Conventional Assessment Methods Research Articles

A CASE STUDY OF LOW-FREQUENCY NOISE

Sound waves below 20 Hz, which are generally inaudible to humans, are called infrasound. When infrasound and the less audible sound of the lowest frequencies (10 to 200Hz) appear as an element that disturbs people and is harmful to health, it is low-frequency noise (LFN). This air-borne noise has a much greater range than normal audible frequency sound and LFN travels much further on the ground. Stronger or longer-lasting infrasound around 7-20 Hz directly affects the human central nervous system and can cause disorientation, anxiety, panic, depression, nausea, discomfort, vomiting, etc. Sooner or later, the waves can lead to damage to the nervous and/or cardiovascular system. It is estimated that about 2.5% of the population may have a low-frequency threshold that is at least 12 dB more sensitive than the average threshold, which corresponds to almost 1,000,000 persons in the age group 50 to 59 years in the EU-15 countries. LFN is recognized as a particular environmental noise problem, especially for sensitive people in their homes. Conventional noise assessment methods are not suitable for LFN and lead to wrong conclusions and consequently to wrong decisions. An LFN source is more difficult to localize, difficult to suppress, and spreads rapidly in all directions so that it can be heard over great distances. All existing sound field visualization methods have a lower frequency limit of 125 HZ. Low-frequency waves surround the noise source and such a source is practically "undirected". In the paper, we described the LFN assessment carried out, which we identified in a commercial/residential building. The measurements were carried out in the immediate vicinity (3-4 km as the crow flies) of the object in question, at the site of industrial facilities (V1 and OM2), the pond complex, and the gas and transformer station. The LFN in the facility was initially unidentified, and the source search diagnostics were based on the method of comparing the generic spectral distributions of the LFN in the facility with other selected measurement positions.

Bioorthogonal Synthesis of Biomimetic Nanoparticles for Screening Chemical Hazards in Food Samples.

The ability to identify unknown risks is the key to improving the level of food safety. However, the conventional nontargeted screening methods for new contaminant identification and risk assessment remain difficult work. Herein, a toxic-oriented screening platform based on high-expression epidermal growth factor receptor HEK293 cell membrane-coated magnetic nanoparticles (EGFR/MNPs) was first used for the discovery of unknown contaminants from food samples. The EGFR served as a bait to bind the risk compounds, considering that the abnormal activation of EGFR was related to the incidence of numerous chronic inflammatory diseases. The cell membranes were specifically covalently immobilized on magnetic carriers through metabolic glycoengineering and strain-promoted alkyne-azide cycloaddition (SPAAC) to ensure the stability and bioactivity of the EGFR/MNPs. The synthesized EGFR/MNPs possessed an excellent receptor loading amount and were used to screen potential risk substances from thermally processed food. Finally, two compounds, harman and norharman, were rapidly identified. The toxicological experiments confirmed that the screened compounds could promote EGFR phosphorylation and further activate downstream signaling pathways, thereby inducing cellular oxidative damage. The proposed method provided a toxic-oriented method for identifying risk compounds from food samples.

Distress Manifestation in Asphalt Pavements: Comparison between Local and Unmanned Aerial Vehicle (UAV) Measurements

Surface surveys of asphalt concrete pavement condition the maintenance and rehabilitation solutions employed; however, they are laborious protocols for field operators. The use of Unmanned Aerial Vehicles (UAVs) in the remote assessment of these pavements has become an alternative, but still lacks validation about current Brazilian regulations. This study sought to compare conventional surface assessment methods (DNIT 006/2003 - PRO, DNIT 007/2003 – PRO, and DNIT 433/2021 - PRO) with the survey carried out by UAV, and to compare the DNIT’s cracked area assessment methods. The assessment was carried out over two 400-meter stretches. The DNIT 433/2021 - PRO standard was more conservative than the DNIT 007/2003 - PRO protocol in determining the cracked area. In addition, the two segments result in the same Global Gravity Index (GGI) classification between the walking and UAV methodsonly when rutting is disregarded. It was also found that the device achieved, on average, 82% of the cracked area values obtained by the DNIT 007/2003 - PRO and DNIT 433/2021 - PRO standards. Thus, it was possible to verify that the Brazilian regulations generate different cracked area results, being used for other purposes and that the UAV, in the scope of this work, the UAV was a technically viable alternative for speeding up the evaluations but was unable to identify cracks with smaller openings (FC-1 class) and threedimensional distresses.

Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning

Conventional personality assessment methods depend on subjective input, while game-based AI predictive methods offer a dynamic and objective framework. However, training these models requires large and labeled datasets, which are challenging to obtain from real players with diverse personality traits. In this paper, we propose a multi-agent system using Deep Reinforcement Learning in a game environment to generate the necessary labeled data. Each agent is trained with custom reward functions based on the HiDAC system that encourages trait-aligned behaviors to emulate specific personality traits based on the OCEAN personality trait model. The Multi-Agent Posthumous Credit Assignment (MA-POCA) algorithm facilitates continuous learning, allowing agents to emulate behaviors through self-play. The resulting gameplay data provide diverse, high-quality samples. This approach allows for robust individual and team assessments, as agent interactions reveal the impact of personality traits on team dynamics and performance. Ultimately, this methodology provides a scalable, unbiased methodology for human personality evaluation in various settings, establishing new standards for data-driven assessment methods.

Optimal configuration strategy of energy storage for enhancing the comprehensive resilience and power quality of distribution networks

Resilience assessment and enhancement in distribution networks primarily focus on the ability to support and recover critical loads after extreme events. With the increasing integration of new energy sources and power electronics, distribution networks have gained a degree of resilience. However, the impact of power quality issues on these networks has become more severe. In some cases, even networks assessed as highly resilient by users suffer equipment damage and substantial economic losses due to power quality issues. To address this issue, this paper builds upon conventional distribution network resilience assessment methods by supplementing and modifying indices in the dimensions of resistance and recovery to account for power quality issues. Furthermore, an optimized energy storage system (ESS) configuration model is proposed as a technical means to minimize the total operational cost of the distribution network while enhancing comprehensive resilience indices. The proposed nonlinear optimization model is solved using second-order cone relaxation techniques. Finally, the proposed strategy is simulated on the IEEE 33-node distribution network. The simulation results demonstrate that the proposed strategy effectively improves the comprehensive resilience indices of the distribution network and reduces the total operational cost.

Role of PRMT5 mediated HOXA10 arginine 337 methylation in endometrial epithelial cell receptivity

A successful embryo implantation relies heavily on the receptivity of the endometrial epithelium, a process regulated by various molecular mechanisms. Evaluating endometrial receptivity in infertility patients undergoing assisted reproductive treatment, particularly those with adenomyosis related infertility, poses significant challenges due to limitations associated with conventional assessment methods. In this study, we collected residual endometrial epithelial cells from the tips of embryo transfer catheters in patients with adenomyosis related infertility. High throughput sequencing revealed a marked downregulation of protein arginine methyltransferase 5 (PRMT5) in these cells. Functional assays demonstrated that PRMT5 interacts with and methylates homeobox A10 (HOXA10), a crucial transcription factor for endometrial receptivity and implantation. The methylation of HOXA10 at arginine 337 by PRMT5 enhances its stability and promotes the transcriptional activation of genes essential for endometrial differentiation and adhesion. The downregulation of PRMT5 led to decreased HOXA10 activity, resulting in impaired endometrial receptivity and subsequent implantation failure. These findings elucidate a critical pathway where PRMT5 downregulation negatively impacts HOXA10 function, providing new insights into the molecular mechanisms underlying implantation failure in adenomyosis related infertility. This study not only advances our understanding of the regulatory mechanisms governing endometrial receptivity but also identifies potential therapeutic targets for enhancing endometrial function in affected patients.

