Search results for: goal-oriented benchmarking

Authors: Stephanie Boue, Nicolas Sierro, Julia Hoeng, Manuel C. Peitsch

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Systems biology relies on large numbers of data points and sophisticated methods to extract biologically meaningful signal and mechanistic understanding. For example, analyses of transcriptomics and proteomics data enable to gain insights into the molecular differences in tissues exposed to diverse stimuli or test items. Whereas the interpretation of endpoints specifically measuring a mechanism is relatively straightforward, the interpretation of big data is more complex and would benefit from comparing results obtained with diverse analysis methods. The sbv IMPROVER project was created to implement solutions to verify systems biology data, methods, and conclusions. Computational challenges leveraging the wisdom of the crowd allow benchmarking methods for specific tasks, such as signature extraction and/or samples classification. Four challenges have already been successfully conducted and confirmed that the aggregation of predictions often leads to better results than individual predictions and that methods perform best in specific contexts. Whenever the scientific question of interest does not have a gold standard, but may greatly benefit from the scientific community to come together and discuss their approaches and results, datathons are set up. The inaugural sbv IMPROVER datathon was held in Singapore on 23-24 September 2016. It allowed bioinformaticians and data scientists to consolidate their ideas and work on the most promising methods as teams, after having initially reflected on the problem on their own. The outcome is a set of visualization and analysis methods that will be shared with the scientific community via the Garuda platform, an open connectivity platform that provides a framework to navigate through different applications, databases and services in biology and medicine. We will present the results we obtained when analyzing data with our network-based method, and introduce a datathon that will take place in Japan to encourage the analysis of the same datasets with other methods to allow for the consolidation of conclusions.

Keywords: big data interpretation, datathon, systems toxicology, verification

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

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Authors: Abdul Rashid, Varsha Rokade

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Service Quality within retail units is pivotal for satisfying customers and retaining them. This study on customer perception towards Service Quality variables in Retail aims to identify the dimensions and their impact on customers. An analytical study of the different retail service quality variables was done to understand the relationship between them. The study tries exploring the factors that attract the customers towards the organised and unorganised retail stores in the capital city of Madhya Pradesh, India. As organised retailers are seen as offering similar products in the outlets, improving service quality is seen as critical to ensuring competitive advantage over unorganised retailers. Data were collected through a structured questionnaire on a five-point Likert scale from existing walk-in customers of selected organised and unorganised retail stores in Bhopal City of Madhya Pradesh, India. The data was then analysed by factor analysis using (SPSS) Statistical Package for the Social Sciences especially Percentage analysis, ANOVA and Chi-Square. This study tries to find interrelationship between various Retail Service Quality dimensions, which will help the retailers to identify the steps needed to improve the overall quality of service. Thus, the findings of the study prove to be helpful in understanding the service quality variables which should be considered by organised and unorganised retail stores in Capital city of Madhya Pradesh, India.Also, findings of this empirical research reiterate the point of view that dimensions of Service Quality in Retail play an important role in enhancing customer satisfaction – a sector with high growth potential and tremendous opportunities in rapidly growing economies like India’s. With the introduction of FDI in multi-brand retailing, a large number of international retail players are expected to enter the Indian market, this intern will bring more competition in the retail sector. For benchmarking themselves with global standards, the Indian retailers will have to improve their service quality.

Keywords: organized retail, unorganised retail, retail service quality, service quality dimension

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Jovian Cheung, Thomas Kwok, Victor Wong

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Buildings are entwined with smart city developments. Building performance relies heavily on electrical and mechanical (E&M) systems and services accounting for about 40 percent of global energy use. By cohering the advancement of technology as well as energy and operation-efficient initiatives into the buildings, people are enabled to raise building performance and enhance the sustainability of the built environment in their daily lives. Digital transformation in the buildings is the profound development of the city to leverage the changes and opportunities of digital technologies To optimize the building performance, intelligent power quality and energy management system is developed for transforming data into actions. The system is formed by interfacing and integrating legacy metering and internet of things technologies in the building and applying big data techniques. It provides operation and energy profile and actionable insights of a building, which enables to optimize the building performance through raising people awareness on E&M services and energy consumption, predicting the operation of E&M systems, benchmarking the building performance, and prioritizing assets and energy management opportunities. The intelligent power quality and energy management system comprises four elements, namely the Integrated Building Performance Map, Building Performance Dashboard, Power Quality Analysis, and Energy Performance Analysis. It provides predictive operation sequence of E&M systems response to the built environment and building activities. The system collects the live operating conditions of E&M systems over time to identify abnormal system performance, predict failure trends and alert users before anticipating system failure. The actionable insights collected can also be used for system design enhancement in future. This paper will illustrate how intelligent power quality and energy management system provides operation and energy profile to optimize the building performance and actionable insights to revitalize an existing building into a smart building. The system is driving building performance optimization and supporting in developing Hong Kong into a suitable smart city to be admired.

Keywords: intelligent buildings, internet of things technologies, big data analytics, predictive operation and maintenance, building performance

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Authors: Henry Lau, Dilupa Nakandala, Li Zhao

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All along the length of the supply chain, fresh food firms face the challenge of managing both product quality, due to the perishable nature of the products, and product cost. This paper develops a method to assist logistics managers upstream in the fresh food supply chain in making cost optimized decisions regarding transportation, with the objective of minimizing the total cost while maintaining the quality of food products above acceptable levels. Considering the case of multiple fresh food products collected from multiple farms being transported to a warehouse or a retailer, this study develops a total cost model that includes various costs incurred during transportation. The practical application of the model is illustrated by using several computational intelligence approaches including Genetic Algorithms (GA), Fuzzy Genetic Algorithms (FGA) as well as an improved Simulated Annealing (SA) procedure applied with a repair mechanism for efficiency benchmarking. We demonstrate the practical viability of these approaches by using a simulation study based on pertinent data and evaluate the simulation outcomes. The application of the proposed total cost model was demonstrated using three approaches of GA, FGA and SA with a repair mechanism. All three approaches are adoptable; however, based on the performance evaluation, it was evident that the FGA is more likely to produce a better performance than the other two approaches of GA and SA. This study provides a pragmatic approach for supporting logistics and supply chain practitioners in fresh food industry in making important decisions on the arrangements and procedures related to the transportation of multiple fresh food products to a warehouse from multiple farms in a cost-effective way without compromising product quality. This study extends the literature on cold supply chain management by investigating cost and quality optimization in a multi-product scenario from farms to a retailer and, minimizing cost by managing the quality above expected quality levels at delivery. The scalability of the proposed generic function enables the application to alternative situations in practice such as different storage environments and transportation conditions.

