Search results for: artificial potential approach

Authors: Yahya Almurtadha, Mukhtar Ghaleb, Ahmed M. Shamsan Saleh

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The world witnessed a fierce attack from the Covid-19 virus, which affected public life socially, economically, healthily and psychologically. The world's governments tried to confront the pandemic by imposing a number of precautionary measures such as general closure, curfews and social distancing. Scientists have also made strenuous efforts to develop an effective vaccine to train the immune system to develop antibodies to combat the virus, thus reducing its symptoms and limiting its spread. Artificial intelligence, along with researchers and medical authorities, has accelerated the vaccine development process through big data processing and simulation. On the other hand, one of the most important negatives of the impact of Covid 19 was the state of anxiety and fear due to the blowout of rumors through social media, which prompted governments to try to reassure the public with the available means. This study aims to proposed using Sentiment Analysis (AKA Opinion Mining) and deep learning as efficient artificial intelligence techniques to work on retrieving the tweets of the public from Twitter and then analyze it automatically to extract their opinions, expression and feelings, negatively or positively, about the symptoms they may feel after vaccination. Sentiment analysis is characterized by its ability to access what the public post in social media within a record time and at a lower cost than traditional means such as questionnaires and interviews, not to mention the accuracy of the information as it comes from what the public expresses voluntarily.

Keywords: deep learning, opinion mining, natural language processing, sentiment analysis

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Authors: Chander Kant, Arun Kumar

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Biometric systems automatically recognize an individual based on his/her physiological and behavioral characteristics. There are also some traits like weight, age, height etc. that may not provide reliable user recognition because of there common and temporary nature. These traits are called soft bio metric traits. Although soft bio metric traits are lack of permanence to uniquely and reliably identify an individual, yet they provide some beneficial evidence about the user identity and may improve the system performance. Here in this paper, we have proposed an approach for integrating the soft bio metrics with fingerprint and face to improve the performance of personal authentication system. In our approach we have proposed a combined architecture of three different sensors to elevate the system performance. The approach includes, soft bio metrics, fingerprint and face traits. We have also proven the efficiency of proposed system regarding FAR (False Acceptance Ratio) and total response time, with the help of MUBI (Multimodal Bio metrics Integration) software.

Keywords: FAR, minutiae point, multimodal bio metrics, primary bio metric, soft bio metric

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Authors: Samir Kumar Pal

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Background: Targeted rapid degradation of bilirubin has the potential to thwart incipient bilirubin encephalopathy. Uncontrolled hyperbilirubinemia is a potential problem in developing countries, including India, because of the lack of reliable healthcare institutes for conventional phototherapy. In India, most of the rural subjects duel in the exchange limit during transport, leading to a risk of kernicterus when they arrive at the treatment centre. Thus, an alternative pharmaceutical agent is needed for the hours. Objective: Exploration of a distinct therapeutic strategy for the control of neonatal hyperbilirubinemia compared to conventional phototherapy in a clinical setting. Method: We synthesized, characterized and investigated a spinel-structured Manganese citrate nanocomplex (C-Mn₃O₄ NC, the nanodrug) along with conventional phototherapy in neonatal subjects. We have also observed BIND scores in order to assess neurological dysfunctions. Results: Our observational study clearly reveals that the rate of declination of bilirubin in neonatal subjects with nanodrug oral administration and phototherapy is faster compared to that in the case of phototherapy only. The associated neural dysfunctions were also found to be significantly lower in the case of combined therapy. Conclusion: This study demonstrates that combined therapy works better than conventional phototherapy only for the control of hyperbilirubinemia. We have observed that a significant portion of neonatal subjects requiring blood exchange has been prevented with the combined therapeutic strategy. Further compilation of a drug-safety-dossier is warranted to translate this novel therapeutic chemo preventive approach to clinical settings.

Keywords: nanodrug, nanoparticle, Neonatal hyperbilirubinemia, alternative to phototherapy, redox modulation, redox medicine

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Authors: Quentin C.P. Bourgogne, Vanessa Bouchart, Pierre Chevrier, Emmanuel Dattoli

