Expert Decision Research Articles

Unraveling sex differences in Parkinson's disease through explainable machine learning

Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a higher score in males for a certain feature doesn't necessarily mean it's less important for characterizing PD in females. This article proposes an explainable Machine Learning (ML) model to elucidate these underlying factors, emphasizing the importance of features. This insight could be critical for personalized medicine, suggesting the need to tailor data collection and analysis for males and females. The model identifies sex-specific differences in PD, aiding in predicting outcomes as “Healthy” or “Pathological”. It adopts a system-level approach, integrating heterogeneous data - clinical, imaging, genetics, and demographics - to study new biomarkers for diagnosis. The explainable ML approach aids non-ML experts in understanding model decisions, fostering trust and facilitating interpretation of complex ML outcomes, thus enhancing usability and translational research. The ML model identifies muscle rigidity, autonomic and cognitive assessments, and family history as key contributors to PD diagnosis, with sex differences noted. The genetic variant SNCA-rs356181 may be more significant in characterizing PD in males. Interaction analysis reveals a greater occurrence of feature interplay among males compared to females. These disparities offer insights into PD pathophysiology and could guide the development of sex-specific diagnostic and therapeutic approaches.

Knowledge-based decisions for asset management of urban water infrastructure in Iran and Brazil using a nominal-based multi-criteria group decision method

ABSTRACT Asset management of the Urban Water Infrastructure (UWI) is typically faced with limitations regarding uncertain operational data. This shortcoming can be improved using the knowledge-based decisions of experienced experts. However, since knowledge-based decisions depend on decision-makers’ knowledge and experience, verifying their results in various regions is essential. This subject was addressed in this research. Hence, four groups of experts in Iran and Brazil collaborated to assess the criteria effective in the Maintenance-Rehabilitation Planning (MRP) of urban water pipes. For this purpose, a nominal-based multi-criteria group decision-making method was developed. To verify the results of this method, the viewpoints of non-weighted and weighted decision-makers were investigated and compared pairwise. The findings of this research improved the asset management of the UWI in Iran and Brazil by identifying the criteria applicable to the MRP of the pipes based on knowledge-based decisions. Likewise, it was found that weighting the experts has no decisive effect on the results and is only effective in groups where the decision-makers’ weights are identically distributed. Moreover, it was shown that there is a relationship between the similarity of results and the similarity of experts’ profiles in knowledge-based decision groups of various countries such as Iran and Brazil.

An integrated group decision-making framework for assessing S3PRLPs based on MULTIMOORA-WASPAS with q-rung orthopair fuzzy information

Sustainable third-party reverse logistics has gradually risen to prominence as a component of contemporary commercial development as a result of the acceleration of global economic integration and the prominent growth of information technology in the logistics industry. In the procedure of sustainable third-party reverse logistics providers (S3PRLPs) selection, indeterminacy and conflict information bring great challenges to decision experts. In view of the significant superiority of q-rung orthopair fuzzy (q-ROF) set in expressing uncertain and vague assessment information, this essay designs a comprehensive assessment framework through merging the best and worst method (BWM), Multiplicative Multi-objective Optimization by Ratio Analysis with Full Multiplicative Form (MULTIMOORA) and weighted aggregated sum product assessment (WASPAS) method to address the S3PRLPs selection issue with entirely unknown weight information under q-ROF setting. Firstly, we present a novel score function for comparing q-ROF numbers after analyzing the inadequacies of previous works. Secondly, the q-ROF Frank interactive weighted average (q-ROFFIWA) and q-ROF Frank interactive weighted geometric (q-ROFFIWG) operators are advanced based on the constructed operations to take into consideration the interactive impact of information fusion procedure. Thirdly, the q-ROF-MULTIMOORA-WASPAS decision framework is built based on novel score function and the developed operators, in which the synthetic weights of the criterion are determined by the modified BWM and entropy weight method to reflect both the subjectivity of the decision expert and the objectivity of the decision information. Ultimately, an empirical example was used to evaluate S3PRLPs to demonstrate the applicability and feasibility of the developed methodology, and a comparative analysis was conducted with other existing methods to highlight its advantages in dealing with complex decision problems. The discussion from the research indicates that the developed methodology can be used to evaluate S3PRLPs and further improve the quality of logistics services for organizations.

