Search results for: multi stage flash distillation

Authors: Sumita Srivastava

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Despite several measures taken by the Government of India, entrepreneurship is still not perceived as a viable career option by the young generation. Although the rate of startups has improved a little after the penetration of e portals as business platforms, still the numbers are not very significant. It is also important to note that entrepreneurial initiatives are mostly taken up by graduates of premier institutions of India like Indian Institute of Technology (IITs) and Indian Institute of Management (IIMs). The scenario is not very satisfactory amongst the masses graduating from mainstream universities of the country. Indian youth at large are not attracted towards entrepreneurship as a career choice. The reason probably lies in the social fabric of the country and inappropriate education system which does not support the entrepreneurship at large amongst youth in the country. Education is critical to the development of an economy from the poverty level to the level of self-sustenance and development. The current curriculum in the majority of business schools in India prepares the average graduate to become employed by the available firms or business owners in society. For graduates in other streams, employment opportunities are very limited. The aim of this study was to identify and design entrepreneurship education contents to encourage undergraduates to pursue entrepreneurship as a career choice. This comprehensive study was conducted in multiple stages. Extensive research was conducted at each stage with an appropriate methodology. These stages of the project study were interconnected with each other, and each preceding stage provided inputs for the following stage of the study. In the first stage of the study, an empirical analysis was conducted to understand the current state of entrepreneurial intentions of undergraduates of Agra city. Various stakeholders were contacted at the stage, including students (n = 500), entrepreneurs (n = 20) and academicians and field experts (n = 10). At the second stage of the project study, a systems science technique, Nominal Group Technique (NGT) was used to identify the critical elements of entrepreneurship education in India based upon the findings of stage 1. The application of the Nominal Group Technique involved a workshop format; 15 domain experts participated in the workshop. Throughout the process, a democratic process was followed to avoid individual dominance and premature focusing on a single idea. The study obtained 63 responses from experts for effective entrepreneurship education in India. The responses were reduced to seven elements after a few thematic iterations. These elements were then segregated into content (knowledge, skills and attitude) and learning interaction on the basis of experts’ responses. After identifying critical elements of entrepreneurship education in the previous stage, the course was designed and validated at stage 3 of the project. Scientific methods were used at this stage to validate the curriculum contents and training interventions experimentally. The educational and training interventions designed through this study would not only help in developing entrepreneurial intentions but also creating skills relevant to the local entrepreneurial opportunities in the vicinity.

Keywords: curriculum design, entrepreneurial intention, entrepreneuship education, nominal group technique

Procedia PDF Downloads 135

Authors: Haroon Jan Khan, Xian-Feng Xu, Syed Nasir Shah, Anooshay Niazi

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CNC machines are not only used on a large scale but also now become a prominent necessity among households and smaller businesses. Printed Circuit Boards manufactured by the chemical process are not only risky and unsafe but also expensive and time-consuming. A 3-axis precise CNC machine has been developed, which not only fabricates PCB but has also been used for multi-tasks just by changing the materials used and tools, making it versatile. The advanced CNC machine takes data from CAM software. The TB-6560 controller is used in the CNC machine to adjust variation in the X, Y, and Z axes. The advanced machine is efficient in automatic drilling, engraving, and cutting.

Keywords: CNC, G-code, CAD, CAM, Proteus, FLATCAM, Easel

Procedia PDF Downloads 166

Authors: Luís Rocha, Adam Gąska, Enrico Savio, Michael Marxer, Christoph Battaglia

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The changes observed in the last years in the field of manufacturing and production engineering, popularly known as "Fourth Industry Revolution", utilizes the achievements in the different areas of computer sciences, introducing new solutions at almost every stage of the production process, just to mention such concepts as mass customization, cloud computing, knowledge-based engineering, virtual reality, rapid prototyping, or virtual models of measuring systems. To effectively speed up the production process and make it more flexible, it is necessary to tighten the bonds connecting individual stages of the production process and to raise the awareness and knowledge of employees of individual sectors about the nature and specificity of work in other stages. It is important to discover and develop a suitable education method adapted to the specificities of each stage of the production process, becoming an extremely crucial issue to exploit the potential of the fourth industrial revolution properly. Because of it, the project “Train4Dim” (T4D) intends to develop complex training material for digital manufacturing, including content for design, manufacturing, and quality control, with a focus on coordinate metrology and portable measuring systems. In this paper, the authors present an approach to using an active learning methodology for digital manufacturing. T4D main objective is to develop a multi-degree (apprenticeship up to master’s degree studies) and educational approach that can be adapted to different teaching levels. It’s also described the process of creating the underneath methodology. The paper will share the steps to achieve the aims of the project (training model for digital manufacturing): 1) surveying the stakeholders, 2) Defining the learning aims, 3) producing all contents and curriculum, 4) training for tutors, and 5) Pilot courses test and improvements.

