Search results for: functional optimization

Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: Usarat Thawornchaisit, Kamalasiri Juthaisong, Kasama Parsongjeen, Phonsiri Phoengchan

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In this study, sludge samples that were collected from the wastewater treatment plant of a printed circuit board manufacturing industry in Thailand were subjected to acid extraction using sulfuric acid as the chemical extracting agent. The effects of sulfuric acid concentration (A), the ratio of a volume of acid to a quantity of sludge (B) and extraction time (C) on the efficiency of copper extraction were investigated with the aim of finding the optimal conditions for maximum removal of copper from the wastewater treatment sludge. Factorial experimental design was employed to model the copper extraction process. The results were analyzed statistically using analysis of variance to identify the process variables that were significantly affected the copper extraction efficiency. Results showed that all linear terms and an interaction term between volume of acid to quantity of sludge ratio and extraction time (BC), had statistically significant influence on the efficiency of copper extraction under tested conditions in which the most significant effect was ascribed to volume of acid to quantity of sludge ratio (B), followed by sulfuric acid concentration (A), extraction time (C) and interaction term of BC, respectively. The remaining two-way interaction terms, (AB, AC) and the three-way interaction term (ABC) is not statistically significant at the significance level of 0.05. The model equation was derived for the copper extraction process and the optimization of the process was performed using a multiple response method called desirability (D) function to optimize the extraction parameters by targeting maximum removal. The optimum extraction conditions of 99% of copper were found to be sulfuric acid concentration: 0.9 M, ratio of the volume of acid (mL) to the quantity of sludge (g) at 100:1 with an extraction time of 80 min. Experiments under the optimized conditions have been carried out to validate the accuracy of the Model.

Keywords: acid treatment, chemical extraction, sludge, waste management

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Authors: B. Bouadjemi, S. Bentata, W. Benstaali, A. Abbad

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The electronic and magnetic properties of the cubic praseodymium oxides perovskites PrMnO3 were calculated using the density functional theory (DFT) with both generalized gradient approximation (GGA) and GGA+U approaches, where U is on-site Coulomb interaction correction. The results show a half-metallic ferromagnetic ground state for PrMnO3 in GGA+U approached, while semi-metallic ferromagnetic character is observed in GGA. The results obtained, make the cubic PrMnO3 a promising candidate for application in spintronics.

Keywords: first-principles, electronic properties, transition metal, materials science

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Authors: I. Kamika, S. Azizi, M. Tekere

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Nanotechnology promises significant improvements of advanced materials and manufacturing techniques with a vast range of applications, which are critical for the future competitiveness of national industries. The manipulations and productions of materials, whilst, controlling the optical properties and surface area to a nanosize scale enabled a birth of a new field known as nanotechnology. However, their rapidly developing industry raises concerns about the environmental impacts of nanoparticles, as their effects on functional bacterial community in wastewater treatment remain unclear. The present research assessed the impact of cerium Oxide nanoparticles (nCeO) on the bacterial microbiome of an activated sludge system, which influenced its performance of this system on nutrient removal. Out of 15875 reads sequenced, a total of 13133 reads were non-chimeric. The wastewater samples were more dominant to the unclassified bacteria (51.07% of bacteria community) followed with the classified bacteria (48.93). Proteobacteria was the most dominant phylum in both classified and unclassified bacteria, whereas 18% of bacteria could even not be assigned a phylum and remained unclassified suggesting hitherto vast untapped microbial diversity. The bacterial operational taxonomic units (OTUs) ranged from 1014 to 2629 over the experimental period. The denitrification related species including Diaphorobacter species, Thauera species and those in the Sphaerotilus and Leptothrix group were found to be inhibited in a high concentration of CeO-NP. The diversity indices suggested that the bacterial community inhabiting the wastewater samples were less diverse as the concentration of CeO increases. The canonical correspondence analysis (CCA) results highlighted that the bacterial community variance had the strongest relationship with water temperature, conductivity, pH, and dissolved oxygen (DO) content as well as nCeO. The results provided the relationships between the microbial community and environmental variables in the wastewater samples.

Keywords: bacterial community, next generation, cerium oxide, wastewater, activated sludge, nanoparticles, nanotechnology

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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Authors: Mohamed M. Mashaly, Ahmed M. F. El Shiwi

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Background: Shoulder impingement syndrome is an encroachment of subacromial tissues, rotator cuff, subacromial bursa, and the long head of the biceps tendon, as a result of narrowing of the subacromial space. Activities requiring repetitive or sustained use of the arms over head often predispose the rotator cuff tendon to injury. Purpose: To compare between Combined effect therapeutic exercises and Shockwave therapy versus therapeutic exercises and phonophoresis in the treatment of shoulder impingement syndrome. Methods: Thirty patients diagnosed as shoulder impingement syndrome stage II Neer classification due to mechanical causes. Patients were randomly distributed into two equal groups. The first group consisted of 15 patients with a mean age of (45.46+8.64) received therapeutic exercises (stretching exercise of posterior shoulder capsule and strengthening exercises of shoulder muscles) and shockwave therapy (6000 shocks, 2000/session, 3 sessions, 2 weeks apart, 0.22mJ/mm^2) years. The second group consisted of 15 patients with a mean age of 46.26 (+ 8.05) received same therapeutic exercises and phonophoresis (3 times per week, each other day, for 4 consecutive weeks). Patients were evaluated pretreatment and post treatment for shoulder pain severity, shoulder functional disability, shoulder flexion, abduction and internal rotation motions. Results: Patients of both groups showed significant improvement in all the measured variables. In between groups difference the shock wave group showed a significant improvement in all measured variables than phonophoresis group. Interpretation/Conclusion: Combined effect of therapeutic exercises and shock wave were more effective than therapeutic exercises and phonophoresis on decreasing shoulder pain severity, shoulder functional disability, increasing in shoulder flexion, abduction, internal rotation in patients with shoulder impingement syndrome.

