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This paper introduces a methodology to identify, analyse and represent heritage site attributes, emphasizing their impact on value, authenticity, integrity and management, with a case study on Ghadames, Libya. Inscribed in 1986 and moved to the In-Danger List in 2016 due to conflict, this work seeks to update the site's attributes and values for improved management.

This methodology, focusing on Ghadames, leverages recent heritage management advancements to monitor conflict-induced changes, aiming to enhance decision-making through a detailed analysis of the site's natural and cultural attributes.

Our findings highlight the need for systematic and holistic assessments of heritage site attributes and values, crucial for managing sites of both local and global significance. This approach is a key to understanding their identity, guiding interpretation, management and preserving cultural significance.

Developed for Ghadames, the methodology requires adaptation for other sites, underscoring the importance of identifying core tangible and intangible attributes that define a site's uniqueness.

Our developed methodology offers a replicable framework that can be modified by local heritage professionals to map attributes and assess the direct and indirect impact of conflict on heritage sites.

The detailed assessment provides a foundation for crafting informed policies and effective management strategies. It specifically targets minimizing the adverse effects of conflict on heritage sites' attributes. This effort is instrumental in preparing the necessary documentation to support the delisting of these sites from the UNESCO World Heritage Site In-Danger List, promoting their preservation and recovery.

This study aims to examine how an educational intervention, using the lens of the LEADS framework, can influence the development of primary care doctors’ leadership skills in Aceh, Indonesia. In order to persevere in the face of inadequate resources and infrastructure, particularly in rural and remote settings of low- and middle‐income countries, physicians require strong leadership skills. However, there is a lack of information on leadership development in these settings.

This study applied an educational intervention consisting of a two-day workshop. The authors evaluated the impact of the workshop on participants’ knowledge and skill by combining quantitative pre- and post-intervention questionnaires (based on Levels 1 and 2 of Kirkpatrick’s model) with qualitative post-intervention in-depth interviews, using a phenomenological approach and thematic analysis.

The workshop yielded positive results, as evidenced by participants’ increased confidence to apply and use the information and skills acquired during the workshop. Critical success factors were as follows: participants were curiosity-driven; the use of multiple learning methodologies that attracted participants; and the use of authentic scenarios as a critical feature of the program.

The intervention may offer a preliminary model for improving physician leadership skills in rural and remote settings by incorporating multiple teaching approaches and considering local cultural norms.

This paper presents Quick Changeover Design (QCD), which is a structured methodological approach for Original Equipment Manufacturers to drive and support the design of machines in terms of rapid changeover capability.

To improve the performance in terms of set up time, QCD addresses machine design from a single-minute digit exchange of die (SMED). Although conceived to aid the design of completely new machines, QCD can be adapted to support for simple design upgrades on pre-existing machines. The QCD is structured in three consecutive steps, each supported by specific tools and analysis forms to facilitate and better structure the designers' activities.

QCD helps equipment manufacturers to understand the current and future needs of the manufacturers' customers to: (1) anticipate the requirements for new and different set-up process; (2) prioritize the possible technical solutions; (3) build machines and equipment that are easy and fast to set-up under variable contexts. When applied to a production system consisting of machines subject to frequent or time-consuming set-up processes, QCD enhances both responsiveness to external market demands and internal control of factory operations.

The QCD approach is a support system for the development of completely new machines and is also particularly effective in upgrading existing ones. QCD's practical application is demonstrated using a case study concerning a vertical spindle machine.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

Mental well-being is associated with positive outcomes throughout the life course. This study aims to examine interventions delivered by allied health professionals (AHPs) to alleviate community stressors adversely impacting public mental health and well-being.

Review inclusion criteria comprised experimental and qualitative process evaluations of public mental health interventions delivered by AHPs. Electronic searches in Cinahl, Embase, Medline, PsycINFO and the Cochrane Library, were combined with grey literature searches of National Institute for Health and Care Excellence public health guidance. A narrative synthesis and the Effective Public Health Practice Project appraisal tool were used to evaluate the evidence.

A total of 45-articles were included in the review describing AHP-delivered interventions addressing social disadvantage, trauma, bullying, loneliness, work-related stress, transitional stress, intersectionality, pain and bereavement. No articles were identified evaluating interventions delivered by operating department practitioners or orthoptists. A conceptual map was developed summarising the stressors, and a typology of public mental health interventions defined including: place-based interventions, discrete/one-off interventions, multi-component lifestyle and social connector interventions and interventions targeting groups at risk of mental health conditions.

Many mental health conditions begin in childhood, and a strength of the review is the life course perspective. A further strength is compiling a compendium of public mental health outcome measures used by AHPs to inform future research. The authors excluded many articles focussed on clinical interventions/populations, which did not meet review inclusion criteria. While playing a key role in delivering public mental health interventions, clinical psychologists are not defined as AHPs and were excluded from the review, and this may be construed a limitation. Given heterogeneity of study designs and interventions evaluated numerical analyses of pooled findings was not appropriate.

The review highlights the breadth of community stressors on which AHPs can intervene and contribute in public mental health contexts, stressors which correspond to those identified in UK Government guidance as currently important and relevant to address. The findings can inform developing community public mental health pathways that align with the UK National Health Service (NHS) Long Term Plan, on prevention and early intervention to protect community mental health and well-being. Further can inform the NHS strategic direction for AHPs including informing ways of increasing utilisation of core AHP skills to optimise contributions in public mental health agendas.

