Search results for: machine tools

Authors: Candela Contero Urgal

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Using realia to teach English for Specific Purposes (ESP) is a must, as it is thought to be designed to meet the students’ real needs in their professional life. Teachers are then expected to offer authentic materials and set students in authentic contexts where their learning outcomes can be highly meaningful. One way of engaging students is using social networks as a way to bridge the gap between their everyday life and their ESP learning outcomes. It is in ESP, particularly in Business English teaching, that our study focuses, as the ongoing process of digitalization is leading firms to use social media to communicate with potential clients. The present paper is aimed at carrying out a case study in which different digital tools are employed as a way to offer a collection of formats businesses are currently using so as to internationalize and advertise their products and services. A secondary objective of our study will then be to progress on the development of multidisciplinary competencies students are to acquire during their degree. A two-phased study will be presented. The first phase will cover the analysis of course tasks accomplished by undergraduate students at the University of Cadiz (Spain) in their third year of the Degree in Business Management and Administration by comparing the results obtained during the years 2019 to 2021. The second part of our study will present a survey conducted to these students in 2021 and 2022 so as to verify their interest in learning new ways to digitalize as well as internationalize their future businesses. Findings will confirm students’ interest in working with updated realia in their Business English lessons, as a consequence of their strong belief in the necessity to have authentic contexts and didactic resources. Despite the limitations social media can have as a means to teach business English, students will still find it highly beneficial since it will foster their familiarisation with the digital tools they will need to use when they get to the labour market.

Keywords: English for specific purposes, business English, internationalization of higher education, foreign language teaching

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Authors: Ling Liyun

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In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

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Authors: Qiuying Li

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It is a shared opinion that sustainable development requires a system discontinuity, meaning that radical changes in the way we produce and consume are needed. Within this framework there is an emerging understanding that an important contribution to this change can be directly linked to decisions taken in the design phase of products, services and systems. Design schools have therefore to be able to provide design students with a broad knowledge and effective Design for Sustainability tools, in order to enable a new generation of designers in playing an active role in reorienting our consumption and production patterns.

Keywords: design for sustainability, services, systems, materials, ecomaterials

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Authors: Rebecca Lynn

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The successful inclusion of students with disabilities depends upon many factors, including the collaboration between general and special education teachers for meeting student learning goals as outlined in the Individualized Education Plan (IEP). However, Individualized Education Plans do not provide sufficient information on accommodations and modifications for the variety of general education contexts and content areas in which a student may participate. In addition, general and special education teachers lack observation skills and tools for gathering essential information about the strengths and needs of students with disabilities in relation to general education instruction and classrooms. More research and tools are needed for planning adaptations that increase access to content in general education classrooms. This paper will discuss the outcomes of a qualitative field-based study of a structured observation instrument used for gathering information on student strengths and needs in relation to social, academic and regulatory expectations during instruction in general education classrooms. The study explores the following questions: To what extent does the observation structure and instrument increase collaborative planning of adaptations in general education classrooms for students with disabilities? To what extent does the observation structure and instrument change pedagogical practices and collaboration in general education classrooms for fostering successful inclusion? A hypothesis of this study was that use of the instrument in the context of lessons and in collaborative debriefing would increase awareness and use of meaningful adaptations, and lead to universal design in the planning of instruction. A finding of the study is a shift from viewing students with disabilities as passive participants to a more pedagogical inclusion as teachers developed skills in observation and created content/context-specific adaptations for students with disabilities in the general education classroom.

Keywords: adaptations, collaboration, inclusion, observations

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Authors: Denise Levy, Helen J. Khoury