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This study presents an experimental and theoretical work conducted on a PolyPhenylene Sulfide reinforced with 40%wt of short glass fibers (PPS GF40) and its matrix. Thermoplastics are widely used in the automotive industry to lightweight automotive parts. The replacement of metallic parts by thermoplastics is reaching under-the-hood parts, near the engine. In this area, the parts are subjected to high temperatures and are immersed in cooling liquid. This liquid is composed of water and glycol and can affect the mechanical properties of the composite. The aim of this work was thus to quantify the evolution of mechanical properties of the thermoplastic composite, as a function of temperature and liquid aging effects, in order to develop a reliable design of parts. An experimental campaign in the tensile mode was carried out at different temperatures and for various glycol proportions in the cooling liquid, for monotonic and cyclic loadings on a neat and a reinforced PPS. The results of these tests allowed to highlight some of the main physical phenomena occurring during these solicitations under tough hydro-thermal conditions. Indeed, the performed tests showed that temperature and liquid cooling aging can affect the mechanical behavior of the material in several ways. The more the cooling liquid contains water, the more the mechanical behavior is affected. It was observed that PPS showed a higher sensitivity to absorption than to chemical aggressiveness of the cooling liquid, explaining this dominant sensitivity. Two kinds of behaviors were noted: an elasto-plastic type under the glass transition temperature and a visco-pseudo-plastic one above it. It was also shown that viscosity is the leading phenomenon above the glass transition temperature for the PPS and could also be important under this temperature, mostly under cyclic conditions and when the stress rate is low. Finally, it was observed that soliciting this composite at high temperatures is decreasing the advantages of the presence of fibers. A new phenomenological model was then built to take into account these experimental observations. This new model allowed the prediction of the evolution of mechanical properties as a function of the loading environment, with a reduced number of parameters compared to precedent studies. It was also shown that the presented approach enables the description and the prediction of the mechanical response with very good accuracy (2% of average error at worst), over a wide range of hydrothermal conditions. A temperature-humidity equivalence principle was underlined for the PPS, allowing the consideration of aging effects within the proposed model. Then, a limit of improvement of the reachable accuracy was determinate for all models using this set of data by the application of an artificial intelligence-based model allowing a comparison between artificial intelligence-based models and phenomenological based ones.

Keywords: aging, analytical modeling, mechanical testing, polymer matrix composites, sequential model, thermomechanical

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Authors: Pothula Madhusudhan Reddy

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Introduction: Yoga is a way of life. of uniting the mind, body and soul. It is also an art of living the right way. The techniques of Yoga are very practical, so they can always be applied. This is the reason why Yoga has been practiced for thousands of years and is still valid today. Importance of Yoga: Yoga that helps to inculcate healthy habits and adopt a healthy lifestyle to achieve good health Research Aim: The aim of this study is to explore the potential benefits of yoga for holistic health and wellbeing, both at an individual and societal level The ultimate goal of human being is to attain the state of perfect freedom from the shackles of ignorance, which is the generator of all the pangs and miseries of life. Methodology: This research follows a thematic and practical experience approach. Yoga includes body postures and movements (stretching), breathing practices, imagery, meditation, and progressive relaxation techniques. Data Collection: The data for this research is collected through a combination of literature review, expert interviews, and practical yoga sessions. The literature review provides a comprehensive understanding of the principles and practices of yoga, while expert interviews offer insights from experienced practitioners. Practical yoga sessions allow for first hand experiences and observations, facilitating a deeper understanding of the subject matter. Analysis Procedures: The collected data is analyzed thematically, where key themes and patterns related to the benefits and effects of yoga on holistic health and wellbeing are identified. The findings are then interpreted and synthesized to draw meaningful conclusions. Questions Addressed: This research addresses the following questions: What are the potential benefits of yoga for holistic health and wellbeing? How does yoga promote rejuvenate the body, mind, and senses? What are the implications of a society embracing yoga for overall societal wellbeing and happiness? Findings: The research highlights that practicing yoga can lead to increased awareness of the body, mind, and senses. It promotes overall physical and mental health, helping individuals achieve a state of happiness and contentment. Moreover, the study emphasizes that a society embracing yoga can contribute to the development of a healthy and happy community. Theoretical Importance: The study of yoga for holistic health and wellbeing holds theoretical importance as it provides insights into the science of yoga and its impact on individuals and society. It contributes to the existing body of knowledge on the subject and further establishes yoga as a potential tool for enhancing overall wellness. Conclusion: The study concludes that yoga is a powerful practice for achieving holistic health and wellbeing. This research provides valuable insights into the science of yoga and its potential as a tool for promoting overall wellness.