Keywords: learning, Industry 4.0, active learning, digital manufacturing

Procedia PDF Downloads 103

Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

Procedia PDF Downloads 444

Authors: Seyed Hossein Iranmanesh, Hassan Nouri, Seyed Reza Iranmanesh

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In the dynamic and complex landscape of today’s business, organizations often face challenges in impactful decision-making across multi-project settings. To efficiently allocate resources, coordinate tasks, and optimize project outcomes, establishing robust decision-making processes is essential. Furthermore, the increasing importance of information systems and their integration within organizational workflows introduces an additional layer of complexity. This research proposes the use of blockchain technology as a suitable solution to enhance decision-making in multi-project environments, particularly within the realm of information systems. The conceptual framework in this study comprises four independent variables and one dependent variable. The identified independent variables for the targeted research include: Blockchain Layer in Integrated Systems, Quality of Generated Information ,User Satisfaction with Integrated Systems and Utilization of Integrated Systems. The project’s performance, considered as the dependent variable and moderated by organizational policies and procedures, reflects the impact of blockchain technology adoption on organizational effectiveness1. The results highlight the significant influence of blockchain implementation on organizational performance.

Keywords: multi-project environments, decision support systems, information systems, blockchain technology, decentralized systems.

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Authors: Seungwoo Kim, Yeongcheol Cho

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In this paper, a holonomic mobile robot is designed in omnidirectional wheels and an adaptive fuzzy controller is presented for its precise trajectories. A kind of adaptive controller based on fuzzy multi-layered algorithm is used to solve the big parametric uncertainty of motor-controlled dynamic system of 3-wheels omnidirectional mobile robot. The system parameters such as a tracking force are so time-varying due to the kinematic structure of omnidirectional wheels. The fuzzy adaptive control method is able to solve the problems of classical adaptive controller and conventional fuzzy adaptive controllers. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a fuzzy multi-layered architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. This paper proposes a design procedure which can be carried out mathematically and systematically from the model of a controlled system. Finally, the good performance of a holonomic mobile robot is confirmed through live tests of the tracking control task.

Keywords: fuzzy adaptive control, fuzzy multi-layered controller, holonomic mobile robot, omnidirectional wheels, robustness and stability.

Procedia PDF Downloads 364

Authors: Rabia Waseem, Low Kah Hin, Najihah Mohamed Hashim

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The variability in the chemical components of the Syzygium species essential oils has been evaluated. The leaves of Syzygium species have been collected from Perak, Malaysia. The essential oils extracted by using the conventional Hydro-distillation extraction procedure and analyzed by using Gas chromatography System attached with Mass Spectrometry (GCMS). Twenty-seven constituents were found in Syzygium species in which the major constituents include: α-Pinene (3.94%), α-Thujene (2.16%), α-Terpineol (2.95%), g-Elemene (2.89%) and D-Limonene (14.59%). The aim of this study was the comparison between the evaluated data and existing literature to fortify the major variability through statistical analysis.

Keywords: chemotaxonomy, cluster analysis, essential oil, medicinal plants, statistical analysis

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Authors: K. Zehouani, I. Oldal

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Purpose: In the human knee joint, degenerative joint disease may happen with time. The standard treatment of this disease is the total knee replacement through prosthesis implanting. The reason lies in the fact that this phenomenon causes different material abrasion as compare to pure sliding or rolling alone. This study focuses on developing a knee prosthesis geometry, which fulfills the mechanical and kinematical requirements. Method: The MSC ADAMS program is used to describe the rotation of the human knee joint as a function of flexion, and to investigate how the flexion and rotation movement changes between the condyles of a multi-body model of the knee prosthesis as a function of flexion angle (in the functional arc of the knee (20-120º)). Moreover, the multi-body model with identical boundary conditions is constituted, and the numerical simulations are carried out using the MSC ADAMS program system. Results: It is concluded that the use of the multi-body model reduces time and cost since it does not need to manufacture the tibia and the femur as it requires for the knee prosthesis of the test machine. Moreover, without measuring or by dispensing with a test machine for the knee prosthesis geometry, approximation of the results of our model to a human knee is carried out directly. Conclusion: The pattern obtained by the multi-body model provides an insight for future experimental tests related to the rotation and flexion of the knee joint concerning the actual average and friction load.

