.5, a SEEC of 49 kWh/ m3, and a STEC of 145 kWh/ m3, with each parameter reaching its optimum at different operational settings. The paper explores the impact of different working fluids in the ORC system on water and power generation. It also discusses recent research on combined PVT and ORC systems in Riyadh, Saudi Arabia, considering the region's specific meteorological conditions. The findings reveal that the highest electricity generation occurs in June, with the ORC system producing 18,540 kWh when R123 is used and the PVT system generating 13,536 kWh. The cumulative electricity output for the year reaches 266,564 kWh. Moreover, the V-MEMD system produces 1333 m3 of water over the year, with the highest water yield of 178 m3 observed in June.

The Influence of the Assembly Line Configuration and Reliability Parameter Symmetry on the Key Performance Indicators

In the context of the demand for mass customization of products, a trade-off between highly efficient automated systems and flexible manual operators is sought. The linear arrangement of workstations made it possible to divide the process into many simple operations, which increases production efficiency, but also results in an increase in the number of workstations and a significant extension of the line. A human operator is usually treated as a quasi-mechanical object, and a human error is considered, similarly, as a failure of a technical component. However, human behavior is more complex and difficult to predict. A mathematical model of a new production organization is presented, including dividing the traditional production line into shorter sections or replacing the serial assembly line with a U-line with cells. Moreover, the reliability of operator and technical means are distinguished. Work-in-progress inventories are located between line sections to improve system stability. The stability of the assembly line is examined based on the system configuration and probabilistic estimates of human failure. The influence of the symmetry of reliability parameters of people on key performance indicators (KPI (headcount), KPI (surface) and KPI (Overall Equipment Effectiveness) is examined. KPI (solution robustness) and KPI (quality robustness) are also presented in order to evaluate the impact of a disruption on the assembly line performance. New rules for assigning tasks to stations are proposed, taking into account the risk of disruptions in the execution of tasks. For comparison of assembly problems, heuristic methods with newly developed criteria are used. The results show the impact of symmetry/asymmetry on assembly line performance and an asymmetric distribution of manual assembly times that is significantly skewed to the right due to human errors. On the assembly line, the effects of these errors are cumulative and lead to longer assembly times and lower KPIs.

Nice to value. Unraveling the nexus between management accounting use and financial performance: A quantitative study of SMEs

ABSTRACT This study investigates the intricate relationship between management accounting (MA) practices and financial performance (FP) in small and medium-sized enterprises (SMEs) in Italy and Germany, offering practical insights for practitioners and policy makers. Despite the theoretical benefits of MA practices like cost accounting, budgeting, and key performance indicators, their implementation in SMEs remains limited. Prior research suggests that SMEs often rely on informal control systems due to resource constraints and a lack of familiarity with MA tools. The COVID-19 pandemic and recent European conflicts have stressed the need for robust MA practices to navigate economic volatility and enhance resilience. Using a quantitative approach, data were collected via questionnaires from 238 SMEs in Italy and Germany, targeting chief financial officers, board members, and majority shareholders. The analysis, employing partial least squares structural equation modeling, reveals a significant positive impact of MA practices on FP. Factors such as resource availability, training, and the culture of control play crucial roles in this relationship. These findings underscore the strategic importance of MA in driving organizational performance and highlight the need for SMEs to adopt these practices more comprehensively. This study offers valuable guidance for SME managers in enhancing operational efficiency and strategic decision making. Moreover, it provides policy makers with evidence-based recommendations to support SME growth and resilience through targeted training programs and resource allocation, ensuring a robust framework for economic stability and development.

Investigations of Cell Tower Antennas Parameters on Reduction of Radio Frequency Radiation Levels from Radio Base Stations

populated areas has raised public health concerns due to increased exposure to radio frequency (RF) radiation emissions. Exposure to high levels of RF radiation can have potential thermal and non-thermal biological effects. Optimizing the configuration of cell tower antenna parameters is crucial for mitigating these radiation levels. This study therefore aims at systematically investigating the influence of different cell tower antenna parameters on reducing the RF radiation levels from mobile base stations. Field measurements were conducted at two cell sites shared by multiple mobile operators. Electric field strengths were measured at multiple locations around the cell sites. The impacts of various antenna parameters were analyzed, including the deployment of GSM spectrum within LTE services, number of carrier frequencies, antenna down-tilt, and transmission power. The analysis revealed that deploying the GSM spectrum within LTE services can reduce the overall radiation levels. Additionally, decreasing the number of carrier frequencies, increasing antenna down-tilt, and lowering transmission power were found to contribute to significant reductions in measured radiation levels. However, optimizing these antenna parameters to mitigate radiation can also degrade key mobile network performance indicators, such as coverage and capacity. These findings provide valuable insights into effective strategies for balancing the need to minimize RF radiation exposure from mobile base stations while maintaining acceptable mobile network service quality. Careful optimization of cell tower antenna configurations is essential to address public health concerns without compromising network performance.

