Search results for: progressive learning

Authors: David Dean Hampton, William Morrison, Mary Rizza, Jan Osborn

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This evaluation examined the effects of a supplemental reading intervThis evaluation examined the effects of a supplemental reading intervention for students with specific learning disabilities in reading who were presented with below grade level on fall benchmark scores on DIBELS 6th ed. Revised. Participants consisted of a condition group, those who received supplemental reading instruction in addition to core + special education services and a comparison group of students who were at grade level in their fall benchmark scores. The students in the condition group received 26 weeks of Project MORE instruction delivered multiple times each week from trained volunteer tutors. Using a regression-discontinuity design, condition and comparison groups were compared on reading development growth using DIBELS ORF. Significant findings were reported for grade 2, 3, and 4. ntion for students with specific learning disabilities in reading who presented with below grade level on fall benchmark scores on DIBELS 6th ed. Revised. Participants consisted of a condition group, those who received supplemental reading instruction in addition to core + special education services and a comparison group of students who were at grade level in their fall benchmark scores. The students in the condition group received 26 weeks of Project MORE instruction delivered multiple times each week from trained volunteer tutors. Using a regression-discontinuity design, condition and comparison groups were compared on reading development growth using DIBELS ORF. Significant findings were reported for grade 2, 3, and 4.

Keywords: special education, evidence-based practices, curriculum, tutoring

Procedia PDF Downloads 62

Authors: Kevin Henry

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Introduction: This research identifies the main struggles of non-binary people in an organizational setting using the ZMET – method of in-depth interviews. The research was done in Iceland, a country that is repeatedly listed in the top countries for gender equality and found three main categories of non-binary struggles in organizations. These categories can be used to improve organizational non-binary inclusion. Aim: The main questions this paper will answer are: Which unique obstacles are non-binary people facing in their daily organizational life? Which organizational and individual measures help with more inclusion of non-binary people? How can organizational gender equality measures be made more inclusive of non-binary issues? Background: Even though gender equality is a much-researched topic, the struggles of non-binary people are often overlooked in gender equality research. Additionally, non-binary and transgender people are frequently researched together, even though their struggles can be very different. Research focused on non-binary people is, in many cases, done on a more structural or organizational level with quantitative data such as salary or position within an organization. This research focuses on the individual and their struggles with qualitative data to derive measures for non-binary inclusion and equality. Method: An adapted approach of the ZMET-Method (Zaltman Metaphor Elicitation Technique) will be used, during which in-depth interviews are held with individuals, utilizing pictures as a metaphorical starting point to discuss their main thoughts and feelings on being non-binary in an organizational setting. Interviewees prepared five pictures, each representing one key thought or feeling about their organizational life. The interviewer then lets the interviewee describe each picture and asks probing questions to get a deeper understanding of each individual topic. This method helps with a mostly unbiased data collection process by only asking probing questions during the interview and not leading the interviewee in any certain direction. Results: This research has identified three main categories of struggles non-binary people are facing in an organizational setting: internal (personal) struggles, external struggles and structural struggles. Internal struggles refer to struggles that originate from the person themselves (e.g., struggles with their own identity). External struggles refer to struggles from the outside (e.g. harassment from coworkers, exclusion). Structural struggles refer to struggles that are built into the organizational policy or facilities (e.g. restrooms, gendered language). Conclusion: This study shows that there are many struggles for non-binary people in organizations and that even in countries that pride themselves on being progressive and having a high level of gender equality, there is still much to be done for non-binary inclusion. Implications for Organizations: Organizations that strive to improve the inclusion of all genders should pay attention to how their structures are built, how their training is conducted, and how their policies affect people of various genders. Simple changes like making restrooms gender-neutral and using neutral language in company communications are good examples of small structural steps for more inclusion.

Keywords: gender equality, non-binary, organizations, ZMET

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

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Authors: Noureddine Ben Ayed, Abdelkader Soumaya, Saïd Maouche, Ali Kadri, Mongi Gueddiche, Hayet Khayati-Ammar, Ahmed Braham

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The archaeological monument of Utica, located in north-eastern Tunisia, was founded (8th century BC) By the Phoenicians as a port installed on the trade route connecting Phoenicia and the Straits of Gibraltar in the Mediterranean Sea. The flourishment of this city as an important settlement during the Roman period was followed by a sudden abandonment, disuse and progressive oblivion in the first half of the fifth century AD. This decadence can be attributed to the destructive earthquake of 5 July 408 - 410 AD, affecting this historic city as documented in 1906 by the seismologist Fernand De Montessus De Ballore. The magnitude of the Utica earthquake was estimated at 6.8 by the Tunisian National Institute of Meteorology (INM). In order to highlight the damage caused by this earthquake, a field survey was carried out at the Utica ruins to detect and analyse the earthquake archaeological effects (EAEs) using structural geology methods. This approach allowed us to highlight several structural damages, including: (1) folded mortar pavements, (2) cracks affecting the mosaic and walls of a water basin in the "House of the Grand Oecus", (3) displaced columns, (4) block extrusion in masonry walls, (5) undulations in mosaic pavements, (6) tilted walls. The structural analysis of these EAEs and data measurements reveal a seismic cause for all evidence of deformation in the Utica monument. The maximum horizontal strain of the ground (e.g. SHmax) inferred from the building oriented damage in Utica shows a NNW-SSE direction under a compressive tectonic regime. For the seismogenic source of this earthquake, we propose the active E-W to NE-SW trending Utique - Ghar El Melh reverse fault, passing through the Utica Monument and extending towards the Ghar El Melh Lake, as the causative tectonic structure. The active fault trace is well supported by instrumental seismicity, geophysical data (e.g., gravity, seismic profiles) and geomorphological analyses. In summary, we find that the archaeoseismic records detected at Utica are similar to those observed at many other archaeological sites affected by destructive ancient earthquakes around the world. Furthermore, the calculated orientation of the average maximum horizontal stress (SHmax) closely match the state of the actual stress field, as highlighted by some earthquake focal mechanisms in this region.

