Search results for: predictive maintenance

Authors: F. S. Alavi, D. Mowla, F. Esmaeilzadeh

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In this paper, the Eclipse 300 simulator was used to perform compositional modeling of gas injection process for enhanced condensate recovery of a real gas condensate well in south of Iran here referred to as SA4. Some experimental data were used to tune the Peng-Robinson equation of state for this case. Different scenarios of gas injection at current reservoir pressure and at abandonment reservoir pressure had been considered with different gas compositions. Methane, carbon dioxide, nitrogen and two other gases with specified compositions were considered as potential gases for injection. According to the obtained results, nitrogen leads to highest pressure maintenance in the reservoir but methane results in highest condensate recovery among the selected injection gases. At low injection rates, condensate recovery percent is strongly affected by gas injection rate but this dependency shifts to zero at high injection rates. Condensate recovery is higher in all cases of injection at current reservoir pressure than injection at abandonment pressure. Using a constant injection rate, increasing the production well bottom hole pressure results in increasing the condensate recovery percent and time of gas breakthrough.

Keywords: gas-condensate reservoir, case-study, compositional modelling, enhanced condensate recovery, gas injection

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Adi Narayana S Sudhakar. I

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Energy conversion is one of the inevitable parts of any industries. It involves either conversion of mechanical energy in to electrical or vice versa. The later conversion of energy i.e. electrical to mechanical emphasizes the need of motor .Statistics reveals, about 8 % of industries’ annual turnover met on maintenance. Thus substantial numbers of efforts are required to minimize in incurring expenditure met towards break down maintenance. Condition monitoring is one of such techniques based on vibration widely used to recognize premature failures and paves a way to minimize cumbersome involved during breakdown of machinery. The present investigation involves a case study of squirrel cage induction motor (frequently in the electro machines) has been chosen for the conditional monitoring to predict its soundness on the basis of results of FFT analyser. Accelerometer which measures the acceleration converts in to impulses by FFT analyser generates vibration spectrum and time spectrum has been located at various positions on motor under different conditions. Results obtained from the FFT analyzer are compared to that of ISO standard vibration severity charts are taken to predict the preventative condition of considered machinery. Initial inspection of motor revealed that stator faults, broken end rings in rotor, eccentricity faults and misalignment between bearings are trouble shootings areas for present investigation. From the results of the shaft frequencies, it can be perceived that there is a misalignment between the bearings at both the ends. The higher order harmonics of FTF shows the presence of cracks on the race of the bearings at both the ends which are in the incipient stage. Replacement of the bearings at both the drive end (6306) and non-drive end (6206) and the alignment check between the bearings in the shaft are suggested as the constructive measures towards preventive maintenance of considered squirrel cage induction motor.

Keywords: FFT analyser, condition monitoring, vibration spectrum, time spectrum accelerometer

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Authors: Seong-Jin Cho, Jin Ho Kim

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Energy harvesting is the technology which gathers and converts external energies such as light, vibration and heat which are disposed into reusable electrical energy and uses such electrical energy. The vibration energy harvesting is very interesting technology because it produces very high density of energy and unaffected by the climate. Vibration energy can be harvested by the electrostatic, electromagnetic and piezoelectric systems. The electrostatic system has low energy conversion efficiency, and the piezoelectric system is expensive and needs the frequent maintenance because it is made of piezoelectric ceramic. On the other hand, the electromagnetic system has a long life time and high harvesting efficiency, and it is relatively cheap. The electromagnetic harvesting system includes the linear generator and the rotary-type generator. The rotary-type generators require the additional mechanical conversion device if it uses linear motion of vibration. But, the linear generator uses directly linear motion of vibration without a mechanical conversion device, and it has uncomplicated structure and light weight compared with the rotary-type generator. Therefore, the linear electromagnetic generator can be useful in using vibration energy harvesting. The pole transformer systems need electricity sensor system for sending voltage and power information to administrator. Therefore, the battery is essential, and its regular maintenance of replacement is required. In case of the transformer of high location in mountainous areas, the person can’t easily access it resulting in high maintenance cost. To overcome these problems, we designed and developed the linear electromagnetic generator which can replace battery in electricity sensor system for sending voltage and power information of the pole transformer. And, it uses vibration energy of frequency more than 50 Hz by the pole transformer. In order to analyze the electromagnetic characteristics of small linear electric generator, a commercial electromagnetic finite element analysis program "MAXWELL" was used. Then, through the actual production and experiment of linear generator, we confirmed output power of linear generator.

