Lack Of Resources Research Articles

Has Family Engagement Finally Gained Foothold in Forensic Mental Healthcare?

Despite advancements in promoting family engagement in mental health settings, limited involvement of family members persists in forensic mental healthcare. Forensic mental healthcare professionals face various barriers in engaging families, including a patient-centered approach and resource constraints. However, limited understanding exists of professionals' experiences with family engagement, which is crucial for improving care practices in this setting. Consequently, this study investigates the evolution of professionals' experiences with family engagement from 2015 to 2021 in Flanders, Belgium. Qualitative methods were employed, including focus group interviews in 2015 and individual interviews in 2021 with 23 forensic mental healthcare professionals. Thematic analysis is employed to identify patterns and changes over time. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist was utilised to report the study. The analysis revealed that while initial steps towards family engagement in forensic mental healthcare have been taken, full integration in organisational structures is still lacking. Future efforts should focus on involving family organisations and caregivers, addressing barriers like time and resource constraints, and fostering a cultural shift towards family engagement. Further research involving a broader range of stakeholders is needed to enhance family engagement initiatives in forensic mental healthcare settings.

Biomedical, Neurodiverse, and Mad Affinities: The Constraints of Collective Epistemic Resources.

Biomedical, Neurodiverse, and Mad Affinities: The Constraints of Collective Epistemic Resources.

Orchestrating Method Ensembles to Adapt to Resource Requirements and Constraints During Robotic Task Execution

Orchestrating Method Ensembles to Adapt to Resource Requirements and Constraints During Robotic Task Execution

Sailfish-cat algorithm-enhanced generative adversarial network for attack detection in internet of things-Fog network authentication

The internet of things (IoT) has emerged as a prominent and influential concept within the realm of computing. Various attack detection methods are devised for detecting attacks in IoT-Fog environment. Despite all these efforts, attack detection still remained as a challenging task due to factors such as low latency, resource constraints of IoT devices, scalability issues, and distribution complexities. All these challenges are addressed in this paper by designing an efficient attack detection technique named as sailfish- cat optimization-based generative adversarial network (SaCO-based GAN) tailored for the IoT-Fog framework. This proposed approach introduces the SaCO-based GAN for IoT-Fog attack detection utilizing deep learning and feature-based classification, validated through experiments showing superior performance metrics. Notably, the SaCO optimization technique is utilized to train the GAN. Experimental results demonstrate the efficacy of the SaCO-based GAN with a maximum recall of 92.15%, a maximum precision of 91.21%, and a maximum F-Measure of 92.16%, outperforming existing techniques in IoT-Fog attack detection. The paper recommends enhancing scalability, implementing real-time detection strategies, rigorously testing robustness against diverse attack scenarios, and integrating with existing IoT security frameworks for practical deployment.

Resource allocation models and heuristics for the multi-project scheduling with global resource transfers and local resource constraints

Resource allocation models and heuristics for the multi-project scheduling with global resource transfers and local resource constraints

Prioritizing questions and topics for the development of guidelines and consensus-based recommendations supported by ILAE: A scoping review and proposal of prioritization criteria.

Clinical practice guidelines (CPGs) and consensus-based recommendations (CBRs) require considerable effort, collaboration, and time-all within the constraints of finite resources. Professional societies, such as the International League Against Epilepsy (ILAE), must prioritize what topics and questions to address. Implementing evidence-based care remains a crucial challenge in clinical practice. Using rigorous processes to ensure that the best available research evidence informs health care recommendations is of the utmost importance. We aimed to develop a structured and transparent process for prioritizing future CPGs and CBRs supported by the ILAE. A multidisciplinary group of researchers and experts from the ILAE Prioritization Task Force conducted a scoping review to identify prioritization approaches for CPG and CBR development. This scoping review was reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Cochrane recommendations. A Problem/population, Concept, and Context (PCC) strategy was applied to the literature search and selection of the studies. We searched Medline/PubMed, Embase, Web of Science, and Scopus without time or language limits. The findings were synthesized qualitatively. A consensus-based process was followed to develop a prioritization scoring tool for CPGs and another for CBRs. Thirty-nine participants, including clinicians, experts in the field, methodologists, and other relevant stakeholders, contributed to developing the final instrument (based on a 5-point Likert scale). Of 721 unique citations, 8 papers reporting prioritization approaches for guideline development were included. Based on these, we developed an initial tool with 10 criteria. It was iteratively optimized and revised by the ILAE Standards and Best Practice Council, which unanimously approved the instrument. The ILAE Executive Committee subsequently approved its final version. The ILAE Prioritization Tool is intended to standardize the prioritization processes and optimize the ILAE's use of resources to select CPGs and CBRs for endorsement.

