Search results for: predictive decision

Authors: Mwehe Mathenge, Ben G. J. S. Sonneveld, Jacqueline E. W. Broerse

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Smallholder farmers are important drivers of agriculture productivity, food security, and poverty reduction in Sub-Saharan Africa. However, they are faced with myriad challenges in their efforts at participating in agribusiness markets. How the geographic explicit factors existing at the local level interact to impede smallholder farmers' decision to participates (or not) in agribusiness markets is not well understood. Deconstructing the spatial complexity of the local environment could provide a deeper insight into how geographically explicit determinants promote or impede resource-poor smallholder farmers from participating in agribusiness. This paper’s objective was to identify, map, and analyze local spatial autocorrelation in factors that impede poor smallholders from participating in agribusiness markets. Data were collected using geocoded researcher-administered survey questionnaires from 392 households in Western Kenya. Three spatial statistics methods in geographic information system (GIS) were used to analyze data -Global Moran’s I, Cluster and Outliers Analysis (Anselin Local Moran’s I), and geographically weighted regression. The results of Global Moran’s I reveal the presence of spatial patterns in the dataset that was not caused by spatial randomness of data. Subsequently, Anselin Local Moran’s I result identified spatially and statistically significant local spatial clustering (hot spots and cold spots) in factors hindering smallholder participation. Finally, the geographically weighted regression results unearthed those specific geographic explicit factors impeding market participation in the study area. The results confirm that geographically explicit factors are indispensable in influencing the smallholder farming decisions, and policymakers should take cognizance of them. Additionally, this research demonstrated how geospatial explicit analysis conducted at the local level, using geographically disaggregated data, could help in identifying households and localities where the most impoverished and resource-poor smallholder households reside. In designing spatially targeted interventions, policymakers could benefit from geospatial analysis methods in understanding complex geographic factors and processes that interact to influence smallholder farmers' decision-making processes and choices.

Keywords: agribusiness markets, GIS, smallholder farmers, spatial statistics, disaggregated spatial data

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Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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Authors: Srikanth Bade

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This paper discusses how the usage of social media has altered certain discourses and communicated with the political institutions for major actions in Indian scenario. The advent of new technology in the form of social media has engrossed the general public to discuss in the open forum. How they promulgated their ideas into action is captured in this study. Moreover, these discourses happening in the social media is analyzed from certain philosophical traditions by adopting a framework. Hence, this paper analyses the role of social media in political communication and change the political discourse. Also, this paper tries to address the issue that whether the deliberation made through social media had indeed communicated the issue of political matters to the decision making authorities.

Keywords: collective action and social capital, political communication, political discourse, social media

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Authors: Osama Zayed

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Aim of the work: The aim of this study is to assess the predictors of pelvic vascular injuries in patients with pelvic fractures from major blunt trauma. Methods: This study was conducted as a tool-assessment study. Forty six patients with pelvic fractures from major blunt trauma will be recruited to the study arriving to department of emergency, Suez Canal University Hospital. Data were collected from questionnaire including; personal data of the studied patients and full medical history, clinical examinations, outcome measures (The Physiological and Operative Severity Score for enumeration of Mortality and morbidity (POSSUM), laboratory and imaging studies. Patients underwent surgical interventions or further investigations based on the conventional standards for interventions. All patients were followed up during conservative, operative and post-operative periods in the hospital for interpretation the predictive scores of vascular injuries. Results: Significant predictors of vascular injuries according to computed tomography (CT) scan include age, male gender, lower Glasgow coma (GCS) scores, occurrence of hypotension, mortality rate, higher physical POSSUM scores, presence of ultrasound collection, type of management, higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) POSSUM scores, presence of abdominal injuries, and poor outcome. Conclusions: There was higher frequency of males than females in the studied patients. There were high probability of morbidity and low probability of mortality among patients. Our study demonstrates that POSSUM score can be used as a predictor of vascular injury in pelvis fracture patients.

