Search results for: learning activities

Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 241

Authors: Maria V. Mishatkina

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The modelling of dialogical environment is an essential feature of modern education. The dialogue of cultures is a foundation and an important prerequisite for a formation of a human’s main moral qualities such as an ability to understand another person, which is manifested in such values as tolerance, respect, mutual assistance and mercy. A formation of a modern expert occurs in an educational environment that is significantly different from what we had several years ago. Nowadays university education has qualitatively new characteristics. They may be observed in Peoples’ Friendship University of Russia (RUDN University), a top Russian higher education institution which unites representatives of more than 150 countries. The content of its educational strategies is not an adapted cultural experience but material between science and innovation. Besides, RUDN University’s profiles and specialization are not equal to the professional structures. People study not a profession in a strict sense but a basic scientific foundation of an activity in different socio-cultural areas (science, business and education). RUDN University also provides a considerable unit of professional education components. They are foreign languages skills, economic, political, ethnic, communication and computer culture, theory of information and basic management skills. Moreover, there is a rich social life (festive multicultural events, theme parties, journeys) and prospects concerning the inclusive approach to education (for example, a special course ‘Social Pedagogy: Issues of Tolerance’). In our research, we use such methods as analysis of modern and contemporary scientific literature, opinion poll (involving students, teachers and research workers) and comparative data analysis. We came to the conclusion that knowledge transfer of RUDN student in the activity happens through making goals, problems, issues, tasks and situations which simulate future innovative ambiguous environment that potentially prepares him/her to dialogical way of life. However, all these factors may not take effect if there is no ‘personal inspiration’ of students by communicative and dialogic values, their participation in a system of meanings and tools of learning activity that is represented by cooperation within the framework of scientific and pedagogical schools dialogue. We also found out that dominating strategies of ensuring the quality of education are those that put students in the position of the subject of their own education. Today these strategies and approaches should involve such approaches and methods as task, contextual, modelling, specialized, game-imitating and dialogical approaches, the method of practical situations, etc. Therefore, University in the modern sense is not only an educational institution, but also a generator of innovation, cooperation among nations and cultural progress. RUDN University has been performing exactly this mission for many decades.

Keywords: dialogical developing situation, dialogue of cultures, readiness for dialogue, university graduate

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Kim Ji-Hyun, Jeon Hye-Seon, Kwon Oh-Yun, Park Eun-Young, Hwang Ui-Jae, Gwak Gyeong-Tae, Yoon Hyeo-Bin

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Stress urinary incontinence (SUI), a common women health problem, is an involuntary leakage of urine while sneezing, coughing, or physical exertion caused by insufficient strength of the pelvic floor and sphincter muscles. SUI also leads to decrease in quality of life and limits sexual activities. SUI is related to the increased bladder neck angle, bladder neck movement, funneling index, urethral width, and decreased urethral length. Various pelvic floor muscle electrical stimulation (ES) interventions have been applied to improve the symptoms of the people with SUI. ES activates afferent fibers of pudendal nerve and smoothly induces contractions of the pelvic floor muscles such as striated periurethral muscles and striated pelvic floor muscles. ES via intravaginal electrodes are the most frequently used types of the pelvic floor muscle ES for the female SUI. However, inserted electrode is uncomfortable and it increases the risks of infection. The purpose of this preliminary study was to determine if the 8-week transcutaneous pelvic floor ES would be effective to improve the symptoms and satisfaction of the females with SUI. Easy-K, specially designed ES equipment for the people with SUI, was used in this study. The oval shape stimulator can be placed on a toilet seat, and the surface has invaded electrode fit to contact with the entire vulva area while users are sitting on the stimulator. Five women with SUI were included in this experiment. Prior to the participation, subjects were instructed about procedures and precautions in using the ES. They have used the stimulator once a day for 20 minutes for each session at home. Outcome data was collected 3 times at the baseline, 4 weeks and 8 weeks after the intervention. Intravaginal sonography was used to measure the bladder neck angle, bladder neck movement, funneling index, thickness of an anterior rhabdosphincter and a posterior rhabdosphincter, urethral length, and urethral width. Leavator ani muscle (LAM) contraction strength was assessed by manual palpation according to the oxford scoring system. In addition, incontinence quality of life (IQOL) and female sexual function index (FSFI) questionnaires were used to obtain addition subjective information. Friedman test, a nonparametric statistical test, was used to determine the effectiveness of the ES. The Wilcoxon test was used for the post-hoc analysis and the significance level was set at .05. The bladder neck angle, funneling index and urethral width were significantly decreased after 8-weeks of intervention (p<.05). LAM contraction score, urethral length and anterior and posterior rhabdosphicter thickness were statistically increased by the intervention (p<.05). However, no significant change was found in the bladder neck movement. Although total score of the IQOL did not improve, the score of the ‘avoidance’ subscale of IQOL had significant improved (p<.05). FSFI had statistical difference in FSFI total score and ‘desire’ subscale (p<.05). In conclusion, 8-week use of a transcutaneous ES on peri-vulva area improved dynamic mechanical structures of the pelvic floor musculature as well as IQOL and conjugal relationship.

Keywords: electrical stimulation, Pelvic floor muscle, sonography, stress urinary incontinence, women health

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Authors: Paulo Roberto Vasconcellos-Silva

