Search results for: dimensional affect prediction

Authors: Christos Lampropoulos, Maria Konsta, Vicky Dradaki, Irini Dri, Konstantina Panouria, Tamta Sirbilatze, Ifigenia Apostolou, Vaggelis Lambas, Christina Kordali, Georgios Mavras

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Objectives: Malnutrition in hospitalized patients is related to increased morbidity and mortality. The purpose of our study was to compare various nutritional scores in order to detect the most suitable one for assessing the nutritional status of elderly, hospitalized patients and correlate them with mortality and extension of admission duration, due to patients’ critical condition. Methods: Sample population included 150 patients (78 men, 72 women, mean age 80±8.2). Nutritional status was assessed by Mini Nutritional Assessment (MNA full, short-form), Malnutrition Universal Screening Tool (MUST) and short Nutritional Appetite Questionnaire (sNAQ). Sensitivity, specificity, positive and negative predictive values and ROC curves were assessed after adjustment for the cause of current admission, a known prognostic factor according to previously applied multivariate models. Primary endpoints were mortality (from admission until 6 months afterwards) and duration of hospitalization, compared to national guidelines for closed consolidated medical expenses. Results: Concerning mortality, MNA (short-form and full) and SNAQ had similar, low sensitivity (25.8%, 25.8% and 35.5% respectively) while MUST had higher sensitivity (48.4%). In contrast, all the questionnaires had high specificity (94%-97.5%). Short-form MNA and sNAQ had the best positive predictive value (72.7% and 78.6% respectively) whereas all the questionnaires had similar negative predictive value (83.2%-87.5%). MUST had the highest ROC curve (0.83) in contrast to the rest questionnaires (0.73-0.77). With regard to extension of admission duration, all four scores had relatively low sensitivity (48.7%-56.7%), specificity (68.4%-77.6%), positive predictive value (63.1%-69.6%), negative predictive value (61%-63%) and ROC curve (0.67-0.69). Conclusion: MUST questionnaire is more advantageous in predicting mortality due to its higher sensitivity and ROC curve. None of the nutritional scores is suitable for prediction of extended hospitalization.

Keywords: duration of admission, malnutrition, nutritional assessment scores, prognostic factors for mortality

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Authors: Susan K. Steele-Moses, Aimee Badeaux

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Background Literature: Nurse anesthesia education placed high demands on students both personally and professionally. High levels of anxiety affect student’s mental, emotional, and physical well-being, which impacts their student success. Whereas more research has focused on the health and well-being of graduate students, far less has focused specifically on nurse anesthesia students (SNRAs), who may experience higher levels of anxiety due to the rigor of their academic program. Current literature describes stressors experienced by SRNAs that cause anxiety and affect their performance, including personal, academic, clinical, interpersonal, emotional, and financial. Sample: DNP-NA 2025 and DNP-NA 2024 cohorts (N = 36). Eighteen (66.7%) students participated in the study. Instrumentation: The DASS-21 was used to measure stress (7 items; α = .87) and anxiety (7 items; α = .74) from the participants. Intervention: The mind-shift meditation app, based on cognitive behavioral therapy, is being used daily before clinical and exams to decrease nurse anesthesia students’ stress and anxiety over time. Results: At baseline, the students exhibited a moderate level of stress, but their anxiety levels were low. The range of scores was 4-21 (out of 28) for stress (M = 12.88; SD = 5.40) and 0-16 (out of 28) for anxiety (M = 6.81; SD = 5.04). Both stress and anxiety were normally distributed [SW = .242 (stress); SW = .210 (anxiety)] without any outliers. There was a significant difference between their stress and anxiety levels (t = 5.55; p < .001) at baseline. Stress and anxiety will be measured over time, with the change analyzed using repeated measures ANOVA. Implications for Practice: The use of purposeful mindfulness meditation has been shown to decrease stress and anxiety in nursing students.

Keywords: mindfulness, meditation, graduate nursing education, nursing education

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Authors: Guity Karim, Valiollah Ayareh

