Assessment Metrics Research Articles

Greenhouse Gas Reduction Potential and Economics of Green Hydrogen via Water Electrolysis: A Systematic Review of Value-Chain-Wide Decarbonization

Green hydrogen generated via water electrolysis has become an essential energy carrier for achieving carbon neutrality globally because of its versatility in renewable energy consumption and decarbonization applications in hard-to-abate sectors; however, there is a lack of systematic analyses of its abatement potential and economics as an alternative to traditional technological decarbonization pathways. Based on bibliometric analysis and systematic evaluation methods, this study characterizes and analyzes the literature on the Web of Science from 1996 to 2023, identifying research hotspots, methodological models, and research trends in green hydrogen for mitigating climate change across total value chain systems. Our review shows that this research theme has entered a rapid development phase since 2016, with developed countries possessing more scientific results and closer partnerships. Difficult-to-abate sectoral applications and cleaner production are the most famous value chain links, and research hotspots focus on three major influencing factors: the environment; techno-economics; and energy. Green hydrogen applications, which include carbon avoidance and embedding to realize carbon recycling, have considerable carbon reduction potential; however, uncertainty limits the influence of carbon reduction cost assessment indicators based on financial analysis methods for policy guidance. The abatement costs in the decarbonization sector vary widely across value chains, electricity sources, baseline scenarios, technology mixes, and time scenarios. This review shows that thematic research trends are focused on improving and optimizing solutions to uncertainties, as well as studying multisectoral synergies and the application of abatement assessment metrics.

The FAIR Assessment Conundrum: Reflections on Tools and Metrics

Several tools for assessing FAIRness have been developed. Although their purpose is common, they use different assessment techniques, they are designed to work with diverse research products, and they are applied in specific scientific disciplines. It is thus inevitable that they perform the assessment using different metrics. This paper provides an overview of the actual FAIR assessment tools and metrics landscape to highlight the challenges characterising this task. In particular, 20 relevant FAIR assessment tools and 1180 relevant metrics were identified and analysed concerning (i) the tool’s distinguishing aspects and their trends, (ii) the gaps between the metric intents and the FAIR principles, (iii) the discrepancies between the declared intent of the metrics and the actual aspects assessed, including the most recurring issues, (iv) the technologies used or mentioned the most in the assessment metrics. The findings highlight (a) the distinguishing characteristics of the tools and the emergence of trends over time concerning those characteristics, (b) the identification of gaps at both metric and tool levels, (c) discrepancies observed in 345 metrics between their declared intent and the actual aspects assessed, pointing at several recurring issues, and (d) the variety in the technology used for the assessments, the majority of which can be ascribed to linked data solutions. This work also highlights some open issues that FAIR assessment still needs to address.

Recommendations on Robotic Hepato-Pancreato-Biliary Surgery. The Paris Jury-Based Consensus Conference.

To establish the first consensus guidelines on the safety and indications of robotics in Hepato-Pancreatic-Biliary (HPB) surgery. The secondary aim was to identify priorities for future research. HPB robotic surgery is reaching the IDEAL 2b exploration phase for innovative technology. An objective assessment endorsed by the HPB community is timely and needed. The ROBOT4HPB conference developed consensus guidelines using the Zurich-Danish model. An impartial and multidisciplinary jury produced unbiased guidelines based on the work of ten expert panels answering predefined key questions and considering the best-quality evidence retrieved after a systematic review. The recommendations conformed with the GRADE and SIGN50 methodologies. Fifty-four experts from 20 countries considered 285 studies, and the conference included an audience of 220 attendees. The jury (n=10) produced recommendations or statements covering five sections of robotic HPB surgery: technology, training and expertise, outcome assessment, and liver and pancreatic procedures. The recommendations supported the feasibility of robotics for most HPB procedures and its potential value in extending minimally invasive indications, emphasizing however the importance of expertise to ensure safety. The concept of expertise was defined broadly, encompassing requirements for credentialing HPB robotics at a given center. The jury prioritized relevant questions for future trials and emphasized the need for prospective registries, including validated outcome metrics for the forthcoming assessment of HPB robotics. The ROBOT4HPB consensus represents a collaborative and multidisciplinary initiative, defining state-of-the-art expertise in HPB robotics procedures. It produced the first guidelines to encourage their safe use and promotion.

