Search results for: thermal cycling machine

Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Ioannis Andrianakis, Manolis Falelakis, Maria Pavlidou, Konstantinos Papakonstantinou, Ermioni Avramidou, Dimitrios Kalogiannis, Nikolaos Milios, Katerina Bountakidou, Kiriakos Chatzidimitriou, Panagiotis Panagiotopoulos

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Artistic events, such as concerts and performances, are challenging to organize because they involve many people with different skill sets. Small and medium venues often struggle to afford the costs and overheads of booking and hosting remote artists, especially if they lack sponsors or subsidies. This limits the opportunities for both venues and artists, especially those outside of big cities. However, more and more research shows that audiences prefer smaller-scale events and concerts, which benefit local economies and communities. To address this challenge, our project “PerformAnts: Digital Event Manager-Organizer” aims to develop a smart digital tool that automates and optimizes the processes and costs of live shows and tours. By using machine learning, applying best practices and training users through workshops, our platform offers a comprehensive solution for a growing market, enhances the mobility of artists and the accessibility of venues and allows professionals to focus on the creative aspects of concert production.

Keywords: event organization, creative industries, event promotion, machine learning

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Authors: Emmanuel Bernard, Rebecca L. Cordell, Akeem A. Abayomi, Rose Alani, Paul S. Monks

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This study investigates the prevalence and extent of BTEX (Benzene, Toluene, Ethylbenzene, and Xylene) contamination in Leicester, United Kingdom, and Lagos, Nigeria, through field measurements at roadside (RS) and urban background (UB) sites. Using thermal desorption gas chromatography mass spectrometry (TD-GC-MS), BTEX concentrations were quantified. In Leicester, the average RS concentration was 24.9 ± 8.9 μg/m³, and the UB concentration was 12.7 ± 5.7 μg/m³. In Lagos, the RS concentration was significantly higher at 106 ± 39.3 μg/m³, and the UB concentration was 20.1 ± 8.9 μg/m³. The RS concentration in Lagos was approximately 4.3 times higher than in Leicester, while the UB concentration was about 1.6 times higher. These disparities are attributed to differences in road infrastructure, traffic regulation compliance, fuel and oil quality, and local activities. In Leicester, the highest UB concentration (20.5 ± 1.7 μg/m³) was at Knighton Village, near the heavily polluted RS Wigston roundabout. In Lagos, the highest concentration (172.1 ± 12.2 μg/m³) was at Ojuelegba, a major transportation hub. Correlation analysis revealed strong positive relationships between the concentrations of BTEX compounds in both cities, suggesting common sources such as vehicular emissions and industrial activities. The ratios of toluene to benzene (T:B) and m/p xylene to ethylbenzene (m/p X:E) were analysed to infer source contributions and the photochemical age of air masses. The T:B ratio in Leicester ranged from 0.44 to 0.71, while in Lagos, it ranged from 1.36 to 2.17. The m/p X:E ratio in Leicester ranged from 2.11 to 2.19, like other UK cities, while in Lagos, it ranged from 1.65 to 2.32, indicating relatively fresh emissions. This study highlights significant differences in BTEX concentrations between Leicester and Lagos, emphasizing the need for tailored pollution control strategies to address the specific sources and conditions in different urban environments.

Keywords: BTEX contamination, urban air quality, thermal desorption GC-MS, roadside emissions, urban background sites, vehicular emissions, pollution control strategies

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Authors: Hong Yu, Dirk Heider, Suresh Advani

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Increasing demands for high strength and lightweight materials in aircraft industry prompted the wide use of carbon composites in recent decades. Carbon composite laminates used on aircraft structures are subject to lightning strikes. Unlike its metal/alloy counterparts, carbon fiber reinforced composites demonstrate smaller electrical conductivity, yielding more severe damages due to Joule heating. The anisotropic nature of composite laminates makes the electrical and thermal conduction within carbon composite laminates even more complicated. Good understanding of the electrical conduction behavior of carbon composites is the key to effective lightning protection design. The goal of this study is to numerically and experimentally investigate the impact of ultra-high temperature induced by simulated lightning strike on the electrical conduction of carbon composites. A lightning simulator is designed to apply standard lightning current waveform to composite laminates. Multiple carbon composite laminates made from IM7 and AS4 carbon fiber are tested and the transient resistance data is recorded. A microstructure based resistor network model is developed to describe the electrical and thermal conduction behavior, with consideration of temperature dependent material properties. Material degradations such as thermal and electrical breakdown are also modeled to include the effect of high current and high temperature induced by lightning strikes. Good match between the simulation results and experimental data indicates that the developed model captures the major conduction mechanisms. A parametric study is then conducted using the validated model to investigate the effect of system parameters such as fiber volume fraction, inter-ply interface quality, and lightning current waveforms.