Keywords: biomechanics, knee joint, rotation, flexion, kinematics, MSC ADAMS

Procedia PDF Downloads 149

Authors: Wong Yat Cheung Maggie

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Occupational Safety and Health Ordinance (OS&HO) (Chap 509) was enacted in 1997, OHS in HK should be growing and maturing, with a holistic approach to occupational health and safety in the workplace including physical, mental, social and spiritual well-being. The question is “How effective are OHSPs in meeting the current needs of HK health workers?” This study was designed to explore the issue for the first time, to empirically analyse the views of those who work in the system. The study employed a mixed method approach to collect various data from Occupational Health Service Providers (OHSPs), Occupational Health Service Consumers (OHSC): Registered nurses working in the hospital setting. This study was designed in two phases and two stages. Phase I Stage I was a paper survey to collect the data on OHSP. Then Phase I Stage II was a follow-up interview. Phase II Stage I was a paper survey to collect the data on OHSC. Then Phase II Stage II was a follow-up focus group study on OHSC for further clarification of the Phase II and Stage I result. The Phase I result reflects HK OHSPs point of view and their experience in the existing OHS practice in the local hospitals. It reflects various styles of reporting systems, staff profiles background and resource in providing OHS in HK hospitals. However, the basic OHS concern is similar between hospitals. In general, the OHS policies and procedures are available on site even though they may have different foci. The Phase II result is reflecting the HKs OHSCs echoes the OHSP feedback at providing of OHS, OHS concern and related policies and procedure are available on site. However, the most significant feedback from the OHSC at Phase II Stage II shows, nurses experienced various OHS concern most commonly work stress, workplace harassment and back strain without formal or official report to the related parties. The lack of reporting was due to the management handling attitude, stakeholders’ compliance and term of definition still have room to be improved even the related policies and procedures are available on site.

Keywords: occupational health service, registered nurse, Hong Kong hospital, mixed method

Procedia PDF Downloads 338

Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

Procedia PDF Downloads 141

Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

Procedia PDF Downloads 545

Authors: Farid Risheq, M. Zaid Alrisheq, Shuaa Al-Sadoon, Karim Al-Faqih, Mays Abdulazeez

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Breast cancer is the most frequently diagnosed cancer worldwide, and a common cause of death among women. Various conventional anatomical imaging tools are utilized for diagnosis, histological assessment and TNM (Tumor, Node, Metastases) staging of breast cancer. Biopsy of sentinel lymph node is becoming an alternative to the axillary lymph node dissection. Advances in 18-Fluoro-Deoxi-Glucose Positron Emission Tomography/Computed Tomography (18FDG-PET/CT) imaging have facilitated breast cancer diagnosis utilizing biological trapping of 18FDG inside lesion cells, expressed as Standardized Uptake Value (SUVmax). Objective: To present the utility of 18FDG uptake PET/CT scans in detecting active extra lymph nodes and distant occult metastases for breast cancer staging. Subjects and Methods: Four female patients were presented with initially classified TNM stages of breast cancer based on conventional anatomical diagnostic techniques. 18FDG-PET/CT scans were performed one hour post 18FDG intra-venous injection of (300-370) MBq, and (7-8) bed/130sec. Transverse, sagittal, and coronal views; fused PET/CT and MIP modality were reconstructed for each patient. Results: A total of twenty four lesions in breast, extended lesions to lung, liver, bone and active extra lymph nodes were detected among patients. The initial TNM stage was significantly changed post 18FDG-PET/CT scan for each patient, as follows: Patient-1: Initial TNM-stage: T1N1M0-(stage I). Finding: Two lesions in right breast (3.2cm2, SUVmax=10.2), (1.8cm2, SUVmax=6.7), associated with metastases to two right axillary lymph nodes. Final TNM-stage: T1N2M0-(stage II). Patient-2: Initial TNM-stage: T2N2M0-(stage III). Finding: Right breast lesion (6.1cm2, SUVmax=15.2), associated with metastases to right internal mammary lymph node, two right axillary lymph nodes, and sclerotic lesions in right scapula. Final TNM-stage: T2N3M1-(stage IV). Patient-3: Initial TNM-stage: T2N0M1-(stage III). Finding: Left breast lesion (11.1cm2, SUVmax=18.8), associated with metastases to two lymph nodes in left hilum, and three lesions in both lungs. Final TNM-stage: T2N2M1-(stage IV). Patient-4: Initial TNM-stage: T4N1M1-(stage III). Finding: Four lesions in upper outer quadrant area of right breast (largest: 12.7cm2, SUVmax=18.6), in addition to one lesion in left breast (4.8cm2, SUVmax=7.1), associated with metastases to multiple lesions in liver (largest: 11.4cm2, SUV=8.0), and two bony-lytic lesions in left scapula and cervicle-1. No evidence of regional or distant lymph node involvement. Final TNM-stage: T4N0M2-(stage IV). Conclusions: Our results demonstrated that 18FDG-PET/CT scans had significantly changed the TNM stages of breast cancer patients. While the T factor was unchanged, N and M factors showed significant variations. A single session of PET/CT scan was effective in detecting active extra lymph nodes and distant occult metastases, which were not identified by conventional diagnostic techniques, and might advantageously replace bone scan, and contrast enhanced CT of chest, abdomen and pelvis. Applying 18FDG-PET/CT scan early in the investigation, might shorten diagnosis time, helps deciding adequate treatment protocol, and could improve patients’ quality of life and survival. Trapping of 18FDG in malignant lesion cells, after a PET/CT scan, increases the retention index (RI%) for a considerable time, which might help localize sentinel lymph node for biopsy using a hand held gamma probe detector. Future work is required to demonstrate its utility.