Keywords: Tunisia, utica, seimogenic fault, archaeological earthquake effects

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Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Ana Maria Reis D'Azevedo Breda, Catarina Maria Neto da Cruz

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The Japanese art of origami provides a rich context for designing exploratory mathematical activities for children and young people. By folding a simple sheet of paper, fascinating and surprising planar and spatial configurations emerge. Equally surprising is the unfolding process, which also produces striking patterns. The procedure of folding, unfolding, and folding again allows the exploration of interesting geometric patterns. When adequately and systematically done, we may deduce some of the mathematical rules ruling origami. As the child/youth folds the sheet of paper repeatedly, he can physically observe how the forms he obtains are transformed and how they relate to the pattern of the corresponding unfolding, creating space for the understanding/discovery of mathematical principles regulating the folding-unfolding process. As part of a 2023 Summer Academy organized by a Portuguese university, a session entitled “Folding, Thinking and Generalizing” took place. Twenty-three students attended the session, all enrolled in the 2nd cycle of Portuguese Basic Education and aged between 10 and 12 years old. The main focus of this session was to foster the development of critical cognitive and socio-emotional skills among these young learners using origami. These skills included creativity, critical analysis, mathematical reasoning, collaboration, and communication. Employing a qualitative, descriptive, and interpretative analysis of data collected during the session through field notes and students’ written productions, our findings reveal that structured origami-based activities not only promote student engagement with mathematical concepts in a playful and interactive but also facilitate the development of socio-emotional skills, which include collaboration and effective communication between participants. This research highlights the value of integrating origami into educational practices, highlighting its role in supporting comprehensive cognitive and emotional learning experiences.

Keywords: skills, origami rules, active learning, hands-on activities

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Martina Dickson, Hanadi Kadbey, Melissa Mcminn

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Research has shown that anxiety and trepidation towards learning about science is prevalent among elementary school teachers in Western countries. It has also been shown repeatedly that pre-service and in-service teachers who show signs of anxiety towards science are; a) less likely to teach it at all, where they have some autonomy over this, b) less likely to teach it effectively c) ultimately that their students have lower attainment scores in science. It is therefore critically important to gauge pre-service teachers’ science anxiety levels early on whilst there are still possibilities to overturn some of the reasons behind these fears and avert these serious issues occurring later on. This study takes place in the capital of the United Arab Emirates (U.A.E.) in the context of training local elementary school teachers. In the U.A.E., where Emirati teachers are already in the vast minority and attrition rates are high, it is important to offer as much support to pre-service teachers as possible. If pre-service teachers are graduating with high levels of science anxiety unabated, according to the research there is a very real concern that as generalist primary school teachers, their science teaching will be far from optimal. The aims of this research study were to ascertain the science anxiety levels of pre-service elementary teachers and to identify particular areas of their science anxiety, if appropriate. We surveyed 200 Emirati pre-service teachers and found that levels of science anxiety were directly related to their perceptions of performance in science exams, laboratory experiments and inquiry approaches to science learning. Whilst some studies have shown that science anxiety can decrease as students gain confidence in science knowledge by studying courses, we did not see this effect in our study. This is based upon a theoretical framework which holds that in some cases, science anxiety is related to lack of exposure to, or insecurity with science content itself which in some cases is alleviated by the students’ covering of material and greater confidence in the subject. Exploring this variable allowed us to explore whether students educated in schools influenced by the educational reform in Abu Dhabi have differing science anxiety levels from those who were educated prior to the reforms. We discuss the possible implications of these findings to the future teaching of science in Abu Dhabi public schools.

