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This study aims to evaluate the usability of a recycled alkaline solution, a residue from cleaning iron plates in the car manufacturing industry, to enhance the strength and stiffness of clayey soil by carrying out unconfined compressive strength (UCS), indirect tensile strength (ITS), ultrasonic pulse velocity (UPV) and microstructural (SEM, XRD) tests. High- and low- calcium fly ashes with percentages of 10% to 40% and 10% to 30%, respectively, were independently introduced into the alkaline activation process to determine the proper type and content of precursors at room temperature. Two mixtures of the residual cleaning solution blended with 2 M and 4 M NaOH solutions at a weight ratio of one were also utilized to determine how efficiently binary solutions could improve the performance of the parent clay soil. These experiments revealed that all mixtures of the residual alkaline solution work better to activate high-calcium fly ash as compared to the low-calcium fly ash in terms of both mechanical strength and stiffness. The sample of high-calcium fly ash with fly ash/solid and Na/ash ratios equal to 30% and 0.037, respectively, experienced the highest improvement. A microstructural analysis showed the formation of C-(A)-S-H and N–(C)–A–S–H gels in the corresponding sample, thus providing a considerably dense structure, which in turn significantly contributed to the enhancement of the maximum strength and stiffness of the treated soil. In fact, the high-calcium precursor proved to be the best candidate for a low-range alkaline activator because it contributes more calcium to a low-alkaline environment. An environmental impact assessment was also carried out to compare the CO2 emissions of clays treated with the 4 M NaOH binary solutions tested in this study with those conventionally stabilized by cement and lime and also those treated traditionally with commercial 2, 4, and 8 M NaOH. The samples in this study treated with environmentally friendly binary solution

Efficacy of orally used of red yeast (Phaffia rhodozyma) (1.6 mg astaxanthin/g product) at 15.5 g (P1), 37.3 g (P2) and 47 (P3) g per kg diet were studied on performance, carcass composition, antioxidant activity and fillet pigmentation in rainbow trout (Oncorhynchus mykiss) weighing 208–212 g for eight weeks at 11 °C. Synthetic astaxanthin (AX) (0.5 g/kg diet) and basal diet were used as controls. With an increase in P. rhodozyma concentration in diet, weight gain and specific growth rate exhibited an increase compared to basal diet (P < 0.05%). A significant difference was seen among the treatments but only P3 diet significantly demonstrated a better growth than AX diet (P < 0.05). Activity of antioxidant enzymes; superoxidase dismutase, glutathione peroxidase, total antioxidant activity, glutathione reductase, and catalase exhibited an enhancement in serum or liver samples of fish fed P. rhodozyma compared to basal diet (P < 0.05). Protein content in fillet of fish fed P3 diet was higher than both basal and AX diets (P < 0.05), but no significant difference was seen in contents of lipid, moisture, ash and pH values either among the treatments or between treatments and basal diet (P > 0.05). Water holding capacity and lipid loss in fillets of treatments were lower than fish fed basal diet (P < 0.05). By increasing P. rhodozyma concentration in fish diets, redness value exhibited a progressive increase, and the highest value was seen in P3 diet compared to other treatments (P < 0.05). These data show application of P. rhodozyma at 47 g/kg diet in trout could provide a better performance, antioxidant activities, and fillet pigmentation.

Efficacy of orally used of red yeast (Phaffia rhodozyma) (1.6 mg astaxanthin/g product) at 15.5 g (P1), 37.3 g (P2) and 47 (P3) g per kg diet were studied on performance, carcass composition, antioxidant activity and fillet pigmentation in rainbow trout (Oncorhynchus mykiss) weighing 208–212 g for eight weeks at 11 °C. Synthetic astaxanthin (AX) (0.5 g/kg diet) and basal diet were used as controls. With an increase in P. rhodozyma concentration in diet, weight gain and specific growth rate exhibited an increase compared to basal diet (P < 0.05%). A significant difference was seen among the treatments but only P3 diet significantly demonstrated a better growth than AX diet (P < 0.05). Activity of antioxidant enzymes; superoxidase dismutase, glutathione peroxidase, total antioxidant activity, glutathione reductase, and catalase exhibited an enhancement in serum or liver samples of fish fed P. rhodozyma compared to basal diet (P < 0.05). Protein content in fillet of fish fed P3 diet was higher than both basal and AX diets (P < 0.05), but no significant difference was seen in contents of lipid, moisture, ash and pH values either among the treatments or between treatments and basal diet (P > 0.05). Water holding capacity and lipid loss in fillets of treatments were lower than fish fed basal diet (P < 0.05). By increasing P. rhodozyma concentration in fish diets, redness value exhibited a progressive increase, and the highest value was seen in P3 diet compared to other treatments (P < 0.05). These data show application of P. rhodozyma at 47 g/kg diet in trout could provide a better performance, antioxidant activities, and fillet pigmentation.