Keywords: energy harvesting, frequency, linear generator, experiment

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Authors: Joseph Salim

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This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

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Authors: Muhammad Badar Habib, Muhammad Shakir, Asif Ali, Muhammad Zia ul Haq

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Purpose of the research is to examine the university teachers’ health and physical activities regarding their academic competencies. Major objectives of this piece research were (a) to identify health problems of teachers at university level that affects academic competencies of university teachers and (b) to evaluate educational betterment through physical balance. This research is descriptive in nature and questionnaire was used as source of collecting data. Population of the present research comprises teachers, professors and professionals teaching in the universities of Pakistan. 580 university teachers were selected as a population of the study. Random sampling technique was used to identify recipients. Data was feed and filter in Ms-Excel. In the light of the analysis of the study following findings were drawn out. This study found that the university teachers in Pakistan do not adopt proper physical exercise program. They were less interested to burn their extra calories and face diseases such as cramping, contraction of the muscles, diabetics and stomach diseases. This study recommends that seminars/workshops may be held by University establishment; to develop overall awareness among the teachers.

Keywords: evaluating effects of health and physical maintenance, academic competencies, university teachers, Pakistan

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Nouira Mariem, Ben Rayana Hazem, Ennigrou Samir

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Background and aim: During the past decade, the frequency of extrapulmonary forms of tuberculosis has increased. These forms are under-diagnosed using conventional tests. The aim of this study was to evaluate the performance of the Tuberculin Skin Test (TST) for the diagnosis of tuberculosis, using the ROC curve and Fagan’s Nomogram methodology. Methods: This was a case-control, multicenter study in 11 anti-tuberculosis centers in Tunisia, during the period from June to November2014. The cases were adults aged between 18 and 55 years with confirmed tuberculosis. Controls were free from tuberculosis. A data collection sheet was filled out and a TST was performed for each participant. Diagnostic accuracy measures of TST were estimated using ROC curve and Area Under Curve to estimate sensitivity and specificity of a determined cut-off point. Fagan’s nomogram was used to estimate its predictive values. Results: Overall, 1053 patients were enrolled, composed of 339 cases (sex-ratio (M/F)=0.87) and 714 controls (sex-ratio (M/F)=0.99). The mean age was 38.3±11.8 years for cases and 33.6±11 years for controls. The mean diameter of the TST induration was significantly higher among cases than controls (13.7mm vs.6.2mm;p=10-6). Area Under Curve was 0.789 [95% CI: 0.758-0.819; p=0.01], corresponding to a moderate discriminating power for this test. The most discriminative cut-off value of the TST, which were associated with the best sensitivity (73.7%) and specificity (76.6%) couple was about 11 mm with a Youden index of 0.503. Positive and Negative predictive values were 3.11% and 99.52%, respectively. Conclusion: In view of these results, we can conclude that the TST can be used for tuberculosis diagnosis with a good sensitivity and specificity. However, the skin induration measurement and its interpretation is operator dependent and remains difficult and subjective. The combination of the TST with another test such as the Quantiferon test would be a good alternative.

Keywords: tuberculosis, tuberculin skin test, ROC curve, cut-off

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Authors: John Segars

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Built out of a real-world need to have better, more detailed, asset and infrastructure documentation, this project will lay out the case for using the database functionality of ArcGIS as a tool to track and maintain infrastructure location, status, maintenance and serviceability. Workflows and processes will be presented and detailed which may be applied to an organizations’ infrastructure needs that might allow them to make use of the robust tools which surround the ArcGIS platform. The end result is a value-added information system framework with a geographic component e.g., the spatial location of various I.T. assets, a detailed set of records which not only documents location but also captures the maintenance history for assets along with photographs and documentation of these various assets as attachments to the numerous feature class items. In addition to the asset location and documentation benefits, the staff will be able to log into the devices and pull SNMP (Simple Network Management Protocol) based query information from within the user interface. The entire collection of information may be displayed in ArcGIS, via a JavaScript based web application or via queries to the back-end database. The project is applicable to all organizations which maintain an IT infrastructure but specifically targets post-secondary educational institutions where access to ESRI resources is generally already available in house.