Keywords: pre-service teachers, science anxiety, United Arab Emirates, educational reform

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Authors: Tunde Ope-Davies

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Transmediality and platformization as online content-sharing protocols have continued to accentuate the growing impact of the unprecedented digital revolution across the world. The rapid transformation across all sectors as a result of this revolution has continued to spotlight the increasing importance of new media technologies in redefining and reshaping the rhythm and dynamics of our private and public discursive practices. Equally, social and political activities are being impacted daily through the creation and transmission of political discourse content through multi-channel platforms such as mobile telephone communication, social media networks and the internet. It has been observed that digital platforms have become central to the production, processing, and distribution of multimodal social data and cultural content. The platformization paradigm thus underpins our understanding of how digital platforms enhance the production and heterogenous distribution of media and cultural content through these platforms and how this process facilitates socioeconomic and political activities. The use of multiple digital platforms to share and transmit political discourse material synchronously and asynchronously has gained some exciting momentum in the last few years. Nigeria’s 2023 general elections amplified the usage of social media and other online platforms as tools for electioneering campaigns, socio-political mobilizations and civic engagement. The study, therefore, focuses on transmedia and platformed political discourse as a new strategy to promote political candidates and their manifesto in order to mobilize support and woo voters. This innovative transmedia digital discourse model involves a constellation of online texts and images transmitted through different online platforms almost simultaneously. The data for the study was extracted from the 2023 general elections campaigns in Nigeria between January- March 2023 through media monitoring, manual download and the use of software to harvest the online electioneering campaign material. I adopted a discursive-analytic qualitative technique with toolkits drawn from a computer-mediated multimodal discourse paradigm. The study maps the progressive development of digital political discourse in this young democracy. The findings also demonstrate the inevitable transformation of modern democratic practice through platform-dependent and transmedia political discourse. Political actors and media practitioners now deploy layers of social media network platforms to convey messages and mobilize supporters in order to aggregate and maximize the impact of their media campaign projects and audience reach.

Keywords: social media, digital humanities, political discourse, platformized discourse, multimodal discourse

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Authors: Joanna Peksa, Faith Dillon-Lee

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Current developments in the Western economies have turned some universities into corporate institutions driven by practices of production and commodity. Academia is increasingly becoming integrated into national economies as a result of students paying fees and is consequently using business practices in student retention and engagement. With these changes, pedagogy status as a priority within the institution has been changing in light of these new demands. New strategies have blurred the boundaries that separate a student from a client. This led to a change of the dynamic, disrupting the traditional idea of the knowledge market, and emphasizing the corporate aspect of universities. In some cases, where students are seen primarily as a customer, the purpose of academia is no longer to educate but sell a commodity and retain fee-paying students. This paper considers opposing viewpoints on the commodification of higher education, reflecting on the reality of maintaining a pedagogic grounding in an increasingly commercialized sector. By analysing a case study of the Student Success Festival, an event that involved academic and marketing teams, the differences are considered between the respective visions of the pedagogic arm of the university and the corporate. This study argues that the initial concept of the event, based on the principles of gamification, independent learning, and cognitive criticality, was more clearly linked to a grounded pedagogic approach. However, when liaising with the marketing team in a crucial step in the creative process, it became apparent that these principles were not considered a priority in terms of their remit. While the study acknowledges in the power of pedagogy, the findings show that a pact of concord is necessary between different stakeholders in order for students to benefit fully from their learning experience. Nevertheless, while issues of power prevail and whenever power is unevenly distributed, reaching a consensus becomes increasingly challenging and further research should closely monitor the developments in pedagogy in the UK higher education.

Keywords: economic pressure, commodification, pedagogy, gamification, public service, marketization

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Authors: Lami Mohammed, Hussain J. Al Faifi, Abdel Aziz Al Bassam, Osama M. K. Kassem

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The Neoproterozoic basement rocks of the Ar Rayn terrane have been identified as parts of the Eastern Arabian Shield. It focuses on the petrological and finite strain properties to display the tectonic setting of the Al Amar suture for high deformed volcanic and granitoids rocks. The volcanic rocks are classified into two major series: the eastern side cycle, which includes dacite, rhyodacite, rhyolite, and ignimbrites, and the western side cycle, which includes andesite and pyroclastics. Granitoids rocks also contain monzodiorite, tonalite, granodiorite, and alkali-feldspar granite. To evaluate the proportions of shear contributions in penetratively deformed rocks. Asymmetrical porphyroclast and sigmoidal structural markers along the suture's strike, namely the Al Amar, are expected to reveal strain factors. The Rf/phi and Fry techniques are used to characterize quartz and feldspar porphyroclast, biotite, and hornblende grains in Abt schist, high deformed volcanic rock, and granitoids. The findings exposed that these rocks had experienced shape flattening, finite strain accumulation, and overall volume loss. The magnitude of the strain appears to increase across the nappe contacts with neighboring lithologies. Subhorizontal foliation likely developed in tandem with thrusting and nappe stacking, almost parallel to tectonic contacts. The ductile strain accumulation that occurred during thrusting along the Al Amar suture mostly includes a considerable pure shear component. Progressive thrusting by overlaid transpression and oblique convergence is shown by stacked nappes and diagonal stretching lineations along the thrust axes. The subhorizontal lineation might be the result of the suture's most recent activity. The current study's findings contradict the widely accepted model that links orogen-scale structures in the Arabian Shield to oblique convergence with dominant simple shear deformation. A significant pure shear component/crustal thickening increment should have played a significant role in the evolution of the suture and thus in the Shield's overall deformation history. This foliation was primarily generated by thrusting nappes together, showing that nappe stacking was linked to substantial vertical shortening induced by the active Al Amar suture on a massive scale.