Transforming Healthcare Delivery: Strengthening Nurse Engagement in Patient-Centered Care Practice

The evolving landscape of healthcare delivery necessitates a robust commitment to patient-centered care (PCC), with nurse engagement playing a pivotal role in achieving this goal. This study explores strategies to enhance nurse engagement in PCC practices by employing qualitative research methods, including a comprehensive literature review and library research. The analysis draws on scholarly articles, case studies, and frameworks that underscore the importance of empowering nurses in clinical decision-making, fostering interdisciplinary collaboration, and integrating holistic approaches to patient care. Findings reveal that nurse engagement significantly contributes to improved patient outcomes, satisfaction, and care efficiency. Furthermore, institutional support, continuous professional development, and a culture of shared leadership are critical enablers of nurse engagement in PCC. By synthesizing existing evidence, this study offers actionable recommendations for healthcare policymakers, practitioners, and educators to foster a supportive environment for nurses. These recommendations emphasize the need for targeted interventions to address barriers such as workload, resource constraints, and organizational hierarchies that impede effective engagement. This research underscores the transformative potential of engaged nursing practices in advancing healthcare systems toward a more compassionate and patient-centered future.

The negative expectation–performance gap and internationalization speed of EMNEs: the moderating effect of organizational slack

PurposeThe purpose of this study is to investigate the impact of the negative expectation–performance gap on the internationalization speed as well as the moderating role of organizational slack, based on the performance feedback theory and the springboard perspective.Design/methodology/approachThis paper takes the Chinese A-share listed companies engaged in outward foreign direct investment (OFDI) between 2010 and 2022 as the research sample. A two-way fixed effects model is employed to test the research hypotheses, using a dataset comprising 6,868 observations.FindingsThe findings show that there is a positive relationship between the negative expectation–performance gap and internationalization speed for Chinese multinational enterprises (CMNEs). In addition, this effect is stronger in private CMNEs. Furthermore, the relationship is negatively moderated by organizational slack. Additional findings suggest that that negative industry expectation-performance gap has a stronger impact on the internationalization speed. The moderating effect of unabsorbed slack resources is more pronounced.Practical implicationsWhen emerging market multinational enterprises (EMNEs) face the dilemma of underperforming, they may consider a rapid internationalization strategy as a solution to improve performance. However, EMNEs should be wary of falling into a resource trap. Organizational slack reduces managers’ incentives to identify issues associated with negative performance feedback, thereby diminishing the likelihood of addressing performance challenges through rapid internationalization. For state-owned EMNEs, optimizing organizational structure and improving the efficiency of responses to negative performance feedback are essential.Originality/valueUnlike previous studies, this paper integrates performance feedback theory and the springboard perspective to explore in depth the relationship between performance feedback, internationalization speed and organizational slack within the context of managers’ cognitive and decision-making mechanisms. It also examines the distinct impacts of historical and industry negative expectation–performance gaps as well as the different moderating roles of absorbed and unabsorbed organizational slack, which have not been explored together before.

Determinants maintaining healthcare personnel’s motivation during COVID-19 pandemic in Uganda

BackgroundMaintaining the physical and psychological well-being of healthcare workers (HCWs) is crucial for health system resilience. In sub-Saharan Africa, particularly Uganda, HCWs faced significant challenges during the coronavirus disease 2019 (COVID-19) pandemic, compounded by pre-existing resource constraints. This study investigated challenges faced by HCWs at a designated COVID-19 hospital (‘the Hospital’) and explored determinants of maintaining healthcare personnel’s motivation during the COVID-19 pandemic in Uganda.MethodsA facility-based cross-sectional study was conducted at the Hospital from May to July 2023, with 120 HCWs categorised by profession. Data were collected using a structured questionnaire. Descriptive and logistic regression analyses were performed to identify motivation-related factors. The questionnaire assessed sociodemographic data, COVID-19 knowledge, medical history, support received, and pandemic-related challenges. Psychological impact was measured using a four-point Likert scale.ResultsAmong respondents, 61.7% worked during the first lockdown and 93.3% during the second. COVID-19 knowledge was high, with over 70% answering questions correctly, although vaccine effectiveness had a slightly lower accuracy rate. The study revealed that 32.5% of HCWs tested positive for COVID-19 and 25% displayed vaccine hesitancy. Key challenges included fear of infection, transportation disruptions, income reduction, and social isolation. Notably, 92.5% reported significant anxiety related to infection. Factors positively associated with remaining motivated were age (≤ 30 versus > 30 years; adjusted odds ratio [AOR]: 10.34, 95% CI; 1.92–55.74), profession (co-medical/non-medical staff versus medical doctors/medical officers; AOR: 11.66, 95% CI: 1.03–132.22), receiving mentoring/tutoring (AOR: 18.87, 95% CI: 2.55–139.72), and information from supervisors/management (AOR: 12.5, 95% CI: 2.60–60.42). In contrast, psychological impact was negatively affected by isolation from family and friends (AOR: 0.61, 95% CI: 0.41–0.92).ConclusionsDespite challenges, most HCWs at the Hospital remained motivated. These findings emphasised the importance of technical and psychological support, such as mentoring and effective communication from upper-level supervisors, rather than financial incentives, in maintaining motivation among the HCWs. Comprehensive support systems are essential for sustaining HCWs’ motivation during the pandemic, especially in resource-limited settings. Further research should address the long-term mental health effects and enhance HCWs’ resilience in future health crises.