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Probiotic dairy products are those that contain biologically active components that may affect beneficially one or more target functions in the body, beyond their adequate nutritional effects. As far as chocolate milk is a popular dairy product in the country especially among children and youth, production of a symbiotic (probiotic + peribiotic) new product using chocolate milk, Bifidobacterium lactis (DSM, Netherland) and inulin (Bene, Belgium) would help to promote the nutritional and functional properties of this product. Bifidobacterium Lactis is used as a probiotic in a variety of foods, particularly dairy products like yogurt and as a probiotic bacterium has benefit effects on the human health. Inulin as a peribiotic agent is considered as functional food ingredient. Experimental studies have shown its use as bifidogenic agent. Chocolate milk with different percent of fat (1 and 2 percent), 6 % of sugar and 0.9 % cacao was made, sterilized (UHT) and supplemented with Bifidobacterium lactis and inulin (0.5 %) after cooling . A sample was made without inulin as a control. Bifidobacterium lactis population was enumerated at days 0, 4, 8 and 12 together with measurement of pH, acidity and viscosity of the samples. Also sensory property of the product was evaluated by a 15 panel testers. The number of live bacterial cells was maintained at the functional level of 106-108 cfu/ml after keeping for 12 days in refrigerated temperature (4°C). Coliforms were found to be absent in the products during the storage. Chocolate milk containing 1% fat and inulin has the best effect on the survival and number of B. lactis at day 8 and after that. Moreover, the addition of inulin did not affect the sensorial quality of the product. In this work, chocolate has been evaluated as a potential protective carrier for oral delivery of B. lactis and inulin.

Keywords: chocolate milk, synbiotic, bifidobacterium lactis, inulin

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Authors: Muhammad Abu-Khaizaran, Ahmad Faza’, Tariq Othman, Yahia Yousef

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This paper presents a study to provide sufficient and reliable information about constructing a Photovoltaic energy profile of the Birzeit University campus (BZU) based on the weather forecast. The developed Photovoltaic energy profile helps to predict the energy yield of the Photovoltaic systems based on the weather forecast and hence helps planning energy production and consumption. Two models will be developed in this paper; a Clear Sky Irradiance model and a Cloud-Cover Radiation model to predict the irradiance for a clear sky day and a cloudy day, respectively. The adopted procedure for developing such models takes into consideration two levels of abstraction. First, irradiance and weather data were acquired by a sensory (measurement) system installed on the rooftop of the Information Technology College building at Birzeit University campus. Second, power readings of a fully operational 51kW commercial Photovoltaic system installed in the University at the rooftop of the adjacent College of Pharmacy-Nursing and Health Professions building are used to validate the output of a simulation model and to help refine its structure. Based on a comparison between a mathematical model, which calculates Clear Sky Irradiance for the University location and two sets of accumulated measured data, it is found that the simulation system offers an accurate resemblance to the installed PV power station on clear sky days. However, these comparisons show a divergence between the expected energy yield and actual energy yield in extreme weather conditions, including clouding and soiling effects. Therefore, a more accurate prediction model for irradiance that takes into consideration weather factors, such as relative humidity and cloudiness, which affect irradiance, was developed; Cloud-Cover Radiation Model (CRM). The equivalent mathematical formulas implement corrections to provide more accurate inputs to the simulation system. The results of the CRM show a very good match with the actual measured irradiance during a cloudy day. The developed Photovoltaic profile helps in predicting the output energy yield of the Photovoltaic system installed at the University campus based on the predicted weather conditions. The simulation and practical results for both models are in a very good match.

Keywords: clear-sky irradiance model, cloud-cover radiation model, photovoltaic, weather forecast

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Authors: C. F. Alegretti, F. R. Cunha, R. G. Gontijo

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This work investigates the effects of an applied magnetic field on the geometry-driven boundary layer detachment flow of a ferrofluid over a sudden expansion. Both constitutive equation and global magnetization equation for a ferrofluid are considered. Therefore, the proposed formulation consists in a coupled magnetic-hydrodynamic problem. Computational simulations are carried out in order to explore, not only the viability to control flow instabilities, but also to evaluate the consistency of theoretical aspects. The unidirectional sudden expansion in a ferrofluid flow is investigated numerically under the perspective of Ferrohydrodynamics in a two-dimensional domain using a Finite Differences Method. The boundary layer detachment induced by the sudden expansion results in a recirculating zone, which has been extensively studied in non-magnetic hydrodynamic problems for a wide range of Reynolds numbers. Similar investigations can be found in literature regarding the sudden expansion under the magnetohydrodynamics framework, but none considering a colloidal suspension of magnetic particles out of the superparamagnetic regime. The vorticity-stream function formulation is implemented and results in a clear coupling between the flow vorticity and its magnetization field. Our simulations indicate a systematic decay on the length of the recirculation zone as increasing physical parameters of the flow, such as the intensity of the applied field and the volume fraction of particles. The results all are discussed from a physical point of view in terms of the dynamical non-dimensional parameters. We argue that the decrease/reduction in the recirculation region of the flow is a direct consequence of the magnetic torque balancing the action of the torque produced by viscous and inertial forces of the flow. For the limit of small Reynolds and magnetic Reynolds parameters, the diffusion of vorticity balances the diffusion of the magnetic torque on the flow. These mechanics control the growth of the recirculation region.