Keywords: petrology, finite strain analysis, al amar region, ar-rayn terrane, Arabian shield

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Authors: Suleiman Garba, Haruna Ishaku

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Information and communication technology (ICT) includes any communication device encompassing: radio, television, cellular phones, computer, satellite systems and so on, as well as the various services and applications associated with them. The significance of ICT cannot be over-emphasized in education. The teaching and learning processes have integrated with the application of ICTs for effectiveness and enhancement of academic performance among the students. Today, as the educational sector is faced with series of changes and reforms, it was noted that the problem of information technology illiteracy was a serious one among the schools’ teachers in the country as it cuts across primary, secondary schools and tertiary institutions. This study investigated the impact of ICT on the academic performance of senior secondary schools students in Gwagwalada Area Council of Federal Capital Territory (FCT), Abuja. A sample of 120 SSS III students was involved in the study. They were selected by using simple random sampling technique. A questionnaire was developed and validated through expert judgement and reliability co-efficient of 0.81 was obtained. It was used to gather relevant data from the respondents. Findings revealed that there was positive impact of ICT on academic performance of senior secondary schools students. The findings indicated the causes of poor academic performance among the students as lack of qualified teachers to teach in schools, peer group influence, and bullying. Significantly, the findings revealed that ICT had a positive impact on students’ academic performance. The null hypotheses were tested using t-test at 0.05 level of significance. It was discovered that there was significant difference between male and female secondary schools’ students' impact of ICT on academic performance in Gwagawalada Area Council of FCT-Abuja. Based on these findings, some recommendations were made which include: adequate funds should be provided towards procurement of ICT resources, relevant textbooks to enhance students’ active participation in learning processes and students should be provided with internet accessibility at inexpensive rate so as to create a platform for accessing useful information in the pursuit of academic excellence.

Keywords: academic performance, impact, information communication technology, schools, students

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Authors: T. Frawley, G. O'Kelly

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The UCD School of Nursing, Midwifery and Health Systems Higher Diploma in Mental Health (HDMH) nursing programme commenced in January 2017. Forty students successfully completed the programme. Programme evaluation was conducted from the outset. Research ethics approval was granted by the UCD Human Research Ethics Committee – Sciences in November 2016 (LS-E-16-163). Plan for Sustainability: Each iteration of the programme continues to be evaluated and adjusted accordingly. Aims: The ultimate purpose of the HDMH programme is to prepare registered nurses (registered children’s nurse (RCN), registered nurse in intellectual disability (RNID) and registered general nurse (RGN)) to function as effective registered psychiatric nurses in all settings which provide care and treatment for people experiencing mental health difficulties. Curriculum evaluation is essential to ensure that the programme achieves its purpose, that aims and expected outcomes are met and that required changes are highlighted for the programme’s continuing positive development. Methods: Both quantitative and qualitative methods were used in the evaluation. A series of questionnaires were used (the majority pre and post programme) to determine student perceptions of the programme, behaviour and attitudinal change from commencement to completion. These included the student assessment of learning gains (SALG); mental health knowledge schedule (MAKS); mental health clinician attitudes scale (MICA); reported and intended behaviour scale (RIBS); and community attitudes towards the mentally ill (CAMI). In addition, student and staff focus groups were conducted. Evaluation methods also incorporated module feedback. Outcome/Results: The evaluation highlighted a very positive response in relation to the achievement of programme outcomes and preparation for future work as registered psychiatric nursing. Some areas were highlighted for further development, which have been taken cognisance of in the 2019 iteration of the programme.

Keywords: learning gains, mental health, nursing, stigma

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Authors: Pothula Madhusudhan Reddy

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Introduction: Yoga is a way of life. of uniting the mind, body and soul. It is also an art of living the right way. The techniques of Yoga are very practical, so they can always be applied. This is the reason why Yoga has been practiced for thousands of years and is still valid today. Importance of Yoga: Yoga that helps to inculcate healthy habits and adopt a healthy lifestyle to achieve good health Research Aim: The aim of this study is to explore the potential benefits of yoga for holistic health and wellbeing, both at an individual and societal level The ultimate goal of human being is to attain the state of perfect freedom from the shackles of ignorance, which is the generator of all the pangs and miseries of life. Methodology: This research follows a thematic and practical experience approach. Yoga includes body postures and movements (stretching), breathing practices, imagery, meditation, and progressive relaxation techniques. Data Collection: The data for this research is collected through a combination of literature review, expert interviews, and practical yoga sessions. The literature review provides a comprehensive understanding of the principles and practices of yoga, while expert interviews offer insights from experienced practitioners. Practical yoga sessions allow for first hand experiences and observations, facilitating a deeper understanding of the subject matter. Analysis Procedures: The collected data is analyzed thematically, where key themes and patterns related to the benefits and effects of yoga on holistic health and wellbeing are identified. The findings are then interpreted and synthesized to draw meaningful conclusions. Questions Addressed: This research addresses the following questions: What are the potential benefits of yoga for holistic health and wellbeing? How does yoga promote rejuvenate the body, mind, and senses? What are the implications of a society embracing yoga for overall societal wellbeing and happiness? Findings: The research highlights that practicing yoga can lead to increased awareness of the body, mind, and senses. It promotes overall physical and mental health, helping individuals achieve a state of happiness and contentment. Moreover, the study emphasizes that a society embracing yoga can contribute to the development of a healthy and happy community. Theoretical Importance: The study of yoga for holistic health and wellbeing holds theoretical importance as it provides insights into the science of yoga and its impact on individuals and society. It contributes to the existing body of knowledge on the subject and further establishes yoga as a potential tool for enhancing overall wellness. Conclusion: The study concludes that yoga is a powerful practice for achieving holistic health and wellbeing. This research provides valuable insights into the science of yoga and its potential as a tool for promoting overall wellness.

