Search results for: machine capacity

Authors: Shubham Bhonde, Nekzad Doctor, Shashwat Gawande

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For the use and the import of Keys and ID Transmitter as well as Body Control Modules with radio transmission in a lot of countries, homologation is required. Final deliverables in homologation of the product are certificates. In considering the world of homologation, there are approximately 200 certificates per product, with most of the certificates in local languages. It is challenging to manually investigate each certificate and extract relevant data from the certificate, such as expiry date, approval date, etc. It is most important to get accurate data from the certificate as inaccuracy may lead to missing re-homologation of certificates that will result in an incompliance situation. There is a scope of automation in reading the certificate data in the field of homologation. We are using deep learning as a tool for automation. We have first trained a model using machine learning by providing all country's basic data. We have trained this model only once. We trained the model by feeding pdf and jpg files using the ETL process. Eventually, that trained model will give more accurate results later. As an outcome, we will get the expiry date and approval date of the certificate with a single click. This will eventually help to implement automation features on a broader level in the database where certificates are stored. This automation will help to minimize human error to almost negligible.

Keywords: homologation, re-homologation, data science, deep learning, machine learning, ETL (extract transform loading)

Procedia PDF Downloads 158

Authors: Peter O. Osifo, Hein W. J. P. Neomagus

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In this study, a predicted pH model was used to determine adsorption equilibrium properties of copper, lead, zinc and cadmium. Chitosan was prepared from the exoskeleton of Cape rock-lobsters, collected from the surroundings of Cape Town, South Africa. The beads were cross-linked with gluteraldehyde to restore its chemical stability in acid media. The chitosan beads were characterized; the beads water contents and pKa varied in the range of 90-96% and 4.3-6.0 respectively and the degree of crosslinking for the beads was 18%. A pH-model, which described the reversibility of the metal adsorbed onto the beads, was used to predict the equilibrium properties of copper, lead, zinc and cadmium adsorption onto the cross-linked beads. The model accounts for the effect of pH and the important model parameters; the equilibrium adsorption constant (Kads) and to a lesser extent the adsorbent adsorption capacity (qmax). The adsorption equilibrium constant for copper, lead, zinc and cadmium were found to be 2.58×10-3, 2.22×0-3, 9.55×0-3, and 4.79×0-3, respectively. The adsorbent maximum capacity was determined to be 4.2 mmol/g.

Keywords: chitosan beads, adsorption, heavy metals, waste water

Procedia PDF Downloads 375

Authors: Nelson Kimeli Kemboi Yego, Juma Kasozi, Joseph Nkruzinza, Francis Kipkogei

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The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector.

Keywords: health insurance, unsupervised learning, clustering algorithms, machine learning

Procedia PDF Downloads 133

Authors: Choy Sonny Yip Hong

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This abstract presents an exploration into the creation of a sustainable bio-polymer compound for additive manufacturing, specifically 3D printing, with a focus on eggshells and polylactide (PLA) polymer. The project initially conducted experiments using a variety of food by-products to create bio-polymers, and promising results were obtained when combining eggshells with PLA polymer. The research journey involved precise measurements, drying of PLA to remove moisture, and the utilization of a filament-making machine to produce 3D printable filaments. The project began with exploratory research and experiments, testing various combinations of food by-products to create bio-polymers. After careful evaluation, it was discovered that eggshells and PLA polymer produced promising results. The initial mixing of the two materials involved heating them just above the melting point. To make the compound 3D printable, the research focused on finding the optimal formulation and production process. The process started with precise measurements of the PLA and eggshell materials. The PLA was placed in a heating oven to remove any absorbed moisture. Handmade testing samples were created to guide the planning for 3D-printed versions. The scrap PLA was recycled and ground into a powdered state. The drying process involved gradual moisture evaporation, which required several hours. The PLA and eggshell materials were then placed into the hopper of a filament-making machine. The machine's four heating elements controlled the temperature of the melted compound mixture, allowing for optimal filament production with accurate and consistent thickness. The filament-making machine extruded the compound, producing filament that could be wound on a wheel. During the testing phase, trials were conducted with different percentages of eggshell in the PLA mixture, including a high percentage (20%). However, poor extrusion results were observed for high eggshell percentage mixtures. Samples were created, and continuous improvement and optimization were pursued to achieve filaments with good performance. To test the 3D printability of the DIY filament, a 3D printer was utilized, set to print the DIY filament smoothly and consistently. Samples were printed and mechanically tested using a universal testing machine to determine their mechanical properties. This testing process allowed for the evaluation of the filament's performance and suitability for additive manufacturing applications. In conclusion, the project explores the creation of a sustainable bio-polymer compound using eggshells and PLA polymer for 3D printing. The research journey involved precise measurements, drying of PLA, and the utilization of a filament-making machine to produce 3D printable filaments. Continuous improvement and optimization were pursued to achieve filaments with good performance. The project's findings contribute to the advancement of additive manufacturing, offering opportunities for design innovation, carbon footprint reduction, supply chain optimization, and collaborative potential. The utilization of eggshell PLA polymer in additive manufacturing has the potential to revolutionize the manufacturing industry, providing a sustainable alternative and enabling the production of intricate and customized products.