Keywords: axillary lymph nodes, breast cancer staging, fluorodeoxyglucose positron emission tomography/computed tomography, lymph nodes

Procedia PDF Downloads 316

Authors: Demet Ozgur-Unluakin, Busenur Turkali, Ayse Karacaorenli

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Maintenance strategies can be classified into two types, which are reactive and proactive, with respect to the time of the failure and maintenance. If the maintenance activity is done after a breakdown, it is called reactive maintenance. On the other hand, proactive maintenance, which is further divided as preventive and predictive, focuses on maintaining components before a failure occurs to prevent expensive halts. Recently, the number of interacting components in a system has increased rapidly and therefore, the structure of the systems have become more complex. This situation has made it difficult to provide the right maintenance decisions. Herewith, determining effective decisions has played a significant role. In multi-component systems, many methodologies and strategies can be applied when a component or a system has already broken down or when it is desired to identify and avoid proactively defects that could lead to future failure. This study focuses on the comparison of various maintenance strategies on a multi-component dynamic system. Components in the system are hidden, although there exists partial observability to the decision maker and they deteriorate in time. Several predefined policies under corrective, preventive and predictive maintenance strategies are considered to minimize the total maintenance cost in a planning horizon. The policies are simulated via Dynamic Bayesian Networks on a multi-component system with different policy parameters and cost scenarios, and their performances are evaluated. Results show that when the difference between the corrective and proactive maintenance cost is low, none of the proactive maintenance policies is significantly better than the corrective maintenance. However, when the difference is increased, at least one policy parameter for each proactive maintenance strategy gives significantly lower cost than the corrective maintenance.

Keywords: decision making, dynamic Bayesian networks, maintenance, multi-component systems, reliability

Procedia PDF Downloads 134

Authors: William Li

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Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving.

Keywords: lane change, path planning, autonomous driving, deep reinforcement learning, 5G, V2V communications, connected vehicles

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Authors: P. C. Chiu, Jehnming Lin

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In this study, a 50-W CO₂ laser was used for the clad of 304L powders on the stainless steel substrate with a temperature sensor and image monitoring system. The laser power and cladding speed and focal position were modified to achieve the requirement of the workpiece flatness and mechanical properties. The numerical calculation is based on ANSYS to analyze the temperature change of the moving heat source at different surface positions when coating the workpiece, and the effect of the process parameters on the bath size was discussed. The temperature of stainless steel powder in the nozzle outlet reacting with the laser was simulated as a process parameter. In the experiment, the difference of the thermal conductivity in three-dimensional space is compared with single-layer cladding and multi-layer cladding. The heat dissipation pattern of the single-layer cladding is the steel plate and the multi-layer coating is the workpiece itself. The relationship between the multi-clad temperature and the profile was analyzed by the temperature signal from an IR pyrometer.

Keywords: laser cladding, temperature, profile, microstructure

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Authors: Chetna Mohabeer, Luis Reyes, Lokmane Abdelouahed, Bechara Taouk

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In the transition to sustainability and the increasing use of renewable energy, hydrogen will play a key role as an energy carrier. Biomass has the potential to accelerate the realization of hydrogen as a major fuel of the future. Pyro-gasification allows the conversion of organic matter mainly into synthesis gas, or “syngas”, majorly constituted by CO, H2, CH4, and CO2. A second, condensable fraction of biomass pyro-gasification products are “tars”. Under certain conditions, tars may decompose into hydrogen and other light hydrocarbons. These conditions include two types of cracking: homogeneous cracking, where tars decompose under the effect of temperature ( > 1000 °C), and heterogeneous cracking, where catalysts such as olivine, dolomite or biochar are used. The latter process favors cracking of tars at temperatures close to pyro-gasification temperatures (~ 850 °C). Pyro-gasification of biomass coupled with water-gas shift is the most widely practiced process route for biomass to hydrogen today. In this work, an innovating solution will be proposed for this conversion route, in that all the pyro-gasification products, not only methane, will undergo processes that aim to optimize hydrogen production. First, a heterogeneous cracking step was included in the reaction scheme, using biochar (remaining solid from the pyro-gasification reaction) as catalyst and CO2 and H2O as gasifying agents. This process was followed by a catalytic steam methane reforming (SMR) step. For this, a Ni-based catalyst was tested under different reaction conditions to optimize H2 yield. Finally, a water-gas shift (WGS) reaction step with a Fe-based catalyst was added to optimize the H2 yield from CO. The reactor used for cracking was a fluidized bed reactor, and the one used for SMR and WGS was a fixed bed reactor. The gaseous products were analyzed continuously using a µ-GC (Fusion PN 074-594-P1F). With biochar as bed material, it was seen that more H2 was obtained with steam as a gasifying agent (32 mol. % vs. 15 mol. % with CO2 at 900 °C). CO and CH4 productions were also higher with steam than with CO2. Steam as gasifying agent and biochar as bed material were hence deemed efficient parameters for the first step. Among all parameters tested, CH4 conversions approaching 100 % were obtained from SMR reactions using Ni/γ-Al2O3 as a catalyst, 800 °C, and a steam/methane ratio of 5. This gave rise to about 45 mol % H2. Experiments about WGS reaction are currently being conducted. At the end of this phase, the four reactions are performed consecutively, and the results analyzed. The final aim is the development of a global kinetic model of the whole system in a multi-stage fluidized bed reactor that can be transferred on ASPEN PlusTM.