Keywords: yoga, asana, pranayama, meditation

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Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

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Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Apurva Patil, Ashay Aswale, Akshay Kulkarni, Shubham Bharadiya

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The potential benefit of gravity compensation of linkages in mechanisms using springs to reduce actuator requirements is well recognized, but practical applications have been elusive. Although existing methods provide exact spring balance, they require additional masses or auxiliary links, or all the springs used originate from the ground, which makes the resulting device bulky and space-inefficient. This paper uses a method of static balancing of mechanisms with conservative loads such as gravity and spring loads using non-zero-free-length springs with child–parent connections and no auxiliary links. Application of this method to the developed arm of a humanoid robot is presented here. Spring balancing is particularly important in this case because the serial chain of linkages has to work against gravity.This work involves approximate spring balancing of the open-loop chain of linkages using minimization of potential energy variance. It uses the approach of flattening the potential energy distribution over the workspace and fuses it with numerical optimization. The results show the considerable reduction in actuator torque requirement with practical spring design and arrangement. Reduced actuator torque facilitates the use of lower end actuators which are generally smaller in weight and volume thereby lowering the space requirements and the total weight of the arm. This is particularly important for humanoid robots where the parent actuator has to handle the weight of the subsequent actuators as well. Actuators with lower actuation requirements are more energy efficient, thereby reduce the energy consumption of the mechanism. Lower end actuators are lower in cost and facilitate the development of low-cost devices. Although the method provides only an approximate balancing, it is versatile, flexible in choosing appropriate control variables that are relevant to the design problem and easy to implement. The true potential of this technique lies in the fact that it uses a very simple optimization to find the spring constant, free-length of the spring and the optimal attachment points subject to the optimization constraints. Also, it uses physically realizable non-zero-free-length springs directly, thereby reducing the complexity involved in simulating zero-free-length springs from non-zero-free-length springs. This method allows springs to be attached to the preceding parent link, which makes the implementation of spring balancing practical. Because auxiliary linkages can be avoided, the resultant arm of the humanoid robot is compact. The cost benefits and reduced complexity can be significant advantages in the development of this arm of the humanoid robot.

Keywords: actuator torque, child-parent connections, spring balancing, the arm of a humanoid robot

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Authors: Adel Ahmadi Hosseini, Fatemeh Tavanaei, Alessandro Navarra, Ferri Hassani

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Mining engineering, as a part of geoscience, must address modern concerns. Traditional mining methods incorporate drill and blast technologies, which are followed by different issues, including excessive noise, vibration, air pollution, and safety hazards. Over the past two decades, mining engineers have sought alternative solutions to move from drill and blast to continuous methods to prevent such issues and improve sustainability in mining. Among the suggested methods, microwave treatment has shown promising results by creating micro/macro cracks in the rock structure prior to the operations. This research utilizes an energy-based analysis methodology to evaluate the efficiency of the microwave treatment in improving mining operations. The data analysis shows that increasing the input microwave energy dosage intensifies the rock damage. However, this approach can decrease the energy efficiency of the method by more than 50% in some cases. In this study, rock samples were treated with three power levels (3 kW, 7 kW, and 12 kW) and two energy dosages (20 kWh/t and 50 kWh/t), resulting in six conditions. To evaluate the impact of microwave treatment on the geomechanical behavior of the rocks, Unconfined Compressive Strength (UCS) tests were conducted on the microwave-treated samples, yielding stress-strain curves. Using the stress-strain curves, the effect of the different powers and energy dosages of microwaves are discussed. This research shows the potential of using microwave treatment to lead the industry to more sustainable mining.

Keywords: microwave treatment, microwave energy dosage, sustainable mining, rock fragmentation

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Authors: Maha Salman, Gada Al-Nuwaibit, Ahmed Al-Haji, Saleh Al-Haddad, Abbas Al-Mesri, Mansour Al-Rugeeb

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The high salinity of oil-produced water and its complicated chemical composition, makes designing a suitable treatment system for oil-produced water is extremely difficult and costly. On the current study, a new innovative method was proposed to treat the complicated oil-produced water through a simple mixing with brine stream produced from waste water treatment plant. The proposal will investigate the scaling potential of oil-produce water, seawater and the selected brine water (BW) produced from Sulaibiya waste water treatment and reclamation plant (SWWTRP) before and after the mixing with oil-produced water, and will calculate the scaling potential of all expected precipitated salts using different conversion and different % of mixing to optimize the % of mixing between the oil-produced water and the selected stream. The result shows a great, feasible and economic solution to treat oil produced with a very low capital cost.

Keywords: brine water, oil-produced water, scaling potential, Sulaibiyah waste water and reclaminatin plant

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Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Michael Stokes

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Using a disability studies approach, this paper analyzes the American science fiction novel Ballroom of the Skies as way to address and access narratives of American exceptionalism in relation to global struggle. Combined with a critical race studies analysis of identity and cultural practice, this essay seeks to find parallels between the treatment of disability and the treatment of the racialized body in literature to forcibly reread potential for multiple assemblages of identity in the speculated futures of science fiction. Thinking through this relationship, the essay constructs a thematic understanding of social eugenics as practiced in American culture.