Distribution reconstruction and reliability assessment of complex LSFs via an adaptive Non-parametric Density Estimation Method

Complex limit state functions (LSFs), often characterized by strong nonlinearity, non-smoothness, or discontinuity, pose challenges for structural reliability analysis in engineering practices. Conventional methods for uncertainty propagation and reliability assessment may struggle to handle these issues effectively. This paper introduces a novel approach to adaptively reconstruct the unknown distributions of complex LSFs. The Non-parametric Density Estimation Method based on Harmonic Transform (NDEM-HT) is employed as the tool for this purpose. An adaptive strategy is then proposed to determine the number of harmonic moments required in NDEM-HT for achieving high accuracy. Specifically, the Adaptive Kernel Density Estimation (AKDE) method is also adopted to provide an initial estimation of the rough distribution. Subsequently, the optimal number of harmonic moments is determined by minimizing the relative entropy between the distributions obtained by AKDE and NDEM-HT. The efficacy of the proposed method is demonstrated through five numerical examples, considering various types of complex LSFs. Comparative results are also provided employing MCS along with both conventional and state-of-the-art methods.

Workflow improvement and financial gain after integration of high-throughput sample processing system with flow cytometer in a high-volume pathology laboratory: Results from a prospective comparative study using Lean principles

BackgroundHighly efficient clinical laboratories are essential for monitoring many human illnesses. Ampath Laboratory Services, the largest pathology lab in South Africa, analyzes large numbers of peripheral blood samples for CD4 levels yearly. ObjectiveTo assess productivity and quality of a newer integrated automated solution, the BD FACSDuet™ Sample Preparation System/BD FACSLyric™ Flow Cytometer using conventional assessment methods and Lean concepts. Materials and methodsThis prospective study compared the performance of the BD FACS™ Sample Preparation Assistant [SPA] III and BD FACSCanto™ II Flow Cytometer with the newly introduced integrated system (BD FACSDuet™/BD FACSLyric™). They were validated for accuracy, precision, and external quality assessment. Process mapping and Lean assessment helped identify steps leading to waste. An economic model was developed to characterize workflow and economic impact associated with total daily hands-on time, processing time, and reworks. ResultsStrong linear correlation was present between both systems. Precision and accuracy studies revealed that all coefficient of variation (CV)% values were below 20% of allowable limits. External proficiency assessments were within limits. The fully automated workflow of BD FACSDuet™/BD FACSLyric™ permitted better consistency with significantly shorter processing time and batch processing and reduced operator interventions. Lean assessment identified defects with motion, over-processing, waiting, and non-utilized talent. Significant reductions in hands-on and total daily processing time that could increase daily specimen testing efficiency and fewer reworks were associated with the BD FACSDuet™/BD FACSLyric™. Lean improvements translated to significant economic savings associated with operator costs and unnecessary reagent consumption. ConclusionBD FACSDuet™/BD FACSLyric™ is an accurate, reliable, and cost-effective fully automated system for high-volume flow cytometry labs that perform T-cell enumeration using a single-platform and single-tube approach.

Multi-Scale Risk-Informed Comprehensive Assessment Methodology for Lithium-Ion Battery Energy Storage System

Lithium-ion batteries (LIB) are prone to thermal runaway, which can potentially result in serious incidents. These challenges are more prominent in large-scale lithium-ion battery energy storage system (Li-BESS) infrastructures. The conventional risk assessment method has a limited perspective, resulting in inadequately comprehensive evaluation outcomes, which impedes the provision of dependable technical support for the scientific appraisal of intricate large-scale Li-BESS systems. This study presents a novel Li-BESS-oriented multi-scale risk-informed comprehensive assessment framework, realizing the seamless transmission of assessment information across various scales. The findings from a previous smaller-scale analysis serve as inputs for a larger scale. The evaluation process of this method is more scientifically rigorous and yields more comprehensive results compared to assessment technologies just relying on a single perspective. By utilizing the proposed comprehensive assessment methodology, this study utilized the emergency power supply of nuclear power plants (NPPs) as an application scenario, demonstrating the complete implementation process of the framework and conducting a comprehensive assessment of Li-BESS feasibility as an emergency power source for NPPs. Our findings propose a novel paradigm for the comprehensive assessment of Li-BESS, which is expected to serve as a scientific foundation for decision-making and technical guidance in practical applications.

