Dar Al-Hekma University

The authors would like to make the following corrections to the published paper [...]

Internet of Drones (IoD) plays a critical role in remote monitoring operations. It has been heavily deployed in applications such as traffic management, air traffic control, disaster and agricultural monitoring. In such operations, high volumes of sensitive and private data is collected and transmitted to the ground stations. The usage of unreliable and open wireless channels during the message exchange process exposes the IoD to a myriad of security threats. Therefore, numerous authentication protocols have been developed in the recent past to mitigate these threats. However, most of these protocols are still inadequate in the provision of robust security against typical IoD attacks. Because, they involve resource-intensive operations and make them unsuitable for an IoD environment. Therefore, we put forward a new authentication protocol which exhibits costeffective performance. To develop the new protocol for secure IoD communication, we leverage on biometrics, physically unclonable function and elliptic curve cryptography for the robust performance. Extensive formal security analysis using the Random Oracle Model (ROM) is showing that proposed protocol is provably secure. The informal security analysis is also performed which demonstrates its robustness against all the threat assumptions in the Canetti-Krawczyk (C-K) model. Specifically, it is found to offer conditional privacy, unlinkability, anonymity, mutual authentication, session key establishment and perfect key secrecy. Moreover, it is capable enough to mitigate numerous threats such as forgery, impersonation, stolen verifier, replay, physical capture, de-synchronization, known secret key and smart card loss attacks. The performance is cost-effective as evident from the comparative analysis showing that the proposed protocol incurs the lowest energy consumptions and computation overheads at relatively low communication costs compared to many other authentication schemes.

Purpose: Our study investigates the combined effects of financial technologies (fintech) and the digital economy on sustainable development, considering geopolitical risks as a moderating factor. Origin: While sustainable development is a global imperative, the integrated roles of digital transformation and fintech remain insufficiently explored. Our research addresses this gap by analyzing their impacts on socioeconomic advancement and environmental sustainability across diverse contexts. Methodology: Employing panel data from 30 developed and developing countries between 1990 and 2023, we assess sustainable development using the Environmental Performance Index (EPI) and the Human Development Index (HDI). Independent variables include proxies for the digital economy (e.g., internet usage, mobile subscriptions, and high-tech exports) and fintech (e.g., digital payments, digital currency, and peer-to-peer lending). The Geopolitical Risk Index (GPRI) is used to evaluate the effect of political instability. We apply generalized least squares (GLS) and fixed-effects estimation (within) to ensure robustness. Findings: Our results indicate that digital transformation and fintech significantly foster socioeconomic development and environmental performance, even amidst geopolitical instability. Key variables such as digital payments and internet access show substantial positive impacts, providing valuable insights for policymakers aiming to enhance resilience and sustainability. Contributions: Our article offers a comprehensive evaluation of how the digital economy and fintech jointly influence sustainable development under geopolitical risks, providing a nuanced understanding for policymakers and researchers.

As cyberattacks become more advanced, conventional centralized threat intelligence models often fail to keep up with these threats’ growing complexity and frequency, highlighting the requirement for innovative approaches to strengthen cybersecurity resilience. Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. The efficiency of FL in enhancing attack intelligence skills emphasizes its probability of driving a novel period of robust and privacy-protecting cybersecurity practices. Furthermore, combining FL into cybersecurity structures can strengthen attack intelligence models by permitting real upgrades and adaptive learning mechanisms. Recently, ML and Deep Learning (DL) approaches have drawn the study community to advance security solutions for cyberattack defence mechanism models. Conventional ML and DL techniques that function with data kept on a federal server increase the main privacy issues for user information. This manuscript presents a Cyberattack Defence Mechanism System for Federated Learning Framework using Attention Induced Deep Convolution Neural Networks (CDMFL-AIDCNN) technique. The CDMFL-AIDCNN model presents an improved structure incorporating self-guided FL with attack intelligence to improve defence mechanisms across varied cybersecurity applications in distributed systems. Initially, the data preprocessing stage utilizes Z-score normalization to transform input data into a beneficial format. The Dung Beetle Optimization (DBO) technique is used in the feature selection process to identify the most relevant and non-redundant features. Furthermore, the fusion of convolutional neural networks, bidirectional long short-term memory, gated recurrent units, and attention (CBLG-A) models are employed to classify cyberattack defence mechanisms. Finally, the parameter tuning of the CBLG-A approach is performed by the growth optimizer (GO) approach. The CDMFL-AIDCNN technique is extensively analyzed using the CIC-IDS-2017 and UNSW-NB15 datasets. The comparison analysis of the CDMFL-AIDCNN technique portrayed a superior accuracy value of 99.07% and 98.64% under the CIC-IDS-2017 and UNSW-NB15 datasets.