Keywords: yoga, asana, pranayama, meditation

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Faizan Tariq, Moayid Ali Zaidi

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Facial emotion recognition algorithms are expanding rapidly now a day. People are using different algorithms with different combinations to generate best results. There are six basic emotions which are being studied in this area. Author tried to recognize the facial expressions using object detector algorithms instead of traditional algorithms. Two object detection algorithms were chosen which are Faster R-CNN and YOLO. For pre-processing we used image rotation and batch normalization. The dataset I have chosen for the experiments is Static Facial Expression in Wild (SFEW). Our approach worked well but there is still a lot of room to improve it, which will be a future direction.

Keywords: face recognition, emotion recognition, deep learning, CNN

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Authors: Osakwe Jude Odiakaosa

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Today’s digital society is characterized by e-education or e-learning, e-commerce, and so on. All these have been propelled by digital revolution. Digital technology such as computer technology, Global Positioning System (GPS) and Geographic Information System (GIS) has been having a tremendous impact on the field of technology. This development has positively affected the scope, methods, speed of data acquisition, data management and the rate of delivery of the results (map and other map products) of data processing. This paper tries to address the impact of revolution brought by digital technology.

Keywords: digital revolution, internet, technology, data management

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Authors: Faris Alkhalil, Rana Bitar, Amer Azaz, Hisham Natour, Noora Almeraikhi, Mohamad Miqdady

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Background: Children with liver transplantation are achieving very good survival and so there is now a need to concentrate on achieving good health in these patients and preventing disease. Immunosuppressive medications have side effects that need to be monitored and if possible avoided. Glucocorticoids and calcineurin inhibitors are detrimental to bone and mineral homeostasis in addition steroids can also affect linear growth. Steroid sparing regimes in renal transplant children has shown to improve children’s height. Aim: We aim to review the growth and bone health of children post liver transplant by measuring bone mineral density (BMD) using dual energy X-ray absorptiometry (DEXA) scan and assessing if there is a clear link between poor growth and impaired bone health and use of long term steroids. Subjects and Methods: This is a single centre retrospective Cohort study, we reviewed the medical notes of children (0-16 years) who underwent a liver transplantation between November 2000 to November 2016 and currently being followed at our centre. Results: 39 patients were identified (25 males and 14 females), the median transplant age was 2 years (range 9 months - 16 years), and the median follow up was 6 years. Four patients received a combined transplant, 2 kidney and liver transplant and 2 received a liver and small bowel transplant. The indications for transplant included, Biliary Atresia (31%), Acute Liver failure (18%), Progressive Familial Intrahepatic Cholestasis (15%), transplantable metabolic disease (10%), TPN related liver disease (8%), Primary Hyperoxaluria (5%), Hepatocellular carcinoma (3%) and other causes (10%). 36 patients (95%) were on a calcineurin inhibitor (34 patients were on Tacrolimus and 2 on Cyclosporin). The other three patients were on Sirolimus. Low dose long-term steroids was used in 21% of the patients. A considerable proportion of the patients had poor growth. 15% were below the 3rd centile for weight for age and 21% were below the 3rd centile for height for age. Most of our patients with poor growth were not on long term steroids. 49% of patients had a DEXA scan post transplantation. 21% of these children had low bone mineral density, one patient had met osteoporosis criteria with a vertebral fracture. Most of our patients with impaired bone health were not on long term steroids. 20% of the patients who did not undergo a DEXA scan developed long bone fractures and 50% of them were on long term steroid use which may suggest impaired bone health in these patients. Summary and Conclusion: The incidence of impaired bone health, although studied in limited number of patients; was high. Early recognition and treatment should be instituted to avoid fractures and improve bone health. Many of the patients were below the 3rd centile for weight and height however there was no clear relationship between steroid use and impaired bone health, reduced weight and reduced linear height.

