Search results for: predictive mining

Authors: Dongseok Cho, Mitchell Driedger, Sera Han, Noman Khan, Mohammed Elmorsy, Mohamad El-Hajj

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Since the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated its population within its first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. The results of these correlations identify countries with stronger health indicators, such as lower mortality rates, lower age dependency ratios, and higher rates of immunization to other diseases, displaying higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.

Keywords: data mining, Pearson correlation, COVID-19, vaccination rates and hesitancy

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Authors: Beloulou Mohamed Lamine, Fedili Benamar, Meliani Walid, Chaid Dalila, Lamara Abdelhak

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Introduction: Post-surgical chronic pain (PSCP) is a pathological condition with a rather complex etiopathogenesis that extensively involves sensitization processes and neuronal damage. The neuropathic component of these pains is almost always present, with variable expression depending on the type of surgery. Objective: To assess the presumed beneficial effect of Regional Anesthesia-Analgesia Techniques (RAAT) on the development of post-surgical chronic neuropathic pain (PSCNP) in various surgical procedures. Patients and Methods: A comparative study involving 510 patients distributed across five surgical models (mastectomy, thoracotomy, hernioplasty, cholecystectomy, and major abdominal-pelvic surgery) and randomized into two groups: Group A (240) receiving conventional postoperative analgesia and Group B (270) receiving balanced analgesia, including the implementation of a Regional Anesthesia-Analgesia Technique (RAAT). These patients were longitudinally followed over a 6-month period, with postsurgical chronic neuropathic pain (PSCNP) defined by a Neuropathic Pain Score DN2≥ 3. Comparative measurements through univariate and multivariable analyses were performed to identify associations between the development of PSCNP and certain predictive factors, including the presumed preventive impact (protective effect) of RAAT. Results: At the 6th month post-surgery, 419 patients were analyzed (Group A= 196 and Group B= 223). The incidence of PSCNP was 32.2% (n=135). Among these patients with chronic pain, the prevalence of neuropathic pain was 37.8% (95% CI: [29.6; 46.5]), with n=51/135. It was significantly lower in Group B compared to Group A, with respective percentages of 31.4% vs. 48.8% (p-value = 0.035). The most significant differences were observed in breast and thoracopulmonary surgeries. In a multiple regression analysis, two predictors of PSCNP were identified: the presence of preoperative pain at the surgical site as a risk factor (OR: 3.198; 95% CI [1.326; 7.714]) and RAAT as a protective factor (OR: 0.408; 95% CI [0.173; 0.961]). Conclusion: The neuropathic component of PSCNP can be observed in different types of surgeries. Regional analgesia included in a multimodal approach to postoperative pain management has proven to be effective for acute pain and seems to have a preventive impact on the development of PSCNP and its neuropathic nature, particularly in surgeries that are more prone to chronicization.

Keywords: post-surgical chronic pain, post-surgical chronic neuropathic pain, regional anesthesia-analgesia techniques, neuropathic pain score DN2, preventive impact

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Authors: Muhammad Mohsin, Mir Md Abdus Salam, Pertti Pulkkinen, Ari Pappinen

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Soil and water contamination by heavy metals is a major challenging issue for the environment. Phytoextraction is an emerging, environmentally friendly and cost-efficient technology in which plants are used to eliminate pollutants from the soil and water. We aimed to assess the copper (Cu) and zinc (Zn) removal efficiency by two willow varieties such as Klara (S. viminalis x S. schwerinii x S. dasyclados) and Karin ((S.schwerinii x S. viminalis) x (S. viminalis x S.burjatica)) under different soil treatments (control/unpolluted, polluted, lime with polluted, wood ash with polluted). In 180 days of pot experiment, these willow varieties were grown in a highly polluted soil collected from Pyhasalmi mining area in Finland. The lime and wood ash were added to the polluted soil to improve the soil pH and observe their effects on metals accumulation in plant biomass. The Inductively Coupled Plasma Optical Emission Spectrometer (ELAN 6000 ICP-EOS, Perkin-Elmer Corporation) was used in this study to assess the heavy metals concentration in the plant biomass. The result shows that both varieties of willow have the capability to accumulate the considerable amount of Cu and Zn varying from 36.95 to 314.80 mg kg⁻¹ and 260.66 to 858.70 mg kg⁻¹, respectively. The application of lime and wood ash substantially affected the stimulation of the plant height, dry biomass and deposition of Cu and Zn into total plant biomass. Besides, the lime application appeared to upsurge Cu and Zn concentrations in the shoots and leaves in both willow varieties when planted in polluted soil. However, wood ash application was found more efficient to mobilize the metals in the roots of both varieties. The study recommends willow plantations to rehabilitate the Cu and Zn polluted soils.