The Learning Management System (LMS) is an essential tool for educational institutions that facilitates content delivery, assessments, lecture delivery, and collaboration to enhance the learning experience. This study explores the role of LMS in creating an effective learning environment to improve students’ academic performance. To achieve the main objective of this study, we utilized a dataset [xAPI-Edu-Data] comprising multiple factors, such as academic, psychological, and cognitive engagement. Various machine learning techniques are employed to assess the impact of engagement activities on students’ performance. Initially, a class imbalance issue identified in the dataset and addressed using SMOTE technique. In addition, other resampling strategies applied to compare the effectiveness of proposed work. The model performance evaluated and compared using different evaluation metrics before and after data enrichment. In addition, hyperparameter optimization is conducted using a grid search approach to enhance models’ accuracy. The performance of individual models such as support vector machine (0.81), logistic regression (0.80), and decision tree (0.75) enhanced using the enriched dataset. The integration of multiple base learners into an ensemble model, with random forest as the stacking learner, achieved a weighted precision of 0.83, improving from 0.60 with the original dataset. The implementation of the stacking approach with enriched dataset has identified a better result and improved accuracy by 23%. The key contribution of this study includes identifying the effectiveness of data enrichment in improving prediction accuracy. Moreover, the research highlights the role of student engagement and behavior in measuring academic performance. The proposed model can identify the factors behind low performance, allowing further actions to be taken. Based on the prediction, the educators can work on the associated factors that could be low engagement, participation, or attendance. The findings further indicate that better use of LMS by creating more engagement activities can enhance students’ learning.

Cognitive fatigue is a psychological condition characterized by opinions of fatigue and weakened cognitive functioning owing to constant stress. Cognitive fatigue is a critical condition that can significantly impair attention and performance, among other cognitive abilities. Monitoring this condition in real-world settings is crucial for detecting and managing adequate break periods. Bridging this research gap is significant, as it has substantial implications for developing more effectual and less intrusive wearable devices to track cognitive fatigue. Many models consider intricate biosignals, like electrooculogram (EOG), electroencephalogram (EEG), or detection of basic heart rate inconstancy parameters. Artificial Intelligence (AI)-driven methods aid in handling and categorizing these biosignals, recognizing fatigue-related patterns with higher accuracy. This technique is essential in high-demand surroundings such as education, healthcare, and workplaces or where cognitive fatigue may affect decision-making and performance. Therefore, the study presents an Exploratory Analysis of Longitudinal Artificial Intelligence for Cognitive Fatigue Detection Using Neurophysiological Based Biosignal Data (EALAI-CFDNBD) approach. The main aim of the EALAI-CFDNBD model is to detect cognitive fatigue using neurophysiological-based biosignal data. Primarily, the EALAI-CFDNBD model utilized the linear scaling normalization (LSN) model to ensure that the input features were appropriately scaled for subsequent analysis. Furthermore, the binary olympiad optimization algorithm (BOOA)-based feature selection is utilized to extract the most informative features, reducing the data dimensionality. The graph convolutional autoencoder (GCA) classifier is employed to classify cognitive fatigue detection. Finally, the multi-objective hippopotamus optimization (MOHO) method is utilized for parameter tuning, optimizing the model’s hyperparameters to enhance overall detection accuracy. An extensive range of simulations is accomplished using the MEFAR dataset to establish a good classification outcome of the EALAI-CFDNBD method. The experimental validation of the EALAI-CFDNBD technique portrayed a superior accuracy value of 97.59% over the recent methods.