Cloud-Enabled Mobile-Based Approach for Enhancing Psychosis Cognitive Assessment.

The study aims to assess the feasibility of developing cognitive tools and integrating the cloud-enabled mobile-based technology into routine clinical practice for psychotic patients. Furthermore, it aims to investigate the correlation between the results obtained using developed tools and established clinical measures, offering valuable insights into tools for enhancing the accuracy, efficiency and ease of administration of cognitive evaluation. A total of 160 participants were recruited (83 outpatients with early course of schizophrenia (SZs) and 77 healthy controls [HCs]). The participants were subjected to cognitive assessments, and the data were collected by cognitive assessment digital smart tool (CADST) and PGI memory scale (PGIMS) to assess attention (ATT) and working memory (WM). Outcome measures of these parameters were digit span (score,time) for ATT and delayed recall (score,time) for WM. The total average score in HCs was significantly higher than in SZs for ATT and WM, and CADST was significantly correlated with PGIMS for evaluating ATT and WM. Furthermore, test completion times for ATT and WM were observed more in SZs although most of SZs had achieved scores as high as that of HCs. The potential of CADST as a valuable addition to the conventional cognitive assessment method is highlighted, showing promising feasibility and strong correlations with the established tool. The importance of integrating time parameters suggests broader implications for understanding cognitive function beyond conventional scoring metrics. It demonstrates the effective and accurate approach for large-scale screening of cognitive parameters in public service settings.

Machine Learning Approaches for the Prediction of Postoperative Major Complications in Patients Undergoing Surgery for Bowel Obstruction.

Performing emergency surgery for bowel obstruction continues to place a significant strain on the healthcare system. Conventional assessment methods for outcomes in bowel obstruction cases often concentrate on isolated factors, and the evaluation of results for individuals with bowel obstruction remains poorly studied. This study aimed to examine the risk factors associated with major postoperative complications. We retrospectively analyzed 99 patients undergoing surgery from 2015 to 2022. We divided the patients into two groups: (1) benign-related obstruction (n = 68) and (2) cancer-related obstruction (n = 31). We used logistic regression, KNN, and XGBOOST. We calculated the receiver operating characteristic curve and accuracy of the model. Colon obstructions were more frequent in the cancer group (p = 0.005). Operative time, intestinal resection, and stoma were significantly more frequent in the cancer group. Major complications were at 41% for the cancer group vs. 20% in the benign group (p = 0.03). Uni- and multivariate analysis showed that the significant risk factors for major complications were cancer-related obstruction and CRP. The best model was KNN, with an accuracy of 0.82. Colonic obstruction is associated with tumor-related blockage. Malignant cancer and an increase in C-reactive protein (CRP) are significant risk factors for patients who have undergone emergency surgery due to major complications. KNN could improve the process of counseling and the perioperative management of patients with intestinal obstruction in emergency settings.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

Assessment of the Relationship Between Anterior Teeth Colour and Skin Colour by assessing different measurement tools in Adult Patients: A Systematic Review

Background: Previous research conducted since 1968 have extensively examined the correlation between tooth color and skin color. The purpose of these studies was to determine whether skin color could effectively influence dentists in choosing tooth color. The objective of this systematic review is to examine the methodologies employed in assessing the relationship between tooth and skin color, and to identify the most precise and dependable approaches for modeling and evaluating this relationship. The systematic review was registered in the PROSPERO (CRD42022356022). This systematic review was done on four databases using PRISMA 2009 search strategy, and selected studies were chosen according to PICO model. JBI Critical Appraisal Checklist was used for quality assessment. Out of 1611 articles that were found in the databases, 10 articles matched the criteria and were selected for this systematic review study. Three main methods were found in the 10 studies: conventional methods, digital methods, conventional-digital (mixed) methods. Digital methods were found to be the more reliable methods for color evaluation. Specific settings should be applied during digital color measuring methods. Conventional and digital assessment methods showed either a negative or a positive significant association between the color of teeth and skin. However, the combined method (digital for tooth and visual for skin) did not show any such relation. It is imperative to conduct and apply research in Artificial Intelligence to forecast tooth color based on knowledge of skin color.