Background: Syringomyelia is a serious complication of spinal cord trauma, occurring in approximately 28% of spinal cord injuries. Treatment options are limited and often produce unsatisfactory results. Post-traumatic syringomyelia (PTS) is presumably related to abnormalities of cerebrospinal fluid (CSF) and interstitial fluid hydrodynamics, but the exact mechanisms are unknown. Methods: Transmission electron microscopy (TEM) was used to investigate in detail the interfaces between fluid and tissue in the spinal cords of healthy Sprague-Dawley rats (n = 3) and in a rat model of PTS (n = 3). PTS was induced by computer-controlled impact (75 kDyn) to the spinal cord between C6 and C8, followed by a subarachnoid injection of kaolin to produce focal arachnoiditis. Control animals received a laminectomy only to C6 and C7 vertebrae. Animals were sacrificed 12 weeks post-surgery, and spinal cords were prepared for TEM. Ultra-thin spinal cord sections at the level of the injury were counterstained for structural anatomy. Results: Spinal cords from animals with PTS displayed several abnormalities including enlarged perivascular spaces, extracellular edema, cell death and loss of tissue integrity. Additionally, alterations to endothelial tight junctions and an abundance of pinocytotic vesicles, in tissue adjacent to syrinx, suggested perturbations to blood-spinal cord barrier (BSCB) function. Conclusions: These findings support the hypothesis that perivascular spaces are important pathways for CSF flow into and out of the spinal cord, but also suggest that fluid may enter the cord through vesicular transport and an altered BSCB.

Background: Fluid homeostasis in the central nervous system (CNS) is essential for normal neurological function. Cerebrospinal fluid (CSF) in the subarachnoid space and interstitial fluid circulation in the CNS parenchyma clears metabolites and neurotransmitters and removes pathogens and excess proteins. A thorough understanding of the normal physiology is required in order to understand CNS fluid disorders, including post-traumatic syringomyelia. The aim of this project was to compare fluid transport, using quantitative imaging of tracers, in the spinal cord from animals with normal and obstructed spinal subarachnoid spaces. Methods: A modified extradural constriction model was used to obstruct CSF flow in the subarachnoid space at the cervicothoracic junction (C7-T1) in Sprague-Dawley rats. Alexa-Fluor 647 Ovalbumin conjugate was injected into the cisterna magna at either 1 or 6 weeks post-surgery. Macroscopic and microscopic fluorescent imaging were performed in animals sacrificed at 10 or 20 min post-injection. Tracer fluorescence intensity was compared at cervical and thoracic spinal cord levels between control and constriction animals at each post-surgery and post-injection time point. The distribution of tracer around arterioles, venules and capillaries was also compared. Results: Macroscopically, the fluorescence intensity of CSF tracer was significantly greater in spinal cords from animals with a constricted subarachnoid space compared to controls, except at 1 week post-surgery and 10 min post-injection. CSF tracer fluorescence intensity from microscopic images was significantly higher in the white matter of constriction animals 1 week post surgery and 10 min post-injection. At 6 weeks post-constriction surgery, fluorescence intensity in both gray and white matter was significantly increased in animals sacrificed 10 min post-injection. At 20 min post-injection this difference was significant only in the white matter and was less prominent. CSF tracer was fo

Perivascular spaces play a pivotal role in the exchange between cerebrospinal and interstitial fluids, and in the clearance of waste in the CNS, yet their precise anatomical components are not well described. The aim of this study was to characterise the ultrastructure of perivascular spaces and their role in the transport of fluid, in the spinal cord of healthy rats, using transmission electron microscopy. The distribution of cerebrospinal fluid tracers injected into the subarachnoid space was studied using light, confocal and electron microscopy. Perivascular spaces were found around arterioles and venules, but not capillaries, throughout the spinal cord white and grey matter. They contained fibroblasts and collagen fibres, and were continuous with the extracellular spaces of the surrounding tissue. At 5 min post injection, tracers were seen in the subarachnoid space, the peripheral white matter, the perivascular spaces, basement membranes, extracellular spaces of the surrounding tissue, and surprisingly, in the lumen of blood vessels, suggesting trans-vascular clearance. These findings point out an unrecognised outflow pathway for CNS fluids, with potential implications for volume regulation in health and disease states, but also clinically for the detection of CNS-derived biomarkers in plasma, the immune response and drug pharmacokinetics.