Keywords: shoulder impingement syndrome, therapeutic exercises, shockwave, phonophoresis

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Authors: Arash Parehkar, S. Jamshid Mousavi, Shoubo Bayazidi, Vahid Karami, Laleh Shahidi, Arash Azaranfar, Ali Moridi, M. Shabakhti, Tayebeh Ariyan, Mitra Tofigh, Kaveh Masoumi, Alireza Motahari

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Water resource systems modelling have constantly been a challenge through history for human being. As the innovative methodological development is evolving alongside computer sciences on one hand, researches are likely to confront more complex and larger water resources systems due to new challenges regarding increased water demands, climate change and human interventions, socio-economic concerns, and environment protection and sustainability. In this research, an automatic calibration scheme has been applied on the Gilan’s large-scale water resource model using mathematical programming. The water resource model’s calibration is developed in order to attune unknown water return flows from demand sites in the complex Sefidroud irrigation network and other related areas. The calibration procedure is validated by comparing several gauged river outflows from the system in the past with model results. The calibration results are pleasantly reasonable presenting a rational insight of the system. Subsequently, the unknown optimized parameters were used in a basin-scale linear optimization model with the ability to evaluate the system’s performance against a reduced inflow scenario in future. Results showed an acceptable match between predicted and observed outflows from the system at selected hydrometric stations. Moreover, an efficient operating policy was determined for Sefidroud dam leading to a minimum water shortage in the reduced inflow scenario.

Keywords: auto-calibration, Gilan, large-scale water resources, simulation

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Authors: Rima Alfaraj, Abeer Alarawi, Murtadha AlTammar

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The global energy landscape heavily relies on the oil and gas industry, which faces the critical challenge of reducing its carbon footprint. To address this issue, the integration of advanced materials like aerogels has emerged as a promising solution to enhance sustainability and environmental performance within the industry. This study thoroughly examines the application of aerogel-based technologies in the oil and gas sector, focusing particularly on their role in carbon capture and storage (CCS) initiatives. Aerogels, known for their exceptional properties, such as high surface area, low density, and customizable pore structure, have garnered attention for their potential in various CCS strategies. The review delves into various fabrication techniques utilized in producing aerogel materials, including sol-gel, supercritical drying, and freeze-drying methods, to assess their suitability for specific industry applications. Beyond fabrication, the practicality of aerogel materials in critical areas such as flow assurance, enhanced oil recovery, and thermal insulation is explored. The analysis spans a wide range of applications, from potential use in pipelines and equipment to subsea installations, offering valuable insights into the real-world implementation of aerogels in the oil and gas sector. The paper also investigates the adsorption and storage capabilities of aerogel-based sorbents, showcasing their effectiveness in capturing and storing carbon dioxide (CO₂) molecules. Optimization of pore size distribution and surface chemistry is examined to enhance the affinity and selectivity of aerogels towards CO₂, thereby improving the efficiency and capacity of CCS systems. Additionally, the study explores the potential of aerogel-based membranes for separating and purifying CO₂ from oil and gas streams, emphasizing their role in the carbon capture and utilization (CCU) value chain in the industry. Emerging trends and future perspectives in integrating aerogel-based technologies within the oil and gas sector are also discussed, including the development of hybrid aerogel composites and advanced functional components to further enhance material performance and versatility. By synthesizing the latest advancements and future directions in aerogel used for CCS applications in the oil and gas industry, this review offers a comprehensive understanding of how these innovative materials can aid in transitioning towards a more sustainable and environmentally conscious energy landscape. The insights provided can assist in strategic decision-making, drive technology development, and foster collaborations among academia, industry, and policymakers to promote the widespread adoption of aerogel-based solutions in the oil and gas sector.

Keywords: CCS, porous, carbon capture, oil and gas, sustainability

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Authors: Sina Moradi, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Patrick Hayde, Rose Amal

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Chloramine is commonly used as a disinfectant in drinking water distribution systems (DWDSs), particularly in Australia and the USA. Maintaining a chloramine residual throughout the DWDS is important in ensuring microbiologically safe water is supplied at the customer’s tap. In order to simulate how chloramine behaves when it moves through the distribution system, a water quality network model (WQNM) can be applied. In this work, the WQNM was based on mono-chloramine decomposition reactions, which enabled prediction of mono-chloramine residual at different locations through a DWDS in Australia, using the Bentley commercial hydraulic package (Water GEMS). The accuracy of WQNM predictions is influenced by a number of water quality parameters. Optimization of these parameters in order to obtain the closest results in comparison with actual measured data in a real DWDS would result in both cost reduction as well as reduction in consumption of valuable resources such as energy and materials. In this work, the optimum operating conditions of water quality parameters (i.e. temperature, pH, and initial mono-chloramine concentration) to maximize the accuracy of mono-chloramine residual predictions for two water supply scenarios in an entire network were determined using response surface methodology (RSM). To obtain feasible and economical water quality parameters for highest model predictability, Design Expert 8.0 software (Stat-Ease, Inc.) was applied to conduct the optimization of three independent water quality parameters. High and low levels of the water quality parameters were considered, inevitably, as explicit constraints, in order to avoid extrapolation. The independent variables were pH, temperature and initial mono-chloramine concentration. The lower and upper limits of each variable for two water supply scenarios were defined and the experimental levels for each variable were selected based on the actual conditions in studied DWDS. It was found that at pH of 7.75, temperature of 34.16 ºC, and initial mono-chloramine concentration of 3.89 (mg/L) during peak water supply patterns, root mean square error (RMSE) of WQNM for the whole network would be minimized to 0.189, and the optimum conditions for averaged water supply occurred at pH of 7.71, temperature of 18.12 ºC, and initial mono-chloramine concentration of 4.60 (mg/L). The proposed methodology to predict mono-chloramine residual can have a great potential for water treatment plant operators in accurately estimating the mono-chloramine residual through a water distribution network. Additional studies from other water distribution systems are warranted to confirm the applicability of the proposed methodology for other water samples.