Keywords: ESRI, GIS, infrastructure, network documentation, PostgreSQL

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Authors: Khaled S. Abdallah, Abdel-Aziz M. Mohamed

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Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.

Keywords: modeling, truck rental, supply chains management.

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Authors: S. Cafiso, B. Capace, A. Di Graziano, C. D’Agostino

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Systematic monitoring of the trackbed is necessary to assure safety and quality of service in the railway system. Moreover, to produce effective management of the maintenance treatments, the assessment of bearing capacity of the railway trackbed must include ballast, sub-ballast and subgrade layers at different depths. Consequently, there is an increasing interest in obtaining a consistent measure of ballast bearing capacity with no destructive tests (NDTs) able to work in the physical and time restrictions of railway tracks in operation. Moreover, in the case of the local railway with reduced gauge, the use of the traditional high-speed track monitoring systems is not feasible. In that framework, this paper presents results from in site investigation carried out on ballast and sleepers with Ground Penetrating Radar (GPR) and Light Falling Weight Deflectometer (LWD). These equipment are currently used in road pavement maintenance where they have shown their reliability and effectiveness. Application of such Non-Destructive Tests in railway maintenance is promising but in the early stage of the investigation. More specifically, LWD was used to estimate the stiffness of ballast and sleeper support, as well. LWD, despite the limited load (6 kN in the trial test) applied directly on the sleeper, was able to detect defects in the bearing capacity at the Sleeper/Ballast interface. A dual frequency GPR was applied to detect the presence of layers’ discontinuities at different depths due to fouling phenomena that are the main causes of changing in the layer dielectric proprieties within the ballast thickness. The frequency of 2000Mhz provided high-resolution data to approximately 0.4m depth, while frequency of 600Mhz showed greater depth penetration up to 1.5 m. In the paper literature review and trial in site experience are used to identify Strengths, Weaknesses, Opportunities, and Threats (SWOT analysis) of the application of GPR and LWD for the assessment of bearing capacity of railway track-bed.

Keywords: bearing capacity, GPR, LWD, no destructive test, railway track

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Authors: Bongs Lainjo

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The evolution and escalation of bike-sharing programs (BSP) continue unabated. Since the sixties, many countries have introduced different models and strategies of BSP. These include variations ranging from dockless models to electronic real-time monitoring systems. Reasons for using this BSP include recreation, errands, work, etc. And there is all indication that complex, and more innovative rider-friendly systems are yet to be introduced. The objective of this paper is to analyze current variables established by different operators and streamline them identifying the most compelling ones using analytics. Given the contents of available databases, there is a lack of uniformity and common standard on what is required and what is not. Two factors appear to be common: user type (registered and unregistered, and duration of each trip). This article uses historical data provided by one operator based in the greater Washington, District of Columbia, USA area. Several variables including categorical and continuous data types were screened. Eight out of 18 were considered acceptable and significantly contribute to determining a useful and reliable predictive model. Bike-sharing systems have become popular in recent years all around the world. Although this trend has resulted in many studies on public cycling systems, there have been few previous studies on the factors influencing public bicycle travel behavior. A bike-sharing system is a computer-controlled system in which individuals can borrow bikes for a fee or free for a limited period. This study has identified unprecedented useful, and pragmatic parameters required in improving BSP ridership dynamics.

Keywords: sharing program, historical data, parameters, ridership dynamics, trip duration

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Authors: Shanza Akram, Samir Salah, Imran Saleem, Jassim Abdou, Ashraf Al Zaabi

