Search results for: mixed models

Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

Procedia PDF Downloads 118

Authors: E. Mas de les Valls, M. Castells-Sanabra, R. Capdevila, N. Pla, Rosa M. Fernandez-Canti, V. de Medina, A. Mujal, C. Barahona, E. Velo, M. Vigo, M. A. Santos, T. Soto

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Absenteeism in universities, including polytechnical universities, is influenced by a variety of factors. Some factors overlap with those causing absenteeism in schools, while others are specific to the university and work-related environments. Indeed, these factors may stem from various sources, including students, educators, the institution itself, or even the alignment of degree curricula with professional requirements. In Spain, there has been an increase in absenteeism in polytechnical university studies, especially after the Covid crisis, posing a significant challenge for institutions to address. This study focuses on Universitat Politècnica de Catalunya• BarcelonaTech (UPC) and aims to quantify the current level of absenteeism and identify its main causes. The study is part of the teaching innovation project ASAP-UPC, which aims to minimize absenteeism through the redesign of teaching methodologies. By understanding the factors contributing to absenteeism, the study seeks to inform the subsequent phases of the ASAP-UPC project, which involve implementing methodologies to minimize absenteeism and evaluating their effectiveness. The study utilizes surveys conducted among students and polytechnical companies. Students' perspectives are gathered through both online surveys and in-person interviews. The surveys inquire about students' interest in attending classes, skill development throughout their UPC experience, and their perception of the skills required for a career in a polytechnical field. Additionally, polytechnical companies are surveyed regarding the skills they seek in prospective employees. The collected data is then analyzed to identify patterns and trends. This analysis involves organizing and categorizing the data, identifying common themes, and drawing conclusions based on the findings. This mixed-method approach has revealed that higher levels of absenteeism are observed in large student groups at both the Bachelor's and Master's degree levels. However, the main causes of absenteeism differ between these two levels. At the Bachelor's level, many students express dissatisfaction with in-person classes, perceiving them as overly theoretical and lacking a balance between theory, experimental practice, and problem-solving components. They also find a lack of relevance to professional needs. Consequently, they resort to using online available materials developed during the Covid crisis and attending private academies for exam preparation instead. On the other hand, at the Master's level, absenteeism primarily arises from schedule incompatibility between university and professional work. There is a discrepancy between the skills highly valued by companies and the skills emphasized during the studies, aligning partially with students' perceptions. These findings are of theoretical importance as they shed light on areas that can be improved to offer a more beneficial educational experience to students at UPC. The study also has potential applicability to other polytechnic universities, allowing them to adapt the surveys and apply the findings to their specific contexts. By addressing the identified causes of absenteeism, universities can enhance the educational experience and better prepare students for successful careers in polytechnical fields.

Keywords: absenteeism, polytechnical studies, professional skills, university challenges

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

Procedia PDF Downloads 108

Authors: Zofija Tupikovskaja-Omovie, David Tyler, Sam Dhanapala, Steve Hayes

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The UK is leading in online retail and mobile adoption. However, there is a dearth of information relating to mobile apparel retail, and developing an understanding about consumer browsing and purchase behavior in m-retail channel would provide apparel marketers, mobile website and app developers with the necessary understanding of consumers’ needs. Despite the rapid growth of mobile retail businesses, no published study has examined shopping behaviour on fashion mobile websites and apps. A mixed method approach helped to understand why fashion consumers prefer websites on mobile devices, when mobile apps are also available. The following research methods were employed: survey, eye-tracking experiments, observation, and interview with retrospective think aloud. The mobile gaze tracking device by SensoMotoric Instruments was used to understand frustrations in navigation and other issues facing consumers in mobile channel. This method helped to validate and compliment other traditional user-testing approaches in order to optimize user experience and enhance the development of mobile retail channel. The study involved eight participants - females aged 18 to 35 years old, who are existing mobile shoppers. The participants used the Topshop mobile app and website on a smart phone to complete a task according to a specified scenario leading to a purchase. The comparative study was based on: duration and time spent at different stages of the shopping journey, number of steps involved and product pages visited, search approaches used, layout and visual clues, as well as consumer perceptions and expectations. The results from the data analysis show significant differences in consumer behaviour when using a mobile app or website on a smart phone. Moreover, two types of problems were identified, namely technical issues and human errors. Having a mobile app does not guarantee success in satisfying mobile fashion consumers. The differences in the layout and visual clues seem to influence the overall shopping experience on a smart phone. The layout of search results on the website was different from the mobile app. Therefore, participants, in most cases, behaved differently on different platforms. The number of product pages visited on the mobile app was triple the number visited on the website due to a limited visibility of products in the search results. Although, the data on traffic trends held by retailers to date, including retail sector breakdowns for visits and views, data on device splits and duration, might seem a valuable source of information, it cannot explain why consumers visit many product pages, stay longer on the website or mobile app, or abandon the basket. A comprehensive list of pros and cons was developed by highlighting issues for website and mobile app, and recommendations provided. The findings suggest that fashion retailers need to be aware of actual consumers’ behaviour on the mobile channel and their expectations in order to offer a seamless shopping experience. Added to which is the challenge of retaining existing and acquiring new customers. There seem to be differences in the way fashion consumers search and shop on mobile, which need to be explored in further studies.

Keywords: consumer behavior, eye-tracking technology, fashion retail, mobile app, m-retail, smart phones, topshop, user experience, website

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Authors: Kholoud Sheba

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Women refugees expectedly are physical, socially and mentally vulnerable due to their past traumatic experiences and their novel circumstances in their receiving countries. They may have a wide range of general, mental, and reproductive health problems, but reportedly avoid visiting health care facilities owing to complex elements. Women refugees are usually unfamiliar with their new country health system and unable to navigate it efficiently. They have limited English language skills, which makes it even harder to access culturally insensitive health services. This study examines barriers to health care for refugee women in Ottawa and offers suggestions to address these challenges. Drawing from culturally congruent health care models in Canada, the United Kingdom, and some parts of the United States, this study highlights the importance of cultivating compassion in the provision of health care for women refugees as a way of addressing some of the disparities in health care in Canada. To address the study purpose, a survey questionnaire was designed and pretested questionnaire and was administrated using SurveyMonkey, a paid source survey application, over a period of two weeks. Snowballing sampling procedures were used to recruit the participants. Data was measured using frequencies, percentages, t-test, ANOVA, and chi-square. The test of significance is set at p < .05. The study asked how refugees perceive their experience in accessing and navigating public health services in Ottawa; what challenges refugees face with healthcare in Canada, and, if gender is related to refugees’ perceptions of the health care system they are forced to use? Results show refugees perceived their experience accessing the healthcare services in Canada to be a positive experience and the health providers to be culturally sensitive and allowing enough time listening to their complaints. The language stood tall in their barriers accessing the services due to low English proficiency and the need for interpretation services to encourage them attending the services.