Keywords: heavy metals, lime, phytoextraction, wood ash, willow

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Authors: Daphne Sobolev, Niklas Voege

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During the last three decades, consumer ethics perceptions have attracted the attention of a large number of researchers. Nevertheless, little is known about the effect of the cognitive and situational contexts of the decision on ethics judgments. In this paper, the interrelationship between consumers’ ethics perceptions, legality evaluations and mind-sets are explored. Legality evaluations represent the cognitive context of the ethical judgments, whereas mind-sets represent their situational context. Drawing on moral development theories and priming theories, it is hypothesized that both factors are significantly related to consumer ethics perceptions. To test this hypothesis, 289 participants were allocated to three mind-set experimental conditions and a control group. Participants in the mind-set conditions were primed for aggressiveness, politeness or awareness to the negative legal consequences of breaking the law. Mind-sets were induced using a sentence-unscrambling task, in which target words were included. Ethics and legality judgments were assessed using consumer ethics and internet ethics questionnaires. All participants were asked to rate the ethicality and legality of consumer actions described in the questionnaires. The results showed that consumer ethics and legality perceptions were significantly correlated. Moreover, including legality evaluations as a variable in ethics judgment models increased the predictive power of the models. In addition, inducing aggressiveness in participants reduced their sensitivity to ethical issues; priming awareness to negative legal consequences increased their sensitivity to ethics when uncertainty about the legality of the judged scenario was high. Furthermore, the correlation between ethics and legality judgments was significant overall mind-set conditions. However, the results revealed conflicts between ethics and legality perceptions: consumers considered 10%-14% of the presented behaviors unethical and legal, or ethical and illegal. In 10-23% of the questions, participants indicated that they did not know whether the described action was legal or not. In addition, an asymmetry between the effects of aggressiveness and politeness priming was found. The results show that the legality judgments and mind-sets interact with consumer ethics perceptions. Thus, they portray consumer ethical judgments as dynamical processes which are inseparable from other cognitive processes and situational variables. They highlight that legal and ethical education, as well as adequate situational cues at the service place, could have a positive effect on consumer ethics perceptions. Theoretical contribution is discussed.

Keywords: consumer ethics, legality judgments, mind-set, priming, aggressiveness

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Ayesha Waqar Khan, Mariam Altaf, Jamil Ahmad, Shaheen Shahzad

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Kidney fibrosis is an anticipated outcome of possibly all types of progressive chronic kidney disease (CKD). Epithelial-mesenchymal transition (EMT) signaling pathway is responsible for production of matrix-producing fibroblasts and myofibroblasts in diseased kidney. In this study, a discrete model of TGF-beta (transforming growth factor) and CTGF (connective tissue growth factor) was constructed using Rene Thomas formalism to investigate renal fibrosis turn over. The kinetic logic proposed by Rene Thomas is a renowned approach for modeling of Biological Regulatory Networks (BRNs). This modeling approach uses a set of constraints which represents the dynamics of the BRN thus analyzing the pathway and predicting critical trajectories that lead to a normal or diseased state. The molecular connection between TGF-beta, Smad 2/3 (transcription factor) phosphorylation and CTGF is modeled using GenoTech. The order of BRN is CTGF, TGF-B, and SMAD3 respectively. The predicted cycle depicts activation of TGF-B (TGF-β) via cleavage of its own pro-domain (0,1,0) and presentation to TGFR-II receptor phosphorylating SMAD3 (Smad2/3) in the state (0,1,1). Later TGF-B is turned off (0,0,1) thereby activating SMAD3 that further stimulates the expression of CTGF in the state (1,0,1) and itself turns off in (1,0,0). Elevated CTGF expression reactivates TGF-B (1,1,0) and the cycle continues. The predicted model has generated one cycle and two steady states. Cyclic behavior in this study represents the diseased state in which all three proteins contribute to renal fibrosis. The proposed model is in accordance with the experimental findings of the existing diseased state. Extended cycle results in enhanced CTGF expression through Smad2/3 and Smad4 translocation in the nucleus. The results suggest that the system converges towards organ fibrogenesis if CTGF remains constructively active along with Smad2/3 and Smad 4 that plays an important role in kidney fibrosis. Therefore, modeling regulatory pathways of kidney fibrosis will escort to the progress of therapeutic tools and real-world useful applications such as predictive and preventive medicine.

Keywords: CTGF, renal fibrosis signaling pathway, system biology, qualitative modeling

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Authors: Cyrillus Prabandana

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Supply chain optimisation requires multiple factors as consideration or constraints. These factors are including but not limited to demand forecasting, raw material fulfilment, production capacity, inventory level, facilities locations, transportation means, and manpower availability. By knowing all manageable factors involved and assuming the uncertainty with pre-defined percentage factors, an integrated supply chain model could be developed to manage various business scenarios. This paper analyse the utilisation of geographical point of view to develop an integrated supply chain network model to optimise the distribution of finished product appropriately according to forecasted demand and available supply. The supply chain optimisation model shows that small change in one supply chain constraint is possible to largely impact other constraints, and the new information from the model should be able to support the decision making process. The model was focused on three areas, i.e. raw material fulfilment, production capacity and finished products transportation. To validate the model suitability, it was implemented in a project aimed to optimise the concrete supply chain in a mining location. The high level of operations complexity and involvement of multiple stakeholders in the concrete supply chain is believed to be sufficient to give the illustration of the larger scope. The implementation of this geographical supply chain network modeling resulted an optimised concrete supply chain from raw material fulfilment until finished products distribution to each customer, which indicated by lower percentage of missed concrete order fulfilment to customer.