It is surprising there were not more AHP delivered evaluations of interventions for other stressors important to address in public mental health settings, for example gambling, domestic violence or that used digital technology, and these are areas for future research. Future research should identify the most active/effective dimensions of multi-component interventions which could be informed by frameworks to guide complex intervention development. The relative paucity of research identified, highlights the predominant focus of research to date on interventions for clinical mental health conditions and populations. The lack of preventative approaches is evident, and an important area for future research to align with UK health and social care priorities.

The review highlights AHP-delivered interventions impacting diverse community stressors across the life course. The findings can inform developing public mental health pathways aligned with government health service priorities to protect mental health and well-being, prioritise prevention and early intervention and increase utilisation of AHP skills across public mental health settings.

During the COVID-19 pandemic, Local Educational Management Units (UGELs), the key government stakeholders in the provision of education services in Peru, implemented responsive interventions. This paper analyses the relationship between UGEL organisational resilience and their responses during this period.

A survey was conducted to measure UGEL management practices, with 251 valid responses from directors and managers. Based on organisational resilience theory, 67 questions were grouped into 13 factors and 3 components: (1) leadership and organisational culture, (2) preparation for change, and (3) networks building on the Organisational Resilience Index (ORI). These factors correlated with the number of interventions and the impact of those interventions implemented by UGELs.

The findings indicated that of all ORI components, leadership and organisational culture ranked the highest. Moreover, the ORI is positively associated with the number of interventions and the perceived impact produced by those interventions. Interestingly, it was found that when the gender variable is included in the correlation between the ORI and the number of interventions, women leading UGELs display a higher number of interventions than their male counterparts; and the coefficient increases even more when women lead a UGEL in a more challenging context (i.e., when the UGEL is located in a low-income area and operates under scarce resources).

This is the first study in Peru which analyses organisational resilience in the education sector, specifically about UGELs during the COVID-19 pandemic. It may help set priorities for institutional strengthening initiatives aimed at improving organisational resilience, which is particularly important in such uncertain and changing contexts.

Using real-time support systems may help operators in road construction to improve paving and compaction operations. Nowadays, these systems transform from descriptive to prescriptive systems. Prescriptive or operator guidance systems propose operators actionable compaction strategies and guidance, based on the data collected. It is investigated how these systems mediate the perceptions and actions of operators in road pavement practice.

A case study is conducted on the specific application of an operator guidance system in a road pavement project. In this case study, comprehensive information is presented regarding the process of converting input in the form of data from cameras and sensors into useful output. The ways in which the operator guidance systems translate data into actionable guidance for operators are analyzed from the technological mediation perspective.

Operator guidance systems mediate actions of operators physically, cognitively and contextually. These different types of action mediation are related to preconditions for successful implementation and use of these systems. Coercive interventions only succeed if there is widespread agreement among the operators. Persuasive interventions are most effective when collective and individual interests align. Contextual influence relates to designs of the operator guidance systems that determine human-technology interactions when using them.

This is the first study that analyzes the functioning of an operator guidance system using the technological mediation approach. It adds a new perspective on the interaction between this system and its users in road pavement practice.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This study aims to describe and evaluate the impact of a participatory age-management intervention on the knowledge, awareness and engagement of line managers and their HR partners from six health-care organizations in Sweden.

The learning workshops consisted of lectures, discussions, feedback and exchange of experiences with colleagues and invited experts. A total of 19 participants were interviewed six months after the final workshop, and qualitative thematic analysis was used to analyze the transcribed interviews.

The intervention design produced promising results in improving line managers’ and HR partners’ knowledge and increasing awareness and engagement. On some occasions, the participants also initiated changes in organizational policies and practices. However, the intervention primarily became a personal learning experience as participants lacked resources and mandates to initiate change in their daily work. To stimulate engagement and change at the organizational level, the authors believe that an intervention must receive support from higher managers, be anchored at the workplace and be aligned with the organization’s goals; moreover, participants must be provided with sufficient resources and mandates to coordinate the implementation of age-management strategies.

Prolonged working life policies and skill shortages are affecting organizations and societies, and for many employers, there are strong reasons for developing strategies to attract, recruit and retain older workers.

This study offers lessons and guidance for future workplace interventions to attract, recruit and retain older workers.

The outbreak of the COVID-19 pandemic has significantly impacted healthcare systems, presenting unforeseen challenges that necessitated the implementation of change management strategies to adapt to the new contextual conditions. This study aims to analyze organizational changes within the total hip replacement (THR) surgery pathway at multiple levels, including macro, meso and micro. It employs data triangulation from various sources to gauge the complexity of the change process and comprehend how multi-level decision-making influenced an unexpected shift.

A multicentric, single in-depth case study was conducted using a mixed-methods approach. Data sources included patient-reported outcome measures specific to the THR pathway and carefully structured in-depth interviews administered to managers and clinicians in two healthcare organizations serving the same population.

Decisions made at the macro level resulted in an overall reduction in surgical activities. Organizational changes at the meso level led to a complete cessation or partial reorganization of activities. Micro-level actions for change and adaptation revealed diverse and fragmented change management strategies.

Organizations with segmented structures may require a robust and structured department for coordinating change management responses to prevent the entire system from becoming stuck in the absorptive phase of change. However, it is important to recognize that absorptive solutions can serve as a starting point for genuine innovations in change management.

The utilization of data triangulation enables the authors to visualize how specific changes implemented in response to the pandemic have influenced the observed outcomes. From a managerial perspective, it provides insights into how future innovations could be introduced.