Keywords: chloramine decay, modelling, response surface methodology, water quality parameters

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Authors: Masome Moeni, Roya Abedizadeh, Elham Aram, Hamid Sadeghi-Abandansari, Davood Sabour, Robert Menzel, Ali Hassanpour

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Mitochondria- targeting immunogenic cell death inducers (MT-ICD) have been designed to trigger intrinsic apoptosis signalling pathway in malignant cells and revive the antitumour immune system. MT-ICD inducers have considered to be non-specific, which can deteriorate the ability to initiate mitochondria-selective oxidative stress, causing high toxicity. Iron oxide nanoparticles (IONPs) can be an ideal candidate as vehicles for utilizing in immunotherapy due to their biocompatibility, modifiable surface chemistry, magnetic characteristics and multi-functional applications in single platform. These types of NPs can facilitate a real time imaging which can provide an effective strategy to analyse pharmacokinetic parameters of nano-formula, including blood circulation time, targeted and controlled release at tumour microenvironment. To our knowledge, the conjugation of IONPs with MT-ICD and oxaliplatin (a chemotherapeutic agent used for the treatment of colorectal cancer) for immunotherapy have not been investigated. Herein, IONPs were generated via co-precipitation reaction at high temperatures, followed by coating the colloidal suspension with tetraethyl orthosilicate and 3-aminopropyltriethoxysilane to optimize their bio-compatibility, preventing aggregation and maintaining stability at physiological pH, then functionalized with (3-carboxypropyl) triphenyl phosphonium bromide for mitochondrial delivery. Analytical results demonstrated the successful process of IONPs functionalization. In particular, the colloidal particles of doped IONPs exhibited an excellent stability and dispersibility. The resultant particles were also successfully loaded with the oxaliplatin for an active mitochondrial targeting in immunotherapy, resulting in well-maintained super-paramagnetic characteristics and stable structure of the functionalized IONPs with nanoscale particle sizes.

Keywords: Immunotherapy, mitochondria, cancer, iron oxide nanoparticle

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper presents a Guidance and Control (G&C) strategy to address spacecraft maneuvering problem for future Rendezvous and Docking (RVD) missions. The proposed strategy allows safe and propellant efficient trajectories for space servicing missions including tasks such as approaching, inspecting and capturing. This work provides the validation test results of the G&C laws using a Hardware-In-the-Loop (HIL) setup with two robotic mockups representing the chaser and the target spacecraft. Through this paper, the challenges of the relative motion control in space are first summarized, and in particular, the constraints imposed by the mission, spacecraft and, onboard processing capabilities. Second, the proposed algorithm is introduced by presenting the formulation of constrained Model Predictive Control (MPC) to optimize the fuel consumption and explicitly handle the physical and geometric constraints in the system, e.g. thruster or Line-Of-Sight (LOS) constraints. Additionally, the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description and accordingly explained. The resulting convex optimization problem allows real-time implementation capability based on a detailed discussion on the computational time requirements and the obtained results with respect to the onboard computer and future trends of space processors capabilities. Finally, the performance of the algorithm is presented in the scope of a potential future mission and of the available equipment. The results also cover a comparison between the proposed algorithms with Linear–quadratic regulator (LQR) based control law to highlight the clear advantages of the MPC formulation.

Keywords: autonomous vehicles, embedded optimization, real-time experiment, rendezvous and docking, space robotics

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Authors: Feridun Demir, Pelin Okdem

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Polymers are versatile materials with many unique properties, such as low density, reasonable strength, flexibility, and easy processability. However, the mechanical properties of these materials are insufficient for many engineering applications. Therefore, there is a continuous search for new polymeric materials with improved properties. Polymeric nanocomposites are an advanced class of composite materials that have attracted great attention in both academic and industrial fields. Since nano-reinforcement materials are very small in size, they provide ultra-large interfacial area per volume between the nano-element and the polymer matrix. This allows the nano-reinforcement composites to exhibit enhanced toughness without compromising hardness or optical clarity. PPy and MWCNT/PPy nanocomposites were synthesized by the chemical oxidative polymerization method and the supercapacitor properties of the obtained nanocomposites were investigated. In addition, pure MWCNT was functionalized with acid (H₂SO₄/H₂O₂) and base (NH₄OH/H₂O₂) solutions at a ratio of 3:1 and a-MWCNT/d-PPy, and b-MWCNT/d-PPy nanocomposites were obtained. The homogeneous distribution of MWCNTs in the polypyrrole matrix and shell-core type morphological structures of the nanocomposites was observed with SEM images. It was observed with SEM, FTIR and XRD analyses that the functional groups formed by the functionalization of MWCNTs caused the MWCNTs to come together and partially agglomerate. It was found that the conductivity of the nanocomposites consisting of MWCNT and d-PPy was higher than that of pure d-PPy. CV, GCD and EIS results show that the use of a-MWCNT and b-MWCNTs in nanocomposites with low particle content positively affects the supercapacitor properties of the materials but negatively at high particle content. It was revealed that the functional MWCNT particles combined in nanocomposites with high particle content cause a decrease in the conductivity and distribution of ions in the electrodes and, thus, a decrease in their energy storage capacity.

