Search results for: visual sensor networks

Authors: Michael Rahme

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Over the past decade, there is an overwhelming consensus spanning across academia, government commissions, and civil societies that concede that individuals with disabilities (IWDs), particularly those with intellectual differences, are a demographic most ‘vulnerable’ to experiences of sexual violence. From this global accord, numerous policies have sprouted in the protection of this ‘pregnable’ sector of society, primarily framed around liberal obligations of stewardship over the ‘defenceless.’ As such, these initiatives mainly target post-incident or victim-based factors of sexual violence, which is apparent in proposals for more inclusive sexual education and accessible contact lines for IWDs. Yet despite the necessity of these initiatives, sexual incidents among this demographic persist and, in nations such as Australia, continue to rise. Culture of Violence theory reveals that such discrepancies in theory and practice stem from societal structures that frame individuals as ‘vulnerable’, ‘impregnable’, or ‘defenceless’ because of their disability, thus propagating their own likelihood of abuse. These structures, as embodied by the Australian experience, allow these sexual violences to endure through cultural ideologies that place the IWDs ‘failures’ at fault while sidelining the institutions that permit this abuse. Such is representative of the initiatives of preventative organizations like People with Disabilities Australia, which have singularly strengthened victim protection networks, despite abuse continuing to rise dramatically among individuals with intellectual disabilities alone. Yet regardless of this rise, screenings of families and workers remain inadequate and practically untouched, a reflection of a tremendous societal warp in understanding surrounding the lived experiences of IWDs. This theory is also representative of broader literature, where the study of the perpetrators of disability rights, particularly sexual rights, is almost unapparent in a field that is already seldom studied. Therefore, placing power on the abuser via stripping that of the victims. As such, the Culture of Violence theory (CVT) sheds light on the institutions that allow these perpetrators to prosper. This paper, taking a CVT approach, aims to dissipate this discrepancy in the Australian experience by way of a qualitative analysis of all available court proceedings and tribunals between 2020-2022. Through an analysis of the perpetrator, their relation to the IWD, and the motives for their actions granted by court and tribunal transcripts and the psychological, and behavioural reports, among other material, that have been presented and consulted during these proceedings. All of which would be made available under the 1982 Freedom of Information Act. The findings from this study, through the incorporation of CVT, determine the institutions in which these abusers function and the ideologies which motivate such behaviour; while being conscious of the issue of re-traumatization and language barriers of the abusees. Henceforth, this study aims to be a potential policy guide on strengthening support institutions that provide IWDs with their basic rights. In turn, undermining sexual violence among individuals with intellectual disabilities at its roots.

Keywords: criminal profiling, intellectual disabilities, prevention, sexual violence

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Rodrigo Calili, Anna Carolina Sermarini, João Henrique Azevedo, Vanessa Cardoso de Albuquerque, Felipe Gonçalves, Gilberto Jannuzzi

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The diffusion of Distributed Energy Resources (DER) has caused structural changes in the relationship between consumers and electrical systems. The Photovoltaic Distributed Generation (PVDG), in particular, is an essential strategy for achieving the 2030 Agenda goals, especially SDG 7 and SDG 13. However, it is observed that most projects involving this technology in Brazil are restricted to the wealthiest classes of society, not yet reaching the low-income population, aligned with theories of energy justice. Considering the research for energy equality, one of the policies adopted by governments is the social electricity tariff (SET), which provides discounts on energy tariffs/bills. However, just granting this benefit may not be effective, and it is possible to merge it with DER technologies, such as the PVDG. Thus, this work aims to evaluate the economic viability of the policy to replace the social electricity tariff (the current policy aimed at the low-income population in Brazil) by PVDG projects. To this end, a proprietary methodology was developed that included: mapping the stakeholders, identifying critical variables, simulating policy options, and carrying out an analysis in the Brazilian context. The simulation answered two key questions: in which municipalities low-income consumers would have lower bills with PVDG compared to SET; which consumers in a given city would have increased subsidies, which are now provided for solar energy in Brazil and for the social tariff. An economic model was created for verifying the feasibility of the proposed policy in each municipality in the country, considering geographic issues (tariff of a particular distribution utility, radiation from a specific location, etc.). To validate these results, four sensitivity analyzes were performed: variation of the simultaneity factor between generation and consumption, variation of the tariff readjustment rate, zeroing CAPEX, and exemption from state tax. The behind-the-meter modality of generation proved to be more promising than the construction of a shared plant. However, although the behind-the-meter modality presents better results than the shared plant, there is a greater complexity in adopting this modality due to issues related to the infrastructure of the most vulnerable communities (e.g., precarious electrical networks, need to reinforce roofs). Considering the shared power plant modality, many opportunities are still envisaged since the risk of investing in such a policy can be mitigated. Furthermore, this modality can be an alternative due to the mitigation of the risk of default, as it allows greater control of users and facilitates the process of operation and maintenance. Finally, it was also found, that in some regions of Brazil, the continuity of the SET presents more economic benefits than its replacement by PVDG. However, the proposed policy offers many opportunities. For future works, the model may include other parameters, such as cost with low-income populations’ engagement, and business risk. In addition, other renewable sources of distributed generation can be studied for this purpose.

Keywords: low income, subsidy policy, distributed energy resources, energy justice

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Authors: E. Cascarosa-Salillas, J. Pozuelo-Muñoz, C. Rodríguez-Casals, A. de Echave

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This study focuses on improving the understanding of the capillarity phenomenon among Primary and Secondary Education students. Despite being a common concept in daily life and covered in various subjects, students’ comprehension remains limited. This work explores inquiry-based teaching methods to build a conceptual foundation of capillarity by examining the forces involved. The study adopts an inquiry-based teaching approach supported by research emphasizing the importance of modeling in science education. Scientific modeling aids students in applying knowledge across varied contexts and developing systemic thinking, allowing them to construct scientific models applicable to everyday situations. This methodology fosters the development of scientific competencies such as observation, hypothesis formulation, and communication. The research was structured as a case study with activities designed for Spanish Primary and Secondary Education students aged 9 to 13. The process included curriculum analysis, the design of an activity sequence, and its implementation in classrooms. Implementation began with questions that students needed to resolve using available materials, encouraging observation, experimentation, and the re-contextualization of activities to everyday phenomena where capillarity is observed. Data collection tools included audio and video recordings of the sessions, which were transcribed and analyzed alongside the students' written work. Students' drawings on capillarity were also collected and categorized. Qualitative analyses of the activities showed that, through inquiry, students managed to construct various models of capillarity, reflecting an improved understanding of the phenomenon. Initial activities allowed students to express prior ideas and formulate hypotheses, which were then refined and expanded in subsequent sessions. The generalization and use of graphical representations of their ideas on capillarity, analyzed alongside their written work, enabled the categorization of capillarity models: Intuitive Model: A visual and straightforward representation without explanations of how or why it occurs. Simple symbolic elements, such as arrows to indicate water rising, are used without detailed or causal understanding. It reflects an initial, immediate perception of the phenomenon, interpreted as something that happens "on its own" without delving into the microscopic level. Explanatory Intuitive Model: Students begin to incorporate causal explanations, though still limited and without complete scientific accuracy. They represent the role of materials and use basic terms such as ‘absorption’ or ‘attraction’ to describe the rise of water. This model shows a more complex understanding where the phenomenon is not only observed but also partially explained in terms of interaction, though without microscopic detail. School Scientific Model: This model reflects a more advanced and detailed understanding. Students represent the phenomenon using specific scientific concepts like ‘surface tension,’ cohesion,’ and ‘adhesion,’ including structured explanations connecting microscopic and macroscopic levels. At this level, students model the phenomenon as a coherent system, demonstrating how various forces or properties interact in the capillarity process, with representations on a microscopic level. The study demonstrated that the capillarity phenomenon can be effectively approached in class through the experimental observation of everyday phenomena, explained through guided inquiry learning. The methodology facilitated students’ construction of capillarity models and served to analyze an interaction phenomenon of different forces occurring at the microscopic level.

