Search results for: fuzzy multiple objective programming

Authors: Monica Lia

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This paper has set the objective of researching how education is influenced by the information society. The first step was to define more precisely the information space. Second step was to identify how information space intersects the family space and institutional space educational levels represented by pre-school / school and pre-university (kindergarten, at elementary / middle school / high school). Interrelationship between the above-mentioned areas was another objective of the research. All these elements have been investigated through the original intention to identify how the information space can become an educational tool to support for the family space, education and institutional space. In addition, the aim of this research is to offer some solutions in this regard. Often the educational efforts appear to be blocked by the existence of this space. However, this paper demonstrates that Informational space can be an enemy of the educational system or be support systems if we know the internal structure and mechanisms. We can make the Informational Space to work for accomplish the educational objectives.

Keywords: informational space, education, educational tool, social diagram, information, information structure, lessons

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Petr Daniel Kovarik, Matt Kennedy, James Adams, Ajay Wilson, Andy Burns, Charles Kelly, Malcolm Jackson, Rahul Patil, Shahid Iqbal

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Introduction: Osteoradionecrosis (ORN) is a recognised toxicity of radiotherapy (RT) for head and neck cancer (HNC). Existing literature lacks any generally accepted definition and staging system for this toxicity. Objective: The objective is to analyse the outcome of the surgical and nonsurgical treatments of ORN. Material and Method: Data on 2303 patients treated for HNC with radical or adjuvant RT or RT-chemotherapy from January 2010 - December 2021 were retrospectively analysed. Median follow-up to the whole group of patients was 37 months (range 0–148 months). Results: ORN developed in 185 patients (8.1%). The location of ORN was as follows; mandible=170, maxilla=10, and extra oral cavity=5. Multiple ORNs developed in 7 patients. 5 patients with extra oral cavity ORN were excluded from treatment analysis as the management is different. In 180 patients with oral cavity ORN, median follow-up was 59 months (range 5–148 months). ORN healed in 106 patients, treatment failed in 74 patients (improving=10, stable=43, and deteriorating=21). Median healing time was 14 months (range 3-86 months). Notani staging is available in 158 patients with jaw ORN with no previous surgery to the mandible (Notani class I=56, Notani class II=27, and Notani class III=76). 28 ORN (mandible=27, maxilla=1; Notani class I=23, Notani II=3, Notani III=1) healed spontaneously with a median healing time 7 months (range 3–46 months). In 20 patients, ORN developed after dental extraction, in 1 patient in the neomandible after radical surgery as a part of the primary treatment. In 7 patients, ORN developed and spontaneously healed in irradiated bone with no previous surgical/dental intervention. Radical resection of the ORN (segmentectomy, hemi-mandibulectomy with fibula flap) was performed in 43 patients (all mandible; Notani II=1, Notani III=39, Notani class was not established in 3 patients as ORN developed in the neomandible). 27 patients healed (63%); 15 patients failed (improving=2, stable=5, deteriorating=8). The median time from resection to healing was 6 months (range 2–30 months). 109 patients (mandible=100, maxilla=9; Notani I=3, Notani II=23, Notani III=35, Notani class was not established in 9 patients as ORN developed in the maxilla/neomandible) were treated conservatively using a combination of debridement, antibiotics and Pentoclo. 50 patients healed (46%) with a median healing time 14 months (range 3–70 months), 59 patients are recorded with persistent ORN (improving=8, stable=38, deteriorating=13). Out of 109 patients treated conservatively, 13 patients were treated with Pentoclo only (all mandible; Notani I=6, Notani II=3, Notani III=3, 1 patient with neomandible). In total, 8 patients healed (61.5%), treatment failed in 5 patients (stable=4, deteriorating=1). Median healing time was 14 months (range 4–24 months). Extra orally (n=5), 3 cases of ORN were in the auditory canal and 2 in mastoid. ORN healed in one patient (auditory canal after 32 months. Treatment failed in 4 patients (improving=3, stable=1). Conclusion: The outcome of the treatment of ORN remains in general, poor. Every effort should therefore be made to minimise the risk of development of this devastating toxicity.