Keywords: women refugee, access barriers, Ottawa, resettlement

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Authors: Abdullah Al. Morshed, Mohammad Nahid Mia

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Introduction: Food security refers to the availability of food and a person’s access to it. It is a complex sustainable development issue, which is closely related to under-nutrition. Food security, in turn, can widely affect the living standard, and is rooted in poverty and leads to poor health, low productivity, low income, food shortage, and hunger. The study's aim was to identify the most vulnerable women who are in insecure positions. Method: 17,842 married women were selected for analysis from the Bangladesh Demographic and Health Survey 2011. Food security defined as dichotomous variables of skipped meals and eaten less food at least once in the last year. The outcome variables were cross-tabulated with women's socio-demographic characteristics and chi2 test was applied to see the significance. Logistic regression models were applied to identify the most vulnerable groups in terms of food security. Result: Only 18.5% of women said that they ever had to skip meals in the last year. 45.7% women from low socioeconomic status had skip meal for at least once whereas only 3.6% were from women with highest socioeconomic status. Women meal skipping was ranged from 1.4% to 34.2% by their educational status. 22% of women were eaten less food during the last year. The rate was higher among the poorest (51.6%), illiterate (39.9%) and household have no electricity connection (38.1) in compared with richest (4.4%), higher educated (2.0%), and household has electricity connection (14.0%). The logistic regression analysis indicated that household socioeconomic status, and women education show strong gradients to skip meals. Poorest have had higher odds (20.9) than richest and illiterate women had 7.7 higher odds than higher educated. In terms of religion, Christianity was 2.3 times more likely to skip their meals than Islam. On the other hand, a similar trend was observed in our other outcome variable eat less food. Conclusion: In this study we able to identify women with lower economics status and women with no education were mostly suffered group from starvation.

Keywords: food security, hunger, under-nutrition, women

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Authors: Ali Ayatic, Emad Mozaffari, Bahareh Tanhaei, Maryam Khajenoori, Saeedeh Movaghar Khoshkho, Ali Ayati

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The abuse of antibiotics, such as tetracycline (TC), is a great global threat to people and the use of topical antibiotics is a promising tact that can help to solve this problem. Antibiotic therapy is often appropriate and necessary for acute wound infections, while topical tetracycline can be highly efficient in improving the wound healing process in diabetics. Due to the advantages of drug-loaded hydrogels as wound dressing, such as ease of handling, high moisture resistance, excellent biocompatibility, and the ability to activate immune cells to speed wound healing, it was found as an ideal wound treatment. In this work, the tetracycline-loaded hydrogels combining agar (AG) and κ-carrageenan (k-CAR) as polymer materials were prepared, in which span60 surfactant was introduced inside as a drug carrier. The Field Emission Scanning Electron Microscopes (FESEM) and Fourier-transform infrared spectroscopy (FTIR) techniques were employed to provide detailed information on the morphology, composition, and structure of fabricated drug-loaded hydrogels and their mechanical properties, and hydrogel permeability to water vapor was investigated as well. Two types of gram-negative and gram-positive bacteria were used to explore the antibacterial properties of prepared tetracycline-contained hydrogels. Their swelling and drug release behavior was studied using the changing factors such as the ratio of polysaccharides (MAG/MCAR), the span60 surfactant concentration, potassium chloride (KCl) concentration and different release media (deionized water (DW), phosphate-buffered saline (PBS), and simulated wound fluid (SWF)) at different times. Finally, the kinetic behavior of hydrogel swelling was studied. Also, the experimental data of TC release to DW, PBS, and SWF using various mathematical models such as Higuchi, Korsmeyer-Peppas, zero-order, and first-order in the linear and nonlinear modes were evaluated.

Keywords: drug release, hydrogel, tetracycline, wound healing

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Authors: Adamu Abubakar, Abdulganiyu Yunusa, Likkason Othniel Kamfani, Abdulrahman Idris Augie

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This research study is being carried out in accordance with the Gwandu formation's potential exploratory activities in the inland basin of northwest Nigeria.The present research aims to identify and characterize subsurface anomalies within Gwandu formation using electrical resistivity tomography (ERT) and magnetic surveys, providing valuable insights for mineral exploration. The study utilizes various data enhancement techniques like derivatives, upward continuation, and spectral analysis alongside 2D modeling of electrical imaging profiles to analyze subsurface structures and anomalies. Data was collected through ERT and magnetic surveys, with subsequent processing including derivatives, spectral analysis, and 2D modeling. The results indicate significant subsurface structures such as faults, folds, and sedimentary layers. The study area's geoelectric and magnetic sections illustrate the depth and distribution of sedimentary formations, enhancing understanding of the geological framework. Thus, showed that the entire formations of Eocene sediment of Gwandu are overprinted by the study area's Tertiary strata. The NE to SW and E to W cross-profile for the pseudo geoelectric sections beneath the study area were generated using a two-dimensional (2D) electrical resistivity imaging. 2D magnetic modelling, upward continuation, and derivative analysis are used to delineate the signatures of subsurface magnetic anomalies. The results also revealed The sediment thickness by surface depth ranges from ∼4.06 km and ∼23.31 km. The Moho interface, the lower and upper mantle crusts boundary, and magnetic crust are all located at depths of around ∼10.23 km. The vertical distance between the local models of the foundation rocks to the north and south of the Sokoto Group was approximately ∼6 to ∼8 km and ∼4.5 km, respectively.