Keywords: bone, growth, pediatric, liver, transplantation

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Authors: Kamilia Mebarki

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The promotion of intercultural competence plays an important role in foreign language education. The outcome of intercultural educationalists‟ studies was the adoption of intercultural language learning and a modified version of the Communicative Competence that encompasses an intercultural component enabling language learners to communicate successfully interculturally. Intercultural Competencehas an even more central role in teaching English as a foreign language (EFL) since efforts are critical to preparing learners for intercultural communisation in our global world. In these efforts, EFL learning materials are a crucial stimulus for developing learners’ intercultural competence. There has been a continuous interest in the analysis of EFL textbooks by researcher all over the world. One specific area that has received prominent attention in recent years is a focus on how the cultural content of EFL materials promote intercultural competence. In the Algerian context, research on the locally produced EFL textbooks tend to focus on investigating the linguistic and communicative competence. The cultural content of the materials has not yet been systematically researched. Therefore, this study contributes to filling this gap by evaluating the locally published EFL textbook ‘New Prospects’ used at the high school level as well as investigating teachers’ views and attitudes on the cultural content of ‘New Prospects’ alongside two others locally produced EFL textbooks ‘Getting Through’ and ‘At the Crossroad’ used at high school level. To estimate the textbook’s potential of developing intercultural competence, mixed methods, a combination of quantitative and qualitative data collection, was used in the material evaluation analysed via content analysis and in the survey questionnaire and interview with teachers.Data collection and analysis were supported by the frameworks developed by the researcher for analysing the textbook, questionnaire, and interview. Indeed, based on the literature, three frameworks/ models are developed in this study to analyse, on one hand, the cultural contexts and themes discussed in the material that play an important role in fostering learners’ intercultural awareness. On the other hand, to evaluate the promotion of developing intercultural competence.

Keywords: intercultural communication, intercultural communicative competence, intercultural competence, EFL materials

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Authors: A. Giannakopoulos, S. B. Buckley

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Problem solving in any field is recognised as a prerequisite for any advancement in knowledge. For example in South Africa it is one of the seven critical outcomes of education together with critical thinking. As a systematic way to problem solving was initiated in mathematics by the great mathematician George Polya (the father of problem solving), more detailed and comprehensive ways in problem solving have been developed. This paper is based on the findings by the author and subsequent recommendations for further research in problem solving and critical thinking. Although the study was done in mathematics, there is no doubt by now in almost anyone’s mind that mathematics is involved to a greater or a lesser extent in all fields, from symbols, to variables, to equations, to logic, to critical thinking. Therefore it stands to reason that mathematical principles and learning cannot be divorced from any field. In management of knowledge situations, the types of problems are similar to mathematics problems varying from simple to analogical to complex; from well-structured to ill-structured problems. While simple problems could be solved by employees by adhering to prescribed sequential steps (the process), analogical and complex problems cannot be proceduralised and that diminishes the capacity of the organisation of knowledge creation and innovation. The low efficiency in some organisations and the low pass rates in mathematics prompted the author to view problem solving as a product. The authors argue that using mathematical approaches to knowledge management problem solving and treating problem solving as a product will empower the employee through further training to tackle analogical and complex problems. The question the authors asked was: If it is true that problem solving and critical thinking are indeed basic skills necessary for advancement of knowledge why is there so little literature of knowledge management (KM) about them and how they are connected and advance KM?This paper concludes with a conceptual model which is based on general accepted principles of knowledge acquisition (developing a learning organisation), knowledge creation, sharing, disseminating and storing thereof, the five pillars of knowledge management (KM). This model, also expands on Gray’s framework on KM practices and problem solving and opens the doors to a new approach to training employees in general and domain specific areas problems which can be adapted in any type of organisation.

Keywords: critical thinking, knowledge management, mathematics, problem solving

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Authors: Huimin Liu, Li Xiang, Yue Liu, Jing Wang

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Background: As one of typical case for the developing countries who are stepping into the aging times globally, more and more older people in China might face the problem of which they could not maintain normal life due to the functional disability. While the government take efforts to build long-term care system and further carry out related policies for the core concept, there is still lack of strong evidence to evaluating the profile of disability states in the elderly population and its transition rate. It has been proved that disability is a dynamic condition of the person rather than irreversible so it means possible to intervene timely on them who might be in a risk of severe disability. Objective: The aim of this study was to depict the picture of the disability transferring status of the older people in China, and then find out individual characteristics that change the state of disability to provide theory basis for disability prevention and early intervention among elderly people. Methods: Data for this study came from the 2011 baseline survey and the 2013 follow-up survey of the China Health and Retirement Longitudinal Study (CHARLS). Normal ADL function, 1~2 ADLs disability,3 or above ADLs disability and death were defined from state 1 to state 4. Multi-state Markov model was applied and the four-state homogeneous model with discrete states and discrete times from two visits follow-up data was constructed to explore factors for various progressive stages. We modeled the effect of explanatory variables on the rates of transition by using a proportional intensities model with covariate, such as gender. Result: In the total sample, state 2 constituent ratio is nearly about 17.0%, while state 3 proportion is blow the former, accounting for 8.5%. Moreover, ADL disability statistics difference is not obvious between two years. About half of the state 2 in 2011 improved to become normal in 2013 even though they get elder. However, state 3 transferred into the proportion of death increased obviously, closed to the proportion back to state 2 or normal functions. From the estimated intensities, we see the older people are eleven times as likely to develop at 1~2 ADLs disability than dying. After disability onset (state 2), progression to state 3 is 30% more likely than recovery. Once in state 3, a mean of 0.76 years is spent before death or recovery. In this model, a typical person in state 2 has a probability of 0.5 of disability-free one year from now while the moderate disabled or above has a probability of 0.14 being dead. Conclusion: On the long-term care cost considerations, preventive programs for delay the disability progression of the elderly could be adopted based on the current disabled state and main factors of each stage. And in general terms, those focusing elderly individuals who are moderate or above disabled should go first.