Keywords: capillarity, inquiry-based learning, scientific modeling, primary and secondary education, conceptual understanding, Drawing analysis.

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Authors: M. Grandcolas, N. Rival, H. Bu, S. Jahren, R. Schmid, H. Johnsen

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The concept of an autonomic self-healing material, where initiation of repair is integrated to the material, is now being considered for engineering applications and is a hot topic in the literature. Among several concepts/techniques, two are most interesting: i) Capsules: Integration of microcapsules in or at the surface of coatings or fibre-like structures has recently gained much attention. Upon damage-induced cracking, the microcapsules are broken by the propagating crack fronts resulting in a release of an active chemical (healing agent) by capillary action, subsequently repairing and avoiding further crack growth. ii) Self-healing polymers: Interestingly, the introduction of dynamic covalent bonds into polymer networks has also recently been used as a powerful approach towards the design of various intrinsically self-healing polymer systems. The idea behind this is to reconnect the chemical crosslinks which are broken when a material fractures, restoring the integrity of the material and thereby prolonging its lifetime. We propose here to integrate both self-healing concepts (capsules, self-healing polymers) in electrospun fibres and coatings. Different capsule preparation approaches have been investigated in SINTEF. The most advanced method to produce capsules is based on emulsification to create a water-in-oil emulsion before polymerisation. The healing agent is a polyurethane-based dispersion that was encapsulated in shell materials consisting of urea-benzaldehyde resins. Results showed the successful preparation of microcapsules and release of the agent when capsules break. Since capsules are produced in water-in-oil systems we mainly investigated organic solvent based coatings while a major challenge resides in the incorporation of capsules into water-based coatings. We also focused on developing more robust microcapsules to prevent premature rupture of the capsules. The capsules have been characterized in terms of size, and encapsulation and release might be visualized by incorporating fluorescent dyes and examine the capsules by microscopy techniques. Alternatively, electrospinning is an innovative technique that has attracted enormous attention due to unique properties of the produced nano-to-micro fibers, ease of fabrication and functionalization, and versatility in controlling parameters. Especially roll-to-roll electrospinning is a unique method which has been used in industry to produce nanofibers continuously. Electrospun nanofibers can usually reach a diameter down to 100 nm, depending on the polymer used, which is of interest for the concept with self-healing polymer systems. In this work, we proved the feasibility of fabrication of POSS-based (POSS: polyhedral oligomeric silsesquioxanes, tradename FunzioNano™) nanofibers via electrospinning. Two different formulations based on aqueous or organic solvents have shown nanofibres with a diameter between 200 – 450nm with low defects. The addition of FunzioNano™ in the polymer blend also showed enhanced properties in term of wettability, promising for e.g. membrane technology. The self-healing polymer systems developed are here POSS-based materials synthesized to develop dynamic soft brushes.

Keywords: capsules, coatings, electrospinning, fibers

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Authors: Hatem Abou-Senna, Essam Radwan

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With increasing traffic and limited resources, accommodating left-turning traffic has been a challenge for traffic engineers as they seek balance between intersection capacity and safety; these are two conflicting goals in the operation of a signalized intersection that are mitigated through signal phasing techniques. Hence, to increase the left-turn capacity and reduce the delay at the intersections, the Florida Department of Transportation (FDOT) moves forward with a vision of optimizing intersection control using innovative intersection designs through the Transportation Systems Management & Operations (TSM&O) program. These alternative designs successfully eliminate the left-turn phase, which otherwise reduces the conventional intersection’s (CI) efficiency considerably, and divide the intersection into smaller networks that would operate in a one-way fashion. This study focused on the Crossover Displaced Left-turn intersections (XDL), also known as Continuous Flow Intersections (CFI). The XDL concept is best suited for intersections with moderate to high overall traffic volumes, especially those with very high or unbalanced left turn volumes. There is little guidance on determining whether partial XDL intersections are adequate to mitigate the overall intersection condition or full XDL is always required. The primary objective of this paper was to evaluate the overall intersection performance in the case of different partial XDL designs compared to a full XDL. The XDL alternative was investigated for 4 different scenarios; partial XDL on the east-west approaches, partial XDL on the north-south approaches, partial XDL on the north and east approaches and full XDL on all 4 approaches. Also, the impact of increasing volume on the intersection performance was considered by modeling the unbalanced volumes with 10% increment resulting in 5 different traffic scenarios. The study intersection, located in Orlando Florida, is experiencing recurring congestion in the PM peak hour and is operating near capacity with volume to a capacity ratio closer to 1.00 due to the presence of two heavy conflicting movements; southbound and westbound. The results showed that a partial EN XDL alternative proved to be effective and compared favorably to a full XDL alternative followed by the partial EW XDL alternative. The analysis also showed that Full, EW and EN XDL alternatives outperformed the NS XDL and the CI alternatives with respect to the throughput, delay and queue lengths. Significant throughput improvements were remarkable at the higher volume level with percent increase in capacity of 25%. The percent reduction in delay for the critical movements in the XDL scenarios compared to the CI scenario ranged from 30-45%. Similarly, queue lengths showed percent reduction in the XDL scenarios ranging from 25-40%. The analysis revealed how partial XDL design can improve the overall intersection performance at various demands, reduce the costs associated with full XDL and proved to outperform the conventional intersection. However, partial XDL serving low volumes or only one of the critical movements while other critical movements are operating near or above capacity do not provide significant benefits when compared to the conventional intersection.

Keywords: continuous flow intersections, crossover displaced left-turn, microscopic traffic simulation, transportation system management and operations, VISSIM simulation model

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

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Authors: Muhammad Umer Mehmood, Muhammad Luqman, Muhammad Yaseen, Imtiaz Hussain

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Climate change is an arduous fact, which could not be unheeded easily. It is a phenomenon which has brought a collection of challenges for the mankind. Scientists have found many of its negative impacts on the life of human being and the resources on which the life of humanity is dependent. There are many issues which are associated with the factor of prime importance in this study, 'climate change'. Whenever changes happen in nature, they strike the whole globe. Effects of these changes vary from region to region. Climate of every region of this globe is different from the other. Even within a state, country or the province has different climatic conditions. So it is mandatory that the response in that specific region and the coping strategy of this specific region should be according to the prevailing risk. In the present study, the objective was to assess the coping strategies and vulnerability of small landholders. So that a professional suggestion could be made to cope with the vulnerability factor of small farmers. The cross-sectional research design was used with the intervention of quantitative approach. The study was conducted in the Khanewal district, of Punjab, Pakistan. 120 small farmers were interviewed after randomized sampling from the population of respective area. All respondents were above the age of 15 years. A questionnaire was developed after keen observation of facts in the respective area. Content and face validity of the instrument was assessed with SPSS and experts in the field. Data were analyzed through SPSS using descriptive statistics. From the sample of 120, 81.67% of the respondents claimed that the environment is getting warmer and not fit for their present agricultural practices. 84.17% of the sample expressed serious concern that they are disturbed due to change in rainfall pattern and vulnerability towards the climatic effects. On the other hand, they expressed that they are not good at tackling the effects of climate change. Adaptation of coping strategies like change in cropping pattern, use of resistant varieties, varieties with minimum water requirement, intercropping and tree planting was low by more than half of the sample. From the sample 63.33% small farmers said that the coping strategies they adopt are not effective enough. The present study showed that subsistence farming, lack of marketing and overall infrastructure, lack of access to social security networks, limited access to agriculture extension services, inappropriate access to agrometeorological system, unawareness and access to scientific development and low crop yield are the prominent factors which are responsible for the vulnerability of small farmers. A comprehensive study should be conducted at national level so that a national policy could be formulated to cope with the dilemma in future with relevance to climate change. Mainstreaming and collaboration among the researchers and academicians could prove beneficiary in this regard the interest of national leaders’ does matter. Proper policies to avoid the vulnerability factors should be the top priority. The world is taking up this issue with full responsibility as should we, keeping in view the local situation.