Machine Learning Algorithms for Intrusion Detection in WSNs

Machine learning (ML) has emerged as a potent tool for optimizing network performance and extending network lifespans. By automating complex data processing tasks, ML algorithms enable real-time solutions that optimize resource allocation and utilization. ML's ability to process vast, intricate datasets with speed and precision empowers networks to adapt dynamically to evolving demands. [13]. Wireless Sensor Networks (WSNs), composed of interconnected sensor and sink nodes, exemplify the transformative potential of ML. These distributed, decentralized networks inherently possess self-organization and self-healing capabilities. By integrating ML techniques, WSNs can significantly enhance their efficiency, reliability, and scalability, enabling a wide array of applications, from environmental monitoring to military operations. As advancements in electronics and wireless communication continue to drive WSN evolution, the fusion of ML and WSNs promises to unlock new horizons in network intelligence and performance. However, WSNs face several challenges, including resource constraints such as limited memory, processing power, and energy. Additionally, the physical infrastructure of WSNs must be secured to protect sensitive data, especially in privacy-critical applications. To address these challenges, the integration of machine learning offers a promising solution. By leveraging ML techniques, WSNs can adapt to dynamic environments, optimize energy consumption, and detect and mitigate potential security threats. This enables the deployment of WSNs in diverse applications, from environmental monitoring to smart cities, while ensuring data privacy and system security. Keywords: Intrusion Detection System (IDS), Security, Wireless Sensor Network (WSN), Attacks, Reinforcement Learning (RL), Denial-of-Service (DoS), Networks, Machine Learning (ML)

Navigating the Post-Pandemic Landscape: The Challenges and Opportunities of Community-Based Networks in Substance Prevention.

The COVID-19 pandemic has significantly influenced substance prevention work, exacerbating existing challenges and providing new opportunities for community-based substance prevention networks (CSPNs). CSPNs are interorganizational networks that include various providers, such as nonprofits and government agencies, that collaborate to provide substance prevention services and are embedded in local communities. This study analyzes the post-pandemic adaptation of a CSPN in the United States. Through qualitative thematic analysis of interview data with network leaders, this study identifies two primary challenges: resource constraints and engagement obstacles and two primary opportunities: innovation in service delivery and data importance. The findings affirm what is known about the barriers to substance prevention and point to a novel shift in community-based substance prevention work. Particularly, CSPNs are adopting harm-reduction tactics and expanding digital outreach to support clients. As CSPNs continue to emerge, this research provides insight into the evolving landscape of substance prevention work and the critical role of community-based networks. The findings in this study offer practical implications for enhancing CSPN effectiveness and resilience in substance prevention.

Giardia duodenalis in the Philippines: A Review of Prevalence, Epidemiology, and Diagnostic and Treatment Challenges

Giardia duodenalis, commonly referred to as Giardia intestinalis or Giardia lamblia, is a worldwide relevant protozoan parasite responsible for giardiasis, a gastrointestinal disorder. Giardiasis continues to pose a public health challenge in the Philippines, especially in areas with inadequate sanitation and hygiene. Prevalence rates differ nationwide, with elevated infection rates observed in children, institutionalized groups, and regions with restricted access to clean water. Assemblage B, the predominant genotype identified, underscores the danger of zoonotic transmission. Notwithstanding progress in molecular diagnoses, conventional microscopy continues to be the principal diagnostic instrument, limited by resource constraints. Chronic giardiasis leads to malnutrition, development retardation, and cognitive deficits, especially in children from disadvantaged backgrounds. Addressing these deficiencies requires enhanced diagnostic techniques, focused public health initiatives, and investments in water sanitation infrastructure. This review highlights significant research deficiencies and emphasizes the necessity for extensive initiatives to alleviate the impact of giardiasis in the Philippines.