Keywords: additive manufacturing, 3D printing, eggshell PLA polymer, design innovation, carbon footprint reduction, supply chain optimization, collaborative potential

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: Mustafa Al-Refai

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This paper presents the optimal design and simulation of a grid-connected Photovoltaic (PV) system to supply electric power to meet the energy demand by Electrical Department in University of Tripoli Libya. Solar radiation is the key factor determining electricity produced by photovoltaic (PV) systems. This paper is designed to develop a novel method to calculate the solar photovoltaic generation capacity on the basis of Mean Global Solar Radiation data available for Tripoli Libya and finally develop a system design of possible plant capacity for the available roof area. MatLab/Simulink Programming tools and monthly average solar radiation data are used for this design and simulation. The specifications of equipments are provided based on the availability of the components in the market. Simulation results and analyses are presented to validate the proposed system configuration.

Keywords: photovoltaic (PV), grid, Simulink, solar energy, power plant, solar irradiation

Procedia PDF Downloads 297

Authors: Kristin Drexler

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Climate change has exacerbated food and livelihood insecurity for Mayan milpa farmers in Central America. For centuries, milpa farming has been sustainable for subsistence; however, in the last 50 years, milpas have become less reliable due to accelerating climate change, resource degradation, declining markets, poverty, and other factors. Using interviews with extension leaders and milpa farmers in Belize, this qualitative study examines the capacity for increasing climate-smart agriculture (CSA) aspects of existing traditional milpa practices, specifically no-burn mulching, soil enrichment, and the use of cover plants. Applying community capitals and socio-ecological systems frameworks, this study finds four key capitals were perceived by farmers and agriculture extension leaders as barriers for increasing CSA practices: (1) human-capacity, (2) financial, (3) infrastructure, and (4) governance-justice capitals. The key barriers include a lack of CSA technology and pest management knowledge-sharing (human-capacity), unreliable roads and utility services (infrastructure), the closure of small markets and crop-buying programs in Belize (financial), and constraints on extension services and exacerbating a sense of marginalization in Maya communities (governance-justice). Recommendations are presented for government action to reduce barriers and facilitate an increase in milpa crop productivity, promote food and livelihood security, and enable climate resilience of Mayan milpa communities in Belize.

Keywords: socio-ecological systems, community capitals, climate-smart agriculture, food security, milpa, Belize

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Eric Sitorus, Septian Prahastudhi, Turgod Nainggolan, Erwin Riyanto

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Mining by block or panel caving is a mining method that takes advantage of fractures within an ore body, coupled with gravity, to extract material from a predetermined column of ore. The caving column is weakened from beneath through the use of undercutting, after which the ore breaks up and is extracted from below in a continuous cycle. The nature of this method induces cyclical stresses on the pillars of excavations as stress is built up and released over time, which has a detrimental effect on both the installed ground support and the rock mass itself. Ground support capacity, especially on the production where excavation void ratio is highest, is subjected to heavy loading. Strain above threshold of the elongation of support capacity can yield resulting in damage to excavations. Geotechnical engineers must evaluate not only the remnant capacity of ground support systems but also investigate depth of rock mass yield within pillars, backs and floors. Ground Penetrating Radar (GPR) is a geophysical method that has the ability to evaluate rock mass damage using electromagnetic waves. This paper illustrates a case study from the Grasberg mining complex where non-invasive information on the depth of damage and condition of the remaining rock mass was required. GPR with 100 MHz antenna resolution was used to obtain images of the subsurface to determine rehabilitation requirements prior to recommencing production activities. The GPR surveys were used to calibrate the reflection coefficient response of varying rock mass conditions to known Rock Quality Designation (RQD) parameters observed at the mine. The calibrated GPR survey allowed site engineers to map subsurface conditions and plan rehabilitation accordingly.