Lithium-ion batteries are commonly employed in various applications owing to high energy density and long service life. Lithium-ion battery models are used for analysing batteries and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based lithium-ion battery model which represents solid-state and electrolyte diffusion dynamics and accurately predicts the current/voltage response using a set of nonlinear partial differential equations. However, implementation of the full DFN model requires significant computation time. This paper proposes a computationally efficient implementation of the full DFN battery model, which is convenient for real-time applications. The proposed implementation is based on applying model order reduction to a spatial and temporal discretisation of the governing model equations. For model order reduction, we apply proper orthogonal decomposition and discrete empirical interpolation method, which leads to a set of reduced order nonlinear algebraic equations. These equations are solved using a particular numerical scheme, based on a damped Newton’s method. In a simulation study, the computational efficiency of the proposed implementation is shown and the resulting accuracy is presented.

Lithium-ion batteries are commonly employed in various applications owing to high energy density and long service life. Lithium-ion battery models are used for analysing batteries and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based lithium-ion battery model which represents solid-state and electrolyte diffusion dynamics and accurately predicts the current/voltage response using a set of nonlinear partial differential equations. However, implementation of the full DFN model requires significant computation time. This paper proposes a computationally efficient implementation of the full DFN battery model, which is convenient for real-time applications. The proposed implementation is based on applying model order reduction to a spatial and temporal discretisation of the governing model equations. For model order reduction, we apply proper orthogonal decomposition and discrete empirical interpolation method, which leads to a set of reduced order nonlinear algebraic equations. These equations are solved using a particular numerical scheme, based on a damped Newton’s method. In a simulation study, the computational efficiency of the proposed implementation is shown and the resulting accuracy is presented.

Post-traumatic syringomyelia (PTS) is a serious neurological disorder characterized by fluid filled cavities that develop in the spinal cord. PTS is thought to be caused by an imbalance between fluid inflow and outflow in the spinal cord, but the underlying mechanisms are unknown. The ion channel Kir4.1 plays an important role in the uptake of K+ ions from the extracellular space and release of K+ ions into the microvasculature, generating an osmotic gradient that drives water movement. Changes in Kir4.1 expression may contribute to disturbances in K+ homeostasis and subsequently fluid imbalance. Here we investigated whether changes in Kir4.1 protein expression occur in PTS. Western blotting and immunohistochemistry were used to evaluate Kir4.1 and glial fibrillary acidic protein (GFAP) expression in a rodent model of PTS at 3days, 1, 6 or 12weeks post-surgery. In Western blotting experiments, Kir4.1 expression increased 1week post-surgery at the level of the cavity. Immunohistochemical analysis examined changes in the spinal parenchyma directly in contact with the syrinx cavity. In these experiments, there was a significant decrease in Kir4.1 expression in PTS animals compared to controls at 3days and 6weeks post-surgery, while an up-regulation of GFAP in PTS animals was observed at 1 and 12weeks. This suggests that while overall Kir4.1 expression is unchanged at these time-points, there are many astrocytes surrounding the syrinx cavity that are not expressing Kir4.1. The results demonstrate a disturbance in the removal of K+ ions in tissue surrounding a post-traumatic syrinx cavity. It is possible this contributes to water accumulation in the injured spinal cord leading to syrinx formation or exacerbation of the underlying pathology.

Most stabilizing controllers designed for nonlinear systems are valid only within a specific region of the state space, called the domain of attraction (DOA). Computation of the DoA is usually costly and time-consuming. This paper proposes a computationally effective sampling approach to estimate the DoAs of nonlinear systems in real time. This method is validated to approximate the DoAs of stable equilibria in several nonlinear systems. In addition, it is implemented for the passivity-based learning controller designed for a second-order dynamical system. Simulation and experimental results show that, in all cases studied, the proposed sampling technique quickly estimates the DoAs, corroborating its suitability for realtime applications.