Keywords: predictors, pelvic vascular injuries, pelvic fractures, major blunt trauma, POSSUM

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Authors: Kate Parkinson

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A Family Group Conference (FGC) is a family-led decision-making process through which a family/kinship group, rather than the professionals involved, is asked to develop a plan for the care or the protection of children in the family. In England and Wales, FGCs are used in 76% of local authorities and in recent years, have tended to be used in cases where the local authority are considering the court process to remove children from their immediate family, to explore kinship alternatives to local authority care. Some local authorities offer the service much earlier, when families first come to the attention of children's social care, in line with research that suggests the earlier an FGC is held, the more likely they are to be successful. Family plans that result from FGCs are different from professional plans in that they are unique to a family and, as a result, reflect the diversity of families. Despite the fact that FGCs are arguable the most researched area of social work globally, there is a dearth of research that examines the nature of family plans and their substance. This paper presents the findings of a documentary analysis of 42 Early Help FGC plans from local authorities in England, with the aim of exploring the level and type of support that family members offer at a FGC. A thematic analysis identified 5 broad areas of support: Practical Support, Building Relationships, Child-care Support, Emotional Support and Social Support. In the majority of cases, family members did not want or ask for any formal support from the local authority or other agencies. Rather, the families came together to agree a plan of support, which was within the parameters of the resources that they as a family could provide. Perhaps then the role of the Early Help professional should be one of a facilitating and enabling role, to support families to develop plans that address their own specific difficulties, rather than the current default option, which is to either close the case because the family do not meet service thresholds or refer to formal support if they do, which may offer very specific support, have rigid referral criteria, long waiting lists and may not reflect the diverse and unique nature of families. FGCs are argued to be culturally appropriate social work practices in that they are appropriate for families from a range of cultural backgrounds and can be adapted to meet particular cultural needs. Furthermore, research on the efficacy of FGCs at an Early Help Level has demonstrated that Early Help FGCs have the potential to address difficulties in family life and prevent the need for formal support services, which are potentially stigmatising and do not reflect the uniqueness and diversity of families. The paper concludes with a recommendation for the use of FGCs across Early Help Services in England and Wales.

Keywords: family group conferences, family led decision making, early help, prevention

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Authors: Babak H. Tabrizi, Seyed Farid Ghaderi

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Concurrent planning of project scheduling and material ordering can provide more flexibility to the project scheduling problem, as the project execution costs can be enhanced. Hence, the issue has been taken into account in this paper. To do so, a mixed-integer mathematical model is developed which considers the aforementioned flexibility, in addition to the materials quantity discount and space availability restrictions. Moreover, the activities duration has been treated as decision variables. Finally, the efficiency of the proposed model is tested by different instances. Additionally, the influence of the aforementioned parameters is investigated on the model performance.

Keywords: material ordering, project scheduling, quantity discount, space availability

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Authors: Dawit Abay Tesfamariam

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Ethiopia’s Climate- Resilient Green Economy strategy focuses mainly on generating and utilization of Renewable Energy (RE). The data collected in 2016 by Ethiopian Electric Power (EEP) indicates that the intermittent RE sources on the grid from solar and wind energy were only 8 % of the total energy produced. On the other hand, the EEP electricity generation plan in 2030 indicates that 36 % of the energy generation share will be covered by solar and wind sources. Thus, a case study was initiated to model and compute the balance and consumption of electricity in three different scenarios: 2016, 2025, and 2030 using the Energy PLAN Model (EPM). Initially, the model was validated using the 2016 annual power-generated data to conduct the EPM analysis for two predictive scenarios. The EPM simulation analysis using EPM for 2016 showed that there was no significant excess power generated. Hence, the model’s results are in line with the actual 2016 output. Thus, the EPM was applied to analyze the role of energy storage in RE in Ethiopian grid systems. The results of the EPM simulation analysis showed there will be excess production of 402 /7963 MW average and maximum, respectively, in 2025. The excess power was dominant in all months except in the three rainy months of the year (June, July, and August). Consequently, based on the validated outcomes of EPM indicates, there is a good reason to think about other alternatives for the utilization of excess energy and storage of RE. Thus, from the scenarios and model results obtained, it is realistic to infer that; if the excess power is utilized with a storage mechanism that can stabilize the grid system; as a result, the extra RE generated can be exported to support the economy. Therefore, researchers must continue to upgrade the current and upcoming energy storage system to synchronize with RE potentials that can be generated from RE.