Keywords: boundary layer detachment, ferrofluid, ferrohydrodynamics, magnetization, sudden expansion

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Abdullah M. Alzahrani, Abdel Halim Boussabaine

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There is an international consensus on the climate change in the entire world and this is as a result of the combination of the natural factors, such as volcanoes and hurricanes with increased of human activity on the earth, such as industrial renaissance. Where this solidarity increases emissions of greenhouse gases GHGs that considered as the main driver of climate change scenarios and related emerging risks and impacts on buildings. These climatic risks including damages, disruption and disquiet are set to increase and it is considered as the main challenges and difficulties facing built environment due to major implications on assets sector. Consequently, the threat from climate change patterns has a significant impact on a variety of complex human decisions, which affect all aspects of living. Understanding the relationship between buildings and such risks arising from climate change scenarios on buildings are the key in insuring the optimal timing and design of policies and systems, which affect all aspects of the built environment. This paper will uncovering this correlation between emerging climate change risks and the building assets. In addition, how these emerging risks can be classified in practical way in terms of their impact type on buildings. Hence, this mapping will assist professionals and interested parties in the building sector to cope with such risks in several systematic ways including development and designing of mitigation and adaptation strategies and processes of design, specification, construction, and operation; all these leads to successful management of assets.

Keywords: climate change, climate change risks, built environment, building sector, impacts

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Maryam Parsian, Pelin Mutlu, Ufuk Gunduz

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Two-dimensional cell culture affords simplicity and low cost, but it has serious limitations; lacking cell-cell and cell-matrix interactions that are present in tissues. Cancer cells grown in 3D culture systems have distinct phenotypes of adhesion, growth, migration, invasion as well as profiles of gene and protein expression. These interactions cause the 3D-cultured cells to acquire morphological and cellular characteristics relevant to in vivo tumors. Palbociclib is a chemotherapeutic agent for the treatment of ER-positive and HER-negative metastatic breast cancer. Poly-amidoamine (PAMAM) dendrimer is a well-defined, special three-dimensional structure and has a multivalent surface and internal cavities that can play an essential role in drug delivery systems. In this study, palbociclib is loaded onto the magnetic PAMAM dendrimer. Hanging droplet method was used in order to form 3D spheroids. The possible toxic effects of both free drug and drug loaded nanoparticles were evaluated in 2D and 3D MCF-7, MD-MB-231 and SKBR-3 breast cancer cell culture models by performing MTT cell viability and Alamar Blue assays. MTT analysis was performed with six different doses from 1000 µg/ml to 25 µg/ml. Drug unloaded PAMAM dendrimer did not demonstrate significant toxicity on all breast cancer cell lines. The results showed that 3D spheroids are clearly less sensitive than 2D cell cultures to free palbociclib. Also, palbociclib loaded PAMAM dendrimers showed more toxic effect than free palbociclib in all cell lines at 2D and 3D cultures. The results suggest that the traditional cell culture method (2D) is insufficient for mimicking the actual tumor tissue. The response of the cancer cells to anticancer drugs is different in the 2D and 3D culture conditions. This study showed that breast cancer cells are more resistant to free palbociclib in 3D cultures than in 2D cultures. However, nanoparticle loaded drugs can be more cytotoxic when compared to free drug.

Keywords: 2D and 3D cell culture, breast cancer, palbociclibe, PAMAM magnetic nanoparticles

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Authors: Nadir Ali Mugheri, Shaukat Ali Lohar

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In a multilingual society like Pakistan, code switching is commonly observed in different contexts. Mostly people use L1 (Native Languages) and L2 for common communications and L3 (i.e. English, Urdu, Sindhi) in formal contexts and for academic writings. Such a frequent code switching does affect EFL learners' acquisition of grammar and lexis of the target language which in the long run result in different types of errors in their writings. The current study is to investigate and identify common elements of L1 and L2 (spoken by students of the Universities in Pakistan) which create hindrances for EFL learners. Case study method was used for this research. Formal writings of 400 EFL learners (as participants from various Universities of the country) were observed. Among 400 participants, 200 were female and 200 were male EFL learners having different academic backgrounds. Errors found were categorized into different types according to grammatical items, the difference in meanings, structure of sentences and identifiers of tenses of L1 or L2 in comparison with those of the target language. The findings showed that EFL learners in Pakistani varsities have serious problems in their writings and they committed serious errors related to the grammar and meanings of the target language. After analysis of the committed errors, the results were found in the affirmation of the hypothesis that L1 or L2 does affect EFL learners. The research suggests in the end to adopt natural ways in pedagogy like task-based learning or communicative methods using contextualized material so as to avoid impediments of L1 or L2 in acquisition the target language.