Keywords: multi-staged fluidized bed reactor, pyro-gasification, steam methane reforming, water-gas shift

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Authors: Hameed Alshammari

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The decarbonisation of the energy sector beyond the 21ˢᵗ century is akin to establishing morally responsible mechanisms that can propagate sustainable livelihoods (Denina et al., 2021). It implies that Kuwait undertakes a re-evaluation of energy generation gaps so as to tap the potential to reduce overreliance on fossil fuel (Si et al., 2020) and align with global views on sustainable energy generation and consumption.(Herrero, Pineda, Villar, & Zambrano, 2020). Without the economic pressure to switch to alternative sources of energy, Kuwait requires a multi-dimensional analysis the energy policies andsources of energy other than fossil fuels (Alsaad, 2021).Currently, Kuwait has an energy system that is highly skewed towards fossil fuels (Alsaad, 2021); hence, the reliance on burning fossil fuels forms part of the core elements of the general inefficient energy systems that have negative consequences to global environmental and economic systems (Kang et al., 2020). This paper undertakes a detailed literature review on factors needed for the development of a framework for the multi-dimensional energy resource analysis in Kuwait. The framework aims aligning the current energy policies in Kuwait with the global decarbonisation drive, to promote sustainable energy strategies.

Keywords: decarbonisation, energy, fossil fuels, multi-dimensional analysis, sustainability

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Authors: Irfan Ali, Srikant Gupta, Aquil Ahmed

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Every country needs a sustainable development (SD) for its economic growth by forming suitable policies and initiative programs for the development of different sectors of the country. This paper is comprised of modeling and optimization of different sectors of India that form a multi-criterion model. In this paper, we developed a fractional goal programming (FGP) model that helps in providing the efficient allocation of resources simultaneously by achieving the sustainable goals in gross domestic product (GDP), electricity consumption (EC) and greenhouse gasses (GHG) emission by the year 2030. Also, a weighted model of FGP is presented to obtain varying solution according to the priorities set by the policy maker for achieving future goals of GDP growth, EC, and GHG emission. The presented models provide a useful insight to the decision makers for implementing strategies in a different sector.

Keywords: sustainable and economic development, multi-objective fractional programming, fuzzy goal programming, weighted fuzzy goal programming

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Authors: Moses C. Siame, Kazutoshi Haga, Atsushi Shibayama

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This study investigates the removal of silica, alumina and phosphorus as impurities from Sanje iron ore using wet high-intensity magnetic separation (WHIMS). Sanje iron ore contains low-grade hematite ore found in Nampundwe area of Zambia from which iron is to be used as the feed in the steelmaking process. The chemical composition analysis using X-ray Florence spectrometer showed that Sanje low-grade ore contains 48.90 mass% of hematite (Fe₂O₃) with 34.18 mass% as an iron grade. The ore also contains silica (SiO₂) and alumina (Al₂O₃) of 31.10 mass% and 7.65 mass% respectively. The mineralogical analysis using X-ray diffraction spectrometer showed hematite and silica as the major mineral components of the ore while magnetite and alumina exist as minor mineral components. Mineral particle distribution analysis was done using scanning electron microscope with an X-ray energy dispersion spectrometry (SEM-EDS) and images showed that the average mineral size distribution of alumina-silicate gangue particles is in order of 100 μm and exists as iron-bearing interlocked particles. Magnetic separation was done using series L model 4 Magnetic Separator. The effect of various magnetic separation parameters such as magnetic flux density, particle size, and pulp density of the feed was studied during magnetic separation experiments. The ore with average particle size of 25 µm and pulp density of 2.5% was concentrated using pulp flow of 7 L/min. The results showed that 10 T was optimal magnetic flux density which enhanced the recovery of 93.08% of iron with 53.22 mass% grade. The gangue mineral particles containing 12 mass% silica and 3.94 mass% alumna remained in the concentrate, therefore the concentrate was further treated in the second stage WHIMS using the same parameters from the first stage. The second stage process recovered 83.41% of iron with 67.07 mass% grade. Silica was reduced to 2.14 mass% and alumina to 1.30 mass%. Accordingly, phosphorus was also reduced to 0.02 mass%. Therefore, the two stage magnetic separation process was established using these results.