Sequential composition is an effective supervisory control scheme for addressing control problems in complex dynamical systems. It executes a set of controllers sequentially to achieve a challenging task. These classes of controllers are typically designed offline. Although sequential composition is a well-defined control methodology, it is not designed to address unmodeled situations that might occur during runtime. This paper proposes a learning approach that augments the standard supervisory framework using online learning to handle unpredicted situations. New controllers based on the acquired knowledge via learning are added to the existing supervisory control structure. Since the learning experiments are restricted to explore just within the domain of attraction (DOA) of the existing controllers, it is guaranteed that the learning process is safe. In addition, the DOA of the new learned controller is approximated after each learning trial. Thus, the learning process can last a short amount of time and is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two different nonlinear systems. The results show that, in both cases, a new controller can be rapidly learned via safe reinforcement learning experiments and then added to the supervisory control structure.

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed controllers, where each of them is associated with a domain of attraction (DoA) and a goal set. By design, if the goal set of one controller lies in the DoA of another controller (this is called the prepare relation), the supervisor can instantly switch from the first controller to the second without affecting the stability of the system. As these controllers are designed offline, sequential composition cannot address unmodeled situations that might occur during runtime. Moreover, sequential composition has not been developed for cooperative settings where the collaboration of multiple systems is required in order to fulfill the control specifications. This thesis studies automatic synthesis of supervisory control systems using the framework of sequential composition. First, a learning sequential composition control algorithm is developed so as to learn new controllers on demand, by means of reinforcement learning. Once learning is completed, the supervisory control structure is augmented with the learned controllers. As a consequence, the supervisor is able to cope with unforeseen situations for which new controllers are required. Second, a cooperative sequential composition control algorithm is proposed to enable the coordination between a set of sequential composition controllers, without any change in their low-level structures. Finally, a robot contact language is designed for the manipulation of multiple objects by multiple robots., OLD Intelligent Control & Robotics

Most stabilizing controllers designed for nonlinear systems are valid only within a specific region of the state space, called the domain of attraction (DoA). Computation of the DoA is usually costly and time-consuming. This paper proposes a computationally effective sampling approach to estimate the DoAs of nonlinear systems in real time. This method is validated to approximate the DoAs of stable equilibria in several nonlinear systems. In addition, it is implemented for the passivity-based learning controller designed for a second-order dynamical system. Simulation and experimental results show that, in all cases studied, the proposed sampling technique quickly estimates the DoAs, corroborating its suitability for real-time applications., Intelligent Control & Robotics

Sequential composition is an effective supervisory control scheme for addressing control problems in complex dynamical systems. It executes a set of controllers sequentially to achieve a challenging task. These classes of controllers are typically designed offline. Although sequential composition is a well-defined control methodology, it is not designed to address unmodeled situations that might occur during runtime. This paper proposes a learning approach that augments the standard supervisory framework using online learning to handle unpredicted situations. New controllers based on the acquired knowledge via learning are added to the existing supervisory control structure. Since the learning experiments are restricted to explore just within the domain of attraction (DOA) of the existing controllers, it is guaranteed that the learning process is safe. In addition, the DOA of the new learned controller is approximated after each learning trial. Thus, the learning process can last a short amount of time and is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two different nonlinear systems. The results show that, in both cases, a new controller can be rapidly learned via safe reinforcement learning experiments and then added to the supervisory control structure.

Most stabilizing controllers designed for nonlinear systems are valid only within a specific region of the state space, called the domain of attraction (DOA). Computation of the DoA is usually costly and time-consuming. This paper proposes a computationally effective sampling approach to estimate the DoAs of nonlinear systems in real time. This method is validated to approximate the DoAs of stable equilibria in several nonlinear systems. In addition, it is implemented for the passivity-based learning controller designed for a second-order dynamical system. Simulation and experimental results show that, in all cases studied, the proposed sampling technique quickly estimates the DoAs, corroborating its suitability for realtime applications.