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Estimated prevalence of asthma in UAE is around 10% (900,000 people). Patients with persistent symptoms despite using high dose ICS plus a second controller +/- Oral steroids are considered to have severe asthma. Omalizumab (Xolair) is an anti-IgE monoclonal antibody approved as add-on therapy for severe allergic asthma. The objective of our study was to obtain baseline characteristics of our local cohort, to determine the efficacy of omalizumab based on clinical outcomes pre and post 52 weeks of treatment and to assess safety and tolerability. Medical records of patients receiving omalizumab therapy for asthma at Zayed Military Hospital, Abu Dhabi were retrospectively reviewed. Patients fulfilling the criteria for severe allergic asthma as per GINA guidelines were included. Asthma control over 12 months pre and post omalizumab were analyzed by taking into account the number of exacerbations, hospitalizations, maintenance of medication dosages, the need for reliever therapy and PFT’s. 21 patients (5 females) with mean age 41 years were included. The mean duration of therapy was 22 months. 19 (91%) patients had Allergic Rhinitis/Sinusitis. Mean serum total IgE level was 648 IU/ml (65-1859). 11 (52%) patients were on oral maintenance steroids pre-treatment. 7 patients managed to stop steroids on treatment while 4 were able to decrease the dosage. Mean exacerbation rate decreased from 5 per year pre-treatment to 1.36 while on treatment. The number of hospitalizations decreased from a mean of 2 per year to 0.9 per year. Reliever inhaler usage decreased from mean of 40 to 15 puffs per week.2 patients discontinued therapy, 1 due to lack of benefit (2 doses) and 2nd due to severe persistent side effects. Patient compliance was poor in some cases. Treatment with omalizumab reduced the number of exacerbations, hospitalizations, maintenance and reliever medications, and is generally well tolerated. Our results show that there is room for improved documentation in terms of symptom recording and use of rescue medication at our institution. There is also need for better patient education and counseling in order to improve compliance.

Keywords: asthma, exacerbations, omalizumab, IgE

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Authors: Vandna Raghuvanshi, Vikrant Thakur, Anupam Jhobta

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Objective: To design HRCT criterion to forecast the threat of pulmonary tuberculosis. Material and methods: This was a prospective study of 69 patients with clinical suspicion of pulmonary tuberculosis. We studied their medical characteristics, numerous separate HRCT-results, and a combination of HRCT findings to foresee the danger for PTB by utilizing univariate and multivariate investigation. Temporary HRCT diagnostic criteria were planned in view of these outcomes to find out the risk of PTB and tested these criteria on our patients. Results: The results of HRCT chest were analyzed, and Rank was given from 1 to 4 according to the HRCT chest findings. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Rank 1: Highly suspected PTB. Rank 2: Probable PTB Rank 3: Nonspecific or difficult to differentiate from other diseases Rank 4: Other suspected diseases • Rank 1 (Highly suspected TB) was present in 22 (31.9%) patients, all of them finally diagnosed to have pulmonary tuberculosis. The sensitivity, specificity, and negative likelihood ratio for RANK 1 on HRCT chest was 53.6%, 100%, and 0.43, respectively. • Rank 2 (Probable TB) was present in 13 patients, out of which 12 were tubercular, and 1 was non-tubercular. • The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of the combination of Rank 1 and Rank 2 was 82.9%, 96.4%, 23.22, and 0.18, respectively. • Rank 3 (Non-specific TB) was present in 25 patients, and out of these, 7 were tubercular, and 18 were non-tubercular. • When all these 3 ranks were considered together, the sensitivity approached 100% however, the specificity reduced to 35.7%. The positive likelihood ratio and negative likelihood ratio were 1.56 and 0, respectively. • Rank 4 (Other specific findings) was given to 9 patients, and all of these were non-tubercular. Conclusion: HRCT is useful in selecting individuals with greater chances of pulmonary tuberculosis.

Keywords: pulmonary, tuberculosis, multivariate, HRCT

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Authors: Jiashu Shen

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Objectives: Spirometry is a basic reference for health evaluation which is widely used in clinical. Previous reference of spirometry is not applicable because of drastic changes of social and natural circumstance in China. A new reference values for the spirometry of the Chinese population is extremely needed. Method: Spirometric reference value was established using the statistical modeling method Generalized Additive Models for Location, Scale and Shape for forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, and maximal mid-expiratory flow (MMEF). Results: Data from 236,606 healthy non-smokers aged 4–90 years was collected from the MJ Health Check database. Spirometry equations for FEV1, FVC, MMEF, and FEV1/FVC were established, including the predicted values and lower limits of normal (LLNs) by sex. The predictive equations that were developed for the spirometric results elaborated the relationship between spirometry and age, and they eliminated the effects of height as a variable. Most previous predictive equations for Chinese spirometry were significantly overestimated (to be exact, with mean differences of 22.21% in FEV1 and 31.39% in FVC for males, along with differences of 26.93% in FEV1 and 35.76% in FVC for females) or underestimated (with mean differences of -5.81% in MMEF and -14.56% in FEV1/FVC for males, along with a difference of -14.54% in FEV1/FVC for females) the results of lung function measurements as found in this study. Through cross-validation, our equations were established as having good fit, and the means of the measured value and the estimated value were compared, with good results. Conclusions: Our study updates the spirometric reference equations for Chinese people of all ages and provides comprehensive values for both physical examination and clinical diagnosis.