Keywords: renewable energy, storage, wind, energyplan

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Authors: Matthew Montebello

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Student engagement within a virtual learning environment generates masses of data points that can significantly contribute to the learning analytics that lead to decision support. Ideally, similar data is collected during student interaction with a physical learning space, and as a consequence, data is present at a large scale, even in relatively small classes. In this paper, we report of such an occurrence during classes held in a HiFlex modality as we investigate the advantages of adopting such a methodology. We plan to take full advantage of the learner-generated data in an attempt to further enhance the effectiveness of the adopted learning environment. This could shed crucial light on operating modalities that higher education institutions around the world will switch to in a post-COVID era.

Keywords: HiFlex, big data in higher education, learning analytics, virtual learning environment

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: Felicia Iyabode Olasehinde, Michael Maiye Olumorin

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To achieve a liveable and sustainable city, decision makers must engage in holistic approach to the planning and development of infrastructure such as roads. From observation there is great disparity in the development of roads in the northern part of the city while the south is being starved with this infrastructure. This paper attempts to make a comparison between the natures of roads in the north as against the south. The methodology to be adopted is survey research using clusters in the four local government making Kaduna Metropolis. The analysis of the road will be based on existing planning standards for roads in urban areas. This will now provide useful information for critical stakeholders at all levels of governance responsible for achieving liveable and sustainable cities.

Keywords: infrastructure, liveable, sustainable, urbanroads

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Authors: Sevinj Asgarova

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Within the last few decades, major advances in the field of prenatal testing have transpired yet little research regarding the experiences of mothers who chose to continue their pregnancies after prenatally receiving a diagnosis of Down Syndrome (DS) has been undertaken. Using social constructionism and interpretive description, this retrospective research study explores this topic from the point of view of the mothers involved and provides insight as to how the experience could be improved. Using purposive sampling, 23 mothers were recruited from British Columbia (n=11) and Ontario (n=12) in Canada. Data retrieved through semi-structured in-depth interviews were analyzed using inductive, constant comparative analysis, the major analytical techniques of interpretive description. Four primary phases emerged from the data analysis 1) healthcare professional-mothers communications, 2) initial emotional response, 3) subsequent decision-making and 4) an adjustment and reorganization of lifestyle to the preparation for the birth of the child. This study validates the individualized and contextualized nature of mothers’ decisions as influenced by multiple factors, with moral values/spiritual beliefs being significant. The mothers’ ability to cope was affected by the information communicated to them about their unborn baby’s diagnosis and the manner in which that information was delivered to them. Mothers used emotional coping strategies, dependent upon support from partners, family, and friends, as well as from other families who have children with DS. Additionally, they employed practical coping strategies, such as engaging in healthcare planning, seeking relevant information, and reimagining and reorganizing their lifestyle. Over time many families gained a sense of control over their situation and readjusted to the preparation for the birth of the child. Many mothers expressed the importance of maintaining positivity and hopefulness with respect to positive outcomes and opportunities for their children. The comprehensive information generated through this study will also provide healthcare professionals with relevant information to assist them in understanding the informational and emotional needs of these mothers. This should lead to an improvement in their practice and enhance their ability to intervene appropriately and effectively, better offering improved support to parents dealing with a diagnosis of DS for their child.

Keywords: continuing affected pregnancy, decision making, disability, down syndrome, eugenic social attitudes, inequalities, life change events, prenatal care, prenatal testing, qualitative research, social change, social justice

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Authors: Alia Aldarmaki, Ahmad Elshennawy