Keywords: cost optimization, food supply chain, fuzzy sets, genetic algorithms, product quality, transportation

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Authors: Caroline Bradbury-Jones, Maria Clark, Eleanor Molloy, Nicki Ward

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Background: Significant developments in the protection of Lesbian, Gay, Bisexual, Transgender and Queer (LGBTQ) rights culminated in their inclusion in the Equality Act 2010. This provides legal protection against discrimination including the Public Sector Equality Duty requiring public bodies to consider all individuals when carrying out their day-to-day work. In the UK, whilst the Higher Education sector has made some commitment to eliminating discrimination and addressing LGBTQ inclusivity, there are two particular problems specifically affecting students on professional programmes: -All students will come into contact with LGBTQ patients/clients/students and need to be equipped to respond appropriately to their diverse needs but evidence suggests that this is not always the case. -Many LGBTQ students have specific concerns on professional placements; often ‘going back in the closet’ or feeling uncertain how to respond to questions about their personal lives and being reticent to challenge discrimination against LGBTQ patients/clients/students for fear of reprisal. Study aim: To investigate how best to prepare all students to deal with the issue of gender and sexual diversity and to support LGBTQ students in negotiating (non) disclosure in practice placements. Methods: This multi-method study was conducted in 2017 in the UK. It comprised a student survey, focus group interview with students and a national benchmarking exercise. Findings: Preliminary findings are that there is considerable variation across professional programmes regarding the preparation of students to respond to LGBTQ issues. Similarly, there is considerable difference between the level of preparedness experienced by students irrespective of whether they identify as LGBTQ. Discussion: Nationally there are a number of ‘best practice’ examples that we share in this presentation. These contain important details and guidance about how to better prepare university students for professional practice, and to contribute to eliminating discrimination and addressing LGBTQ inclusivity. Conclusions: The presentation will appeal to delegates who are interested in the equality agenda regarding LGBTQ people. The study findings will be discussed and debated to explore their impact on higher education and learning and to identify ways to integrate best practice into professional curricula across the UK and beyond.

Keywords: diversity, equality, practice, sexuality, students, university

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Authors: Muhammad Tahir Hassan, Stas Burek, Muhammad Asif, Mohamed Emad

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Industrial sector in Pakistan accounts for 25% of total energy consumption in the country. The performance of this sector has been severely affected due to the adverse effect of current energy crises in the country. Energy conservation potentials of Pakistan’s industrial sectors through energy management can save wasted energy which would ultimately leads to economic and environmental benefits. However due to lack of financial incentives of energy efficiency and absence of energy benchmarking within same industrial sectors are some of the main challenges in the implementation of energy management. In Pakistan, this area has not been adequately explored, and there is a lack of focus on the need for industrial energy efficiency and proper management. The main objective of this research is to evaluate the current energy management performance of Pakistani industrial sector and empirical investigation of the existence of various barriers to industrial energy efficiency. Data was collected from the respondents of 192 small and medium-sized enterprises (SME’s) of Pakistan i.e. foundries, textile, plastic industries, light engineering, auto and spare parts and ceramic manufacturers and analysed using Statistical Package for the Social Sciences (SPSS) software. Current energy management performance of manufacturing SME’s in Pakistan has been evaluated by employing two significant indicators, ‘Energy Management Matrix’ and ‘pay-off criteria’, with modified approach. Using the energy management matrix, energy management profiles of overall industry and the individual sectors have been drawn to assess the energy management performance and identify the weak and strong areas as well. Results reveal that, energy management practices in overall surveyed industries are at very low level. Energy management profiles drawn against each sector suggest that performance of textile sector is better among all the surveyed manufacturing SME’s. The empirical barriers to industrial energy efficiency have also been ranked according to the overall responses. The results further reveal that there is a significant relationship exists among the industrial size, sector type and nature of barriers to industrial energy efficiency for the manufacturing SME’s in Pakistan. The findings of this study may help the industries and policy makers in Pakistan to formulate a sustainable energy policy to support industrial energy efficiency keeping in view the actual existing energy efficiency scenario in the industrial sector.

Keywords: barriers, energy conservation, energy management profile, environment, manufacturing SME's of Pakistan

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Authors: I. Dangeard, S. Meineri, M. Dupré

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large body of research on energy consumption reveals that regular information on energy consumption produces a positive effect on behavior. The present research aims to test this feedback paradigm on waste management. A small-scale experiment on residual household waste was performed in a large french urban area, in partnership with local authorities, as part of the development of larger-scale project. A two-step door-to-door recruitment scheme led to 85 households answering a questionnaire. Among them, 54 accepted to participate in a study on waste (second step). Participants were then randomly assigned to one of the 3 experimental conditions : self-reported feedback on curbside waste, external feedback on waste weight based on information technologies, and no feedback for the control group. An additional control group was added, including households who were not requested to answer the questionnaire. Household residual waste was collected every week, and tags on curbside bins fed a database with waste weight of households. The feedback period lasted 14 weeks (february-may 2014). Quantitative data on waste weight were analysed, including these 14 weeks and the 7 previous weeks. Households were then contacted by phone in order to confirm the quantitative results. Regarding the recruitment questionnaire, results revealed high pro-environmental attitude on the NEP scale, high recycling behavior level and moderate level of source reduction behavior on the adapted 3R scale, but no statistical difference between the 3 experimental groups. Regarding the feedback manipulation paradigm, waste weight reveals important differences between households, but doesn't prove any statistical difference between the experimental conditions. Qualitative phone interviews confirm that recycling is a current practice among participants, whereas source reduction of waste is not, and mainly appears as a producer problem of packaging limitation. We conclude that triggering waste prevention behaviors among recycling households involves long-term feedback and should promote benchmarking, in order to clearly set waste reduction as an objective to be managed through feedback figures.