Keywords: polypyrrole, multi-walled carbon nanotube (MWCNT), conducting polymer, chemical oxidative polymerization, nanocomposite, supercapacitor

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Authors: Ajith J. Kings, L. R. Monisha Miriam, R. Edwin Raj, S. Julyes Jaisingh, S. Gavaskar

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Microalgae are a potential bio-source for rejuvenated solutions in various disciplines of science and technology, especially in medicine and energy. Biodiesel is being replaced for conventional fuels in automobile industries with reduced pollution and equivalent performance. Since it is a carbon neutral fuel by recycling CO2 in photosynthesis, global warming potential can be held in control using this fuel source. One of the ways to meet the rising demand of automotive fuel is to adopt with eco-friendly, green alternative fuels called sustainable microalgal biodiesel. In this work, a microalga Chlamydomonas reinhardtii was cultivated and optimized in different media compositions developed from under-utilized waste materials in lab scale. Using the optimized process conditions, they are then mass propagated in out-door ponds, harvested, dried and oils extracted for optimization in ambient conditions. The microalgal oil was subjected to two step esterification processes using acid catalyst to reduce the acid value (0.52 mg kOH/g) in the initial stage, followed by transesterification to maximize the biodiesel yield. The optimized esterification process parameters are methanol/oil ratio 0.32 (v/v), sulphuric acid 10 vol.%, duration 45 min at 65 ºC. In the transesterification process, commercially available alkali catalyst (KOH) is used and optimized to obtain a maximum biodiesel yield of 95.4%. The optimized parameters are methanol/oil ratio 0.33(v/v), alkali catalyst 0.1 wt.%, duration 90 min at 65 ºC 90 with smooth stirring. Response Surface Methodology (RSM) is employed as a tool for optimizing the process parameters. The biodiesel was then characterized with standard procedures and especially by GC-MS to confirm its compatibility for usage in internal combustion engine.

Keywords: microalgae, organic media, optimization, transesterification, characterization

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Authors: Yuekun Chen, Yousef Sardahi, Salam Hajjar, Christopher Greer

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This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems.

Keywords: cascade control, multi-Loop control systems, multiobjective optimization, optimal control

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Authors: Majid Manawer Alenezi, Gavin Lawrence, Hans-Peter Kubis

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Imagery training could be an important treatment for muscle function improvements in patients who are facing limitations in exercise training by pain or other adverse symptoms. However, recent studies are mostly limited to small muscle groups and are often contradictory. Moreover, a possible bilateral transfer effect of imagery training has not been examined. We, therefore, investigated the effectiveness of unilateral imagery training in comparison with exercise training on hip abductor muscle strength and EMG. Additionally, both limbs were assessed to investigate bilateral transfer effects. Healthy individuals took part in an imagery or exercise training intervention for two weeks and were assesses pre and post training. Participants (n=30), after randomization into an imagery and an exercise group, trained 5 times a week under supervision with additional self-performed training on the weekends. The training consisted of performing, or to imagine, 5 maximal isometric hip abductor contractions (= one set), repeating the set 7 times. All measurements and trainings were performed laying on the side on a dynamometer table. The imagery script combined kinesthetic and visual imagery with internal perspective for producing imagined maximal hip abduction contractions. The exercise group performed the same number of tasks but performing the maximal hip abductor contractions. Maximal hip abduction strength and EMG amplitudes were measured of right and left limbs pre- and post-training period. Additionally, handgrip strength and right shoulder abduction (Strength and EMG) were measured. Using mixed model ANOVA (strength measures) and Wilcoxen-tests (EMGs), data revealed a significant increase in hip abductor strength production in the imagery group on the trained right limb (~6%). However, this was not reported for the exercise group. Additionally, the left hip abduction strength (not used for training) did not show a main effect in strength, however, there was a significant interaction of group and time revealing that the strength increased in the imagery group while it remained constant in the exercise group. EMG recordings supported the strength findings showing significant elevation of EMG amplitudes after imagery training on right and left side, while the exercise training group did not show any changes. Moreover, measures of handgrip strength and shoulder abduction showed no effects over time and no interactions in both groups. Experiments showed that imagery training is a suitable method for effectively increasing functional parameters of larger limb muscles (strength and EMG) which were enhanced on both sides (trained and untrained) confirming a bilateral transfer effect. Indeed, exercise training did not reveal any increases in the parameters above omitting functional improvements. The healthy individuals tested might not easily achieve benefits from exercise training within the time tested. However, it is evident that imagery training is effective in increasing the central motor command towards the muscles and that the effect seems to be segmental (no increase in handgrip strength and shoulder abduction parameters) and affects both sides (trained and untrained). In conclusion, imagery training was effective in functional improvements in limb muscles and produced a bilateral transfer on strength and EMG measures.

Keywords: imagery, exercise, physiotherapy, motor imagery

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Authors: Djeffal Asma, Zemmouri Noureddine

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In order for building designer to be able to balance environmental concerns with other performance requirements, they need clear and concise information. For certain decisions during the design process, qualitative guidance, such as design checklists or guidelines information may not be sufficient for evaluating the environmental benefits between different building materials, products and designs. In this case, quantitative information, such as that generated through a life cycle assessment, provides the most value. LCA provides a systematic approach to evaluating the environmental impacts of a product or system over its entire life. In the case of buildings life cycle includes the extraction of raw materials, manufacturing, transporting and installing building components or products, operating and maintaining the building. By integrating LCA into building design process, designers can evaluate the life cycle impacts of building design, materials, components and systems and choose the combinations that reduce the building life cycle environmental impact. This article attempts to give an overview of the integration of LCA methodology in the context of building design, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. A multiple case study was conducted in order to assess the benefits of the LCA as a decision making aid tool during the first stages of the building design under specific climate conditions of the North East region of Algeria. It is clear that the LCA methodology can help to assess and reduce the impact of a building design and components on the environment even if the process implementation is rather long and complicated and lacks of global approach including human factors. It is also demonstrated that using LCA as a multi objective optimization of building process will certainly facilitates the improvement in design and decision making for both new design and retrofit projects.