Keywords: head and neck cancer, radiotherapy, osteoradionecrosis, treatment outcome

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Authors: Bingjie Wang, Qianqian Qang, Nan Lu, Xiubing Liang, Baolong Shen

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Novel composites of HfNbTaTiZrV refractory high-entropy alloy (RHEA) reinforced with 0-5 vol.% Al₂O₃ particles have been synthesized by vacuum arc melting. The microstructure evolution, compressive mechanical properties at room and elevated temperatures, as well as strengthening mechanism of the composites, are analyzed. The HfNbTaTiZrV RHEA reinforced with 4 vol.% Al₂O₃ displays excellent phase stability at elevated temperatures. A superior compressive yield strength of 2700 MPa at room temperature, 1392 MPa at 800 °C, and 693 MPa at 1000 °C has been obtained for this composite. The improved yield strength results from multiple strengthening mechanisms caused by Al₂O₃ addition, including interstitial strengthening, grain boundary strengthening, and dispersion strengthening. Besides, the effects of interstitial strengthening increase with the temperature and is the main strengthening mechanism at elevated temperatures. These findings not only promote the development of oxide-reinforced RHEAs for challenging engineering applications but also provide guidelines for the design of light refractory materials with multiple strengthening mechanisms.

Keywords: Al₂O₃-reinforcement, HfNbTaTiZrV, refractory high-entropy alloy, interstitial strengthening

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Authors: Metrek Almetrek, Naser Alqahtani, Eisa Ghazwani, Mona Asiri, Mohammed Alqahtani, Magboolah Balobaid

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Background: Advocacy of potential for community coalitions to positively address social determinants of health and quality of life, little is known about the views of stakeholders involved in such efforts. This study sought to assess the provinces leaders’ perspectives about social determinants related to the Health Neighborhood Initiative (HNI), a new county effort to support community coalitions. Method and Subjects: We used a descriptive, qualitative study using personal interviews in 2022. We conducted it in the community coalition's “main cities committees” set across service planning areas that serve vulnerable groups located in the seven registered healthy cities to WHO (Abha, Tareeb, Muhayel, Balqarn, Alharajah, Alamwah, and Bisha). We conducted key informant interviews with 76 governmental, profit, non-profit, and community leaders to understand their perspectives about the HNI. As part of a larger project, this study focused on leaders’ views about social determinants of health related to the HNI. All interviews were audio-recorded and transcribed. An inductive approach to coding was used, with text segments grouped by social determinant categories. Results: Provinces leaders described multiple social determinants of health and quality of life that were relevant to the HNI community coalitions: housing and safety, community violence, economic stability, city services coordination and employment and education. Leaders discussed how social determinants were interconnected with each other and the need for efforts to address multiple social determinants simultaneously to effectively improve health and quality of life. Conclusions: Community coalitions have an opportunity to address multiple social determinants of health and quality of life to meet the social needs of vulnerable groups. Future research should examine how community coalitions, like those in the HNI, can actively engage with community members to identify needs and then deliver evidence-based care.

Keywords: social determinants, health and quality of life, vulnerable groups, qualitative research

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Authors: Michael Stewart

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Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs. This paper used reinforcement learning and parallel processing within the context of mutation testing for the selection of mutation operators and test cases that reduced the computational cost of testing and improved test suite effectiveness. Experiments were conducted using sample programs to determine how well the reinforcement learning-based algorithm performed with one live mutation, multiple live mutations and no live mutations. The experiments, measured by mutation score, were used to update the algorithm and improved accuracy for predictions. The performance was then evaluated on multiple processor computers. With reinforcement learning, the mutation operators utilized were reduced by 50 – 100%.

Keywords: automated-testing, machine learning, mutation testing, parallel processing, reinforcement learning, software engineering, software testing

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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Authors: Debabrata Biswas