Artificial intelligence (AI) is transforming the internationalisation activities of multinational corporations (MNCs) through enhanced operational efficiencies and optimised decision-making; though the moderating factors influencing its impact on export-led internationalisation remain underexplored. This research adopts a Resource-Based View (RBV) approach to examine the complex relationship between AI capabilities and the export performance of Indian MNCs, with cultural distance serving as a moderating factor, analysing how AI adoption influences export intensity, trade expansion, and market penetration strategies. Data from a 2024 survey of 449 Indian exporters across various industries, analysed using Structural Equation Modelling, reveal that AI capabilities positively impact export performance particularly in markets characterised by high institutional uncertainty and complex regulatory environments. Moreover, cultural distance acts as a significant moderator, amplifying the role of AI in navigating consumer preferences, language barriers, and localised business practices. AI-powered analytics help firms better understand foreign markets, adapt to cultural differences, and optimise international operations. This study advances the scholarly understanding and contributes to internationalisation theory by integrating AI-driven trade strategies with institutional and cultural moderating factors and offers a structured framework for corporate managers and policymakers to formulate AI-based strategic decisions that leverage AI to mitigate trade-related uncertainties, improve their compliance with international regulations, and strengthen global trade competitiveness in emerging economies.

This study employs Design Builder software to evaluate advanced glazing technologies for enhancing the thermal performance of residential buildings in Jeddah, Saudi Arabia. Recognizing the energy inefficiencies caused by adopting Western architectural styles unsuited to local climatic conditions, and given that buildings consume 44% of national energy, we conducted a systematic parametric analysis to isolate the effects of key glazing parameters. The study examines six polycarbonate (PC) configurations and three critical comparative cases: (1) a selective double-glazed unit representing a new baseline glazing; (2) a low-U configuration to isolate thermal insulation effects; and (3) a low-SHGC configuration to evaluate solar heat gain mitigation independently. These controlled comparisons address a critical research gap by decoupling the traditionally confounded impacts of U-value and SHGC in hot climates. The simulations reveal that the 36 mm aerogel glazing (U = 0.9 W/m²·K, SHGC = 0.3) reduces cooling demand by 48.6% annually compared to single-pane glazing while maintaining indoor temperatures at 30.09 °C versus 38.43 °C at baseline. Notably, the findings demonstrate that 87% of these savings derive from SHGC reduction, with only 3.02 percentage points attributable to U-value improvements. The selective DGU benchmark delivers 85% of aerogel’s benefits at 40% lower cost, establishing it as a practical solution for most applications. These findings provide evidence-based guidance for Saudi Vision 2030’s sustainability goals, emphasizing that while aerogel glazing excels in extreme solar exposures, strategic SHGC optimization in conventional glazing can achieve the most energy savings in hot climates.

With accelerating surface warming trends in urban regions, cities like Algiers are increasingly exposed to extreme heat, contributing to a growing concern over heat-related illnesses. For a comprehensive long-term assessment (2001–2023) of heat-related risks in Algiers, multi-decade satellite, meteorological, and census data were used in this study to map and assess spatial patterns of the Heat Health Risk Index (HHRI) within the framework established by the Intergovernmental Panel on Climate Change (IPCC) incorporating hazard, exposure and vulnerability components. The Universal Thermal Climate Index (UTCI) was then calculated to assess thermal stress levels during the same period. Following this, the study addressed a critical research gap by coupling the HHRI and UTCI and identified hotspots using the Getis-Ord Gi* statistical analysis tool. Our findings reveal that the intensity of HHRI has increased over time since “very-low” risk areas had an outstanding decrease (93%) and a 6 °C UTCI rise over 23 years reaching the “very strong heat stress” level. The coupled index demonstrated greater and different risk areas compared to the HHRI alone, suggesting that the coupling of both indicators enhances the sensitivity of heat risk assessment. Finally, persistently identified hotspots in central and eastern regions call for localized, targeted interventions in those areas and highlight the value of remote sensing in informing policymakers and enhancing climate resilience.

The research concentrates on determining the degree of internationalization of born global SMEs, believing that some push factors determine internationalization, pull factors, and internal firm-specific factors. Three important factors were found in looking into the causes of internationalization in born global firms: push, pull, and internal firm-specific factors. The study used a survey instrument with a sample of 280 manufacturing-related SMEs chosen from manufacturing clusters in India. A metric called the “index of internationalization” is used to gauge how internationalization in SMEs takes shape. The results demonstrated that internal firm-specific factors influence the internationalization of firms relatively highly compared to push and pull factors. The results unequivocally demonstrate that developing economies have distinct factors that cause internationalization, opening up new avenues for further study. The research aids in the identification of the elements that will enhance early internationalization and tries to draw the attention of young entrepreneurs. This research also helps prioritize the factors responsible for early internationalization. These findings are pertinent for the practitioners and researchers working in this area. This research is helpful for start-ups looking for global opportunities; this research categorizes factors significant in the global journey of the born global firms.