Keywords: life cycle assessment, buildings, sustainability, elementary schools, environmental impacts

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Authors: Francesca Nicolosi, Teresa Cosola

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Climate changes in Mediterranean areas, such as the increase of average seasonal temperatures, the urban heat island phenomenon, the intensification of solar radiation and the extreme weather threats, cause disruption events, so that climate adaptation has become a pressing issue. Due to the strategic role that the built heritage holds in terms of environmental impact and energy waste and its potentiality, it is necessary to assess the vulnerability and the adaptive capacity of the existing building to climate change, in order to define different mitigation scenarios. The aim of this research work is to define an optimized and integrated methodology for the assessment of resilience levels and adaptation scenarios for existing buildings in Mediterranean urban areas. Moreover, the study of resilience indicators allows us to define building environmental and energy performance in order to identify the design and technological solutions for the improvement of the building and its urban area potentialities. The methodology identifies step-by-step different phases, starting from the detailed study of characteristic elements of urban system: climatic, natural, human, typological and functional components are analyzed in their critical factors and their potential. Through the individuation of the main perturbing factors and the vulnerability degree of the system to the risks linked to climate change, it is possible to define mitigation and adaptation scenarios. They can be different, according to the typological, functional and constructive features of the analyzed system, divided into categories of intervention, and characterized by different analysis levels (from the single building to the urban area). The use of software simulations allows obtaining information on the overall behavior of the building and the urban system, to generate predictive models in the medium and long-term environmental and energy retrofit and to make a comparative study of the mitigation scenarios identified. The studied methodology is validated on a case study.

Keywords: climate impact mitigation, energy efficiency, existing building heritage, resilience

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Authors: David Dolbow, Daniel Credeur, Mujtaba Rahimi, Dobrivoje Stokic, Jennifer Lemacks, Andrew Courtner

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Introduction: Obesity is at epidemic proportions in the spinal cord injury (SCI) population (66-75%), as individuals who suffer from paralysis undergo a dramatic decrease in muscle mass and a dramatic increase in adipose deposition. Obesity is a major public health concern which includes a doubling of the risk of heart disease, stroke and type II diabetes mellitus. It has been demonstrated that physical activity, and especially HIIT, can promote a healthy body composition and decrease the risk cardiometabolic disease in the able-bodied population. However, SCI typically limits voluntary exercise to the arms, but a high prevalence of shoulder pain in persons with chronic SCI (60-90%) can cause increased arm exercise to be problematic. Functional electrical stimulation (FES) cycling has proven to be a safe and effective way to exercise paralyzed leg muscles in clinical and home settings, saving the often overworked arms. Yet, HIIT-FES cycling had not been investigated prior to the current study. The purpose of this study was to investigate the body composition changes with combined HIIT-FES cycling and nutritional counseling on individuals with SCI. Design: A matched (level of injury, time since injury, body mass index) and controlled trail. Setting: University exercise performance laboratory. Subjects: Ten individuals with chronic SCI (C5-T9) ASIA impairment classification (A & B) were divided into the treatment group (n=5) for 30 minutes of HIIT-FES cycling 3 times per week for 8 weeks and nutritional counseling over the phone for 30 minutes once per week for 8 weeks and the control group (n=5) who received nutritional counseling only. Results: There was a statistically significant difference between the HIIT-FES group and the control group in mean body fat percentage change (-1.14 to +0.24) respectively, p = .030). There was also a statistically significant difference between the HIIT-FES and control groups in mean change in legs lean mass (+0.78 kg to -1.5 kg) respectively, p = 0.004. There was a nominal decrease in weight, BMI, total fat mass and a nominal increase in total lean mass for the HIIT-FES group over the control group. However, these changes were not found to be statistically significant. Additionally, there was a nominal decrease in the mean blood glucose levels for both groups 101.8 to 97.8 mg/dl for the HIIT-FES group and 94.6 to 93 mg/dl for the Nutrition only group, however, neither were found to be statistically significant. Conclusion: HIIT-FES cycling combined with nutritional counseling can provide healthful body composition changes including decreased body fat percentage in just 8 weeks. Future study recommendations include a greater number of participants, a primer electrical stimulation exercise program to better ready participants for HIIT-FES cycling and a greater volume of training above 30 minutes, 3 times per week for 8 weeks.

Keywords: body composition, functional electrical stimulation cycling, high-intensity interval training, spinal cord injury

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Authors: Abdulrahman Aloufi

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E-commerce has evolved to become a functional and mainstream tool of global trading, including in the Kingdom of Saudi Arabia. Consequently, online consumers need protection just as much as consumers in the offline world. In 2019, the Ministry of Commerce in Saudi Arabia established a so-called ‘e-commerce law’; however, this law does not cover the court enforcement of contracts entered into by international vendors, so it is not applicable in cross-border situations. The purpose of this paper is to identify the gaps present in this new e-commerce law in Saudi Arabia.