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The "Janeiro Roxo" (Purple January) campaign in Brazil aims to promote awareness of leprosy and its early symptoms. The COVID-19 pandemic has adversely affected institutional campaigns, mostly considering leprosy a neglected disease by the media. Google Trends (GT) is a tool that tracks user searches on Google, providing insights into the popularity of specific search terms. Our prior research has categorized online searches into two types: "Reactive searches," driven by transient campaign-related stimuli, and "Proactive searches," driven by personal interest in early symptoms and self-diagnosis. Using GT we studied: (i) the impact of "Janeiro Roxo" on public interest in leprosy (assessed through reactive searches) and its early symptoms (evaluated through proactive searches) over the past five years; (ii) changes in public interest during and after the COVID-19 pandemic; (iii) patterns in the dynamics of reactive and proactive searches Methods: We used GT's "Relative Search Volume" (RSV) to gauge public interest on a scale from 0 to 100. "HANSENÍASE" (HAN) was a proxy for reactive searches, and "HANSENÍASE SINTOMAS" (leprosy symptoms) (H.SIN) for proactive searches (interest in leprosy or in self-diagnosis). We analyzed 261 weeks of data from 2018 to 2023, using polynomial trend lines to model trends over this period. Analysis of Variance (ANOVA) was used to compare weekly RSV, monthly (MM) and annual means (AM). Results: Over a span of 261 weeks, there was consistently higher Relative Search Volume (RSV) for HAN compared to H.SIN. Both search terms exhibited their highest (MM) in January months during all periods. COVID-19 pandemic: a decline was observed during the pandemic years (2020-2021). There was a 24% decrease in RSV for HAN and a 32.5% decrease for H.SIN. Both HAN and H.SIN regained their pre-pandemic search levels in January 2022-2023. Breakpoints indicated abrupt changes - in the 26th week (February 2019), 55th and 213th weeks (September 2019 and 2022) related to September regional campaigns (interrupted in 2020-2021). Trend lines for HAN exhibited an upward curve between 33rd-45th week (April to June 2019), a pandemic-related downward trend between 120th-136th week (December 2020 to March 2021), and an upward trend between 220th-240th week (November 2022 to March 2023). Conclusion: The "Janeiro Roxo" campaign, along with other media-driven activities, exerts a notable influence on both reactive and proactive searches related to leprosy topics. Reactive searches, driven by campaign stimuli, significantly outnumber proactive searches. Despite the interruption of the campaign due to the pandemic, there was a subsequent resurgence in both types of searches. The recovery observed in reactive and proactive searches post-campaign interruption underscores the effectiveness of such initiatives, particularly at the national level. This suggests that regional campaigns aimed at leprosy awareness can be considered highly successful in stimulating proactive public engagement. The evaluation of internet-based campaign programs proves valuable not only for assessing their impact but also for identifying the needs of vulnerable regions. These programs can play a crucial role in integrating regions and highlighting their needs for assistance services in the context of leprosy awareness.

Keywords: health communication, leprosy, health campaigns, information seeking behavior, Google Trends, reactive searches, proactive searches, leprosy early identification

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Authors: Irakli Gamkrelidze, David Shengelia, Giorgi Chichinadze, Tamara Tsutsunava, Giorgi Beridze, Tamara Tsamalashvili, Ketevan Tedliashvili, Irakli Javakhishvili

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The Caucasus is situated between the Eurasian and Africa-Arabian plates and represents a component of the Mediterranean (Alpine-Himalayan) collision belt. The Loki crystalline massif crops out within one of the terranes of the Caucasus – Baiburt-Sevanian terrane. By the end of 2018, a digital geological map (1:50 000) of the Loki massif was compiled. The presented map is of great importance for the region since there is no large-scale geological map which reflects the present standards of the geological study of the massif up to the last time. The existing State Geological Map of the Loki massif is very outdated. A new map drown by using GIS (Geographic Information System) technology is loaded with multi-informative details that include: specified contours of geological units and separate tectonic scales, key mineral assemblages and facies of metamorphism, temperature conditions of metamorphism, ages of metamorphism events and the massif rocks, genetic-geodynamic types of magmatic rocks. Explanatory note, attached to the map includes the large specter of scientific information. It contains characterization of the geological setting, composition and petrogenetic and geodynamic models of the massif formation. To create a geological map of the Loki crystalline massif, appropriate methodologies were applied: a sampling of rocks, GIS technology-based mapping of geological units, microscopic description of the material, composition analysis of rocks, microprobe analysis of minerals and a new interpretation of obtained data. To prepare a digital version of the map the appropriated activities were held including the creation of a common database. Finally, the design was created that includes the elaboration of legend and the final visualization of the map. The results of the study presented in the explanatory note are given below. The autochthonous gneissose quartz diorites of normal alkalinity and sub-alkaline gabbro-diorites included in them belong to different phases of magmatism. They represent “igneous” granites corresponding to mixed mantle-crustal type granites. Four tectonic plates of the allochthonous metamorphic complex–Lower Gorastskali, Sapharlo–Lok-Jandari, Moshevani, and Lower Gorastskali differ from each other by structure and degree of metamorphism. The initial rocks of these plates are formed in different geodynamic conditions and during the Early Bretonian orogeny while overthrusting due to tectonic compression they form a thick tectonic sheet. The Lower Gorastskali overthrust sheet is a fragment of ophiolitic association corresponding to the Paleotethys oceanic crust. The protolith of the ophiolitic complex basites corresponds to the tholeiitic series of basalts. The Sapharlo–Lok-Jandari overthrust sheet is metapelites, metamorphosed in conditions of greenschist facies of regional metamorphism. The regional metamorphism of Moshevani overthrust sheet crystalline schists quartzites corresponds to a range from greenschist to hornfels facies. The “mélange” is built of rock fragments and blocks of above-mentioned overthrust sheets. Sub-alkaline and normal alkaline post-metamorphic granites of the Loki crystalline massif belong to “igneous” and rarely to “sialic” and “anorogenic” types of granites.

Keywords: digital geological map, 1:50 000 scale, crystalline massif, the caucasus

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Authors: Ripudaman S. Minhas, Pardeep K. Benipal, Aisha K. Yousafzai

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Background: Children of refugee or asylum-seeking background have multiple, complex needs (e.g. trauma, mental health concerns, separation, relocation, poverty, etc.) that places them at an increased risk for developing learning problems. Families encounter challenges accessing support during resettlement, preventing children from achieving their full developmental potential. There are very few studies in literature that examine the unique parenting challenges refugee families’ face. Providing appropriate support services and educational resources that address these distinctive concerns of refugee parents, will alleviate these challenges allowing for a better developmental outcome for children. Objective: To identify the characteristics of effective parenting interventions that address the unique needs of refugee families. Methods: English-language articles published from 1997 onwards were included if they described or evaluated programmes or interventions for parents of refugee or asylum-seeking background, globally. Data were extracted and analyzed according to Arksey and O’Malley’s descriptive analysis model for scoping reviews. Results: Seven studies met criteria and were included, primarily studying families settled in high-income countries. Refugee parents identified parenting to be a major concern, citing they experienced: alienation/unwelcoming services, language barriers, and lack of familiarity with school and early years services. Services that focused on building the resilience of parents, parent education, or provided services in the family’s native language, and offered families safe spaces to promote parent-child interactions were most successful. Home-visit and family-centered programs showed particular success, minimizing barriers such as transportation and inflexible work schedules, while allowing caregivers to receive feedback from facilitators. The vast majority of studies evaluated programs implementing existing curricula and frameworks. Interventions were designed in a prescriptive manner, without direct participation by family members and not directly addressing accessibility barriers. The studies also did not employ evaluation measures of parenting practices or the caregiving environment, or child development outcomes, primarily focusing on parental perceptions. Conclusion: There is scarce literature describing parenting interventions for refugee families. Successful interventions focused on building parenting resilience and capacity in their native language. To date, there are no studies that employ a participatory approach to program design to tailor content or accessibility, and few that employ parenting, developmental, behavioural, or environmental outcome measures.