Keywords: eco-feedback, household waste, waste reduction, experimental research

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Authors: Gaurav Vaidya, N. R. Mandal

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UN Summit, 2000 had resolved to provide access to sanitation to whole humanity as part of ‘Millennium Development Goals -2015’. However, more than one third of world’s population still did not have the access to basic sanitation facilities by 2015. Therefore, it will be a gigantic challenge to achieve goal-6 of ‘UN Sustainable Development Goal’ to ensure availability and sustainable management of sanitation for all by the year 2030. Countries attempt to find out own ways of meeting this challenge of providing access to safe sanitation and as part of monitoring the actions have prepared varied types of ‘performance assessment frameworks (PAF)’. India introduced Service Level Benchmarking (SLB) in 2010 to set targets and achieve the goals of NUSP. Further, a method of reviewing performance was introduced as ‘Swachh Sarvekshan’ (Cleanliness Surveys) in 2016 and in 2017 guidelines for the same was revised. This study, as a first step, reviews the documents in use in India with a conclusion that the frameworks adopted are based on target setting, financial allocation and performance in achieving the targets set. However, it does not focus upon sanitation needs holistically i.e., areas and aspects not targeted through projects are not covered in the performance assessment. In this context, as a second step, this study reviews literature available on performance assessment frameworks for urban sanitation in selected other countries and compares the same with that in India. The outcome of the comparative review resulted in identification of unaddressed aspects as well as inadequacy of parameters in Indian context. Thirdly, in an attempt to restructure the performance assessment process and develop an index in urban sanitation, researches done in other urban services such as health and education were studied focusing on methods of measuring under-performance. As a fourth step, a tentative modified framework is suggested with the help of understanding drawn from above for urban sanitation using stages of Urban Sanitation Service Chain Management (SSCM) and modified set of parameters drawn from the literature review in the first and second steps. This paper reviews existing literature on SSCM procedures, Performance Index in sanitation and other urban services and identifies a tentative list of parameters and a framework for measuring under-performance in sanitation services. This may aid in preparation of a Service Delivery Under-performance Index (SDUI) in future.

Keywords: assessment, performance, sanitation, services

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Authors: Michael Howard

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The growing popularity of electric vehicles in the United States requires an ever-expanding infrastructure of commercial DC fast charging stations. The U.S. Department of Energy estimates 33,355 publicly available DC fast charging stations as of September 2023. In 2017, 115,370 gasoline stations were operating in the United States, much more ubiquitous than DC fast chargers. Range anxiety is an important impediment to the adoption of electric vehicles and is even more relevant in underserved regions in the country. The peer-to-peer energy marketplace helps fill the demand by allowing private home and small business owners to rent their 240 Volt, level-2 charging facilities. The existing, publicly accessible outlets are wrapped with a Cloud-connected microcontroller managing security and charging sessions. These microcontrollers act as Edge devices communicating with a Cloud message broker, while both buyer and seller users interact with the framework via a web-based user interface. The database storage used by the marketplace framework is a key component in both the cost of development and the performance that contributes to the user experience. A traditional storage solution is the SQL database. The architecture and query language have been in existence since the 1970s and are well understood and documented. The Structured Query Language supported by the query engine provides fine granularity with user query conditions. However, difficulty in scaling across multiple nodes and cost of its server-based compute have resulted in a trend in the last 20 years towards other NoSQL, serverless approaches. In this study, we evaluate the NoSQL vs. SQL solutions through a comparison of Google Cloud Firestore and Cloud SQL MySQL offerings. The comparison pits Google's serverless, document-model, non-relational, NoSQL against the server-base, table-model, relational, SQL service. The evaluation is based on query latency, flexibility/scalability, and cost criteria. Through benchmarking and analysis of the architecture, we determine whether Firestore can support the energy marketplace storage needs and if the introduction of a GraphQL middleware layer can overcome its deficiencies.

Keywords: non-relational, relational, MySQL, mitigate, Firestore, SQL, NoSQL, serverless, database, GraphQL

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Authors: Malek Makki

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The main purpose of this research study is to assist non-profit organizations (NPOs) to better segment a group of least developing countries and to optimally target the most needier areas, so that the provided aids make positive and lasting differences. We applied international marketing and strategy approaches to segment a sub-group of candidates among a group of 151 countries identified by the UN-G77 list, and furthermore, we point out the areas of priorities. We use reliable and well known criteria on the basis of economics, geography, demography and behavioral. These criteria can be objectively estimated and updated so that a follow-up can be performed to measure the outcomes of any program. We selected 12 socio-economic criteria that complement each other: GDP per capita, GDP growth, industry value added, export per capita, fragile state index, corruption perceived index, environment protection index, ease of doing business index, global competitiveness index, Internet use, public spending on education, and employment rate. A weight was attributed to each variable to highlight the relative importance of each criterion within the country. Care was taken to collect the most recent available data from trusted well-known international organizations (IMF, WB, WEF, and WTO). Construct of equivalence was carried out to compare the same variables across countries. The combination of all these weighted estimated criteria provides us with a global index that represents the level of development per country. An absolute index that combines wars and risks was introduced to exclude or include a country on the basis of conflicts and a collapsing state. The final step applied to the included countries consists of a benchmarking method to select the segment of countries and the percentile of each criterion. The results of this study allowed us to exclude 16 countries for risks and security. We also excluded four countries because they lack reliable and complete data. The other countries were classified per percentile thru their global index, and we identified the needier and the areas where aids are highly required to help any NPO to prioritize the area of implementation. This new concept is based on defined, actionable, accessible and accurate variables by which NPO can implement their program and it can be extended to profit companies to perform their corporate social responsibility acts.

Keywords: developing countries, international marketing, non-profit organization, segmentation

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Authors: Awadh O. Al-Anazi

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As part of the organizational needs toward successful maintaining activities, a proper management system need to be put in place, ensuring the effectiveness of maintenance activities. The management system shall clearly describes the process of identifying, prioritizing, planning, scheduling, execution, and providing valuable feedback for all maintenance activities. Completion and accuracy of the system with proper implementation shall provide the organization with a strong platform for effective maintenance activities that are resulted in efficient outcomes toward business success. The purpose of this research was to introduce a practical tool for measuring the maintenance efficiency level within Saudi organizations. A comprehensive study was launched across many maintenance professionals throughout Saudi leading organizations. The study covered five main categories: work process, identification, planning and scheduling, execution, and performance monitoring. Each category was evaluated across many dimensions to determine its current effectiveness through a five-level scale from 'process is not there' to 'mature implementation'. Wide participation was received, responses were analyzed, and the study was concluded by highlighting major gaps and improvement opportunities within Saudi organizations. One effective implementation of the efficiency enhancement efforts was deployed in Saudi Kayan (one of Sabic affiliates). Below details describes the project outcomes: SK overall maintenance wrench time was measured at 20% (on average) from the total daily working time. The assessment indicates the appearance of several organizational gaps, such as a high amount of reactive work, poor coordination and teamwork, Unclear roles and responsibilities, as well as underutilization of resources. Multidiscipline team was assigned to design and implement an appropriate work process that is capable to govern the execution process, improve the maintenance workforce efficiency, and maximize wrench time (targeting > 50%). The enhanced work process was introduced through brainstorming and wide benchmarking, incorporated with a proper change management plan and leadership sponsorship. The project was completed in 2018. Achieved Results: SK WT was improved to 50%, which resulted in 1) reducing the Average Notification completion time. 2) reducing maintenance expenses on OT and manpower support (3.6 MSAR Actual Saving from Budget within 6 months).