Keywords: Chinese, GAMLSS model, reference values, spirometry

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Jyun-Huei Huang, Ting-I Lee

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Urban heat island effect is derived from the reduction of vegetative cover by urban development. Because plants can improve air quality and microclimate, green walls have been applied as a sustainable design approach to cool building temperature. By using plants to green vertical surfaces, they decrease room temperature and, as a result, decrease the energy use for air conditioning. Based on their structures, green walls can be divided into two categories, green façades and living walls. A green façade uses the climbing ability of a plant itself, while a living wall assembles planter modules. The latter one is widely adopted in public space, as it is time-effective and less limited. Although a living wall saves energy spent on cooling, it is not necessarily cost-effective from the perspective of a lifecycle analysis. The Italian study shows that the overall benefit of a living wall is only greater than its costs after 47 years of its establishment. In Taiwan, urban greening policies encourage establishment of green walls by referring to their benefits of energy saving while neglecting their low performance on cost-effectiveness. Thus, this research aims at understanding the perception of appliers and consumers on the cost-effectiveness of their living wall products from the lifecycle viewpoint. It adopts semi-structured interviews and field observations on the maintenance of the products. By comparing the two results, it generates insights for sustainable urban greening policies. The preliminary finding shows that stakeholders do not have a holistic sense of lifecycle or cost-effectiveness. Most importantly, a living wall well maintained is often with high input due to the availability of its maintenance budget, and thus less sustainable. In conclusion, without a comprehensive sense of cost-effectiveness throughout a product’s lifecycle, it is very difficult for suppliers and consumers to maintain a living wall system while achieve sustainability.

Keywords: case study, maintenance, post-occupancy evaluation, vertical greening

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Authors: Eduardo Rodrigues, Gisela Mendes, José M. Torres, José E. Sousa

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In the sequence of the liberalisation of the electricity sector a progressive engagement of consumers has been considered and targeted by sector regulatory policies. With the objective of promoting market competition while protecting consumers interests, by transferring some of the upstream benefits to the end users while reaching a fair distribution of system costs, different market models to value consumers’ demand flexibility at the energy community level are envisioned. Local Energy and Flexibility Markets (LEFM) involve stakeholders interested in providing or procure local flexibility for community, services and markets’ value. Under the scope of DOMINOES, a European research project supported by Horizon 2020, the local market concept developed is expected to: • Enable consumers/prosumers empowerment, by allowing them to value their demand flexibility and Distributed Energy Resources (DER); • Value local liquid flexibility to support innovative distribution grid management, e.g., local balancing and congestion management, voltage control and grid restoration; • Ease the wholesale market uptake of DER, namely small-scale flexible loads aggregation as Virtual Power Plants (VPPs), facilitating Demand Response (DR) service provision; • Optimise the management and local sharing of Renewable Energy Sources (RES) in Medium Voltage (MV) and Low Voltage (LV) grids, trough energy transactions within an energy community; • Enhance the development of energy markets through innovative business models, compatible with ongoing policy developments, that promote the easy access of retailers and other service providers to the local markets, allowing them to take advantage of communities’ flexibility to optimise their portfolio and subsequently their participation in external markets. The general concept proposed foresees a flow of market actions, technical validations, subsequent deliveries of energy and/or flexibility and balance settlements. Since the market operation should be dynamic and capable of addressing different requests, either prioritising balancing and prosumer services or system’s operation, direct procurement of flexibility within the local market must also be considered. This paper aims to highlight the research on the definition of suitable DR models to be used by the Distribution System Operator (DSO), in case of technical needs, and by the retailer, mainly for portfolio optimisation and solve unbalances. The models to be proposed and implemented within relevant smart distribution grid and microgrid validation environments, are focused on day-ahead and intraday operation scenarios, for predictive management and near-real-time control respectively under the DSO’s perspective. At local level, the DSO will be able to procure flexibility in advance to tackle different grid constrains (e.g., demand peaks, forecasted voltage and current problems and maintenance works), or during the operating day-to-day, to answer unpredictable constraints (e.g., outages, frequency deviations and voltage problems). Due to the inherent risks of their active market participation retailers may resort to DR models to manage their portfolio, by optimising their market actions and solve unbalances. The interaction among the market actors involved in the DR activation and in flexibility exchange is explained by a set of sequence diagrams for the DR modes of use from the DSO and the energy provider perspectives. • DR for DSO’s predictive management – before the operating day; • DR for DSO’s real-time control – during the operating day; • DR for retailer’s day-ahead operation; • DR for retailer’s intraday operation.