Keywords: adaptation, coping strategies, climate change, Pakistan, small farmers, vulnerability

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Authors: Areej Al-Hamad, Yasin Yasin, Kateryna Metersky

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This mixed-method systematic review and meta-analysis provide an encompassing understanding of the barriers, facilitators, and predictors of refugee women's employment and economic inclusion. The study sheds light on the complex interplay of sociocultural, personal, political, and environmental factors influencing these outcomes, underlining the urgent need for a multifaceted, tailored approach to devising strategies, policies, and interventions aimed at boosting refugee women's economic empowerment. Our findings suggest that sociocultural factors, including gender norms, societal attitudes, language proficiency, and social networks, profoundly shape refugee women's access to and participation in the labor market. Personal factors such as age, educational attainment, health status, skills, and previous work experience also play significant roles. Political factors like immigration policies, regulations, and rights to work, alongside environmental factors like labor market conditions, availability of employment opportunities, and access to resources and support services, further contribute to the complex dynamics influencing refugee women's economic inclusion. The significant variability observed in the impacts of these factors across different contexts underscores the necessity of adopting population and region-specific strategies. A one-size-fits-all approach may prove to be ineffective due to the diversity and unique circumstances of refugee women across different geographical, cultural, and political contexts. The study's findings have profound implications for policy-making, practice, education, and research. The insights garnered a call for coordinated efforts across these domains to bolster refugee women's economic participation. In policy-making, the findings necessitate a reassessment of current immigration and labor market policies to ensure they adequately support refugee women's employment and economic integration. In practice, they highlight the need for comprehensive, tailored employment services and interventions that address the specific barriers and leverage the facilitators identified. In education, they underline the importance of language and skills training programs that cater to the unique needs and circumstances of refugee women. Lastly, in research, they emphasize the need for ongoing investigations into the multifaceted factors influencing refugee women's employment experiences, allowing for continuous refinement of our understanding and interventions. Through this comprehensive exploration, the study contributes to ongoing efforts aimed at creating more inclusive, equitable societies. By continually refining our understanding of the complex factors influencing refugee women's employment experiences, we can pave the way toward enhanced economic empowerment for this vulnerable population.

Keywords: refugee women, employment barriers, systematic review, employment facilitators

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Authors: Jolanta Jonak, Sylvia Tolczyk

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Educational systems are tasked with preparing students for future success in academic or work environments. Schools strive to achieve this goal, but often it is challenging as conventional teaching approaches are often ineffective in increasingly diverse educational systems. In today’s ever-increasing global society, educational systems become increasingly diverse in terms of cultural and linguistic differences, learning preferences and styles, ability and disability. Through increased understanding of disabilities and improved identification processes, students having some form of disabilities tend to be identified earlier than in the past, meaning that more students with identified disabilities are being supported in our classrooms. Also, a large majority of students with disabilities are educated in general education environments. Due to cognitive makeup and life experiences, students have varying learning styles and preferences impacting how they receive and express what they are learning. Many students come from bi or multilingual households and with varying proficiencies in the English language, further impacting their learning. All these factors need to be seriously considered when developing learning opportunities for student's. Educators try to adjust their teaching practices as they discover that conventional methods are often ineffective in reaching each student’s potential. Many teachers do not have the necessary educational background or training to know how to teach students whose learning needs are more unique and may vary from the norm. This is further complicated by the fact that many classrooms lack consistent access to interventionists/coaches that are adequately trained in evidence-based approaches to meet the needs of all students, regardless of what their academic needs may be. One evidence-based way for providing successful education for all students is by incorporating cognitive approaches and strategies that tap into affective, recognition, and strategic networks in the student's brain. This can be done through Differentiated Instruction (DI). Differentiated Instruction is increasingly recognized model that is established on the basic principles of Universal Design for Learning. This form of support ensures that regardless of the students’ learning preferences and cognitive learning profiles, they have opportunities to learn through approaches that are suitable to their needs. This approach improves the educational outcomes of students with special needs and it benefits other students as it accommodates learning styles as well as the scope of unique learning needs that are evident in the typical classroom setting. Differentiated Instruction also is recognized as an evidence-based best practice in education and is highly effective when it is implemented within the tiered system of the Response to Intervention (RTI) model. Recognition of DI becomes more common; however, there is still limited understanding of the effective implementation and use of strategies that can create unique learning environments for each student within the same setting. Through employing knowledge of a variety of instructional strategies, general and special education teachers can facilitate optimal learning for all students, with and without a disability. A desired byproduct of DI is that it can eliminate inaccurate perceptions about the students’ learning abilities, unnecessary referrals for special education evaluations, and inaccurate decisions about the presence of a disability.

Keywords: differentiated instruction, universal design for learning, special education, diversity

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Authors: Gabriela R. Silveira, Claudia S. Rocha, Lais S. Vitti, Jeane L. Borges, Helen B. Durgante

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Emerging adulthood occurs after adolescence in a period that maybe be marked by experimentation, identity reconfigurations, labor life demands, and insertion in the work environment, which tends to generate stress and emotional instability. Health promotion programs for the development of strengths and virtues, based on Positive Psychology, for emerging adults are sparse in Brazil. The aim of this study was to evaluate the preliminary effects of an online multi-component Positive Psychology program for the health promotion of emerging adults based on Cognitive Behavioural Therapy and Positive Psychology. The program included six online (synchronous) weekly group sessions of approximately two hours each and homework (asynchronous) activities. The themes worked were Values and self-care/Prudence, Optimism, Empathy, Gratitude, Forgiveness, and Meaning of life and work. This study presents data from a longitudinal, pre-experimental design with pre (T1) and post-test (T2) evaluation in the intervention group. 47 individuals aged between 19-30 years old participated, mean age of 24.53 years (SD=3.13), 37 females (78.7%). 42 (89.4%) self-defined as heterosexual, four (8.5%) as homosexual, and one (2.5%) as bisexual. 33 (70.2%) had incomplete higher education, four (8.5%) completed higher education, and seven (14.9%) had a graduate level of education. 27 participants worked (57.4%), out of which 25 were health workers (53.2%). 14 (29.8%) were caregivers, 27 (57.4%) had a spiritual belief, 36 (76.6%) had access to leisure, and 38 (80.9%) had perceived social support. The instruments used were a sociodemographic questionnaire, the 10-item Perceived Stress Scale, and the 12-item General Health Questionnaire. The program was advertised on social networks and interested participants filled out the Consent Form and the evaluation protocol at T1 and T2 via Google Docs form. The main research was approved (CEP n.1,899,368; 4,143,219; CAAE: 61997516.5.0000.5334) and complied with sanitary and Ethics criteria in research with human beings. Wilcoxon statistics revealed significant improvements in indicators of perceived stress between T1 (X=22.21, SD=6.79) and T2 (X=15.10, SD=5.82); (Z=-4.353; p=0.001) as well as depression and anxiety symptoms (T1:X=26.72, SD=8.84; T2: X=19.23, SD=4.68); (Z=-3.945, p=0.001) of the emerging adults after their participation in the programme. The programme has an innovative character not only for presenting an online Positive Psychology approach but also for being based on an intervention developed, evaluated, and manualized in Brazil. By focusing on emerging adults, this study contributes to advancing research on a relatively new field in developmental studies. As a limitation, this is a pre-experimental and pilot study, requiring an increase in sample size for greater statistical robustness, also qualitative data analysis is crucial for methodological complementarity. The importance of investing efforts to accompany this age group and provide advances in longitudinal research in the area of health promotion and disease prevention is highlighted.