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map. The map demonstrates remarkable chaotic dynamics over a wide range of parameters. We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map, which allows us to select optimal parameter configurations for the encryption process. Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors, an essential characteristic for effective encryption. The encryption technique is based on bit-plane decomposition, wherein a plain image is divided into distinct bit planes. These planes are organized into two matrices: one containing the most significant bit planes and the other housing the least significant ones. The subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance security. An auxiliary matrix is then generated, comprising the combined bit planes that yield the final encrypted image. Experimental results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical images. As a result, image quality is evaluated using the Structural Similarity Index (SSIM), yielding values close to zero for encrypted images and approaching one for decrypted images. Additionally, the entropy values of the encrypted images are near 8, with a Number of Pixel Change Rate (NPCR) and Unified Average Change Intensity (UACI) exceeding 99.50% and 33%, respectively. Furthermore, quantitative assessments of occlusion attacks, along with comparisons to leading algorithms, validate the integrity and efficacy of our medical image encryption approach.

This paper presents the International Skin Spectra Archive (ISSA), a multicultural human skin phenotype dataset, containing 15,256 records of both spectral and colorimetric data derived from 2,113 subjects. These measurements, collected between 2012 and 2024, come from eleven different datasets gathered by international laboratories across eight countries, all adhering to a uniform measurement protocol to ensure data consistency. The ISSA dataset addresses the inherent challenges in measuring human skin colour due to its complex structure and covers a wide variability in skin characteristics such as geography, ethnicity, age, gender, and body location. Providing a broad spectrum of human skin data, the ISSA dataset will advance our understanding of skin colour variations and their biological, cultural, and environmental influences. It will also serve as a crucial resource for scientific research and technological development across various fields where diverse and precise spectral and colour data of real human skin are essential.

Vitamin B6 (pyridoxine) vitamins are of interest in preventative and protective strategies in cardiovascular disease. However, the safety and efficacy of vitamin B6 has been questioned. The aim of this study was to study the protective effect of pyridoxine, amlodipine, and their combination against vasopressin-induced angina model in rats. The administration of vasopressin (1 IU/kg, i.v.) to the rats elevated the S-wave level of the electrocardiogram reflecting the presence of subendocardial ischemia, whereas it decreased of the heart rate, resulting in the increase of the cardiac enzymes, creatine kinase MB (CK MB), and lactate dehydrogenase (LDH). In the vasopressin-induced angina model, oral administration of pyridoxine in dose of 5, 7, 10 mg/kg revealed dose-dependent suppression of vasopressin-triggered of ST elevation and in reduced of heart rat. In addition, pyridoxine produced dose-dependent suppression of cardiac enzymes, creatine kinase MB (CK MB), and lactate dehydrogenase (LDH) more than amlodipine and isosorbide; while in contrast, the combination of pyridoxine with amlodipine resulted in a trend towards increased adverse cardiovascular events; pyridoxine in dose 7 mg/kg was found to be more potent than pyridoxine in doses 5, 10 mg/kg, amlodipine and isosorbide on vasopressin-induced angina in rats. Pyridoxine in dose of (5, 7 mg) prevents cardiac necrosis and artery well thickened on vasopressin-induced angina modal. Pyridoxine’s protective effects may be mediated by improved endothelial nitric oxide synthase (eNOS) function, reduction of homocysteine levels, and modulation of sympathetic activity. Pyridoxine at optimal doses shows promise as a novel therapeutic agent for coronary heart disease prevention, warranting further investigation into its potential clinical applications.

