Implementation Of Corrective Actions Research Articles

QUANTITATIVE ASSESSMENT OF OCCUPATIONAL RISK BASED ON DETERMINING THE COSTS OF ENSURING LIFE AND HEALTH OF EMPLOYEES

Implementing a quantitative occupational risk assessment is legislated as an employer’s responsibility nowadays. Yet, there is no unified approach to risk assessment. Employers make decision on a certain methodological approach depending on current industrial processes and existing occupational health and management system on their own. The study aims to develop a unified methodological approach to quantitative occupational risk assessment. The research methodology is based on the provisions of regulatory documents in occupational health and safety, scientific publications on occupational risk assessment. The research method involves analyzing normative and methodological literature on occupational risk management and the statistical data on occupational safety expenditures of companies. The article presents a comparison of the main available approaches used in companies to implement a quantitative occupational risk assessment. The results show there is no possibility to justify the costs of risk response activities in a vast majority of methods. The authors propose a methodological approach to occupational risk assessment based on determining the costs of ensuring life and health of employees. This approach allows to develop and substantiate activities on occupational risk reduction and the improvement of industrial safety level in a company as well as to assess the effect expected of the implementation of corrective actions. The actual vector of scientific research on the subject is to create and implement information systems for occupational risk management and decision-making support based on the developed approach in companies.

A comprehensive review of safety audits: ensuring workplace safety and compliance in Zimbabwe

Safety audits are systematic evaluations of an organisation’s safety management system, policies, procedures, and practices to identify potential hazards and assess compliance with relevant regulations and standards. This article provides a comprehensive review of safety audits, focusing on their role in ensuring workplace safety and compliance in Zimbabwe. A comprehensive search was conducted across various databases, including PubMed, Science Direct, Scopus, Google Scholar, and Sage Publications, to gather relevant articles. The relevance of the literature to the topic being studied was assessed by utilising abstracts and key words such safety audits and safety standards. The article examines multiple research studies carried out on the topic and presents a comprehensive summary of the present condition of safety audits in Zimbabwe. The findings indicated that the objective of safety audits is to proactively identify areas for improvement and implement corrective actions to prevent accidents, injuries, and occupational illnesses. The study indicates types of safety audits including compliance audits and management system audits. The article delves into the key components of safety audit processes, benefits of safety audits and challenges and limitations of safety audits in Zimbabwe. The article concludes by emphasising the importance of continuous improvement in safety audits.

Critical Role of Manufacturing Execution Systems in Digital Transformation of Manufacturing Industry

The manufacturing industry is undergoing a significant digital transformation, propelled by advancements in technology. Central to this evolution is the implementation of Manufacturing Execution Systems (MES), which act as a vital link between the shop floor and enterprise-level systems. This paper explores the critical role of MES in the digital transformation of the manufacturing industry, focusing on how it enhances operational efficiency, improves product quality, and enables real-time decision-making. Manufacturing Execution Systems are software platforms designed to monitor, control, and optimize production processes from the initiation of orders to the delivery of finished goods. MES provides real-time data on production activities, enabling manufacturers to make informed decisions, reduce production costs, and increase overall productivity. The integration of MES with advanced technologies such as the Internet of Things (IoT), Big Data analytics, and cloud computing has further amplified its impact, leading to smarter and more agile manufacturing processes. One of the key benefits of MES is its ability to improve operational efficiency. MES offers real-time visibility into the production process, allowing manufacturers to identify and resolve bottlenecks, minimize downtime, and optimize resource utilization. Predictive maintenance capabilities, enabled by IoT integration, prevent unexpected equipment failures and reduce maintenance costs. MES also plays a crucial role in enhancing product quality. By monitoring each stage of production, MES ensures adherence to quality standards and facilitates traceability. In the event of a quality issue, MES allows for quick identification of the root cause and implementation of corrective actions. Real-time quality checks at critical points in the production process help detect and address defects promptly. Moreover, MES supports real-time decision-making by providing up-to-date data that allows production managers to respond swiftly to changes in demand or production conditions. This agility is essential in today's fast-paced manufacturing environment. In conclusion, Manufacturing Execution Systems are indispensable in the digital transformation of the manufacturing industry. By improving efficiency, product quality, and decision-making capabilities, MES equips manufacturers to succeed in the era of Industry 4.0. This paper highlights the benefits and challenges of MES implementation, offering valuable insights for manufacturers aiming to leverage MES in their digital transformation efforts