Keywords: emerging adults, disease prevention, health promotion, online program

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Authors: Aleksandra Jonas, Aleksandra Gronowska, Maciej Ścigacz, Szymon Jadczak

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Exact Data is a smart tool which helps with meaningful marketing content creation. It helps marketers achieve this by analyzing the text of an advertisement before and after its publication on social media sites like Facebook or Instagram. In our research we focus on four areas of natural language processing (NLP): grammar correction, sentiment analysis, irony detection and advertisement interpretation. Our research has identified a considerable lack of NLP tools for the Polish language, which specifically aid online marketers. In light of this, our research team has set out to create a robust and versatile NLP tool for the Polish language. The primary objective of our research is to develop a tool that can perform a range of language processing tasks in this language, such as sentiment analysis, text classification, text correction and text interpretation. Our team has been working diligently to create a tool that is accurate, reliable, and adaptable to the specific linguistic features of Polish, and that can provide valuable insights for a wide range of marketers needs. In addition to the Polish language version, we are also developing an English version of the tool, which will enable us to expand the reach and impact of our research to a wider audience. Another area of focus in our research involves tackling the challenge of the limited availability of linguistically diverse corpora for non-English languages, which presents a significant barrier in the development of NLP applications. One approach we have been pursuing is the translation of existing English corpora, which would enable us to use the wealth of linguistic resources available in English for other languages. Furthermore, we are looking into other methods, such as gathering language samples from social media platforms. By analyzing the language used in social media posts, we can collect a wide range of data that reflects the unique linguistic characteristics of specific regions and communities, which can then be used to enhance the accuracy and performance of NLP algorithms for non-English languages. In doing so, we hope to broaden the scope and capabilities of NLP applications. Our research focuses on several key NLP techniques including sentiment analysis, text classification, text interpretation and text correction. To ensure that we can achieve the best possible performance for these techniques, we are evaluating and comparing different approaches and strategies for implementing them. We are exploring a range of different methods, including transformers and convolutional neural networks (CNNs), to determine which ones are most effective for different types of NLP tasks. By analyzing the strengths and weaknesses of each approach, we can identify the most effective techniques for specific use cases, and further enhance the performance of our tool. Our research aims to create a tool, which can provide a comprehensive analysis of advertising effectiveness, allowing marketers to identify areas for improvement and optimize their advertising strategies. The results of this study suggest that a smart tool for advertisement analysis can provide valuable insights for businesses seeking to create effective advertising campaigns.

Keywords: NLP, AI, IT, language, marketing, analysis

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Authors: Brandy Yee, Dianne Yee

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Through the exploration of the lived experiences, beliefs and values of instructional leaders, teachers and students in Finland, Germany and Canada, we investigated the factors which contribute to developmentally responsive, intellectually engaging middle-level learning environments for early adolescents. Student-centred leadership dimensions, effective instructional practices and student agency were examined through the lens of current policy and research on middle-level learning environments emerging from the Canadian province of Manitoba. Consideration of these three research perspectives in the context of early adolescent learning, placed against an international backdrop, provided a previously undocumented perspective on leading, teaching and learning in the middle years. Aligning with a social constructivist, qualitative research paradigm, the study incorporated collective case study methodology, along with constructivist grounded theory methods of data analysis. Data were collected through semi-structured individual and focus group interviews and document review, as well as direct and participant observation. Three case study narratives were developed to share the rich stories of study participants, who had been selected using maximum variation and intensity sampling techniques. Interview transcript data were coded using processes from constructivist grounded theory. A cross-case analysis yielded a conceptual framework highlighting key factors that were found to be significant in the establishment of developmentally responsive, intellectually engaging middle-level learning environments. Seven core categories emerged from the cross-case analysis as common to all three countries. Within the visual conceptual framework (which depicts the interconnected nature of leading, teaching and learning in middle-level learning environments), these seven core categories were grouped into Essential Factors (student agency, voice and choice), Contextual Factors (instructional practices; school culture; engaging families and the community), Synergistic Factors (instructional leadership) and Cornerstone Factors (education as a fundamental cultural value; preservice, in-service and ongoing teacher development). In addition, sub-factors emerged from recurring codes in the data and identified specific characteristics and actions found in developmentally responsive, intellectually engaging middle-level learning environments. Although this study focused on 12 schools in Finland, Germany and Canada, it informs the practice of educators working with early adolescent learners in middle-level learning environments internationally. The authentic voices of early adolescent learners are the most important resource educators have to gauge if they are creating effective learning environments for their students. Ongoing professional dialogue and learning is essential to ensure teachers are supported in their work and develop the pedagogical practices needed to meet the needs of early adolescent learners. It is critical to balance consistency, coherence and dependability in the school environment with the necessary flexibility in order to support the unique learning needs of early adolescents. Educators must intentionally create a school culture that unites teachers, students and their families in support of a common purpose, as well as nurture positive relationships between the school and its community. A large, urban school district in Canada has implemented a school cohort-based model to begin to bring developmentally responsive, intellectually engaging middle-level learning environments to scale.

Keywords: developmentally responsive learning environments, early adolescents, middle level learning, middle years, instructional leadership, instructional practices, intellectually engaging learning environments, leadership dimensions, student agency

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Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

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Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

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Authors: Gu Sihao

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The coordinated development of "ecology-production-life" in cities has been highly concerned by the country, and the transformation development and sustainable development of resource-based cities have become a hot research topic at present. As an important part of China's resource-based cities, coal resource-based cities have the characteristics of large number and wide distribution. However, due to the adjustment of national energy structure and the gradual exhaustion of urban coal resources, the development vitality of coal resource-based cities is gradually reduced. In many studies, the deterioration of ecological environment in coal resource-based cities has become the main problem restricting their urban transformation and sustainable development due to the "emphasis on economy and neglect of ecology". Since the 18th National Congress of the Communist Party of China (CPC), the Central Government has been deepening territorial space planning and development. On the premise of optimizing territorial space development pattern, it has completed the demarcation of ecological protection red lines, carried out ecological zoning and ecosystem evaluation, which have become an important basis and scientific guarantee for ecological modernization and ecological civilization construction. Grasp the regional multiple ecosystem services is the precondition of the ecosystem management, and the relationship between the multiple ecosystem services study, ecosystem services cluster can identify the interactions between multiple ecosystem services, and on the basis of the characteristics of the clusters on regional ecological function zoning, to better Social-Ecological system management. Based on this cognition, this study optimizes the spatial function zoning of Gujiao, a coal resource-based city, in order to provide a new theoretical basis for its sustainable development. This study is based on the detailed analysis of characteristics and utilization of Gujiao city land space, using SOFM neural networks to identify local ecosystem service clusters, according to the cluster scope and function of ecological function zoning of space partition balance and coordination between different ecosystem services strength, establish a relationship between clusters and land use, and adjust the functions of territorial space within each zone. Then, according to the characteristics of coal resources city and national spatial function zoning characteristics, as the driving factors of land change, by cellular automata simulation program, such as simulation under different restoration strategy situation of urban future development trend, and provides relevant theories and technical methods for the "third-line" demarcations of Gujiao's territorial space planning, optimizes territorial space functions, and puts forward targeted strategies for the promotion of regional ecosystem services, providing theoretical support for the improvement of human well-being and sustainable development of resource-based cities.