Keywords: carbon composite, joule heating, lightning strike, resistor network

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Swati Sundararajan, , Asit B. Samui, Prashant S. Kulkarni

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The global energy crisis has led to extensive research on alternative sources of energy. The gap between energy supply and demand can be met by thermal energy storage techniques, of which latent heat storage is most effective in the form of phase change materials (PCMs). Phase change materials utilize latent heat absorbed or released over a narrow temperature range of the material undergoing phase transformation, to store energy. The latent heat can be utilized for heating or cooling purposes. It can also be used for converting to electricity. All these actions amount to minimizing the load on electricity demand. These materials retain this property over repeated number of cycles. Different PCMs differ in the phase change temperature and the heat storage capacities. Poly(ethylene glycol) (PEG) was cross-linked to hydroxyl-terminated poly(dimethyl siloxane) (PDMS) in the presence of cross-linker, tetraethyl orthosilicate (TEOS) and catalyst, dibutyltin dilaurate. Four different ratios of PEG and PDMS were reacted together, and the composition with the lowest PEG concentration resulted in the formation of a flexible solid-solid phase change membrane. The other compositions are obtained in powder form. The enthalpy values of the prepared PCMs were studied by using differential scanning calorimetry and the crystallization properties were analyzed by using X-ray diffraction and polarized optical microscopy. The incorporation of silicone moiety was expected to reduce the hydrophilic character of PEG, which was evaluated by measurement of contact angle. The membrane forming ability of this crosslinked polymer can be extended to several smart packaging, building and textile applications. The detailed synthesis, characterization and performance evaluation of the crosslinked polymer blend will be incorporated in the presentation.

Keywords: phase change materials, poly(ethylene glycol), poly(dimethyl siloxane), thermal energy storage

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Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: Muhammad Nasar-u-Minallah, Dagmar Haase, Salman Qureshi

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In a rapidly growing developing country cities are becoming more urbanized leading to modifications in urban climate. Rapid urbanization, especially unplanned urban land expansion, together with climate change has a profound impact on the urban settlement and urban thermal environment. Cities, particularly Pakistan are facing remarkably environmental issues and uneven development, and thus it is important to strengthen the investigation of urban environmental pressure brought by land-use changes and urbanization. The present study investigated the long term modification of the urban environment by urbanization utilizing Spatio-temporal dynamics of land-use change, urban population data, urban heat islands, monthly maximum, and minimum temperature of thirty years, multi remote sensing imageries, and spectral indices such as Normalized Difference Built-up Index and Normalized Difference Vegetation Index. The results indicate rapid growth in an urban built-up area and a reduction in vegetation cover in the last three decades (1990-2020). A positive correlation between urban heat islands and Normalized Difference Built-up Index, whereas a negative correlation between urban heat islands and the Normalized Difference Vegetation Index clearly shows how urbanization is affecting the local environment. The increase in air and land surface temperature temperatures is dangerous to human comfort. Practical approaches, such as increasing the urban green spaces and proper planning of the cities, have been suggested to help prevent further modification of the urban thermal environment by urbanization. The findings of this work are thus important for multi-sectorial use in the cities of Pakistan. By taking into consideration these results, the urban planners, decision-makers, and local government can make different policies to mitigate the urban land use impacts on the urban thermal environment in Pakistan.

Keywords: land use, urban environment, local climate, Lahore

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Authors: Jinan Fiaidhi, Sabah Mohammed

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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

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Authors: Ormanee Patarathipakorn

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Background: Stress was the major psychological problem that affecting to physical and mental health among undergraduate students. Aim of study was to determine the effective of meditation training program (MTP) for stress reduction measured by biofeedback (BB) machine. Material and Methods: This was quasi-experimental study conducted in Faculty of Dentistry, Thammasat University, Thailand. Study period was between August and December 2023. Participants were the first-year Dentistry students. MTP was concentration meditation (Anapana meditation). Stress measurement was evaluated by using Thai version perceived stress scale (T-PSS-10) was performed at one week before study, 14 and 18 weeks. Stress evaluation by biofeedback machine (skin conductance: SC and skin temperature: ST) were performed at one week before study, 4, 8, 14 and 18 weeks. Data from T-PSS-10 and SC/ST biofeedback were collected and analyzed. Results: A total of 28 subjects were recruited. The mean age of participant was 18.4 years old. Two-thirds (19/28) was female. Stress reduction from MTP was detected since 4 and 8 weeks by STBB and SCBB, respectively. T-PSS 10 scores before MTP, 14 and 18 weeks were 17.7± 5.4, 9.8 ± 3.1 and 8.4 ± 3.1 with statistical significance. Conclusion: Meditation training program could reduce stress and measured by skin conductance and temperature biofeedback.