The risk-based operational safety strategy for Perkin-Elmer/Cetus DNA Thermal Cycler PCR systems

Introduction: this article was conducted in the field of polymerase chain reaction (PCR) test safety, where it is essential to understand the contribution and impact of risk analysis methods to improve test safety medical diagnosis at the institutional level. In this context, the current study focuses on failure modes and effects analysis (FMEA) applied to the Perkin-Elmer/Cetus deoxyribonucleic acid (DNA) thermocycler.Objective: the objective of this study is to provide practitioners with a comprehensive overview of the application of FMEA to improve the operational safety of Perkin-Elmer/Cetus DNA thermocyclers used in medical diagnostics in ataxic patients.Methods: to carry out this study, a methodology based on an exhaustive review of the scientific literature available in academic databases and relevant sources was used. Previous studies on FMEA analysis of DNA thermocyclers in common and expanded forms were collected and analyzed.Results: the results obtained demonstrated that FMEA is an effective tool to identify and evaluate possible failure modes of Perkin-Elmer/Cetus DNA thermal cyclers. Several defects were identified and their consequences, causes and classification based on their severity and frequency were determined. Likewise, corrective actions are proposed to reduce the identified risks.Conclusions: early identification of possible failure modes and implementation of corrective actions can reduce risk and guarantee the quality of the diagnoses carried out and therefore improve operational safety.

Navigating Uncertainties: A Holistic Framework for Identifying and Assessing Risks in Third-Party Logistics (3PL)

To review the extant literature on 3PL risk management (SCRM, including risk identification, assessment, treatment, and monitoring), developing a comprehensive definition and conceptual framework; to evaluate prior theory use; and to identify future research directions. Additionally, our framework emphasizes the importance of aligning performance metrics with risk objectives, facilitating ongoing monitoring and evaluation of 3PL partnerships. By establishing clear performance benchmarks and feedback mechanisms, organizations can promptly identify emerging risks and implement corrective actions as necessary. Methodology: A systematic literature review of couple of articles (published 2000-2016) based on descriptive, thematic, and content analysis. Drawing from extensive literature review and industry best practices, our framework integrates multiple dimensions of risk identification and evaluation into a cohesive methodology. We propose a structured approach that encompasses both qualitative and quantitative techniques, enabling organizations to comprehensively assess risks across various stages of the 3PL relationship lifecycle. Through the application of our holistic framework, organizations can enhance their ability to navigate uncertainties in 3PL relationships, fostering resilience and competitiveness in today's dynamic business environment. Findings: There has been a considerable focus on identifying risk types and proposing risk mitigation strategies. Research has emphasised organisational responses to 3PL risks. Research implications: A broad, contemporary understanding of 3PL risk management is provided; leading to a conceptual framework. The research agenda guides future work towards maturation of the discipline. Practical implications: Managers are encouraged to adopt a holistic approach to risk management. Guidance is provided on how to select appropriate risk treatment actions according to the probability and impact of a risk. Key components of our framework include risk categorization, stakeholder engagement, scenario analysis, and performance metrics alignment. By systematically categorizing risks according to their nature and impact, organizations can prioritize mitigation efforts and allocate resources effectively. Stakeholder engagement ensures that diverse perspectives are considered in the risk assessment process, enhancing its accuracy and relevance. Furthermore, scenario analysis allows for the exploration of potential future events and their implications on 3PL operations, enabling proactive risk management strategies. Keywords: 3PL, Risk management, Systematic literature review (SLR), theoretical approach