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The current trend in the development of antibiotic resistance by multiple bacterial pathogens has resulted in a troubling loss of effective antibiotic options for human. The emergence of multi-drug-resistant pathogens has necessitated higher dosages and combinations of multiple antibiotics, further exacerbating the problem of antibiotic resistance. Zoonotic bacterial pathogens, such as Salmonella, Campylobacter, Shiga toxin-producing Escherichia coli (such as enterohaemorrhagic E. coli or EHEC), and Listeria are the most common and predominant foodborne enteric infectious agents. It was observed that these pathogens gained/developed their ability to survive in the presence of antibiotics either in farm animal gut or farm environment and researchers believe that therapeutic and sub-therapeutic antibiotic use in farm animal production might play an important role in it. The mechanism of action of antimicrobial components used in farm animal production in genomic interplay in the gut and farm environment, has not been fully characterized. Even the risk of promoting the exchange of mobile genetic elements between microbes specifically pathogens needs to be evaluated in depth, to ensure sustainable farm animal production, safety of our food and to mitigate/limit the enteric infection with multiple antibiotic resistant bacterial pathogens. Due to the consumer’s demand and considering the current emerging situation, many countries are in process to withdraw antibiotic use in farm animal production. Before withdrawing use of the sub-therapeutic antibiotic or restricting the use of therapeutic antibiotics in farm animal production, it is essential to find alternative natural antimicrobials for promoting the growth of farm animal and/or treating animal diseases. Further, it is also necessary to consider whether that compound(s) has the potential to trigger the acquisition or loss of genetic materials in zoonotic and any other bacterial pathogens. Development of alternative therapeutic and sub-therapeutic antimicrobials for farm animal production and food processing and preservation and their effective implementation for sustainable strategies for farm animal production as well as the possible risk for horizontal gene transfer in major enteric pathogens will be focus in the study.

Keywords: food safety, natural antimicrobial, sustainable farming, antibiotic resistance

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Authors: Maureen Kamau, Gulnaz Mohamoud, Adelaide Lusambili, Njeri Nyanja

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Life expectancy has increased over the last century amongst older individuals, and in particular, those 60 years and over. The World Health Organization estimates that the world's population of persons over 60 years will rise to 22 per cent by the year 2050. Ageing is associated with increasing disability, multiple chronic conditions, and an increase in the use of health services. These multiple chronic conditions are managed with polypharmacy. Polypharmacy has numerous adverse effects including non-adherence, poor compliance to the various medications, reduced appetite, and risk of fall. Studies on polypharmacy and ageing are few and poorly understood especially in low and middle - income countries. The aim of this study was to explore the knowledge, attitudes and beliefs of older people towards polypharmacy. A qualitative study of 15 patients aged 60 years and above, taking more than five medications per day were conducted at the Aga Khan University using Semi-structured in-depth interviews. Three interviews were pilot interviews, and data analysis was performed on 12 interviews. Data were analyzed using NVIVO 12 software. A thematic qualitative analysis was carried out guided by Braun and Clarke (2006) framework. Themes identified; - knowledge of their co-morbidities and of the medication that older persons take, sources of information about medicines, and storage of the medication, experiences and attitudes of older patients towards polypharmacy both positive and negative, older peoples beliefs and their coping mechanisms with polypharmacy. The study participants had good knowledge on their multiple co-morbidities, and on the medication they took. The patients had positive attitudes towards medication as it enhanced their health and well-being, and enabled them to perform their activities of daily living. There was a strong belief among older patients that the medications were necessary for their health. All these factors enhanced compliance to the multiple medication. However, some older patients had negative attitudes due to the pill burden, side effects of the medication, and stigma associated with being ill. Cost of healthcare was a concern, with most of the patients interviewed relying on insurance to cover the cost of their medication. Older patients had accepted that the medication they were prescribed were necessary for their health, as it enabled them to complete their activities of daily living. Some concerns about the side effects of the medication arose, and brought about the need for patient education that would ensure that the patients are aware of the medications they take, and potential side effects. The effect that the COVID 19 pandemic had in the healthcare of the older patients was evident by the number of the older patients avoided coming to the hospital during the period of the pandemic. The relationship with the primary care physician and the older patients is an important one, especially in LMICs such as Kenya, as many of the older patients trusted the doctors wholeheartedly to make the best decision about their health and about their medication. Prescription review is important to avoid the use of potentially inappropriate medication.

Keywords: polypharmacy, older patients, multiple chronic conditions, Kenya, Africa, qualitative study, indepth interviews, primary care

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Authors: Ting Gao, Mingyue He

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Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

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Authors: Mohammad Nadeem, Haider Banka, R. Venugopal

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Granulation or agglomeration is a size enlargement process to transform the fine particulates into larger aggregates since the fine size of available materials and minerals poses difficulty in their utilization. Though a long list of methods is available in the literature for the modeling of granulation process to facilitate the in-depth understanding and interpretation of the system, there is still scope of improvements using novel tools and techniques. Intelligent techniques, such as artificial neural network, fuzzy logic, self-organizing map, support vector machine and others, have emerged as compelling alternatives for dealing with imprecision and complex non-linearity of the systems. The present study tries to review the applications of intelligent techniques in the modeling of size enlargement process for fine materials.