Keywords: Markov model, elderly people, disability, transition intensity

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Authors: Tommy Krieger, Klaus Gruendler

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We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies.

Keywords: democracy, democracy index, machine learning, support vector machines

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Authors: Volker Koch

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This paper deals with the education of purchasing professionals in Austria. In this education, equivalent and measurable criteria are collected in order to create a comparison. The comparison shows the problem. To make the aforementioned comparison possible, methodologies such as KODE-Competence Atlas or presentations in a matrix form are used. The result shows the content taught and whether there are any similarities or interesting differences in the current Austrian purchasers’ formations. Purchasing professionals learning competencies are also illustrated in the study result.

Keywords: competencies, education, purchasing professional, technological-oriented

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Authors: Sereen Itani

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As the world aspires for technological innovation, Innovative Robotics Technology-Supported Pedagogic Models in STEAM Education (Science, Technology, Engineering, Arts, and Mathematics) are critical in our global education system to build and enhance the next generation 21st century skills. Thus, diverse international schools endeavor in attempts to construct an integrated robotics and technology enhanced curriculum based on interdisciplinary subjects. Accordingly, it is vital that the globe remains resilient in STEAM fields by equipping the future learners and educators with Innovative Technology Experiences through robotics to support such fields. A variety of advanced teaching methods is employed to learn about Robotics Technology-integrated pedagogic models. Therefore, it is only when STEAM and innovations in Robotic Technology becomes integrated with real-world applications that transformational learning can occur. Robotics STEAM education implementation faces major challenges globally. Moreover, STEAM skills and concepts are communicated in separation from the real world. Instilling the passion for robotics and STEAM subjects and educators’ preparation could lead to the students’ majoring in such fields by acquiring enough knowledge to make vital contributions to the global STEAM industries. Thus, this necessitates the establishment of Pedagogic models such as Innovative Robotics Technologies to enhance STEAM education and develop students’ 21st-century skills. Moreover, an ICT innovative supported robotics classroom will help educators empower and assess students academically. Globally, the Robotics Design System and platforms are developing in schools and university labs creating a suitable environment for the robotics cross-discipline STEAM learning. Accordingly, the research aims at raising awareness about the importance of robotics design systems and methodologies of effective employment of robotics innovative technology-supported pedagogic models to enhance and develop (STEAM) education globally and enhance the next generation 21st century skills.

Keywords: education, robotics, STEAM (Science, Technology, Engineering, Arts and Mathematics Education), challenges

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Authors: Yichao Liu, Peter Fransson, Julian Heidecke, Jonas Wallin, Joacim Rockloev

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Dengue, a mosquito-borne viral disease, poses a significant public health challenge in endemic tropical or subtropical countries, including Sri Lanka. To reveal insights into the complexity of the dynamics of this disease and study the drivers, a comprehensive model capable of both robust forecasting and insightful inference of drivers while capturing the co-circulating of several virus strains is essential. However, existing studies mostly focus on only one aspect at a time and do not integrate and carry insights across the siloed approach. While mechanistic models are developed to capture immunity dynamics, they are often oversimplified and lack integration of all the diverse drivers of disease transmission. On the other hand, purely data-driven methods lack constraints imposed by immuno-epidemiological processes, making them prone to overfitting and inference bias. This research presents a hybrid model that combines machine learning techniques with mechanistic modelling to overcome the limitations of existing approaches. Leveraging eight years of newly reported dengue case data, along with socioeconomic factors, such as human mobility, weekly climate data from 2011 to 2018, genetic data detecting the introduction and presence of new strains, and estimates of seropositivity for different districts in Sri Lanka, we derive a data-driven vector (SEI) to human (SEIR) model across 16 regions in Sri Lanka at the weekly time scale. By conducting ablation studies, the lag effects allowing delays up to 12 weeks of time-varying climate factors were determined. The model demonstrates superior predictive performance over a pure machine learning approach when considering lead times of 5 and 10 weeks on data withheld from model fitting. It further reveals several interesting interpretable findings of drivers while adjusting for the dynamics and influences of immunity and introduction of a new strain. The study uncovers strong influences of socioeconomic variables: population density, mobility, household income and rural vs. urban population. The study reveals substantial sensitivity to the diurnal temperature range and precipitation, while mean temperature and humidity appear less important in the study location. Additionally, the model indicated sensitivity to vegetation index, both max and average. Predictions on testing data reveal high model accuracy. Overall, this study advances the knowledge of dengue transmission in Sri Lanka and demonstrates the importance of incorporating hybrid modelling techniques to use biologically informed model structures with flexible data-driven estimates of model parameters. The findings show the potential to both inference of drivers in situations of complex disease dynamics and robust forecasting models.