Keywords: coal resource-based city, territorial spatial planning, ecosystem service cluster, gmop model, geosos-FLUS model, functional zoning optimization and upgrading

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Authors: Francesco Saverio Angiò

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The Islamic State of Iraq and the Levant / Syria (ISIL/S), Al-Qaeda in the Islamic Maghreb (AQIM) and People Committed to the Propagation of the Prophet's Teachings and Jihad, also known as ‘Boko Haram’ (BH), have fought successfully against Syria and Iraq, Mali, Nigeria’s government, respectively. According to Napoleoni, the ‘shell-state’ concept can explain the economic dimension and the financing model of the ISIL insurgency. However, she argues that AQIM and BH did not properly plan their financial model. Consequently, her idea would not be suitable to these groups. Nevertheless, AQIM and BH’s economic performances and their (short) territorialisation suggest that their financing models respond to a well-defined strategy, which they were able to adapt to new circumstances. Therefore, Napoleoni’s idea of ‘shell-state’ can be applied to the three jihadist armed groups. In the last five years, together with other similar entities, ISIL/S, AQIM and BH have been fighting against governments with insurgent tactics and terrorism acts, conquering and ruling a quasi-state; a physical space they presented as legitimate territorial entity, thanks to a puritan version of the Islamic law. In these territories, they have exploited the traditional local economic networks. In addition, they have contributed to the development of legal and illegal transnational business activities. They have also established a justice system and created an administrative structure to supply services. Napoleoni’s ‘shell-state’ can describe the evolution of ISIL/S, AQIM and BH, which has switched from an insurgency to a proto or a quasi-state entity, enjoying a significant share of power over territories and populations. Napoleoni first developed and applied the ‘Shell-state’ concept to describe the nature of groups such as the Palestine Liberation Organisation (PLO), before using it to explain the expansion of ISIL. However, her original conceptualisation emphasises on the economic dimension of the rise of the insurgency, focusing on the ‘business’ model and the insurgents’ financing management skills, which permits them to turn into an organisation. However, the idea of groups which use, coordinate and grab some territorial economic activities (at the same time, encouraging new criminal ones), can also be applied to administrative, social, infrastructural, legal and military levels of their insurgency, since they contribute to transform the insurgency to the same extent the economic dimension does. In addition, according to Napoleoni’s view, the ‘shell-state’ prism is valid to understand the ISIL/S phenomenon, because the group has carefully planned their financial steps. Napoleoni affirmed that ISIL/S carries out activities in order to promote their conversion from a group relying on external sponsors to an entity that can penetrate and condition local economies. On the contrary, ‘shell-state’ could not be applied to AQIM or BH, which are acting more like smugglers. Nevertheless, despite its failure to control territories, as ISIL has been able to do, AQIM and BH have responded strategically to their economic circumstances and have defined specific dynamics to ensure a flow of stable funds. Therefore, Napoleoni’s theory is applicable.

Keywords: shell-state, jihadist insurgency, proto or quasi-state entity economic planning, strategic financing

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Authors: Lakshmi N. Reddi, Akanksha Varma Sagi

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Studies on biomimetics are largely developed, deriving inspiration from natural processes in our objective world to develop novel technologies. Recent studies are diverse in nature, making their categorization quite challenging. Based on an exhaustive survey, we developed categorizations based on either the essential elements of nature - air, water, land, fire, and space, or on form/shape, functionality, and process. Such diverse studies as aircraft wings inspired by bird wings, a self-cleaning coating inspired by a lotus petal, wetsuits inspired by beaver fur, and search algorithms inspired by arboreal ant path networks lend themselves to these categorizations. Our categorizations of biomimetic studies allowed us to define a different dimension of biomimetics. This new dimension is not restricted to inspiration from the objective world. It is based on the premise that the biological processes observed in the objective world find their reflections in our human bodies in a variety of ways. For example, the lungs provide the most efficient example for liquid-gas phase exchange, the heart exemplifies a very efficient pumping and circulatory system, and the kidneys epitomize the most effective cleaning system. The main focus of this paper is to bring out the magnificence of the cerebro-spinal system (CSS) insofar as it relates to our current computer architecture. In particular, the paper uses four key measures to analyze the differences between CSS and human- engineered computational systems. These are adaptability, sustainability, energy efficiency, and resilience. We found that the cerebrospinal system reveals some important challenges in the development and evolution of our current computer architectures. In particular, the myriad ways in which the CSS is integrated with other systems/processes (circulatory, respiration, etc) offer useful insights on how the human-engineered computational systems could be made more sustainable, energy-efficient, resilient, and adaptable. In our paper, we highlight the energy consumption differences between CSS and our current computational designs. Apart from the obvious differences in materials used between the two, the systemic nature of how CSS functions provides clues to enhance life-cycles of our current computational systems. The rapid formation and changes in the physiology of dendritic spines and their synaptic plasticity causing memory changes (ex., long-term potentiation and long-term depression) allowed us to formulate differences in the adaptability and resilience of CSS. In addition, the CSS is sustained by integrative functions of various organs, and its robustness comes from its interdependence with the circulatory system. The paper documents and analyzes quantifiable differences between the two in terms of the four measures. Our analyses point out the possibilities in the development of computational systems that are more adaptable, sustainable, energy efficient, and resilient. It concludes with the potential approaches for technological advancement through creation of more interconnected and interdependent systems to replicate the effective operation of cerebro-spinal system.

Keywords: cerebrospinal system, computer architecture, adaptability, sustainability, resilience, energy efficiency

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Authors: Julia Terlet, Thomas H. Beach, Yacine Rezgui

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Urbanization, population growth, climate change and the current increase in water demand have made the adoption of innovative demand management strategies crucial to the water industry. Water conservation in urban areas has to be improved by encouraging consumers to adopt more sustainable habits and behaviours. This includes informing and educating them about their households’ water consumption and advising them about ways to achieve significant savings on a daily basis. This paper presents a study conducted in the context of the European FP7 WISDOM Project. By integrating innovative Information and Communication Technologies (ICT) frameworks, this project aims at achieving a change in water savings. More specifically, behavioural change will be attempted by implementing smart meters and in-home displays in a trial group of selected households within Cardiff (UK). Using this device, consumers will be able to receive feedback and information about their consumption but will also have the opportunity to compare their consumption to the consumption of other consumers and similar households. Following an initial survey, it appeared necessary to implement these in-home displays in a way that matches consumer's motivations to save water. The results demonstrated the importance of various factors influencing people’s daily water consumption. Both the relevant literature on the subject and the results of our survey therefore led us to include within the in-home device a variety of elements. It first appeared crucial to make consumers aware of the economic aspect of water conservation and especially of the significant financial savings that can be achieved by reducing their household’s water consumption on the long term. Likewise, reminding participants of the impact of their consumption on the environment by making them more aware of water scarcity issues around the world will help increasing their motivation to save water. Additionally, peer pressure and social comparisons with neighbours and other consumers, accentuated by the use of online social networks such as Facebook or Twitter, will likely encourage consumers to reduce their consumption. Participants will also be able to compare their current consumption to their past consumption and to observe the consequences of their efforts to save water through diverse graphs and charts. Finally, including a virtual water game within the display will help the whole household, children and adults, to achieve significant reductions by providing them with simple tips and advice to save water on a daily basis. Moreover, by setting daily and weekly goals for them to reach, the game will expectantly generate cooperation between family members. Members of each household will indeed be encouraged to work together to reduce their water consumption within different rooms of the house, such as the bathroom, the kitchen, or the toilets. Overall, this study will allow us to understand the elements that attract consumers the most and the features that are most commonly used by the participants. In this way, we intend to determine the main factors influencing water consumption in order to identify the measures that will most encourage water conservation in both the long and short term.