Keywords: demand response, energy communities, flexible demand, local energy and flexibility markets

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Authors: Prof. Ammal Mokhtar Metwally, Samia M. Sami, Nihad A. Ibrahim, Fatma A. Shaaban, Iman I. Salama

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Objectives: Reducing neonatal mortality by 2030 is still a challenging goal in developing countries. low birth weight (LBW) is a significant contributor to this, especially where weighing newborns is not possible routinely. The present study aimed to determine a simple, easy, reliable anthropometric measure(s) that can predict LBW) and neonatal mortality. Methods: A prospective cohort study of 570 babies born in districts of El Menia governorate, Egypt (where most deliveries occurred at home) was examined at birth. Newborn weight, length, head, chest, mid-arm, and thigh circumferences were measured. Follow up of the examined neonates took place during their first four weeks of life to report any mortalities. The most predictable anthropometric measures were determined using the statistical package of SPSS, and multiple Logistic regression analysis was performed.: Results: Head and chest circumferences with cut-off points < 33 cm and ≤ 31.5 cm, respectively, were the significant predictors for LBW. They carried the best combination of having the highest sensitivity (89.8 % & 86.4 %) and least false negative predictive value (1.4 % & 1.7 %). Chest circumference with a cut-off point ≤ 31.5 cm was the significant predictor for neonatal mortality with 83.3 % sensitivity and 0.43 % false negative predictive value. Conclusion: Using chest circumference with a cut-off point ≤ 31.5 cm is recommended as a single simple anthropometric measurement for the prediction of both LBW and neonatal mortality. The predicted measure could act as a substitute for weighting newborns in communities where scales to weigh them are not routinely available.

Keywords: low birth weight, neonatal mortality, anthropometric measures, practical screening

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Authors: Francesco Pantano, Marta Fogolari, Michele Iuliani, Sonia Simonetti, Silvia Cavaliere, Marco Russano, Fabrizio Citarella, Bruno Vincenzi, Silvia Angeletti, Giuseppe Tonini

Abstract:

Background: Although immune checkpoint inhibitors (ICIs) have changed the treatment paradigm of non–small cell lung cancer (NSCLC), these drugs fail to elicit durable responses in the majority of NSCLC patients. The gut microbiota, able to regulate immune responsiveness, is emerging as a promising, modifiable target to improve ICIs response rates. Since the oral microbiome has been demonstrated to be the primary source of bacterial microbiota in the lungs, we investigated its composition as a potential predictive biomarker to identify and select patients who could benefit from immunotherapy. Methods: Thirty-five patients with stage IV squamous and non-squamous cell NSCLC eligible for an anti-PD-1/PD-L1 as monotherapy were enrolled. Saliva samples were collected from patients prior to the start of treatment, bacterial DNA was extracted using the QIAamp® DNA Microbiome Kit (QIAGEN) and the 16S rRNA gene was sequenced on a MiSeq sequencing instrument (Illumina). Results: NSCLC patients were dichotomized as “Responders” (partial or complete response) and “Non-Responders” (progressive disease), after 12 weeks of treatment, based on RECIST criteria. A prevalence of the phylum Candidatus Saccharibacteria was found in the 10 responders compared to non-responders (abundance 5% vs 1% respectively; p-value = 1.46 x 10-7; False Discovery Rate (FDR) = 1.02 x 10-6). Moreover, a higher prevalence of Saccharibacteria Genera Incertae Sedis genus (belonging to the Candidatus Saccharibacteria phylum) was observed in "responders" (p-value = 6.01 x 10-7 and FDR = 2.46 x 10-5). Finally, the patients who benefit from immunotherapy showed a significant abundance of TM7 Phylum Sp Oral Clone FR058 strain, member of Saccharibacteria Genera Incertae Sedis genus (p-value = 6.13 x 10-7 and FDR=7.66 x 10-5). Conclusions: These preliminary results showed a significant association between oral microbiota and ICIs response in NSCLC patients. In particular, the higher prevalence of Candidatus Saccharibacteria phylum and TM7 Phylum Sp Oral Clone FR058 strain in responders suggests their potential immunomodulatory role. The study is still ongoing and updated data will be presented at the congress.