Keywords: efficiency, enhancement, maintenance, work force, wrench time

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Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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Authors: Jemima C. A. Sumboh

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Given the urgency and consensus for countries to move towards Universal Health Coverage (UHC), health financing systems need to be accurately and consistently monitored to provide valuable data to inform policy and practice. Most of the indicators for monitoring UHC, particularly catastrophe and impoverishment, are established based on the impact of out-of-pocket health payments (OOPHP) on households’ living standards, collected through varied household surveys. These surveys, however, vary substantially in survey methods such as the length of the recall period or the number of items included in the survey questionnaire or the farming of questions, potentially influencing the level of OOPHP. Using different survey instruments can provide inaccurate, inconsistent, erroneous and misleading estimates of UHC, subsequently influencing wrong policy decisions. Using data from a household budget survey conducted by the Navrongo Health Research Center in Ghana from May 2017 to December 2018, this study intends to explore the potential implications of using surveys with varied levels of disaggregation of OOPHP data on estimates of financial risk protection. The household budget survey, structured around food and non-food expenditure, compared three OOPHP measuring instruments: Version I (existing questions used to measure OOPHP in household budget surveys), Version II (new questions developed through benchmarking the existing Classification of the Individual Consumption by Purpose (COICOP) OOPHP questions in household surveys) and Version III (existing questions used to measure OOPHP in health surveys integrated into household budget surveys- for this, the demographic and health surveillance (DHS) health survey was used). Version I, II and III contained 11, 44, and 56 health items, respectively. However, the choice of recall periods was held constant across versions. The sample size for Version I, II and III were 930, 1032 and 1068 households, respectively. Financial risk protection will be measured based on the catastrophic and impoverishment methodologies using STATA 15 and Adept Software for each version. It is expected that findings from this study will present valuable contributions to the repository of knowledge on standardizing survey instruments to obtain estimates of financial risk protection that are valid and consistent.

Keywords: Ghana, household budget surveys, measuring financial risk protection, out-of-pocket health payments, survey instruments, universal health coverage

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Authors: Ilaria Barletta, Mahesh Mani, Björn Johansson

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The concept of sustainability encompasses economic, environmental, social and institutional considerations. Sustainable manufacturing (SM) is, therefore, a multi-faceted concept. It broadly implies the development and implementation of technologies, projects and initiatives that are concerned with the life cycle of products and services, and are able to bring positive impacts to the environment, company stakeholders and profitability. Because of this, achieving SM-related goals requires a holistic, life-cycle-thinking approach from manufacturing companies. Further, such an approach must rely on a logic of continuous improvement and ease of implementation in order to be effective. Currently, there exists in the academic literature no comprehensively structured frameworks that support manufacturing companies in the identification of the issues and the capabilities that can either hinder or foster sustainability. This scarcity of support extends to difficulties in obtaining quantifiable measurements in order to objectively evaluate solutions and programs and identify improvement areas within SM for standards conformance. To bridge this gap, this paper proposes the concept of a framework for assessing and continuously improving the sustainability of manufacturing companies. The framework addresses strategies and projects for SM and operates in three sequential phases: analysis of the issues, design of solutions and continuous improvement. A set of interviews, observations and questionnaires are the research methods to be used for the implementation of the framework. Different decision-support methods - either already-existing or novel ones - can be 'plugged into' each of the phases. These methods can assess anything from business capabilities to process maturity. In particular, the authors are working on the development of a sustainable manufacturing maturity model (SMMM) as decision support within the phase of 'continuous improvement'. The SMMM, inspired by previous maturity models, is made up of four maturity levels stemming from 'non-existing' to 'thriving'. Aggregate findings from the use of the framework should ultimately reveal to managers and CEOs the roadmap for achieving SM goals and identify the maturity of their companies’ processes and capabilities. Two cases from two manufacturing companies in Australia are currently being employed to develop and test the framework. The use of this framework will bring two main benefits: enable visual, intuitive internal sustainability benchmarking and raise awareness of improvement areas that lead companies towards an increasingly developed SM.

Keywords: life cycle management, continuous improvement, maturity model, sustainable manufacturing

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Authors: Albert Kwabena Osei Konadu

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Pterocarpus erinaceus, also known as Rosewood, is tropical wood, endemic in forest savannah transition zones within the middle and northern portion of Ghana. Its economic viability has made it increasingly popular and in high demand, leading to widespread conservation concerns. Ghana’s forest resource management regime for these ecozones is mainly on conservation and very little on resource utilization. Consequently, commercial logging management standards are at teething stage and not fully developed, leading to a deficiency in the monitoring of logging operations and quantification of harvested trees volumes. Tree information form (TIF); a volume estimation and tracking regime, has proven to be an effective, sustainable management tool for regulating timber resource extraction in the high forest zones of the country. This work aims to generate TIF that can track and capture requisite parameters to accurately estimate the volume of harvested rosewood within forest savannah transition zones. Tree information forms were created on three scenarios of individual billets, stacked billets and conveying vessel basis. These TIFs were field-tested to deduce the most viable option for the tracking and estimation of harvested volumes of rosewood using the smallian and cubic volume estimation formula. Overall, four districts were covered with individual billets, stacked billets and conveying vessel scenarios registering mean volumes of 25.83m3,45.08m3 and 32.6m3, respectively. These adduced volumes were validated by benchmarking to assigned volumes of the Forestry Commission of Ghana and known standard volumes of conveying vessels. The results did indicate an underestimation of extracted volumes under the quotas regime, a situation that could lead to unintended overexploitation of the species. The research revealed conveying vessels route is the most viable volume estimation and tracking regime for the sustainable management of the Pterocarpous erinaceus species as it provided a more practical volume estimate and data extraction protocol.