Leveraging Six Sigma for Enhanced Reliability in Heavy Equipment Operations

This study addresses the prevalent issue of mechanical failures in heavy equipment and dump trucks within a leading special steel production firm. Utilizing the Six Sigma methodology, the research conducted in August 2023 aimed to systematically design and implement corrective actions to improve maintenance processes. Preliminary results from the IRGA Department's interventions suggest a significant reduction in breakdown frequency and severity, indicating enhanced operational efficiency and reduced downtime. These findings highlight the potential of Six Sigma as an effective tool in optimizing industrial maintenance strategies, with implications for boosting productivity and sustainability in heavy industries globally. Highlights: Regular maintenance prevents costly damage. Six Sigma ensures systematic improvement. Vision: Leading in quality and environmental care. Keywords: Steel Bar Casting (billet), Flat Bar, Round Bar Production

Analyzing clinical laboratory specimen rejection rates at a specialized hospital in Ethiopia: A 2-year document review.

Accurate laboratory diagnosis is essential for effective patient care, but the rejection of specimens within laboratories can have serious consequences. A retrospective cross-sectional study was conducted from September to November 2021 at the University of Gondar Comprehensive Specialized Hospital laboratory. Two years of laboratory data were collected from laboratory log books and analyzed to determine trends in specimen rejection rates and identify potential reasons for those rejections. We analyzed 114,439 specimens, of which 786 (0.70%) were rejected. The hematology service exhibited the highest rejection rate, at 273 (0.2%). The main reasons for specimen rejection were specimens without requests or requests without specimens (40.2%), poor smear preparation (12.3%), clotted specimens (11.3%), and labeling problems (8.0%). This study emphasized a significant incidence of specimen rejection, particularly in the hematology laboratory, underscoring the need for immediate implementation of corrective actions and preventive measures. Furthermore, conducting comprehensive larger-scale studies is recommended to deepen our understanding of and investigate the specific factors contributing to specimen rejection in greater detail.

Supervisi Model Behaviour dalam Konteks Analisis Supervisi Pendidikan

This literature research article discusses the role and implementation of the Supervision Behaviour Model in the analysis of educational supervision. Behaviour Model Supervision is an approach that not only focuses on supervision, but also aims to increase positive behaviour in the education system. With a focus on teachers, students, and administration, this supervision involves observing teaching practices, social interactions, and communication in the classroom. Through performance evaluation and implementation of corrective actions, behavioural model supervision forms the basis for professional development and character building of students. This article presents a synthesis of literature regarding the concepts, benefits, challenges and solutions in implementing Behaviour Model Supervision, as well as its implications for the quality of education and the development of the education system as a whole.

Reducing the Risk of Fall among Oncology Patients using Failure Modes and Effects Analysis.

This project aimed to mitigate the risk of falls among oncology patients using Failure Modes and Effects Analysis (FMEA) in the outpatient setting.<br /><br /><br /><br />Methods: The project was conducted within outpatient settings, specifically encompassing outpatient clinics, daycare, radiology and radiotherapy, and rehabilitation at the SQCCCRC. The project employed an observational analytical design to assess the fall risk assessment procedure in outpatient settings. The project integrated a 7-step procedure for conducting an FMEA methodology, including defining the system or process, identifying potential failure mode, evaluating the effects of each failure mode, Assigning severity, likelihood, and detection of occurrence ratings, and identifying and implement corrective actions. In addition, Risk Priority Numbers (RPNs) were used to identify the impact of the interventions in reducing the risk of patient fall assessment and management.<br /><br /><br /><br />Result: In the patient fall screening process, interventions yielded substantial reductions in RPNs for failure modes like "Wrong assessment" (57% decrease) and "Complex risk assessment scale" (63% decrease), addressing knowledge gaps and simplifying risk assessment. Similarly, the "Missed fall assessment" failure mode saw an impressive 80% reduction in RPN, rectifying unclear processes and knowledge gaps. In the Fall risk precaution measures process, interventions led to noteworthy RPN reductions, such as 80% for "Unclear fall precaution measures-responsibilities" and 57% for "Missed bracelets for high risk," demonstrating successful risk mitigation. Moreover, interventions in the Patient Education process achieved significant RPN reductions (57% and 55%) for "No/improper education" and "Unuse of educational material and resources," enhancing staff education and patient awareness. The total reduction in RPNs was 62% in all failure modes in the fall assessment and management process. <br /><br /><br /><br />Conclusion: Overall, FMEA is a valuable strategy for reducing fall risks among oncology patients, but its success depends on addressing these limitations and ensuring the thorough execution and maintenance of the identified corrective actions.