This study presents an assessment methodology to evaluate design studio facility effectiveness by conducting a post-occupancy evaluation case study. The effectiveness of architectural studios determines how students learn in architecture schools because it affects their creativity levels and productivity and educational achievement. POE represents an essential strategy for educational facility assessment which helps verify their match with user requirements. The study follows a sequential method that initiates with a study of architectural studio importance and POE performance in academic spaces. The researchers conducted their study at Onaizah Colleges located in Qassim, Saudi Arabia by implementing both qualitative and quantitative data gathering techniques which included walkthrough inspections and semi-structured interviews and the distribution of questionnaires. The study identifies a methodical several-step system to evaluate architectural studio performance. A structured categorization of performance criteria included ten groups that evaluated functional and technical operations with behavioral capabilities across environmental comfort and spatial organization and technological implementation and user satisfaction. Educational architecture proves its dependency on fundamental features of comfort together with functionality based on the study outcomes. The framework enables professional users to methodically analyze studio layouts for enhancing their educational performance and user satisfaction. The research analysis demonstrates how user-centered design approaches must be used to improve student learning because it identifies important performance elements. The research uniquely utilizes a systematic approach to studio assessment which delivers essential information to facilities management regarding studio administration to enhance educational outcomes.

The study’s primary objective is to observe the upshot of intellectual capital (IC) on the financial performance of Indian public sector companies. The article also sheds light on the effect of the global financial crisis of 2008 on the financial performance of Indian public companies. Secondary data were collected for 24 Indian “Central Public Sector Enterprises (CPSEs)” between 1999 and 2018. The “Value Added Intellectual Coefficient (VAIC™)” methodology was employed for measuring IC and its dimensions. The result shows that public Indian firms effectively utilize their IC to enhance their sales growth. At the same time, they failed to leverage their IC to improve their profitability and productivity. Additionally, among the dimensions of VAIC, human capital efficiency (HCE) and structural capital efficiency (SCE) enhance only the firm’s sales growth. Lastly, the results confirm the adverse effect of the global economic crisis of 2008 on the firm’s financial performance.

Background and aims In recent years, increased awareness of the psychological wellbeing of healthcare professionals and students has become a pressing public health issue affecting care delivery. Medical students undergo rigorous training programs that can affect their psychological wellbeing. Despite increased awareness of mental health issues among medical students, research often focuses on negative aspects, overlooking potential positive contributors to wellbeing. This study aims to explore both negative and positive factors influencing medical students’ psychological wellbeing, considering coping strategies and personality traits to inform targeted support measures for diverse student needs. Methods A mixed-methods approach was employed to investigate medical students’ psychological wellbeing, coping strategies, and personality traits. Quantitative data was gathered via self-report questionnaires and analysed using regression models. Additionally, qualitative insights were obtained from semi-structured interviews and analysed thematically to capture students’ perceptions and experiences. Results The analysis revealed moderate to high levels of stress, anxiety, and depression among medical students, along with decreased life satisfaction. Regression analysis showed that problem-focused coping positively impacted medical students’ psychological wellbeing, whereas emotion-focused and avoidance coping showed less favourable effects. Notably, problem-focused coping partially mediated the relationship between stress and depression. Furthermore, personality traits, particularly agreeableness and conscientiousness, played a pivotal role in shaping medical students’ coping strategies and mental health outcomes. Based on thematic analysis, codes gave rise to three overarching themes and corresponding subthemes. Conclusions The study underscores the significance of addressing both positive and negative factors impacting medical students’ wellbeing and highlights the need for tailored support considering individual personality traits that influence coping strategies and mental health. It also identifies challenges within medical education, emphasising the necessity for stress management programs, mental health support, and curricula promoting problem-solving skills. Prioritising medical students’ wellbeing may not only foster good mental health among future professionals but may also enhance future healthcare quality.

Saudi Vision 2030, a strategic framework aimed at diversifying the economy and enhancing societal inclusivity, aligns with the UN’s Sustainable Development Goals (SDGs) by promoting gender equality and sustainable economic growth. Sustainability is central to fostering women’s entrepreneurship, as it drives social equity, economic diversification, and innovation, elements which are crucial to sustainable development. While the existing literature has primarily focused on women’s entrepreneurship in the Western world, limited attention has been given to its development in the Global South, particularly in Saudi Arabia. As a nation undergoing transformative social, cultural, and economic shifts, women entrepreneurs play a critical role in aligning entrepreneurial efforts with global sustainability goals. This research investigates the factors influencing Saudi women to become entrepreneurs, specifically examining the factors that inspire or hinder them from creating their own ventures. Drawing upon cognitive and social capital theories, which have proven their soundness in the existing literature, this research utilizes a dataset of 1715 women entrepreneurs analyzed through binomial logistic regression. The findings indicate that social desirability, relational capital, experience as angel investors, age, income, and education significantly increase the likelihood of women’s entrepreneurship. By contextualizing women’s entrepreneurship within Saudi Arabia’s evolving societal and economic landscape, this research highlights their potential as drivers of inclusive growth and sustainable economic empowerment. Furthermore, the research outlines strategies to enhance women’s entrepreneurial participation, contributing both to the entrepreneurship literature and the realization of Saudi Vision 2030.