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Nuclear technology is a controversial issue among a great share of the Brazilian population. Misinformation and common wrong beliefs confuse public’s perceptions and the scientific community is expected to offer a wider perspective on the benefits and risks resulting from ionizing radiation in everyday life. Attentive to the need of new approaches between science and society, the Nuclear Energy Museum, in northeast Brazil, is an initiative created to communicate the growing impact of the beneficial applications of nuclear technology in medicine, industry, agriculture and electric power generation. Providing accessible scientific information, the museum offers a rich learning environment, making use of different educational strategies, such as films, interactive panels and multimedia learning tools, which not only increase the enjoyment of visitors, but also maximize their learning potential. Developed according to modern active learning instructional strategies, multimedia materials are designed to present the increasingly role of nuclear science in modern life, transforming science education into a meaningful learning experience. In year 2016, nine different interactive computer-based activities were developed, presenting curiosities about ionizing radiation in different landmarks around the world, such as radiocarbon dating works in Egypt, nuclear power generation in France and X-radiography of famous paintings in Italy. Feedback surveys have reported a high level of visitors’ satisfaction, proving the high quality experience in learning nuclear science at the museum. The Nuclear Energy Museum is the first and, up to the present time, the only permanent museum in Brazil devoted entirely to nuclear science.

Keywords: nuclear technology, multimedia learning tools, science museum, society and education

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Evgeny Obraztsov, Ilya Kremnev, Vitaly Sokolov, Maksim Gavrilov, Evgeny Tretyakov, Vladimir Kukhtevich, Vladimir Bezlepkin

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Over the last decade, a specific set of connected software tools and calculation codes has been gradually developed. It allows simulating I&C systems, thermal-hydraulic, neutron-physical and electrical processes in elements and systems at the Unit of NPP (initially with WWER (pressurized water reactor)). In 2012 it was called a complex simulation suite “Virtual Unit of NPP” (or CSS “VEB” for short). Proper application of this complex tool should result in a complex coupled mathematical computational model. And for a specific design of NPP, it is called the Virtual Power Unit (or VPU for short). VPU can be used for comprehensive modelling of a power unit operation, checking operator's functions on a virtual main control room, and modelling complicated scenarios for normal modes and accidents. In addition, CSS “VEB” contains a combination of thermal hydraulic codes: the best-estimate (two-liquid) calculation codes KORSAR and CORTES and a homogenous calculation code TPP. So to analyze a specific technological system one can build thermal-hydraulic simulation models with different detalization levels up to a nodalization scheme with real geometry. And the result at some points is similar to the notion “engineering/testing simulator” described by the European utility requirements (EUR) for LWR nuclear power plants. The paper is dedicated to description of the tools mentioned above and an example of the application of the engineering thermal-hydraulic simulator in analysis of the boron acid concentration in the primary coolant (changed by the make-up and boron control system).

Keywords: best-estimate code, complex simulation suite, engineering simulator, power plant, thermal hydraulic, VEB, virtual power unit

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Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Shaurya Chauhan, Sagar Gupta

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Prominent urbanizing centres across the globe like Delhi, Dhaka, or Manila have exhibited that development often faces a challenge in bridging the gap among the top-down collective requirements of the city and the bottom-up individual aspirations of the ever-diversifying population. When this exclusion is intertwined with rapid urbanization and diversifying urban demography: unplanned sprawl, poor planning, and low-density development emerge as automated responses. In parallel, new ideas and methods of densification and public participation are being widely adopted as sustainable alternatives for the future of urban development. This research advocates a collaborative design method for future development: one that allows rapid application with its prototypical nature and an inclusive approach with mediation between the 'user' and the 'urban', purely with the use of empirical tools. Building upon the concepts and principles of 'open-sourcing' in design, the research establishes a design framework that serves the current user requirements while allowing for future citizen-driven modifications. This is synthesized as a 3-tiered model: user needs – design ideology – adaptive details. The research culminates into a context-responsive 'open source project development framework' (hereinafter, referred to as OSPDF) that can be used for on-ground field applications. To bring forward specifics, the research looks at a 300-acre redevelopment in the core of a rapidly urbanizing city as a case encompassing extreme physical, demographic, and economic diversity. The suggestive measures also integrate the region’s cultural identity and social character with the diverse citizen aspirations, using architecture and urban design tools, and references from recognized literature. This framework, based on a vision – feedback – execution loop, is used for hypothetical development at the five prevalent scales in design: master planning, urban design, architecture, tectonics, and modularity, in a chronological manner. At each of these scales, the possible approaches and avenues for open- sourcing are identified and validated, through hit-and-trial, and subsequently recorded. The research attempts to re-calibrate the architectural design process and make it more responsive and people-centric. Analytical tools such as Space, Event, and Movement by Bernard Tschumi and Five-Point Mental Map by Kevin Lynch, among others, are deep rooted in the research process. Over the five-part OSPDF, a two-part subsidiary process is also suggested after each cycle of application, for a continued appraisal and refinement of the framework and urban fabric with time. The research is an exploration – of the possibilities for an architect – to adopt the new role of a 'mediator' in development of the contemporary urbanity.