Keywords: high-resolution aeromagnetic data, electrical resistivity imaging, subsurface anomalies, 2d dorward modeling

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Authors: Vijaya Dixit Aasheesh Dixit

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Shipbuilding industry operates in Engineer Procure Construct (EPC) context. Product mix of a shipyard comprises of various types of ships like bulk carriers, tankers, barges, coast guard vessels, sub-marines etc. Each order is unique based on the type of ship and customized requirements, which are engineered into the product right from design stage. Thus, to execute every new project, a shipyard needs to upgrade its production expertise. As a result, over the long run, holistic learning occurs across different types of projects which contributes to the knowledge base of the shipyard. Simultaneously, in the short term, during execution of a project comprising of multiple sister ships, repetition of similar tasks leads to learning at activity level. This research aims to capture above learnings of a shipyard and incorporate learning curve effect in project scheduling and materials procurement to improve project performance. Extant literature provides support for the existence of such learnings in an organization. In shipbuilding, there are sequences of similar activities which are expected to exhibit learning curve behavior. For example, the nearly identical structural sub-blocks which are successively fabricated, erected, and outfitted with piping and electrical systems. Learning curve representation can model not only a decrease in mean completion time of an activity, but also a decrease in uncertainty of activity duration. Sister ships have similar material requirements. The same supplier base supplies materials for all the sister ships within a project. On one hand, this provides an opportunity to reduce transportation cost by batching the order quantities of multiple ships. On the other hand, it increases the inventory holding cost at shipyard and the risk of obsolescence. Further, due to learning curve effect the production scheduled of each consequent ship gets compressed. Thus, the material requirement schedule of every next ship differs from its previous ship. As more and more ships get constructed, compressed production schedules increase the possibility of batching the orders of sister ships. This work aims at integrating materials management with project scheduling of long duration projects for manufacturing of multiple sister ships. It incorporates the learning curve effect on progressively compressing material requirement schedules and addresses the above trade-off of transportation cost and inventory holding and shortage costs while satisfying budget constraints of various stages of the project. The activity durations and lead time of items are not crisp and are available in the form of probabilistic distribution. A Stochastic Mixed Integer Programming (SMIP) model is formulated which is solved using evolutionary algorithm. Its output provides ordering dates of items and degree of order batching for all types of items. Sensitivity analysis determines the threshold number of sister ships required in a project to leverage the advantage of learning curve effect in materials management decisions. This analysis will help materials managers to gain insights about the scenarios: when and to what degree is it beneficial to treat a multiple ship project as an integrated one by batching the order quantities and when and to what degree to practice distinctive procurement for individual ship.

Keywords: learning curve, materials management, shipbuilding, sister ships

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Authors: Sotirios Longinos, Anna Loskutova, Assel Tolegenova, Assem Imanzhussip, Lei Wang

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Hot, dry rock (HDR) has substantial potential as a thermal energy source. It has been exploited by hydraulic fracturing to extract heat and generate electricity, which is a well-developed technique known for creating the enhanced geothermal systems (EGS). These days, LN2 is being tested as an environmental friendly fracturing fluid to generate densely interconnected crevices to augment heat exchange efficiency and production. This study examines experimentally the efficacy of LN2 cryogenic fracturing for granite samples in Kazakhstan with immersion method. A comparison of two different experimental models is carried out. The first mode is rock heating along with liquid nitrogen treatment (heating with freezing time), and the second mode is multiple times of heating along with liquid nitrogen treatment (heating with LN2 freezing-thawing cycles). The experimental results indicated that with multiple heating and LN2-treatment cycles, the permeability of granite first ameliorates with increasing number of cycles and later reaches a plateau after a certain number of cycles. On the other hand, density, P-wave velocity, uniaxial compressive strength, elastic modulus, and tensile strength indicate a downward trend with increasing heating and treatment cycles. The thermal treatment cycles do not seem to have an obvious effect on the Poisson’s ratio. The changing rate of granite rock properties decreases as the number of cycles increases. The deterioration of granite primarily happens within the early few cycles. The heating temperature during the cycles shows an important influence on the deterioration of granite. More specifically, mechanical deterioration and permeability amelioration become more remarkable as the heating temperature increases.LN2 fracturing generates many positives compared to conventional fracturing methods such as little water consumption, requirement of zero chemical additives, lessening of reservoir damage, and so forth. Based on the experimental observations, LN2 can work as a promising waterless fracturing fluid to stimulate hot, dry rock reservoirs.

Keywords: granite, hydraulic fracturing, liquid nitrogen, Kazakhstan

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Authors: Erika Takidah, Salina Kassim

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Digital transformation has accelerated in the new millennium. It is reshaping the financial services industry from a traditional system to financial technology. Moreover, the number of financial inclusion rates in Indonesia is less than 60%. An innovative model needed to elucidate this national problem. On the other hand, the Islamic financial service industry and financial technology grow fast as a new aspire in economic development. An Islamic bank, takaful, Islamic microfinance, Islamic financial technology and Islamic social finance institution could collaborate to intensify the financial inclusion number in Indonesia. The primary motive of this paper is to examine the strategy of collaboration-based Islamic financial services to enhance financial inclusion in Indonesia, particularly facing the digital era. The fundamental findings for the main problems are the foundations and key ecosystems aspect involved in the development of collaboration-based Islamic financial services. By using the Interpretive Structural Model (ISM) approach, the core problems faced in the development of the models have lacked policy instruments guarding the collaboration-based Islamic financial services with fintech work process and availability of human resources for fintech. The core strategies or foundations that are needed in the framework of collaboration-based Islamic financial services are the ability to manage and analyze data in the big data era. For the aspects of the Ecosystem or actors involved in the development of this model, the important actor is government or regulator, educational institutions, and also existing industries (Islamic financial services). The outcome of the study designates that strategy collaboration of Islamic financial services institution supported by robust technology, a legal and regulatory commitment of the regulators and policymakers of the Islamic financial institutions, extensive public awareness of financial inclusion in Indonesia. The study limited itself to realize financial inclusion, particularly in Islamic finance development in Indonesia. The study will have an inference for the concerned professional bodies, regulators, policymakers, stakeholders, and practitioners of Islamic financial service institutions.