Metricizing policy texts: Comprehensive dataset on China’s Agri-policy intensity spanning 1982–2023

Due to the lack of direct assessment metrics, existing studies on the intensity of agricultural policies often utilize indicators such as Gross Domestic Product (GDP) of agriculture or the quantity of agricultural policies as measures. Optimizing methods for analyzing the intensity of agricultural policies will significantly impact parameter selection in agricultural policy research and the evaluation of policy effectiveness. In this study, we constructed a Chinese Agricultural Policy Corpus using agricultural policies released by various governmental agencies at the national level in China from 1982 to April 2023. We quantified the values of agricultural domain terms in the corpus and evaluated the intensity of each agricultural policy document. The validation results of this study indicate a strong correlation between the intensity of agricultural policies and agricultural GDP. The trend in agricultural GDP changes lags behind policy intensity by 2.5 years (at a 95% confidence level), thus validating the rationality of our constructed corpus, agricultural policy scoring dataset, and methodology.

Methodological shifts and departures in music education research: embracing complexity with diffraction, intra-action, and agentic assemblages

ABSTRACT In England, music education policy is deeply rooted in neoliberal ideologies, shaping classroom practice and assessments. This paper presents findings from a four-year funded longitudinal project exploring the impact of musical partnerships on young people at risk of educational exclusion. Through a posthuman lens, the researchers made a methodological shift, acknowledging the agency of non-human elements and physical spaces in musical interactions. Posthumanism offered alternative insights, challenging traditional notions of music education practice and extended human-centric perspectives and school assessment metrics. It expanded our understanding of what it means to be a musician and pedagogue. This paper proposes that posthumanism can challenge existing notions of music education practice while simultaneously moving the discussion of important matters in music education research into new areas of thinking, doing, and being.

A reliable hybrid autoregressive integrated moving average and deep reinforcement machine learning strategy for resiliency enhancement in microgrid

Machine learning algorithms are crucial for energy transition and climate change system resilience. This paper presents the essential issue of improved microgrid resilience after catastrophes, with a focus on the Root Mean Square Error (RMSE) assessment metric. By using a Hybrid Autoregressive Integrated Moving Average- Deep Reinforcement Learning (HARIMA-DRL) based machine learning microgrid restoration method for post-disaster power recovery, resilience in the operation aspect is enhanced. The local power grid's resilience during a simulated disaster is examined using a quantitative resilience measure that is based on two significant quality functions. When the hybrid HARIMA-DRL model final resilience value is 3.07 %, 7.78 %, and 11.51 % higher for 5000, 8000, and 10000 training datasets, as compared to Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) and Gauss Reinforcement Memory (GRM). Simulation findings comparing the proposed HARIMA-DRL method to CNN-LSTM and GRM models trained on separate datasets show that the HARIMA-DRL enhances grid instantaneous and average resilience by 3.38 % over T1 and 4.96 % over T2. Flexible kernel settings and smooth nonlinear prediction behaviour allow the proposed HARIMA-DRL approach to provide the highest resilience enhancement and lowest prediction error.

Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications

ABSTRACT This study explores the fusion of Sentinel-2A (S2) and different bands of UAV imagery (Blue, Green, Red and Panchromatic (PAN)) to enhance spatial and spectral information for crop health monitoring. It assesses the quality of fused images, accuracy of the classified fused images and statistical analysis between the fused and S2 vegetation indices. Various fusion techniques are examined to select an optimal combination of bands. The quality of the fused images is evaluated using image quality assessment metrics and classification employs the Random Forest algorithm, assessing accuracy with metrics like F-score and Kappa Coefficient. Statistical analysis involves comparing vegetation indices from fused and S2 imagery. Notably, the fusion of UAV Green and Red bands with S2 imagery, using BT and PCA techniques, emerges as an effective combination for plant-level agricultural health monitoring. This research contributes to advancing precision agriculture techniques by leveraging multispectral imaging fusion for enhanced crop monitoring and management.

Evaluating the Impact of Flossing Band Integration in Conventional Physiotherapy for Patellofemoral Pain Syndrome.