Keywords: Sanje iron ore, magnetic separation, silica, alumina, recovery

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Authors: Yuxiao Jiang, Lingjun Meng, Mengyu Zhan, Lichunyi Zhang, Yingxia Yun

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On August 8th of 2015, a serious explosion occurred in Binhai New Area of Tianjin. This explosion led to the suspension of Tianjin-Binhai Light Rail Line 9 which was an important transportation mean connecting the old and new urban areas and the suspension causes inconvenience to commuters traveling from Tianjin to Binhai or Binhai to Tianjin and residents living by Line 9. On this regard, this paper intends to give suggestions on how to develop multi-mode transportation system rapidly and effectively after a disaster and tackle with the problems in terms of transportation infrastructure facilities. The paper proposes the idea of traffic resilience which refers to the city’s ability to restore its transportation system and reduce risks when the transportation system is destroyed by a disaster. By doing questionnaire research, on the spot study and collecting data from the internet, a GIS model is established so as to analyze the alternative traffic means used by different types of residents and study the transportation supply and demand. The result shows that along the Line 9, there is a larger demand for alternative traffic means in the place which is nearer to the downtown area. Also, the distribution of bus stations is more reasonable in the place nearer to downtown area, however, the traffic speed in the area is slower. Based on traffic resilience, the paper raises strategies to develop post-disaster multi-mode transportation system such as establishing traffic management mechanism timely and effectively, building multi-mode traffic networks, improving intelligent traffic systems and so on.

Keywords: traffic resilience, multi-mode transportation system, public traffic, transportation demand

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Authors: V. Howard, R. Maguire, S. Corrigan

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Background: Type 1 Diabetes (T1D) is a chronic autoimmune disease, typically diagnosed in childhood. T1D puts an enormous strain on families; controlling blood-glucose in children is difficult and the consequences of poor control for patient health are significant. Successful illness management and better health outcomes can be dependent on quality of caregiving. On diagnosis, parent-caregivers face a steep learning curve as T1D care requires a significant level of knowledge to inform complex decision making throughout the day. The majority of illness management is carried out in the home setting, independent of clinical health providers. Parent-caregivers vary in their level of knowledge and their level of engagement in applying this knowledge in the practice of illness management. Enabling researchers to quantify these aspects of the caregiver experience is key to identifying targets for psychosocial support interventions, which are desirable for reducing stress and anxiety in this highly burdened cohort, and supporting better health outcomes in children. Currently, there are limited tools available that are designed to capture this information. Where tools do exist, they are not comprehensive and do not adequately capture the lived experience. Objectives: Development of quantitative tools, informed by lived experience, to enable researchers gather data on parent-caregiver knowledge and engagement, which accurately represents the experience/cohort and enables exploration of questions that are of real-world value to the cohort themselves. Methods: This research employed an engaged approach to address the problem of quantifying two key aspects of caregiver diabetes management: Knowledge and engagement. The research process was multi-staged and iterative. Stage 1: Working from a constructivist standpoint, literature was reviewed to identify relevant questionnaires, scales and single-item measures of T1D caregiver knowledge and engagement, and harvest candidate questionnaire items. Stage 2: Aggregated findings from the review were circulated among a PPI (patient and public involvement) expert panel of caregivers (n=6), for discussion and feedback. Stage 3: In collaboration with the expert panel, data were interpreted through the lens of lived experience to create a long-list of candidate items for novel questionnaires. Items were categorized as either ‘knowledge’ or ‘engagement’. Stage 4: A Delphi-method process (iterative surveys) was used to prioritize question items and generate novel questions that further captured the lived experience. Stage 5: Both questionnaires were piloted to refine wording of text to increase accessibility and limit socially desirable responding. Stage 6: Tools were piloted using an online survey that was deployed using an online peer-support group for caregivers for Juveniles with T1D. Ongoing Research: 123 parent-caregivers completed the survey. Data analysis is ongoing to establish face and content validity qualitatively and through exploratory factor analysis. Reliability will be established using an alternative-form method and Cronbach’s alpha will assess internal consistency. Work will be completed by early 2024. Conclusion: These tools will enable researchers to gain deeper insights into caregiving practices among parents of juveniles with T1D. Development was driven by lived experience, illustrating the value of engaged research at all levels of the research process.