Keywords: decision making, geographical supply chain modeling, supply chain optimisation, supply chain

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Authors: Victor Emery David Jr., Jiang Wenchao, Daniel Mmereki, Yasinta John

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The fundamentals of public health are access to safe and clean drinking water. The presence of arsenic and other contaminants in drinking water leads to the potential risk to public health and the environment particularly in most developing countries where there’s inadequate access to safe and clean water and adequate sanitation. Liberia has taken steps to improve its drinking water status so as to achieve the Sustainable Development Goals (SDGs) target of ensuring clean water and effective sanitation but there is still a lot to be done. The Sustainable Development Goals are a United Nation initiative also known as transforming our world: The 2030 agenda for sustainable development. It contains seventeen goals with 169 targets to be met by respective countries. Liberia is situated within in the gold belt region where there exist the presence of arsenic and other contaminants in the underground water due to mining and other related activities. While there are limited or no epidemiological studies conducted in Liberia to confirm illness or death as a result of arsenic contamination in Liberia, it remains a public health concern. This paper assesses the drinking water quality, the presence of arsenic in groundwater/drinking water in Liberia, and proposes strategies for mitigating contaminants in drinking water and suggests options for improvement with regards to achieving the Sustainable Development Goals of ensuring clean water and effective sanitation in Liberia by 2030.

Keywords: arsenic, action plan, contaminants, environment, groundwater, sustainable development goals (SDGs), Monrovia, Liberia, public health, drinking water

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Authors: Gayan Mapitiya

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Maintaining good occupational health and safety (OHS) performance is significant at the coalface, especially in industries such as mining, power, and construction. Coalface workers are vulnerable to high OHS risks such as working at heights, working with mobile plants and vehicles, working with underground and above ground services, chemical emissions, radiation hazards and explosions at everyday work. To improve OHS performance of workers, OHS key performance indicators (KPIs) (for example, lost time injuries (LTI), serious injury frequency rate (SIFR), total reportable injury frequency rate (TRIFR) and number of near misses) are widely used by managers in making OHS business decisions such as investing in safety equipment and training programs. However, in many organizations, workers at the coalface hardly see any relevance or value addition of OHS KPIs to their everyday work. Therefore, the aim of the study was to understand why coalface workers perceive that OHS KPIs are not practically relevant to their jobs. Accordingly, this study was conducted as a qualitative case study focusing on a large electricity and gas firm in Australia. Semi-structured face to face interviews were conducted with selected coalface workers to gather data on their attitude towards OHS KPIs. The findings of the study revealed that workers at the coalface generally have no understanding of the purpose of KPIs, the meaning of each KPI, origin of KPIs, and how KPIs are correlated to organizational performance. Indeed, KPIs are perceived as ‘meaningless obstacles’ imposed on workers by managers without a rationale. It is recommended to engage coalface workers (a fair number of representatives) in both KPIs setting and revising processes while maintaining a continuous dialogue between workers and managers in regards OHS KPIs.

Keywords: KPIs, coalface, OHS risks, case-study

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Authors: Rafeza Begum, Mohammad Mokhlesur Rahman, Safikul Moula, Rafiqul Islam

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Potassium (K) is crucial for healthy soil and plant growth. However, K fertilization is either disregarded or poorly underutilized in Bangladesh agriculture, despite the great demand for crops. This could eventually result in a significant depletion of the soil's potassium reserves, irreversible alteration of the minerals that contain potassium, and detrimental effects on crop productivity. Soil K mining in Bangladesh is a worrying problem, and we need to evaluate it thoroughly and find remedies. A pot culture experiment was conducted in the greenhouse of Bangladesh Institute of Nuclear Agriculture (BINA) using eleven soil series of Bangladesh in order to see the depletion behaviour of potassium (K) by continuous cropping using rice (var. Iratom-24) as the test crop. The soil series were Ranishankhail, Kaonia. Sonatala, Silmondi, Gopalpur, Ishurdi, Sara, Kongsha, Nunni, Lauta and Amnura on which four successive rice plants (45 days duration) were raised with (100 ppm K) or without addition of potassium. Nitrogen, phosphorus, sulfur and zinc were applied as basal to all pots. Potassium application resulted in higher dry matter yield, increased K concentration and uptake in all the soils compared with no K treatment; which gradually decreased in the subsequent harvests. Furthermore, plant takes up K not only from exchangeable pool but also from non-exchangeable sites and a minimum replenishment of K from the soil reserve was observed. Continuous cropping has resulted in the depletion of available K of the soil. The result indicated that in order to sustain higher crop yield under intensive cultivation, the addition of potash fertilizer is necessary.