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed controllers, where each of them is associated with a domain of attraction (DoA) and a goal set. By design, if the goal set of one controller lies in the DoA of another controller (this is called the prepare relation), the supervisor can instantly switch from the first controller to the second without affecting the stability of the system. As these controllers are designed offline, sequential composition cannot address unmodeled situations that might occur during runtime. Moreover, sequential composition has not been developed for cooperative settings where the collaboration of multiple systems is required in order to fulfill the control specifications. This thesis studies automatic synthesis of supervisory control systems using the framework of sequential composition. First, a learning sequential composition control algorithm is developed so as to learn new controllers on demand, by means of reinforcement learning. Once learning is completed, the supervisory control structure is augmented with the learned controllers. As a consequence, the supervisor is able to cope with unforeseen situations for which new controllers are required. Second, a cooperative sequential composition control algorithm is proposed to enable the coordination between a set of sequential composition controllers, without any change in their low-level structures. Finally, a robot contact language is designed for the manipulation of multiple objects by multiple robots., OLD Intelligent Control & Robotics

Most stabilizing controllers designed for nonlinear systems are valid only within a specific region of the state space, called the domain of attraction (DoA). Computation of the DoA is usually costly and time-consuming. This paper proposes a computationally effective sampling approach to estimate the DoAs of nonlinear systems in real time. This method is validated to approximate the DoAs of stable equilibria in several nonlinear systems. In addition, it is implemented for the passivity-based learning controller designed for a second-order dynamical system. Simulation and experimental results show that, in all cases studied, the proposed sampling technique quickly estimates the DoAs, corroborating its suitability for real-time applications., OLD Intelligent Control & Robotics

The present study investigates the influence of ferrous iron (as FeSO4) and ferric iron (as Fe2 (SO4)3), and pyrite (FeS2) on the ability of bacterial leaching of a high-grade sulphide Zn-Pb ore. In this regard, shake flask experiments were carried out at 5% (w/v) pulp density of the ore sample (having 40.7% Zn and 12.4% Pb initial metal content) using a consortium of mesophilic iron and sulphur-oxidising acidophiles. A concentration of 0.04 mol/l of ferric iron in the leaching media was found to be optimum for zinc extraction without affecting growth of the microorganisms. Under this concentration, the dissolution of Zn, Pb, Cd, and As was found to be 57%, 0.2%, 0.03%, and 9.9% in 25 days. Using ferrous iron in the media, 0.16 mol/l of Fe2+ was found to be the optimum concentration for efficient bacterial growth and metal dissolution (54.6% Zn, 0.08% Pb, 0.03% Cd, and 10.2% As) from the sample in 25 days. On the other hand, using pyrite as the source of energy for bacterial growth, an initial 12-day lag period was observed when compared to the effect of ferrous iron in the media. Under the optimum concentration (test with 0.24 mol/l iron in the form of pyrite), the dissolution of Zn, Pb, Cd, and As was found to be 39.8%, 0.1%, 0.03%, and 10% in 25 days. The surface chemistry analysis indicated formation of a sulphur layer over the particle surface that hindered reagent diffusion and affected metal recovery through bioleaching. (Authors.), The present study investigates the influence of ferrous iron (as FeSO4) and ferric iron (as Fe2 (SO4)3), and pyrite (FeS2) on the ability of bacterial leaching of a high-grade sulphide Zn-Pb ore. In this regard, shake flask experiments were carried out at 5% (w/v) pulp density of the ore sample (having 40.7% Zn and 12.4% Pb initial metal content) using a consortium of mesophilic iron and sulphur-oxidising acidophiles. A concentration of 0.04 mol/l of ferric iron in the leaching media was found to be optimum for zinc extraction without affecting growth of the microorganisms. Under this concentration, the dissolution of Zn, Pb, Cd, and As was found to be 57%, 0.2%, 0.03%, and 9.9% in 25 days. Using ferrous iron in the media, 0.16 mol/l of Fe2+ was found to be the optimum concentration for efficient bacterial growth and metal dissolution (54.6% Zn, 0.08% Pb, 0.03% Cd, and 10.2% As) from the sample in 25 days. On the other hand, using pyrite as the source of energy for bacterial growth, an initial 12-day lag period was observed when compared to the effect of ferrous iron in the media. Under the optimum concentration (test with 0.24 mol/l iron in the form of pyrite), the dissolution of Zn, Pb, Cd, and As was found to be 39.8%, 0.1%, 0.03%, and 10% in 25 days. The surface chemistry analysis indicated formation of a sulphur layer over the particle surface that hindered reagent diffusion and affected metal recovery through bioleaching. (Authors.)