Keywords: asylum-seekers, developmental pediatrics, parenting interventions, refugee families

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Authors: Silvia Baralla, Raffaella Zucaro, Romina Lorenzetti

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Water management needs to cope with economic, societal, and environmental changes. This could be guaranteed through 'shifting from government to governance'. In the last decades, it was applied in Europe through and within important legislative pillars (Water Framework Directive and Common Agricultural Policy) and their measures focused on resilience and adaptation to climate change, with particular attention to the creation of synergies among policies and all the actors involved at different levels. Within the climate change context, the agricultural sector can play, through sustainable water management, a leading role for climate-resilient growth and environmental integrity. A recent analysis on the water management governance of different countries identified some common gaps dealing with administrative, policy, information, capacity building, funding, objective, and accountability. The ability of a country to fill these gaps is an essential requirement to make some of the changes requested by Europe, in particular the improvement of the agro-ecosystem resilience to the effect of climatic change, supporting green and digital transitions, and sustainable water use. This research aims to contribute in sharing examples of water governances and related advantages useful to fill the highlighted gaps. Italy has developed a strong and exhaustive model of water governance in order to react with strategic and synergic actions since it is one of the European countries most threatened by climate change and its extreme events (drought, floods). In particular, the Italian water governance model was able to overcome several gaps, specifically as concerns the water use in agriculture, adopting strategies as a systemic/integrated approach, the stakeholder engagement, capacity building, the improvement of planning and monitoring ability, and an adaptive/resilient strategy for funding activities. They were carried out, putting in place regulatory, structural, and management actions. Regulatory actions include both the institution of technical committees grouping together water decision-makers and the elaboration of operative manuals and guidelines by means of a participative and cross-cutting approach. Structural actions deal with the funding of interventions within European and national funds according to the principles of coherence and complementarity. Finally, management actions regard the introduction of operational tools to support decision-makers in order to improve planning and monitoring ability. In particular, two cross-functional and interoperable web databases were introduced: SIGRIAN (National Information System for Water Resources Management in Agriculture) and DANIA (National Database of Investments for Irrigation and the Environment). Their interconnection allows to support sustainable investments, taking into account the compliance about irrigation volumes quantified in SIGRIAN, ensuring a high level of attention on water saving, and monitoring the efficiency of funding. Main positive results from the Italian water governance model deal with a synergic and coordinated work at the national, regional, and local level among institutions, the transparency on water use in agriculture, a deeper understanding from the stakeholder side of the importance of their roles and of their own potential benefits and the capacity to guarantee continuity to this model, through a sensitization process and the combined use of management operational tools.

Keywords: agricultural sustainability, governance model, water management, water policies

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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Authors: Àidan Higgins

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Higher education will be crucial in the coming decades in helping to make Ireland a nation is known for innovation, competitive enterprise, and ongoing academic success, as well as a desirable location to live and work with a high quality of life, vibrant culture, and inclusive social structures. Higher education institutions will actively connect with each student community, society, and business; they will help students develop a sense of place and identity in Ireland and provide the tools they need to contribute significantly to the global community. It will also serve as a catalyst for novel ideas through research, many of which will become the foundation for long-lasting inventive businesses in the future as part of the 2030 National Strategy on Education focuses on change and developing our education system with a focus on how we carry out Research. The emphasis is central to knowledge transfer and a consistent research framework with exploiting opportunities and having the necessary expertise. The newly formed Technological Universities (TU) in Ireland are based on a government initiative to create a new type of higher education institution that focuses on applied and industry-focused research and education. The basis of the TU is to bring together two or more existing institutes of technology to create a larger and more comprehensive institution that offers a wider range of programs and services to students and industry partners. The TU model aims to promote collaboration between academia, industry, and community organizations to foster innovation, research, and economic development. The TU model also aims to enhance the student experience by providing a more seamless pathway from undergraduate to postgraduate studies, as well as greater opportunities for work placements and engagement with industry partners. Additionally, the TUs are designed to provide a greater emphasis on applied research, technology transfer, and entrepreneurship, with the goal of fostering innovation and contributing to economic growth. A project is a collection of organised tasks carried out precisely to produce a singular output (product or service) within a given time frame. Project management is a set of activities that facilitates the successful implementation of a project. The significant differences between research and development projects are the (lack of) precise requirements and (the inability to) plan an outcome from the beginning of the project. The evaluation criteria for a research project must consider these and other "particularities" in works; for instance, proving something cannot be done may be a successful outcome. This study intends to explore how a newly established multi-campus technological university manages research projects effectively. The study will identify the potential and difficulties of managing research projects, the tools, resources and processes available in a multi-campus Technological University context and the methods and approaches employed to deal with these difficulties. Key stakeholders like project managers, academics, and administrators will be surveyed as part of the study, which will also involve an explorative investigation of current literature and data. The findings of this study will contribute significantly to creating best practices for project management in this setting and offer insightful information about the efficient management of research projects within a multi-campus technological university.