Keywords: convention on international trade in endangered species, cubic volume formula, forest transition savannah zones, pterocarpus erinaceus, smallian’s volume formula, tree information form

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Authors: Albert Kwabena Osei Konadu

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One of the endemic forest species of the savannah transition zones enlisted by the Convention of International Treaty for Endangered Species (CITES) in Appendix II is the Rosewood, also known as Pterocarpus erinaceus or Krayie. Its economic viability has made it increasingly popular and in high demand. Ghana’s forest resource management regime for these ecozones is mainly on conservation and very little on resource utilization. Consequently, commercial logging management standards are at teething stage and not fully developed, leading to a deficiency in the monitoring of logging operations and quantification of harvested trees volumes. Tree information form (TIF); a volume estimation and tracking regime, has proven to be an effective sustainable management tool for regulating timber resource extraction in the high forest zones of the country. This work aims to generate TIF that can track and capture requisite parameters to accurately estimate the volume of harvested rosewood within forest savannah transition zones. Tree information forms were created on three scenarios of individual billets, stacked billets and conveying vessel basis. The study was limited by the usage of regulators assigned volume as benchmark and also fraught with potential volume measurement error in the stacked billet scenario due to the existence of spaces within packed billets. These TIFs were field-tested to deduce the most viable option for the tracking and estimation of harvested volumes of rosewood using the smallian and cubic volume estimation formula. Overall, four districts were covered with individual billets, stacked billets and conveying vessel scenarios registering mean volumes of 25.83m3,45.08m3 and 32.6m3, respectively. These adduced volumes were validated by benchmarking to assigned volumes of the Forestry Commission of Ghana and known standard volumes of conveying vessels. The results did indicate an underestimation of extracted volumes under the quotas regime, a situation that could lead to unintended overexploitation of the species. The research revealed conveying vessels route is the most viable volume estimation and tracking regime for the sustainable management of the Pterocarpous erinaceus species as it provided a more practical volume estimate and data extraction protocol.

Keywords: cubic volume formula, smallian volume formula, pterocarpus erinaceus, tree information form, forest transition and savannah zones, harvested tree volume

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Authors: Francesca Schiavello

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This work aims to create a hybrid algorithm, combining Quantum Approximate Optimization Algorithm (QAOA) with an Evolutionary Algorithm (EA) in the place of traditional gradient based optimization processes. QAOA’s were first introduced in 2014, where, at the time, their algorithm performed better than the traditional best known classical algorithm for Max-cut graphs. Whilst classical algorithms have improved since then and have returned to being faster and more efficient, this was a huge milestone for quantum computing, and their work is often used as a benchmarking tool and a foundational tool to explore variants of QAOA’s. This, alongside with other famous algorithms like Grover’s or Shor’s, highlights to the world the potential that quantum computing holds. It also presents the reality of a real quantum advantage where, if the hardware continues to improve, this could constitute a revolutionary era. Given that the hardware is not there yet, many scientists are working on the software side of things in the hopes of future progress. Some of the major limitations holding back quantum computing are the quality of qubits and the noisy interference they generate in creating solutions, the barren plateaus that effectively hinder the optimization search in the latent space, and the availability of number of qubits limiting the scale of the problem that can be solved. These three issues are intertwined and are part of the motivation for using EAs in this work. Firstly, EAs are not based on gradient or linear optimization methods for the search in the latent space, and because of their freedom from gradients, they should suffer less from barren plateaus. Secondly, given that this algorithm performs a search in the solution space through a population of solutions, it can also be parallelized to speed up the search and optimization problem. The evaluation of the cost function, like in many other algorithms, is notoriously slow, and the ability to parallelize it can drastically improve the competitiveness of QAOA’s with respect to purely classical algorithms. Thirdly, because of the nature and structure of EA’s, solutions can be carried forward in time, making them more robust to noise and uncertainty. Preliminary results show that the EA algorithm attached to QAOA can perform on par with the traditional QAOA with a Cobyla optimizer, which is a linear based method, and in some instances, it can even create a better Max-Cut. Whilst the final objective of the work is to create an algorithm that can consistently beat the original QAOA, or its variants, due to either speedups or quality of the solution, this initial result is promising and show the potential of EAs in this field. Further tests need to be performed on an array of different graphs with the parallelization aspect of the work commencing in October 2023 and tests on real hardware scheduled for early 2024.

Keywords: evolutionary algorithm, max cut, parallel simulation, quantum optimization

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Authors: Rose Antony, Vivekanand B. Khanapuri, Karuna Jain

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Online groceries have transformed the way the supply chains are managed. These are facing numerous challenges in terms of product wastages, low margins, long breakeven to achieve and low market penetration to mention a few. The e-grocery chains need to overcome these challenges in order to survive the competition. The purpose of this paper is to carry out a structural analysis of the enablers in e-grocery chains by applying Interpretive Structural Modeling (ISM) and MICMAC analysis in the Indian context. The research design is descriptive-explanatory in nature. The enablers have been identified from the literature and through semi-structured interviews conducted among the managers having relevant experience in e-grocery supply chains. The experts have been contacted through professional/social networks by adopting a purposive snowball sampling technique. The interviews have been transcribed, and manual coding is carried using open and axial coding method. The key enablers are categorized into themes, and the contextual relationship between these and the performance measures is sought from the Industry veterans. Using ISM, the hierarchical model of the enablers is developed and MICMAC analysis identifies the driver and dependence powers. Based on the driver-dependence power the enablers are categorized into four clusters namely independent, autonomous, dependent and linkage. The analysis found that information technology (IT) and manpower training acts as key enablers towards reducing the lead time and enhancing the online service quality. Many of the enablers fall under the linkage cluster viz., frequent software updating, branding, the number of delivery boys, order processing, benchmarking, product freshness and customized applications for different stakeholders, depicting these as critical in online food/grocery supply chains. Considering the perishability nature of the product being handled, the impact of the enablers on the product quality is also identified. Hence, study aids as a tool to identify and prioritize the vital enablers in the e-grocery supply chain. The work is perhaps unique, which identifies the complex relationships among the supply chain enablers in fresh food for e-groceries and linking them to the performance measures. It contributes to the knowledge of supply chain management in general and e-retailing in particular. The approach focus on the fresh food supply chains in the Indian context and hence will be applicable in developing economies context, where supply chains are evolving.