Keywords: stress, meditation, biofeedback, student

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Authors: Theresia Gita Hapsari

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There has been major concern about urban heating as urban clusters emerge and population migration from rural to urban areas continues. Green roof has been one of the main practice for urban heat island mitigation for the past decades, thus, this study was conducted to predict the cooling potential of retentive green roof in mitigating urban heat island. Retentive green roof was developed by Han in 2010. It has 320 mm height of retention wall surrounding the vegetation and 65mm depth of retention board underneath the soil, while most conventional green roof doesn’t have any retention wall and only maximum of 25 mm depth of drainage board. Seoul National University retentive green roof significantly reduced sensible heat movement towards the air by 0.5 kWh/m2, and highly enhanced the evaporation process as much as 0.5 – 5.4 kg/m2 which equals to 0.3 – 3.6 kWh/m2 of latent heat flux. These results indicate that with design enhancement, serving as a viable alternate for conventional green roof, retentive green roof contributes to overcome the limitation of conventional green roof which is the main solution for mitigating urban heat island.

Keywords: green roof, low impact development, retention board, thermal behavior, urban heat island

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Authors: R. S. Sheu, H. Usman, M. S. Lawal

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Takagi-Sugeno (T-S) fuzzy model based control of a load frequency deviation in a single machine with limit nonlinearity on power coefficient is presented in the paper. Two T-S fuzzy rules with only rotor angle variable as input in the premise part, and linear state space models in the consequent part involving characteristic matrices determined from limits set on the power coefficient constant are formulated, state feedback control gains for closed loop control was determined from the formulated Linear Matrix Inequality (LMI) with eigenvalue optimization scheme for asymptotic and exponential stability (speed of esponse). Numerical evaluation of the closed loop object was carried out in Matlab. Simulation results generated of both the open and closed loop system showed the effectiveness of the control scheme in maintaining load frequency stability.

Keywords: T-S fuzzy model, state feedback control, linear matrix inequality (LMI), frequency deviation control

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Authors: Vimal Kumar Chouksey, S. P. Sharma

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This paper deals with theoretical analysis of performance of solar air collector having its duct packed with blackened wire screen matrices. The heat transfer equations for two-dimensional fully developed fluid flows under quasi-steady-state conditions have been developed in order to analyze the effect of bed depth on performance. A computer programme is developed in C++ language to estimate the temperature rise of entering air for evaluation of performance by solving the governing equations numerically using relevant correlations for heat transfer coefficient for packed bed systems. Results of air temperature rise and thermal efficiency obtained from the analysis have been compared with available experimental results and results have been found fairly in closed agreement. It has been found that there is considerable enhancement in performance with packed bed collector upto a certain total bed depth. Effect of total bed depth on efficiency show that there is an upper limiting value of total bed depth beyond which the thermal efficiency begins to fall again and this type of characteristics behavior is observed at all mass flow rate.

Keywords: plane collector, solar air heater, solar energy, wire screen packed bed

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Authors: Cleyton S. Stampa

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This paper addresses the perspectives of using low melting point metals (LMPMs) as phase change materials (PCMs) in latent thermal energy storage (LTES) units, through a numerical approach. This is a new class of PCMs that has been one of the most prospective alternatives to be considered in LTES, due to these materials present high thermal conductivity and elevated heat of fusion, per unit volume. The chosen type of LTES consists of several horizontal parallel slabs filled with PCM. The heat transfer fluid (HTF) circulates through the channel formed between each two consecutive slabs on a laminar regime through forced convection. The study deals with the LTES charging process (heat-storing) by using pure gallium as PCM, and it considers heat conduction in the solid phase during melting driven by natural convection in the melt. The transient heat transfer problem is analyzed in one arbitrary slab under the influence of the HTF. The mathematical model to simulate the isothermal phase change is based on a volume-averaged enthalpy method, which is successfully verified by comparing its predictions with experimental data from works available in the pertinent literature. Regarding the convective heat transfer problem in the HTF, it is assumed that the flow is thermally developing, whereas the velocity profile is already fully developed. The study aims to learn about the effect of the solid subcooling in the melting rate through comparisons with the melting process of the solid in which it starts to melt from its fusion temperature. In order to best understand this effect in a metallic compound, as it is the case of pure gallium, the study also evaluates under the same conditions established for the gallium, the melting process of commercial paraffin wax (organic compound) and of the calcium chloride hexahydrate (CaCl₂ 6H₂O-inorganic compound). In the present work, it is adopted the best options that have been established by several researchers in their parametric studies with respect to this type of LTES, which lead to high values of thermal efficiency. To do so, concerning with the geometric aspects, one considers a gap of the channel formed by two consecutive slabs, thickness and length of the slab. About the HTF, one considers the type of fluid, the mass flow rate, and inlet temperature.