Keywords: multilingualism, L2 acquisition, code switching, language acquisition, communicative language teaching

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Authors: J. B. Athavale, Sean Clarke

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Introduction According to ancient Indian scriptures, a person’s consciousness includes the physical body, the vital energy sheath (Pranshakti), the mental body (which includes one’s feelings and emotions), the intellectual body (which refers to one’s decision-making ability), and the Soul (which is the God Principle that resides in every person). Apart from the physical body, all the other aspects are subtle in nature. In today’s world, much attention is given to one’s physical appearance and intellectual prowess. While there have been improvements in the attention given to mental health, its complete nature is not understood, and in many cultures, mental ill health is considered taboo and looked down upon. Regarding the spiritual well-being of a person, our spiritual research has shown that people’s understanding and efforts are mostly lacking and superficial as they do not conform to Universal Spiritual Principles. Also, true well-being occurs only when all the bodies are healthy. Methodology The spiritual research team at the University has found that the spiritual aspect of a person’s life affects all the physical, psychological, and intellectual bodies of a person resulting in ill health. Cleansing these bodies at a spiritual level is essential to regain well-being. Using Aura and Energy Scanners and advanced sixth sense, we studied what causes spiritual impurity in various bodies and how to cleanse them. We measured the spiritual vibrations of a person and how they get affected due to various daily activities. For example, we studied the difference in a person’s aura before and after applying chemical-based makeup vs. natural makeup. Key Findings From the various spiritual research experiments we conducted, we found that: • All our actions and our thoughts affect our various bodies and have the potential to change the aura for the better or worse. • When there is an increase in negative vibrations around a person, negative energies from the subtle dimension are more likely to affect a person. • As the person’s spiritual level increases, the positivity in their aura also increases, and it is much easier to cleanse the various bodies spiritually. • Spiritual practice is like a general spiritual tonic that increases the positivity in one’s aura. The benefits of this are that it leads to mental stability and intellectual clarity. • Spiritual healing remedies augment any spiritual practice to obtain a faster healing effect. Conclusion Taking care of oneself spiritually has a positive halo effect on all one’s bodies. Spiritual cleansing is required regularly if one wants to attain a state of well-being. Spiritual practice and spiritual healing lead to spiritual growth, stability of mind, and less stress and reactions. Spiritually purer people affect the environment positively, and there is less unrest and more harmony between man and nature.

Keywords: body, spirituality, cleansing, consciousness

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Authors: Helmi Temimi

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In this paper, we study the super-convergence properties of the discontinuous Galerkin (DG) method applied to one-dimensional mth-order ordinary differential equations without introducing auxiliary variables. We found that nth−derivative of the DG solution exhibits an optimal O (hp+1−n) convergence rates in the L2-norm when p-degree piecewise polynomials with p≥1 are used. We further found that the odd-derivatives and the even derivatives are super convergent, respectively, at the upwind and downwind endpoints.

Keywords: discontinuous, galerkin, superconvergence, higherorder, error, estimates

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Antonio Aguilar Aguilar, Eliezer Braun Guitler

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Using a previously developed procedure and with the aid of algebraic software, a two-dimensional generalized Ising model with a 4×2 unitary cell (UC), we obtain a Kagome Lattice with twelve different spin-spin values of interaction, in order to determine the partition function per spin L(T). From the partition function we can study the magnetic behavior of the system. Because of the competition phenomenon between spins, a very complex behavior among them in a variety of magnetic states can be observed.

Keywords: correlations, Ising, Kagome, exact functions

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Authors: Yung-Chung Chuang

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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

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Authors: Ali S. Gargoum

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Graphical modeling plays an important role in providing efficient probability calculations in high dimensional problems (computational efficiency). In this paper, we address one of such problems where we discuss fragmenting puff models and some distributional assumptions concerning models for the instantaneous, emission readings and for the fragmenting process. A graphical representation in terms of a junction tree of the conditional probability breakdown of puffs and puff fragments is proposed.

Keywords: graphical models, influence diagrams, junction trees, Bayesian nets

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Authors: Eiji Nakamachi, Ryota Sakiyama, Koji Yamamoto, Yusuke Morita, Hidetoshi Sakamoto