Multi-Model Assessing and Visualizing Consistency and Compatibility of Experts in Group Decision-Making

Multi-Model Assessing and Visualizing Consistency and Compatibility of Experts in Group Decision-Making

Multidisciplinary Team Practitioners Working in High Performance Sport: Skilled Intuitive ‘Doers’ or Novel Problem-Solving Innovators

Practitioners operating in performance sports are required to problem solve, enabling them to offer tailored performance solutions while making expert decisions with high precision. Problem-solving and decision-making are intertwined and tangled in practice. However, the reality is that practitioners require two independent skill sets. This study aims to investigate performance practitioner’s approaches to problem-solving and decision-making, analysing the meta-cognitive skills required by multidisciplinary team (MDT) practitioners to be effective in their daily practice. Using a 71-statement Likert scale survey, 115 performance- and medical-related MDT practitioners were surveyed to gain insight into their strengths and perceptions of how they think about problem-solving and decision-making in their work. We tabulated descriptive data and created heat maps to visualise correlations between responses. Findings suggest that practitioners rely on a mixed bag of approaches, cognitively toggling between problem types, approaches, and decision styles. In this study, practitioners preferred skilled procedural doing and intuitive expertise to overcome simple problems over rationalistic, logical innovation to address complex problems. Findings suggest the need for MDT practitioners to differentiate between problem types, problem-solving approaches, and decision-making styles while deepening our comprehension of practitioners’ expertise. It offers insight into the cognition that forms the foundation of their approaches, providing a valuable perspective.

Developing a career and education framework for advanced clinical practice in midwifery.

This study outlines the nature of Advanced Clinical Practice in Midwifery (ACPiM), reporting on a stakeholder analysis as part of a national project to develop a career framework for advanced practice in midwifery on behalf of the National Health Service (NHS) in England. Between June and July 2022, 31 advanced practice midwives were recruited across England within the NHS settings. Convenience sampling was used to identify midwives working as advanced practitioners, and those pursuing this career route. Focus group and one-to-one interviews were conducted, recorded, and transcribed. These stakeholder data were then analyzed using a reflexive thematic approach. ACP midwives were active across many professional settings. The findings resulted in three themes: Midwifery autonomy, Desire for progression, and Avenues of support. Midwifery autonomy highlighted a midwifery desire to utilize specialist skills and expert decision-making to provide holistic care directly to women and families. Desire for progression highlighted that, regardless of career stage, midwives aspired to advance their practice requiring a range of pathways to fulfil career satisfaction and meet local population health needs. Avenues of support discussed the barriers and facilitators to progression, highlighting the need for service vision, a multi-disciplinary approach to facilitate support for individuals, and strong midwifery leadership. Although the ACPiM role is desired by maternity institutions and organizations, midwives remain unclear about how to achieve this position, and employers remain unsure of how an ACPiM could transform services. If midwives are to successfully achieve ACPiM status, organizational support is needed to facilitate individuals drive for career progression, resulting in a strengthened workforce and improved patient experience.

Resilience evaluation research on coping with rainstorm inundation under the perspective of full cycle: A case study of Beijing-Tianjin-Hebei region