Keywords: fine material, granulation, intelligent technique, modelling

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Authors: Ameni Mejri, Moufida Hajjaj, Salem Hasnaoui, Ridha Bouallegue

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The technology advances have most lately made the millimeter wave (mmWave) communication possible. Due to the huge amount of spectrum that is available in MmWave frequency bands, this promising candidate is considered as a key technology for the deployment of 5G cellular networks. In order to enhance system capacity and achieve spectral efficiency, very large antenna arrays are employed at mmWave systems by exploiting array gain. However, it has been shown that conventional beamforming strategies are not suitable for mmWave hardware implementation. Therefore, new features are required for mmWave cellular applications. Unlike traditional multiple-input-multiple-output (MIMO) systems for which only digital precoders are essential to accomplish precoding, MIMO technology seems to be different at mmWave because of digital precoding limitations. Moreover, precoding implements a greater number of radio frequency (RF) chains supporting more signal mixers and analog-to-digital converters. As RF chain cost and power consumption is increasing, we need to resort to another alternative. Although the hybrid precoding architecture has been regarded as the best solution based on a combination between a baseband precoder and an RF precoder, we still do not get the optimal design of hybrid precoders. According to the mapping strategies from RF chains to the different antenna elements, there are two main categories of hybrid precoding architecture. Given as a hybrid precoding sub-array architecture, the partially-connected structure reduces hardware complexity by using a less number of phase shifters, whereas it sacrifices some beamforming gain. In this paper, we treat the hybrid precoder design in mmWave MIMO systems as a problem of matrix factorization. Thus, we adopt the alternating minimization principle in order to solve the design problem. Further, we present our proposed algorithm for the partially-connected structure, which is based on the iterative hard thresholding method. Through simulation results, we show that our hybrid precoding algorithm provides significant performance gains over existing algorithms. We also show that the proposed approach reduces significantly the computational complexity. Furthermore, valuable design insights are provided when we use the proposed algorithm to make simulation comparisons between the hybrid precoding partially-connected structure and the fully-connected structure.

Keywords: alternating minimization, hybrid precoding, iterative hard thresholding, low-complexity, millimeter wave communication, partially-connected structure

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Authors: Ida Srdić, Luka Mandić, LidijaMandić

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Digitization and intensive use of tablets, smartphones, the internet, mobile, and web applications have massively disrupted our habits, and the way audiences (especially youth) consume content. To introduce educational content in games and animations, and at the same time to keep it interesting and compelling for kids, is a challenge. In our work, we are comparing the different possibilities and potentials that digital games could provide to successfully mitigate direct connection with education. We analyze the main directions and educational methods in game-based learning and the possibilities of interactive modeling through questionnaires for user experience and requirements. A pre and post-quantitative survey will be conducted in order to measure levels of objective knowledge as well as the games perception. This approach enables quantitative and objective evaluation of the impact the game has on participants. Also, we will discuss the main barriers to the use of games in education and how games can be best used for learning.

Keywords: Bloom’s taxonomy, epistemic games, learning objectives, virtual learning environments

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Authors: Noah Emil Glisik

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Multiple sclerosis (MS) is a chronic, immune-mediated disorder characterized by neurodegenerative processes and a highly variable disease course. Recent research highlights a complex interplay between psychological stress and MS progression, with both acute and chronic stressors linked to heightened inflammatory activity, increased relapse risk, and accelerated disability. This review synthesizes findings from systematic analyses, cohort studies, and neuroimaging investigations to examine how stress contributes to disease dynamics in MS. Evidence suggests that psychological stress influences MS progression through neural and physiological pathways, including dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and heightened activity in specific brain regions, such as the insular cortex. Notably, functional MRI studies indicate that stress-induced neural activity may predict future atrophy in gray matter regions implicated in motor and cognitive function, thus supporting a neurobiological link between stress and neurodegeneration in MS. Longitudinal studies further associate chronic stress with reduced quality of life and higher relapse frequency, emphasizing the need for a multifaceted therapeutic approach that addresses both the physical and psychological dimensions of MS. Evidence from intervention studies suggests that stress management strategies, such as cognitive-behavioral therapy and mindfulness-based programs, may reduce relapse rates and mitigate lesion formation in MS patients. These findings underscore the importance of integrating stress-reducing interventions into standard MS care, with potential to improve disease outcomes and patient well-being. Further research is essential to clarify the causal pathways and develop targeted interventions that could modify the stress response in MS, offering an avenue to address disease progression and enhance quality of life.