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The regeneration of injured central nerve network caused by the cerebrovascular accidents is difficult, because of poor regeneration capability of central nerve system composed of the brain and the spinal cord. Recently, new regeneration methods such as transplant of nerve cells and supply of nerve nutritional factor were proposed and examined. However, there still remain many problems with the canceration of engrafted cells and so on and it is strongly required to establish an efficacious treating method of a central nerve system. Blackman proposed the electromagnetic stimulation method to enhance the axonal nerve extension. In this study, we try to design and fabricate a new three-dimensional (3D) bio-reactor, which can load a uniform AC magnetic field stimulation on PC12 cells in the extracellular environment for enhancement of an axonal nerve extension and 3D nerve network generation. Simultaneously, we measure the morphology of PC12 cell bodies, axons, and dendrites by the multiphoton excitation fluorescence microscope (MPM) and evaluate the effectiveness of the uniform AC magnetic stimulation to enhance the axonal nerve extension. Firstly, we designed and fabricated the uniform AC magnetic field stimulation bio-reactor. For the AC magnetic stimulation system, we used the laminated silicon steel sheets for a yoke structure of 3D chamber, which had a high magnetic permeability. Next, we adopted the pole piece structure and installed similar specification coils on both sides of the yoke. We searched an optimum pole piece structure using the magnetic field finite element (FE) analyses and the response surface methodology. We confirmed that the optimum 3D chamber structure showed a uniform magnetic flux density in the PC12 cell culture area by using FE analysis. Then, we fabricated the uniform AC magnetic field stimulation bio-reactor by adopting analytically determined specifications, such as the size of chamber and electromagnetic conditions. We confirmed that measurement results of magnetic field in the chamber showed a good agreement with FE results. Secondly, we fabricated a dish, which set inside the uniform AC magnetic field stimulation of bio-reactor. PC12 cells were disseminated with collagen gel and could be 3D cultured in the dish. The collagen gel were poured in the dish. The collagen gel, which had a disk shape of 6 mm diameter and 3mm height, was set on the membrane filter, which was located at 4 mm height from the bottom of dish. The disk was full filled with the culture medium inside the dish. Finally, we evaluated the effectiveness of the uniform AC magnetic field stimulation to enhance the nurve axonal extension. We confirmed that a 6.8 increase in the average axonal extension length of PC12 under the uniform AC magnetic field stimulation at 7 days culture in our bio-reactor, and a 24.7 increase in the maximum axonal extension length. Further, we confirmed that a 60 increase in the number of dendrites of PC12 under the uniform AC magnetic field stimulation. Finally, we confirm the availability of our uniform AC magnetic stimulation bio-reactor for the nerve axonal extension and the nerve network generation.

Keywords: nerve regeneration, axonal extension , PC12 cell, magnetic field, three-dimensional bio-reactor

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Authors: Athena Daniilidou, Maria Platsidou

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Resilience is considered to greatly affect the personal and occupational wellbeing and efficacy of individuals; therefore, it has been widely studied in the social and behavioral sciences. Given its significance, several scales have been created to assess resilience of children and adults. However, most of these scales focus on examining only the internal protective or risk factors that affect the levels of resilience. The aim of the present study is to create a reliable scale that assesses both the internal and the external protective factors that affect Greek teachers’ levels of resilience. Participants were 136 secondary school teachers (89 females, 47 males) from urban areas of Greece. Connor-Davidson Resilience Scale (CD-Risc) and Resilience Scale for Adults (RSA) were used to collect the data. First, exploratory factor analysis was employed to investigate the inner structure of each scale. For both scales, the analyses revealed a differentiated factor solution compared to the ones proposed by the creators. That prompt us to create a scale that would combine the best fitting subscales of the CD-Risc and the RSA. To this end, the items of the four factors with the best fit and highest reliability were used to create the ‘Teachers' resilience scale’. Exploratory factor analysis revealed that the scale assesses the following protective/risk factors: Personal Competence and Strength (9 items, α=.83), Family Cohesion Spiritual Influences (7 items, α=.80), Social Competence and Peers Support (7 items, α=.78) and Spiritual Influence (3 items, α=.58). This four-factor model explained 49,50% of the total variance. In the next step, a confirmatory factor analysis was performed on the 26 items of the derived scale to test the above factor solution. The fit of the model to the data was good (χ2/292 = 1.245, CFI = .921, GFI = .829, SRMR = .074, CI90% = .026-,056, RMSEA = 0.43), indicating that the proposed scale can validly measure the aforementioned four aspects of teachers' resilience and thus confirmed its factorial validity. Finally, analyses of variance were performed to check for individual differences in the levels of teachers' resilience in relation to their gender, age, marital status, level of studies, and teaching specialty. Results were consistent to previous findings, thus providing an indication of discriminant validity for the instrument. This scale has the advantage of assessing both the internal and the external protective factors of resilience in a brief yet comprehensive way, since it consists 26 items instead of the total of 58 of the CD-Risc and RSA scales. Its factorial inner structure is supported by the relevant literature on resilience, as it captures the major protective factors of resilience identified in previous studies.