Keywords: oral microbiota, immune checkpoint inhibitors, non-small cell lung cancer, predictive biomarker

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Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto

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Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT

Keywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start

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Authors: Y. Baba, A. Archibong-Eso, H. Yeung

Abstract:

Experimental study of high viscosity oil-gas flows in horizontal pipelines published in literature has indicated that hydrodynamic slug flow is the dominant flow pattern observed. Investigations have shown that hydrodynamic slugging brings about high instabilities in pressure that can damage production facilities thereby making it inherent to study high viscous slug flow regime so as to improve the understanding of its flow dynamics. Most slug flow models used in the petroleum industry for the design of pipelines together with their closure relationships were formulated based on observations of low viscosity liquid-gas flows. New experimental investigations and data are therefore required to validate these models. In cases where these models underperform, improving upon or building new predictive models and correlations will also depend on the new experimental dataset and further understanding of the flow dynamics in high viscous oil-gas flows. In this study conducted at the Flow laboratory, Oil and Gas Engineering Centre of Cranfield University, slug flow variables such as pressure gradient, mean liquid holdup, frequency and slug length for oil viscosity ranging from 1..0 – 5.5 Pa.s are experimentally investigated and analysed. The study was carried out in a 0.076m ID pipe, two fast sampling gamma densitometer and pressure transducers (differential and point) were used to obtain experimental measurements. Comparison of the measured slug flow parameters to the existing slug flow prediction models available in the literature showed disagreement with high viscosity experimental data thus highlighting the importance of building new predictive models and correlations.

Keywords: gamma densitometer, mean liquid holdup, pressure gradient, slug frequency and slug length

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Authors: Samuel Fekadu A., Esayas Alemayehu B., Bultum Oljira D., Seid Tiku D., Dessalegn Dadi D., Bart Van Der Bruggen A.

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The peroxi-photoelectrocoagulation process was evaluated for the removal of chemical oxygen demand (COD) and color from healthcare wastewater. A 2-level full factorial design with center points was created to investigate the effect of the process parameters, i.e., initial COD, H₂O₂, pH, reaction time and current density. Furthermore, the total energy consumption and average current efficiency in the system were evaluated. Predictive models for % COD, % color removal and energy consumption were obtained. The initial COD and pH were found to be the most significant variables in the reduction of COD and color in peroxi-photoelectrocoagulation process. Hydrogen peroxide only has a significant effect on the treated wastewater when combined with other input variables in the process like pH, reaction time and current density. In the peroxi-photoelectrocoagulation process, current density appears not as a single effect but rather as an interaction effect with H₂O₂ in reducing COD and color. Lower energy expenditure was observed at higher initial COD, shorter reaction time and lower current density. The average current efficiency was found as low as 13 % and as high as 777 %. Overall, the study showed that hybrid electrochemical oxidation can be applied effectively and efficiently for the removal of pollutants from healthcare wastewater.

Keywords: electrochemical oxidation, UV, healthcare pollutants removals, factorial design

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Authors: Sarah Kalaouze, Maxine Iannucelli, Kristen Dunfield