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There is a global shift in healthcare systems’ emphasizing engaging different stakeholders in selecting quality improvement initiatives and incorporating their preferences to improve the healthcare efficiency and outcomes. Although experts bring scientific knowledge based on the scientific model and their personal experience, other stakeholders can bring new insights and information into the decision-making process. This study attempts to explore the impact of incorporating different stakeholders’ preference in identifying the most significant criteria that should be considered in healthcare for electing the improvement projects. A Framework based on a modified Fuzzy Delphi Method (FDM) was built. In addition to, the subject matter experts, doctors/physicians, nurses, administrators, and managers groups contribute to the selection process. The research identifies potential criteria for evaluating projects in healthcare, then utilizes FDM to capture expertise knowledge. The first round in FDM is intended to validate the identified list of criteria from experts; which includes collecting additional criteria from experts that the literature might have overlooked. When an acceptable level of consensus has been reached, a second round is conducted to obtain experts’ and other related stakeholders’ opinions on the appropriate weight of each criterion’s importance using linguistic variables. FDM analyses eliminate or retain the criteria to produce a final list of the critical criteria to select improvement projects in healthcare. Finally, reliability and validity were investigated using Cronbach’s alpha and factor analysis, respectively. Two case studies were carried out in a public hospital in the United Arab Emirates to test the framework. Both cases demonstrate that even though there were common criteria between the experts and the stakeholders, still stakeholders’ perceptions bring additional critical criteria into the evaluation process, which can impact the outcomes. Experts selected criteria related to strategical and managerial aspects, while the other participants preferred criteria related to social aspects such as health and safety and patients’ satisfaction. The health and safety criterion had the highest important weight in both cases. The analysis showed that Cronbach’s alpha value is 0.977 and all criteria have factor loading greater than 0.3. In conclusion, the inclusion of stakeholders’ perspectives is intended to enhance stakeholders’ engagement, improve transparency throughout the decision process, and take robust decisions.

Keywords: Fuzzy Delphi Method, fuzzy number, healthcare, stakeholders

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Authors: A. Kherraf, M. Bouziane, A. Drighil, L. Azzouzi, R. Habbal

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Objective: Myxomas are rare heart tumors most commonly found in the left atrium. The purpose of this observation is to report a rare case of myxoma of the right atrium revealed by pulmonary embolism. Observation: A 34-year-old patient with no history presented to the emergency room with sudden onset dyspnea. Clinical examination showed arterial pressure at 110/70mmHg, tachycardia at 110bpm, and 90% oxygen saturation. The ECG enrolled in incomplete right bundle branch block. The radio-thorax was normal. Echocardiography revealed the presence of a large homogeneous intra-OD mass, contiguous to the inter-atrial septum, prolapsing through the tricuspid valve, and causing mild tricuspid insufficiency, with dilation of the right ventricle and retained systolic function with PAPs estimated at 45mmHg. A chest scan was performed, revealing the presence of right segmental pulmonary embolism. The patient was put under anticoagulant and underwent surgical resection of the mass; its pathological examination concluded to a myxoma. The post-operative consequences were simple, without recurrence of the mass after one year follow-up. Discussion: Myxomas represent 50% of heart tumors. Most often, they originate in the left atrium, and more rarely in the right atrium or the ventricles. Myxoma of the right atrium can be responsible for life-threatening pulmonary embolism. The most predictive factor for embolization remains the morphology of the myxomas; papillary or villous myxomas are the most friable. Surgery is the standard treatment, with regular postoperative follow-up to detect recurrence. Conclusion: Myxomas of the right atrium are a rare location for these tumors. Pulmonary embolism is the main complication and should routinely involve careful study of the right chambers on echocardiography.

Keywords: pulmonary embolism, myxoma, right atrium, heart tumors

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Authors: I. McAndrew, K. L. Witcher, E. Navarro

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This paper presents the theory and application of low-speed flight for unmanned aerial vehicles when subjected to surface environmental conditions such as ice on the leading edge and upper surface. A model was developed and tested in a wind tunnel to see how theory compares with practice at various speed including take-off, landing and operational applications where head winds substantially alter parameters. Furthermore, a comparison is drawn with maned operations and how that this subject is currently under-supported with accurate theory or knowledge for designers or operators to make informed decision or accommodate individual applications. The effects of ice formation for lift and drag are determined for a range of different angles of attacks.

Keywords: aerodynamics, environmental influences, glide path ratio, unmanned vehicles

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Authors: Tiffany Wicks

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Queer patients can face issues with self-advocating due to the factors of implicit provider bias, lack of tools and resources to self-advocate, and lack of comfortability in self-advocating based on prior experiences. In this study, five participants who identify as queer discussed their interactions with their healthcare providers. This exploratory study revealed that there is a need for healthcare provider education to reduce implicit bias and judgments about queer patients. There is also an important need for peer advocates in order to further inform healthcare promotion and decision-making before and during provider visits in an effort for a better outcome. Through this exploration, queer patients voiced their experiences and concerns to inform a need for change in healthcare collaboration between providers and patients in the queer community.