Keywords: collaboration, financial inclusion, Islamic financial services, Islamic fintech

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Authors: Titus A. Beu

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Many modern gene-delivery protocols rely on condensed complexes of DNA with polycations to introduce the genetic payload into cells by endocytosis. In particular, polyethyleneimine (PEI) stands out by a high buffering capacity (enabling the efficient condensation of DNA) and relatively simple fabrication. Realistic computational studies can offer essential insights into the formation process of DNA-PEI polyplexes, providing hints on efficient designs and engineering routes. We present comprehensive computational investigations of solvated PEI and DNA-PEI polyplexes involving calculations at three levels: ab initio, all-atom (AA), and coarse-grained (CG) molecular mechanics. In the first stage, we developed a rigorous AA CHARMM (Chemistry at Harvard Macromolecular Mechanics) force field (FF) for PEI on the basis of accurate ab initio calculations on protonated model pentamers. We validated this atomistic FF by matching the results of extensive molecular dynamics (MD) simulations of structural and dynamical properties of PEI with experimental data. In a second stage, we developed a CG MARTINI FF for PEI by Boltzmann inversion techniques from bead-based probability distributions obtained from AA simulations and ensuring an optimal match between the AA and CG structural and dynamical properties. In a third stage, we combined the developed CG FF for PEI with the standard MARTINI FF for DNA and performed comprehensive CG simulations of DNA-PEI complex formation and condensation. Various technical aspects which are crucial for the realistic modeling of DNA-PEI polyplexes, such as options of treating electrostatics and the relevance of polarizable water models, are discussed in detail. Massive CG simulations (with up to 500 000 beads) shed light on the mechanism and provide time scales for DNA polyplex formation independence of PEI chain size and protonation pattern. The DNA-PEI condensation mechanism is shown to primarily rely on the formation of DNA bundles, rather than by changes of the DNA-strand curvature. The gained insights are expected to be of significant help for designing effective gene-delivery applications.

Keywords: DNA condensation, gene-delivery, polyethylene-imine, molecular dynamics.

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Authors: Kuan Yin Hsiao, Shyh Ming Kuo, Yi Jhen Wu, Chin Wen Chuang, Chuen-Fu Lin, Wei-qing Yang, Han Hsiang Huang

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Anti-tumor effects of natural products, like compounds from marine sponges and soft corals, have been investigated for decades. Polymeric nanoparticles prepared from biodegradable and biocompatible molecules, such as Hyaluronan (HA), Chitosan (CHI) and gelatin have been widely studied. Encapsulation of anti-cancer therapies by the biopolymeric nanoparticles in drug delivery system is potentially capable of improving the therapeutic effects and attenuating their toxicity. In the current study, the anti-bladder cancer effects of heteronemin extracted from the sponge Hippospongia sp. with or without HA and CHI nanoparticle encapsulation were assessed and evaluated in vitro. Results showed that IC50 (half maximal inhibitory concentration) of heteronemin toward T24 human bladder cancer cell viability is approximately 0.18 µg/mL. Both plain and HA nanoparticles-encapsulated heteronemin at 0.2 and 0.4 µg/mL significantly reduced T24 cell viability (P<0.001) while HA nanoparticles-encapsulated heteronemin showed weaker viability-inhibitory effects on L929 fibroblasts compared with plain heteronemin at the identical concentrations. HA and CHI nanoparticles-encapsulated heteronemin exhibited significantly stronger inhibitory effects against migration of T24 human bladder cancer cell than those exerted by plain heteronemin at the same concentrations (P<0.001). The flow cytometric results showed that 0.2 µg/mL HA and CHI nanoparticles-encapsulated heteronemin induced higher early apoptosis rate than that induced by plain heteronemin at the same concentration. These results show that HA and CHI nanoparticle encapsulation is able to elevate anti-migratory and apoptosis-inducing effects exerted by heteronemin against bladder cancer cells in vitro. The in vivo anti-bladder cancer effects of the compound with or without HA/CHI nanoparticle encapsulation will be further investigated and examined using murine tumor models. The data obtained from this study will extensively evaluate of the anti-bladder cancer effects of heteronemin as well as HA/CHI-encapsulated heteronemin and pave a way to develop potential bladder cancer treatment.

Keywords: heteronemin, nanoparticles, hyaluronan, chitosan, bladder cancer

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Authors: Isadora Bugarin, Taygoara F. de Oliveira

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Binary fluids and emulsions, in general, are present in a vast range of industrial, medical, and scientific applications, showing complex behaviors responsible for defining the flow dynamics and the system operation. However, the literature describing those highlighted fluids in non-isothermal models is currently still limited. The present work brings a detailed investigation on droplet migration due to natural convection in square enclosure, aiming to clarify the effects of drop viscosity on the flow dynamics by showing how distinct viscosity ratios (droplet/ambient fluid) influence the drop motion and the final movement pattern kept on stationary regimes. The analysis was taken by observing distinct combinations of Rayleigh number, drop initial position, and viscosity ratios. The Navier-Stokes and Energy equations were solved considering the Boussinesq approximation in a laminar flow using the finite differences method combined with the Level Set method for binary flow solution. Previous results collected by the authors showed that the Rayleigh number and the drop initial position affect drastically the motion pattern of the droplet. For Ra ≥ 10⁴, two very marked behaviors were observed accordingly with the initial position: the drop can travel either a helical path towards the center or a cyclic circular path resulting in a closed cycle on the stationary regime. The variation of viscosity ratio showed a significant alteration of pattern, exposing a large influence on the droplet path, capable of modifying the flow’s behavior. Analyses on viscosity effects on the flow’s unsteady Nusselt number were also performed. Among the relevant contributions proposed in this work is the potential use of the flow initial conditions as a mechanism to control the droplet migration inside the enclosure.