Assessment of health impacts of quality water provisioning from groundwater sources: a micro-level study in India

Abstract Many nations have implemented policies to improve drinking water quality, but challenges arise from overexploited or contaminated groundwater-based sources. This paper aims at examining the aspects of water scarcity, security, and sustainability within an Indian village context. We attempt to compare two rural water supply sources in Nagla Chandi village in Uttar Pradesh, India: (a) untreated groundwater sourced from bore wells and (b) treated water from a reverse osmosis (RO) plant. We observed that subterranean minerals are the primary pollutants of unprocessed water drawn from borewells, which form the main source of drinking water. Even though water from the RO plant meets all the quality parameters of potability, frequent breakdowns of the plant due to improper management force the villagers to drink untreated water from the dug wells fitted with hand pumps, affecting their health. We also found a high incidence of water-borne diseases. The case analysis suggests enhancing village water treatment projects by training local artisans in system repair and increasing Panchayat staff to include trained engineers for technical advice and maintenance. The paper proposes reducing water testing cost in rural areas to make it possible Q4 for local organizations to regularly assess water quality and implement corrective actions.

Analysing Machine Learning based Approaches for Detecting Late Blight Disease in Potato Crop

Agriculture is a significant contributor in the world economy. With the drastic change in the geographical conditions, the occurrence of extreme events like floods, droughts, heat waves, etc. are increasing, thereby harming crop yield. Additionally, crop yield is adversely impacted by crop diseases causing significant losses towards food production. Protecting against losses incurred by crop diseases can aid in improving food security as well as strengthening the economy. Traditional methods of crop disease detection are time and labor-intensive, whereas the use of machine learning (ML) based methods fastens up the process,thereby helping implement corrective actions at an early stage. Multiple ML algorithms find application in the field of crop disease detection. Nevertheless, there exists a need to investigate the accuracies of different ML algorithm with regardto disease detection for a specific crop and disease combination. The performance of three ML algorithms, namely Random Forest, Linear Discriminant Analysis, and k-Nearest Neighbors with respect to late blight disease in potatoeswas investigated in this work.

APPLIED INFORMATION TECHNOLOGIES IN AGRICULTURE

This paper examines the possibility of transitioning towards sustainable food production, considering the accelerated growth of the world population and increased demand for food on one hand, and the consequences that the previous approach has had on the biosphere and sociosphere at a global level on the other hand. The first part of the paper presents alarming and precise data and facts that require an ultimate paradigm shift, this time based on the most powerful technological and scientific methods and practices to date. The second part establishes the relationships between the concepts of Agricultural Social Network (ASN), Agricultural Value Chain (AVC), new digital technologies and approaches, with the need for transition towards Society 5.0, where a balance between economic progress and a society tailored to human needs is established. In the third part, the technologies on which the new concept of “precision agriculture - PA” relies are identified, and a case study of the Agrosens digital platform is provided as a good practice example to support agricultural producers. Finally, the last part demonstrates, through the concrete example of Project Provenance Ltd., how the application of new technologies can connect processes and actors in Supply Chain Management (SCM) to establish transparency, efficiency, traceability, and other performance measures necessary for effective and responsible SCM. This enables the implementation of corrective actions and measures to promote responsible production, distribution, and consumption of food or other widely used products.