Keywords: disability studies, science fiction, eugenics, cultural studies

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Hoi-Lam Ma, Sai-Ho Chung

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In traditional integrated berth allocation with quay crane assignment models, time dimension is usually assumed in hourly based. However, nowadays, transshipment becomes the main business to many container terminals, especially in Southeast Asia (e.g. Hong Kong and Singapore). In these terminals, vessel arrivals are usually very frequent with small handling volume and very short staying time. Therefore, the traditional hourly-based modeling approach may cause significant berth and quay crane idling, and consequently cannot meet their practical needs. In this connection, a 15-minute-based modeling approach is requested by industrial practitioners. Accordingly, a Three-level Genetic Algorithm (3LGA) with Quay Crane (QC) shifting heuristics is designed to fulfill the research gap. The objective function here is to minimize the total service time. Preliminary numerical results show that the proposed 15-minute-based approach can reduce the berth and QC idling significantly.

Keywords: transshipment, integrated berth allocation, variable-in-time quay crane assignment, quay crane assignment

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Authors: Sergio D’Ambrosioa, Azza Dobousa, Chiara Schiraldia, Donatella Ciminib

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Probiotics are living microorganisms that give beneficial effects while consumed. Lactic acid bacteria and bifidobacteria are among the most representative strains assessed as probiotics and exploited as food supplements. Numerous studies demonstrated their potential as a therapeutic candidate for a variety of diseases (restoring gut flora, lowering cholesterol, immune response-enhancing, anti-inflammation and anti-oxidation activities). These beneficial actions are also due to biomolecules produced by probiotics, such as exopolysaccharides (EPSs), that demonstrate plenty of beneficial properties such as antimicrobial, antitumor, anti-biofilm, antiviral and immunomodulatory activities. Limosilactobacillus fermentum is a widely studied member of probiotics; however, few data are available on the development of fermentation and downstream processes for the production of viable biomasses for potential industrial applications. However, few data are available on the development of fermentation processes for the large-scale production of probiotics biomass for industrial applications and for purification processes of EPSs at an industrial scale. For this purpose, L. fermentum strain was isolated from buffalo milk and used as a test example for biotechnological process development. The strain was able to produce up to 109 CFU/mL on a (glucose-based) semi-defined medium deprived of animal-derived raw materials up to the pilot scale (150 L), demonstrating improved results compared to commonly used, although industrially not suitable, media-rich of casein and beef extract. Biomass concentration via microfiltration on hollow fibers, and subsequent spray-drying allowed to recover of about 5.7 × 1010CFU/gpowder of viable cells, indicating strain resistance to harsh processing conditions. Overall, these data demonstrate the possibility of obtaining and maintaining adequate levels of viable L. fermentum cells by using a simple approach that is potentially suitable for industrial development. A downstream EPS purification protocol based on ultrafiltration, precipitation and activated charcoal treatments showed a purity of the recovered polysaccharides of about 70-80%.

Keywords: probiotics, fermentation, exopolysaccharides (EPSs), purification

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Authors: Ruma Rano, Firoza Sultana, Bishal Bhuyan, Nurul Alam Mazumder

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Fly ash collected from Cachar Paper Mill, Assam, India has been thoroughly characterized in respect of its physico-chemical, morphological and mineralogical features were concerned by using density, LOI, FTIR, XRD, SEM-EDS etc. The results reveal that there is a striking difference in the features and properties of the coarser and finer fractions .The high carbon ash consists of large unburnt carbon (chars), irregular carbonaceous particles in the coarser fraction, which appear to be porous and may be used as domestic fuel. The percentage of char albeit the carbon content decreases with decrease in size of particles. The various fractions essentially contain quartz and mullite as the main mineral phases. For suggesting the potential utilization channels, number of experiments were performed correlating the total characteristic features. Water holding capacities of different size classified fractions were determined, the coarser fractions have unexpectedly higher water holding capacities than the finer ones. An attempt has been made to correlate the results obtained with potential use in agriculture. Another potential application of coarser particles is used as adsorbent for effluents containing waste organic materials. Thus thorough characterization leads to not only a definite direction about the uses of the value added components but also gives useful information regarding the prevailing combustion process.