Keywords: binary fluids, droplet motion, level set method, natural convection, viscosity

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Authors: Arun C. O., Shrijith M. B., Thakur Rajesh Singh

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The study aims to analyze and understand the biomechanical stability of the atlantoaxial complex under different posterior fixation techniques using the finite element method in the Indian context. The conventional cadaveric studies performed show heterogeneity in biomechanical properties. The finite element method being a versatile numerical tool, is being wisely used for biomechanics analysis of atlantoaxial complex. However, the biomechanics of posterior fixation techniques for an Indian subject is missing in the literature. It is essential to study in this context as the bone density and geometry of vertebrae vary from region to region, thereby requiring different screw lengths and it can affect the range of motion(ROM), stresses generated. The current study uses CT images for developing a 3D finite element model with C1-C2 geometry without ligaments. Instrumentation is added to this geometry to develop four models for four fixation techniques, namely C1-C2 TA, C1LM-C2PS, C1LM-C2Pars, C1LM-C2TL. To simulate Flexion, extension, lateral bending, axial rotation, 1.5 Nm is applied to C1 while the bottom nodes of C2 are fixed. Then Range of Motion (ROM) is compared with the unstable model(without ligaments). All the fixation techniques showed more than 97 percent reduction in the Range of Motion. The von-mises stresses developed in the screw constructs are obtained. From the studies, it is observed that Transarticular technique is most stable in Lateral Bending, C1LM-C2 Translaminar is found most stable in Flexion/extension. The Von-Mises stresses developed minimum in Trasarticular technique in lateral bending and axial rotation, whereas stress developed in C2 pars construct minimum in Flexion/ Extension. On average, the TA technique is stable in all motions and also stresses in constructs are less in TA. Tarnsarticular technique is found to be the best fixation technique for Indian subjects among the 4 methods.

Keywords: biomechanics, cervical spine, finite element model, posterior fixation

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Authors: Fleura Shkëmbi, Sevim Mustafa, Naim Fanaj

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Hypnosis, often known as cognitive therapy, is a sort of mind-body psychotherapy. A professional and certified hypnotist or hypnotherapist guides the patient into this extreme level of focus and relaxation during the session by utilizing verbal cues, repetition, and imagery. In recent years, hypnotherapy has gained popularity in the treatment of a variety of disorders, including anxiety and particular phobias. The term "phobia" is commonly used to define fear of a certain trigger. When faced with potentially hazardous situations, the brain naturally experiences dread. While a little dread here and there may keep us safe, phobias can drastically reduce our quality of life. In summary, persons who suffer from anxiety are considered to see particular environmental situations as dangerous, but those who do not suffer from anxiety do not. Hypnosis is essential in the treatment of anxiety disorders. Hypnosis can help patients minimize their anxiety symptoms. This broad concept has aided in the development of models and therapies for anxiety disorders such as generalized anxiety disorder, panic attacks, hypochondria, and obsessional disorders. Hypnosis techniques are supposed to be attentive and mental pictures, which is conceivable; this is why they're associated with improved working memory and visuospatial abilities. In this sense, the purpose of this study is to determine how effectively specific therapeutic methods perform in treating persons with anxiety and phobias. In addition to cognitive-behavioral therapy and other therapies, the approaches emphasized the use of therapeutic hypnosis. This study looks at the use of hypnosis and related psychotherapy procedures in the treatment of anxiety disorders. Following a discussion of the evolution of hypnosis as a therapeutic tool, neurobiological research is used to demonstrate the influence of hypnosis on the change of perception in the brain. The use of hypnosis in the treatment of phobias, stressful situations, and posttraumatic stress disorder is examined, as well as similarities between the hypnotic state and dissociative reactions to trauma. Through an extensive literature evaluation, this study will introduce hypnotherapy procedures that result in more successful anxiety and phobia treatment.

Keywords: anxiety, hypnosis, hypnotherapy, phobia, technique, state

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Authors: Tichaona Nharingo, Hope Tauya, Mambo Moyo

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Wood ash has been demonstrated to have favourable adsorption capacity for heavy metal ions but suffers the application problem of difficult to separate/isolate from the batch adsorption systems. Fabrication of wood ash beads using multifunctional group and non-toxic carbohydrate, alginate, may improve the applicability of wood ash in environmental pollutant remediation. In this work, alginate-wood ash beads (AWAB) were fabricated and applied to the removal of cadmium ions from aqueous systems. The beads were characterized by FTIR, TGA/DSC, SEM-EDX and their pHZPC before and after the adsorption of Cd(II) ions. Important adsorption parameters i.e. pH, AWAB dosage, contact time and ionic strength were optimized and the effect of initial concentration of Cd(II) ions to the adsorption process was established. Adsorption kinetics, adsorption isotherms, adsorption mechanism and application of AWAB to real water samples spiked with Cd(II) ions were ascertained. The composite adsorbent was characterized by a heterogeneous macro pore surface comprising of metal oxides, multiple hydroxyl groups and carbonyl groups that were involved in electrostatic interaction and Lewis acid-base interactions with the Cd(II) ions. The pseudo second order and the Freundlich isotherm models best fitted the adsorption kinetics and isotherm data respectively suggesting chemical sorption process and surface heterogeneity. The presence of Pb(II) ions inhibited the adsorption of Cd(II) ions (reduced by 40 %) attributed to the competition for the adsorption sites. The Cd(II) loaded beads could be regenerated using 0.1 M HCl and could be applied to four sorption-desorption cycles without significant loss in its initial adsorption capacity. The high maximum adsorption capacity, stability, selectivity and reusability of AWAB make the adsorbent ideal for application in the removal of Cd(II) ions from real water samples. Column type adsorption experiments need to be explored to establish the potential of the adsorbent in removing Cd(II) ions using continuous flow systems.

Keywords: adsorption, Cd(II) ions, regeneration, wastewater, wood ash-alginate beads

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Authors: Yichin Chen

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Taiwan is an island characterized by an active tectonics and high erosion rates. Monitoring the dynamic landscape of Taiwan is an important issue for disaster mitigation, geomorphological research, and watershed management. Long-term and high spatiotemporal landform data is essential for quantifying and simulating the geomorphological processes and developing warning systems. Recently, the advances in unmanned aerial vehicle (UAV) and computational photogrammetry technology have provided an effective way to rebuild and monitor the topography changes in high spatio-temporal resolutions. This study rebuilds the 30-years landform change in the Aiyuzi watershed in 1986-2017 by using the aerial photogrammetry-based techniques. The Aiyuzi watershed, located in central Taiwan and has an area of 3.99 Km², is famous for its frequent landslide and debris flow disasters. This study took the aerial photos by using UAV and collected multi-temporal historical, stereo photographs, taken by the Aerial Survey Office of Taiwan’s Forestry Bureau. To rebuild the orthoimages and digital surface models (DSMs), Pix4DMapper, a photogrammetry software, was used. Furthermore, to control model accuracy, a set of ground control points was surveyed by using eGPS. The results show that the generated DSMs have the ground sampling distance (GSD) of ~10 cm and ~0.3 cm from the UAV’s and historical photographs, respectively, and vertical error of ~1 m. By comparing the DSMs, there are many deep-seated landslides (with depth over 20 m) occurred on the upstream in the Aiyuzi watershed. Even though a large amount of sediment is delivered from the landslides, the steep main channel has sufficient capacity to transport sediment from the channel and to erode the river bed to ~20 m in depth. Most sediments are transported to the outlet of watershed and deposits on the downstream channel. This case study shows that UAV and photogrammetry technology are useful for topography change monitoring effectively.