Keywords: caregiving, engaged research, juvenile type 1 diabetes, quantified engagement and knowledge

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Authors: Farzad Sharifi, Raziyeh Shamsi

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In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose molti-objective DEA-R model, because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduces the efficiency score), an efficient DMU is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other case, only the ratio of stochastic data may be available (e.g; the ratio of stochastic inputs to stochastic outputs). Thus, we provide multi objective DEA model without explicit outputs and prove that in-put oriented MOP DEA-R model in the invariable return to scale case can be replacing by MOP- DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model, yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA, MOLP, STOCHASTIC, DEA-R

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Authors: Huinan Zhang, Wenjie Jiang

Abstract:

Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Mersha Tewodros Getnet, Mengistu Ketema, Bamlaku Alemu, Girma Demilew

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The availability of forest products determines the possibilities for forest-based livelihood options. Plantation forest is a widespread economic activity in highland areas of the Amhara regional state, owing primarily to degradation and limited access to natural forests. As a result, tree plantation has become one of the rural livelihood options in the area. Therefore, given the increasing importance of smallholder plantations in highland areas of Amhara Regional States, the aim of this research was to evaluate the extent of smallholder plantations and their socio-economic impact. To address the abovementioned research, a sequential embedded mixed research design was employed. This qualitative and quantitative information was gathered from both primary and secondary sources. Primary data were collected from 385 sample households, which were chosen using a three-stage, multi-stage sampling method based on the Cochran sample size formula. Both descriptive and inferential statistics were used to analyze the data. Smallholder eucalyptus plantations in the study area were discovered to be common, and they are now part of the livelihood portfolio for meeting both household wood consumption and generating cash income. According to the PSM model's ATT results, income from selling farm forest products certainly contributes more to total household income, farm expenditure per cultivated land, and education spending than non-planter households. As a result, the government must strengthen plantation practices by prioritizing specific intervention areas while implementing measures to counteract the plantation's inequality-increasing effect through a variety of means, including progressive taxation.

Keywords: smallholder plantation, Eucalyptus, propensity score matching, average treatment effect and income

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Authors: Seyed Vahid Kamal Alavi

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Italy has experienced a notable quantity and impact of disasters due to natural hazards and technological accidents caused by diverse risk sources on its physical, technological, and human/sociological infrastructures during past decade. This study discusses the frequency and impacts of the most three physical devastating natural hazards in Italy for the period 2000–2013. The approach examines the reliability of a range of open source WebGIS techniques versus a proposed multi-hazard risk management methodology. Spatial and attribute data which include USGS publically available hazard data and thirteen years Munich RE recorded data for Italy with different severities have been processed, visualized in a GIS (Geographic Information System) framework. Comparison of results from the study showed that the multi-hazard risk maps generated using open source techniques do not provide a reliable system to analyze the infrastructures losses in respect to national risk sources while they can be adopted for general international risk management purposes. Additionally, this study establishes the possibility to critically examine and calibrate different integrated techniques in evaluating what better protection measures can be taken in an area.

Keywords: multi-hazard risk mapping, risk management, GIS, Italy

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Authors: Henri El Zakhem, Anthony Dahdah, Lara Frangieh, Jessica Koura

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Nowadays, the removal of whey produced in the dairy processes has been the most important problem in the dairy industry. Every year, about 47% of the 115 million tons of whey produced world-wide are disposed in the environment. Whey is a nutritious liquid, containing whey proteins (β-lactoglobulin, α-lactalbumin, immunoglobulin-G, proteose pepton), lactose, vitamins (B5, B2, C, and B6), minerals (Calcium, Magnesium, Phosphorous, Potassium, Chloride, and Sodium), and trace elements (Zinc, Iron, Iodine, and Copper). The first objective was to increase the economical and commercial value of whey which is considered as by-product. The second objective of this study was to formulate a new baby food with good nutritional, sensory and storage properties and acceptable to consumers using the cheese whey. The creation of the new product must pass through the following stages: idea stage, development stage which includes the business planning and the product development prototype, packaging stage, production stage, test marketing stage, quality control/sanitation. Three types of whey-based food were selected and prepared by mixing whey and apple, whey and banana as well as whey, apple, and banana.To compile with the recommended dietary allowances (RDA) and adequate intakes (AI) for vitamins and minerals, each sample is formed from 114g of sliced and smashed fruits mixed with 8 mL of whey. Mixtures are heated to 72oC for 15 seconds, and filled in pasteurized jars. Jars were conserved at 4oC. Following the experimental part, sensory evaluation made by an experienced panel took place. Hedonic tests results show that the mixture of whey, apple, and banana has the most delicious and sweetness taste followed by the mixture of whey and banana, and finally the mixture of whey and apple. This study was concluded with a managerial and engineering study that reveals that the project is economically profitable to be executed in Lebanon.