Keywords: queer, LGBT, patient, self-advocacy, healthcare

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Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

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Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

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Authors: Rebeca Ramirez Acosta, Sebastian Lenhoff

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Decision makers worldwide need to determine if the implementation of a new market mechanism will contribute to the sustainability and resilience of the power system. Due to smart grid technologies, new products in the distribution and transmission system can be traded; however, the impact of changing a market rule will differ between several regions. To test systematically those impacts, a market categorization has been compiled and organized in a smart grid market testing toolbox. This toolbox maps all actual energy products and sets the basis for running a co-simulation test with the new rule to be implemented. It will help to measure the impact of the new rule, based on the sustainable and resilience indicators.

Keywords: co-simulation, electricity market, smart grid market, market testing

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Authors: Hassan Eshkiki, Benjamin Mora

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The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting.

Keywords: explainable AI, EX AI, feature importance, counterfactual explanations

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Authors: Hussah Alotaishan

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In today’s globally-driven modern cities diverse groups of new visitors face various challenges when attempting to find their desired location if culture and language are barriers. The most common way-showing tools such as directional and identificational signs are the most problematic and their usefulness can be limited or even non-existent. It is argued new methods should be implemented that could support or replace such conventional literacy and language dependent way-finding aids. It has been concluded in recent research studies that local urban features in complex pedestrian spaces are worthy of further study in order to reveal if they do function as way-showing clues. Some researchers propose a more comprehensive approach to the complex perception of buildings, façade design and surface patterns, while some have been questioning whether we necessarily need directional signs or can other methods deliver the same message but in a clearer manner for a wider range of users. This study aimed to test to what extent do existent environmental and urban features through the city center area of Swansea in the UK facilitate the way-finding process of a first time visitor. The three-hour experiment was set to attempt to find 11 visitor attractions ranging from recreational, historical, educational and religious locations. The challenge was attempting to find as many as possible when no prior geographical knowledge of their whereabouts was established. The only clues were 11 pictures representing each of the locations that had been acquired from the city of Swansea official website. An iPhone and a heart-rate tracker wristwatch were used to record the route was taken and stress levels, and take record photographs of destinations or decision-making points throughout the journey. This paper addresses: current limitations in understanding the ways that the physical environment can be intentionally deployed to facilitate pedestrians while finding their way around, without or with a reduction in language dependent signage; investigates visitor perceptions of their surroundings by indicating what urban elements manifested an impact on the way-finding process. The initial findings support the view that building facades and street features, such as width, could facilitate the decision-making process if strategically employed. However, more importantly, the anticipated features of a specific place construed from a promotional picture can also be misleading and create confusion that may lead to getting lost.

Keywords: pedestrian way-finding, environmental features, urban way-showing, environmental affordance

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Authors: Till Gramberg, Max Kellner, Erwin Gross

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This paper presents a comprehensive investigation into the topic of smart services and IIoT-Platforms, focusing on their selection and prioritization in manufacturing organizations. First, a literature review is conducted to provide a basic understanding of the current state of research in the area of smart services. Based on discussed and established definitions, a definition approach for this paper is developed. In addition, value propositions for smart services are identified based on the literature and expert interviews. Furthermore, the general requirements for the provision of smart services are presented. Subsequently, existing approaches for the selection and development of smart services are identified and described. In order to determine the requirements for the selection of smart services, expert opinions from successful companies that have already implemented smart services are collected through semi-structured interviews. Based on the results, criteria for the evaluation of existing methods are derived. The existing methods are then evaluated according to the identified criteria. Furthermore, a novel method for the selection of smart services in manufacturing companies is developed, taking into account the identified criteria and the existing approaches. The developed concept for the method is verified in expert interviews. The method includes a collection of relevant smart services identified in the literature. The actual relevance of the use cases in the industrial environment was validated in an online survey. The required data and sensors are assigned to the smart service use cases. The value proposition of the use cases is evaluated in an expert workshop using different indicators. Based on this, a comparison is made between the identified value proposition and the required data, leading to a prioritization process. The prioritization process follows an established procedure for evaluating technical decision-making processes. In addition to the technical requirements, the prioritization process includes other evaluation criteria such as the economic benefit, the conformity of the new service offering with the company strategy, or the customer retention enabled by the smart service. Finally, the method is applied and validated in an industrial environment. The results of these experiments are critically reflected upon and an outlook on future developments in the area of smart services is given. This research contributes to a deeper understanding of the selection and prioritization process as well as the technical considerations associated with smart service implementation in manufacturing organizations. The proposed method serves as a valuable guide for decision makers, helping them to effectively select the most appropriate smart services for their specific organizational needs.