Keywords: aerial photogrammetry, landslide, landform change, Taiwan

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Authors: Vera Wilden, Benno Hoffmeister, Markus Feldmann

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Glued laminated timber (glulam) is a preferred choice for long span girders, e.g., for gyms or storage halls. While the material provides sufficient strength to resist the bending moments, large spans lead to increased slenderness of such members and to a higher susceptibility to stability issues, in particular to lateral torsional buckling (LTB). Rules for the determination of the ultimate LTB resistance are provided by Eurocode 5. The verifications of the resistance may be performed using the so called equivalent member method or by means of theory 2nd order calculations (direct method), considering equivalent imperfections. Both methods have significant limitations concerning their applicability; the equivalent member method is limited to rather simple cases; the direct method is missing detailed provisions regarding imperfections and requirements for numerical modeling. In this paper, the results of a test series on slender glulam beams in three- and four-point bending are presented. The tests were performed in an innovative, newly developed testing rig, allowing for a very precise definition of loading and boundary conditions. The load was introduced by a hydraulic jack, which follows the lateral deformation of the beam by means of a servo-controller, coupled with the tested member and keeping the load direction vertically. The deformation-controlled tests allowed for the identification of the ultimate limit state (governed by elastic stability) and the corresponding deformations. Prior to the tests, the structural and geometrical imperfections were determined and used later in the numerical models. After the stability tests, the nearly undamaged members were tested again in pure bending until reaching the ultimate moment resistance of the cross-section. These results, accompanied by numerical studies, were compared to resistance values obtained using both methods according to Eurocode 5.

Keywords: experimental tests, glued laminated timber, lateral torsional buckling, numerical simulation

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Authors: P. V. Pramila , V. Mahesh

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Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients esulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF 25, PEF,FEF 25-75, FEF50, and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF 25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects). It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV, multivariate adaptive regression splines pulmonary function test, random forest

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Authors: Nabil Elkhatri, Mohamed Louay Metougui, Ngonidzashe Chirinda

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Mining activities have left a legacy of degraded landscapes, prompting urgent efforts for ecological restoration. Reforestation holds promise as a potent tool to rehabilitate these old mining sites, with the potential to sequester carbon and contribute to climate change mitigation. This study focuses on evaluating the carbon stock and sequestration potential of reforestation species in the context of Morocco's mining areas, employing the DeNitrification-DeComposition (DNDC) model. The research is grounded in recognizing the need to connect theoretical models with practical implementation, ensuring that reforestation efforts are informed by accurate and context-specific data. Field data collection encompasses growth patterns, biomass accumulation, and carbon sequestration rates, establishing an empirical foundation for the study's analyses. By integrating the collected data with the DNDC model, the study aims to provide a comprehensive understanding of carbon dynamics within reforested ecosystems on old mining sites. The major findings reveal varying sequestration rates among different reforestation species, indicating the potential for species-specific optimization of reforestation strategies to enhance carbon capture. This research's significance lies in its potential to contribute to sustainable land management practices and climate change mitigation strategies. By quantifying the carbon stock and sequestration potential of reforestation species, the study serves as a valuable resource for policymakers, land managers, and practitioners involved in ecological restoration and carbon management. Ultimately, the study aligns with global objectives to rejuvenate degraded landscapes while addressing pressing climate challenges.

Keywords: carbon stock, carbon sequestration, DNDC model, ecological restoration, mining sites, Morocco, reforestation, sustainable land management.

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Authors: Darius Jakimavicius

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Artificial intelligence is the subject of intense debate in the international arena, identified both as a technological breakthrough and as a component of the strategic stability effect. Both the kinetic and non-kinetic development of AI and its application in the national strategies of the great powers may trigger a change in the security situation. Artificial intelligence is generally faster, more capable and more efficient than humans, and there is a temptation to transfer decision-making and control responsibilities to artificial intelligence. Artificial intelligence, which, once activated, can select and act on targets without further intervention by a human operator, blurs the boundary between human or robot (machine) warfare, or perhaps human and robot together. Artificial intelligence acts as a force multiplier that speeds up decision-making and reaction times on the battlefield. The role of humans is increasingly moving away from direct decision-making and away from command and control processes involving the use of force. It is worth noting that the autonomy and precision of AI systems make the process of strategic stability more complex. Deterrence theory is currently in a phase of development in which deterrence is undergoing further strain and crisis due to the complexity of the evolving models enabled by artificial intelligence. Based on the concept of strategic stability and deterrence theory, it is appropriate to develop further research on the development and impact of AI in order to assess AI from both a scientific and technical perspective: to capture a new niche in the scientific literature and academic terminology, to clarify the conditions for deterrence, and to identify the potential uses, impacts and possibly quantities of AI. The research problem is the impact of artificial intelligence developed by great powers on strategic stability. This thesis seeks to assess the impact of AI on strategic stability and deterrence principles, with human exclusion from the decision-making and control loop as a key axis. The interaction between AI and human actions and interests can determine fundamental changes in great powers' defense and deterrence, and the development and application of AI-based great powers strategies can lead to a change in strategic stability.