Background/Objectives: Patellofemoral Pain Syndrome (PFPS) is prevalent among physically active individuals, highlighting the need for innovative treatment strategies beyond conventional physiotherapy. This study investigates the effectiveness of integrating flossing band therapy with standard physiotherapy, anticipating improved outcomes in pain reduction, functional ability, and patient satisfaction. Methods: A double-blinded randomized controlled trial involved 50 PFPS-diagnosed participants. They were divided into two groups: Standard Physiotherapy Group (SPG) and Flossing Band and Physiotherapy Group (FBPG), each undergoing an 8-week intervention focusing on resistance training supplemented by respective therapies. Assessment metrics included pain (VAS), strength (Dynamometry), lower limb function (LEFS), and PFPS function (AKPS) measured before and after the intervention. Results: Significant enhancements in all outcome measures were noted for both groups, yet the FBPG exhibited notably superior improvements in pain, knee functionality, muscle strength, and lower extremity function. The FBPG demonstrated statistically significant greater efficacy in pain alleviation and strength enhancement. Conclusions: The addition of flossing band therapy to conventional physiotherapy presents a more effective treatment modality for PFPS, suggesting its potential to redefine therapeutic standards. Future studies should delve into the long-term impacts and mechanistic underpinnings of floss band therapy in PFPS management.

The use of virtual technologies with music in rehabilitation: a scoping systematic review

IntroductionThe use of virtual environments in rehabilitation has been growing. The current systematic review aimed to evaluate how and where virtual technologies are combined with music in rehabilitation.MethodsA literature search was conducted in seven databases (ACM Digital Library, CINAHL Complete, Cochrane Library, Computer Science database, Medline, Nursing & Allied Health database, and Scopus). It included articles published from 2006 to February 2023. The search followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for study selection. Twelve studies with 160 participants were included in the review. In addition, a descriptive approach was used to gather data from the studies for the areas of interest.ResultsVirtual Reality (VR) and Augmented Reality were most familiar and used in rehabilitation, but Mixed Reality, and Extended Reality were less studied. It became evident that equipment, diverse applications, assessment metrics, training environments, and methodologies were highly varied. Most of the studies were pilots, the overall quality of included studies was rated moderate, and the risk of bias was high in the included studies. The studies on motor rehabilitation after stroke showed a clinical improvement in hand functions, specifically fractionation. Furthermore, studies that included motivation, adherence, and mental health, slightly supported the idea that VR technologies with music have the potential to promote motivation and exercise adherence, increase positive emotions, and decrease anxiety. In addition, user experience and feasibility studies presented technologies combined with music to be safe and acceptable in rehabilitation.DiscussionIn light of these findings, it can be inferred that virtual technologies with music offer promising avenues for future rehabilitation research. However, some confusion was obtained related to the included studies’ definitions of technologies and management terms. Furthermore, it is important to note the limited number of studies and participants, the heterogeneity among participants, and the overall quality of the studies that restrict the generalizability of these results to a larger population. Therefore, future researchers should define concepts and terms, delve deeper into this area to uncover the potential benefits, and clarify the specific mechanisms through which virtual technologies with music can positively impact rehabilitation outcomes.

Image inpainting based on GAN-driven structure- and texture-aware learning with application to object removal

In this paper, a novel deep learning-based image inpainting framework consisting of restoring image structure and reconstructing image details from corrupted images is proposed. Most image inpainting methods in the literature aim at restoring image details, outlines, and colors, simultaneously, which may suffer from blurring, deformation, and unreasonable content recovery due to interference among various information. To solve these problems, a two-stage image inpainting deep neural network based on GAN (generative adversarial network) architecture is proposed. The proposed inpainting framework consists of two modules: (1) the first stage, called the structure-aware learning stage, aims at learning a GAN-based structure restoration network, focusing on recovering the low-frequency image component, including colors and outlines of the missing regions of the input corrupted image; and (2) the second stage, called the texture-aware learning stage, aims at learning a GAN-based detail refinement network, focusing on rebuilding the high-frequency image details and texture information. In particular, we also propose to remove details from the training images to better train the structure restoration network to avoid inadequate image structure recovery induced by richer image textures, where the detail reconstruction task is left to the second stage. This strategy achieves to balance the workload between the two stages and the image quality can be progressively enhanced through the two stages. Experimental results have shown that the proposed deep inpainting framework quantitatively and qualitatively achieves state-of-the-art performance on the well-known datasets, including the CelebA, Places2, and ImageNet datasets, compared with existing deep learning-based image inpainting approaches. More specifically, in terms of the two well-known image quality assessment metrics, PSNR (peak signal-to-noise ratio) and SSIM (structural similarity), the improvement percentage of the proposed method, compared with the baseline approach, respectively, ranges from 3.23 % to 11.12 %, and 1.95 % to 13.39 %. The improvements have been shown to stably and significantly outperform the compared state-of-the-art methods in most types of inpainting mask. We also show that the proposed method is applicable to image editing in object removal from a single image.