Keywords: behavioural change, ICT technologies, water consumption, water conservation

Procedia PDF Downloads 335

Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

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Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

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Authors: Gianmarco Taveri, Ivo Dlouhy

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Geopolymers are well-suited materials to abate CO2 emission coming from the Portland cement production, and then replace them, in the near future, in building and other applications. The cost of production of geopolymers may be seen the only weakness, but the use of wastes as raw materials could provide a valid solution to this problem, as demonstrated by the successful incorporation of fly-ash, a by-product of thermal power plants, and waste glasses. Recycled glass in waste-derived geopolymers was lately employed as a further silica source. In this work we present, for the first time, the introduction of recycled borosilicate glass (BSG). BSG is actually a waste glass, since it derives from dismantled pharmaceutical vials and cannot be reused in the manufacturing of the original articles. Owing to the specific chemical composition (BSG is an ‘alumino-boro-silicate’), it was conceived to provide the key components of zeolitic networks, such as amorphous silica and alumina, as well as boria (B2O3), which may replace Al2O3 and contribute to the polycondensation process. The solid–state MAS NMR spectroscopy was used to assess the extent of boron oxide incorporation in the structure of geopolymers, and to define the degree of networking. FTIR spectroscopy was utilized to define the degree of polymerization and to detect boron bond vibration into the structure. Mechanical performance was tested by means of 3 point bending (flexural strength), chevron notch test (fracture toughness), compression test (compressive strength), micro-indentation test (Vicker’s hardness). Spectroscopy (SEM and Confocal spectroscopy) was performed on the specimens conducted to failure. FTIR showed a characteristic absorption band attributed to the stretching modes of tetrahedral boron ions, whose tetrahedral configuration is compatible to the reaction product of geopolymerization. 27Al NMR and 29Si NMR spectra were instrumental in understanding the extent of the reaction. 11B NMR spectroscopies evidenced a change of the trigonal boron (BO3) inside the BSG in favor of a quasi-total tetrahedral boron configuration (BO4). Thanks to these results, it was inferred that boron is part of the geopolymeric structure, replacing the Si in the network, similarly to the aluminum, and therefore improving the quality of the microstructure, in favor of a more cross-linked network. As expected, the material gained as much as 25% in compressive strength (45 MPa) compared to the literature, whereas no improvements were detected in flexural strength (~ 5 MPa) and superficial hardness (~ 78 HV). The material also exhibited a low fracture toughness (0.35 MPa*m1/2), with a tangible brittleness. SEM micrographies corroborated this behavior, showing a ragged surface, along with several cracks, due to the high presence of porosity and impurities, acting as preferential points for crack initiation. The 3D pattern of the surface fracture, following the confocal spectroscopy, evidenced an irregular crack propagation, whose proclivity was mainly, but not always, to follow the porosity. Hence, the crack initiation and propagation are largely unpredictable.

Keywords: borosilicate glass, characterization, fly-ash, geopolymerization

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Authors: Brian J. Biroscak

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In the United States (US), “sex trafficking” is defined at the federal level in the Trafficking Victims Protection Act of 2000 as encompassing a number of processes such as recruitment, transportation, and provision of a person for the purpose of a commercial sex act. It involves the use of force, fraud, or coercion, or in which the person induced to perform such act has not attained 18 years of age. Accumulating evidence suggests that sex trafficking is exacerbated by social and environmental stressors (e.g., pandemics). Given that “provision” is a key part of the definition, “provisioning systems” may offer a useful lens through which to study sex trafficking dynamics. Provisioning systems are the social systems connecting individuals, small groups, entities, and embedded communities as they seek to satisfy their needs and wants for goods, services, experiences and ideas through value-based exchange in communities. This project presents a conceptual framework for understanding sex trafficking dynamics in the context of the COVID pandemic. The framework is developed as a system dynamics simulation model based on published evidence, social and behavioral science theory, and key informant interviews with stakeholders from the Protection, Prevention, Prosecution, and Partnership sectors in one US state. This “4 P Paradigm” has been described as fundamental to the US government’s anti-trafficking strategy. The present research question is: “How do sex trafficking systems (e.g., supply, demand and price) interact with other provisioning systems (e.g., networks of organizations that help sexually exploited persons) to influence trafficking over time vis-à-vis the COVID pandemic?” Semi-structured interviews with stakeholders (n = 19) were analyzed based on grounded theory and combined for computer simulation. The first step (Problem Definition) was completed by open coding video-recorded interviews, supplemented by a literature review. The model depicts provision of sex trafficking services for victims and survivors as declining in March 2020, coincidental with COVID, but eventually rebounding. The second modeling step (Dynamic Hypothesis Formulation) was completed by open- and axial coding of interview segments, as well as consulting peer-reviewed literature. Part of the hypothesized explanation for changes over time is that the sex trafficking system behaves somewhat like a commodities market, with each of the other subsystems exhibiting delayed responses but collectively keeping trafficking levels below what they would be otherwise. Next steps (Model Building & Testing) led to a ‘proof of concept’ model that can be used to conduct simulation experiments and test various action ideas, by taking model users outside the entire system and seeing it whole. If sex trafficking dynamics unfold as hypothesized, e.g., oscillated post-COVID, then one potential leverage point is to address the lack of information feedback loops between the actual occurrence and consequences of sex trafficking and those who seek to prevent its occurrence, prosecute the traffickers, protect the victims and survivors, and partner with the other anti-trafficking advocates. Implications for researchers, administrators, and other stakeholders are discussed.

Keywords: pandemics, provisioning systems, sex trafficking, system dynamics modeling

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Authors: M. Soubadra Devy, Savitha Swamy, Chethana V. Casiker

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Sustainable smart cities are emerging as an important concept in response to the exponential rise in the world’s urbanizing population. While earlier, only technical, economic and governance based solutions were considered, more and more layers are being added in recent times. With the prefix of 'sustainability', solutions which help in judicious use of resources without negatively impacting the environment have become critical. We present a case study of Bangalore city which has transformed from being a garden city and pensioners' paradise to being an IT city with a huge, young population from different regions and diverse cultural backgrounds. This has had a big impact on the green spaces in the city and the biodiversity that they support, as well as on farming/gardening practices. Edible landscapes comprising farms lands, home gardens and neighbourhood parks (NPs henceforth) were examined. The land prices of areas having NPs were higher than those that did not indicate an appreciation of their aesthetic value. NPs were part of old and new residential areas largely managed by the municipality. They comprised manicured gardens which were similar in vegetation structure and composition. Results showed that NPs that occurred in higher density supported reasonable levels of biodiversity. In situations where NPs occurred in lower density, the presence of a larger green space such as a heritage park or botanical garden enhanced the biodiversity of these parks. In contrast, farm lands and home gardens which were common within the city are being lost at an unprecedented scale to developmental projects. However, there is also the emergence of a 'neo-culture' of home-gardening that promotes 'locovory' or consumption of locally grown food as a means to a sustainable living and reduced carbon footprint. This movement overcomes the space constraint by using vertical and terrace gardening techniques. Food that is grown within cities comprises of vegetables and fruits which are largely pollinator dependent. This goes hand in hand with our landscape-level study that has shown that cities support pollinator diversity. Maintaining and improving these man-made ecosystems requires analysing the functioning and characteristics of the existing structures of governance. Social network analysis tool was applied to NPs to examine relationships, between actors and ties. The management structures around NPs, gaps, and means to strengthen the networks from the current state to a near-ideal state were identified for enhanced services. Learnings from NPs were used to build a hypothetical governance structure and functioning of integrated governance of NPs and edible landscapes to enhance ecosystem services such as biodiversity support, food production, and aesthetic value. They also contribute to the sustainability axis of smart cities.