Keywords: potassium, exchangeable pool, depletion behavior., Soil series

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Authors: M. L. Dinis, M. C. Vila, A. Fiúza, A. Futuro, C. Nunes

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The present paper describes the study of paste tailings prepared in laboratory using gold tailings, produced in a Finnish gold mine with the incorporation of coal ash. Natural leaching tests were conducted with the original materials (tailings, fly and bottom ashes) and also with paste mixtures that were prepared with different percentages of tailings and ashes. After leaching, the solid wastes were physically and chemically characterized and the results were compared to those selected as blank – the unleached samples. The tailings and the coal ash, as well as the prepared mixtures, were characterized, in addition to the textural parameters, by the following measurements: grain size distribution, chemical composition and pH. Mixtures were also tested in order to characterize their mechanical behavior by measuring the flexural strength, the compressive strength and the consistency. The original tailing samples presented an alkaline pH because during their processing they were previously submitted to pressure oxidation with destruction of the sulfides. Therefore, it was not possible to ascertain the effect of the coal ashes in the acid mine drainage. However, it was possible to verify that the paste reactivity was affected mostly by the bottom ash and that the tailings blended with bottom ash present lower mechanical strength than when blended with a combination of fly and bottom ash. Surface paste disposal offer an attractive alternative to traditional methods in addition to the environmental benefits of incorporating large-volume wastes (e.g. bottom ash). However, a comprehensive characterization of the paste mixtures is crucial to optimize paste design in order to enhance engineer and environmental properties.

Keywords: coal ash, mine tailings, paste blends, surface disposal

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Authors: Livhuwani Muthelo, R. N. Malema, T. M. Mothiba

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Long term exposure to excessive noise in the mining industry increases the risk of noise induced hearing loss, with consequences for employee’s health, productivity and the overall quality of life. Objective: The objective of this study was to investigate the factors that contribute to Noise Induced Hearing Loss amongst employees at the Platinum mine in the Limpopo Province, South Africa. Study method: A qualitative, phenomenological, exploratory, descriptive, contextual design was applied in order to explore and describe the contributory factors. Purposive non-probability sampling was used to select 10 male employees who were diagnosed with NIHL in the year 2014 in four mine shafts, and 10 managers who were involved in a Hearing Conservation Programme. The data were collected using semi-structured one-on-one interviews. A qualitative data analysis of Tesch’s approach was followed. Results: The following themes emerged: Experiences and challenges faced by employees in the work environment, hearing protective device factors and management and leadership factors. Hearing loss was caused by partial application of guidelines, policies, and procedures from the Department of Minerals and Energy. Conclusion: The study results indicate that although there are guidelines, policies, and procedures available, failure in the implementation of one element will affect the development and maintenance of employees hearing mechanism. It is recommended that the mine management should apply the guidelines, policies, and procedures and promptly repair the broken hearing protective devices.

Keywords: employees, factors, noise induced hearing loss, noise exposure

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Authors: Nor Hafizah Anuar, M. Gul Akdeniz

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This paper attempted on emphasize on the station buildings façade elements. Station buildings were essential part of the transportation that reflected the technology. Comparative analysis on architectural styles will also be made between the railway station buildings of Malaysia and any railway station buildings which have similarities. The Malay Peninsula which is strategically situated between the Straits of Malacca and the South China Sea makes it an ideal location for trade. Malacca became an important trading port whereby merchants from around the world stopover to exchange various products. The Portuguese ruled Malacca for 130 years (1511–1641) and for the next century and a half (1641–1824), the Dutch endeavoured to maintain an economic monopoly along the coasts of Malaya. Malacca came permanently under British rule under the Anglo-Dutch Treaty, 1824. Up to Malaysian independence in 1957, Malaya saw a great influx of Chinese and Indian migrants as workers to support its growing industrial needs facilitated by the British. The growing tin ore mining and rubber industry resulted as the reason of the development of the railways as urgency to transport it from one place to another. The existence of railway transportation becomes more significant when the city started to bloom and the British started to build grandeur buildings that have different functions; administrative buildings, town and city halls, railway stations, public works department, courts, and post offices.