Keywords: multiple sclerosis, psychological stress, disease progression, neuroimaging, stress management

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Authors: G. Shahzadi, A. Soulaimani

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This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety.

Keywords: Rockfill dam, parameter identification, stochastic analysis, regression, PLAXIS

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Authors: L. Aparecida Bastos, J. Leige Lopes, J. Crepaldi, R. Monteiro da Silva

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The objective of this study is to analyze the evolution of some social and economic indicators of Mercosur´s economies from 1980 to 2012, based on the statistics of the Latin American Integration Association (LAIA). The objective is to observe if after the accession of these economies to Mercosur (the first accessions occurred in 1994) these indicators showed better performance, in order to demonstrate if economic integration contributed to improved trade, macroeconomic performance, and level of social and economic development of member countries. To this end, the methodologies used will be a literature review and descriptive statistics. The theoretical framework that guides the work are the theories of Integration: Classical Liberal, Marxist and structural-proactive. The results reveal that most social and economic indicators showed better performance in those economies that joined Mercosur after 1994. This work is the result of an investigation already completed.

Keywords: economic integration, Mercosur, social indicators, economic indicators

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Authors: Mehmet Bozca

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Singular value decomposition based optimisation of geometric design parameters of a 5-speed gearbox is studied. During the optimisation, a four-degree-of freedom torsional vibration model of the pinion gear-wheel gear system is obtained and the minimum singular value of the transfer matrix is considered as the objective functions. The computational cost of the associated singular value problems is quite low for the objective function, because it is only necessary to compute the largest and smallest singular values (µmax and µmin) that can be achieved by using selective eigenvalue solvers; the other singular values are not needed. The design parameters are optimised under several constraints that include bending stress, contact stress and constant distance between gear centres. Thus, by optimising the geometric parameters of the gearbox such as, the module, number of teeth and face width it is possible to obtain a light-weight-gearbox structure. It is concluded that the all optimised geometric design parameters also satisfy all constraints.

Keywords: Singular value, optimisation, gearbox, torsional vibration

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Authors: Pin-Ling Yin, Dung-Ying Lin

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Satellites play an important role in our daily lives, from monitoring the Earth's environment and providing real-time disaster imagery to predicting extreme weather events. As technology advances and demands increase, the tasks undertaken by satellites have become increasingly complex, with more stringent resource management requirements. A common challenge in satellite mission scheduling is the limited availability of resources, including onboard memory, ground station accessibility, and satellite power. In this context, efficiently scheduling and managing the increasingly complex satellite missions under constrained resources has become a critical issue that needs to be addressed. The core of Satellite Onboard Activity Planning (SOAP) lies in optimizing the scheduling of the received tasks, arranging them on a timeline to form an executable onboard mission plan. This study aims to develop an optimization model that considers the various constraints involved in satellite mission scheduling, such as the non-overlapping execution periods for certain types of tasks, the requirement that tasks must fall within the contact range of specified types of ground stations during their execution, onboard memory capacity limits, and the collaborative constraints between different types of tasks. Specifically, this research constructs a mixed-integer programming mathematical model and solves it with a commercial optimization package. Simultaneously, as the problem size increases, the problem becomes more difficult to solve. Therefore, in this study, a heuristic algorithm has been developed to address the challenges of using commercial optimization package as the scale increases. The goal is to effectively plan satellite missions, maximizing the total number of executable tasks while considering task priorities and ensuring that tasks can be completed as early as possible without violating feasibility constraints. To verify the feasibility and effectiveness of the algorithm, test instances of various sizes were generated, and the results were validated through feedback from on-site users and compared against solutions obtained from a commercial optimization package. Numerical results show that the algorithm performs well under various scenarios, consistently meeting user requirements. The satellite mission scheduling algorithm proposed in this study can be flexibly extended to different types of satellite mission demands, achieving optimal resource allocation and enhancing the efficiency and effectiveness of satellite mission execution.