Keywords: project management, research and research-related projects, multi-campus technology university, processes

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Authors: Abdullah Al Mohiuddin

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Bangladesh is one of the poorest countries in the world, and ranks low on almost all measures of economic development, thus leaving the population extremely vulnerable to natural disasters and climate events. 20% of GDP come from agriculture but more than 60% of the population relies on agriculture as their main source of income making the entire economy vulnerable to climate change and natural disasters. High population density exacerbates the exposure to and effect of climate events, and increases the levels of vulnerability, as does the poor institutional development of the country. The most vulnerable sectors to climate change impacts in Bangladesh are agriculture, coastal zones, water resources, forestry, fishery, health, biomass, and energy. High temperatures, heavy rainfall, high humidity and fairly marked seasonal variations characterize the climate in Bangladesh: Mild winter, hot humid summer and humid, warm rainy monsoon. Much of the country is flooded during the summer monsoon. The Department of Environment (DOE) under the Ministry of Environment and Forestry (MoEF) is the focal point for the United Nations Framework Convention on Climate Change (UNFCCC) and coordinates climate related activities in the country. Recently, a Climate Change Cell (CCC) has been established to address several issues including adaptation to climate change. The climate change focus started with The National Environmental Management Action Plan (NEMAP) which was prepared in 1995 in order to initiate the process to address environmental and climate change issues as long-term environmental problems for Bangladesh. Bangladesh was one of the first countries to finalise a NAPA (Preparation of a National Adaptation Plan of Action) which addresses climate change issues. The NAPA was completed in 2005, and is the first official initiative for mainstreaming adaptation to national policies and actions to cope with climate change and vulnerability. The NAPA suggests a number of adaptation strategies, for example: - Providing drinking water to coastal communities to fight the enhanced salinity caused by sea level rise, - Integrating climate change in planning and design of infrastructure, - Including climate change issues in education, - Supporting adaptation of agricultural systems to new weather extremes, - Mainstreaming CCA into policies and programmes in different sectors, e.g. disaster management, water and health, - Dissemination of CCA information and awareness raising on enhanced climate disasters, especially in vulnerable communities. Bangladesh has geared up its environment conservation steps to save the world’s poorest countries from the adverse effects of global warming. Now it is turning towards green economy policies to save the degrading ecosystem. Bangladesh is a developing country and always fights against Natural Disaster. At the same time we also fight for establishing ecological environment through promoting Green Economy/Energy by Youth Networking. ANTAR is coordinating a big Youth Network in the southern part of Bangladesh where 30 Youth group involved. It can be explained as the economic development based on sustainable development which generates growth and improvement in human’s lives while significantly reducing environmental risks and ecological scarcities. Green economy in Bangladesh promotes three bottom lines – sustaining economic, environment and social well-being.

Keywords: resilience, networking, mobilizing, resource

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Authors: H. K. Esfahani, B. Datta

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Groundwater is one of the most important natural resources in many parts of the world; however it is widely polluted due to human activities. Currently, effective and reliable groundwater management and remediation strategies are obtained using characterization of groundwater pollution sources, where the measured data in monitoring locations are utilized to estimate the unknown pollutant source location and magnitude. However, accurately identifying characteristics of contaminant sources is a challenging task due to uncertainties in terms of predicting source flux injection, hydro-geological and geo-chemical parameters, and the concentration field measurement. Reactive transport of chemical species in contaminated groundwater systems, especially with multiple species, is a complex and highly non-linear geochemical process. Although sufficient concentration measurement data is essential to accurately identify sources characteristics, available data are often sparse and limited in quantity. Therefore, this inverse problem-solving method for characterizing unknown groundwater pollution sources is often considered ill-posed, complex and non- unique. Different methods have been utilized to identify pollution sources; however, the linked simulation-optimization approach is one effective method to obtain acceptable results under uncertainties in complex real life scenarios. With this approach, the numerical flow and contaminant transport simulation models are externally linked to an optimization algorithm, with the objective of minimizing the difference between measured concentration and estimated pollutant concentration at observation locations. Concentration measurement data are very important to accurately estimate pollution source properties; therefore, optimal design of the monitoring network is essential to gather adequate measured data at desired times and locations. Due to budget and physical restrictions, an efficient and effective approach for groundwater pollutant source characterization is to design an optimal monitoring network, especially when only inadequate and arbitrary concentration measurement data are initially available. In this approach, preliminary concentration observation data are utilized for preliminary source location, magnitude and duration of source activity identification, and these results are utilized for monitoring network design. Further, feedback information from the monitoring network is used as inputs for sequential monitoring network design, to improve the identification of unknown source characteristics. To design an effective monitoring network of observation wells, optimization and interpolation techniques are used. A simulation model should be utilized to accurately describe the aquifer properties in terms of hydro-geochemical parameters and boundary conditions. However, the simulation of the transport processes becomes complex when the pollutants are chemically reactive. Three dimensional transient flow and reactive contaminant transport process is considered. The proposed methodology uses HYDROGEOCHEM 5.0 (HGCH) as the simulation model for flow and transport processes with chemically multiple reactive species. Adaptive Simulated Annealing (ASA) is used as optimization algorithm in linked simulation-optimization methodology to identify the unknown source characteristics. Therefore, the aim of the present study is to develop a methodology to optimally design an effective monitoring network for pollution source characterization with reactive species in polluted aquifers. The performance of the developed methodology will be evaluated for an illustrative polluted aquifer sites, for example an abandoned mine site in Queensland, Australia.

Keywords: monitoring network design, source characterization, chemical reactive transport process, contaminated mine site

Procedia PDF Downloads 210

Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

Procedia PDF Downloads 47

Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

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Authors: Sarah M. Schoellhammer, Stephen Gibb