Real-time defect detection using online learning for laser metal deposition

Defect prevention and detection are very crucial for the quality improvement of additive manufacturing (AM) processes. Timely identification of imperfections and flaws in the manufactured products will allow effective and early implementation of corrective actions and thus impede the spread of defects to the whole industrial value chain. In laser metal deposition (LMD) AM processes, defects are imperfections which typically include “porosity” and “cracks formation”. This study presents novel data-driven anomaly detection techniques that use both transfer learning and online learning to assess the quality of melt pool images taken during the LMD process of a part. The proposed methods incorporate the characteristics and dynamics of the manufactured part during the printing process along with previous knowledge acquired from historical data. Continual online learning models (K-means and self-organizing maps) are developed whose parameters are adapted to the incoming data collected in real-time as a new part is being manufactured. The proposed models significantly outperformed the performance of their batch-learning counterparts in detecting anomalous melt pool images in an additively manufactured Ti–6Al–4V thin-walled part. Both models required an average of ∼0.07 s to process each incoming melt pool image, update their parameters and give a prediction on the image's health. This shows the potential of continual online learning for real-time anomaly detection.

Does Cerebral Oxygenation Change during Peripherally Inserted Central Catheterization in Preterm Infants?

This study aimed to evaluate the effect of peripherally inserted central venous catheterization on cerebral oxygenation by near-infrared spectroscopy in very low birth weight preterm infants. Forty-one preterm infants (gestational age ≤32 weeks and birth weight ≤1,500 g) requiring peripherally inserted central venous catheter were included. Hemodynamic data and cerebral regional oxygen saturation values measured by near-infrared spectroscopy were prospectively collected before (T0) and every 5 minutes for 30 minutes following catheterization. When compared with baseline (T0) values, those values having relative maximum changes in the first 15 minutes and between 15 and 30 minutes were defined as T15 max and T30 max, respectively. Any change of more than a 10% decrease in baseline cerebral rSO2 was considered clinically significant. Additionally, any changes exceeding 20% in heart rate and mean arterial blood pressure values were considered significant. Following catheterization, the time interval to reach the baseline for cerebral regional oxygen saturation was noted. Cerebral regional oxygen saturation values at T15 max and T30 max were found to have decreased significantly in 46 and 22% of patients, respectively. A statistically significant difference was observed between these two time periods (p = 0.002); no significant differences in heart rate, mean arterial blood pressure, or cerebral fractional oxygen extraction values at T15 max and T30 max were observed. All patients reached their baseline cerebral regional oxygen saturation in a median of 25 (15-60) minutes. In very low birth weight preterm infants, monitoring cerebral regional oxygen saturation by near-infrared spectroscopy before and after peripherally inserted central venous catheterization may be useful in clinical practice. The assessment of factors affecting cerebral oxygenation and, in the case of low cerebral oxygenation, implementation of corrective actions before peripherally inserted central catheterization may offer a neuroprotective strategy. · Monitoring cerebral rSO2 by NIRS during PICC line procedure might be useful in preterm infants.. · In this study, a significant decrease in cerebral rSO2 level following catheterization was observed.. · Implementation of corrective actions before PICC line procedure may offer a neuroprotective strategy..