Keywords: biodiversity support, ecosystem services, edible green spaces, neighbourhood parks, sustainable smart city

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Manasi Mahanty

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In the current phase of globalization, the mobility of capital facilitates outsourcing and subcontracting of production processes to the developing economies for cheap and flexible labour force. In such process, the globalization of production networks operates at multi-locational points within the nation. Under the new quota regime in the globalization period, the Indian manufacturing exporters came under the influence of corporate buyers and large retailers from the importing countries. As part of such process, the garment manufacturing sector is expected to create huge employment opportunities and to expand the export market in the country. While following these, expectations, the apparel and garment industries mostly target to hire female migrant workers with a purpose of establishing more flexible industrial relations through the casual nature of employment contract. It leads to an increasing women’s participation in the labour market as well as the rise in precarious forms of female paid employment. In the context, the main objective of the paper is to understand the wider dynamics of globalization of production and its link with informalization, feminization of labour force and internal migration process of the country. For this purpose, the study examines the changing labour relations in the KINFRA Apparel Park at Kerala’s Special Economic Zone which operates under the scheme ‘Apparel Parks for Export’ (APE) of the Government of India. The present study was based on both quantitative and qualitative analysis. In the first, the secondary sources of data were collected from the source location (SEAM centre) and destination (KINFRA Park). The official figures and data were discussed and analyzed in order to find out the various dimensions of labour relations under globalization of production. In the second, the primary survey was conducted to make a comparative analysis of local and migrant female workers. The study is executed by taking 100 workers in total. The local workers comprised of 53% of the sample whereas the outside state workers were 47%. Even personal interviews with management staff, and workers were also made for collecting the information regarding the organisational structure, nature, and mode of recruitment, work environment, etc. The study shows the enormous presence of rural women migrant workers in KINFRA Apparel Park. A Public Private Partnership (PPP) arranged migration system is found as Skills for Employment in Apparel Manufacturing (SEAM) from where young women and girls are being sent to work in garment factories of Kerala’s KINFRA International Apparel Park under the guise of an apprenticeship based recruitment. The study concludes that such arrangements try to avoid standard employment relationships and strengthen informalization, casualization and contractualization of work. In this process, the recruitment of women migrant workers is to be considered as best option for the employers of private industries which could be more easily hired and fired.

Keywords: female migration, globalization, informalization, KINFRA apparel park

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Authors: Varun Joshi, M. S. Rawat

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The Indian Himalayan Region (IHR) is inherently fragile and susceptible to landslide hazard due to its extremely weak geology, highly rugged topography and heavy monsoonal rainfall. One of the most common hazards in the IHR is landslide, and this event is particularly frequent in Himalayan states of India i.e. Jammu & Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, Manipur and Arunachal Pradesh. Landslides are mostly triggered by extreme rainfall events but the incidence increases during monsoon months (June to September). Natural slopes which are otherwise stable but they get destabilized due to anthropogenic activities like construction of various developmental activities and deforestation. These activities are required to fulfill the developmental needs and upliftment of societal status in the region. Landslides also trigger during major earthquakes and reported most observable and damaging phenomena. Studies indicate that the landslide phenomenon has increased many folds due to developmental activities in Himalayan region. Gradually increasing and devastating consequences of landslides turned into one of the most important hydro-geological hazards in Himalayan states especially in Uttarakhand and Sikkim states of India. The recent most catastrophic rainfall in June 2013 in Uttarakhand lead to colossal loss of life and property. The societal damage due to this incident is still to be recovered even after three years. Sikkim earthquake of September 2011 is witnessed for triggering of large number of coseismic landslides. The rescue and relief team faced huge problem in helping the trapped villagers in remote locations of the state due to road side blockade by landslides. The recent past incidences of landslides in Uttarakhand, as well as Sikkim states, created a new domain of research in terms of understanding the phenomena of landslide and management of disaster in such situation. Every year at many locations landslides trigger which force dwellers to either evacuate their dwelling or lose their life and property. The communication and transportation networks are also severely affected by landslides at several locations. Many times the drinking water supply disturbed and shortage of daily need household items reported during monsoon months. To minimize the severity of landslide in IHR requires in-depth research and developmental planning. For most of the areas in the present study, landslide hazard zonation is done on 1:50,000 scale. The land use planning maps on extensive basis are not available. Therefore, there is a need of large-scale landslide hazard zonation and land use planning maps. If the scientist conduct research on desired aspects and their outcome of research is utilized by the government in developmental planning then the incidents of landslide could be minimized, subsequent impact on society, life and property would be reduced. Along with the scientific research, there is another need of awareness generation in the region for stake holders and local dwellers to combat with the landslide hazard, if triggered in their location.

Keywords: coseismic, Indian Himalayan Region, landslide hazard zonation, Sikkim, societal, Uttarakhand

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Authors: Sebastian Hiergeist

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It is a common view that Unmanned Aerial Vehicles (UAV) tend to migrate into the civil airspace. This trend is challenging UAV manufacturer in plenty ways, as there come up a lot of new requirements and functional aspects. On the higher application levels, this might be collision detection and avoidance and similar features, whereas all these functions only act as input for the flight control components of the aircraft. The flight control computer (FCC) is the central component when it comes up to ensure a continuous safe flight and landing. As these systems are flight critical, they have to be built up redundantly to be able to provide a Fail-Operational behavior. Recent architectural approaches of FCCs used in UAV systems are often based on very simple microprocessors in combination with proprietary Application-Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA) extensions implementing the whole redundancy functionality. In the future, such simple microprocessors may not be available anymore as they are more and more replaced by higher sophisticated System on Chip (SoC). As the avionic industry cannot provide enough market power to significantly influence the development of new semiconductor products, the use of solutions from foreign markets is almost inevitable. Products stemming from the industrial market developed according to IEC 61508, or automotive SoCs, according to ISO 26262, can be seen as candidates as they have been developed for similar environments. Current available SoC from the industrial or automotive sector provides quite a broad selection of interfaces like, i.e., Ethernet, SPI or FlexRay, that might come into account for the implementation of a redundancy network. In this context, possible network architectures shall be investigated which could be established by using the interfaces stated above. Of importance here is the avoidance of any single point of failures, as well as a proper segregation in distinct fault containment regions. The performed analysis is supported by the use of guidelines, published by the aviation authorities (FAA and EASA), on the reliability of data networks. The main focus clearly lies on the reachable level of safety, but also other aspects like performance and determinism play an important role and are considered in the research. Due to the further increase in design complexity of recent and future SoCs, also the risk of design errors, which might lead to common mode faults, increases. Thus in the context of this work also the aspect of dissimilarity will be considered to limit the effect of design errors. To achieve this, the work is limited to broadly available interfaces available in products from the most common silicon manufacturer. The resulting work shall support the design of future UAV FCCs by giving a guideline on building up a redundancy network between SoCs, solely using on board interfaces. Therefore the author will provide a detailed usability analysis on available interfaces provided by recent SoC solutions, suggestions on possible redundancy architectures based on these interfaces and an assessment of the most relevant characteristics of the suggested network architectures, like e.g. safety or performance.