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Bureaucracy, in particular, its core element, a formal and stable hierarchy of authority, is proving less and less appropriate under the conditions of today’s knowledge economy. Centralization and formalization were consistently found to hinder innovation, undermining cross-functional collaboration, personal responsibility, and flexibility. With its focus on systematical planning, controlling and monitoring the development of new or improved solutions for customers, even innovation management as a discipline is to a significant extent based on a mechanistic understanding of organizations. The most important drivers of innovation, human creativity, and initiative, however, can be more hindered than supported by central elements of classic innovation management, such as predefined innovation strategies, rigid stage gate processes, and decisions made in management gate meetings. Heterarchy, as an alternative network form of organization, is essentially characterized by its dynamic influence structures, whereby the biggest influence is allocated by the collective to the persons perceived the most competent in a certain issue. Theoretical arguments that the non-hierarchical concept better supports innovation than bureaucracy have been supported by empirical research. These prior studies either focus on the structure and general functioning of non-hierarchical organizations or on their innovativeness, that means innovation as an outcome. Complementing classic innovation management approaches, this work aims to shed light on how innovations are initiated and realized in heterarchies in order to identify alternative solutions practiced under conditions of the post-bureaucratic organization. Through an initial individual case study, which is part of a multiple-case project, the innovation practices of an innovative and highly heterarchical medium-sized company in the German fire engineering industry are investigated. In a pragmatic mixed methods approach media resonance, company documents, and workspace architecture are analyzed, in addition to qualitative interviews with the CEO and employees of the case company, as well as a quantitative survey aiming to characterize the company along five scaled dimensions of a heterarchy spectrum. The analysis reveals some similarities and striking differences to approaches suggested by classic innovation management. The studied heterarchy has no predefined innovation strategy guiding new product and service development. Instead, strategic direction is provided by the CEO, described as visionary and creative. Procedures for innovation are hardly formalized, with new product ideas being evaluated on the basis of gut feeling and flexible, rather general criteria. Employees still being hesitant to take responsibility and make decisions, hierarchical influence is still prominent. Described as open-minded and collaborative, culture and leadership were found largely congruent with definitions of innovation culture. Overall, innovation efforts at the case company tend to be coordinated more through cultural than through formal organizational mechanisms. To better enable innovation in mainstream organizations, responsible practitioners are recommended not to limit changes to reducing the central elements of the bureaucratic organization, formalization, and centralization. The freedoms this entails need to be sustained through cultural coordination mechanisms, with personal initiative and responsibility by employees as well as common innovation-supportive norms and values. These allow to integrate diverse competencies, opinions, and activities and, thus, to guide innovation efforts.

Keywords: bureaucracy, heterarchy, innovation management, values

Procedia PDF Downloads 167

Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

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Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: Munir Kassa, Mengistu Hailemariam, Meghan Obermeyer, Kefelegn Baruda, Yonas Getachew, Asnakech Dessie

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Improving the quality of sexual and reproductive health services that women receive is expected to have an impact on women’s satisfaction with the services, on their continued use and, ultimately, on their ability to achieve their fertility goals or reproductive intentions. Surprisingly, however, there is little empirical evidence of either whether this expectation is correct, or how best to improve service quality within sexual and reproductive health programs so that these impacts can be achieved. The Recent focus on quality has prompted more physicians to do quality improvement work, but often without the needed skill sets, which results in poorly conceived and ultimately unsuccessful improvement initiatives. As this renders the work unpublishable, it further impedes progress in the field of health care improvement and widens the quality chasm. Moreover, since 2014, the Center for International Reproductive Health Training (CIRHT) has worked diligently with 11 teaching hospitals across Ethiopia to increase access to contraception and abortion care services. This work has included improving pre-service training through education and curriculum development, expanding hands-on training to better learn critical techniques and counseling skills, and fostering a “team science” approach to research by encouraging scientific exploration. This is the first time this systematic approach has been applied and documented to improve access to high-quality services in Ethiopia. The purpose of this article is to report initiatives undertaken, and findings concluded by the clinical service team at CIRHT in an effort to provide a pragmatic approach to quality improvement projects. An audit containing nearly 300 questions about several aspects of patient care, including structure, process, and outcome indicators was completed by each teaching hospital’s quality improvement team. This baseline audit assisted in identifying major gaps and barriers, and each team was responsible for determining specific quality improvement aims and tasks to support change interventions using Shewart’s Cycle for Learning and Improvement (the Plan-Do-Study-Act model). To measure progress over time, quality improvement teams met biweekly and compiled monthly data for review. Also, site visits to each hospital were completed by the clinical service team to ensure monitoring and support. The results indicate that applying an evidence-based, participatory approach to quality improvement has the potential to increase the accessibility and quality of services in a short amount of time. In addition, continued ownership and on-site support are vital in promoting sustainability. This approach could be adapted and applied in similar contexts, particularly in other African countries.

Keywords: abortion, contraception, quality improvement, service provision

Procedia PDF Downloads 185

Authors: Kevin Baxter, Ron Grove, James Pitney, John Harrison, Ozlem Gumus

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The purpose of this research is to determine the mediating effect of effectiveness of the training provided by Northwestern University’s School of Police Staff and Command (SPSC), on the ability of law enforcement mid-level managers to learn transformational and authentic leadership traits. This study will also evaluate the leadership styles, of course, graduates compared to non-attendees using a static group comparison design. The Louisiana State Police pay approximately $40,000 in salary, tuition, housing, and meals for each state police lieutenant attending the 10-week program of the SPSC. This school lists the development of transformational leaders as an increasing element. Additionally, the SPSC curriculum addresses all four components of authentic leadership - self-awareness, transparency, ethical/moral, and balanced processing. Upon return to law enforcement in roles of mid-level management, there are questions as to whether or not students revert to an “autocratic” leadership style. Insufficient evidence exists to support claims for the effectiveness of management training or leadership development. Though it is widely recognized that transformational styles are beneficial to law enforcement, there is little evidence that suggests police leadership styles are changing. Police organizations continue to hold to a more transactional style (i.e., most senior police leaders remain autocrats). Additionally, research in the application of transformational, transactional, and laissez-faire leadership related to police organizations is minimal. The population of the study is law enforcement mid-level managers from various states within the United States who completed leadership training presented by the SPSC. The sample will be composed of 66 active law enforcement mid-level managers (lieutenants and captains) who have graduated from SPSC and 65 active law enforcement mid-level managers (lieutenants and captains) who have not attended SPSC. Participants will answer demographics questions, Multifactor Leadership Questionnaire, Authentic Leadership Questionnaire, and the Kirkpatrick Hybrid Evaluation Survey. Analysis from descriptive statistics, group comparison, one-way MANCOVA, and the Kirkpatrick Evaluation Model survey will be used to determine training effectiveness in the four levels of reaction, learning, behavior, and results. Independent variables are SPSC graduates (two groups: upper and lower) and no-SPSC attendees, and dependent variables are transformational and authentic leadership scores. SPSC graduates are expected to have higher MLQ scores for transformational leadership traits and higher ALQ scores for authentic leadership traits than SPSC non-attendees. We also expect the graduates to rate the efficacy of SPSC leadership training as high. This study will validate (or invalidate) the benefits, costs, and resources required for leadership development from a nationally recognized police leadership program, and it will also help fill the gap in the literature that exists between law enforcement professional development and transformational and authentic leadership styles.