Keywords: interpretive structural modelling (ISM), India, online grocery, retail operations, supply chain management

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Authors: Emily Cherono Sawe, Elisha Ondieki Makori

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Knowledge and digital economies have brought challenges and potential opportunities for universities to innovate and improve the quality of learning. Disruption technologies and information dynamics continue to transform and change the landscape in teaching, scholarship, and research activities across universities. Digital literacy is a fundamental and imperative element in higher education and training, as witnessed during the new norm. COVID-19 caused unprecedented disruption in universities, where teaching and learning depended on digital innovations and applications. Academic services and activities were provided online, including library information services. Information professionals were forced to adopt various digital platforms in order to provide information services to patrons. University libraries’ roles in fulfilling educational responsibilities continue to evolve in response to changes in pedagogy, technology, economy, society, policies, and strategies of parent institutions. Libraries are currently undergoing considerable transformational change as a result of the inclusion of a digital environment. Academic libraries have been at the forefront of providing online learning resources and online information services, as well as supporting students and staff to develop digital literacy skills via online courses, tutorials, and workshops. Digital literacy transformation and information staff are crucial elements reminiscent of the prioritization of skills and knowledge for lifelong learning. The purpose of this baseline research is to assess the implications of digital literacy transformation in institutions of higher learning in Kenya and share appropriate strategies to leverage and sustain teaching and research. Objectives include examining the leverage and preparedness of the digital literacy environment in streamlining learning in the universities, exploring and benchmarking imperative digital competence for information professionals, establishing the perception of information professionals towards digital literacy skills, and determining lessons, best practices, and strategies to accelerate digital literacy transformation for effective research and learning in the universities. The study will adopt a descriptive research design using questionnaires and document analysis as the instruments for data collection. The targeted population is librarians and information professionals, as well as academics in public and private universities teaching information literacy programmes. Data and information are to be collected through an online structured questionnaire and digital face-to-face interviews. Findings and results will provide promising lessons together with best practices and strategies to transform and change digital literacies in university libraries in Kenya.

Keywords: digital literacy, digital innovations, information professionals, librarians, higher education, university libraries, digital information literacy

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Authors: Bhavana Gulaty, Arshia Chaudhri

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Great civic projects contribute integrally to a city, and every city undergoes a recurring cycle of urban transformations and regeneration by their insertion. The M.I.C.E. (Meetings, Incentives, Convention and Exhibitions) industry is the forbearer of one category of such catalytic civic projects. Through a specific focus on M.I.C.E. destinations, this paper illustrates the multifarious dimensions that urban catalysts impact the city on S.P.U.R. (Seed. Profile. Urbane. Reflections), the theoretical framework of this paper aims to unearth these dimensions in the realm of the COEX (Convention & Exhibition) biosphere. The ‘COEX Biosphere’ is the filter of such catalysts being ecosystems unto themselves. Like a ripple in water, the impact of these strategic interventions focusing on art, culture, trade, and promotion expands right from the trigger; the immediate context to the region and subsequently impacts the global scale. These ripples are known to bring about significant economic, social, and political and network changes. The COEX inventory in the Asian context has one such prominent addition; the proposed India International Convention and Exhibition Centre (IICC) at New Delhi. It is envisioned to be the largest facility in Asia currently and would position India on the global M.I.C.E map. With the first phase of the project scheduled to open for use in the end of 2019, this flagship project of the Government of India is projected to cater to a peak daily footfall of 3,20,000 visitors and estimated to generate 5,00,000 jobs. While the economic benefits are yet to manifest in real time and ‘Good design is good business’ holds true, for the urban transformation to be meaningful, the benefits have to go beyond just a balance sheet for the city’s exchequer. This aspect has been found relatively unexplored in research on these developments. The methodology for investigation will comprise of two steps. The first will be establishing an inventory of the global success stories and associated benefits of COEX projects over the past decade. The rationale for capping the timeframe is the significant paradigm shift that has been observed in their recent conceptualization; for instance ‘Innovation Districts’ conceptualised in the city of Albuquerque that converges into the global economy. The second step would entail a comparative benchmarking of the projected transformations by IICC through a toolkit of parameters. This is posited to yield a matrix that can form the test bed for mapping the catalytic trajectory for projects in the pipeline globally. As a ready reckoner, it purports to be a catalyst to substantiate decision making in the planning stage itself for future projects in similar contexts.

Keywords: catalysts, COEX, M.I.C.E., urban transformations

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Authors: Fatih Gundogan, Fatih Kafali, Abdullah Karadag, Alper Baloglu, Ersoy Pehlivan, Mustafa Eruyar, Osman Bayram, Orhan Karademiroglu, Wasim Shoman

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Nowadays many cities around the world are investing their efforts and resources for the purpose of facilitating their citizen’s life and making cities more livable and sustainable by implementing newly emerged phenomena of smart city. For this purpose, related research institutions prepare and publish smart city indices or benchmarking reports aiming to measure the city’s current ‘smartness’ status. Several functional domains, various indicators along different selection and calculation methods are found within such indices and reports. The selection criteria varied for each institution resulting in inconsistency in the ranking and evaluating. This research aims to evaluate the impact of selecting such functional domains, indicators and calculation methods which may cause change in the rank. For that, six functional domains, i.e. Environment, Mobility, Economy, People, Living and governance, were selected covering 19 focus areas and 41 sub-focus (variable) areas. 60 out of 191 indicators were also selected according to several criteria. These were identified as a result of extensive literature review for 13 well known global indices and research and the ISO 37120 standards of sustainable development of communities. The values of the identified indicators were obtained from reliable sources for ten cities. The values of each indicator for the selected cities were normalized and standardized to objectively investigate the impact of the chosen indicators. Moreover, the effect of choosing an integration method to represent the values of indicators for each city is investigated by comparing the results of two of the most used methods i.e. geometric aggregation and fuzzy logic. The essence of these methods is assigning a weight to each indicator its relative significance. However, both methods resulted in different weights for the same indicator. As a result of this study, the alternation in city ranking resulting from each method was investigated and discussed separately. Generally, each method illustrated different ranking for the selected cities. However, it was observed that within certain functional areas the rank remained unchanged in both integration method. Based on the results of the study, it is recommended utilizing a common platform and method to objectively evaluate cities around the world. The common method should provide policymakers proper tools to evaluate their decisions and investments relative to other cities. Moreover, for smart cities indices, at least 481 different indicators were found, which is an immense number of indicators to be considered, especially for a smart city index. Further works should be devoted to finding mutual indicators representing the index purpose globally and objectively.