Solving Manufacturing Problems with 8D Methodology: A Case Study of Leakage Current in a Production Company

Customer satisfaction is critical for the success of any organization. This case study presents a design company and manufacturing supplier that has received customer complaints about defective temperature sensors and power supply units in maritime vessels. The research aims to identify the root cause of the issue and implement a solution to prevent its recurrence using the Lean Eight Disciplines methodology. This includes: (1) a 5 Whys analysis by a cross-functional team, (2) confirmation of the problem description, (3) containment actions, (4) root cause analysis of the occurrence, (5) permanent corrective actions, (6) implementation of the permanent corrective action, (7) actions to prevent recurrence, and (8) closure with an 8D report and congratulations to the cross-functional team from the design, engineering, and supplier production sites. This study presents the development and implementation of the Eight Disciplines (8D) investigation tool for addressing issues in the factories and improving the product life cycle in the manufacturing industry. The 8D tool is designed for the cross-functional teams to work together to identify the root cause of problems using the "five whys" method and implement corrective and preventive actions to prevent similar issues from occurring in the future. The novelty of this case study is the development of an 8D analytics template for product managers and practitioners in manufacturing and production companies to use for improving the quality of products throughout their life cycle. The case study also highlights the challenges and opportunities of using the 8D tool for problem-solving, including the high warranty cost and production failures, the unreliability of products and resources, and different scopes of issues. In one example, the 8D analytics template was used to identify a long-term fix for a circuit prone to leakage current by using a different flux chemistry that can be cleaned with a water-based solvent, rather than the traditional "no-clean" solder flux that requires specific solvents for effective cleaning

An integrated framework to assess and improve the financial soundness of private banks

Banks have faced significant challenges due to national economic problems in recent years. There is now a greater need to assess the existing banking system and implement corrective action and strengthening measures. In this paper, financial data of 11 Iranian private banks are examined for 15 different periods and their performance is evaluated using the CAMELS indicator. Expert opinions were used to determine the weights of indicators and ratios, and their final weights were determined using the Best-Worst Method (BWM). Data Envelopment Analysis (DEA) was then used to calculate the efficiency score units (DMUs) and their ranking. Principal component analysis (PCA) was used to validate the DEA results. Several sensitive analyses were conducted on all private banks in Iran to verify their rankings. All 11 Iranian private banks were evaluated through a comprehensive sensitivity analysis, and they were evaluated independently based on the suggested indicators based on their respective performance. The intuitive results of sensitivity analysis were accurate through statistic test. Also, appropriate strategies for improving the performance of the banking system are presented using Strengths-Weaknesses-Opportunities-Threats (SWOT) matrix. Using the results, designers can build more intelligent system CAMELS perspective.

The Agency to Implement Voice: How Target Hierarchical Position and Competence Changes the Relationship Between Voice and Individual Performance

Although voice is communication that is intended to benefit the performance of collectives, little is known about the benefits or costs to individual task performance and what mechanisms drive these effects. Our research offers new theory to articulate and illustrate the conditions under which voice has positive versus negative effects on individual task performance by directly acknowledging that employees have many options for where to direct their ideas. We introduce an agency perspective on voice by theorizing that one fundamental reason why employees speak up is to generate the implementation of corrective action for issues affecting themselves and to the extent targets of voice have agency to facilitate action through implementation of voice, voicing employees should be more likely to realize performance benefits from speaking up. In a first field study, we present evidence that two characteristics—the hierarchical position of the voice target (boss versus peer) and the competence of the voice target—alter the relationship between voice and the voicing employee’s task performance. In a second field study, using an event-contingent design, we provide evidence of the unique mechanisms underlying how competent managers (via their resources) and competent peers (via their efficacy to act) affect how upward and sideways voices lead to idea implementation. We discuss the theoretical implications of these ideas and findings by highlighting how voice target characteristics influence not just the incidence of voice but also, its outcomes. Funding: This research benefited from financial support through the McCombs Research Excellence Grant.

A Pharmacy Quality and Internal Audit Program Promoting Continuous Survey Readiness with Medication Management Standards.