With the development of global warming and urbanization, the risk of cities being affected by rainstorms is increasing and the safety of lives and properties of residents as well as the sustainable development of the urban economy and society, are facing crucial threats. As a giant complex system, the interior of a city is highly complex and interconnected. The concept of urban resilience has become an effective way of guiding cities to cope with rainstorm inundation perturbations from the perspective of the wholeness of a system. We aimed to integrate the time dimension into the study of resilience in response to rainstorm inundation to gain a deeper understanding of the process of rainstorm inundation generation and development as well as the resilience mechanism in this process. In this study, based on the basic principles of urban resilience and the occurrence and evolution mechanisms of rainstorm inundation, we propose a novel resilience evaluation method to cope with rainstorm inundation from the perspective of the entire cycle. The full-cycle process from ``rainstorm event'' to ``inundation disaster'' is divided into four phases: rainstorm incubation, rainstorm occurrence, inundation persistence, and inundation recession phases and the resilience objectives of each phase are proposed to form a regional resilience indicator set for coping with rainstorm inundation. The Google Earth Engine (GEE) platform was used to identify the spatial and temporal patterns of regional rainstorm inundation, identify the contributing indicators of specific regional rainstorm inundation, combine hierarchical analysis and expert decision analysis to determine the weight of each indicator, and build a regional resilience evaluation model to cope with rainstorm inundation. On this basis, we conducted regional resilience evaluation and type spectrum research to cope with rainstorm inundation, identify the resilience index of the research unit based on the VIKOR method, identify the regional resilience pattern under the full-cycle perspective, divide the resilience types, generate the resilience zoning map and resilience obstacle indicators, and provide guidance on the timeline for improving resilience in the region.

Navigating from data-driven design to designing with ML: a case study of truck HMI system design

AbstractData-driven design is believed to be empowered by machine learning (ML) with advanced pattern classification and prediction. However, research on how ML can be used to support automotive human-machine interface (HMI) design is lacking. We presented a case study of truck HMI design to understand the current data use and expectations of ML in the design process. Findings show decentralized data practices, the role of expertise in decision-making, and the envisioned reactive use of ML, where we underscore the implications for advancing human-ML collaboration in designing future truck HMI systems.

Ongoing decision-making dilemma for treatment of de novo spinal infections: a comparison of the Spinal Infection Treatment Evaluation Score with the Spinal Instability Spondylodiscitis Score and Spine Instability Neoplastic Score.

De novo spinal infections are an increasing medical problem. The decision-making for surgical or nonsurgical treatment for de novo spinal infections is often a non-evidence-based process and commonly a case-by-case decision by single physicians. A scoring system based on the latest evidence might help improve the decision-making process compared with other purely radiology-based scoring systems or the judgment of a single senior physician. Patients older than 18 years with an infection of the spine who underwent nonsurgical or surgical treatment between 2019 and 2021 were identified. Clinical data for neurological status, pain, and existing comorbidities were gathered and transferred to an anonymous spreadsheet. Patients without an MR image and a CT scan of the affected spine region were excluded from the investigation. A multidisciplinary expert panel used the Spine Instability Neoplastic Score (SINS), Spinal Instability Spondylodiscitis Score (SISS), and Spinal Infection Treatment Evaluation Score (SITE Score), previously developed by the authors' group, on every clinical case. Each physician of the expert panel gave an individual treatment recommendation for surgical or nonsurgical treatment for each patient. Treatment recommendations formed the expert panel opinion, which was used to calculate predictive validities for each score. A total of 263 patients with spinal infections were identified. After the exclusion of doubled patients, patients without de novo infections, or those without CT and MRI scans, 123 patients remained for the investigation. Overall, 70.70% of patients were treated surgically and 29.30% were treated nonoperatively. Intraclass correlation coefficients (ICCs) for the SITE Score, SINS, and SISS were 0.94 (95% CI 0.91-0.95, p < 0.01), 0.65 (95% CI 0.91-0.83, p < 0.01), and 0.80 (95% CI 0.91-0.89, p < 0.01). In comparison with the expert panel decision, the SITE Score reached a sensitivity of 96.97% and a specificity of 81.90% for all included patients. For potentially unstable and unstable lesions, the SISS and the SINS yielded sensitivities of 84.42% and 64.07%, respectively, and specificities of 31.16% and 56.52%, respectively. The SITE Score showed higher overall sensitivity with 97.53% and a higher specificity for patients with epidural abscesses (75.00%) compared with potentially unstable and unstable lesions for the SINS and the SISS. The SITE Score showed a significantly higher agreement for the definitive treatment decision regarding the expert panel decision, compared with the decision by a single physician for patients with spondylodiscitis, discitis, or spinal osteomyelitis. The SITE Score shows high sensitivity and specificity regarding the treatment recommendation by a multidisciplinary expert panel. The SITE Score shows higher predictive validity compared with radiology-based scoring systems or a single physician and demonstrates a high validity for patients with epidural abscesses.