Keywords: flat slab, heat storing, pure metal, solid subcooling

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Authors: Wenzheng Fu, Yue Fu, Zhijiang Dong, Yujian Fu

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The integration of artificial intelligence (AI) and machine learning (ML) into project management is transforming how engineering projects are executed, monitored, and controlled. This paper provides a comprehensive survey of AI and ML applications in project management, systematically categorizing their use in key areas such as project data analytics, monitoring, tracking, scheduling, and reporting. As project management becomes increasingly data-driven, AI and ML offer powerful tools for improving decision-making, optimizing resource allocation, and predicting risks, leading to enhanced project outcomes. The review highlights recent research that demonstrates the ability of AI and ML to automate routine tasks, provide predictive insights, and support dynamic decision-making, which in turn increases project efficiency and reduces the likelihood of costly delays. This paper also examines the emerging trends and future opportunities in AI-driven project management, such as the growing emphasis on transparency, ethical governance, and data privacy concerns. The research suggests that AI and ML will continue to shape the future of project management by driving further automation and offering intelligent solutions for real-time project control. Additionally, the review underscores the need for ongoing innovation and the development of governance frameworks to ensure responsible AI deployment in project management. The significance of this review lies in its comprehensive analysis of AI and ML’s current contributions to project management, providing valuable insights for both researchers and practitioners. By offering a structured overview of AI applications across various project phases, this paper serves as a guide for the adoption of intelligent systems, helping organizations achieve greater efficiency, adaptability, and resilience in an increasingly complex project management landscape.

Keywords: artificial intelligence, decision support systems, machine learning, project management, resource optimization, risk prediction

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Authors: Chusak Thanawattano, Roongroj Bhidayasiri

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This paper proposes a novel feature set utilized for classifying the Parkinson tremor and essential tremor. Ten ET and ten PD subjects are asked to perform kinetic, postural and resting tests. The empirical mode decomposition (EMD) is used to decompose collected tremor signal to a set of intrinsic mode functions (IMF). The IMFs are used for reconstructing representative signals. The feature set is composed of peak frequencies of IMFs and reconstructed signals. Hypothesize that the dominant frequency components of subjects with PD and ET change in different directions for different tests, difference of peak frequencies of IMFs and reconstructed signals of pairwise based tests (kinetic-resting, kinetic-postural and postural-resting) are considered as potential features. Sets of features are used to train and test by classifier including the quadratic discriminant classifier (QLC) and the support vector machine (SVM). The best accuracy, the best sensitivity and the best specificity are 90%, 87.5%, and 92.86%, respectively.

Keywords: tremor, Parkinson, essential tremor, empirical mode decomposition, quadratic discriminant, support vector machine, peak frequency, auto-regressive, spectrum estimation

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Authors: S. Al-Enezi, J. Samuel, A. Al-Banna

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Polymer based membranes are one of the low-cost technologies available for the gas separation. Three major elements required for a commercial gas separating membrane are high permeability, high selectivity, and good mechanical strength. Poly(vinylidene fluoride) (PVDF) is a commercially available fluoropolymer and a widely used membrane material in gas separation devices since it possesses remarkable thermal, chemical stability, and excellent mechanical strength. The PVDF membrane was chemically modified by soaking in different ionic liquids and dried. The thermal behavior of modified membranes was investigated by differential scanning calorimetry (DSC), and thermogravimetry (TGA), and the results clearly show the best affinity between the ionic liquid and the polymer support. The porous structure of the PVDF membranes was clearly seen in the scanning electron microscopy (SEM) images. The CO₂ permeability of blended membranes was explored in comparison with the unmodified matrix. The ionic liquid immobilized in the hydrophobic PVDF support exhibited good performance for separations of CO₂/N₂. The improved permeability of modified membrane (PVDF-IL) is attributed to the high concentration of nitrogen rich imidazolium moieties.