Keywords: open source, public participation, urbanization, urban development

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: Sadat Sembatya

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Tantalum metal is used in addressing capacitance challenge in the 21st-century technology growth. Tantalum is rarely found in its elemental form. Hence it’s often found with niobium and the radioactive elements of thorium and uranium. Industrial processes are required to extract pure tantalum. Its deposits are mainly oxide associated and exist in Ta-Nb oxides such as tapiolite, wodginite, ixiolite, rutile and pyrochlore-supergroup minerals are of minor importance. The stability and chemical inertness of tantalum makes it a valuable substance for laboratory equipment and a substitute for platinum. Each period of Tantalum ore formation is characterized by specific mineralogical and geochemical features. Compositions of Columbite-Group Minerals (CGM) are variable: Fe-rich types predominate in the Man Shield (Sierra Leone), the Congo Craton (DR Congo), the Kamativi Belt (Zimbabwe) and the Jos Plateau (Nigeria). Mn-rich columbite-tantalite is typical of the Alto Ligonha Province (Mozambique), the Arabian-Nubian Shield (Egypt, Ethiopia) and the Tantalite Valley pegmatites (southern Namibia). There are large compositional variations through Fe-Mn fractionation, followed by Nb-Ta fractionation. These are typical for pegmatites usually associated with very coarse quartz-feldspar-mica granites. They are young granitic systems of the Kibara Belt of Central Africa and the Older Granites of Nigeria. Unlike ‘simple’ Be-pegmatites, most Ta-Nb rich pegmatites have the most complex zoning. Hence we need systematic exploration tools to find and rapidly assess the potential of different pegmatites. The pegmatites exist as known deposits (e.g., abandoned mines) and the exposed or buried pegmatites. We investigate rocks and minerals to trace for the possibility of the effect of hydrothermal alteration mainly for exposed pegmatites, do mineralogical study to prove evidence of gradual replacement and geochemistry to report the availability of trace elements which are good indicators of mineralisation. Pegmatites are not good geophysical responders resulting to the exclusion of the geophysics option. As for more advanced prospecting, we bulk samples from different zones first to establish their grades and characteristics, then make a pilot test plant because of big samples to aid in the quantitative characterization of zones, and then drill to reveal distribution and extent of different zones but not necessarily grade due to nugget effect. Rapid assessment tools are needed to assess grade and degree of fractionation in order to ‘rule in’ or ‘rule out’ a given pegmatite for future work. Pegmatite exploration is also unique, high risk and expensive hence right traceability system and certification for 3Ts are highly needed.

Keywords: exploration, mineralogy, pegmatites, tantalum

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Maria Angela Bedini, Fabio Bronzini

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The Italian experiences, positive and negative, of the post-earthquake are conditioned by long times and structural bureaucratic constraints, also motivated by the attempt to contain mafia infiltration and corruption. The transition from the operational phase of the emergency to the planning phase of the reconstruction project is thus hampered by a series of inefficiencies and delays, incompatible with the need for rapid recovery of the territories in crisis. In fact, intervening in areas affected by seismic events means at the same time associating the reconstruction plan with an urban and territorial rehabilitation project based on strategies and tools in which prevention and safety play a leading role in the regeneration of territories in crisis and the return of the population. On the contrary, the earthquakes that took place in Italy have instead further deprived the territories affected of the minimum requirements for habitability, in terms of accessibility and services, accentuating the depopulation process, already underway before the earthquake. The objective of this work is to address with implementing and programmatic tools the procedures and strategies to be put in place, today and in the future, in Italy and abroad, to face the challenge of the reconstruction of activities, sociality, services, risk mitigation: a protocol of operational intentions and firm points, open to a continuous updating and implementation. The methodology followed is that of the comparison in a synthetic form between the different Italian experiences of the post-earthquake, based on facts and not on intentions, to highlight elements of excellence or, on the contrary, damage. The main results obtained can be summarized in technical comparison cards on good and bad practices. With this comparison, we intend to make a concrete contribution to the reconstruction process, certainly not only related to the reconstruction of buildings but privileging the primary social and economic needs. In this context, the recent instrument applied in Italy of the strategic urban and territorial SUM (Minimal Urban Structure) and the strategic monitoring process become dynamic tools for supporting reconstruction. The conclusions establish, by points, a protocol of interventions, the priorities for integrated socio-economic strategies, multisectoral and multicultural, and highlight the innovative aspects of 'inversion' of priorities in the reconstruction process, favoring the take-off of 'accelerator' interventions social and economic and a more updated system of coexistence with risks. In this perspective, reconstruction as a necessary response to the calamitous event can and must become a unique opportunity to raise the level of protection from risks and rehabilitation and development of the most fragile places in Italy and abroad.