Keywords: artificial inteligence, strategic stability, deterrence theory, decision making loop

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: drug addiction, artificial neural networks, multilayer perceptron (MLP), decision support system

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Authors: Nancy Retzlaff, Sarah J. Berkemer, Trudie Strauss

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In the last couple of decades, the advent of digitalization of any kind of data was probably one of the major advances in all fields of study. This paves the way for also analysing these data even though they might come from disciplines where there was no initial computational necessity to do so. Especially in linguistics, one can find a rather manual tradition. Still when considering studies that involve the history of language families it is hard to overlook the striking similarities to bioinformatics (phylogenetic) approaches. Alignments of words are such a fairly well studied example of an application of bioinformatics methods to historical linguistics. In this paper we will not only consider alignments of strings, i.e., words in this case, but also alignments of syntax trees of selected Indo-European languages. Based on initial, crude alignments, a sophisticated scoring model is trained on both letters and syntactic features. The aim is to gain a better understanding on which features in two languages are related, i.e., most likely to have the same root. Initially, all words in two languages are pre-aligned with a basic scoring model that primarily selects consonants and adjusts them before fitting in the vowels. Mixture models are subsequently used to filter ‘good’ alignments depending on the alignment length and the number of inserted gaps. Using these selected word alignments it is possible to perform tree alignments of the given syntax trees and consequently find sentences that correspond rather well to each other across languages. The syntax alignments are then filtered for meaningful scores—’good’ scores contain evolutionary information and are therefore used to train the sophisticated scoring model. Further iterations of alignments and training steps are performed until the scoring model saturates, i.e., barely changes anymore. A better evaluation of the trained scoring model and its function in containing evolutionary meaningful information will be given. An assessment of sentence alignment compared to possible phrase structure will also be provided. The method described here may have its flaws because of limited prior information. This, however, may offer a good starting point to study languages where only little prior knowledge is available and a detailed, unbiased study is needed.

Keywords: alignments, bioinformatics, comparative linguistics, historical linguistics, statistical methods

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Authors: Carolyn Scott, Rachel Nugent

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The agricultural industry in Ireland and worldwide is one of the most dangerous occupations with respect to occupational health and safety accidents and fatalities. Many accident causation models have been developed in safety research to understand the underlying and contributory factors that lead to the occurrence of an accident. Due to the uniqueness of the agricultural sector, current accident causation theories cannot be applied. This paper presents an accident causation model named the Irish Farm Accident Causation Model (IFACM) which has been specifically tailored to the needs of Irish farms. The IFACM is a theoretical and practical model of accident causation that arranges the causal factors into a graphic representation of originating, shaping, and contributory factors that lead to accidents when unsafe acts and conditions are created that are not rectified by control measures. Causes of farm accidents were assimilated by means of a thorough literature review and were collated to form a graphical representation of the underlying causes of a farm accident. The IFACM was validated retrospectively through case study analysis and peer review. Participants in the case study (n=10) identified causes that led to a farm accident in which they were involved. A root cause analysis was conducted to understand the contributory factors surrounding the farm accident, traced back to the ‘root cause’. Experts relevant to farm safety accident causation in the agricultural industry have peer reviewed the IFACM. The accident causation process is complex. Accident prevention requires a comprehensive understanding of this complex process because to prevent the occurrence of accidents, the causes of accidents must be known. There is little research on the key causes and contributory factors of unsafe behaviours and accidents on Irish farms. The focus of this research is to gain a deep understanding of the causality of accidents on Irish farms. The results suggest that the IFACM framework is helpful for the analysis of the causes of accidents within the agricultural industry in Ireland. The research also suggests that there may be international applicability if further research is carried out. Furthermore, significant learning can be obtained from considering the underlying causes of accidents.

Keywords: farm safety, farm accidents, accident causation, root cause analysis

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Authors: M. Vlahovic

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This paper presents a model of public finances, which combines three concepts: participatory budgeting, crowdfunding and blockchain technology. Participatory budgeting is defined as a process in which community members decide how to spend a part of community’s budget. Crowdfunding is a practice of funding a project by collecting small monetary contributions from a large number of people via an Internet platform. Blockchain technology is a distributed ledger that enables efficient and reliable transactions that are secure and transparent. In this hypothetical model, the government or authorities on local/regional level would set up a platform where they would propose public projects to citizens. Citizens would browse through projects and support or vote for those which they consider justified and necessary. In return, they would be entitled to a tax relief in the amount of their monetary contribution. Since the blockchain technology enables tracking of transactions, it can be used to mitigate corruption, money laundering and lack of transparency in public finances. Models of its application have already been created for e-voting, health records or land registries. By presenting a model of application of blockchain technology in public finances, this paper takes into consideration the potential of blockchain technology to disrupt governments and make processes more democratic, secure, transparent and efficient. The framework for this paper consists of multiple streams of research, including key concepts of direct democracy, public finance (especially the voluntary theory of public finance), information and communication technology, especially blockchain technology and crowdfunding. The framework defines rules of the game, basic conditions for the implementation of the model, benefits, potential problems and development perspectives. As an oversimplified map of a new form of public finances, the proposed model identifies primary factors, that influence the possibility of implementation of the model, and that could be tracked, measured and controlled in case of experimentation with the model.

Keywords: blockchain technology, distributed ledger, participatory budgeting, crowdfunding, direct democracy, internet platform, e-government, public finance

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Authors: S. S. Kharintsev, E. A. Chernykh, S. K. Saikin, A. I. Fishman, S. G. Kazarian