METHODOLOGICAL APPROACH TO THE ASSESSMENT OF INTERNATIONAL LOGISTICS MANAGEMENT IN RECREATIONAL ENTERPRISES: THE IMPACT OF RISKS ON FINANCIAL SECURITY

In the article, we present a methodological approach to evaluating international logistics management in the context of ensuring financial security through the development of an appropriate model for assessing indicators and risks that influence it. The research focuses on enterprises in the recreational sector. Structural-logical analysis methods were applied for modelling and graphical methods for their visualization. The fundamental method was the technique of evaluation using fuzzy sets. In this context, the MATLAB system served as the basis for more effective modelling. The article demonstrates that the evaluation of international management begins with the analysis of key performance indicators and risks. As a result of the research, a methodological approach to evaluating the effectiveness of international logistics management in the recreational sphere was formed. The main goal of such management is efficient planning and execution of logistics operations, which ensure continuity and high quality of recreational services at an international level, cost optimization, and increased customer satisfaction. However, to achieve this, it is necessary to properly assess risks and key indicators in the context of ensuring an adequate level of financial security. The proposed approach to evaluation focuses on identifying logistics risks, providing a comprehensive understanding of the problems and opportunities in the context of enhancing financial security. Looking ahead, it is necessary to more closely consider the possibility of practical application for specifically selected enterprises in the recreational sphere and to evaluate not only risks but also threats to financial security.

Circular-ESG Model for Regenerative Transition

This paper presents a novel circular-ESG framework integrating circular economy (CE) principles with environmental, social, and governance (ESG) criteria to address the lack of uniform sustainability measures. We introduce normalized sustainability coordinates (NSCs) as a comprehensive metric for sustainability performance, reconciling economic development with environmental balance. The circular-ESG model employs a four-quadrant Cartesian system to map business model impacts on natural and socio-economic systems, ranging from linear open-loop to circular closed-loop ESG models. This framework enables empirical analysis through data-driven NSCs (−1 to 1) and establishes temporal key performance indicators. By incorporating the Human Development Index within ecological limits, the model promotes regenerative development aligned with planetary boundaries. The circular-ESG approach offers a practical tool for businesses, households, organizations, and policymakers to navigate sustainable development complexities. This integrated framework fosters innovation and supports a just transition towards regenerative practices, providing a roadmap for high human development within ecological limits. The circular-ESG model advances sustainability science and management, contributing to the discourse on measuring and implementing sustainable practices across sectors and scales. The model is currently conceptual; we encourage empirical validation and further research to explore its practical applications and effectiveness in real-world scenarios. While the provided examples of use cases serve as conceptual demonstrations, future research could empirically apply the model to real-world data.

Further Evidence That Science-Based Biosecurity Provides Sustainable Prevention of Porcine Reproductive and Respiratory Syndrome Virus Infection and Improved Productivity in Swine Breeding Herds.

Porcine reproductive and respiratory syndrome virus (PRRSV) is a globally significant pathogen of pigs. Preventing the entry of PRRSV into swine breeding herds enhances animal health and welfare. A recently published retrospective cohort study reported significant differences in PRRSV incidence risk between breeding herds that practiced Next Generation Biosecurity (NGB) COMPLETE, versus herds that practiced a partial approach (NGB INCOMPLETE) over a 2-year period. This follow-up communication builds on this previous publication and brings new information regarding statistical differences in key performance indicators (KPIs) from 43 NGB COMPLETE herds and 19 NGB INCOMPLETE herds during disease years 1 and 2. Statistically significant differences included higher total born/farrow and pigs weaned/female along with a reduced pre-weaning mortality and wean to 1st service interval, as well as a 0.91 increase in the number of pigs weaned/mated female/year. In addition, this communication reports that PRRSV incidence risk throughout disease years 1-3 was 8.0%, and that the association of NGB status (COMPLETE vs. INCOMPLETE) and disease burden for the cumulative 3-year period was statistically significant (p < 0.0001). These findings support previously published data that NGB, while not perfect, provides sustainable prevention of PRRSV, and may help improve herd productivity.