Novel intelligent supervised neuro-structures for nonlinear financial crime differential systems

Artificial intelligence (AI)-based applications contribute to monitoring financial transactions and detect fraudulent activity in real-time by analyzing transaction patterns, consumer behavior, and other statistics, making them essential for quickly addressing potential threats in the fight against financial crime dynamics. Leveraging financial crime systems with intelligent supervised neuro-structures exploiting nonlinear autoregressive exogenous networks integrating damped least square (NARX-DLS) optimization methods to achieve an appropriate degree of accuracy and adaptability for the estimation of complex nonlinear financial crime differential systems (NFCDSs). The representative NFCDS for financial crime indicators is expressed as susceptible individuals, financial criminals, individuals under prosecution, imprisoned individuals, and honest individuals. The Adams numerical solver accomplishes the acquisition of synthetic data for the layer structure NARX-DLS algorithm execution to solve NFCDSs for various financial crime parameters, such as recruitment rate, influence rate, conversion rate to honest people, financial criminal prosecution rate per capita, discharge and acquittal rate from prosecutions, percentage of discharge rate from prosecution, transition rate to prison, and freedom rate. A sturdy overlap between the solutions of NARX-DLSs and the reference numerical results of NFCDSs implies that the error value is close to a desirable value of zero. The effectiveness of the NARX-DLSs is evidenced by including a variety of assessment metrics that carefully examine the model’s correctness and efficacy, including mean square error-based convergence arches, adaptive regulating parameters, error distribution, and input-error/cross-correlation analyses.

Carsharing Worldwide: Case Studies on Carsharing Development in China, Europe, Japan, and the United States

Carsharing has received considerable attention as a sustainable mobility paradigm. Various service designs and dynamic business environments have increased the decision complexity for the carsharing business. Therefore, carsharing operators require a tool for assessing business development from holistic perspectives. This research provides a framework for outlining the requirements of the carsharing system with holistic perspectives of stakeholders being considered, as well as to derive assessment metrics for examining carsharing development. To create the framework, the system modeling tool, context diagram, was adopted to map out the interactions of externalities with the system and the requirements of the system. Eight assessment metrics: the market condition, business advantage, parking condition, electric vehicle deployment, self-serving configuration, vehicle reservation, vehicle maintenance, and pricing scheme, were eventually identified from the system modeling. From these dimensions, we review 24 carsharing cases from China, Europe, Japan, and the United States, and we summarize discrepancies among different marketplaces and some managerial insights on carsharing development, such as carsharing motivators and inhibitors, innovations in respect of different business backgrounds, approaches of increasing parking privileges, approaches of increasing electrifications, essential digital features, reservation regimes, methods of vehicle maintenances, and service pricing regimes.

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.

Development and Evaluation of Chemometric Models for the Estimation of Sumatriptan in the Presence of Naproxen and a Degradation Product Using UV Spectrophotometry.