Keywords: Malaysia, station building, architectural styles, facade elements

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Paballo M. Moshupya, Tamiru A. Abiye, Ian Korir

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Mining and processing of naturally occurring radioactive materials could result in elevated levels of natural radionuclides in the environment. The aim of this study was to evaluate the radioactivity levels on a large scale in the West Rand District in South Africa, which is dominated by abandoned gold mine tailings and the consequential radiological exposures to members of the public. The activity concentrations of ²³⁸U, ²³²Th and 40K in mine tailings, soil and rocks were assessed using the BGO Super-Spec (RS-230) gamma spectrometer. The measured activity concentrations for ²³⁸U, ²³²Th and 40K in the studied mine tailings were found to range from 209.95 to 2578.68 Bq/kg, 19.49 to 108.00 Bq/kg and 31.30 to 626.00 Bq/kg, respectively. In surface soils, the overall average activity concentrations were found to be 59.15 Bq/kg, 34.91 and 245.64 Bq/kg for 238U, ²³²Th and 40K, respectively. For the rock samples analyzed, the mean activity concentrations were 32.97 Bq/kg, 32.26 Bq/kg and 351.52 Bg/kg for ²³⁸U, ²³²Th and 40K, respectively. High radioactivity levels were found in mine tailings, with ²³⁸U contributing significantly to the overall activity concentration. The external gamma radiation received from surface soil in the area is generally low, with an average of 0.07 mSv/y. The highest annual effective doses were estimated from the tailings dams and the levels varied between 0.14 mSv/y and 1.09 mSv/y, with an average of 0.51 mSv/y. In certain locations, the recommended dose constraint of 0.25 mSv/y from a single source to the average member of the public within the exposed population was exceeded, indicating the need for further monitoring and regulatory control measures specific to these areas to ensure the protection of resident members of the public.

Keywords: activity concentration, gold mine tailings, in-situ gamma spectrometry, radiological exposures

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Authors: E. M. D. Akuoko, Eliana Vasquez Osorio, Marcel Van Herk, Marianne Aznar

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Radiotherapy is an integral part of treatment for many patients with breast cancer. However, side effects can occur, e.g., fibrosis or erythema. If patients at higher risks of radiation-induced side effects could be identified before treatment, they could be given more individual information about the risks and benefits of radiotherapy. We hypothesize that breast density is correlated with the risk of side effects and present a novel method for automatic evaluation based on radiotherapy planning CT scans. Methods: 799 supine CT scans of breast radiotherapy patients were available from the REQUITE dataset. The methodology was first established in a subset of 114 patients (cohort 1) before being applied to the whole dataset (cohort 2). All patients were scanned in the supine position, with arms up, and the treated breast (ipsilateral) was identified. Manual experts contour available in 96 patients for both the ipsilateral and contralateral breast in cohort 1. Breast tissue was segmented using atlas-based automatic contouring software, ADMIRE® v3.4 (Elekta AB, Sweden). Once validated, the automatic segmentation method was applied to cohort 2. Breast density was then investigated by thresholding voxels within the contours, using Otsu threshold and pixel intensity ranges based on Hounsfield units (-200 to -100 for fatty tissue, and -99 to +100 for fibro-glandular tissue). Volumetric breast density (VBD) was defined as the volume of fibro-glandular tissue / (volume of fibro-glandular tissue + volume of fatty tissue). A sensitivity analysis was performed to verify whether calculated VBD was affected by the choice of breast contour. In addition, we investigated the correlation between volumetric breast density (VBD) and patient age and breast size. VBD values were compared between ipsilateral and contralateral breast contours. Results: Estimated VBD values were 0.40 (range 0.17-0.91) in cohort 1, and 0.43 (0.096-0.99) in cohort 2. We observed ipsilateral breasts to be denser than contralateral breasts. Breast density was negatively associated with breast volume (Spearman: R=-0.5, p-value < 2.2e-16) and age (Spearman: R=-0.24, p-value = 4.6e-10). Conclusion: VBD estimates could be obtained automatically on a large CT dataset. Patients’ age or breast volume may not be the only variables that explain breast density. Future work will focus on assessing the usefulness of VBD as a predictive variable for radiation-induced side effects.

Keywords: breast cancer, automatic image segmentation, radiotherapy, big data, breast density, medical imaging

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Authors: Wajid Rehman, Sirajul Haq, Bakhtiar Muhammad, Syed Fahad Hassan, Amin Badshah, Muhammad Waseem, Fazal Rahim, Obaid-Ur-Rahman Abid, Farzana Latif Ansari, Umer Rashid

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Five novel triorganotin (IV) compounds have been synthesized and characterized. The tin atom is penta-coordinated to assume trigonal-bipyramidal geometry. Using in silico derived parameters; the objective of our study is to design and synthesize promiscuous antibacterials potent enough to combat resistance. Among various synthesized organotin (IV) complexes, compound 5 was found as potent antibacterial agent against various bacterial strains. Further lead optimization of drug-like properties was evaluated through in silico predictions. Data mining and computational analysis were utilized to derive compound promiscuity phenomenon to avoid drug attrition rate in designing antibacterials. Xanthine oxidase and human glucose- 6-phosphatase were found as only true positive off-target hits by ChEMBL database and others utilizing similarity ensemble approach. Propensity towards a-3 receptor, human macrophage migration factor and thiazolidinedione were found as false positive off targets with E-value 1/4> 10^-4 for compound 1, 3, and 4. Further, displaying positive drug-drug interaction of compound 1 as uricosuric was validated by all databases and docked protein targets with sequence similarity and compositional matrix alignment via BLAST software. Promiscuity of the compound 5 was further confirmed by in silico binding to different antibacterial targets.