Keywords: baby food, by-product, cheese whey, formulation

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Authors: Shangqing Song, Bin Xu, Yajun Cheng, Zhong Wang

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miRNAs derived from exosomes exist in a body fluid such as urine were regarded as potential biomarkers for various human cancers diagnosis and prognosis, as mature miRNAs can be steadily preserved by exosomes. However, its potential value in clear cell renal cell carcinoma (ccRCC) diagnosis and prognosis remains unclear. In the present study, differentially expressed miRNAs from urinal exosomes were identified by next-generation sequencing (NGS) technology. The 16 differentially expressed miRNAs were identified between ccRCC patients and healthy donors. To explore the specific diagnosis biomarker of ccRCC, we validated these urinary exosomes from 70 early-stage renal cancer patients, 30 healthy people and other urinary system cancers, including 30 early-stage prostate cancer patients and 30 early-stage bladder cancer patients by qRT-PCR. The results showed that urinary exosome miR-30c-5p could be stably amplified and meanwhile the expression of miR-30c-5p has no significant difference between other urinary system cancers and healthy control, however, expression level of miR-30c-5p in urinary exosomal of ccRCC patients was lower than healthy people and receiver operation characterization (ROC) curve showed that the area under the curve (AUC) values was 0.8192 (95% confidence interval was 0.7388-0.8996, P= 0.0000). In addition, up-regulating miR-30c-5p expression could inhibit renal cell carcinoma cells growth. Lastly, HSP5A was found as a direct target gene of miR-30c-5p. HSP5A depletion reversed the promoting effect of ccRCC growth casued by miR-30c-5p inhibitor, respectively. In conclusion, this study demonstrated that urinary exosomal miR-30c-5p is readily accessible as diagnosis biomarker of early-stage ccRCC, and miR-30c-5p might modulate the expression of HSPA5, which correlated with the progression of ccRCC.

Keywords: clear cell renal cell carcinoma, exosome, HSP5A, miR-30c-5p

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Authors: Mohamed R. Mhereeg

Abstract:

The paper discusses the design of a .NET Windows Service based agent system called MACS (Multi-Agent Classification System). MACS is a system aims to accurately classify spread-sheet developers competency over a network. It is designed to automatically and autonomously monitor spread-sheet users and gather their development activities based on the utilization of the software Multi-Agent Technology (MAS). This is accomplished in such a way that makes management capable to efficiently allow for precise tailor training activities for future spread-sheet development. The monitoring agents of MACS are intended to be distributed over the WWW in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus methodology is used for the design of the agents of MACS. Prometheus has been used to undertake this phase of the system design because it is developed specifically for specifying and designing agent-oriented systems. Additionally, Prometheus specifies also the communication needed between the agents in order to coordinate to achieve their delegated tasks.

Keywords: classification, design, MACS, MAS, prometheus

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Authors: Loyce Nahwera, Joy Wachira, Edwin Kiptolo, Constance Nsibambi, Mshilla Maghanga, Timothy Makubuya

Abstract:

Introduction: Hypertension is one of the most prominent risk factors for cardiovascular diseases globally, and it can be modified through lifestyle interventions such as exercise. The objective of this study was to investigate the effects of a 12-week aerobic dance programme on systolic blood pressure (SBP) in stage 1 hypertensive individuals. Methods: This study employed an experimental research design. A total of 36 stage 1 hypertensive individuals who were randomly assigned into experimental and control groups completed the study. Systolic BP was measured using a mercury sphygmomanometer at baseline, mid-point and after the program. The experimental group participants trained 3 days a week, 45 minutes per session, at a moderate intensity of 40-60% of maximum oxygen consumption (VO2max) monitored by Garmin heart rate monitors. Data were analyzed using SPSS version 20. The significance level was set at p<0.05. A paired sample t-test was used to compare mean differences within the groups. Results: Data from the 36 participants (22 males and 14 females) (experimental; n=18, control; n=18) show that the experimental group had a mean SBP of 143.83±6.382 mmHg at baseline while the control had a mean of 137.61±6.400 mmHg. Following the end of a 6-week aerobic dance, the mean SBP of the experimental group reduced to 138.06±9.539 mmHg while that of the control marginally decreased to 137.00±8.073 mmHg. At the completion of a 12-week program, the mean SBP of the experimental group reduced to 136.33±9.191 mmHg, while that of the control marginally increased to 139.56±9.954 mmHg. This implies that both the 6-week and 12-week aerobic dance program reduced the SBP of the experimental group by 5.77±7.133 mmHg and 7.50±8.487 mmHg, respectively, while the control group fast reduced marginally by 0.61 before ultimately increasing by 1.95±7.974 mmHg at 12-weeks. The changes were statistically significant (p<0.05) at both 6 and 12 weeks of an aerobic dance program. Conclusion: The study concluded that aerobic dance is an effective non-pharmacological method for managing SBP of stage 1 hypertensive individuals both in the short-term (6 weeks) and long-term (12 weeks).

Keywords: aerobic dance, blood pressure, stage 1 hypertension, systolic blood pressure.

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