Hierarchical fuzzy model applied to oil trading

Computer trading is currently relatively limited to quantitative trading in paper markets, such as, futures, swaps, and options, ignoring the large volume of physical commodities trading. Numerical algorithms and mathematical methods are frequently applied in the trading of non-physical commodities and stock markets. However, the physical trade of oil does not have a computer program that represents an unquestionable development that includes more complex and subjective factors that traders take into consideration when entering a transaction. Using fuzzy logic, this work proposes a new approach to base a method that can rank oils and the variables, commonly involved in the trading. It uses fuzzy logic to blend objective and subjective factors and brings in two key hierarchical steps to the process. It is shown that this model can also be useful for apprising a decision-making process, improving internal communication, and for the management of physical commodities trading. Here the options of trading are assessed accordingly to its numerical and linguistic judgments, considering even abrupt outcomes. The wide-ranging options, successive filters, and factors evaluation that mirror the technical and expert trading decision making process make it possible for the future application of artificial intelligence (AI) in physical commodities trading.

Study on the Method of Selecting Sustainable Food Suppliers Considering Interactive Factors

The existing sustainable supplier selection methods are not sufficient to deal with the problem of sustainable food supplier selection with the interaction of criteria under uncertainty. Therefore, this paper proposes a method of sustainable food supplier selection based on an extended decision model. Firstly, a processing method for supplier evaluation information is constructed using the Pythagorean fuzzy set has the function of processing complex uncertain information. Secondly, to obtain the objective weights of decision experts, a Pythagorean fuzzy weighted distance measure model is constructed, and an expert information fusion method based on a weighted power mean operator is proposed to construct the group decision matrix. Then, the decision experiment and evaluation experiment methods are integrated with the traditional MARCOS method, to construct a sustainable food supplier selection method considering the interaction of factors. This method can effectively deal with the complicated and uncertain problem of sustainable food supplier selection with interactive factors. Finally, the feasibility of the proposed method is verified by an example of sustainable food supplier selection. In addition, parameter sensitivity analysis and multi-method comparative analysis verified the rationality of the proposed selection method for sustainable food supplier selection.

RECAM-J 2023-Validation and development of the Japanese version of RECAM for the diagnosis of drug-induced liver injury.

The diagnosis of drug-induced liver injury (DILI) is challenging. We modified the revised electronic version of the Roussel Uclaf Causality Assessment Method (RUCAM) for the diagnosis of DILI (RECAM), the scoring system developed in US and Spanish cohorts in 2022, and developed RECAM-J 2023 to align with the clinical practice in Japan. In the current study, we introduce RECAM-J 2023 and verify its performance in the context of Japanese patients with DILI. After translation of RECAM into Japanese, modifications were made to develop RECAM-J 2023 without any alteration to the scores. To examine the validity and performance of RECAM-J 2023, clinical information on DILI and non-DILI cases in Japan were retrospectively collected. The diagnosis of DILI was made by expert's decision. Then we scored each case using RECAM-J 2023, and calculated area under curve (AUC) values for identification for DILI. We collected data from 538 DILI and 128 non-DILI cases. The sum of highly probable (HP) and probable (PR) cases categorized by RECAM-J 2023 were only 206 (38%) in DILI cases. As the primary cause of low scores was the deduction with missing hepatitis virus markers, which is unlikely to be an issue in prospective applications, we rescored without these deductions. At this time, the sum of HP and PR was raised to 421 (78%). The AUCs of RECAM-J 2023 without deductions were 0.70 and 0.88 for identifying at least HP, and at least PR, respectively. RECAM-J 2023, when prospectively used without any missing hepatitis virus markers, provides acceptable performance for identifying at least PR DILI cases in Japanese daily clinical practice.