Keywords: protective factors, resilience, scale development, teachers

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Authors: Godaye Gobena Gomiole

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The purpose of this study was to investigate school context-related factors that affect principals' instructional leadership competence in primary schools in Tarcha Town, Ethiopia. A qualitative case study research design was used. The data were collected through semi-structured interviews and document analysis. Twelve senior principals were included in the study through purposive sampling. Interviews were used to collect in-depth data. The data was analyzed thematically. The findings of the study indicated that primary school principals face both internal and external challenges. Internally, they face limited knowledge and skills, a lack of courage and commitment, and administrative work overload. Their external challenges included negative attitudes from parents and teachers, a lack of instructional materials, and little support from local education authorities. Consequently, they can't serve effectively as instructional leaders or resource people. Based on the findings, it is recommended that the Ministry of Education, South West Regional Education Bureau, Dawuro Zone Education Department, and Tarcha Town Administration Education Officers and Cluster Supervisors regularly monitor and support school leaders and prepare and provide pertinent teaching materials and training so that the principals can lead in the capacity that is appropriate for the position.

Keywords: instructional leadership, primary school, principals, school context related factors

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Authors: Maria Fontenele, Claude-Gilles Dussap, Vincent Dumouilla, Baptiste Boit

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Roquette Frères is a producer of plant-based ingredients that employs many processes to extract relevant molecules and often transforms them through chemical and physical processes to create desired ingredients with specific functionalities. In this context, Roquette encounters numerous multi-component complex systems in their processes, including fibers, proteins, and carbohydrates, in an aqueous environment. To develop, control, and optimize both new and old processes, Roquette aims to develop new in silico tools. Currently, Roquette uses process modelling tools which include specific thermodynamic models and is willing to develop computational methodologies such as molecular dynamics simulations to gain insights into the complex interactions in such complex media, and especially hydrogen bonding interactions. The issue at hand concerns aqueous mixtures of polyols with high dry matter content. The polyols mannitol and sorbitol molecules are diastereoisomers that have nearly identical chemical structures but very different physicochemical properties: for example, the solubility of sorbitol in water is 2.5 kg/kg of water, while mannitol has a solubility of 0.25 kg/kg of water at 25°C. Therefore, predicting liquid-solid equilibrium properties in this case requires sophisticated solution models that cannot be based solely on chemical group contributions, knowing that for mannitol and sorbitol, the chemical constitutive groups are the same. Recognizing the significance of solvation phenomena in polyols, the GePEB (Chemical Engineering, Applied Thermodynamics, and Biosystems) team at Institut Pascal has developed the COSMO-UCA model, which has the structural advantage of using quantum mechanics tools to predict formation and phase equilibrium properties. In this work, we use molecular dynamics simulations to elucidate the behavior of polyols in aqueous solution. Specifically, we employ simulations to compute essential metrics such as radial distribution functions and hydrogen bond autocorrelation functions. Our findings illuminate a fundamental contrast: sorbitol and mannitol exhibit disparate hydrogen bond lifetimes within aqueous environments. This observation serves as a cornerstone in elucidating the divergent physicochemical properties inherent to each compound, shedding light on the nuanced interplay between their molecular structures and water interactions. We also present a methodology to predict the physicochemical properties of complex solutions, taking as sole input the three-dimensional structure of the molecules in the medium. Finally, by developing knowledge models, we represent some physicochemical properties of aqueous solutions of sorbitol and mannitol.

Keywords: COSMO models, hydrogen bond, molecular dynamics, thermodynamics

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Authors: Kai Liua, Hongbo Sua, Weimin Wangb, Hong Liangb

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Urban heat island (UHI) has attracted attention around the world since they profoundly affect human life and climatological. Better understanding the effects of landscape pattern on UHI is crucial for improving the ecological security and sustainability of cities. This study aims to investigate how landscape composition and configuration would affect UHI in Shenzhen, China, based on the analysis of land surface temperature (LST) in relation landscape metrics, mainly with the aid of three new satellite sensors launched by China. HJ-1B satellite system was utilized to estimate surface temperature and comprehensively explore the urban thermal spatial pattern. The landscape metrics of the high spatial resolution remote sensing satellites (GF-1 and ZY-3) were compared and analyzed to validate the performance of the new launched satellite sensors. Results show that the mean LST is correlated with main landscape metrics involving class-based metrics and landscape-based metrics, suggesting that the landscape composition and the spatial configuration both influence UHI. These relationships also reveal that urban green has a significant effect in mitigating UHI in Shenzhen due to its homogeneous spatial distribution and large spatial extent. Overall, our study not only confirm the applicability and effectiveness of the HJ-1B, GF-1 and ZY-3 satellite system for studying UHI but also reveal the impacts of the urban spatial structure on UHI, which is meaningful for the planning and management of the urban environment.