Chemometrics is a discipline that allows the spectral resolution of drugs in a pharmaceutical formulation along with degradation product and it is an alternative to chromatographic methods. Sumatriptan (SUM) is co-formulated with naproxen (NAP) and used in acute migraine attacks. SUM, which has physiological importance, has not been subjected to any stability-indicating chemometric approaches yet, so there is a need for an accurate and safe method for the assay of the cited drug in its preparations. The greenness and blueness assessment was applied using different ecological metrics, including the Green Analytical Procedure Index (GAPI), Analytical Greenness Metric (AGREE), Analytical Eco-Scale (AES) and new "blueness" evaluation using the Blue Applicability Grade Index (BAGI) tool. SUM was determined in pharmaceutical formulation along with NAP and in presence of alkali-induced degradation product with simple and cost-effective multivariate approaches using spectrophotometric data. Three chemometric approaches were applied for the stability-indicating determination of SUM in the presence of NAP. Classical least-squares (CLS), partial least-squaresregression (PLS), and principal components regression (PCR)-three multivariate calibration numerical models that were applied to the UV spectra of the mixtures-were used to achieve the best resolution. Sumatriptan was analyzed with mean accuracies for PLS (100.29% ± 1.318) and for PCR (100.60% ± 1.564). The presented methods were compared and validated for their quantitative analyses. Moreover, statistical comparison between the results obtained by the proposed models and the official methods showed no significant differences. The proposed multivariate calibrations were accurate and specific for quantitative analysis of the studied component. PLS is the best method that has the capacity for qualitative analysis of SUM and it is suitable for routine analysis and stability studies of SUM in QC laboratories. Various ecological assessment metrics confirmed the long-standing eco-friendliness of the suggested models. Severally overlapped mixtures of SUM along with co-formulated drug NAP and an alkali-induced degradation product were analyzed by three chemometric approaches. The analytical performance of PLS and PCR was compared and validated in terms of root-mean-square error of calibration (RMSEC), SE of prediction, and recoveries. PLS gave the highest predicted concentrations with the lowest RMSEC and root-mean-square error of prediction. The standard addition was applied for accuracy assessment and the results were compared to those of official methods. Proposed models determined SUM in synthetic mixtures and pharmaceutical formulation in QC laboratories and stability studies. Ecological evaluation tools for measuring the environmental friendliness of chemicals were utilized for the first time in the analysis of SUM.

No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real imaging processes, models are usually trained and validated on simulation datasets and then tested on real measurements captured by real HSI imaging systems. However, due to the gap between the simulation imaging process and the real imaging process, the best model validated on the simulation dataset may fail on real measurements. To obtain the best model for the real-world task, it is crucial to design a suitable no-reference HSI quality assessment metric to reflect the reconstruction performance of different models. In this paper, we propose a novel no-reference HSI quality assessment metric via ranking feature learning (R-NHSIQA), which calculates the Wasserstein distance between the distribution of the deep features of the reconstructed HSIs and the benchmark distribution. Additionally, by introducing the spectral self-attention mechanism, we propose a Spectral Transformer (S-Transformer) to extract the spatial-spectral representative deep features of HSIs. Furthermore, to extract quality-sensitive deep features, we use quality ranking as a pre-training task to enhance the representation capability of the S-Transformer. Finally, we introduce the Wasserstein distance to measure the distance between the distribution of the deep features and the benchmark distribution, improving the assessment capacity of our method, even with non-overlapping distributions. The experimental results demonstrate that the proposed metric yields consistent results with multiple full-reference image quality assessment (FR-IQA) metrics, validating the idea that the proposed metric can serve as a substitute for FR-IQA metrics in real-world tasks.

Assessing the accuracy of remote sensing data products: A multi-granular spatial sampling method

Over the past two decades, remote sensing data have demonstrated significant potential across various fields. Accuracy assessment, as a crucial component, plays a vital role in ensuring the effective application of remote sensing data products. To assess the accuracy of remote sensing data products, this paper proposed a multi-granular spatial sampling method (MG-SSM). In the proposed MG-SSM, the assessed remote sensing data products were stratified into different layers based on spatial heterogeneity, quantified by calculating the aggregation index. Subsequently, multi-granular sampling units were defined for each layer based on spatial correlation, quantified using Moran’s I and Z-score. Finally, the accuracy of remote sensing data products was assessed by comparing it with the reference data. Using the classification results of Landsat 9 OLI/TIRS with a 30-m spatial resolution as assessed data, and the classification results of Sentinel-2 MSI with a 10-m spatial resolution as reference data, and employing overall accuracy (OA) and the Kappa coefficient as assessment metrics, the performance of MG-SSM was compared against other sampling methods, including SRS, ST, SY, SSS, and GLCM. The performances of SRS, ST, SY, SSS, GLCM, and MG-SSM were 74.41%, 75.39%, 76.57%, 78.47%, 76.77%, and 67.36% in terms of OA, and 0.6, 0.61, 0.64, 0.66, 0.64, and 0.46 in terms of the Kappa coefficient, respectively. The results indicated that MG-SSM exhibited the lowest values for overall accuracy and the Kappa coefficient, aligning with the objective of assessing the accuracy of remote sensing data products. This outcome is attributed to MG-SSM’s strategy of distributing more sample points in areas with lower aggregation of land-cover classes, which are more prone to classification errors. Collectively, the proposed MG-SSM enhanced the representativeness and reduced the information redundancy of the sample points, making it an efficient sampling method for assessing the accuracy of remote sensing data products.