Keywords: PVDF, polymer membrane, gas permeability, CO₂ separation, nanotubes

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Authors: Blanka Havlickova

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In the last ten years, there are growing numbers of troops with machine guns serving on streets of European cities. We can see them around government buildings, major transport hubs, synagogues, galleries and main tourist landmarks. As the main purpose of armed soldier’s presence in European cities authorities declare the prevention of terrorist attacks and psychological support for tourists and domestic population. The main objective of the following study is to find out whether the deployment of armed soldiers in European cities has a calming and reassuring effect on Czech citizens (if the presence at armed soldiers make the Czech population feel more secure) or rather becomes a stress factor (the presence of soldiers standing guard in full military fatigues recalls serious criminality and terrorist attacks which are reflected in the fears and insecurity of Czech population). The initial hypothesis of this study is connected with the priming theory, the idea that when we are exposed to an image (armed soldier), it makes us unconsciously focus on a topic connected with this image (terrorism). This paper is based on a quantitative public survey, which was carried out in the form of electronic questioning among the citizens of the Czech Republic. Respondents answered 14 questions about two European cities – London and Paris. Besides general questions investigating the respondents' awareness of these cities, some of the questions focused on the fear that the respondents had when picturing themselves leaving next Monday for the given city (London or Paris). The questions asking about respondent´s travel fears and concerns were accompanied by different photos. When answering the question about fear some respondents have been presented with a photo of Westminster Palace and the Eiffel with ordinary citizens while other respondents have been presented with a picture of the Westminster Palace, the and Eiffel's tower not only with ordinary citizens, but also with one soldier holding a machine gun. The main goal of this paper is to analyse and compare data about concerns for these two groups of respondents (presented with different pictures) and find out if and how an armed soldier with a machine gun in front of the Westminster Palace or the Eiffel Tower affects the public's concerns about visiting the site. In other words, the aim of this paper is to confirm or rebut the hypothesis that the look at a soldier with a machine gun in front of the Eiffel Tower or the Westminster Palace automatically triggers the association with a terrorist attack leading to an increase in fear and insecurity among Czech population.

Keywords: terrorism, security measures, priming, risk perception

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Authors: Serdar Yaman, Hanzade Haykiri-Acma

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Biomass is a CO₂-neutral fuel that is renewable and sustainable along with having very huge global potential. Efficient use of biomass in power generation and production of biomass-based biofuels can mitigate the greenhouse gasses (GHG) and reduce dependency on fossil fuels. There are also other beneficial effects of biomass energy use such as employment creation and pollutant reduction. However, most of the biomass materials are not capable of competing with fossil fuels in terms of energy content. High moisture content and high volatile matter yields of biomass make it low calorific fuel, and it is very significant concern over fossil fuels. Besides, the density of biomass is generally low, and it brings difficulty in transportation and storage. These negative aspects of biomass can be overcome by thermal pretreatments that upgrade the fuel property of biomass. That is, torrefaction is such a thermal process in which biomass is heated up to 300ºC under non-oxidizing conditions to avoid burning of the material. The treated biomass is called as biochar that has considerably lower contents of moisture, volatile matter, and oxygen compared to the parent biomass. Accordingly, carbon content and the calorific value of biochar increase to the level which is comparable with that of coal. Moreover, hydrophilic nature of untreated biomass that leads decay in the structure is mostly eliminated, and the surface properties of biochar turn into hydrophobic character upon torrefaction. In order to investigate the effectiveness of torrefaction process on biomass properties, several biomass species such as olive milling residue (OMR), Rhododendron (small shrubby tree with bell-shaped flowers), and ash tree (timber tree) were chosen. The fuel properties of these biomasses were analyzed through proximate and ultimate analyses as well as higher heating value (HHV) determination. For this, samples were first chopped and ground to a particle size lower than 250 µm. Then, samples were subjected to torrefaction in a horizontal tube furnace by heating from ambient up to temperatures of 200, 250, and 300ºC at a heating rate of 10ºC/min. The biochars obtained from this process were also tested by the methods applied to the parent biomass species. Improvement in the fuel properties was interpreted. That is, increasing torrefaction temperature led to regular increases in the HHV in OMR, and the highest HHV (6065 kcal/kg) was gained at 300ºC. Whereas, torrefaction at 250ºC was seen optimum for Rhododendron and ash tree since torrefaction at 300ºC had a detrimental effect on HHV. On the other hand, the increase in carbon contents and reduction in oxygen contents were determined. Burning characteristics of the biochars were also studied using thermal analysis technique. For this purpose, TA Instruments SDT Q600 model thermal analyzer was used and the thermogravimetric analysis (TGA), derivative thermogravimetry (DTG), differential scanning calorimetry (DSC), and differential thermal analysis (DTA) curves were compared and interpreted. It was concluded that torrefaction is an efficient method to upgrade the fuel properties of biomass and the biochars from which have superior characteristics compared to the parent biomasses.