Prosigna Assay for Treatment Decisions in Early Breast Cancer: A Decision Impact Study.

Introduction: Therapeutic decisions in early breast cancer are based on clinico-pathological features which are subject to intra- and inter-observer variability. This single-center decision impact study aimed to evaluate the effects of the Prosigna assay on physicians' adjuvant treatment choices. Methods: Between 09/2017 and 02/2018, formalin-fixed tumor samples from 52 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative breast cancer (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgements and Prosigna test results were compared. Results: The mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) were represented. There was 40.4% discordance between the pre- and post-test risk of recurrences. No significant change was observed in the clinical intermediate risk category, while there was a net reclassification of low-risk patients into a high Prosigna recurrence risk group. In addition, clinically determined intrinsic subtypes were 34.6% discordant with the Prosigna results, which is largely driven by the reclassification of the luminal A tumors into the Prosigna-assessed luminal B group. Before the Prosigna test, endocrine treatment was the primary choice in 20 patients (39.2%), and chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in treatment choice for one patient. Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy and value for risk reduction are suboptimal. According to our results, the Prosigna assay was found to be a relevant tool for the clinical decision making process. Long-term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for treatment.

Urban tree health assessment using multifaceted remote sensing datasets: A case study in Hong Kong

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Duckweed-based optical biosensor for herbicide toxicity assessment

In response to the pervasive issue of herbicide pollution in environmental water bodies, particularly from herbicides used extensively in agriculture, traditional chemical-based water quality analysis methods have proven costly and time-consuming, often failing to meet regulatory standards. To overcome these limitations, global environmental agencies have turned to rapidly-growing species like duckweed as bioindicators for herbicide and pesticide contamination. However, conventional biological assessment methods, such as the 168-h duckweed growth inhibition test, are slow and lack real-time monitoring capabilities. To address this challenge, we developed an innovative approach by integrating opto-mechanical technology with duckweed to create a cost-effective biosensor for herbicide detection, priced under $10 USD per system. This advancement allows for the rapid detection of herbicide impacts on duckweed growth within just 48 h, significantly improving upon traditional methods. Our biosensor achieves detection limits of 10 ppm (p < 0.05) for glyphosate and 1 ppm (p < 0.05) for glufosinate, both prominent herbicides globally. This mini-biosensing platform offers a practical alternative to the official method, which requires 168 h and higher thresholds (36.4 ppm for glyphosate and 34.0 ppm for glufosinate) for routine environmental analysis. Thus, these duckweed-based optical biosensors represent a promising advancement in environmental monitoring, enhancing accessibility and efficacy for widespread adoption globally.

Needs Analysis of English for Specific Purposes for Health Sciences Students: A Cross-Sectional Study at a University in the U.A.E.

This paper presents findings from a needs analysis survey to determine the extent to which the current English for Specific Purposes (ESP) course for healthcare students meets the needs of this population. A structured questionnaire was shared with the students (N=180) from different healthcare departments to examine the preferability of different aspects. Many participants gave great value to ESP courses whose importance was mainly to learn medical or pharmaceutical terminology and communicative skills. Yet, the participants preferred the conversational content over the grammatical one. Most participants perceived the four English skills as important. The overall conclusion shows that the main objective of an ESP course is language fluency to be able to communicate and achieve any requirement in their career. Moreover, the study shows that the conventional methods of assessment are less preferred and that students prefer assessments where teamwork is involved. They also preferred to receive the learning materials via online or technological methods, and they preferred class activities and audio/video aids. Yet, a great number did not prefer using an English Language Laboratory. Finally, most of them agreed that being in a mostly English-speaking community at the university is convenient to enhance their communication skills in English.