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Children’s implicit beliefs regarding intelligence (i.e., intelligence mindsets) influence their motivation, perseverance, and success. Previous research suggests that the way parents perceive failure influences the development of their child’s intelligence mindsets. We invited 151 children-parent dyads (Age= 5–6 years) to complete a series of difficult puzzles over zoom. We assessed parents’ intelligence and failure mindsets using questionnaires and recorded parents’ person/performance-oriented (e.g., “you are smart” or "you were almost able to complete that one) and process-oriented (e.g., “you are trying really hard” or "maybe if you place the bigger pieces first") failure feedback. We were interested in observing the relation between parental mindsets and the type of feedback provided. We found that parents’ intelligence mindsets were not predictive of the feedback they provided children. Failure mindsets, on the other hand, were predictive of failure feedback. Parents who view failure-as-debilitating provided more person-oriented feedback, focusing on performance and personal ability. Whereas parents who view failure-as-enhancing provided process-oriented feedback, focusing on effort and strategies. Taken all together, our results allow us to determine that although parents might already have a growth intelligence mindset, they don’t necessarily have a failure-as-enhancing mindset. Parents adopting a failure-as-enhancing mindset would influence their children to view failure as a learning opportunity, further promoting practice, effort, and perseverance during challenging tasks. The focus placed on a child’s learning, rather than their performance, encourages them to perceive intelligence as malleable (growth mindset) rather than fix (fixed mindset). This implies that parents should not only hold a growth mindset but thoroughly understand their role in the transmission of intelligence beliefs.

Keywords: mindset(s), failure, intelligence, parental feedback, parents

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Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

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Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

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Authors: Mustafa Al-Yaseen, Haider Mohammed Mahdi, Haider Ali Al–Zahid, Nazar S. Haddad

Abstract:

Background: Early diagnosis of musculoskeletal infections is of vital importance to avoid devastating complications. There is no single laboratory marker which is sensitive and specific in diagnosing these infections accurately. White blood cell count, erythrocyte sedimentation rate, and C-reactive protein are not specific as they can also be elevated in conditions other than bacterial infections. Materials Culture and sensitivity is not a true gold standard due to its varied positivity rates. Serum Procalcitonin is one of the new laboratory markers for pyogenic infections. The objective of this study is to assess the value of PCT in the diagnosis of soft tissue, bone, and joint infections. Patients and Methods: Patients of all age groups (seventy-four patients) with a diagnosis of musculoskeletal infection are prospectively included in this study. All patients were subjected to White blood cell count, erythrocyte sedimentation rate, C-reactive protein, and serum Procalcitonin measurements. A healthy non infected outpatient group (twenty-two patients) taken as a control group and underwent the same evaluation steps as the study group. Results: The study group showed mean Procalcitonin levels of 1.3 ng/ml. Procalcitonin, at 0.5 ng/ml, was (42.6%) sensitive and (95.5%) specific in diagnosing of musculoskeletal infections with (positive predictive value of 87.5% and negative predictive value of 48.3%) and (positive likelihood ratio of 9.3 and negative likelihood ratio of 0.6). Conclusion: Serum Procalcitonin, at a cut – off of 0.5 ng/ml, is a specific but not sensitive marker in the diagnosis of musculoskeletal infections, and it can be used effectively to rule in the diagnosis of infection but not to rule out it.

Keywords: procalcitonin, infection, labratory markers, musculoskeletal

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Authors: Seyedeh Fatemeh Nabavi, Hamid Dalir, Anooshiravan Farshidianfar

Abstract:

Laser welding is pivotal in modern manufacturing, offering unmatched precision, speed, and efficiency. Its versatility in minimizing heat-affected zones, seamlessly joining dissimilar materials, and working with various metals makes it indispensable for crafting intricate automotive components. Integration into automated systems ensures consistent delivery of high-quality welds, thereby enhancing overall production efficiency. Noteworthy are the safety benefits of laser welding, including reduced fumes and consumable materials, which align with industry standards and environmental sustainability goals. As the automotive sector increasingly demands advanced materials and stringent safety and quality standards, laser welding emerges as a cornerstone technology. A comprehensive model encompassing thermal dynamic and characteristics models accurately predicts geometrical, metallurgical, and mechanical aspects of the laser beam welding process. Notably, Model 2 showcases exceptional accuracy, achieving remarkably low error rates in predicting primary and secondary dendrite arm spacing (PDAS and SDAS). These findings underscore the model's reliability and effectiveness, providing invaluable insights and predictive capabilities crucial for optimizing welding processes and ensuring superior productivity, efficiency, and quality in the automotive industry.

Keywords: laser welding process, geometrical characteristics, mechanical characteristics, metallurgical characteristics, comprehensive model, thermal dynamic

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Authors: Ella Tyuryumina, Alexey Neznanov

Abstract:

This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

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