Keywords: consumer protection, e-commerce law, Saudi consumer, international vendor

Procedia PDF Downloads 176

Authors: Chih-Hsin Lin, Shyh-Yuan Lee, Yuan-Min Lin

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For many tissue engineering applications, an important goal is to create functional tissues in-vitro, and such tissues to be viable, they have to be vascularized. Endothelial cells (EC) and endothelial progenitor cells (EPC) are promising candidates for vascularization. However, both of them have limited expansion capacity and autologous cells currently do not exist for either ECs or EPCs. Therefore, we use bone marrow mesenchymal stem cells (MSC) as a source material for ECs. Growth supplements are commonly used to induce MSC differentiation, and further improvements in differentiation conditions can be made by modifying the cell's growth environment. An example is pre-treatment of the growth dish with gas plasma, in order to modify the surface functional groups of the material that the cells are seeded on. In this work, we compare the effects of different gas plasmas on the growth and differentiation of MSCs. We treat the dish with different plasmas (CO2, N2, and O2) and then induce MSC differentiation with endothelial growth medium-2 (EGM-2). We find that EGM-2 by itself upregulates EC marker CD31 mRNA expression, but not VEGFR2, CD34, or vWF. However, these additional EC marker expressions were increased for cells seeded on plasma treated substrates. Specifically, for EC markers, we found that N2 plasma treatment upregulated CD31 and VEGFR-2 mRNA expressions; CO2 plasma treatment upregulated CD34 and vWF mRNA expressions. The osteogenic markers ALP and osteopontin mRNA expressions were markedly enhanced on all plasma-treated dishes. We also found that plasma treatment in conjunction with EGM-2 growth medium can enhance MSCs differentiation into endothelial-like cells and osteogenic-like cells. Our work shows that the effect of the growth medium (EGM-2) on MSCs differentiation is influenced by the plasma modified surface chemistry of the substrate. In conclusion, plasma surface modification can enhance EGM-2 effectiveness and induced both endothelial and osteogenic differentiation. Our findings provide a method to enhance EGM-2 based cell differentiation, with consequences for tissue engineering and stem cell biology applications.

Keywords: endothelial differentiation, EGM-2, osteogenesis, plasma treatment, surface modification

Procedia PDF Downloads 331

Authors: Jingwei Du

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The need of optimizing air conditioning systems for existing buildings calls for control methods designed with energy-efficiency as a primary goal. The majority of current control methods boil down to two categories: empirical and model-based. To be effective, the former heavily relies on engineering expertise and the latter requires extensive historical data. Reinforcement Learning (RL), on the other hand, is a model-free approach that explores the environment to obtain an optimal control strategy often referred to as “policy”. This research adopts Proximal Policy Optimization (PPO) to improve chiller plant control, and enable the RL agent to collaborate with experienced engineers. It exploits the fact that while the industry lacks historical data, abundant operational data is available and allows the agent to learn and evolve safely under human supervision. Thanks to the development of language models, renewed interest in RL has led to modern, online, policy-based RL algorithms such as the PPO. This research took inspiration from “alignment”, a process that utilizes human feedback to finetune the pretrained model in case of unsafe content. The methodology can be summarized into three steps. First, an initial policy model is generated based on minimal prior knowledge. Next, the prepared PPO agent is deployed so feedback from both critic model and human experts can be collected for future finetuning. Finally, the agent learns and adapts itself to the specific chiller plant, updates the policy model and is ready for the next iteration. Besides the proposed approach, this study also used traditional RL methods to optimize the same simulated chiller plants for comparison, and it turns out that the proposed method is safe and effective at the same time and needs less to no historical data to start up.

Keywords: chiller plant, control methods, energy efficiency, proximal policy optimization, reinforcement learning

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Authors: Leo Paul Vaurs, Sonia Heaven, Charles Banks

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Municipal solid waste (MSW) is a virtually unlimited source of lignocellulosic material in the form of a waste paper/cardboard mixture which can be converted into fermentable sugars via cellulolytic enzyme hydrolysis in a biorefinery. The extraction of the lignocellulosic fraction and its preparation, however, are energy and water demanding processes. The waste water generated is a rich organic liquor with a high Chemical Oxygen Demand that can be partially cleaned while generating biogas in an Upflow Anaerobic Sludge Blanket bioreactor and be further re-used in the process. In this work, an experiment was designed to determine the critical contaminant concentrations in water affecting either anaerobic digestion or enzymatic hydrolysis by simulating multiple water re-circulations. It was found that re-using more than 16.5 times the same water could decrease the hydrolysis yield by up to 65 % and led to a complete granules desegregation. Due to the complexity of the water stream, the contaminant(s) responsible for the performance decrease could not be identified but it was suspected to be caused by sodium, potassium, lipid accumulation for the anaerobic digestion (AD) process and heavy metal build-up for enzymatic hydrolysis. The experimental data were incorporated into a Water Pinch technology based model that was used to optimize the water re-utilization in the modelled system to reduce fresh water requirement and wastewater generation while ensuring all processes performed at optimal level. Multiple scenarios were modelled in which sub-process requirements were evaluated in term of importance, operational costs and impact on the CAPEX. The best compromise between water usage, AD and enzymatic hydrolysis yield was determined for each assumed contaminant degradations by anaerobic granules. Results from the model will be used to build the first MSW based biorefinery in the USA.

Keywords: anaerobic digestion, enzymatic hydrolysis, municipal solid waste, water optimization

Procedia PDF Downloads 320

Authors: F. Bellali, M. Kharroubi, Y. Rady, N. Bourhim

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In Morocco, fish processing industry is an important source income for a large amount of by-products including skins, bones, heads, guts, and scales. Those underutilized resources particularly scales contain a large amount of proteins and calcium. Sardina plichardus scales from resulting from the transformation operation have the potential to be used as raw material for the collagen production. Taking into account this strong expectation of the regional fish industry, scales sardine upgrading is well justified. In addition, political and societal demands for sustainability and environment-friendly industrial production systems, coupled with the depletion of fish resources, drive this trend forward. Therefore, fish scale used as a potential source to isolate collagen has a wide large of applications in food, cosmetic, and biomedical industry. The main aim of this study is to isolate and characterize the acid solubilize collagen from sardine fish scale, Sardina pilchardus. Experimental design methodology was adopted in collagen processing for extracting optimization. The first stage of this work is to investigate the optimization conditions of the sardine scale deproteinization on using response surface methodology (RSM). The second part focus on the demineralization with HCl solution or EDTA. And the last one is to establish the optimum condition for the isolation of collagen from fish scale by solvent extraction. The advancement from lab scale to pilot scale is a critical stage in the technological development. In this study, the optimal condition for the deproteinization which was validated at laboratory scale was employed in the pilot scale procedure. The deproteinization of fish scale was then demonstrated on a pilot scale (2Kg scales, 20l NaOH), resulting in protein content (0,2mg/ml) and hydroxyproline content (2,11mg/l). These results indicated that the pilot-scale showed similar performances to those of lab-scale one.