Keywords: antibacterial activity, drug promiscuity, ADMET prediction, metallo-pharmaceutical, antimicrobial resistance

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Authors: Steven M. Graham, Anne V. Magee

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Most world religions have sacred texts and traditions that provide instruction about and definitions of marriage, family, and family duties and responsibilities. Given that religious fundamentalism (RF) is defined as the belief that these sacred texts and traditions are literally and completely true to the exclusion of other teachings, RF should be predictive of the attitudes one holds about these topics. The goals of the present research were to: (1) explore the extent to which people think that men and women can be happy without marriage, a significant sexual relationship, a long-term romantic relationship, and having children; (2) determine the extent to which RF is associated with these beliefs; and, (3) to determine how RF is associated with considering certain elements of a relationship to be necessary for thinking of that relationship as a marriage. In Study 1, participants completed a reliable and valid measure of RF and answered questions about the necessity of various elements for a happy life. Higher RF scores were associated with the belief that both men and women require marriage, a sexual relationship, a long-term romantic relationship, and children in order to have a happy life. In Study 2, participants completed these same measures and the pattern of results replicated when controlling for overall religiosity. That is, RF predicted these beliefs over and above religiosity. Additionally, participants indicated the extent to which a variety of characteristics were necessary to consider a particular relationship to be a marriage. Controlling for overall religiosity, higher RF scores were associated with the belief that the following were required to consider a relationship a marriage: religious sanctification, a sexual component, sexual monogamy, emotional monogamy, family approval, children (or the intent to have them), cohabitation, and shared finances. Interestingly, and unexpectedly, higher RF scores were correlated with less importance placed on mutual consent in order to consider a relationship a marriage. RF scores were uncorrelated with the importance placed on legal recognition or lifelong commitment and these null findings do not appear to be attributable to ceiling effects or lack of variability. These results suggest that RF constrains views about both the importance of marriage and family in one’s life and also the characteristics required to consider a relationship a proper marriage. This could have implications for the mental and physical health of believers high in RF, either positive or negative, depending upon the extent to which their lives correspond to these templates prescribed by RF. Additionally, some of these correlations with RF were substantial enough (> .70) that the relevant items could serve as a brief, unobtrusive measure of RF. Future research will investigate these possibilities.

Keywords: attitudes about marriage, fertility intentions, measurement, religious fundamentalism

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

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Authors: Esra Polat

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Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, demographic indicators, economic indicators, European Union, partial least squares discriminant analysis

Procedia PDF Downloads 252

Authors: Patti Kastanias, Wei Wang, Karyn Mackenzie, Sandra Robinson, Susan Wnuk

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Obesity is a worldwide epidemic and is recognized as a chronic disease. Pain in the obese individual is a multidimensional issue. An increase in BMI is positively correlated with pain incidence and severity, especially in central obesity where individuals are twice as likely to have chronic pain. Both obesity and chronic pain are also associated with mood disorders. Pain is worse among obese individuals with depression and anxiety. Bariatric surgery provides patients with an effective solution for long-term weight loss and associated health problems. However, not much is known about acute and chronic pain after bariatric surgery and its contributing factors, including mood disorders. Nurse practitioners (NPs) at one large multidisciplinary bariatric surgery centre led two studies to examine acute and chronic pain and pain management over time after bariatric surgery. The purpose of the initial study was to examine the incidence and severity of acute and chronic pain after bariatric surgery. The aim of the secondary study was to further examine chronic pain, specifically looking at psychological factors that influence severity or incidence of both neuropathic and somatic pain as well as changes in opioid use. The initial study was a prospective, longitudinal study where patients having bariatric surgery at one surgical center were followed up to 6 months postop. Data was collected at 7 time points using validated instruments for pain severity, pain interference, and patient satisfaction. In the second study, subjects were followed longitudinally starting preoperatively and then at 6 months and 1 year postoperatively to capture changes in chronic pain and influencing variables over time. Valid and reliable instruments were utilized for all major study outcomes. In the first study, there was a trend towards decreased acute post-operative pain over time. The incidence and severity of chronic pain was found to be significantly reduced at 6 months post bariatric surgery. Interestingly, interference of chronic pain in daily life such as normal work, mood, and walking ability was significantly improved at 6 months postop however; this was not the case with sleep. Preliminary results of the secondary study indicate that pain severity, pain interference, anxiety and depression are significantly improved at 6 months postoperatively. In addition, preoperative anxiety, depression and emotional regulation were predictive of pain interference, but not pain severity. The results of our regression analyses provide evidence for the impact of pre-existing psychological factors on pain, particularly anxiety in obese populations.