Keywords: an operational protocol for reconstruction, operational priorities for coexistence with seismic risk, social and economic interventions accelerators of building reconstruction, the difficult post-earthquake reconstruction in Italy

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Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: Sameh Al-Natour, Izak Benbasat

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The introduction of a multitude of new and interactive e-commerce information technology (IT) artifacts has impacted adoption research. Rather than solely functioning as productivity tools, new IT artifacts assume the roles of interaction mediators and social actors. This paper describes the varying roles assumed by IT artifacts, and proposes and distinguishes between four distinct foci of how the artifacts are evaluated. It further proposes a theoretical model that maps the different views of IT artifacts to four distinct types of evaluations.

Keywords: IT adoption, IT artifacts, similarity, social actor

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Authors: Ishtiaq Ahmad Gondal, Mubasher Hussain

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Although the world always has spent a lot to counter the terror, thousands of millions of dollars have been spent in this regard after 9/11 that result to thwart some dangerous plots of terrorists. It is also un-ignorable that the terrorists, keeping the counterterrorist actions in their mind, always planned new ways for their operations, yet there is one thing still common in most terrorists' attacks: to use the label of religion, regardless any specific religion, in any form. The terrorism, in past few years, has also hit state's security, its consistency and coherence for achieving their cultural, political and military objectives. So, if they are not treated harshly for making the people's minds and their society dirty they will continue spreading chaos, anarchy and destruction among the ignorant and innocent people. Australia is doing its best to eliminate terrorism by using different tools such as by educating people and reducing poverty. There is still need to improve the tool of education as it can be used as one of the most effective tools to counter the terrorism. It is, as this paper will highlight, the need of contemporary time for establishing some high level educational centers that can educate people and keep them safe from any kind of terror incident. This study also concluded that common man, to keep himself saved from such activities and incidents, can be educated through public awareness movements and campaigns on media and at social gatherings. There is, according to the study, a need to reorganize the curriculum taught in different educational institutions especially in Islamic Schools (Madāris) that are assumed by some western writers as place of extremists, for the better understanding of moral and social obligations, fundamental rights, religious beliefs as well as cultural and social values to promote social justice and equality. This paper is an attempt to show the role of the Sheikh Zayed Islamic Centre in this regard.

Keywords: social justice, counterterrorism, educational policy, religion, peace, terrorism

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Authors: Nwadioha Samuel Iheanacho, Abah Paul, Odimayo Simidele Michael