Keywords: mixed-integer programming, meta-heuristics, optimization, resource management, satellite mission scheduling

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Authors: Rupesh K. Chaudhary, Narinder P. Jain, Rajesh Mahajan, Rajat Manchanda

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Introduction: Delirium is a complex, multifactorial neuropsychiatric syndrome comprising a broad range of cognitive and neurobehavioral symptoms. In critically ill patients, it may develop secondary to multiple predisposing factors. Although it can be transient and irreversible but if left untreated may lead to long term cognitive dysfunction. Early identification and assessment of risk factors usually help in appropriate management of delirium which in turn leads to decreased hospital stay, cost of therapy and mortality. Aim and Objective: Aim of the present study was to estimate the incidence of delirium using a validated scale in medical ICU patients and to determine the associated risk factors and outcomes. Material and Method: A prospective study in an 18-bed medical-intensive care unit (ICU) was undertaken. A total of 357 consecutive patients admitted to ICU for more than 24 hours were assessed. These patients were screened with the help of Confusion Assessment Method for Intensive Care Unit -CAM-ICU, Richmond Agitation and Sedation Scale, Screening Checklist for delirium and APACHE II. Appropiate statistical analysis was done to evaluate the risk factors influencing mortality in delirium. Results: Delirium occurred in 54.6% of 194 patients. Risk of delirium was independently associated with a history of hypertension, diabetes but not with severity of illness APACHE II score. Delirium was linked to longer ICU stay 13.08 ± 9.6 ver 7.07 ± 4.98 days, higher ICU mortality (35.8% % vs. 17.0%). Conclusion: Our study concluded that delirium poses a great risk factor in the outcome of the patient and carries high mortality, so a timely intervention helps in addressing these issues.

Keywords: delirium, risk factors, outcome, intervention

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Authors: Ioana Pisica, Dimitrios Gkritzapis

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The significance of UAS in scientific applications has been amply demonstrated in recent years. The combinations of portability and quasi-static positioning by means of flying in close loop path make them versatile and efficient in the inspection of power systems infrastructure. In this paper, we critically assess several platforms and sensor capabilities to identify their pros and cons in relation to the power systems assets to be monitored. In this respect, it is paramount the flights to be conducted by using UAS which bear certain suitable features, such as responsive and easy control, video capturing in real time, autonomous routing of pre-planned flight programming with differentiating payloads. The outcome of this research is a set of optimal requirements for power system assets monitoring with UAS.

Keywords: platforms, power system, sensors, UAVs

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Authors: Somayeh Nezami

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The management of financial and human resources of police in metropolitans requires many information and exact plans to reduce a rate of crime and increase the safety of the society. Geographical Information Systems have an important role in providing crime maps and their analysis. By using them and identification of crime hot spots along with spatial presentation of the results, it is possible to allocate optimum resources while presenting effective methods for decision making and preventive solutions. In this paper, we try to explain and compare between some of the methods of hot spots analysis such as Mode, Fuzzy Mode and Nearest Neighbour Hierarchical spatial clustering (NNH). Then the spots with the highest crime rates of drug smuggling for one province in Iran with borderline with Afghanistan are obtained. We will show that among these three methods NNH leads to the best result.

Keywords: GIS, Hot spots, nearest neighbor hierarchical spatial clustering, NNH, spatial analysis of crime

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Authors: Sean Goltz, Michael Mayo

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The purpose of this research is to use Global-Regulation.com database of the world laws, focusing on tax treaties between countries, in order to create an AI-driven tax simulator that will run an AI agent through potential tax scenarios across countries. The AI agent goal is to identify the scenario that will result in minimum tax liability based on tax treaties between countries. The results will be visualized by a three dimensional matrix. This will be an online web application. Multinational corporations are running their business through multiple countries. These countries, in turn, have a tax treaty with many other countries to regulate the payment of taxes on income that is transferred between these countries. As a result, planning the best tax scenario across multiple countries and numerous tax treaties is almost impossible. This research propose to use Global-Regulation.com database of word laws in English (machine translated by Google and Microsoft API’s) in order to create a simulator that will include the information in the tax treaties. Once ready, an AI agent will be sent through the simulator to identify the scenario that will result in minimum tax liability. Identifying the best tax scenario across countries may save multinational corporations, like Google, billions of dollars annually. Given the nature of the raw data and the domain of taxes (i.e., numbers), this is a promising ground to employ artificial intelligence towards a practical and beneficial purpose.