Keywords: biochar, biomass, fuel upgrade, torrefaction

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Authors: Juan José Mesas, Luis Sainz

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The motivation for the development of this paper lies in the need for energy networks to reduce losses, improve performance, optimize their operation and try to benefit from the interconnection capacity with other networks enabled for other energy carriers. These interconnections generate interdependencies between some energy networks and others, which requires suitable models and methods for their analysis. Traditionally, the modeling and study of energy networks have been carried out independently for each energy carrier. Thus, there are well-established models and methods for the steady-state analysis of electrical networks, gas networks, and thermal networks separately. What is intended is to extend and combine them adequately to be able to face in an integrated way the steady-state analysis of networks with multiple energy carriers. Firstly, the added value of multi-energy networks, their operation, and the basic principles that characterize them are explained. In addition, two current aspects of great relevance are exposed: the storage technologies and the coupling elements used to interconnect one energy network with another. Secondly, the characteristic equations of the different energy networks necessary to carry out the steady-state analysis are detailed. The electrical network, the natural gas network, and the thermal network of heat and cold are considered in this paper. After the presentation of the equations, a particular case of the steady-state analysis of a specific multi-energy network is studied. This network is represented graphically, the interconnections between the different energy carriers are described, their technical data are exposed and the equations that have previously been presented theoretically are formulated and developed. Finally, the two iterative numerical resolution methods considered in this paper are presented, as well as the resolution procedure and the results obtained. The pros and cons of the application of both methods are explained. It is verified that the results obtained for the electrical network (voltages in modulus and angle), the natural gas network (pressures), and the thermal network (mass flows and temperatures) are correct since they comply with the distribution, operation, consumption and technical characteristics of the multi-energy network under study.

Keywords: coupling elements, energy carriers, multi-energy networks, steady-state analysis

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Authors: Hammadi Larbi

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The treatment of wastewater in sewage plants usually results in the formation of a large amount of sludge. These appear at the outlet of the treatment plant as a viscous fluid loaded with a high concentration of dry matter. This sludge production presents environmental, ecological, and economic risks. That is why it is necessary to find many solutions for minimizing these risks. In the present article, the effect of hydrogen peroxide, thermal treatment, and quicklime on the characteristics of the activated sludge produced in urban wastewater plant were evaluated in order to avoid any risk in the plants. The study shows increasing of the dose of H2O2 from 0 to 0.4 g causes an increase in the solubilization rate of COD from 12% to 45% and a reduction in the organic matter content of sludge (VM/SM) from 74% to 36% . The results also show that the optimum efficiency of the heat treatment corresponds to a temperature of 80 ° C for a treatment time of 40 min is 47% and 51.82% for a temperature equal to 100 ° C and 76.30 % for a temperature of 120 ° C, and 79.38% for a temperature of 140 ° C. The treatment of sludge by quicklime gives the optimum efficiency of 70.62 %. It was shown the increasing of the temperature from 80°C to 140°C, the pH of sludge was increased from 7.12 to 9.59. The obtained results showed that with increasing the dose of quicklime from 0 g/l to 1g/l in activated sludge led to an increase of their pH from 7.12 to 12.06. The study shows the increasing the dose of quicklime from 0 g/l to 1g/l causes also an increase in the solubilization of COD from 0% to 70.62 %

Keywords: activated sludge, hydrogen peroxide, thermal treatment, quicklime

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Authors: Fatih Kılıç, Burak Birol, Necmettin Maraşlı

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The directional solidification process is generally used for homogeneous compound production, single crystal growth, and refining (zone refining), etc. processes. The most important two parameters that control eutectic structures are temperature gradient and grain growth rate which are called as solidification parameters The solidification behavior and microstructure characteristics is an interesting topic due to their effects on the properties and performance of the alloys containing eutectic compositions. The solidification behavior of multicomponent and multiphase systems is an important parameter for determining various properties of these materials. The researches have been conducted mostly on the solidification of pure materials or alloys containing two phases. However, there are very few studies on the literature about multiphase reactions and microstructure formation of multicomponent alloys during solidification. Because of this situation, it is important to study the microstructure formation and the thermodynamical, thermophysical and microstructural properties of these alloys. The production process is difficult due to easy oxidation of magnesium and therefore, there is not a comprehensive study concerning alloys containing high Mg (> 30 wt.% Mg). With the increasing amount of Mg inside Al alloys, the specific weight decreases, and the strength shows a slight increase, while due to formation of β-Al8Mg5 phase, ductility lowers. For this reason, production, examination and development of high Mg containing alloys will initiate the production of new advanced engineering materials. The original value of this research can be described as obtaining high Mg containing (> 30% Mg) Al based multicomponent alloys by melting under vacuum; controlled directional solidification with various growth rates at a constant temperature gradient; and establishing relationship between solidification rate and microstructural, mechanical, electrical and thermal properties. Therefore, within the scope of this research, some > 30% Mg containing ternary or quaternary Al alloy compositions were determined, and it was planned to investigate the effects of directional solidification rate on the mechanical, electrical and thermal properties of these alloys. Within the scope of the research, the influence of the growth rate on microstructure parameters, microhardness, tensile strength, electrical conductivity and thermal conductivity of directionally solidified high Mg containing Al-32,2Mg-0,37Si; Al-30Mg-12Zn; Al-32Mg-1,7Ni; Al-32,2Mg-0,37Fe; Al-32Mg-1,7Ni-0,4Si; Al-33,3Mg-0,35Si-0,11Fe (wt.%) alloys with wide range of growth rate (50-2500 µm/s) and fixed temperature gradient, will be investigated. The work can be planned as; (a) directional solidification of Al-Mg based Al-Mg-Si, Al-Mg-Zn, Al-Mg-Ni, Al-Mg-Fe, Al-Mg-Ni-Si, Al-Mg-Si-Fe within wide range of growth rates (50-2500 µm/s) at a constant temperature gradient by Bridgman type solidification system, (b) analysis of microstructure parameters of directionally solidified alloys by using an optical light microscopy and Scanning Electron Microscopy (SEM), (c) measurement of microhardness and tensile strength of directionally solidified alloys, (d) measurement of electrical conductivity by four point probe technique at room temperature (e) measurement of thermal conductivity by linear heat flow method at room temperature.