The Interaction between Climate Change and Biodiversity Can Be Assessed from a Material Cycle Perspective

This study focused on understanding biodiversity variability in response to climate change as an environmental stressor from a carbon cycle perspective. As the frequency and magnitude of environmental stresses are expected to increase due to rising carbon concentrations, these changes may affect the variability in biodiversity. However, several studies have argued that conventional methods for biodiversity assessment, primarily based on arithmetic indices utilized in national policies, are inadequate for accurately assessing these interactions among species, abiotic changes, and material cycling. Here, we indicate that environmental stressors that occur at various scales and domains, from macroscopic to microscopic, and their effects on ecosystems are multi-dimensional. Furthermore, environmental stresses not only affect organisms inhabiting these ecosystems but these organisms, in return, influence the carbon cycle through life history changes resulting from these stresses. The results of our review suggest that these processes are sequentially interconnected, forming self-reinforcing feedback loops. Ultimately, an integrated approach is necessary to understand biodiversity variability in complex and open ecosystems. This approach should be capable of comprehending changes in the overall flow by considering ecosystem characteristics.

A Predictive Model Using Long Short-Time Memory (LSTM) Technique for Power System Voltage Stability

The stability of the operation of the power system is essential to ensure a continuous supply of electricity to meet the load of the system. In the operational process, voltage stability (VS) should be recognized and predicted as a basic requirement. In electrical systems, deep learning and machine learning algorithms have found widespread applications. These algorithms can learn from previous data to detect and predict future scenarios of potential instability. This study introduces long short-term memory (LSTM) technology to predict the stability of the nominal voltage of the power system. Based on the results, the recommended LSTM technology achieved the highest accuracy target of 99.5%. In addition, the LSTM model outperforms other machine learning (ML) and deep learning techniques, i.e., support vector machines (SVMs), Naive Bayes (NB), and convolutional neural networks (CNNs), when comparing the accuracy of the VS forecast. The results show that the LSTM method is useful to predict the voltage of an electrical system. The IEEE 33-bus system indicates that the recommended approach can rapidly and precisely verify the system stability category. Furthermore, the proposed method outperforms conventional assessment methods that rely on shallow learning.

A new high-level life cycle assessment framework for evaluating environmental performance: An aviation case study

Conventional Life Cycle Assessment (LCA) methods are able to assess environmental impact using significant resources (including time and data). However, due to the challenges associated with data collection these can still suffer from issues including representation accuracy, comparability, data availability, data quality, and uncertainty. This paper describes a new streamlined, high-level framework which seeks to solve these issues through rigorous and iterative application of existing standardised LCA methodologies whilst continually engaging with stakeholders. This new framework has been applied to an aviation case study, which seeks to investigate the potential environmental impact of implementing sustainable aviation fuel (including fuels based on used cooking oil, power to liquid technology, and hydrogen) and digitalisation of training regimes within a UK aircraft manufacturer. These are currently major areas of focus to enable the decarbonisation of the global aviation sector. The proposed framework allowed for efficient joint interpretation of results by different stakeholders, and therefore enabled effective strategic decision making without requiring the granular level of data detail demanded by conventional LCA frameworks. The case study has shown that each scenario offers potential reductions in global warming potential, fine particulate matter formation, and water consumption for an aircraft; but only when the associated supply chain is just as sustainable as the scenario in question. Overall, this research has shown that applying the new framework allows for rapid evaluation of decarbonisation technologies through rigorous environmental assessment to a degree accuracy which still enables strategic decision making, but without the use of unnecessary resources. Although this framework has been developed to work across product, platform, or system, further work should seek to apply it in different contexts as a LCA enabler within technological developments including exploration of other aviation decarbonisation pathways to achieve net zero.