Keywords: bariatric surgery, mood disorders, obesity, pain

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Predicting the risk of diabetic retinopathy (DR) in Indian type 2 diabetes patients is immensely necessary. India, being the second largest country after China in terms of a number of diabetic patients, to the best of our knowledge not a single risk score for complications has ever been investigated. Diabetic retinopathy is a serious complication and is the topmost reason for visual impairment across countries. Any type or form of DR has been taken as the event of interest, be it mild, back, grade I, II, III, and IV DR. A sample was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of DR. Cox proportional hazard regression is used to design risk scores for the prediction of retinopathy. Model calibration and discrimination are assessed from Hosmer Lemeshow and area under receiver operating characteristic curve (ROC). Overfitting and underfitting of the model are checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Optimal cut off point is chosen by Youden’s index. Five-year probability of DR is predicted by both survival function, and Markov chain two state model and the better technique is concluded. The risk scores developed can be applied by doctors and patients themselves for self evaluation. Furthermore, the five-year probabilities can be applied as well to forecast and maintain the condition of patients. This provides immense benefit in real application of DR prediction in T2DM.

Keywords: Cox proportional hazard regression, diabetic retinopathy, ROC curve, type 2 diabetes mellitus

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Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

Procedia PDF Downloads 249

Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik

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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.

Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes

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Authors: K. G. Sorokozherdyev, E. A. Efimov

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The socio-economic development of regions and countries is an important research issue. Today, in the face of many negative events in the global and regional economies, it is especially important to identify those areas that can serve as sources of economic growth and the basis for the well-being of the population. This study aims to identify the most important sectors of the economy of the Arkhangelsk region that can contribute to the socio-economic development of the region as a whole. For research, the Arkhangelsk region was taken as one of the typical Russian regions that do not have significant reserves of hydrocarbons nor there are located any large industrial complexes. In this regard, the question of possible origins of economic growth seems especially relevant. The basis of this study constitutes the distributed lag regression model (ADL model) developed by the authors, which is based on quarterly data on the socio-economic development of the Arkhangelsk region for the period 2004-2016. As a result, we obtained three equations reflecting the dynamics of three indicators of the socio-economic development of the region -the average wage, the regional GRP, and the birth rate. The influencing factors are the shares in GRP of such sectors as agriculture, mining, manufacturing, construction, wholesale and retail trade, hotels and restaurants, as well as the financial sector. The study showed that the greatest influence on the socio-economic development of the region is exerted by such industries as wholesale and retail trade, construction, and industrial sectors. The study can be the basis for forecasting and modeling the socio-economic development of the Arkhangelsk region in the short and medium term. It also can be helpful while analyzing the effectiveness of measures aimed at stimulating those or other industries of the region. The model can be used in developing a regional development strategy.

Keywords: regional economic development, regional sectoral structure, ADL model, Arkhangelsk region

Procedia PDF Downloads 78

Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

Procedia PDF Downloads 127

Authors: Noor Taleb Ismael, Areej Abd Al Kareem Al Titi, Ala'a Fayez Jaber

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Objectives: To investigate self-perceived functional performance among adults with IDD who are Jordanian residential care and rehabilitation centers residents. Also, to investigate their functional abilities (i.e., motor, and cognitive). In addition, to determine the motor and cognitive predictors of their functional performance. Methods: The study utilized a cross-sectional descriptive design; the sample included 180 individuals with IDD (90 males and 90 females) aged 18 to 75 years. The inclusion criteria encompassed: 1) Adults with a confirmed IDD by their physician’s professional and 2) residents in Jordanian Residential Care and Rehabilitation Centers affiliated with the Jordanian Ministry of Social Development. The exclusion criteria were: 1) bedridden or totally dependent on their care providers; 2) who had an accident or acquired neurological conditions. Researchers conducted semi-structured interviews to complete the outcome measures that include the Canadian Occupational Performance Measure (COPM), the Functional Independence Measure (FIM), the Montreal Cognitive Assessment (MoCA), the Mini-Mental Status Examination (MMSE), and the sociodemographic questionnaire. Data analyses consisted of descriptive statistics, analysis of frequencies, correlation, and regression analyses. Result: Individuals with IDD showed low functional performance in all daily life areas, including self-care, productivity, and leisure; there was severe cognitive impairment and poor independence and functional performance. (COPM Performance M= 1.433, SD±.57021, COPM Satisfaction M= 1.31, SD±.54, FIM M= 3.673, SD± 1.7918). Two predictive models were validated for the COPM performance and FIM total scores. First, significant predictors of high self-perceived functional performance on COPM were high scores on FIM Motor sub scores, FIM cognitive sub scores, young age, and having a high school educational level (R2=0.603, p=0.012). Second, significant predictors of high functional capacity on FIM were a high score on the COPM performance subscale, a high MMSE score, and having a cerebral palsy (CP) diagnosis (R2=0.671, p<0.001). Conclusions: Evaluating functional performance and associated factors is important in rehabilitation to provide better services and improve health and QoL for individuals with IDD. This study suggested conducting future studies targeting integrated individuals with IDD who live with their families in the communities.