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Background: The World Health Assembly through the Global Health Sector Strategy on viral hepatitis calls for the elimination of viral hepatitis as a public health threat by 2030. All hands are on deck to actualize this goal through an effective and active vaccination and monitoring tool. Aim: To combine the Epidemiologic with Laboratory Hepatitis B Virus vaccination monitoring tools. Method: Laboratory results analysis of subjects recruited during the World Hepatitis week from July 2020 to July 2021 was done after obtaining their epidemiologic data on Hepatitis B virus risk factors, in the Medical Microbiology Laboratory of Benue State University Teaching Hospital, Nigeria. Result: A total of 500 subjects comprising males 60.0%(n=300/500) and females 40.0%(n=200/500) were recruited. A fifty-three percent majority was of the age range of 26 to 36 years. Serologic profiles were as follows, 15.0%(n=75/500) HBsAg; 7.0% (n=35/500) HBeAg; 8.0% (n=40/500) Anti-Hbe; 20.0% (n=100/500) Anti-HBc and 38.0% (n=190/500) Anti-HBs. Immune responses to vaccination were as follows, 47.0%(n=235/500) Immune naïve {no serologic marker + normal ALT}; 33%(n=165/500) Immunity by vaccination {Anti-HBs + normal ALT}; 5%(n=25/500) Immunity to previous infection {Anti-HBs, Anti-HBc, +/- Anti-HBe + normal ALT}; 8%(n=40/500) Carriers {HBsAg, Anti-HBc, Anti-HBe +normal ALT} and 7% (35/500) Anti-HBe serum- negative infections {HBsAg, HBeAg, Anti-HBc +elevated ALT}. Conclusion: The present 33.0% immunity by vaccination coverage in Central Nigeria was much lower than the 41.0% national peak in 2013, and a far cry from the global expectation of attainment of a Global Health Sector Strategy on the elimination of viral hepatitis as a public health threat by 2030. Therefore, more creative ideas and collective effort are needed to attain this goal of the World Health Assembly.

Keywords: Hepatitis B, vaccination status, laboratory tools, resource-limited settings

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Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

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Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: E. Omanya, E. Mueni, G. Makau, M. Kariuki

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HIV infection is the strongest risk factor for a person to develop TB. Isoniazid preventive therapy (IPT) for People Living with HIV (PLWHIV) not only reduces the individual patients’ risk of developing active TB but mitigates cross infection. In Kenya, IPT for six months was recommended through the National TB, Leprosy and Lung Disease Program to treat latent TB. In spite of this recommendation by the national government, uptake of IPT among PLHIV remained low in Kenya by the end of 2015. The USAID/Kenya and East Africa Afya Jijini project, which supports 42 TBHIV health facilities in Nairobi County, began addressing low uptake of IPT through Quality Improvement (QI) teams set up at the facility level. Quality is characterized by WHO as one of the four main connectors between health systems building blocks and health systems outputs. Afya Jijini implements the Kenya Quality Model for Health, which involves QI teams being formed at the county, sub-county and facility levels. The teams review facility performance to identify gaps in service delivery and use QI tools to monitor and improve performance. Afya Jijini supported the formation of these teams in 42 facilities and built the teams’ capacity to review data and use QI principles to identify and address performance gaps. When the QI teams began working on improving IPT uptake among PLHIV, uptake was at 31.8%. The teams first conducted a root cause analysis using cause and effect diagrams, which help the teams to brainstorm on and to identify barriers to IPT uptake among PLHIV at the facility level. This is a participatory process where program staff provides technical support to the QI teams in problem identification and problem-solving. The gaps identified were inadequate knowledge and skills on the use of IPT among health care workers, lack of awareness of IPT by patients, inadequate monitoring and evaluation tools, and poor quantification and forecasting of IPT commodities. In response, Afya Jijini trained over 300 health care workers on the administration of IPT, supported patient education, supported quantification and forecasting of IPT commodities, and provided IPT data collection tools to help facilities monitor their performance. The facility QI teams conducted monthly meetings to monitor progress on implementation of IPT and took corrective action when necessary. IPT uptake improved from 31.8% to 61.2% during the second year of the Afya Jijini project and improved to 80.1% during the third year of the project’s support. Use of QI teams and root cause analysis to identify and address service delivery gaps, in addition to targeted program interventions and continual performance reviews, can be successful in increasing TB related service delivery uptake at health facilities.