Keywords: block caving, ground penetrating radar, reflectivity, RQD

Procedia PDF Downloads 133

Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Rahim Shahrokhi, Junboum Park

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Per- and Polyfluoroalkyl Substances (PFASs) are environmentally persistent halogenated hydrocarbons that have been widely used in many industrial and commercial applications. Recently, contaminating the soil and groundwater due to the ubiquity of PFAS in environments has raised great concern. Adsorption technology is one of the most promising methods for PFAS removal. Chitosan is a biopolymer substance with abundant amine and hydroxyl functional groups, which render it a good adsorbent. This study has tried to enhance the adsorption capacity of chitosan by grafting more amine functional groups on its surface for the removal of two long (PFOA and PFOS) and two short-chain (PFBA, PFBS) PFAS substances from the aqueous phase. A series of batch adsorption tests have been performed to evaluate the adsorption capacity of the used sorbent. Also, the sorbent was analyzed by SEM, FT-IR, zeta potential, and XRD tests. The results demonstrated that both chitosan beads have good potential for adsorbing short and long-chain PFAS from the aqueous phase.

Keywords: PFAS, chitosan beads, adsorption, grafted chitosan

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Authors: Chularat Leewuttanakul, Khanitta Ruttarattanamongkol, Sasivimon Chittrakorn

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Rice during grain development stage is a rich source of many bioactive compounds. Dough stage rice contains high amounts of photochemical and can be used for rice milling industries. However, rice grain at dough stage had low milling quality due to high moisture content. Thermal processing can be applied to rice grain for improving milled rice yield. This experiment was conducted to study the chemical and physic properties of dough stage rice grain after roasting treatment. Rice were roasted with two different methods including traditional pan roasting at 140 °C for 60 minutes and using the electrical roasting machine at 140 °C for 30, 40, and 50 minutes. The chemical, physical properties, and bioactive compounds of brown rice and milled rice were evaluated. The result of this experiment showed that moisture content of brown and milled rice was less than 10 % and amylose contents were in the range of 26-28 %. Rice grains roasting for 30 min using electrical roasting machine had high head rice yield and length and breadth of grain after milling were close to traditional pan roasting (p > 0.05). The lightness (L*) of rice did not affect by roasting treatment (p > 0.05) and the a* indicated the yellowness of milled rice was lower than brown rice. The bioactive compounds of brown and milled rice significantly decreased with increasing of drying time. Brown rice roasted for 30 minutes had the highest of total phenolic content, antioxidant activity, α-tocopherol, and ɤ-oryzanol content. Volume expansion and elongation of cooked rice decreased as roasting time increased and quality of cooked rice roasted for 30 min was comparable to traditional pan roasting. Hardness of cooked rice as measured by texture analyzer increased with increasing roasting time. The results indicated that rice grains at dough stage, containing a high amount of bioactive compounds, have a great potential for rice milling industries and the electrical roasting machine can be used as an alternative to pan roasting which decreases processing time and labor costs.

Keywords: bioactive compounds, cooked rice, dough stage rice grain, grain development, roasting

Procedia PDF Downloads 159

Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Gheorghe Braniste

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Gender gap has always been in dispute when comparing records and has been a major factor influencing best performances in various sports. Consequently, our study registers the evolution of the difference between men's and women’s best performances within either cyclic or acyclic sports, considering the fact that the training sessions of high performance athletes prove both similarities and differences in long-term adaptation of their body to stress and effort in breaking limits and records. Firstly, for a correct interpretation of the data and tables included in this paper, we must point out that the intense muscular activity has a considerable impact on the structural organization of the organs and systems of the performer's body through the mechanism of motor-visceral reflexes, forming a high working capacity suitable for intense muscular activity. The opportunity to obtaine high sports results during the official competitions is due, on the one hand, to the genetic characteristics of the athlete's body, and on the other hand, to the fact that playing professional sports leaves its mark on the vital morphological and functional parameters. The aim of our research is to study the landmarking differences between male and female athletes and their physical development, together with their growing capacity to stand up to the functional training during the competitive period of their annual training cycle. In order to evaluate the physical development of the athletes, the data of the anthropometric screenings obtained at the Olympic Training Center of the selected teams of the Republic of Moldova were interpreted and rated. During the study of physical development in terms of body height and weight, vital capacity, thoracic excursion, maximum force (Fmax), dynamometry of the hand and back, a further evaluation of the physical development indices that allow an evaluation of complex physical development were registered. The interdependence of the results obtained in performance sports with the morphological and functional particularities of the athletes' body is firmly determined and cannot be disputed. Nevertheless, registered data proved that with the increase of the training capacity, the morphological and functional abilities of the female body increase and, in some respects, approach and even slightly surpass the men in certain sports.