Keywords: directional solidification, electrical conductivity, high Mg containing multicomponent Al alloys, microhardness, microstructure, tensile strength, thermal conductivity

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Authors: Ricky Leung

Abstract:

Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

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Authors: Lydia Novozhilova, Vladimir Urazhdin

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An optimization problem for reinforced thin-walled vessels under uniform external pressure is considered. The conventional approaches to optimization generally start with pre-defined geometric parameters of the vessels, and then employ analytic or numeric calculations and/or experimental testing to verify functionality, such as stability under the projected conditions. The proposed approach consists of two steps. First, the feasibility domain will be identified in the multidimensional parameter space. Every point in the feasibility domain defines a design satisfying both geometric and functional constraints. Second, an objective function defined in this domain is formulated and optimized. The broader applicability of the suggested methodology is maximized by implementing the Support Vector Machines (SVM) classification algorithm of machine learning for identification of the feasible design region. Training data for SVM classifier is obtained using the Simulation package of SOLIDWORKS®. Based on the data, the SVM algorithm produces a curvilinear boundary separating admissible and not admissible sets of design parameters with maximal margins. Then optimization of the vessel parameters in the feasibility domain is performed using the standard algorithms for the constrained optimization. As an example, optimization of a ring-stiffened closed cylindrical thin-walled vessel with semi-spherical caps under high external pressure is implemented. As a functional constraint, von Mises stress criterion is used but any other stability constraint admitting mathematical formulation can be incorporated into the proposed approach. Suggested methodology has a good potential for reducing design time for finding optimal parameters of thin-walled vessels under uniform external pressure.

Keywords: design parameters, feasibility domain, von Mises stress criterion, Support Vector Machine (SVM) classifier

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Authors: J. Bolobajev, M. Trapido, A. Goi

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The practical application of the Fenton-based treatment method for organic compounds-contaminated water purification is limited mainly because of the large amount of ferric sludge formed during the treatment, where ferrous iron (Fe(II)) is used as the activator of the hydrogen peroxide oxidation processes. Reuse of ferric sludge collected from clarifiers to substitute Fe(II) salts allows reducing the total cost of Fenton-type treatment technologies and minimizing the accumulation of hazardous ferric waste. Dissolution of ferric iron (Fe(III)) from the sludge to liquid phase at acidic pH and autocatalytic transformation of Fe(III) to Fe(II) by phenolic compounds (tannic acid, lignin, phenol, catechol, pyrogallol and hydroquinone) added or present as water/wastewater constituents were found to be essentially involved in the Fenton-based oxidation mechanism. Observed enhanced formation of highly reactive species, hydroxyl radicals, resulted in a substantial organic contaminant degradation increase. Sludge reuse at acidic pH and in the presence of ferric iron reductants is a novel strategy in the Fenton-based treatment application for organic compounds-contaminated water purification.

Keywords: ferric sludge recycling, ferric iron reductant, water treatment, organic pollutant

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Authors: Valery Yakubovich, Shuping wu

Abstract:

Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

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Authors: Yahya Lavafpour, Steve Sharples

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This study investigated the potential negative impacts of future UK climate change on dwellings. In particular, the risk of overheating was considered for a Passivhaus dwelling in London. The study used dynamic simulation modelling software to investigate the potential use of building geometry to control current and future overheating risks in the dwelling for London climate. Specifically, the focus was on the optimum inclination of a south façade to make use of the building’s shape to self-protect itself. A range of different inclined façades were examined to test their effectiveness in reducing the overheating risk. The research found that implementing a 115° tilted façade could completely eliminate the risk of overheating in current climate, but with some consequence for natural ventilation and daylighting. Future overheating was significantly reduced by the tilted façade. However, geometric considerations could not eradicate completely the risk of overheating particularly by the 2080s. The study also used CFD modelling and sensitivity analysis to investigate the effect of the façade geometry on the wind pressure distributions on and around the building surface. This was done to assess natural ventilation flows for alternative façade inclinations.