Keywords: taxation, law, multinational, corporation

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Authors: Ilana Singer, Anastasia Lučić, Julie Leclerc

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Introduction: Tics, characterized by repetitive, sudden, non-voluntary motor movements or vocalizations, are prevalent in chronic tic disorder (CT) and Tourette Syndrome (TS). These neurodevelopmental disorders often coexist with various psychiatric conditions, leading to challenges and reduced quality of life. While medication in conjunction with behavioral interventions, such as Habit Reversal Training (HRT), Exposure Response Prevention (ERP), and Comprehensive Behavioral Intervention for Tics (CBIT), has shown efficacy, a significant proportion of patients experience persistent tics. Thus, innovative treatment approaches are necessary to improve therapeutic outcomes, such as mindfulness-based approaches. Nonetheless, the effectiveness of mindfulness-based interventions in the context of CT and TS remains understudied. Objective: The objective of this scoping review is to provide an overview of the current state of research on mindfulness-based interventions for CT and TS, identify knowledge and evidence gaps, discuss the effectiveness of mindfulness-based interventions with other treatment options, and discuss implications for clinical practice and policy development. Method: Using guidelines from Peters (2020) and the PRISMA-ScR, a scoping review was conducted. Multiple electronic databases were searched from inception until June 2023, including MEDLINE, EMBASE, PsychInfo, Global Health, PubMed, Web of Science, and Érudit. Inclusion criteria were applied to select relevant studies, and data extraction was independently performed by two reviewers. Results: Five papers were included in the study. Firstly, we found that mindfulness interventions were found to be effective in reducing anxiety and depression while enhancing overall well-being in individuals with tics. Furthermore, the review highlighted the potential role of mindfulness in enhancing functional connectivity within the Default Mode Network (DMN) as a compensatory function in TS patients. This suggests that mindfulness interventions may complement and support traditional therapeutic approaches, particularly HRT, by positively influencing brain networks associated with tic regulation and control. Conclusion: This scoping review contributes to the understanding of the effectiveness of mindfulness-based interventions in managing CT and TS. By identifying research gaps, this review can guide future investigations and interventions to improve outcomes for individuals with CT or TS. Overall, these findings emphasize the potential benefits of incorporating mindfulness-based interventions as a smaller subset within comprehensive treatment strategies. However, it is essential to acknowledge the limitations of this scoping review, such as the exclusion of a pre-established protocol and the limited number of studies available for inclusion. Further research and clinical exploration are necessary to better understand the specific mechanisms and optimal integration of mindfulness-based interventions with existing behavioral interventions for this population.

Keywords: scoping reviews, Tourette Syndrome, tics, mindfulness-based, therapy, intervention

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Authors: H. R. Kamali, A. Sadegheih, M. A. Vahdat-Zad, H. Khademi-Zare

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In this paper, a single product, multi-echelon, multi-period closed loop supply chain is surveyed, including a variety of costs, time conditions, and capacities, to plan and determine the values and time of the components procurement, production, distribution, recycling and disposal specially for high-tech products that undergo a decreasing production cost and sale price over time. For this purpose, the mathematic model of the problem that is a kind of mixed integer linear programming is presented, and it is finally proved that the problem belongs to the category of NP-hard problems.

Keywords: closed loop supply chain, continuous price decrease, NP-hard, planning

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Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

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Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

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Authors: Amor Jnifene, Cedric Cocaud

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One of the most constraining problems facing the development of autonomous vehicles is the limitations of current technologies. Guidance and navigation controllers need to be faster and more robust. Communication data links need to be more reliable and secure. For an Unmanned Aerial Vehicles (UAV) to be useful, and fully autonomous, one important feature that needs to be an integral part of the navigation system is autonomous trajectory planning. The work discussed in this paper presents a method for on-line trajectory planning for UAV’s. This method takes into account various constraints of different types including specific vectors of approach close to target points, multiple objectives, and other constraints related to speed, altitude, and obstacle avoidance. The trajectory produced by the proposed method ensures a smooth transition between different segments, satisfies the minimum curvature imposed by the dynamics of the UAV, and finds the optimum velocity based on available atmospheric conditions. Given a set of objective points and waypoints a skeleton of the trajectory is constructed first by linking all waypoints with straight segments based on the order in which they are encountered in the path. Secondly, vectors of approach (VoA) are assigned to objective waypoints and their preceding transitional waypoint if any. Thirdly, the straight segments are replaced by 3D curvilinear trajectories taking into account the aircraft dynamics. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircrafts, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircraft, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers.