Keywords: chars, porous, water holding capacity, combustion process

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Authors: Temitope L. Baiyegunhi, Kuiwu Liu, Oswald Gwavava, Christopher Baiyegunhi

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Shale gas has recently been the exploration focus for future energy resource in South Africa. Specifically, the black shales of the lower Ecca Group in the study area are considered to be one of the most prospective targets for shale gas exploration. Evaluation of this potential resource has been restricted due to the lack of exploration and scarcity of existing drill core data. Thus, only limited previous geochemical data exist for these formations. In this study, outcrop and core samples of the Ecca Group were analysed to assess their total organic carbon (TOC), organic matter type, thermal maturity and hydrocarbon generation potential (SP). The results show that these rocks have TOC ranging from 0.11 to 7.35 wt.%. The SP values vary from 0.09 to 0.53 mg HC/g, suggesting poor hydrocarbon generative potential. The plot of S1 versus TOC shows that the source rocks were characterized by autochthonous hydrocarbons. S2/S3 values range between 0.40 and 7.5, indicating Type- II/III, III, and IV kerogen. With the exception of one sample from the collingham formation which has HI value of 53 mg HC/g TOC, all other samples have HI values of less than 50 mg HC/g TOC, thus suggesting Type-IV kerogen, which is mostly derived from reworked organic matter (mainly dead carbon) with little or no potential for hydrocarbon generation. Tmax values range from 318 to 601℃, indicating immature to over-maturity of hydrocarbon. The vitrinite reflectance values range from 2.22 to 3.93%, indicating over-maturity of the kerogen. Binary plots of HI against OI and HI versus Tmax show that the shales are of Type II and mixed Type II-III kerogen, which are capable of generating both natural gas and minor oil at suitable burial depth. Based on the geochemical data, it can be inferred that the source rocks are immature to over-matured variable from localities and have potential of producing wet to dry gas at present-stage. Generally, the Whitehill formation of the Ecca Group is comparable to the Marcellus and Barnett Shales. This further supports the assumption that the Whitehill Formation has a high probability of being a profitable shale gas play, but only when explored in dolerite-free area and away from the Cape Fold Belt.

Keywords: source rock, organic matter type, thermal maturity, hydrocarbon generation potential, Ecca Group

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Authors: Arun Prasad Jaganathan

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In today's rapidly evolving technological landscape, the need for efficient and proactive maintenance management solutions has become increasingly evident across various industries. Traditional approaches often suffer from drawbacks such as reactive strategies, leading to potential downtime, increased costs, and decreased operational efficiency. In response to these challenges, this paper proposes an AI-enabled approach to object-based maintenance management aimed at enhancing reliability and efficiency. The paper contributes to the growing body of research on AI-driven maintenance management systems, highlighting the transformative impact of intelligent technologies on enhancing object reliability and operational efficiency.

Keywords: AI, machine learning, predictive maintenance, object-based maintenance, expert team scheduling

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Authors: Nasir Mustafa

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One of the prerequisites required for an efficient diagnosis in a medical practice is to have a strong command of both functional and clinical anatomy. In this study, we introduce a new collaborative approach to the effective teaching of the knee and hip joints. In the present teaching model, anatomists, orthopedists and physical therapists present the anatomy of the hip and knee joints in small groups. Courses for the hip and knee joints were scheduled during the early stages of the medical curriculum. Students of nursing and physical therapy were grouped together to sensitize to the importance of a collaborative effort. The study results clearly demonstrate that nursing students and physical therapy students appreciated this teaching approach. The collaborative approach further proved to be a suitable method to teach both functional and clinical anatomy of the hip and knee joints. Aside from this training, a collaborative approach between medical students and physical therapy students was also successful for a healthcare organization.

Keywords: hip and knee joint anatomy, collaborative, Anatomy teaching, Nursing students, Physiotherapy students

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Authors: Cesar Torres, Robert Briber, Nam Sun Wang

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Eye drops are the most commonly used treatment for short-term and long-term ophthalmic diseases. However, eye drops could deliver only about 5% of the functional ingredients contained in a burst dosage. To address the limitations of eye drops, the use of therapeutic contact lenses has been introduced. Drug-loaded contact lenses provide drugs a longer residence time in the tear film and hence, decrease the potential risk of side effects. Nevertheless, a major limitation of contact lenses as drug delivery devices is that most of the drug absorbed is released within the first few hours. This fact limits their use for extended release. The present study demonstrates the application of long-alkyl chain ionic surfactants on extending drug release kinetics from commercially available silicone hydrogel contact lenses. In vitro release experiments were carried by immersing drug-containing contact lenses in phosphate buffer saline at physiological pH. The drug concentration as a function of time was monitored using ultraviolet-visible spectroscopy. The results of the study demonstrate that release kinetics is dependent on the ionic surfactant weight percent in the contact lenses, and on the length of the hydrophobic alkyl chain of the ionic surfactants. The use of ionic surfactants in contact lenses can extend the delivery of drugs from a few hours to a few weeks, depending on the physicochemical properties of the drugs. Contact lenses embedded with ionic surfactants could be potential biomaterials to be used for extended drug delivery and in the treatment of ophthalmic diseases. However, ocular irritation and toxicity studies would be needed to evaluate the safety of the approach.