Keywords: physical development, indices, parameters, active body weight, morphological maturity, physical performance

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Michael Stewart

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

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

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Vipan Kumar Sohpal

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Temperature effect on the adsorption of crystal violet dye (CVD) was investigated using a castor leaf powder (CLP) that was prepared from the mature leaves of castor trees, through chemical reaction. The optimum values of pH (8), adsorbent dose (10g/L), initial dye concentration (10g/L), time (2hrs), and stirrer speed (120 rpm) were fixed to investigate the influence of temperature on adsorption capacity, percentage of removal of dye and free energy. A central composite design (CCD) was successfully employed for experimental design and analysis of the results. The combined effect of temperature, absorbance, and concentration on the dye adsorption was studied and optimized using response surface methodology. The optimum values of adsorption capacity, percentage of removal of dye and free energy were found to be 0.965(mg/g), 93.38 %, -8202.7(J/mol) at temperature 55.97 °C having desirability > 90% for removal of crystal violet dye respectively. The experimental values were in good agreement with predicted values.

Keywords: crystal violet dye, CVD, castor leaf powder, CLP, response surface methodology, temperature, optimization

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Authors: Cansu Gülser, Tuğce Ercan

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The construction sector is a key driver of the global economy, contributing significantly to growth and employment through a diverse array of sub-sectors. However, it faces challenges due to its project-based nature, which often hampers long-term collaboration and broader incentives beyond individual projects. These limitations are frequently discussed in scientific literature as obstacles to innovation and industry-wide change. Traditional practices and unwritten rules further hinder the adoption of new processes within the construction industry. The disadvantages of the construction industry’s project-based structure in fostering innovation and long-term relationships include limited continuity, fragmented collaborations, and a focus on short-term goals, which collectively hinder the development of sustained partnerships, inhibit the sharing of knowledge and best practices, and reduce incentives for investing in innovative processes and technologies. This structure typically emphasizes specific projects, which restricts broader collaborations and incentives that extend beyond individual projects, thus impeding innovation and change. The temporal complexities inherent in project-based sectors like construction make it difficult to address societal challenges through collaborative efforts. Traditional management approaches are inadequate for scaling up innovations and adapting to significant changes. For systemic transformation in the construction sector, there is a need for more collaborative relationships and activities beyond traditional supply chains. This study delves into the concept of an innovation ecosystem within the construction sector, highlighting various research findings. It aims to explore key questions about the components that enhance innovation capacity, the relationship between a robust innovation ecosystem and this capacity, and the reasons why innovation is less prevalent and implemented in this sector compared to others. Additionally, it examines the main factors hindering innovation within companies and identifies strategies to improve these efforts, particularly in developing countries. The innovation ecosystem in the construction sector generates various outputs through interactions between business resources and external components. These outputs include innovative value creation, sustainable practices, robust collaborations, knowledge sharing, competitiveness, and advanced project management, all of which contribute significantly to company market performance and competitive advantage. This article offers insights and strategic recommendations for industry professionals, policymakers, and researchers interested in developing and sustaining innovation ecosystems in the construction sector. Future research should focus on broader samples for generalization, comparative sector analysis, and application-focused studies addressing real industry challenges. Additionally, studying the long-term impacts of innovation ecosystems, integrating advanced technologies like AI and machine learning into project management, and developing future application strategies and policies are also important.

Keywords: construction industry, innovation ecosystem, innovation ecosystem components, project management

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Authors: C. E. Chinma, S. O. Azeez, J. C. Anuonye, O. B. Ocheme, C. M. Yakubu, S. James, E. U. Ohuoba, I. A. Baba