Keywords: redundancy, System-on-Chip, UAV, flight control computer (FCC)

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Authors: Joao Ferreira Do Rosario, Maria De Lurdes Calisto, Ana Teresa Machado, Nuno Gustavo, Rui Gonçalves

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The tourism sector is increasingly important to the economic performance of countries and a relevant theme to academic research, increasing the importance of understanding how and why tourists evaluate tourism locations. The city of Lisbon is currently a tourist destination of excellence in the European and world-wide panorama, registering a significant growth of the economic weight of its tourist activities in the Gross Added Value of the region. Although there is research on the feedback of those who visit tourist sites and different methodologies for studying tourist sites have been applied, this research seeks to be innovative in the objective of obtaining insights on the competitiveness in terms of attractiveness of the city of Lisbon as a tourist destination, based the feedback of tourists in the Facebook pages of the most visited museums and monuments of Lisbon, an interpretation that is relevant in the development of strategies of tourist attraction. The intangible dimension of the tourism offer, due to its unique condition of simultaneous production and consumption, makes eWOM particularly relevant. The testimony of consumers is thus a decisive factor in the decision-making and buying process in tourism. Online social networks are one of the most used platforms for tourists to evaluate the attractiveness's points of a tourism destination (e.g. cultural and historical heritage), with this user-generated feedback enabling relevant information about the customer-tourists. This information is related to the tourist experience representing the true voice of the customer. Furthermore, this voice perceived by others as genuine, opposite to marketing messages, may have a powerful word-of-mouth influence on other potential tourists. The relevance of online reviews sharing, however, becomes particularly complex, considering social media users’ different profiles or the possible and different sources of information available, as well as their associated reputation associated with each source. In the light of these trends, our research focuses on the tourists’ feedback on Facebook pages of the most visited museums and monuments of Lisbon that contribute to its attractiveness as a tourism destination. Sentiment Analysis is the methodology selected for this research, using public available information in the online context, which was deemed as an appropriate non-participatory observation method. Data will be collected from two museums (Museu dos Coches and Museu de Arte Antiga) and three monuments ((Mosteiro dos Jerónimos, Torre de Belém and Panteão Nacional) Facebook pages during a period of one year. The research results will help in the evaluation of the considered places by the tourists, their contribution to the city attractiveness and present insights helpful for the management decisions regarding this museums and monuments. The results of this study will also contribute to a better knowledge of the tourism sector, namely the identification of attributes in the evaluation and choice of the city of Lisbon as a tourist destination. Further research will evaluate the Lisbon attraction points for tourists in different categories beyond museums and monuments, will also evaluate the tourist feedback from other sources like TripAdvisor and apply the same methodology in other cities and country regions.

Keywords: Lisbon tourism, opinion mining, sentiment analysis, tourism location attractiveness evaluation

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Authors: Yi Wang, Greg Hearn, Terry Flew

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In order to understand how cultural innovation occurs, this paper explores the interaction in Shenzhen of China between universities, creative industries, and government in creative economic using the Triple Helix framework. During the past two decades, Triple Helix has been recognized as a new theory of innovation to inform and guide policy-making in national and regional development. Universities and governments around the world, especially in developing countries, have taken actions to strengthen connections with creative industries to develop regional economies. To date research based on the Triple Helix model has focused primarily on Science and Technology collaborations, largely ignoring other fields. Hence, there is an opportunity for work to be done in seeking to better understand how the Triple Helix framework might apply in the field of creative industries and what knowledge might be gleaned from such an undertaking. Since the late 1990s, the concept of ‘creative industries’ has been introduced as policy and academic discourse. The development of creative industries policy by city agencies has improved city wealth creation and economic capital. It claims to generate a ‘new economy’ of enterprise dynamics and activities for urban renewal through the arts and digital media, via knowledge transfer in knowledge-based economies. Creative industries also involve commercial inputs to the creative economy, to dynamically reshape the city into an innovative culture. In particular, this paper will concentrate on creative spaces (incubators, digital tech parks, maker spaces, art hubs) where academic, industry and government interact. China has sought to enhance the brand of their manufacturing industry in cultural policy. It aims to transfer the image of ‘Made in China’ to ‘Created in China’ as well as to give Chinese brands more international competitiveness in a global economy. Shenzhen is a notable example in China as an international knowledge-based city following this path. In 2009, the Shenzhen Municipal Government proposed the city slogan ‘Build a Leading Cultural City”’ to show the ambition of government’s strong will to develop Shenzhen’s cultural capacity and creativity. The vision of Shenzhen is to become a cultural innovation center, a regional cultural center and an international cultural city. However, there has been a lack of attention to the triple helix interactions in the creative industries in China. In particular, there is limited knowledge about how interactions in creative spaces co-location within triple helix networks significantly influence city based innovation. That is, the roles of participating institutions need to be better understood. Thus, this paper discusses the interplay between university, creative industries and government in Shenzhen. Secondary analysis and documentary analysis will be used as methods in an effort to practically ground and illustrate this theoretical framework. Furthermore, this paper explores how are creative spaces being used to implement Triple Helix in creative industries. In particular, the new combination of resources generated from the synthesized consolidation and interactions through the institutions. This study will thus provide an innovative lens to understand the components, relationships and functions that exist within creative spaces by applying Triple Helix framework to the creative industries.

Keywords: cultural policy, creative industries, creative city, triple Helix

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Kiana Taheri

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Rural tourism has garnered significant attention as a catalyst for rural development and sustainability, particularly in regions like Tuscany, Italy, where the convergence of cultural heritage, picturesque landscapes, and agricultural traditions provides a fertile ground for tourism activities. This paper investigates the pivotal role of women entrepreneurs in driving sustainable rural tourism development, with a specific focus on Tuscany. Drawing upon a synthesis of literature on rural tourism, entrepreneurship, and gender studies, this research offers insights into how women entrepreneurs contribute to the economic, social, and environmental dimensions of rural tourism in Tuscany. The conceptual framework of this study is rooted in the evolving landscape of rural development, shaped by shifting paradigms in agricultural policies, such as the Common Agricultural Policy (CAP) of the European Union. This framework underscores the transition from traditional agrarian economies to dynamic rural tourism destinations characterized by a consumer-centric approach and a focus on sustainable development. Against this backdrop, the study delves into the multifaceted contributions of women entrepreneurs within the rural tourism sector. Central to the analysis is the recognition of rural tourism as a nexus of social, cultural, economic, and environmental interactions, wherein women entrepreneurs play a pivotal role in leveraging local resources, preserving cultural heritage, and fostering community engagement. By capitalizing on their unique perspectives, skills, and networks, women entrepreneurs drive innovation, diversification, and inclusivity within the tourism sector, thereby enhancing its resilience and long-term viability. Moreover, the study highlights the symbiotic relationship between rural tourism development and women's empowerment, as evidenced by the increasing prominence of women entrepreneurs in Tuscany's rural economy. Through their leadership roles in small and medium enterprises (SMEs) and agritourism ventures, women entrepreneurs not only contribute to economic growth but also challenge traditional gender norms and empower local communities. A key empirical focus of this research is a comprehensive case study of Tuscany, renowned for its successful rural tourism model and vibrant entrepreneurial ecosystem. Through qualitative interviews, surveys, and archival analysis, the study elucidates the strategies, challenges, and impacts of women entrepreneurs on sustainable rural tourism development in Tuscany. By examining the experiences of women entrepreneurs across diverse sectors of rural tourism, including hospitality, gastronomy, and cultural heritage, the study offers nuanced insights into their contributions to regional development and empowerment. In conclusion, this research contributes to the burgeoning scholarship on rural tourism, entrepreneurship, and gender studies by shedding light on the transformative role of women entrepreneurs in driving sustainable development agendas in rural areas. By elucidating the interplay between gender dynamics, entrepreneurial activities, and tourism development, this study seeks to inform policy interventions and strategic initiatives aimed at fostering inclusive and sustainable rural tourism ecosystems.

Keywords: rural tourism, women empowerment, entrepreneurship, sustainable development, small and medium-sized enterprises (SMEs)

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