Keywords: functional domain, urban city index, indicator, smart city

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Authors: Kyoko Monden

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Tourism has moved to the forefront of national attention in Japan since September of 2013 when Tokyo won its bid to host the 2020 Summer Olympics. The number of foreign tourists has continued to break records, reaching 13.4 million in 2014, and is now expected to hit 20 million sooner than initially targeted 2020 due to government stimulus promotions; an increase in low cost carriers; the weakening of the Japanese yen, and strong economic growth in Asia. The tourism industry can be an effective trigger in Japan’s economic recovery as foreign tourists spent two trillion yen ($16.6 million) in Japan in 2014. In addition, 81% of them were all from Asian countries, and it is essential to know that 68.9% of the world’s Muslims, about a billion people, live in South and Southeast Asia. An important question is ‘Do Muslim tourists feel comfortable traveling in Japan?’ This research was initiated by an encounter with Muslim visitors in Hiroshima, a popular international tourist destination, who said they had found very few suitable restaurants in Hiroshima. The purpose of this research is to examine halal implementation in Hiroshima and suggest the next steps to be taken to improve current efforts. The goal will be to provide anyone, Muslims included, with first class hospitality in the near future in preparation for the massive influx of foreign tourists in 2020. The methods of this research were questionnaires, face-to-face interviews, phone interviews, and internet research. First, this research aims to address the significance of growing inbound tourism in Japan, especially the expected growth in Muslim tourists. Additionally, it should address the strong popularity of eating Japanese foods in Asian Muslim countries and as ranked no. 1 thing foreign tourists want to do in Japan. Secondly, the current incipient stage of Hiroshima’s halal implementation at hotels, restaurants, and major public places were exposed, and the existing action plans by Hiroshima Prefecture Government were presented. Furthermore, two surveys were conducted to clarify basic halal awareness of local residents in Hiroshima, and to gauge the inconveniences Muslims living in Hiroshima faced. Thirdly, the reasons for this lapse were observed and compared to the benchmarking data of other major tourist sites, Hiroshima’s halal implementation plans were proposed. The conclusion is, despite increasing demands and interests in halal-friendly businesses, overall halal actions have barely been applied in Hiroshima. 76% of Hiroshima residents had no idea what halal or halaal meant. It is essential to increase halal awareness and its importance to the economy and to launch further actions to make Muslim tourists feel welcome in Hiroshima and the entire country.

Keywords: halaal, halal implementation, Hiroshima, inbound tourists in Japan

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Authors: Samuele Ghignoli, Valentina de Lorenzi, Gianmarco Ferri, Stefano Luin, Francesco Cardarelli

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Insulin and glucagon are the two essential hormones for maintaining proper blood glucose homeostasis, which is disrupted in Diabetes. A constantly growing research interest has been focused on the study of the subcellular structures involved in hormone secretion, namely insulin- and glucagon-containing granules, and on the mechanisms regulating their behaviour. Yet, while several successful attempts were reported describing the dynamic properties of insulin granules, little is known about their counterparts in α cells, the glucagon-containing granules. To fill this gap, we used αTC1 clone 9 cells as a model of α cells and ZIGIR as a fluorescent Zinc chelator for granule labelling. We started by using spatiotemporal fluorescence correlation spectroscopy in the form of imaging-derived mean square displacement (iMSD) analysis. This afforded quantitative information on the average dynamical and structural properties of glucagon granules having insulin granules as a benchmark. Interestingly, the iMSD sensitivity to average granule size allowed us to confirm that glucagon granules are smaller than insulin ones (~1.4 folds, further validated by STORM imaging). To investigate possible heterogeneities in granule dynamic properties, we moved from correlation spectroscopy to single particle tracking (SPT). We developed a MATLAB script to localize and track single granules with high spatial resolution. This enabled us to classify the glucagon granules, based on their dynamic properties, as ‘blocked’ (i.e., trajectories corresponding to immobile granules), ‘confined/diffusive’ (i.e., trajectories corresponding to slowly moving granules in a defined region of the cell), or ‘drifted’ (i.e., trajectories corresponding to fast-moving granules). In cell-culturing control conditions, results show this average distribution: 32.9 ± 9.3% blocked, 59.6 ± 9.3% conf/diff, and 7.4 ± 3.2% drifted. This benchmarking provided us with a foundation for investigating selected experimental conditions of interest, such as the glucagon-granule relationship with the cytoskeleton. For instance, if Nocodazole (10 μM) is used for microtubule depolymerization, the percentage of drifted motion collapses to 3.5 ± 1.7% while immobile granules increase to 56.0 ± 10.7% (remaining 40.4 ± 10.2% of conf/diff). This result confirms the clear link between glucagon-granule motion and cytoskeleton structures, a first step towards understanding the intracellular behaviour of this subcellular compartment. The information collected might now serve to support future investigations on glucagon granules in physiology and disease. Acknowledgment: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 866127, project CAPTUR3D).

Keywords: glucagon granules, single particle tracking, correlation spectroscopy, ZIGIR

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Taha Anjamrooz, Sareh Rajabi, Ayman Alzaatreh