Keywords: smart services, IIoT, industrie 4.0, IIoT-platform, big data

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Authors: Maria Areias, Diogo Simões, Ana Figueiredo, Anishur Rahman, Filipa Figueiredo, João Nunes

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It is expected that close to 7 billion people will live in urban areas by 2050. In order to improve the sustainability of the territories and its transition towards circular economy, it’s necessary to understand its metabolism and promote and guide the entrepreneurship answer. The study of a macroeconomic metabolism involves the quantification of the inputs, outputs and storage of energy, water, materials and wastes for an urban region. This quantification and analysis representing one opportunity for the promotion of green entrepreneurship. There are several methods to assess the environmental impacts of an urban territory, such as human and environmental risk assessment (HERA), life cycle assessment (LCA), ecological footprint assessment (EF), material flow analysis (MFA), physical input-output table (PIOT), ecological network analysis (ENA), multicriteria decision analysis (MCDA) among others. However, no consensus exists about which of those assessment methods are best to analyze the sustainability of these complex systems. Taking into account the weaknesses and needs identified, the CiiM - Circular Innovation Inter-Municipality project aims to define an uniform and globally accepted methodology through the integration of various methodologies and dynamic approaches to increase the efficiency of macroeconomic metabolisms and promoting entrepreneurship in a circular economy. The pilot territory considered in CiiM project has a total area of 969,428 ha, comprising a total of 897,256 inhabitants (about 41% of the population of the Center Region). The main economic activities in the pilot territory, which contribute to a gross domestic product of 14.4 billion euros, are: social support activities for the elderly; construction of buildings; road transport of goods, retailing in supermarkets and hypermarkets; mass production of other garments; inpatient health facilities; and the manufacture of other components and accessories for motor vehicles. The region's business network is mostly constituted of micro and small companies (similar to the Central Region of Portugal), with a total of 53,708 companies identified in the CIM Region of Coimbra (39 large companies), 28,146 in the CIM Viseu Dão Lafões (22 large companies) and 24,953 in CIM Beiras and Serra da Estrela (13 large companies). For the construction of the database was taking into account data available at the National Institute of Statistics (INE), General Directorate of Energy and Geology (DGEG), Eurostat, Pordata, Strategy and Planning Office (GEP), Portuguese Environment Agency (APA), Commission for Coordination and Regional Development (CCDR) and Inter-municipal Community (CIM), as well as dedicated databases. In addition to the collection of statistical data, it was necessary to identify and characterize the different stakeholder groups in the pilot territory that are relevant to the different metabolism components under analysis. The CIIM project also adds the potential of a Geographic Information System (GIS) so that it is be possible to obtain geospatial results of the territorial metabolisms (rural and urban) of the pilot region. This platform will be a powerful visualization tool of flows of products/services that occur within the region and will support the stakeholders, improving their circular performance and identifying new business ideas and symbiotic partnerships.

Keywords: circular economy tools, life cycle assessment macroeconomic metabolism, multicriteria decision analysis, decision support tools, circular entrepreneurship, industrial and regional symbiosis

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Authors: S. Da Costa, D. Páez, F. Sánchez

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We examine the causal role of positive affect on creativity, the association of creativity or innovation in the ideation phase with functional emotional regulation, successful adjustment to stress and dispositional emotional creativity, as well as the predictive role of creativity for positive emotions and social adjustment. The study examines the effects of modification of positive affect on creativity. Participants write three poems, narrate an infatuation episode, answer a scale of personal growth after this episode and perform a creativity task, answer a flow scale after creativity task and fill a dispositional emotional creativity scale. High and low positive effect was induced by asking subjects to write three poems about high and low positive connotation stimuli. In a neutral condition, tasks were performed without previous affect induction. Subjects on the condition of high positive affect report more positive and less negative emotions, more personal growth (effect size r = .24) and their last poem was rated as more original by judges (effect size r = .33). Mediational analysis showed that positive emotions explain the influence of the manipulation on personal growth - positive affect correlates r = .33 to personal growth. The emotional creativity scale correlated to creativity scores of the creative task (r = .14), to the creativity of the narration of the infatuation episode (r = .21). Emotional creativity was also associated, during performing the creativity task, with flow (r = .27) and with affect balance (r = .26). The mediational analysis showed that emotional creativity predicts flow through positive affect. Results suggest that innovation in the phase of ideation is associated with a positive affect balance and satisfactory performance, as well as dispositional emotional creativity is adaptive.