HIV testing and pre-exposure prophylaxis (PrEP) are recommended in Germany for individuals at increased HIV risk. However, data on HIV testing, PrEP use, and PrEP knowledge among trans and non-binary people are limited. We analysed data from the ‘Sexuelle Gesundheit in trans und nicht-binären Communitys’ (TASG) study, a participatory study on HIV/STI and sexual health among trans and non-binary people in Germany. The study was designed, promoted, and analysed with active involvement of community members. Participants were invited to complete an anonymous online survey between 1 March and 1 July 2022. The outcomes included HIV testing within the last 5 years, PrEP use, and PrEP-specific knowledge. Predictors for HIV testing were identified using a bootstrap stepwise selection procedure. Among 2468 HIV-negative participants with information on potential HIV risks, 21.5% had potential needs for HIV testing and PrEP. Of these, only 44.3% (208/470, missing: 60) reported testing for HIV within the last 5 years. Older participants, those living in larger cities, and those with higher education levels were more likely to have tested for HIV. Additionally, only 8.3% (38/459, missing: 71) reported ever using PrEP. Among 451 participants with potential PrEP needs (missing: 79), only 57.4% knew at least one of three key PrEP-related facts at the time of the survey. Our findings highlight substantial gaps in HIV testing and prevention among trans and non-binary individuals in Germany with potential needs for these services. Reducing barriers to testing and prevention is essential to enable broader access to these critical services.

The user’s experience is critical in spatial design, particularly in outdoor spaces like university campuses, where the physical environment significantly influences students’ relaxation and stress relief. This study investigates the combined impact of thermal, luminous, and auditory environments on students’ perceptions within recreational areas at Bordj Bou Arreridj University Campus. A mixed-method approach combined field surveys and on-site measurements across eleven locations within three distinct spatial configurations. The findings from this study indicate that the auditory environment had the most substantial influence on overall perceptions, surpassing luminous and thermal factors. The open courtyard (Area 1) was perceived as less comfortable due to excessive heat and noise exposure. The shaded zone (Area 2) was identified as the most vulnerable, experiencing significant thermal stress and noise disturbances. In contrast, the secluded patio (Area 3) achieved the highest comfort rating and was perceived as the most cheerful and suitable space. Correlation analysis revealed significant interrelationships between physical and perceptual dimensions, highlighting the critical role of factors such as wind velocity, sky view factor, and illuminance in shaping thermal, luminous, and acoustic perceptions. A fuzzy logic model was developed to predict user perceptions of comfort, suitability, and mood based on measured environmental parameters to address the complexity of multisensory interactions. This study highlights the importance of integrating multisensory evaluations into spatial design to optimize the quality of outdoor environments.

This paper presents a systematic post-occupancy evaluation (POE) of a gated apartment building in Onaizah, Qassim, Saudi Arabia, focusing on resident satisfaction and building performance. Employing a mixed-methods approach, the research combines quantitative data from questionnaires and qualitative data from walkthrough observations and interviews to assess various performance aspects, including thermal comfort, visual comfort, acoustic performance, and safety. Results indicate that residents generally expressed satisfaction with thermal comfort, visual comfort, and indoor air quality. However, concerns were highlighted in areas such as safety and security, design adequacy, and construction support services. These findings reveal that while the building meets many occupant needs, there are critical areas requiring improvement. This study underscores the importance of incorporating POE as a valuable tool for assessing building performance and informing future design and management strategies in residential developments. Finally, this study’s methodology excelled in analyzing the quality and performance of residential building elements, which contributes to enriching the literature related to facilities management. It explains the research strategy followed to provide an organized and reliable framework that can be used to evaluate performance and quality in residential buildings.