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Recent advances in improving information storage performance are inseparably linked with circumvention of fundamental constraints such as the supermagnetic limit in heat assisted magnetic recording, charge loss tolerance in solid-state memory and the Abbe’s diffraction limit in optical storage. A substantial breakthrough in the development of nonvolatile storage devices with dimensional scaling has been achieved due to phase-change chalcogenide memory, which nowadays, meets the market needs to the greatest advantage. A further progress is aimed at the development of versatile nonvolatile high-speed memory combining potentials of random access memory and archive storage. The well-established properties of light at the nanoscale empower us to use them for recording optical information with ultrahigh density scaled down to a single molecule, which is the size of a pit. Indeed, diffraction-limited optics is able to record as much information as ~1 Gb/in2. Nonlinear optical effects, for example, two-photon fluorescence recording, allows one to decrease the extent of the pit even more, which results in the recording density up to ~100 Gb/in2. Going beyond the diffraction limit, due to the sub-wavelength confinement of light, pushes the pit size down to a single chromophore, which is, on average, of ~1 nm in length. Thus, the memory capacity can be increased up to the theoretical limit of 1 Pb/in2. Moreover, the field confinement provides faster recording and readout operations due to the enhanced light-matter interaction. This, in turn, leads to the miniaturization of optical devices and the decrease of energy supply down to ~1 μW/cm². Intrinsic features of light such as multimode, mixed polarization and angular momentum in addition to the underlying optical and holographic tools for writing/reading, enriches the storage and encryption of optical information. In particular, the finite extent of the near-field penetration, falling into a range of 50-100 nm, gives the possibility to perform 3D volume (layer-to-layer) recording/readout of optical information. In this study, we demonstrate a comprehensive evidence of isotropic-to-homeotropic phase transition of the azobenzene-functionalized polymer thin film exposed to light and dc electric field using near-field optical microscopy and scanning capacitance microscopy. We unravel a near-field Raman dichroism of a sub-10 nm thick epoxy-based side-chain azo-polymer films with polarization-controlled tip-enhanced Raman scattering. In our study, orientation of azo-chromophores is controlled with a bias voltage gold tip rather than light polarization. Isotropic in-plane and homeotropic out-of-plane arrangement of azo-chromophores in glassy environment can be distinguished with transverse and longitudinal optical near-fields. We demonstrate that both phases are unambiguously visualized by 2D mapping their local dielectric properties with scanning capacity microscopy. The stability of the polar homeotropic phase is strongly sensitive to the thickness of the thin film. We make an analysis of α-transition of the azo-polymer by detecting a temperature-dependent phase jump of an AFM cantilever when passing through the glass temperature. Overall, we anticipate further improvements in optical storage performance, which approaches to a single molecule level.

Keywords: optical memory, azo-dye, near-field, tip-enhanced Raman scattering

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Authors: Leila Baghaarabani, Parvin Razzaghi, Mennatolla Magdy Mostafa, Ahmad Albaqsami, Al Warith Al Rushaidi, Masoud Al Rawahi

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Predicting the interaction between drugs and their molecular targets is pivotal for advancing drug development processes. Due to the time and cost limitations, computational approaches have emerged as an effective approach to drug-target interaction (DTI) prediction. Most of the introduced computational based approaches utilize the drug molecule and protein sequence as input. This study does not only utilize these inputs, it also introduces a protein representation developed using a masked protein language model. In this representation, for every individual amino acid residue within the protein sequence, there exists a corresponding probability distribution that indicates the likelihood of each amino acid being present at that particular position. Then, the similarity between each pair of amino-acids is computed to create similarity matrix. To encode the knowledge of the similarity matrix, Bi-Level Routing Attention (BiFormer) is utilized, which combines aspects of transformer-based models with protein sequence analysis and represents a significant advancement in the field of drug-protein interaction prediction. BiFormer has the ability to pinpoint the most effective regions of the protein sequence that are responsible for facilitating interactions between the protein and drugs, thereby enhancing the understanding of these critical interactions. Thus, it appears promising in its ability to capture the local structural relationship of the proteins by enhancing the understanding of how it contributes to drug protein interactions, thereby facilitating more accurate predictions. To evaluate the proposed method, it was tested on two widely recognized datasets: Davis and KIBA. A comprehensive series of experiments was conducted to illustrate its effectiveness in comparison to cuttingedge techniques.

Keywords: BiFormer, transformer, protein language processing, self-attention mechanism, binding affinity, drug target interaction, similarity matrix, protein masked representation, protein language model

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Authors: Emejeamara Francis

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The future of potentials wind energy applications within suburban/urban areas are currently faced with various problems. These include insufficient assessment of urban wind resource, and the effectiveness of commercial gust control solutions as well as unavailability of effective and cheaper valuable tools for scoping the potentials of urban wind applications within built-up environments. In order to achieve effective assessment of the potentials of urban wind installations, an estimation of the total energy that would be available to them were effective control systems to be used, and evaluating the potential power to be generated by the wind system is required. This paper presents a methodology of predicting the power generated by a wind system operating within an urban wind resource. This method was developed by using high temporal resolution wind measurements from eight potential sites within the urban and suburban environment as inputs to a vertical axis wind turbine multiple stream tube model. A relationship between the unsteady performance coefficient obtained from the stream tube model results and turbulence intensity was demonstrated. Hence, an analytical methodology for estimating the unsteady power coefficient at a potential turbine site is proposed. This is combined with analytical models that were developed to predict the wind speed and the excess energy (EEC) available in estimating the potential power generated by wind systems at different heights within a built environment. Estimates of turbulence intensities, wind speed, EEC and turbine performance based on the current methodology allow a more complete assessment of available wind resource and potential urban wind projects. This methodology is applied to four major UK cities namely Leeds, Manchester, London and Edinburgh and the potential to map the turbine performance at different heights within a typical urban city is demonstrated.

Keywords: small-scale wind, turbine power, urban wind energy, turbulence intensity, excess energy content

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Authors: Hossein Afzali

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Increasingly, decision analytic models are used to inform decisions about whether or not to publicly fund new health technologies. It is well noted that the accuracy of model predictions is strongly influenced by the appropriateness of model structuring. However, there is relatively inadequate methodological guidance surrounding this issue in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC) and The National Institute for Health and Care Excellence (NICE) in the UK. This presentation aims to discuss issues around model structuring within decision making with a focus on (1) the need for a transparent and evidence-based model structuring process to inform the most appropriate set of structural aspects as the base case analysis; (2) the need to characterise structural uncertainty (If there exist alternative plausible structural assumptions (or judgements), there is a need to appropriately characterise the related structural uncertainty). The presentation will provide an opportunity to share ideas and experiences on how the guidelines developed by national funding bodies address the above issues and identify areas for further improvements. First, a review and analysis of the literature and guidelines developed by PBAC and NICE will be provided. Then, it will be discussed how the issues around model structuring (including structural uncertainty) are not handled and justified in a systematic way within the decision-making process, its potential impact on the quality of public funding decisions, and how it should be presented in submissions to national funding bodies. This presentation represents a contribution to the good modelling practice within the decision-making process. Although the presentation focuses on the PBAC and NICE guidelines, the discussion can be applied more widely to many other national funding bodies that use economic evaluation to inform funding decisions but do not transparently address model structuring issues e.g. the Medical Services Advisory Committee (MSAC) in Australia or the Canadian Agency for Drugs and Technologies in Health.

Keywords: decision-making process, economic evaluation, good modelling practice, structural uncertainty

Procedia PDF Downloads 187