Keywords: isoniazid, quality, health care workers, people leaving with HIV

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Authors: Waqas Ashraf, Yasir Nawab, Haritham Khan, Habib Awais, Shahbaz Ahmad

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Seersucker is a popular lightweight fabric widely used in men’s and women’s suiting, casual wear, children’s clothing, house robes, bed spreads and for spring and summer wear. The puckered effect generates air spaces between body and the fabric, keeping the wearer cool in hot conditions. The aim of this work was to develop knitted seersucker fabric on single cylinder weft knitting machine using plain jersey structure. Core spun cotton yarn and cotton spun yarn of same linear density were used. Core spun cotton yarn, contains cotton fiber in the sheath and elastase filament in the core. The both yarn were fed at regular interval to feeders on the machine. The loop length and yarn tension were kept constant at each feeder. The samples were then scoured and bleached. After wet processing, the fabric samples were washed and tumble dried. Parameters like loop length, stitch density and areal density were measured after conditioning these samples for 24 hours in Standard atmospheric condition. Produced sample has a regular puckering stripe along the width of the fabric with same height. The stitch density of both the flat and puckered area of relaxed fabric was found to be different .Air permeability and moisture management tests were performed. The results indicated that the knitted seersucker fabric has better wicking and moisture management properties as the flat area contact, whereas puckered area held away from the skin. Seersucker effect in knitted fabric was achieved by the difference of contraction of both sets of courses produced from different types of yarns. The seer sucker fabric produce by knitting technique is less expensive as compared to woven seer sucker fabric as there is no need of yarn preparation. The knitted seersucker fabric is more practicable for summer dresses, skirts, blouses, shirts, trousers and shorts.

Keywords: air permeability, knitted structure, moisture management, seersucker

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Authors: Anwesha Pathak

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With the increasing number of satellites and space debris, space traffic management (STM) becomes crucial. AI can aid in STM by predicting and preventing potential collisions, optimizing satellite trajectories, and managing orbital slots. Governance frameworks need to address the integration of AI algorithms in STM to ensure safe and sustainable satellite activities. AI and governance play significant roles in the context of satellite activities in space. Artificial intelligence (AI) technologies, such as machine learning and computer vision, can be utilized to process vast amounts of data received from satellites. AI algorithms can analyse satellite imagery, detect patterns, and extract valuable information for applications like weather forecasting, urban planning, agriculture, disaster management, and environmental monitoring. AI can assist in automating and optimizing satellite operations. Autonomous decision-making systems can be developed using AI to handle routine tasks like orbit control, collision avoidance, and antenna pointing. These systems can improve efficiency, reduce human error, and enable real-time responsiveness in satellite operations. AI technologies can be leveraged to enhance the security of satellite systems. AI algorithms can analyze satellite telemetry data to detect anomalies, identify potential cyber threats, and mitigate vulnerabilities. Governance frameworks should encompass regulations and standards for securing satellite systems against cyberattacks and ensuring data privacy. AI can optimize resource allocation and utilization in satellite constellations. By analyzing user demands, traffic patterns, and satellite performance data, AI algorithms can dynamically adjust the deployment and routing of satellites to maximize coverage and minimize latency. Governance frameworks need to address fair and efficient resource allocation among satellite operators to avoid monopolistic practices. Satellite activities involve multiple countries and organizations. Governance frameworks should encourage international cooperation, information sharing, and standardization to address common challenges, ensure interoperability, and prevent conflicts. AI can facilitate cross-border collaborations by providing data analytics and decision support tools for shared satellite missions and data sharing initiatives. AI and governance are critical aspects of satellite activities in space. They enable efficient and secure operations, ensure responsible and ethical use of AI technologies, and promote international cooperation for the benefit of all stakeholders involved in the satellite industry.

Keywords: satellite, space debris, traffic, threats, cyber security.

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Authors: Joseph Bertrand Iboum Kissaaka, Charles Fonyuy Ngum Tchioben, Paul Gustave Fowe Kwetche, Jeannette Ngo Elogan Ntem, Joseph Binyet Njebakal, Ribert Yvan Makosso-Tchapi, François Mvondo Owono, Marie Joseph Ntamak-Nida