Keywords: training effectiveness, transformational leadership, authentic leadership, law enforcement mid-level manager

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Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

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Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

Procedia PDF Downloads 37

Authors: Nurten Çelik

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Under the new developments in science, medicine, sociology, psychology and literary theories, body studies has gained huge importance and the body has become a debatable issue. There has emerged, among sociologists and literary theorists, an overwhelming consensus that body is socially, politically and culturally perceived and constructed and thus, the position of an individual in the society is determined in accordance with his/her body image. In this regard, the most complicated point is the theoretical views propounded upon disability studies, where the disabled body is considered to be a site upon which social and political restrictions as well as repressions are inscribed. There has been the widely-accepted view that no matter what kind of disability it is, those with physical, mental or learning impairments face varied social, political and environmental obstacles that prevent them from being an active citizen, worker, lover and even a family member. In parallel with these approaches, the matter of the sufferings of disabled individuals attains its place in cinema and literature as well as in theatre studies under the category of disability theatre. One of the prominent plays that deal with physical disability came from the contemporary British playwright Nina Raine. In her awarded play Tribes, which premiered at the Royal Court Theatre in 2010, Raine develops the social strata where her deaf protagonist, Billy, caught up between two tribes – namely his family and his lover Slyvia, a member of the deaf community– experiences personal and social hardships due to his hearing impairment. In the play, intransigent and self-opinionated family members foster no sense of empathy towards Billy, there are noisy talking and shouting, but no communication, love, compassion or mutual understanding, and language becomes just a tool for the expression of rage and oppression. In the disordered atmosphere of the family life, Billy experiences isolation and loneliness. Billy’s hopes for success and love are destroyed when Slyvia, troubled between hearing and deafness, rejects him because she does not utterly grasp what Billy is experiencing. Drawing upon the hardships, Billy undergoes in his relationships with his family and his girlfriend, Tribes problematizes the concept of deafness and explores to what extent a deaf person can find a place in the hearing world. Setting ‘the disabled’ bodies against ‘the abled’ bodies in a family, a microcosm of the society where bodies are socially shaped and constructed, Tribes dramatizes how the disabled bodies are disenfranchised, stigmatised, marginalized and othered on the grounds that they are socially misfit. Tribes, with a specific focus on the dysfunctional family, shows that the lack of communication and empathy numbs the characters to the feelings of each other and thereby, they become more disabled than Billy. In conclusion, this paper, with the reference to the embodiment of disability and social theories, aims to explore how disabled bodies are socially marked and segregated from family and society.

Keywords: body, deafness, disability, disability theatre, Nina Raine, tribes

Procedia PDF Downloads 222

Authors: Ana Fonseca, Ana Gouveia, Edgar Ramalho, Rita Henriques, Filipa Figueiredo, João Nunes

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Due to persistent climate pressures, there is a need to find a resilient economic system that is regenerative in nature. Bioeconomy offers the possibility of replacing non-renewable and non-biodegradable materials derived from fossil fuels with ones that are renewable and biodegradable, while a Circular Economy aims at sustainable and resource-efficient operations. The term "Circular Bioeconomy", which can be summarized as all activities that transform biomass for its use in various product streams, expresses the interaction between these two ideas. Portugal has a very favourable context to promote a Circular Bioeconomy due to its variety of climates and ecosystems, availability of biologically based resources, location, and geomorphology. Recently, there have been political and legislative efforts to develop the Portuguese Circular Bioeconomy. The Action Plan for a Sustainable Bioeconomy, approved in 2021, is composed of five axes of intervention, ranging from sustainable production and the use of regionally based biological resources to the development of a circular and sustainable bioindustry through research and innovation. However, as some statistics show, Portugal is still far from achieving circularity. According to Eurostat, Portugal has circularity rates of 2.8%, which is the second lowest among the member states of the European Union. Some challenges contribute to this scenario, including sectorial heterogeneity and fragmentation, prevalence of small producers, lack of attractiveness for younger generations, and absence of implementation of collaborative solutions amongst producers and along value chains.Regarding the Portuguese industrial sector, there is a tendency towards complex bureaucratic processes, which leads to economic and financial obstacles and an unclear national strategy. Together with the limited number of incentives the country has to offer to those that pretend to abandon the linear economic model, many entrepreneurs are hesitant to invest the capital needed to make their companies more circular. Absence of disaggregated, georeferenced, and reliable information regarding the actual availability of biological resources is also a major issue. Low literacy on bioeconomy among many of the sectoral agents and in society in general directly impacts the decisions of production and final consumption. The WinBio project seeks to outline a strategic approach for the management of weaknesses/opportunities in the technology transfer process, given the reality of the territory, through road mapping and national and international benchmarking. The developed work included the identification and analysis of agents in the interior region of Portugal, natural endogenous resources, products, and processes associated with potential development. Specific flow of biological wastes, possible value chains, and the potential for replacing critical raw materials with bio-based products was accessed, taking into consideration other countries with a matured bioeconomy. The study found food industry, agriculture, forestry, and fisheries generate huge amounts of waste streams, which in turn provide an opportunity for the establishment of local bio-industries powered by this biomass. The project identified biological resources with potential for replication and applicability in the Portuguese context. The richness of natural resources and potentials known in the interior region of Portugal is a major key to developing the Circular Economy and sustainability of the country.

Keywords: circular bioeconomy, interior region of portugal, regional development., public policy

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Authors: Andreas Rudisch, Ralf Lampert, Andreas Kolbitsch