Keywords: functional performance, intellectual and developmental disabilty, cognitive abilities, motor abilities

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Authors: Yori Gidron, Maartje Mol, Norbert Foudraine, Frits Van Osch, Joop Van Den Bergh, Moshe Farchi, Maud Straus

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Background: The worldwide pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-COV2), which began in 2019, also known as Covid-19, has infected over 136 million people and tragically took the lives of over 2.9 million people worldwide. Many of the complications and deaths are predicted by the inflammatory “cytokine storm.” One way to progress in the prevention of death is by finding a predictive and protective factor that inhibits inflammation, on the one hand, and which also increases anti-viral immunity on the other hand. The vagal nerve does precisely both actions. This study examined whether vagal nerve activity, indexed by heart-rate variability (HRV), predicts survival in patients with Covid-19. Method: We performed a pseudo-prospective study, where we retroactively obtained ECGs of 271 Covid-19 patients arriving at a large regional hospital in The Netherlands. HRV was indexed by the standard deviation of the intervals between normal heartbeats (SDNN). We examined patients’ survival at 3 weeks and took into account multiple confounders and known prognostic factors (e.g., age, heart disease, diabetes, hypertension). Results: Patients’ mean age was 68 (range: 25-95) and nearly 22% of the patients had died by 3 weeks. Their mean SDNN (17.47msec) was far below the norm (50msec). Importantly, relatively higher HRV significantly predicted a higher chance of survival, after statistically controlling for patients’ age, cardiac diseases, hypertension and diabetes (relative risk, H.R, and 95% confidence interval (95%CI): H.R = 0.49, 95%CI: 0.26 – 0.95, p < 0.05). However, since HRV declines rapidly with age and since age is a profound predictor in Covid-19, we split the sample by median age (40). Subsequently, we found that higher HRV significantly predicted greater survival in patients older than 70 (H.R = 0.35, 95%CI: 0.16 – 0.78, p = 0.01), but HRV did not predict survival in patients below age 70 years (H.R = 1.11, 95%CI: 0.37 – 3.28, p > 0.05). Conclusions: To the best of our knowledge, this is the first study showing that higher vagal nerve activity, as indexed by HRV, is an independent predictor of higher chances for survival in Covid-19. The results are in line with the protective role of the vagal nerve in diseases and extend this to a severe infectious illness. Studies should replicate these findings and then test in controlled trials whether activating the vagus nerve may prevent mortality in Covid-19.

Keywords: Covid-19, heart-rate Variability, prognosis, survival, vagal nerve

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Authors: Jifeng Zhang , Yongpeng Lei

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Continuous carbon fiber reinforced polyamide 6 (CF/PA6) composites are potential for application in the automotive industry due to their high specific strength and stiffness. However, PA6 resin is sensitive to the moisture in the hydrothermal environment and CF/PA6 composites might undergo several physical and chemical changes, such as plasticization, swelling, and hydrolysis, which induces a reduction of mechanical properties. So far, little research has been reported on the assessment of the effects of hydrothermal aging on the mechanical properties of continuous CF/PA6 composite. This study deals with the effects of hydrothermal aging on moisture absorption and mechanical properties of polyamide 6 (PA6) and polyamide 6 reinforced with continuous carbon fibers composites (CF/PA6) by immersion in distilled water at 30 ℃, 50 ℃, 70 ℃, and 90 ℃. Degradation of mechanical performance has been monitored, depending on the water absorption content and the aging temperature. The experimental results reveal that under the same aging condition, the PA6 resin absorbs more water than the CF/PA6 composite, while the water diffusion coefficient of CF/PA6 composite is higher than that of PA6 resin because of interfacial diffusion channel. In mechanical properties degradation process, an exponential reduction in tensile strength and elastic modulus are observed in PA6 resin as aging temperature and water absorption content increases. The degradation trend of flexural properties of CF/PA6 is the same as that of tensile properties of PA6 resin. Moreover, the water content plays a decisive role in mechanical degradation compared with aging temperature. In contrast, hydrothermal environment has mild effect on the tensile properties of CF/PA6 composites. The elongation at breakage of PA6 resin and CF/PA6 reaches the highest value when their water content reaches 6% and 4%, respectively. Dynamic mechanical analysis (DMA) and scanning electron microscope (SEM) were also used to explain the mechanism of mechanical properties alteration. After exposed to the hydrothermal environment, the Tg (glass transition temperature) of samples decreases dramatically with water content increase. This reduction can be ascribed to the plasticization effect of water. For the unaged specimens, the fibers surface is coated with resin and the main fracture mode is fiber breakage, indicating that a good adhesion between fiber and matrix. However, with absorbed water content increasing, the fracture mode transforms to fiber pullout. Finally, based on Arrhenius methodology, a predictive model with relate to the temperature and water content has been presented to estimate the retention of mechanical properties for PA6 and CF/PA6.

Keywords: continuous carbon fiber reinforced polyamide 6 composite, hydrothermal aging, Arrhenius methodology, interface

Procedia PDF Downloads 104