Keywords: deproteinization, pilot scale, scale, sardine pilchardus

Procedia PDF Downloads 446

Authors: Precious Ehiomogue, Ifechukwude Israel Ahuchaogu, Isiguzo Edwin Ahaneku

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Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Several works have verified the adsorption of toxic metals using vermicompost but the application is still scarce for the retention of organic compounds. This research brings to knowledge the effectiveness of earthworm waste (vermicompost) for the remediation of crude oil contaminated soils. The remediation methods adopted in this study were two soil washing methods namely, batch and column process which represent laboratory and in-situ remediation. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental results. This study also investigated the application of machine learning models [Artificial neural network (ANN), Adaptive neuro fuzzy inference system (ANFIS). ANN and ANFIS were evaluated using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency obtained from the Box-Behnken design experiment ranged from 29% to 98.9% for batch process remediation. Optimization of the experimental factors carried out using numerical optimization techniques by applying desirability function method of the response surface methodology (RSM) produce the highest removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively, showing the agreement between experimental and predicted results. For batch and column precess, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between experimental and projected findings. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. It can be concluded that machine learning models can predict the removal of crude oil from polluted soil using vermicompost. Therefore, it is recommended to use machines learning models to predict the removal of crude oil from contaminated soil using vermicompost.

Keywords: ANFIS, ANN, crude-oil, contaminated soil, remediation and vermicompost

Procedia PDF Downloads 111

Authors: Esraa Mohamed El-Fawal

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A facile solvothermal procedure was applied to fabricate zinc ferrite nanoparticles (ZnFe₂O₄ NPs). Activated carbon (AC) derived from peanut shells is synthesized using a microwave through the chemical activation method. The ZnFe₂O₄-AC composite is then mixed with a cerium-based metal-organic framework (CeMOF) by solid-state adding to formulate ZnFe₂O₄-AC/CeMOF composite. The synthesized photo materials were tested by scanning/transmission electron microscope (SEM/TEM), Photoluminescence (PL), (XRD) X-Ray diffraction, (FTIR) Fourier transform infrared, (UV-Vis/DRS) ultraviolet-visible/diffuse reflectance spectroscopy. The prepared ZnFe₂O₄-AC/CeMOFphotomaterial shows significantly boosted efficiency for photodegradation of methyl orange /methylene blue (MO/MB) compared with the pristine ZnFe₂O₄ and ZnFe₂O₄-AC composite under the irradiation of visible-light. The favorable ZnFe₂O₄-AC/CeMOFphotocatalyst displays the highest photocatalytic degradation efficiency of MB/MO (R: 91.5-88.6%, consecutively) compared with the other as-prepared materials after 30 min of visible-light irradiation. The apparent reaction rate K: 1.94-1.31 min-1 is also calculated. The boosted photocatalytic proficiency is ascribed to the heterojunction at the interface of prepared photo material that assists the separation of the charge carriers. To reach optimization, statistical analysis using response surface methodology was applied. The effect of independent parameters (such as A (pH), B (irradiation time), and (c) initial pollutants concentration on the response function (%)photodegradation of MB/MO dyes (as examples of azodyes) was investigated via using central composite design. At the optimum condition, the photodegradation efficiency (%) of the MB/MO is 99.8-97.8%, respectively. ZnFe2O₄-AC/CeMOF hybrid reveals good stability over four consecutive cycles.

Keywords: azo-dyes, photo-catalysis, zinc ferrite, response surface methodology

Procedia PDF Downloads 168

Authors: Oluwatoyin Joseph Gbadeyan, Sarp Adali, Bright Glen, Bruce Sithole

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Sodium lauryl surfactant concentration optimization, for nanoparticle production, provided the platform for advanced research studies. Different concentrations (0.05 %, 0.1 %, and 0.2 %) of sodium lauryl surfactant was added to snail shells powder during milling processes for producing CaCO3 at smaller particle size. Epoxy nanocomposites prepared at filler content 2 wt.% synthesized with different volumes of sodium lauryl surfactant were fabricated using a conventional resin casting method. Mechanical properties such as tensile strength, stiffness, and hardness of prepared nanocomposites was investigated to determine the effect of sodium lauryl surfactant concentration on nanocomposite properties. It was observed that the loading of the synthesized nano-calcium carbonate improved the mechanical properties of neat epoxy at lower concentrations of sodium lauryl surfactant 0.05 %. Meaningfully, loading of achatina fulica snail shell nanoparticles manufactures, with small concentrations of sodium lauryl surfactant 0.05 %, increased the neat epoxy tensile strength by 26%, stiffness by 55%, and hardness by 38%. Homogeneous dispersion facilitated, by the addition of sodium lauryl surfactant during milling processes, improved mechanical properties. Research evidence suggests that nano-CaCO3, synthesized from achatina fulica snail shell, possesses suitable reinforcement properties that can be used for nanocomposite fabrication. The evidence showed that adding small concentrations of sodium lauryl surfactant 0.05 %, improved dispersion of nanoparticles in polymetrix material that provided mechanical properties improvement.