Keywords: affectivity, creativity, induction, innovation, psychological factors

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Authors: Peter Clutterbuck, Terry Rowlands, Owen Seamons

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In this paper we describe one critical research program within a complex, ongoing multi-year project (2010 to 2014 inclusive) with the overall goal to improve the learning outcomes for first year undergraduate commerce/business students within an Information Systems (IS) subject with very large enrolment. The single research program described in this paper is the analysis of student attitudes and decision making in relation to the availability of formative assessment feedback via Web-based real time conferencing and document exchange software (Adobe Connect). The formative assessment feedback between teaching staff and students is in respect of an authentic problem-based, team-completed assignment. The analysis of student attitudes and decision making is investigated via both qualitative (firstly) and quantitative (secondly) application of the Theory of Planned Behavior (TPB) with a two statistically-significant and separate trial samples of the enrolled students. The initial qualitative TPB investigation revealed that perceived self-efficacy, improved time-management, and lecturer-student relationship building were the major factors in shaping an overall favorable student attitude to online feedback, whilst some students expressed valid concerns with perceived control limitations identified within the online feedback protocols. The subsequent quantitative TPB investigation then confirmed that attitude towards usage, subjective norms surrounding usage, and perceived behavioral control of usage were all significant in shaping student intention to use the online feedback protocol, with these three variables explaining 63 percent of the variance in the behavioral intention to use the online feedback protocol. The identification in this research of perceived behavioral control as a significant determinant in student usage of a specific technology component within a virtual learning environment (VLE) suggests that VLEs could now be viewed not as a single, atomic entity, but as a spectrum of technology offerings ranging from the mature and simple (e.g., email, Web downloads) to the cutting-edge and challenging (e.g., Web conferencing and real-time document exchange). That is, that all VLEs should not be considered the same. The results of this research suggest that tertiary students have the technological sophistication to assess a VLE in this more selective manner.

Keywords: formative assessment feedback, virtual learning environment, theory of planned behavior, perceived behavioral control

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Jessica Ho

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AI has helped government, businesses and industries transform the way they do things. AI is used in automating tasks to improve decision-making and efficiency. AI is embedded in sensors and used in automation to help save time and eliminate human errors in repetitive tasks. Today, we saw the growth in AI using the collection of vast amounts of data to forecast with greater accuracy, inform decision-making, adapt to changing market conditions and offer more personalised service based on consumer habits and preferences. Government around the world share the opportunity to leverage these disruptive technologies to improve productivity while reducing costs. In addition, these intelligent solutions can also help streamline government processes to deliver more seamless and intuitive user experiences for employees and citizens. This is a critical challenge for NSW Government as we are unable to determine the risk that is brought by the unprecedented pace of adoption of AI solutions in government. Government agencies must ensure that their use of AI complies with relevant laws and regulatory requirements, including those related to data privacy and security. Furthermore, there will always be ethical concerns surrounding the use of AI, such as the potential for bias, intellectual property rights and its impact on job security. Within NSW’s public sector, agencies are already testing AI for crowd control, infrastructure management, fraud compliance, public safety, transport, and police surveillance. Citizens are also attracted to the ease of use and accessibility of AI solutions without requiring specialised technical skills. This increased accessibility also comes with balancing a higher risk and exposure to the health and safety of citizens. On the other side, public agencies struggle with keeping up with this pace while minimising risks, but the low entry cost and open-source nature of generative AI led to a rapid increase in the development of AI powered apps organically – “There is an AI for That” in Government. Other challenges include the fact that there appeared to be no legislative provisions that expressly authorise the NSW Government to use an AI to make decision. On the global stage, there were too many actors in the regulatory space, and a sovereign response is needed to minimise multiplicity and regulatory burden. Therefore, traditional corporate risk and governance framework and regulation and legislation frameworks will need to be evaluated for AI unique challenges due to their rapidly evolving nature, ethical considerations, and heightened regulatory scrutiny impacting the safety of consumers and increased risks for Government. Creating an effective, efficient NSW Government’s governance regime, adapted to the range of different approaches to the applications of AI, is not a mere matter of overcoming technical challenges. Technologies have a wide range of social effects on our surroundings and behaviours. There is compelling evidence to show that Australia's sustained social and economic advancement depends on AI's ability to spur economic growth, boost productivity, and address a wide range of societal and political issues. AI may also inflict significant damage. If such harm is not addressed, the public's confidence in this kind of innovation will be weakened. This paper suggests several AI regulatory approaches for consideration that is forward-looking and agile while simultaneously fostering innovation and human rights. The anticipated outcome is to ensure that NSW Government matches the rising levels of innovation in AI technologies with the appropriate and balanced innovation in AI governance.