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There is growing interest in the use of rice bran protein in food formulation due to its hypoallergenic protein, high nutritional value and health promoting potentials. For the first time, the amino acid profile, protein digestibility, antioxidant, and functional properties of protein concentrate from some local varieties of rice bran from Nigeria were studied for possible food applications. Protein concentrates were prepared from rice bran and analysed using standard methods. Results showed that protein content of Kwandala, Yardass, Jeep, and Jamila were 69.24%, 69.97%, 68.73%, and 71.62%, respectively while total essential amino acid were 52.71, 53.03, 51.86, and 55.75g/100g protein, respectively. In vitro protein digestibility of protein concentrate from Kwandala, Yardass, Jeep and Jamila were 90.70%, 91.39%, 90.57% and 91.63% respectively. DPPH radical inhibition of protein from Kwandala, Yardass, Jeep, and Jamila were 48.15%, 48.90%, 47.56%, and 53.29%, respectively while ferric reducing ability power were 0.52, 0.55, 0.47 and 0.67mmol TE per gram, respectively. Protein concentrate from Jamila had higher onset (92.57oC) and denaturation temperature (102.13oC), and enthalpy (0.72J/g) than Jeep (91.46oC, 101.76oC, and 0.68J/g, respectively), Kwandala (90.32oC, 100.54oC and 0.57J/g, respectively), and Yardass (88.94oC, 99.45oC, and 0.51J/g, respectively). In vitro digestibility of protein from Kwandala, Yardas, Jeep, and Jamila were 90.70%, 91.39%, 90.57% and 91.63% respectively. Oil absorption capacity of Kwandala, Yardass, Jeep, and Jamila were 3.61, 3.73, 3.40, and 4.23g oil/g sample respectively, while water absorption capacity were 4.19, 4.32, 3.55 and 4.48g water/g sample, respectively. Protein concentrates had low bulk density (0.37-0.43g/ml). Protein concentrate from Jamila rice bran had the highest foam capacity (37.25%), followed by Yardass (34.20%), Kwandala (30.14%) and Jeep (28.90%). Protein concentrates showed low emulsifying and gelling capacities. In conclusion, protein concentrate prepared from these local rice bran varieties could serve as functional ingredients in food formulations and for enriching low protein foods.

Keywords: rice bran protein, amino acid profile, protein digestibility, antioxidant and functional properties

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Authors: S. A. Naeini, M. Hamidzadeh

Abstract:

Today, with the daily advancement of geotechnical engineering on soil improvement and modification of the physical properties and shear strength of soil, it is now possible to construct structures with high-volume and high service load on loose sandy soils. One of such methods is using micropiles, which are mostly used to control asymmetrical subsidence, increase bearing capacity, and prevent soil liquefaction. This study examined the improvement of Anzali's saturated loose silty sand using 192 micropiles with a length of 8 meters and diameter of 75 mm. Bandar-e Anzali is one of Iran's coastal populated cities which are located in a high-seismicity region. The effects of the insertion of micropiles on prevention of liquefaction and improvement of subsidence were examined through comparison of the results of Standard Penetration Test (SPT) and Plate Load Test (PLT) before and after implementation of the micropiles. The results show that the SPT values and the ultimate bearing capacity of silty sand increased after the implementation of the micropiles. Therefore, the installation of micropiles increases the strength of silty sand improving the resistance of soil against liquefaction.

Keywords: soil improvement, silty sand, micropiles, SPT, PLT, strength

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

Abstract:

This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: Tahar Ayadat

Abstract:

The undrained shear strength is an essential parameter for determining the consistency and the ultimate bearing capacity of a clay layer. The undrained shear strength can be determined by field tests such as the in situ vane test or in laboratory, including hand vane test, triaxial, simple compression test, and the consistency penetrometer (i.e. Swedish cone). However, the field vane test and the Swedish cone are the most commonly used tests by geotechnical experts. In this technical note, a comparison between the shear strength results obtained by the in situ vane test and the cone penetration test (Swedish cone) was conducted. A correlation between the results of these two tests, concerning the undrained shear strength of the Champlain sea clay, has been developed. Moreover, some applications of the proposed correlation on some geotechnical problems have been included, such as the determination of the consistency and the bearing capacity of a clay layer.

Keywords: correlation, shear strength, clay, vane test, Swedish cone

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Authors: Lucas Paganin, Viliam Makis

Abstract:

With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy.