Keywords: sodium lauryl surfactant, mechanical properties , achatina fulica snail shel, calcium carbonate nanopowder

Procedia PDF Downloads 146

Authors: D. Dixon, J. Nicol, J. A. Coulter, E. Harrison

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The ease with which they can be functionalized, combined with their excellent biocompatibility, make gold nanoparticles (AuNPs) ideal candidates for various applications in nanomedicine. Indeed several promising treatments are currently undergoing human clinical trials (CYT-6091 and Auroshell). A successful nanoparticle treatment must first evade the immune system, then accumulate within the target tissue, before enter the diseased cells and delivering the payload. In order to create a clinically relevant drug delivery system, contrast agent or radiosensitizer, it is generally necessary to functionalize the AuNP surface with multiple groups; e.g. Polyethylene Glycol (PEG) for enhanced stability, targeting groups such as antibodies, peptides for enhanced internalization, and therapeutic agents. Creating and characterizing the biological response of such complex systems remains a challenge. The two commonly used methods to attach multiple groups to the surface of AuNPs are the creation of a mixed monolayer, or by binding groups to the AuNP surface using a bi-functional PEG linker. While some excellent in-vitro and animal results have been reported for both approaches further work is necessary to directly compare the two methods. In this study AuNPs capped with both PEG and a Receptor Mediated Endocytosis (RME) peptide were prepared using both mixed monolayer and PEG linker approaches. The PEG linker used was SH-PEG-SGA which has a thiol at one end for AuNP attachment, and an NHS ester at the other to bind to the peptide. The work builds upon previous studies carried out at the University of Ulster which have investigated AuNP synthesis, the influence of PEG on stability in a range of media and investigated intracellular payload release. 18-19nm citrate capped AuNPs were prepared using the Turkevich method via the sodium citrate reduction of boiling 0.01wt% Chloroauric acid. To produce PEG capped AuNPs, the required amount of PEG-SH (5000Mw) or SH-PEG-SGA (3000Mw Jenkem Technologies) was added, and the solution stirred overnight at room temperature. The RME (sequence: CKKKKKKSEDEYPYVPN, Biomatik) co-functionalised samples were prepared by adding the required amount of peptide to the PEG capped samples and stirring overnight. The appropriate amounts of PEG-SH and RME peptide were added to the AuNP to produce a mixed monolayer consisting of approximately 50% PEG and 50% RME. The PEG linker samples were first fully capped with bi-functional PEG before being capped with RME peptide. An increase in diameter from 18-19mm for the ‘as synthesized’ AuNPs to 40-42nm after PEG capping was observed via DLS. The presence of PEG and RME peptide on both the mixed monolayer and PEG linker co-functionalized samples was confirmed by both FTIR and TGA. Bi-functional PEG linkers allow the entire AuNP surface to be capped with PEG, enabling in-vitro stability to be achieved using a lower molecular weight PEG. The approach also allows the entire outer surface to be coated with peptide or other biologically active groups, whilst also offering the promise of enhanced biological availability. The effect of mixed monolayer versus PEG linker attachment on both stability and non-specific protein corona interactions was also studied.

Keywords: nanomedicine, gold nanoparticles, PEG, biocompatibility

Procedia PDF Downloads 339

Authors: Tim Giesen, Raphael Adamietz, Pablo Mayer, Philipp Stiefel, Patrick Alle, Dirk Schlenker

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The competitiveness and success of new electrical energy storages such as battery cells are significantly dependent on a short time-to-market. Producers who decide to supply new battery cells to the market need to be easily adaptable in manufacturing with respect to the early customers’ needs in terms of cell size, materials, delivery time and quantity. In the initial state, the required output rates do not yet allow the producers to have a fully automated manufacturing line nor to supply handmade battery cells. Yet there was no solution for manufacturing battery cells in low to medium volumes in a reproducible way. Thus, in terms of cell format and output quantity, a concept for the flexible assembly of battery cells was developed by the Fraunhofer-Institute for Manufacturing Engineering and Automation. Based on clustered processes, the modular system platform can be modified, enlarged or retrofitted in a short time frame according to the ordered product. The paper shows the analysis of the production steps from a conventional battery cell assembly line. Process solutions were found by using I/O-analysis, functional structures, and morphological boxes. The identified elementary functions were subsequently clustered by functional coherences for automation solutions and thus the single process cluster was generated. The result presented in this paper enables to manufacture different cell products on the same production system using seven process clusters. The paper shows the solution for a batch-wise flexible battery cell production using advanced process control. Further, the performed tests and benefits by using the process clusters as cyber-physical systems for an integrated production and value chain are discussed. The solution lowers the hurdles for SMEs to launch innovative cell products on the global market.

Keywords: automation, battery production, carrier, advanced process control, cyber-physical system

Procedia PDF Downloads 337

Authors: Zi-Yan Chao

Abstract:

With the rapid development of urbanization and increasing demand for efficient transportation systems, the interaction between land use diversity and transportation resource allocation has become a critical issue in urban planning. Achieving a balance of land use types, such as residential, commercial, and industrial areas, is crucial role in ensuring social equity and sustainable urban development. Simultaneously, optimizing multimodal transportation networks, including bus, subway, and car routes, is essential for minimizing total travel time and costs, while ensuring fairness for all social groups, particularly in meeting the transportation needs of low-income populations. This study develops a bilevel programming model to address these challenges, with land use diversity as the foundation for measuring equity. The upper-level model maximizes land use diversity for balanced land distribution across regions. The lower-level model optimizes multimodal transportation networks to minimize travel time and costs while maintaining user equilibrium. The model also incorporates constraints to ensure fair resource allocation, such as balancing transportation accessibility and cost differences across various social groups. A solution approach is developed to solve the bilevel optimization problem, ensuring efficient exploration of the solution space for land use and transportation resource allocation. This study maximizes social equity by maximizing land use diversity and achieving user equilibrium with optimal transportation resource distribution. The proposed method provides a robust framework for addressing urban planning challenges, contributing to sustainable and equitable urban development.

Keywords: bilevel programming model, genetic algorithms, land use diversity, multimodal transportation optimization, social equity

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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