Showcasing compliance with Joint Commission Medication Management (MM) standards during accreditation visits is important, but it is equally vital to maintain a state of continuous survey readiness. This article describes a Pharmacy Quality and Internal Audit Program to promote continuous survey readiness and sustained compliance with MM standards. A comprehensive MM audit was developed to assess for compliance in all inpatient, ambulatory, and procedural areas that use medications, with each area undergoing the internal auditing process at least twice per calendar year. The audit is performed by an MM subject matter expert from the Department of Pharmacy and incorporates electronic chart review, clinical staff interviews, and observations of area-specific medication practices. Notable findings and recommendations are communicated to area-specific leadership. Institutionwide scorecards are compiled to track performance with MM standards, identify opportunities, and determine future focus areas to improve and sustain compliance. Results revealed consistent compliance (≥ 90%) with a majority of MM standards. These included hazardous medication management, controlled substances compliance, look-alike/sound-alike drugs, medication storage/security, and labeling of medication syringes. Several areas of opportunity were identified: awareness of high-alert medications and risk-reduction safety strategies, compliance with pill cutter standards, and therapeutic duplication of "as needed" medication orders. Subsequent implementation of corrective actions and mitigation strategies resulted in improved compliance rates from < 50% to ≥ 90% for awareness of high-alert medications and risk-reduction strategies, from < 60% to ≥ 80% for compliance with pill cutter standards, and from < 90% to ≥ 90% for therapeutic duplication of "as needed" orders. Establishment of a Pharmacy Quality and Internal Audit Program allowed for performance tracking, identification of compliance deficiencies, implementation of focused corrective actions, and promotion of continuous survey readiness with MM standards.

Towards the establishment of national diagnostic reference levels in Tunisia: a multicentre survey in paediatric CT

This work focuses on the determination of the radiation doses for a total sample of 916 children, categorised into four age groups (<1, 1–5, <5–10, <10–15 years) undergoing the most frequent paediatric CT scans performed in different scan facilities in Tunisia in order to establish the national diagnostic reference levels (DRLs). Dose evaluation concerned the dosimetric indicators: volume computed tomography dose index (CTDIvol) and dose–length product (DLP). The different paediatric CT protocols and practices were also evaluated. The results show a large variation in doses between different radiology departments. For head scans, the respective DRLs for children aged <1, 1–5, 5–10 and 10–15 years were 26, 38, 51 and 51 mGy, respectively, for CTDIvol and 384, 664, 873 and 978 mGy cm, respectively, for DLP. For the chest, the equivalent respective DRLs were 8, 10, 12 and 15 mGy for CTDIvol and 118, 330, 442 and 526 mGy cm for DLP. For the abdomen, the respective DRLs were 9, 13, 19 and 18 mGy for CTDIvol and 353, 485, 592 and 1073 mGy cm for DLP. This study shows that the optimisation of paediatric CT procedures should be a priority, especially within regional hospitals. The implementation of corrective actions will take place after the initial DRLs. These actions, including recommendations and guidelines to good practice, should be a joint effort of all stakeholders, including health authorities, the radiation protection regulator, professional societies and universities.

Web-based dashboard of data integration for green highway performance management

Highway concessionaires in Malaysia spend considerable assets for managing different forms of data during highway projects' life cycles. In this context, and based on the use of modern technologies, an enormous volume of highway data is generated daily. These data can be utilized to extract and evaluate indicators to analyze highway green performance. Feedback from indicators allows to grasp the sustainable practices and implement corrective actions. Dashboards have become popular in recent years as uniquely powerful tools for communicating important information. This research adopted the approach of a centralized data provider to develop a web-based visualization dashboard, as a technique to reinforce decisions in the highway sector. The dashboard will be employed to gain information about highway green performance to enhance decisions. In this dashboard, data integration of Malaysia Green Highway Index and Carbon Footprint Calculator has been used in illustrating the visualization. The development utilized a web-based multi-programming language. The quantitative and qualitative measures were used to validate the dashboard. Findings demonstrated an increase in accuracy of data monitoring and quicker decision-making using the Dashboard. The outcome of this research will add value to the green highway development and map the highway towards a better and sustainable quality of life nationally and internationally. Furthermore, this research will pave the way for future researchers to integrate other efficient tools and parameters that contribute towards sustainable development.