Keywords: contact lenses, drug delivery, controlled release, ionic surfactant

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Authors: Jiahui Song, Ravindra P. Joshi

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The use of high-intensity, short-duration electric pulses is a promising development with many biomedical applications. The uses include irreversible electroporation for killing abnormal cells, reversible poration for drug and gene delivery, neuromuscular manipulation, and the shrinkage of tumors, etc. High intensity, short-duration electric pulses result in the creation of high-density, nanometer-sized pores in the cellular membrane. This electroporation amounts to localized modulation of the transverse membrane conductance, and effectively provides a voltage shunt. The electrically controlled changes in the trans-membrane conductivity could be used to affect neural traffic and action potential propagation. A rat was taken as the representative example in this research. The simulation study shows the pathway from the sensorimotor cortex down to the spinal motoneurons, and effector muscles could be reversibly blocked by using high-intensity, short-duration electrical pulses. Also, actual experimental observations were compared against simulation predictions.

Keywords: action potential, electroporation, high-intensity, short-duration

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Authors: Francisco Pereira, António Augusto Vicente, Filipe Vaz, Joel Borges, Pedro Geada

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Light is a growth-limiting factor in microalgae cultivation, where factors like spectral components, intensity, and duration, often characterized by its wavelength, are well-reported to have a substantial impact on cell growth rates and, consequently, photosynthetic performance and mitigation of CO2, one of the most significant greenhouse gases (GHGs). Photobioreactors (PBRs) are commonly used to grow microalgae under controlled conditions, but they often fail to provide an even light distribution to the cultures. For this reason, there is a pressing need for innovations aiming at enhancing the efficient utilization of light. So, one potential approach to address this issue is by implementing plasmonic films, such as the localized surface plasmon resonance (LSPR). LSPR is an optical phenomenon connected to the interaction of light with metallic nanostructures. LSPR excitation is characterized by the oscillation of unbound conduction electrons of the nanoparticles coupled with the electromagnetic field from incident light. As a result of this excitation, highly energetic electrons and a strong electromagnetic field are generated. These effects lead to an amplification of light scattering, absorption, and extinction of specific wavelengths, contingent on the nature of the employed nanoparticle. Thus, microalgae might benefit from this biotechnology as it enables the selective filtration of inhibitory wavelengths and harnesses the electromagnetic fields produced, which could lead to enhancements in both biomass and metabolite productivity. This study aimed at implementing and evaluating a “plasmon-enhanced PBR”. The goal was to utilize LSPR thin films to enhance the growth and CO2 bio-fixation rate of Chlorella vulgaris. The internal/external walls of the PBRs were coated with a TiO2 matrix containing different nanoparticles (Au, Ag, and Au-Ag) in order to evaluate the impact of this approach on microalgae’s performance. Plasmonic films with distinct compositions resulted in different Chlorella vulgaris growth, ranging from 4.85 to 6.13 g.L-1. The highest cell concentrations were obtained with the metallic Ag films, demonstrating a 14% increase compared to the control condition. Moreover, it appeared to be no differences in growth between PBRs with inner and outer wall coatings. In terms of CO2 bio-fixation, distinct rates were obtained depending on the coating applied, ranging from 0.42 to 0.53 gCO2L-1d-1. Ag coating was demonstrated to be the most effective condition for carbon fixation by C. vulgaris. The impact of LSPR films on the biochemical characteristics of biomass (e.g., proteins, lipids, pigments) was analysed as well. Interestingly, Au coating yielded the most significant enhancements in protein content and total pigments, with increments of 15 % and 173 %, respectively, when compared to the PBR without any coating (control condition). Overall, the incorporation of plasmonic films in PBRs seems to have the potential to improve the performance and efficiency of microalgae cultivation, thereby representing an interesting approach to increase both biomass production and GHGs bio-mitigation.

Keywords: CO₂ bio-fixation, plasmonic effect, photobioreactor, photosynthetic microalgae