Identifying the influence of inlet velocity changes to pressure drop and collecting efficiency in Stairmand and Lapple type cyclone separators

The object of this research is to compare the performance of Stairmand and Lapple type cyclone separators. The main problem to be solved in this research is determining which Stairmand or Lapple type cyclone separator is more suitable for integration into the pyrolysis system. The comparison is based on key performance indicators: pressure drop and collecting efficiency. The research findings indicate that both Stairmand and Lapple cyclone separators exhibit similar trends in pressure drop and collecting efficiency. As inlet velocity increases, the pressure drop also increases for both types. However, the collecting efficiency initially rises but then declines when inlet velocities exceed 13 m/s. The Lapple variant achieved a peak collecting efficiency of 98.94 % and pressure drop 16.26 mbar at an inlet velocity of 13 m/s, whereas the Stairmand design reached 97.33 % and pressure drop 12.16 mbar at 13 m/s inlet velocity. The Lapple type cyclone separator outperformed the Stairmand type in terms of both of pressure drop and collecting efficiency. This superiority is attributed to the specific design features and characteristics of the Lapple type. The superior performance of the Lapple type cyclone separator can be explained by its unique design elements that contribute to improved particulate matter separation and pressure drop. These elements may include differences in cylinder height and particulate matter outlet diameter. Based on the findings of this research, the Lapple type cyclone separator is recommended for integration into pyrolysis systems. However, it is important to consider the specific operating conditions of the pyrolysis process, such as temperature, the particulate matter size distribution, flow rate, and desired separation efficiency

People Don’t Practice What They Preach? Inclusive Educational Policy and Exclusionary Practices for Children with Autism Spectrum Disorders in China’s Compulsory Education: A Critical Assessment

Purpose: Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by behavioral deficits. In China, children with ASD find themselves critically challenged due to inadequate support and insufficient awareness among the general public. Methods: This paper illustrates the implementation of Learning in Regular Classroom Program (LRC) in China, comparing and deconstructing the special education policies in two consecutive phases in a southern metropolis in China. Results: A Critical assessment reveals 1) retrogression in guiding principles, 2) inconsistent models of disabilities, 3) consistent use of enrolment rate as Key Performance Index (KPI) and 4) non-mandatory use of language to guide implementation. Conclusion: The widening gap between the conceptualization and realization of the LRC in China is beyond question. Suggested solutions are: 1) professional projection of possible challenges for children with ASD; 2) adequate supply of resource classrooms and special education teachers appointed for the implementation of the Individual Education Plan (IEP); 3) a statutory code of practice in ASD studies.

Using Digital Storytelling and Social Media to Combat COVID-19 Vaccine Hesitancy: A Public Service Social Marketing Campaign.

Disparities in vaccine confidence and uptake among racial and ethnic minorities have resulted in a disproportionate burden of COVID-19 in these populations. Social media campaigns have shown promise in public health promotion and behavioral interventions. In January 2022, an academic-community partnership launched #Vax4Community, a 6-month social media campaign centered around the use of digital storytelling videos. The campaign purpose was to decrease vaccine hesitancy, combat vaccine misinformation and disinformation, and increase vaccine confidence within three distinct target communities: the justice-involved population, South Asian residents, and public housing youth in the metropolitan area of New York City (NYC). Our approach included the production and dissemination of digital storytelling videos featuring personal vaccine experiences from target populations. We evaluated key performance indicators (KPIs) of the campaign, including post impressions, reach and engagement across social media platforms, and shares from partner organizations. Overall, we received 1,910,662 post impressions, 699,722 unique users reached, and 2,880 post engagements across Instagram, Facebook, LinkedIn, and Twitter, and 147 shares from 48 partner organizations. Social media campaigns require strategic design in branding, messaging and outreach channels and could serve as an important tool to disseminate emotionally relatable content and trusted information to prime target populations to respond more optimally to public health interventions. The purpose of this paper is to describe the process of creating and disseminating these digital stories and the KPIs of the social media campaign.