Keywords: urban heat island, Shenzhen, new remote sensing sensor, remote sensing satellites

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Authors: Filippo Portera

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Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

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Authors: Amber Dummett, Efstathia Tzemou

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Narcissism is a personality trait characterized by selfishness, entitlement, and superiority. Narcissism is split into two subtypes, grandiose narcissism (GN) and vulnerable narcissism (VN). Grandiose narcissists are extraverted and arrogant, while vulnerable narcissists are introverted and insecure. This study investigates the psychological mechanisms that lead to differences in academic achievement (AA) between grandiose and vulnerable narcissists, specifically the mediating effects of self-esteem and self-efficacy. While narcissism is considered to be a negative trait, one of the Dark Triads, GN, has been found to have some benefits; therefore, this study considers if better AA is one of them. Moreover, further research into VN is essential to fully compare and contrast it with GN. We hypothesize that grandiose narcissists achieve higher marks due to having high self-esteem and self-efficacy. In comparison, we hypothesize that vulnerable narcissists underperform and achieve lower marks due to having low self-esteem and self-efficacy. Two online surveys were distributed to undergraduate university students. The first was a collection of scales measuring the mentioned dimensions and semester one AA, and the second investigated end of year AA. Sequential mediation analyses were conducted using the gathered data. Our analysis shows that neither self-esteem nor self-efficacy mediates the relationship between GN and AA. GN positively predicts self-esteem but has no relationship with self-efficacy. Self-esteem does not mediate the relationship between VN and AA. VN has a negative indirect effect on AA via self-efficacy, and VN negatively predicts self-esteem. Self-efficacy positively predicts AA. GN does not affect AA through the mediation of self-esteem and then self-efficacy, and neither does VN in this way. Overall, having grandiose or vulnerable narcissistic traits does not affect students’ AA. However, being highly efficacious does lead to academic success; therefore, universities should employ methods to improve the self-efficacy of their students.

Keywords: academic achievement, grandiose narcissism, self-efficacy, self-esteem, vulnerable narcissism

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Authors: Magali Mari, Misha Muller

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The present study’s objectives were to determine how diverse implicit stereotypes affect the processing of written information and linguistic inferential processes, such as presupposition accommodation. When reading a text, one constructs a representation of the described situation, which is then updated, according to new outputs and based on stereotypes inscribed within society. If the new output contradicts stereotypical expectations, the representation must be corrected, resulting in longer reading times. A similar process occurs in cases of linguistic inferential processes like presupposition accommodation. Presupposition accommodation is traditionally regarded as fast, automatic processing of background information (e.g., ‘Mary stopped eating meat’ is quickly processed as Mary used to eat meat). However, very few accounts have investigated if this process is likely to be influenced by domains of social cognition, such as implicit stereotypes. To study the effects of implicit stereotypes on presupposition accommodation, adults were recorded while they read sentences in French, combining two methods, an eye-tracking task and a classic self-paced reading task (where participants read sentence segments at their own pace by pressing a computer key). In one condition, presuppositions were activated with the French definite articles ‘le/la/les,’ whereas in the other condition, the French indefinite articles ‘un/une/des’ was used, triggering no presupposition. Using a definite article presupposes that the object has already been uttered and is thus part of background information, whereas using an indefinite article is understood as the introduction of new information. Two types of stereotypes were under examination in order to enlarge the scope of stereotypes traditionally analyzed. Study 1 investigated gender stereotypes linked to professional occupations to replicate previous findings. Study 2 focused on nationality-related stereotypes (e.g. ‘the French are seducers’ versus ‘the Japanese are seducers’) to determine if the effects of implicit stereotypes on reading are generalizable to other types of implicit stereotypes. The results show that reading is influenced by the two types of implicit stereotypes; in the two studies, the reading pace slowed down when a counter-stereotype was presented. However, presupposition accommodation did not affect participants’ processing of information. Altogether these results show that (a) implicit stereotypes affect the processing of written information, regardless of the type of stereotypes presented, and (b) that implicit stereotypes prevail over the superficial linguistic treatment of presuppositions, which suggests faster processing for treating social information compared to linguistic information.

Keywords: eye-tracking, implicit stereotypes, reading, social cognition

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Authors: I. Wlasny, Z. Klusek, A. Wysmolek