Keywords: trajectory planning, unmanned autonomous air vehicle, vector of approach, waypoints

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Authors: Luísa Pedro, José Pais-Ribeiro, João Páscoa Pinheiro

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Multiple Sclerosis (MS) is a chronic disease of the central nervous system that affects more often young adults in the prime of his career and personal development, with no cure and unknown causes. The most common signs and symptoms are fatigue, muscle weakness, changes in sensation, ataxia, changes in balance, gait difficulties, memory difficulties, cognitive impairment and difficulties in problem solving. MS is a relatively common neurological disorder in which various impairments and disabilities impact strongly on function and daily life activities. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in MS patients. MS is a relatively common neurological disorder in which various impairments and disabilities impact strongly on function and daily life activities. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in MS patients. After this, a set of exercises was implemented to be used in daily life activities, according to studies developed with MS patients. We asked the subjects the question “Please classify the severity of your disease?” and used the domain of psychological well-being, the Mental Health Inventory (MHI-38) at the beginning (time A) and end (time B) of the program of self-regulation. We used the Statistical Package for the Social Sciences (SPSS) version 20. A non-parametric statistical hypothesis test (Wilcoxon test) was used for the variable analysis. The intervention followed the recommendations of the Helsinki Declaration. The age range of the subjects was between 20 and 58 years with a mean age of 44 years. 58.3 % were women, 37.5 % were currently married, 67% were retired and the mean level of education was 12.5 years. In the correlation between the severity of the disease perception and psychological well before the self-regulation program, an obtained result (r = 0.26, p <0.05), then the self-regulation program, was (r = 0.37, p <0.01), from a low to moderate correlation. We conclude that the program of self-regulation for physical activity in patients with MS can improve the relationship between the perception of disease severity and psychological well-being.

Keywords: psychological well-being, multiple sclerosis, self-regulation, physical activity

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Authors: Berfin Yildiz

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These days, increasing global warming effects, limited amount of energy resources, etc., necessitates the awareness that must be present in every profession group. The architecture and construction sectors are responsible for both the embodied and operational energy of the materials. This responsibility has led designers to seek alternative solutions for energy-efficient material selection. The choice of energy-efficient material requires consideration of the entire life cycle, including the building's production, use, and disposal energy. The aim of this study is to investigate the method of material selection of external thermal insulation composite systems (ETICS). Embodied and in-use energy values of material alternatives were used for the evaluation in this study. The operational energy is calculated according to the u-value calculation method defined in the TS 825 (Thermal Insulation Requirements) standard for Turkey, and the embodied energy is calculated based on the manufacturer's Energy Performance Declaration (EPD). ETICS consists of a wall, adhesive, insulation, lining, mechanical, mesh, and exterior finishing materials. In this study, lining, mechanical, and mesh materials were ignored because EPD documents could not be obtained. The material selection problem is designed as a hypothetical volume area (5x5x3m) and defined as a multi-objective discrete optimization problem for external thermal insulation composite systems. Defining the problem as a discrete optimization problem is important in order to choose between materials of various thicknesses and sizes. Since production and use energy values, which are determined as optimization objectives in the study, are often conflicting values, material selection is defined as a multi-objective optimization problem, and it is aimed to obtain many solution alternatives by using Hypervolume (HypE) algorithm. The enrollment process started with 100 individuals and continued for 50 generations. According to the obtained results, it was observed that autoclaved aerated concrete and Ponce block as wall material, glass wool, as insulation material gave better results.

Keywords: embodied energy, multi-objective discrete optimization, performative design, thermal insulation

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Authors: Mehtap Dursun, Zeynep Şener

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Supplier selection is considered as one of the most critical issues encountered by operations and purchasing managers to sharpen the company’s competitive advantage. In this paper, a novel fuzzy multi-criteria group decision making approach integrating quality function deployment (QFD) and decision making trial and evaluation laboratory (DEMATEL) method is proposed for supplier selection. The proposed methodology enables to consider the impacts of inner dependence among supplier assessment criteria. A house of quality (HOQ) which translates purchased product features into supplier assessment criteria is built using the weights obtained by DEMATEL approach to determine the desired levels of supplier assessment criteria. Supplier alternatives are ranked by a distance-based method.

Keywords: DEMATEL, group decision making, QFD, supplier selection

Procedia PDF Downloads 438