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Smart cities are playing important roles in real life. Integration and automation between different features of modern cities and information technologies improve smart city efficiency, energy management, human and equipment resource management, life quality and better utilization of resources for the customers. One of difficulties in this path, is use, interface and link between software, hardware, and other IT technologies to develop and optimize processes in various business fields such as construction, supply chain management and transportation in parallel to cost-effective and resource reduction impacts. Also, Smart cities are certainly intended to demonstrate a vital role in offering a sustainable and efficient model for smart houses while mitigating environmental and ecological matters. Energy management is one of the most important matters within smart houses in the smart cities and communities, because of the sensitivity of energy systems, reduction in energy wastage and maximization in utilizing the required energy. Specially, the consumption of energy in the smart houses is important and considerable in the economic balance and energy management in smart city as it causes significant increment in energy-saving and energy-wastage reduction. This research paper develops features and concept of smart city in term of overall efficiency through various effective variables. The selected variables and observations are analyzed through data analysis processes to demonstrate the efficiency of smart city and compare the effectiveness of each variable. There are ten chosen variables in this study to improve overall efficiency of smart city through increasing effectiveness of smart houses using an automated solar photovoltaic system, RFID System, smart meter and other major elements by interfacing between software and hardware devices as well as IT technologies. Secondly to enhance aspect of energy management by energy-saving within smart house through efficient variables. The main objective of smart city and smart houses is to reproduce energy and increase its efficiency through selected variables with a comfortable and harmless atmosphere for the customers within a smart city in combination of control over the energy consumption in smart house using developed IT technologies. Initially the comparison between traditional housing and smart city samples is conducted to indicate more efficient system. Moreover, the main variables involved in measuring overall efficiency of system are analyzed through various processes to identify and prioritize the variables in accordance to their influence over the model. The result analysis of this model can be used as comparison and benchmarking with traditional life style to demonstrate the privileges of smart cities. Furthermore, due to expensive and expected shortage of natural resources in near future, insufficient and developed research study in the region, and available potential due to climate and governmental vision, the result and analysis of this study can be used as key indicator to select most effective variables or devices during construction phase and design

Keywords: smart city, traditional housing, RFID, photovoltaic system, energy efficiency, energy saving

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Authors: Sandra Carrillo Hoyos

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Mining activities in South America have significantly grown during the last decades, given the abundance of natural resources, the implemented governmental policies to incentivize foreign investment as well as the boom in international prices for metals and oil between 2002 and 2008. While this context allowed the region to occupy a leading position between the top producers of minerals around the world, it has also meant an increase in socio-environmental conflicts which have generated costs and negative impacts not only for the companies but especially for the governments and local communities.During the latest decade, the mining sector in Peru has faced with the social resistance of a large number of communities, which began organizing actions against the implementation of high investing projects. The dissatisfaction has derived in the prevalence of socio-environmental conflicts associated with mining activities, some of them never solved into an agreement. In order to prevent those socio-environmental conflicts and obtain the social license from local communities, most of the mining companies have developed diverse initiatives within the framework of policies and practices of corporate social responsibility (CSR). This paper has assessed the mining sector’s contribution toward the local development management along the last decade, as part of CSR strategies as well as the policies promoted by the Peruvian State. This assessment found that, in the beginning, these initiatives have been based on a philanthropic approach and were reacting to pressures from local stakeholders to maintain the consent to operate from the surrounding communities as well as to create, as a result, a harmonious atmosphere for operations. Due to the weak State presence, such practices have increased the expectations of communities related to the participation of mining companies in solving structural development problems, especially those related to primary needs, infrastructure, education, health, among others. In other words, this paper was focused on analyze in what extent these initiatives have promoted local empowerment for development planning and integrated management of natural resources from a territorial approach. From this perspective, the analysis demonstrates that, while the design and planning of social investment initiatives have improved due to the sector´s sustainability approach, many companies have developed actions beyond their competence during this process. In some cases, the referenced actions have generated dependency with communities, even though this relationship has not exempted the companies of conflict situations with unfortunate consequences. Furthermore, the social programs developed have not necessarily generated a significant impact in improving the quality of life of affected populations. In fact, it is possible to identify that those regions with high mining resources and investment are facing with a situation of poverty and high dependency on mining production. In spite of the revenues derived from mining industry, local governments have not been able to translate the royalties into sustainable development opportunities. For this reason, the proposed paper suggests some challenges for the mining sector contribution to local development based on the best practices and lessons learnt from a benchmarking for the leading mining companies.

Keywords: corporate social responsibility, local development, mining, socio-environmental conflict

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Authors: A. Augustyn, M. Florek, M. Herezniak

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Since the public authorities are constantly pressured by the public opinion to showcase the tangible and measurable results of their efforts, the evaluation of place brand-related activities becomes a necessity. Given the political and social character of place branding process, the legitimization of the branding efforts requires the compliance of the objectives set out in the city brand strategy with the actual needs, expectations, and aspirations of various internal stakeholders. To deliver on the diverse promises, city authorities and brand managers need to translate them into the measurable indicators against which the brand strategy effectiveness will be evaluated. In concert with these observations are the findings from branding and marketing literature with a widespread consensus that places should adopt a more systematic and holistic approach in order to ensure the performance of their brands. However, the measurement of the effectiveness of place branding remains insufficiently explored in theory, even though it is considered a significant step in the process of place brand management. Therefore, the aim of the research presented in the current paper was to collect insights on the nature of effectiveness measurement of city brand strategies and to juxtapose these findings with the theoretical assumptions formed on the basis of the state-of-the-art literature review. To this end, 15 international academic experts (out of 18 initially selected) with affiliation from ten countries (five continents), were individually interviewed. The standardized set of 19 open-ended questions was used for all the interviewees, who had been selected based on their expertise and reputation in the fields of place branding/marketing. Findings were categorized into four modules: (i) conceptualizations of city brand effectiveness, (ii) methodological issues of city brand effectiveness measurement, (iii) the nature of measurement process, (iv) articulation of key performance indicators (KPIs). Within each module, the interviewees offered diverse insights into the subject based on their academic expertise and professional activity as consultants. They proposed that there should be a twofold understanding of effectiveness. The narrow one when it is conceived as the aptitude to achieve specific goals, and the broad one in which city brand effectiveness is seen as an increase in social and economic reality of a place, which in turn poses diverse challenges for the measurement concepts and processes. Moreover, the respondents offered a variety of insights into the methodological issues, particularly about the need for customization and flexibility of the measurement systems, for the employment of interdisciplinary approach to measurement and implications resulting therefrom. Considerable emphasis was put on the inward approach to measurement, namely the necessity to monitor the resident’s evaluation of brand related activities instead of benchmarking cities against the competitive set. Other findings encompass the issues of developing appropriate KPIs for the city brand, managing the measurement process and the inclusion of diverse stakeholders to produce a sound measurement system. Furthermore, the interviewees enumerated the most frequently made mistakes in measurement mainly resulting from the misunderstanding of the nature of city brands. This research was financed by the National Science Centre, Poland, research project no. 2015/19/B/HS4/00380 Towards the categorization of place brand strategy effectiveness indicators – findings from strategic documents of Polish district cities – theoretical and empirical approach.

Keywords: city branding, effectiveness, experts’ insights, measurement

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