Keywords: climate change, tilt façade, thermal comfort, passivhaus, overheating

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Authors: Neha Singh, Ananya Arora

Abstract:

Facial expression measurement plays a crucial role in the identification of emotion. Facial expression plays a key role in psychophysiology, neural bases, and emotional disorder, to name a few. The Facial Action Coding System (FACS) has proven to be the most efficient and widely used of the various systems used to describe facial expressions. Coders can manually code facial expressions with FACS and, by viewing video-recorded facial behaviour at a specified frame rate and slow motion, can decompose into action units (AUs). Action units are the most minor visually discriminable facial movements. FACS explicitly differentiates between facial actions and inferences about what the actions mean. Action units are the fundamental unit of FACS methodology. It is regarded as the standard measure for facial behaviour and finds its application in various fields of study beyond emotion science. These include facial neuromuscular disorders, neuroscience, computer vision, computer graphics and animation, and face encoding for digital processing. This paper discusses the conceptual basis for FACS, a numerical listing of discrete facial movements identified by the system, the system's psychometric evaluation, and the software's recommended training requirements.

Keywords: facial action, action units, coding, machine learning

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Authors: Hong-Jin Joo, Hee-Yoel Kwak

Abstract:

Vacuum Membrane Distillation (VMD) uses pressure lower than the atmospheric pressure. The feed seawater is capable of producing more vapor at the same temperature than Direct Contact Membrane Distillation (DCMD), Air Gap Membrane Distillation (AGMD) or Sweep Gas Membrane Distillation (SGMD). It is advantageous because it is operable at a lower temperature than other membrane distillations. However, no commercial product is available that uses the VMD method, as it is still in the study stage. In this study, therefore, thermal performance test according to the feed water conditions was performed prior to both construction of the demonstration plant, which uses VMD module of the capacity of 400m³/d in South Korea, and commercialization of VMD module with hollow fiber membrane. Such study was performed by designing and constructing the VMD module of the capacity of 2 m³/day which utilizes the polyvinylidene fluoride (PVDF) hollow fiber membrane. The results obtained from the VMD module manufactured by ECONITY Co., Ltd in South Korea, showed that the maximum performance ratio (PR) value of 0.904, feed water temperature of 75 ℃, and the flow rate of 8 m3/h. As the temperature of and flow rate of the feed water increased, the PR value of the VMD module also increased.

Keywords: membrane distillation, vacuum membrane distillation, hollow fiber membrane, desalination

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Authors: Annapurna Basavaraju, Andreas Marn, Franz Heitmeir

Abstract:

The Advisory Council for Aeronautics Research in Europe (ACARE) is cognizant for the overall reduction of NOx emissions by 80% in its vision 2020. Moreover small turbo engines have higher fuel specific emissions compared to large engines due to their limited combustion chamber size. In order to fulfill these requirements, novel combustion concepts are essential. This motivates to carry out the research on the current state of art, catalytic stabilized combustion chamber using hydrogen in small jet engines which are designed and investigated both numerically and experimentally during this project. Catalytic combustion concepts can also be adopted for low caloric fuels and are therefore not constrained to only hydrogen. However, hydrogen has high heating value and has the major advantage of producing only the nitrogen oxides as pollutants during the combustion, thus eliminating the interest on other emissions such as Carbon monoxides etc. In the present work, the combustion chamber is designed based on the ‘Rich catalytic Lean burn’ concept. The experiments are conducted for the characteristic operating range of an existing engine. This engine has been tested successfully at Institute of Thermal Turbomachinery and Machine Dynamics (ITTM), Technical University Graz. One of the facts that the efficient combustion is a result of proper mixing of fuel-air mixture, considerable significance is given to the selection of appropriate mixer. This led to the design of three diverse configurations of mixers and is investigated experimentally and numerically. Subsequently the best mixer would be equipped in the main combustion chamber and used throughout the experimentation. Furthermore, temperatures and pressures would be recorded at various locations inside the combustion chamber and the exhaust emissions will also be analyzed. The instrumented combustion chamber would be inspected at the engine relevant inlet conditions for nine different sets of catalysts at the Hot Flow Test Facility (HFTF) of the institute.

Keywords: catalytic combustion, gas turbine, hydrogen, mixer, NOx emissions

Procedia PDF Downloads 302