Navigating Educational Disruptions

This research article investigates the impact of the COVID-19 pandemic on secondary schools in Uganda, focusing on the strategies adopted to cope with disruptions in educational processes. It utilises a mixed-methods approach, gathering data from 16 schools in Uganda's Western, Northern, and Eastern regions, and focusing on vocational subjects. Its findings reveal that schools observed Standard Operating Procedures (SOPs), including social distancing, mask-wearing, and handwashing, despite resource constraints and spatial limitations. Remote learning for candidate classes reduced exposure but highlighted rural disparities due to inadequate internet infrastructure. Despite significant challenges, school administrators exhibited resilience by implementing innovative measures, including staggered attendance and restricted student movement to minimize contact. However, vocational education encountered notable setbacks, such as inadequate practical facilities, especially within private institutions. It highlights the critical need for enhanced government support, particularly for vocational teachers, to sustain education post-pandemic. While Uganda's education sector has shown resilience during the pandemic, sustained investment in infrastructure, teacher support, and vocational education is vital for long-term stability and growth .

“Let It Be” or “Nip It in the Bud”? A Study of the Impact of Population Outflow on Economic Growth and High-Quality Economic Development in Forest Areas

In the face of the realistic background of population loss, resource constraints and factor constraints, it explores the high-quality development of forest areas, aiming to analyze the impacts of population exodus from forest areas on economic growth and high-quality development. The focus is on China’s key state-owned forest areas, especially in Heilongjiang Province, Jilin Province and Inner Mongolia Autonomous Region. A set of indicators centered on the concept of green development is constructed and designed to measure the dynamics of high-quality economic development and the impact of population outflow in the three provinces and regions from 2000 to 2022. The study found that (1) Heilongjiang Province excels in high-quality economic development, followed by Jilin Province, while the Inner Mongolia Autonomous Region lags behind. During the observation period, the high-quality development trend in the forest areas showed a steady upward trend until 2014 and then began to slow down or even decline. (2) The pulling effect of population mobility on economic growth in forest areas has gradually weakened; nevertheless, the effective agglomeration of labor and capital is still a key factor driving economic growth. (3) Population exodus poses an obstacle to high-quality economic development. Increased population mobility has a negative impact on both economic growth and high-quality development, exacerbating economic fluctuations, and is not conducive to stability and high-quality development. It is worth noting that although the population base plays a fundamental role in economic growth, the direct effect of population on development is not significant. On the contrary, there is a negative correlation between population outflow and high-quality economic development. Based on the findings, strategic recommendations are made with the aim of promoting a shift from a growth model that relies on population numbers to a development model that focuses on the improvement of population quality in forest areas, thereby realizing a fundamental innovation in the economic development model. This study enriches the theory of regional development in forest areas and is of great significance in promoting sustainable development in forest areas.

Sustainable Business Practices: The Future of Corporate Responsibility

Sustainable business practices have emerged as an essential facet of corporate responsibility in response to mounting global concerns over environmental degradation, socio-economic inequalities, and the climate crisis. This paper examines the growing significance of sustainability in the modern business landscape, underscoring its dual role as an ethical obligation and strategic opportunity. It also examines key strategies for integrating sustainable practices, such as employee engagement, supply chain optimization, and the adoption of innovative technologies. Additionally, it delves into methods for measuring sustainability impacts, emphasizing accountability and stakeholder engagement. While recognizing challenges such as resource constraints and greenwashing risks, the paper highlights how these obstacles can be reframed as opportunities for innovation and long-term resilience. By adopting progressive sustainability strategies, businesses can align their operations with the principles of environmental stewardship and social equity, ensuring sustained economic growth and enhanced corporate reputation. Keywords: Sustainable Business Practices, Corporate Responsibility, Environmental Stewardship, Social Equity, Supply Chain Optimization.

Disaster Risk Management and Emergency Response Legislations and Strategies Awareness in Tanzania: A Case of Dar Es Salaam City