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Located in the Gulf of Guinea, the Kribi-Campo sub-basin belongs to the Aptian salt basins along the West African Margin. In this paper, we investigated the tectono-stratigraphic architecture of the basin, focusing on the role of salt tectonics and strike-slip faults along the Kribi Fracture Zone with implications for reservoir prediction. Using 2D seismic data and well data interpreted through sequence stratigraphy with integrated seismic attributes analysis with Python Programming and unsupervised Machine Learning, at least six second-order sequences, indicating three main stages of tectono-stratigraphic evolution, were determined: pre-salt syn-rift, post-salt rift climax and post-rift stages. The pre-salt syn-rift stage with KTS1 tectonosequence (Barremian-Aptian) reveals a transform rifting along NE-SW transfer faults associated with N-S to NNE-SSW syn-rift longitudinal faults bounding a NW-SE half-graben filled with alluvial to lacustrine-fan delta deposits. The post-salt rift-climax stage (Lower to Upper Cretaceous) includes two second-order tectonosequences (KTS2 and KTS3) associated with the salt tectonics and Campo High uplift. During the rift-climax stage, the growth of salt diapirs developed syncline withdrawal basins filled by early forced regression, mid transgressive and late normal regressive systems tracts. The early rift climax underlines some fine-grained hangingwall fans or delta deposits and coarse-grained fans from the footwall of fault scarps. The post-rift stage (Paleogene to Neogene) contains at least three main tectonosequences KTS4, KTS5 and KTS6-7. The first one developed some turbiditic lobe complexes considered as mass transport complexes and feeder channel-lobe complexes cutting the unstable shelf edge of the Campo High. The last two developed submarine Channel Complexes associated with lobes towards the southern part and braided delta to tidal channels towards the northern part of the Kribi-Campo sub-basin. The reservoir distribution in the Kribi-Campo sub-basin reveals some channels, fan lobes reservoirs and stacked channels reaching up to the polygonal fault systems.

Keywords: tectono-stratigraphic architecture, Kribi-Campo sub-basin, machine learning, pre-salt sequences, post-salt sequences

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Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

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Authors: Marcin Baranski

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This article presents a vibration diagnostic method designed for permanent magnets (PM) traction motors. Those machines are commonly used in traction drives of electrical vehicles. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. This work presents: field-circuit model, results of static tests, results of calculations and simulations.

Keywords: electrical vehicle, permanent magnet, traction drive, vibrations, electrical machine, eccentricity

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Authors: Okba Belahssen, Said Benramache

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This paper presents wear behavior of the plasma-nitrided 20MnCr5 steel. Untreated and plasma nitrided samples were tested. The morphology was observed by scanning electron microscopy (SEM). The plasma nitriding behaviors of 20MnCr5 steel have been assessed by evaluating tribological properties and surface hardness by using a pin-on-disk wear machine and microhardness tester. Experimental results showed that the nitrides ε-Fe2−3N and γ′-Fe4N present in the white layer improve the wear resistance.

Keywords: plasma-nitriding, alloy 20mncr5, steel, friction, wear

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Authors: Virakti Dhaval Shah

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Advances in technology and the changes in lifestyle and work expectations amid the COVID-19 pandemic are requiring changes to vocational assessment, provision of vocational training, and identification of job matches for individuals with intellectual disability. Vocational assessment involves the assessment of interests, skills, abilities, and strengths/weaknesses, as well as a detailed understanding of healthcare, familial, social, community, workplace-related and policy-level protective and risk factors impacting the individual. In India, vocational assessment procedures such as interviews play a major role in vocational placement today. Unfortunately, many of the most widely used vocational assessment instruments in India were developed in the 1970s to 2000s and have not been revised since. Hence, there is a dire need to update existing tools and prepare a structured approach for vocational service providers to meet the unique employment needs of individuals with intellectual disabilities. To address this need, this project designed a multi-domain assessment toolkit for a structured approach to vocational assessment, training, and job placement for individuals with intellectual disabilities in India. Methods included conducting an empirical review of the available tools currently in use in India for the vocational assessment of persons with intellectual disabilities. Domains addressed in the instrument review were organized into a structured system, and additional items related to contemporary technology, pandemic-related experiences of persons with disabilities, and changes in lifestyle and work expectations due to the pandemic were added. Items assessing behaviors, provision of vocational training, and identification of job matches for individuals with intellectual disabilities were developed. The proposed tool has the potential to benefit organizations working with and preparing individuals with intellectual disabilities to find successful employment by undertaking a structured approach to vocational assessment, rehabilitation, training, and placement. It can be particularly useful for guiding new professionals doing vocational rehabilitation in India.

Keywords: intellectual disability, rehabilitation, vocational assessment, vocational rehabilitation

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