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Authors: Tugba Saricaoglu

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This study argues about the potential representation and interpretation of Basilica of Smyrna through gamification. Representation can be defined as a key which plays a role as a converter in order to provide interpretation of something according to the person who perceives. Representation of cultural heritage is a hypothetical and factual approach in terms of its sustainable conservation. Today, both site interpreters and public of cultural heritage have varying perspectives due to their different demographic, social, and even cultural backgrounds. Additionally, gamification application offers diversion of methods suchlike video games to improve user perspective of non-game platforms, contexts, and issues. Hence, cultural heritage and video game decided to be analyzed. Moreover, there are basically different ways of representation of cultural heritage such as digital, physical, and virtual methods in terms of conservation. Virtual reality (VR) and augmented reality (AR) technologies are two of the contemporary digital methods of heritage conservation. In this study, 3D documented ruins of the Basilica will be presented in the virtual and augmented reality based technology as a theoretical gamification sample. Also, this paper will focus on two sub-topics: First, evaluation of the video-game platforms applied to cultural heritage sites, and second, potentials of cultural heritage to be represented in video game platforms. The former will cover the analysis of some case(s) with regard to the concepts and representational aspects of cultural heritage. The latter will include the investigation of cultural heritage sites which carry such a potential and their sustainable conversation. Consequently, after mutual collection of information from cultural heritage and video game platforms, a perspective will be provided in terms of interpretation of representation of cultural heritage by sampling that on Basilica of Smyrna by using VR and AR based technologies.

Keywords: Basilica of Smyrna, cultural heritage, digital heritage, gamification

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Authors: Urszula Stachewicz

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Surface properties of materials are the key parameter in many applications, especially in the biomedical field, to control cell-material interactions. In our work, we want to achieve the controllability of surface properties of polymer fibers via a single-step electrospinning process by alternating voltage polarities. Voltage polarity defines the charge accumulated on the surface of the liquid jet and the surface of the fibers. Positive polarity attracts negatively charged groups to fibers’ surface, whereas negative polarity moves the negatively charged functional groups away from the surface. This way, we can control the surface chemistry, wettability, and additionally surface potential of electrospun fibers. Within our research, we characterized surface chemistry using X-ray photoelectron microscopy (XPS) and surface potential with Kelvin probe force microscopy (KPFM) on electrospun fibers of commonly used polymers such as PCL, PVDF, and PMMA, often used as biomaterials. We proved the significant effect of fibers' surface potential on cell integration with the scaffolds and further cells development for the regeneration processes based on the osteoblast and fibroblast culture studies. Acknowledgments: The study was conducted within ‘Nanofiber-based sponges for atopic skin treatment’ project, which is carried out within the First TEAM programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund, project no POIR.04.04.00-00- 4571/18-00.

Keywords: cell attachment, fibers, fibroblasts, osteoblast, proliferation, surface potential

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Akshita Jindal, Renu Chadha, Maninder Karan

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Supramolecular chemistry has gained recent eminence in a flurry of research documents demonstrating the formation of new crystalline forms with potentially advantageous characteristics. Mesalamine (5-amino salicylic acid) belongs to anti-inflammatory class of drugs, is used to treat ulcerative colitis and Crohn’s disease. Unfortunately, mesalamine suffer from poor solubility and therefore very low bioavailability. This work is focused on preparation and characterization of cocrystal of mesalamine with nicotinamide (MNIC) a coformer of GRAS status. Cocrystallisation was achieved by solvent drop grinding in stoichiometric ratio of 1:1 using acetonitrile as solvent and was characterized by various techniques including DSC (Differential Scanning Calorimetry), PXRD (X-ray Powder Diffraction), and FTIR (Fourier Transform Infrared Spectrometer). The co-crystal depicted single endothermic transitions (254°C) which were different from the melting peaks of both drug (288°C) and coformer (128°C) indicating the formation of a new solid phase. Different XRPD patterns and FTIR spectrums for the co-crystals from those of individual components confirms the formation of new phase. Enhancement in apparent solubility study and intrinsic dissolution study showed effectiveness of this cocrystal. Further improvement in pharmacokinetic profile has also been observed with 2 folds increase in bioavailability. To conclude, our results show that application of nicotinamide as a coformer is a viable approach towards the preparation of cocrystals of potential drug molecule having limited solubility.

Keywords: cocrystal, mesalamine, nicotinamide, solvent drop grinding

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Authors: Mateen A Khan, Taj Mohammad, Md. Imtaiyaz Hassan