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It is a well-known fact among the engineering community that earthquakes with comparatively low magnitudes can cause serious damage to nonstructural components (NSCs) of buildings, even when the supporting structure performs relatively well. Past research works focused mainly on NSCs of nuclear power plants and industrial plants. Particular attention should also be given to architectural façade elements of old masonry buildings (e.g. ornamental figures, balustrades, vases), which are very vulnerable under seismic excitation. Large numbers of these historical nonstructural components (HiNSCs) can be found in highly frequented historical city centers and in the event of failure, they pose a significant danger to persons. In order to estimate the vulnerability of acceleration sensitive HiNSCs, the peak horizontal floor acceleration (PHFA) is used. The PHFA depends on the dynamic characteristics of the building, the ground excitation, and induced nonlinearities. Consequently, the PHFA can not be generalized as a simple function of height. In the present research work, an extensive case study was conducted to investigate the influence of induced nonlinearity on the PHFA for old masonry buildings. Probabilistic nonlinear FE time-history analyses considering three different hazard levels were performed. A set of eighteen synthetically generated ground motions was used as input to the structure models. An elastoplastic macro-model (multiPlas) for nonlinear homogenized continuum FE-calculation was calibrated to multiple scales and applied, taking specific failure mechanisms of masonry into account. The macro-model was calibrated according to the results of specific laboratory and cyclic in situ shear tests. The nonlinear macro-model is based on the concept of multi-surface rate-independent plasticity. Material damage or crack formation are detected by reducing the initial strength after failure due to shear or tensile stress. As a result, shear forces can only be transmitted to a limited extent by friction when the cracking begins. The tensile strength is reduced to zero. The first goal of the calibration was the consistency of the load-displacement curves between experiment and simulation. The calibrated macro-model matches well with regard to the initial stiffness and the maximum horizontal load. Another goal was the correct reproduction of the observed crack image and the plastic strain activities. Again the macro-model proved to work well in this case and shows very good correlation. The results of the case study show that there is significant scatter in the absolute distribution of the PHFA between the applied ground excitations. An absolute distribution along the normalized building height was determined in the framework of probability theory. It can be observed that the extent of nonlinear behavior varies for the three hazard levels. Due to the detailed scope of the present research work, a robust comparison with code-recommendations and simplified PHFA distributions are possible. The chosen methodology offers a chance to determine the distribution of PHFA along the building height of old masonry structures. This permits a proper hazard assessment of HiNSCs under seismic loads.

Keywords: nonlinear macro-model, nonstructural components, time-history analysis, unreinforced masonry

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Authors: Lya Porto, Giulia Giacchè, Mario Aquino Alves

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The paper aims to describe the linkage between government and civil society proposing a study on agro-ecological agriculture policy and urban action in São Paulo city underling the main achievements obtained. The negotiation processes between social movements and the government (inputs) and its results on political regulation and public action for Urban Agriculture (UA) in São Paulo city (outputs) have been investigated. The method adopted is qualitative, with techniques of semi-structured interviews, participant observation, and documental analysis. The authors conducted 30 semi-structured interviews with organic farmers, activists, governmental and non-governmental managers. Participant observation was conducted in public gardens, urban farms, public audiences, democratic councils, and social movements meetings. Finally, public plans and laws were also analyzed. São Paulo city with around 12 million inhabitants spread out in a 1522 km2 is the economic capital of Brazil, marked by spatial and socioeconomic segregation, currently aggravated by environmental crisis, characterized by water scarcity, pollution, and climate changes. In recent years, Urban Agriculture (UA) social movements gained strength and struggle for a different city with more green areas, organic food production, and public occupation. As the dynamics of UA occurs by the action of multiple actresses and institutions that struggle to build multiple senses on UA, the analysis will be based on literature about solidarity economy, governance, public action and networks. Those theories will mark out the analysis that will emphasize the approach of inter-subjectivity built between subjects, as well as the hybrid dynamics of multiple actors and spaces in the construction of policies for UA. Concerning UA we identified four main typologies based on land ownership, main function (economic or activist), form of organization of the space, and type of production (organic or not). The City Hall registers 500 productive unities of agriculture, with around 1500 producers, but researcher estimated a larger number of unities. Concerning the social movements we identified three categories that differ in goals and types of organization, but all of them work by networks of activists and/or organizations. The first category does not consider themselves as a movement, but a network. They occupy public spaces to grow organic food and to propose another type of social relations in the city. This action is similar to what became known as the green guerrillas. The second is configured as a movement that is structured to raise awareness about agro-ecological activities. The third one is a network of social movements, farmers, organizations and politicians that work focused on pressure and negotiation with executive and legislative government to approve regulations and policies on organic and agro-ecological Urban Agriculture. We conclude by highlighting how the interaction among institutions and civil society produced important achievements for recognition and implementation of UA within the city. Some results of this process are awareness for local production, legal and institutional recognition of the rural zone around the city into the planning tool, the investment on organic school public procurements, the establishment of participatory management of public squares, the inclusion of UA on Municipal Strategic Plan and Master Plan.

Keywords: public action, policies, agroecology, urban and peri-urban agriculture, Sao Paulo

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Authors: Farnaz Jafari

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The surge in mass timber construction represents a pivotal moment in sustainable building practices, yet the lack of comprehensive education in construction management poses a challenge in harnessing this innovation effectively. This research endeavors to bridge this gap by developing a curriculum framework integrating mass timber construction into undergraduate and industry certificate programs. To optimize learning outcomes, the study explores the impact of two prototype formats -Virtual Reality (VR) simulations and physical mock-ups- on students' understanding and skill development. The curriculum framework aims to equip future construction managers with a holistic understanding of mass timber, covering its unique properties, construction methods, building codes, and sustainable advantages. The study adopts a mixed-methods approach, commencing with a systematic literature review and leveraging surveys and interviews with educators and industry professionals to identify existing educational gaps. The iterative development process involves incorporating stakeholder feedback into the curriculum. The evaluation of prototype impact employs pre- and post-tests administered to participants engaged in pilot programs. Through qualitative content analysis and quantitative statistical methods, the study seeks to compare the effectiveness of VR simulations and physical mock-ups in conveying knowledge and skills related to mass timber construction. The anticipated findings will illuminate the strengths and weaknesses of each approach, providing insights for future curriculum development. The curriculum's expected contribution to sustainable construction education lies in its emphasis on practical application, bridging the gap between theoretical knowledge and hands-on skills. The research also seeks to establish a standard for mass timber construction education, contributing to the field through a unique comparative analysis of VR simulations and physical mock-ups. The study's significance extends to the development of best practices and evidence-based recommendations for integrating technology and hands-on experiences in construction education. By addressing current educational gaps and offering a comparative analysis, this research aims to enrich the construction management education experience and pave the way for broader adoption of sustainable practices in the industry. The envisioned curriculum framework is designed for versatile integration, catering to undergraduate programs and industry training modules, thereby enhancing the educational landscape for aspiring construction professionals. Ultimately, this study underscores the importance of proactive educational strategies in preparing industry professionals for the evolving demands of the construction landscape, facilitating a seamless transition towards sustainable building practices.