Keywords: compartmental model, climate, dengue, machine learning, social-economic

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Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

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Authors: Carly Nesvat, Dominic Jarrett, Colin Thomson, Wilma Coltart, Thelma Bowers, Jan Thomson

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Introduction: Within Scotland, the Same As You strategy committed to moving people with learning disabilities out of long-stay hospital accommodation into homes in the community. Much of the focus of this movement was on the placement of people within individual homes. In order to achieve this, potentially excessive supports were put in place which created dependence, and carried significant ongoing cost primarily for local authorities. The greater focus on empowerment and community participation which has been evident in more recent learning disability strategy, along with the financial pressures being experienced across the public sector, created an imperative to re-examine that provision, particularly in relation to the use of expensive sleepover supports to individuals, and the potential for this to be appropriately scaled back through the use of telecare. Method: As part of a broader programme of redesigning overnight supports within North Ayrshire, a cluster of individuals living in close proximity were identified, who were in receipt of overnight supports, but who were identified as having the capacity to potentially benefit from their removal. In their place, a responder service was established (an individual staying overnight in a nearby service user’s home), and a variety of telecare solutions were placed within individual’s homes. Active and passive technology was connected to an Alarm Receiving Centre, which would alert the local responder service when necessary. Individuals and their families were prepared for the change, and continued to be informed about progress with the pilot. Results: 4 individuals, 2 of whom shared a tenancy, had their sleepover supports removed as part of the pilot. Extensive data collection in relation to alarm activation was combined with feedback from the 4 individuals, their families, and staff involved in their support. Varying perspectives emerged within the feedback. 3 of the individuals were clearly described as benefitting from the change, and the greater sense of independence it brought, while more concerns were evident in relation to the fourth. Some family members expressed a need for greater preparation in relation to the change and ongoing information provision. Some support staff also expressed a need for more information, to help them understand the new support arrangements for an individual, as well as noting concerns in relation to the outcomes for one participant. Conclusion: Developing a telecare response in relation to a cluster of individuals was facilitated by them all being supported by the same care provider. The number of similar clusters of individuals being identified within North Ayrshire is limited. Developing other solutions such as a response service for redesign will potentially require greater collaboration between different providers of home support, as well as continuing to explore the full range of telecare, including digital options. The pilot has highlighted the need for effective preparatory and ongoing engagement with staff and families, as well as the challenges which can accompany making changes to long-standing packages of support.

Keywords: challenges, change, engagement, telecare

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Joël Passicousset, David Gilbert, Chloé Chervier-Legourd, Emmanuel Caron-Garant, Didier Labarre

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Living soil agriculture tries to reconciliate food production while improving soil health, soil biodiversity, soil fertility and more generally attenuating the inherent environmental drawbacks induced by modern agriculture. Using appropriate organic materials as soil amendments has a role to play in the aim of increasing the soil organic matter, improving soil fertility, sequestering carbon, and diminishing the dependence on both mineral fertilizer and pesticides. Insect farming consists in producing insects that can be used as a rich-in-protein and entomo-based food. Usually, detritivores are chosen, thus they can be fed with food wastes, which contributes to circular economy while producing low-carbon food. This process also produces frass, made of insect feces, exuvial material, and non-digested fibrous material, that have valuable fertilizer and biostimulation properties. But frass, used as a sole fertilizer on a crop may be not completely adequate for plants’ needs. This is why this project considers black soldier fly (termed BSF, one of the three main insect species grown commercially) frass as a complementary fertilizer, both in organic and in conventional contexts. Three kinds of experiments are made to understand the behaviour of fertilizer treatments based on frass incorporation. Lab-scale mineralization experiments suggest that BSF frass alone mineralizes more slowly than chicken manure alone (CM), but at a ratio of 90% CM-10% BSF frass, the mineralization rate of the mixture is higher than both frass and CM individually. For example, in the 7 days following the fertilization with same nitrogen amount introduced among treatments, around 80% of the nitrogen content supplied through 90% CM-10% BSF frass fertilization is present in the soil under mineral forms, compared to roughly 60% for commercial CM fertilization and 45% with BSF-frass. This suggests that BSF frass contains a more recalcitrant form of organic nitrogen than CM, but also that BSF frass has a highly active microbiota that can increase CM mineralization rate. Consequently, when progressive mineralization is needed, pure BSF-frass may be a consistent option from an agronomic aspect whereas, for specific crops that require spikes of readily available nitrogen sources (like cranberry), fast release 90CM-10BSF frass biofertilizer are more appropriate. Field experiments on cranberry suggests that, indeed, 90CM-10BSF frass is a potent candidate for organic cranberry production, as currently, organic growers rely solely on CM, whose mineralization kinetics are known to imperfectly match plant’s needs, which is known to be a major reason that sustains the current yield gap between conventional and organic cranberry sectors.

Keywords: soil mineralization, biofertilizer, BSF-frass, chicken manure, soil functions, nitrogen, soil microbiota

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