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Alzheimer’s disease is characterized by the accumulation of the processing products of the amyloid beta peptide cleaved by amyloid precursor protein (APP). Iron increases the synthesis of amyloid beta peptides, which is why iron is present in Alzheimer's disease patients' amyloid plaques. Iron misregulation in the brain is linked to the overexpression of APP protein, which is directly related to amyloid-β aggregation in Alzheimer’s disease. The APP 5'-UTR region encodes a functional iron-responsive element (IRE) stem-loop that represents a potential target for modulating amyloid production. Targeted regulation of APP gene expression through the modulation of 5’-UTR sequence function represents a novel approach for the potential treatment of AD because altering APP translation can be used to improve both the protective brain iron balance and provide anti-amyloid efficacy. The molecular docking analysis of APP IRE RNA with eukaryotic translation initiation factors yields several models exhibiting substantial binding affinity. The finding revealed that the interaction involved a set of functionally active residues within the binding sites of eIF4F. Notably, APP IRE RNA and eIF4F interaction were stabilized by multiple hydrogen bonds with residues of APP IRE RNA and eIF4F. It was evident that APP IRE RNA exhibited a structural complementarity that tightly fit within binding pockets of eIF4F. The simulation studies further revealed the stability of the complexes formed between RNA and eIF4F, which is crucial for assessing the strength of these interactions and subsequent roles in the pathophysiology of Alzheimer’s disease. In addition, MD simulations would capture conformational changes in the IRE RNA and protein molecules during their interactions, illustrating the mechanism of interaction, conformational change, and unbinding events and how it may affect aggregation propensity and subsequent therapeutic implications. Our binding studies correlated well with the translation efficiency of APP mRNA. Overall, the outcome of this study suggests that the genomic modification and/or inhibiting the expression of amyloid protein by targeting APP IRE RNA can be a viable strategy to identify potential therapeutic targets for AD and subsequently be exploited for developing novel therapeutic approaches.

Keywords: Alzheimer's disease, Protein-RNA interaction analysis, molecular docking simulations, conformational dynamics, binding stability, binding kinetics, protein synthesis.

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Authors: Ana M. Guzman, Claudia M. Rodriguez, Pedro F. B. Brandao, Elianna Castillo

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Cadmium (Cd) is a carcinogenic metal to which humans are exposed mainly due to its presence in the food chain. Lactic acid bacteria have the capability to bind cadmium and thus the potential to be used as probiotics to treat this metal toxicity in the human body. The main objective of this study is to evaluate the potential of native lactic acid bacteria, isolated from Colombian fermented cocoa, to remove cadmium from aqueous solutions. An initial screening was made with the Lactobacillus plantarum JCM 1055 type strain, and Cd was quantified by atomic absorption spectroscopy (AAS). Lb. plantarum JCM 1055 was grown in ½ MRS medium to follow growth kinetics during 32 h at 37 °C, by measuring optical density at 600 nm. Washed cells, grown for 18 h, were adjusted to obtain dry biomass concentrations of 1.5 g/L and 0.5 g/L for removal assays in 10 mL of Cd(NO₃)₂ solution with final concentrations of 10 mg/Kg or 1.0 mg/Kg. The assays were performed at two different pH values (2.0 and 5.0), and results showed better adsorption abilities at higher pH. After incubation for 1 h at 37 °C and 150 rpm, the removal percentages for 10 mg/Kg Cd with 1.5 g/L and 0.5 g/L biomass concentration at pH 5.0 were, respectively, 71% and 50%, while the efficiency was 9.15 and 4.52 mg Cd/g dry biomass, respectively. For the assay with 1.0 mg/Kg Cd at pH 5.0, the removal was 100% and 98%, respectively for the same biomass concentrations, and the efficiency was 1.63 and 0.56 mg Cd/g dry biomass, respectively. These results suggest the efficiency of Lactobacillus strains to remove cadmium and their potential to be used as probiotics to treat cadmium toxicity and reduce its accumulation in the human body.

Keywords: cadmium removal, fermented cocoa, lactic acid bacteria, probiotics

Procedia PDF Downloads 159

Authors: M. Iommi, G. Losco

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Current building sector is focused on reduction of energy requirements, on renewable energy generation and on regeneration of existing urban areas. These targets need to be solved with a systemic approach, considering several aspects simultaneously such as climate conditions, lighting conditions, solar radiation, PV potential, etc. The solar access analysis is an already known method to analyze the solar potentials, but in current years, simulation tools have provided more effective opportunities to perform this type of analysis, in particular in the early design stage. Nowadays, the study of the solar access is related to the easiness of the use of simulation tools, in rapid and easy way, during the design process. This study presents a comparison of three simulation tools, from the point of view of the user, with the aim to highlight differences in the easy-to-use of these tools. Using a real urban context as case study, three tools; Ecotect, Townscope and Heliodon, are tested, performing models and simulations and examining the capabilities and output results of solar access analysis. The evaluation of the ease-to-use of these tools is based on some detected parameters and features, such as the types of simulation, requirements of input data, types of results, etc. As a result, a framework is provided in which features and capabilities of each tool are shown. This framework shows the differences among these tools about functions, features and capabilities. The aim of this study is to support users and to improve the integration of simulation tools for solar access with the design process.

Keywords: energy building design tools, solar access analysis, solar potential, urban planning

Procedia PDF Downloads 333