Keywords: curriculum framework, mass timber construction, physical vs. virtual prototypes, sustainable building practices

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Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have played an indispensable role in controlling illness and death associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global healthcare problem. Many antibiotics had lost their effectiveness since the beginning of the antibiotic era because many bacteria have adapted defenses against these antibiotics. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing require the isolation of the pathogen from a clinical specimen by culturing on the appropriate media (this culturing stage lasts 24 h-first culturing). Then, chosen colonies are grown on media containing antibiotic(s), using micro-diffusion discs (second culturing time is also 24 h) in order to determine its bacterial susceptibility. Other methods, genotyping methods, E-test and automated methods were also developed for testing antimicrobial susceptibility. Most of these methods are expensive and time-consuming. Fourier transform infrared (FTIR) microscopy is rapid, safe, effective and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria; nonetheless, its true potential in routine clinical diagnosis has not yet been established. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The UTI E.coli bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 700 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 90% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E.coli, FTIR, multivariate analysis, susceptibility, UTI

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Authors: Efstratios Karavasilis, Foteini Christidi, Georgios Velonakis, Agapi Plousi, Kalliopi Platoni, Nikolaos Kelekis, Ioannis Evdokimidis, Efstathios Efstathopoulos

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Introduction: Advanced structural and functional neuroimaging techniques contribute to the study of anatomical and functional brain connectivity and its role in the pathophysiology and symptoms’ heterogeneity in several neurodegenerative disorders, including multiple sclerosis (MS). Aim: In the present study, we applied multiparametric neuroimaging techniques to investigate the structural and functional cortico-cerebellar changes in MS patients. Material: We included 51 MS patients (28 with clinically isolated syndrome [CIS], 31 with relapsing-remitting MS [RRMS]) and 51 age- and gender-matched healthy controls (HC) who underwent MRI in a 3.0T MRI scanner. Methodology: The acquisition protocol included high-resolution 3D T1 weighted, diffusion-weighted imaging and echo planar imaging sequences for the analysis of volumetric, tractography and functional resting state data, respectively. We performed between-group comparisons (CIS, RRMS, HC) using CAT12 and CONN16 MATLAB toolboxes for the analysis of volumetric (cerebellar gray matter density) and functional (cortico-cerebellar resting-state functional connectivity) data, respectively. Brainance suite was used for the analysis of tractography data (cortico-cerebellar white matter integrity; fractional anisotropy [FA]; axial and radial diffusivity [AD; RD]) to reconstruct the cerebellum tracts. Results: Patients with CIS did not show significant gray matter (GM) density differences compared with HC. However, they showed decreased FA and increased diffusivity measures in cortico-cerebellar tracts, and increased cortico-cerebellar functional connectivity. Patients with RRMS showed decreased GM density in cerebellar regions, decreased FA and increased diffusivity measures in cortico-cerebellar WM tracts, as well as a pattern of increased and mostly decreased functional cortico-cerebellar connectivity compared to HC. The comparison between CIS and RRMS patients revealed significant GM density difference, reduced FA and increased diffusivity measures in WM cortico-cerebellar tracts and increased/decreased functional connectivity. The identification of decreased WM integrity and increased functional cortico-cerebellar connectivity without GM changes in CIS and the pattern of decreased GM density decreased WM integrity and mostly decreased functional connectivity in RRMS patients emphasizes the role of compensatory mechanisms in early disease stages and the disintegration of structural and functional networks with disease progression. Conclusions: In conclusion, our study highlights the added value of multimodal neuroimaging techniques for the in vivo investigation of cortico-cerebellar brain changes in neurodegenerative disorders. An extension and future opportunity to leverage multimodal neuroimaging data inevitably remain the integration of such data in the recently-applied mathematical approaches of machine learning algorithms to more accurately classify and predict patients’ disease course.

Keywords: advanced neuroimaging techniques, cerebellum, MRI, multiple sclerosis

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Authors: Cassandra Branham, Danielle Farrar

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As writing instructors and writing center administrators at a large research university with a significant population of English language learners (ELLs), we are interested in how peer feedback pedagogy can be effectively translated for writing center purposes, as well as how various modes of peer feedback can enrich the learning experiences of L1 and L2 writers in these spaces. Although peer feedback is widely used in classrooms and centers, instructor, student, and researcher opinions vary in respect to its effectiveness. We argue that peer feedback - traditional and digital, synchronous and asynchronous - is an indispensable element for both classrooms and centers and emphasize that it should occur with both L1 and L2 students to further develop an array of reading and writing skills. We also believe that further understanding of the best practices of peer feedback in such cross-cultural spaces, like the classroom and center, can optimize the benefits of peer feedback. After a critical review of the literature, we implemented an embedded tutoring program in our university’s writing center in collaboration with its First-Year Composition (FYC) program and Language Institute. The embedded tutoring program matches a graduate writing consultant with L1 and L2 writers enrolled in controlled-matriculation composition courses where ELLs make up at least 50% of each class. Furthermore, this program is informed by what we argue to be some best practices of peer feedback for both classroom and center purposes, including expectation-based training through rubrics, modeling effective feedback, hybridizing traditional and digital modes of feedback, recognizing the significance the body in composition (what we call writer embodiment), and maximizing digital technologies to exploit extended cognition. After conducting surveys and follow-up interviews with students, instructors, and writing consultants in the embedded tutoring program, we found that not only did students see an increased value in peer feedback, but also instructors saw an improvement in both writing style and critical thinking skills. Our L2 participants noted improvements in language acquisition while our L1 students recognized a broadening of their worldviews. We believe that both L1 and L2 students developed self-efficacy and agency in their identities as writers because they gained confidence in their abilities to offer feedback, as well as in the legitimacy of feedback they received from peers. We also argue that these best practices situate novice writers as experts, as writers become a valued and integral part of the revision process with their own and their peers’ papers. Finally, the use of iPads in embedded tutoring recovered the importance of the body and its senses in writing; the highly sensory feedback from these multi-modal sessions that offer audio and visual input underscores the significant role both the body and mind play in compositional practices. After beginning with a brief review of the literature that sparked this research, this paper will discuss the embedded tutoring program in detail, report on the results of the pilot program, and will conclude with a discussion of the pedagogical implications that arise from this research for both classroom and center.

Keywords: English language learners, peer feedback, writing center, writing classroom

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