Contextual knowledge graph approach to bias-reduced decision support systems

ABSTRACT Recent research shows that automated decision-making systems based on Artificial Intelligence (AI) may lead to perceived unfairness and bias, especially in some sensitive areas. There is no universal debiasing solution for AI applications and systems. This paper proposes a bias-reducing framework based on contextual knowledge graphs for decision-making systems to help analyse and detect potential bias factors during system operation in near-real time. In particular, the contextual knowledge graph is designed to learn the relations between current tasks and corresponding features and explore the correlation among data, context and tasks. Three bias assessment metrics (i.e., label bias, sampling bias and timeliness bias) are proposed to measure, quantify and qualitatively define the bias level for the pre- and post-modelling. The trained model using debiased datasets combines contextual knowledge to support fairer decision-making. Experimental results show that the proposed method is more effective in supporting fairer decision- making than the existing methods.

Evaluating the reliability of remote video based assessment metrics for the advanced training in laparoscopic suturing (ATLAS) curriculum–a pilot study

Evaluating the reliability of remote video based assessment metrics for the advanced training in laparoscopic suturing (ATLAS) curriculum–a pilot study

Precision in Insurance Forecasting: Enhancing Potential with Ensemble and Combination Models based on the Adaptive Neuro-Fuzzy Inference System in the Egyptian Insurance Industry

ABSTRACT Enhancing the precision of retention ratio predictions holds profound significance for insurance industry decision-makers and those vested in advancing insurance services. Precision helps insurance companies navigate inflationary pressures and evaluate underwriting profitability, enabling reliable prognoses of future underwriting gains. As far as we know, although there have been multiple attempts to construct a predictive model for retention ratio, none of these attempts have used combining models or studied the Egyptian market. Therefore, this study contributes significantly to this developing field by providing combining models, which combined statistical time series models such as Exponential Smoothing (ES), and Autoregressive Integrated Moving Average (ARIMA), with Adaptive Neuro-Fuzzy Inference System (ANFIS). Two different types of combinations are employed with these models. Furthermore, the study introduces three ensemble models designed for the purpose of predicting the retention ratio within the Egyptian insurance market. Dataset was carefully gathered from the EFSA’s annual reports, focused on the property-liability insurance sector within the Egyptian insurance market and covers the time period from 1989 to 2021. Next, the proposed models are assessed employing well-established statistical assessment metrics, namely, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), R Square (R2), and Root Mean Square Error (RMSE). The results show that combining and ensemble methods improve predicted accuracy. A multi-linear regression-based ensemble model that combines ARIMA, ES, and ANFIS models outperforms both single and combined models in robustness. The article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

Identifying the criteria for community-centred Life Cycle Sustainability Assessment of estate regeneration schemes

Identifying the overall environmental, and socioeconomic impacts of different estate regeneration scenarios can contribute to the overall sustainability of such schemes. Life Cycle Sustainability Assessment (LCSA) is an appropriate tool for assessing holistic sustainability. To achieve resilient societies, the interests of communities should be considered in decision making. This paper proposes a method for incorporating community needs in identifying sustainability metrics for the sustainability assessment of estate regeneration schemes. A literature review in the field of sustainability assessment of buildings is conducted followed by a mixed methods empirical research. Collection of data has been through surveys, an interview, and an evaluation questionnaire. Data has been analysed through statistical and thematic analysis and triangulation of the results. The findings have consistently yielded the limitations of the scope of the current sustainability assessment methodologies, especially for lack of attention to societal impacts of regeneration. The results have justified the need for this research to employ participatory approaches for identifying a relevant set of sustainability indicators and criteria for assessing the lifetime impacts of estate regeneration schemes. Issues related to community involvement in decision making, maintenance and management, community facilities, refurbishment, and disruption have been identified as the stakeholders’ top priorities. Mental Health and Socioeconomic Values have been introduced as new criteria. The findings confirm the need for an in-depth approach towards identifying the regeneration priorities of the communities for the scope of LCSA studies. The identified list of criteria can apply to other studies of this context for an equitable approach for selecting the indicators across different criteria and for communicating the LCSA results with different stakeholders.