Keywords: artificial inteligence, machine learning, rules, governance, government

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Authors: An-Shik Yang, Chiang-Ho Cheng, Jen-Hao Wu, Yu-Hsuan Juan

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Natural ventilation has played an important role for many low energy-building designs. It has been also noticed as a essential subject to persistently bring the fresh cool air from the outside into a building. This study carried out the computational fluid dynamics (CFD)-based simulations to examine the natural ventilation development of a work area in a public building. The simulated results can be useful to better understand the indoor microclimate and the interaction of wind with buildings. Besides, this CFD simulation procedure can serve as an effective analysis tool to characterize the airing performance, and thereby optimize the building ventilation for strengthening the architects, planners and other decision makers on improving the natural ventilation design of public buildings.

Keywords: CFD simulations, natural ventilation, microclimate, wind environment

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Authors: N. Perko, N. Kavran, M. Bukljas, I. Berbic

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This paper is a result of implemented research on proposed introduced methodology for nautical ports capacity planning by introducing a multi-criteria approach of defined criteria and impacts at the Adriatic Sea. The purpose was analysing the determinants -characteristics of infrastructure and services of nautical ports capacity allocated, especially nowadays due to COVID-19 pandemic, as crucial for the successful operation of nautical ports. Giving the importance of the defined priorities for short-term and long-term planning is essential not only in terms of the development of nautical tourism but also in terms of developing the maritime system, but unfortunately, this is not always carried out. Evaluation of the use of resources should follow from a detailed analysis of all aspects of resources bearing in mind that nautical tourism used resources in a sustainable manner and generate effects in the tourism and maritime sectors. Consequently, the identified multiplier effect of nautical tourism, which should be defined and quantified in detail, should be one of the major competitive products on the Croatian Adriatic and the Mediterranean. Research of nautical tourism is necessary to quantify the effects and required planning system development. In the future, the greatest threat to the long-term sustainable development of nautical tourism can be its further uncontrolled or unlimited and undirected development, especially under pressure markedly higher demand than supply for new moorings in the Mediterranean. Results of this implemented research are applicable to nautical ports management and decision-makers of maritime transport system development. This paper will present implemented research and obtained result-developed methodology for nautical port capacity planning -port capacity planning multi-criteria decision-making. A proposed methodological approach of multi-criteria capacity planning includes four criteria (spatial - transport, cost - infrastructure, ecological and organizational criteria, and additional services). The importance of the criteria and sub-criteria is evaluated and carried out as the basis for sensitivity analysis of the importance of the criteria and sub-criteria. Based on the analysis of the identified and quantified importance of certain criteria and sub-criteria, as well as sensitivity analysis and analysis of changes of the quantified importance, scientific and applicable results will be presented. These obtained results have practical applicability by management of nautical ports in the planning of increasing capacity and further development and for the adaptation of existing nautical ports. Obtained research is applicable and replicable in other seas, and results are especially important and useful in this COVID-19 pandemic challenging maritime development framework.

Keywords: Adriatic Sea, capacity, infrastructures, maritime system, methodology, nautical ports, nautical tourism, service

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

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Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

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Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

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