A Study on AI in Marketing and Improving the Business Procedure

Everything will be accessible online in early 2020. To put it briefly, everything is bought and sold online. It represents a wise company with a wise customer. Artificial intelligence is being used in various industries, including business and engineering, because it is dependable, economical, capable of solving complex issues, and able to make expert system decisions. Perhaps the most prevalent and dynamic kind of marketing available today is social media. Artificial intelligence (AI) assists marketers in analyzing brand visibility and interactions to gauge client happiness. Consequently, a marketer can effectively promote his goods and services and run his firm thanks to the strength of artificial intelligence systems in digital marketing tactics. AI is becoming more and more sophisticated in marketing, and marketers have the ability to use it to their advantage by properly implementing and overseeing AI solutions, which will become increasingly important for enhancing the company.

Promoting Equity: Examining Participatory Justice in Free Lunch Program Planning to Combat Stunting in Indonesia

This study examines the organisation of complimentary meal initiatives in Indonesia through the lens of participatory justice. The study methodology employed is a normative juridical approach within a descriptive-analytical framework. Data were gathered via literature reviews and subjectively assessed. The results indicate that public policy should consider the active involvement of different societal stakeholders. Participatory justice fosters socioeconomic inclusivity and equity in the process of decision-making. While the free lunch program is a tangible measure to combat stunting, active involvement from the impacted individuals is crucial to guarantee the efficiency and equity of this program. Challenges in implementing participatory justice are the credibility of public involvement, dispute resolution, and the influence of experts in decision making procedures.

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.

Selection of a Green Contractor for the Implementation of a Solar Power Plant Project

This study is focused on the problem of contractor selection for the implementation of a solar power plant project to produce electricity from sustainable sources for the needs of the company Voćar. The goal of this research is to select a construction contractor to install a solar power plant using sustainability criteria. With this power plant, the company Voćar can reduce its electricity costs and contribute to the production of sustainable energy. A total of three main sustainability criteria were used, in which six auxiliary criteria were symmetrically distributed. With these criteria, six suppliers were analyzed, and expert decision making was carried out with the application of the fuzzy–rough approach. To define the weights of the criteria, the SWARA method was utilized in this study. Based on the findings of this method, the most important criteria are the ecological criteria. Using the CRADIS method, the contractors were ranked, and the results show that contractor C6 has the best results and is the first choice for implementing this project. Choosing the best supplier increases the sustainability of project implementation and the realization of the expected effects.

Sarcoidosis-Associated Pulmonary Hypertension.

Sarcoidosis-associated pulmonary hypertension (SAPH) is a very severe complication of the disease, largely impacting its morbidity and being one of its strongest predictors of mortality. With the recent modifications of the hemodynamic definition of pulmonary hypertension (mean arterial pulmonary pressure >20 instead of <25 mmHg,) its prevalence is presently not precisely known, but it affects from 3 to 20% of sarcoid patients; mostly, although not exclusively, those with an advanced, fibrotic pulmonary disease. Its gold-standard diagnostic tool remains right heart catheterization (RHC). The decision to perform it relies on an expert decision after a non-invasive work-up, in which echocardiography remains the screening tool of choice. The mechanisms underlying SAPH, very often entangled, are crucial to define, as appropriate and personalized therapeutic strategies will aim at targeting the most significant ones. There are no recommendations so far as to the indications and modalities of the medical treatment of SAPH, which is based upon the opinion of a multidisciplinary team of sarcoidosis, pulmonary hypertension and sometimes lung transplant experts.