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Hexagonal boron nitride is a representative of a widely popular class of two-dimensional Van Der Waals materials. It finds its uses, among others, in construction of complexly layered heterostructures. Hexagonal boron nitride attracts great interest because of its properties characteristic for wide-gap semiconductors as well as an ultra-flat surface.Van Der Waals heterostructures composed of two-dimensional layered materials, such as transition metal dichalcogenides or graphene give hope for miniaturization of various electronic and optoelectronic elements. In our presentation, we will show the results of our investigations of the not previously reported modification of the hexagonal boron nitride layers with focused laser beam. The electrostatic force microscopy (EFM) images reveal that the irradiation leads to changes of the local electric fields for a wide range of laser wavelengths (from 442 to 785 nm). These changes are also accompanied by alterations of crystallographic structure of the material, as reflected by Raman spectra. They exhibit high stability and remain visible after at least five months. This behavior can be explained in terms of photoionization of the defect centers in h-BN which influence non-uniform electrostatic field screening by the photo-excited charge carriers. Analyzed changes influence local defect structure, and thus the interatomic distances within the lattice. These effects can be amplified by the piezoelectric character of hexagonal boron nitride, similar to that found in nitrides (e.g., GaN, AlN). Our results shed new light on the optical properties of the hexagonal boron nitride, in particular, those associated with electron-phonon coupling. Our study also opens new possibilities for h-BN applications in layered heterostructures where electrostatic fields can be used in tailoring of the local properties of the structures for use in micro- and nanoelectronics or field-controlled memory storage. This work is supported by National Science Centre project granted on the basis of the decision number DEC-2015/16/S/ST3/00451.

Keywords: atomic force microscopy, hexagonal boron nitride, optical properties, raman spectroscopy

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Authors: Julia Zimmerman, Gaurav Savant

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This study aims to evaluate the efficacy of the U.S. Army Corp of Engineers’ River Analysis System (HEC-RAS) application to modeling the hydraulics of estuaries. HEC-RAS has been broadly used for a variety of riverine applications. However, it has not been widely applied to the study of circulation in estuaries. This report details the model development and validation of a combined 1D/2D unsteady flow hydraulic model using HEC-RAS for estuaries and they are associated with tidally influenced rivers. Two estuaries, Galveston Bay and Delaware Bay, were used as case studies. Galveston Bay, a bar-built, vertically mixed estuary, was modeled for the 2005 calendar year. Delaware Bay, a drowned river valley estuary, was modeled from October 22, 2019, to November 5, 2019. Water surface elevation was used to validate both models by comparing simulation results to NOAA’s Center for Operational Oceanographic Products and Services (CO-OPS) gauge data. Simulations were run using the Diffusion Wave Equations (DW), the Shallow Water Equations, Eulerian-Lagrangian Method (SWE-ELM), and the Shallow Water Equations Eulerian Method (SWE-EM) and compared for both accuracy and computational resources required. In general, the Diffusion Wave Equations results were found to be comparable to the two Shallow Water equations sets while requiring less computational power. The 1D/2D combined approach was valid for study areas within the 2D flow area, with the 1D flow serving mainly as an inflow boundary condition. Within the Delaware Bay estuary, the HEC-RAS DW model ran in 22 minutes and had an average R² value of 0.94 within the 2-D mesh. The Galveston Bay HEC-RAS DW ran in 6 hours and 47 minutes and had an average R² value of 0.83 within the 2-D mesh. The longer run time and lower R² for Galveston Bay can be attributed to the increased length of the time frame modeled and the greater complexity of the estuarine system. The models did not accurately capture tidal effects within the 1D flow area.

Keywords: Delaware bay, estuarine hydraulics, Galveston bay, HEC-RAS, one-dimensional modeling, two-dimensional modeling

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Authors: Tsung Chieh Ma, I-Ling Chen

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Disclosing family origins to adoptees is an important topic in the adoption process. Adoption agencies usually educate adoptive parents on how to disclose to adoptees, but many adoptive parents worry that the disclosure will affect the parent–child relationship. Thus, how adoptees would like to receive the disclosure and whether they subjectively feel that the parent–child relationship is affected are both topics worthy of further discussion. This research takes a qualitative approach and connects with adoption agencies to interview six adoptees who are now adults. The purpose of the interviews is to learn about their experience receiving disclosures and their subjective feelings after learning of their family origins. The aim is to reveal the changes disclosure brought to the parent–child relationship and whether common concerns are raised due to the adoptive status. We also want to know about factors that affect their identification with their adopted status so that we can consequently give advice to other adoptive families. in this study finds that adoptees see disclosure as a process rather than an isolated event. The majority want to be told their family origin as early and proactively as possible and expect to learn the reasons they were given up for adoption and taken in as adoptees. The disclosure does not necessarily influence the parent–child relationship, and adoptees care more about the positive experiences they had with adoptive parents in their childhood. Moreover, adopted children seek contact with their original family mostly to understand why they were given up for adoption. The effects of disclosure depend on how the adoptive parents or other significant people in the lives of adoptees interpret the identity of the adoptees. That is, their response and attitude toward the identity have a lasting impact on the adoptees. The study suggests that early disclosure gives adoptees a chance to internalize the experience in the process and find self-identification.

Keywords: adoption, adoptees, disclosure of family origins, parent–child relationship, self-identity

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Authors: Bliss Singhal

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

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

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: M. Pienaar, J. W. Odendaal, J. C. Smit, J. Joubert

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Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

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