Keywords: maintenance, semi-Markov decision process, statistical process control, Xbar control chart

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Authors: Rohalla Moghimi, Sirus Mohammadi

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Batteries for storage of electricity from solar and wind generation farms are a key element in the success of sustainability. In recent years, due to large integration of Renewable Energy Sources (RESs) like wind turbine and photovoltaic unit into the Micro-Grid (MG), the necessity of Battery Energy Storage (BES) has increased dramatically. The BES has several benefits and advantages in the MG-based applications such as short term power supply, power quality improvement, facilitating integration of RES, ancillary service and arbitrage. This paper presents the cost-based formulation to determine the optimal size of the BES in the operation management of MG. Also, some restrictions, i.e. power capacity of Distributed Generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction should be considered as well. In this paper, a methodology is proposed for the optimal allocation and economic analysis of ESS in MGs on the basis of net present value (NPV). As the optimal operation of an MG strongly depends on the arrangement and allocation of its ESS, economic operation strategies and optimal allocation methods of the ESS devices are required for the MG.

Keywords: microgrid, energy storage system, optimal sizing, net present value

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Authors: Akanimo Emene, Robert Edyvean

Abstract:

In developing countries, a low cost method of wastewater treatment is highly recommended. Adsorption is an efficient and economically viable treatment process for wastewater. The utilisation of this process is based on the understanding of the relationship between the growth environment and the metal capacity of the biomaterial. Luffa cylindrica (LC), a plant material, was used as an adsorbent in adsorption design system of heavy metals. The chemically modified LC was used to adsorb heavy metals ions, lead and cadmium, from aqueous environmental solution at varying experimental conditions. Experimental factors, adsorption time, initial metal ion concentration, ionic strength and pH of solution were studied. The chemical nature and surface area of the tissues adsorbing heavy metals in LC biosorption systems were characterised by using electron microscopy and infra-red spectroscopy. It showed an increase in the surface area and improved adhesion capacity after chemical treatment. Metal speciation of the metal ions showed the binary interaction between the ions and the LC surface as the pH increases. Maximum adsorption was shown between pH 5 and pH 6. The ionic strength of the metal ion solution has an effect on the adsorption capacity based on the surface charge and the availability of the adsorption sites on the LC. The nature of the metal-surface complexes formed as a result of the experimental data were analysed with kinetic and isotherm models. The pseudo second order kinetic model and the two-site Langmuir isotherm model showed the best fit. Through the understanding of this process, there will be an opportunity to provide an alternative method for water purification. This will be provide an option, for when expensive water treatment technologies are not viable in developing countries.

Keywords: adsorption, luffa cylindrica, metal-surface complexes, pH

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Authors: Antonio Lopez-Villegas, Daniel Catalan-Matamoros, Remedios Lopez-Liria

Abstract:

Objectives: The purpose of this study was to analyze clinical data, health-related quality of life (HRQoL) and functional capacity of patients using a telemonitoring follow-up system (TM) compared to patients followed-up through standard outpatient visits (HM) 5 years after the implantation of a pacemaker. Methods: This is a controlled, non-randomised, nonblinded clinical trial, with data collection carried out at 5 years after the pacemakers implant. The study was developed at Hospital de Poniente (Almeria, Spain), between October 2012 and November 2013. The same clinical outcomes were analyzed in both follow-up groups. Health-Related Quality of Life and Functional Capacity was assessed through EuroQol-5D (EQ-5D) questionnaire and Duke Activity Status Index (DASI) respectively. Sociodemographic characteristics and clinical data were also analyzed. Results: 5 years after pacemaker implant, 55 of 82 initial patients finished the study. Users with pacemakers were assigned to either a conventional follow-up group at hospital (HM=34, 50 initials) or a telemonitoring system group (TM=21, 32 initials). No significant differences were found between both groups according to sociodemographic characteristics, clinical data, Health-Related Quality of Life and Functional Capacity according to medical record and EQ5D and DASI questionnaires. In addition, conventional follow-up visits to hospital were reduced in 44,84% (p < 0,001) in the telemonitoring group in relation to hospital monitoring group. Conclusion: Results obtained in this study suggest that the telemonitoring of users with pacemakers is an equivalent option to conventional follow-up at hospital, in terms of Health-Related Quality of Life and Functional Capacity. Furthermore, it allows for the early detection of cardiovascular and pacemakers-related problem events and significantly reduces the number of in-hospital visits. Trial registration: ClinicalTrials.gov NCT02234245. The PONIENTE study has been funded by the General Secretariat for Research, Development and Innovation, Regional Government of Andalusia (Spain), project reference number PI/0256/2017, under the research call 'Development and Innovation Projects in the Field of Biomedicine and Health Sciences', 2017.

Keywords: cardiovascular diseases, health-related quality of life, pacemakers follow-up, remote monitoring, telemedicine

Procedia PDF Downloads 122