Fuzzy causal model of enterprise flexibility dimensions using Fermatean Fuzzy DEMATEL: a case study of Indian pharmaceutical industry

PurposeTo develop a fuzzy causal model of enterprise flexibility dimensions in a case study of Indian pharmaceutical industry.Design/methodology/approachThe eight dimensions of enterprise flexibility were identified based on literature review. Fermatean fuzzy decision-making trail and evaluation laboratory (FF-DEMATEL) technique is applied to develop the cause-and-effect interrelationship model among various enterprise flexibility dimensions.FindingsThe information technology flexibility, supply chain flexibility, technical flexibility and marketing flexibility are found to be causing/influencing other flexibilities and contributing to overall enterprise flexibilities. Therefore, more attention needs to be paid to develop and sustain them for competitive advantage.Research limitations/implicationsFermatean fuzzy sets offer more flexibility and more accurate handling complex uncertain group decision making. FF-DEMATEL is a more accurate method to develop inter-dependencies and causal model than ISM, TISM. Ratings from the limited number of decision experts (DEs) from few pharmaceutical firms were done. Future study should take bigger sample of firms and more number of DEs to generalize the findings.Practical implicationsThe model will help managers in pharmaceutical industry to prioritize the dimensions of enterprise flexibility to achieve agility, responsiveness, resilience and competitive advantage.Originality/valueTo the best knowledge of the authors, causal modeling enterprise flexibility dimensions using FF-DEMATEL has been studied for the first time in a developing economy context.

Earthquake Risk Assessment Using GIS-Based Analytical Hierarchy Process (AHP): The Case of Bitlis Province (Türkiye)

The risk level of natural disasters such as earthquakes depends on many factors. Some of these are direct hazards, while others are vulnerability factors that increase the risk. In this regard, risk assessment should be performed by evaluating the hazard and vulnerability factors together. The Analytical Hierarchy Process (AHP) is a powerful tool for co-evaluating such multiple decision criteria. The spatial visualization of the results facilitates the direct assessment of risks as well. In the present study, the seismic risk assessment of the Bitlis province in Eastern Anatolia, which has a high seismic risk, was performed by using the GIS-based AHP method. Among many criteria, six effective criteria on earthquake risk such as seismicity, demographic and topographic criteria were taken into consideration based on expert decision makers. It is concluded that the results obtained from the study were quite successful in terms of determining the seismic risks of the study area. Accordingly, while the risks are high in densely populated settlements with high peak ground acceleration (PGA), the risk decreases according to soil and land use.

The Role of Actuaries in Sustainable Development Goals

Purpose: The general purpose of this study was to investigate the role of actuaries in Sustainable Development Goals.&#x0D; Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of actuaries in Sustainable Development Goals. The study underscored the indispensable role of actuaries in advancing sustainable development goals (SDGs) across multiple sectors. Actuaries contribute significantly to promoting environmental sustainability by assessing and managing risks associated with climate change, while also fostering social equity and inclusivity through the design of insurance and pension schemes. Moreover, they facilitate long-term investment and infrastructure development aligned with SDGs 8 and 9, and support health and well-being initiatives, particularly in healthcare financing and risk assessment. Overall, through their expertise in risk assessment, financial management, and strategic decision-making, actuaries play a critical role in driving transformative change towards a more sustainable, inclusive, and resilient future.&#x0D; Unique Contribution to Theory, Practice and Policy: The Stakeholder theory, Institutional theory and Systems theory may be used to anchor future studies on the role of actuaries in Sustainable Development Goals. The study provided recommendations aimed at enhancing the contributions of actuaries to sustainable development agendas. It advocated for increased awareness among actuaries about their role in supporting SDGs, fostering collaboration between stakeholders, integrating sustainability considerations into actuarial practices, and promoting transparency and accountability within the profession. These recommendations emphasized the importance of aligning actuarial efforts with sustainability goals, leveraging collective expertise, and ensuring ethical and responsible decision-making. By implementing these recommendations, actuaries could play a more proactive and impactful role in advancing sustainable development goals and addressing societal challenges.&#x0D; Keywords: Actuaries, Sustainable Development Goals, Contributions, Recommendations, Awareness, Collaboration, Sustainability Considerations, Transparency