This study evaluated major cities’ compliance and awareness to the disaster management and emergency response legislations and strategies in Tanzania with a focus on Dar es salaam city that have experienced persistent undesirable disasters and emergencies that arise from both natural and manmade. The study also focused at actions and programs embarked by the Dar es Salaam City Governments (Ilala, Temeke, Ubungo and Kinondoni) to implement the established disaster and emergency response legislations and strategies in dealing with the challenges identified and their way forward. The outcome of this research was contemplated to significantly assist individuals, community, the local government and other interested parties in dealing with disasters and emergencies, and enhance understanding and compliance and awareness to the disaster management and emergency response legislations and strategies urban and rural dwellers with disaster-resilient. This study deployed descriptive research design and survey to gather information about the research problem under the study. The survey provided quantitative account of views of sampled population and their attitudes, and provided chance to test correlation among population variables of the population samples in which 105 respondents were conveniently selected for this study. The study found that the majority of the population are aware the about the disasters that are likely to occur in their localities and their associated impacts both health and economic impacts, but however they are not satisfied with the strategies implemented by the government to deal with them. The study revealed that, the public officials responsible for emergency response and disaster management in Dar es Salaam are aware about the legislations and plans in dealing with disasters and emergency response but the compliance to them are below average due to various factors like resources constraints, insufficient personnel, low involvement of community, insufficient institutional coordination between emergency responders, insufficient infrastructures that affect response time to emergencies, inadequate urban land use planning and strategies and political influenced decision making. The study proposed that the government have to review and follow up implementation of developed urban land use plans base on disaster risk assessment and management reports, to intensify and provide public awareness campaigns on disaster risk management and emergency response strategies, capacity building to government officials and emergency responders on new urban development technologies and enhance their knowledge and competencies, and to review and establish a coordinated emergency response plans in major cities and establishment of early warning systems with coordinated communication systems among responders.

LEVERAGING BLOCKCHAIN WITH CHAOTIC OPPOSITIONAL BARNACLES MATING OPTIMIZER-BASED DEEP LEARNING MODEL FOR SECURE IOT ENVIRONMENT IN CONSUMER ELECTRONICS

The security of IoT networks has become a major concern with the ubiquity of Internet of Things (IoT) technology. The concept of intrusion detection system (IDS) is a complex system to discover an intruder in the IoT platform, where the intruder can be a host that tries to gain some other nodes without authorization. Due to the complexity and resource constraints, classical IDS have their limitations in the context of IoT networks. There has been significant research in integrating both IDS and blockchain (BC) to detect existing and emerging cyberattacks and improve data privacy, correspondingly. In these approaches, learning-based ensemble algorithms can concurrently ensure data privacy and facilitate the detection of sophisticated malicious events. This study introduces a BC with fractal chaotic oppositional barnacles mating optimizer-based deep learning (BCOBMO-DL) Model for Secure IoT environment. The BCOBMO-DL technique exploits BC technology with DL-based intrusion detection to protect the IoT environment. In the BCOBMO-DL technique, the linear scaling normalization (LSN) approach can be used to scale the input data into a uniform format. In addition, the BCOBMO-DL technique designs the COBMO algorithm for electing the optimal subset of features. For intrusion detection, the self-attention bidirectional gated recurrent unit (SA-BiGRU) model is applied with fractal theory. Finally, the reptile search algorithm (RSA)-based hyperparameter tuning process is utilized for tuning the parameters based on the SA-BiGRU model. Furthermore, the BC technology is useful for achieving security in the IoT network. The experimental evaluation of the BCOBMO-DL method takes place on the benchmark NSLKDD dataset. The widespread experimental outcomes highlighted the enhanced detection outcomes of the BCOBMO-DL algorithm over other models.

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced data requirements. Notably, delta learning trained with just 20-30% of high-level data matched or exceeded the performance of other methods trained with full data sets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large data sets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target data sets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly in thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints.

MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today’s Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this paper, we consider the challenge of scheduling a RT application modelled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTGs on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6 % and area usage reductions of 46.3 % are possible compared to full Software SW and HW execution, respectively.

Evaluating Sustainability in Post-Conflict Reconstruction: A Case Study of Blast-Damaged Buildings Without Structural Collapse Risk in Syria

In the context of post-war reconstruction, this study introduces a novel assessment model tailored for the preliminary management of blast-damaged reinforced concrete buildings without structural collapse risk. The model addresses the critical challenge of prioritizing reconstruction efforts within constraints of time and resources while integrating economic, environmental, and social dimensions. Developed using the Integrated Value Model for Sustainable Assessment and refined through the Delphi method, the model provides stakeholders with a practical tool to evaluate alternative reconstruction scenarios, including refurbishment, demolition, reconstruction with retained identity, and preservation for future work. Validation is carried out through a case study of a tourist hotel in the historic area of Damascus, where a report confirmed the building posed no structural collapse risk. The results indicate that “preservation for future work” is the most sustainable option environmentally, while “refurbishment” emerges as the most sustainable economically and socially. This study highlights the importance of comprehensive damage assessments and sustainability-oriented tools for resilient and sustainable post-war reconstruction strategies that respect heritage contexts.