79 research outputs found
Characterization of multi-layered impact damage in polymer matrix composites using lateral thermography
No full textCoexisting Z-type charge and bond order in metallic NaRu2O4
No full text© 2022, The Author(s).How particular bonds form in quantum materials has been a long-standing puzzle. Two key concepts dealing with charge degrees of freedom are dimerization (forming metal-metal bonds) and charge ordering. Since the 1930s, these two concepts have been frequently invoked to explain numerous exciting quantum materials, typically insulators. Here we report dimerization and charge ordering within the dimers coexisting in metallic NaRu2O4. By combining high-resolution x-ray diffraction studies and theoretical calculations, we demonstrate that this unique phenomenon occurs through a new type of bonding, which we call Z-type ordering. The low-temperature superstructure has strong dimerization in legs of zigzag ladders, with short dimers in legs connected by short zigzag bonds, forming Z-shape clusters: simultaneously, site-centered charge ordering also appears. Our results demonstrate the yet unknown flexibility of quantum materials with the intricate interplay among orbital, charge, and lattice degrees of freedom.11Nscopu
Anaerobic Conversion of Proteins in Aerobic Granular Sludge
No full textIn an aerobic granular sludge (AGS) reactor treating urban wastewater, nutrient removal depends on the availability of carbon source. Domestic wastewater consists of 40-60% of slowly biodegradable complex substrates, out of which proteins form a major fraction. Despite this, little is known about the mechanisms of protein degradation in AGS. This research assessed the anaerobic availability of protein substrates for enhanced biological phosphorous removal (EBPR) in an aerobic granular sludge reactor. Proteins have to be first hydrolyzed before being assimilated by the bacteria, and nutrient removal is often limited by the rate of hydrolysis. Therefore, a major part of this thesis attempted to look into the mechanisms of proteolysis in AGS. Next, the utilization of amino acids - the hydrolysis product of proteins - by PAO was explored, based on critical evaluation of available literature. Firstly, it is proposed that the important aspect likely to govern the hydrolysis of proteins in AGS is the substrate-granule interaction, taking into account the diffusion limitation of particulate substrates within the granules and the probable presence of hydrolytic enzymes on the granular surface. Further, it is seen that the amino acids may be utilized by the polyphosphate accumulating organisms (PAOs) either directly or after the anaerobic degradation of amino acids to simple VFAs (volatile fatty acids), which are then taken up by the PAOs. The anaerobic degradation or fermentation of amino acids may occur via two well-known pathways- Stickland pathway and the non-Stickland pathway. Non-Stickland reaction requires syntrophy with hydrogen consuming bacteria whose presence in AGS is questionable. The bacteria responsible for Stickland reaction are obligate anaerobes belonging to the genus Clostridium which has not been found in aerobic granular sludge. Thus, it seems more likely that the amino acids are directly taken up by the PAOs in an AGS reactor. However, the direct uptake of amino acids by the PAOs has been reported only for eleven amino acids in total. More research on the likely fate of the remaining amino acids is recommended, considering that very little is known about the fate of amino acids in AGS. Few laboratory experiments were also conducted to study the effect of substrate size and granule size on the rate of hydrolysis of proteins. From preliminary experiments, it was seen that aerobic granular sludge exhibited significant anaerobic phosphate-release activity when casein (protein) was the only available carbon source. In the lab experiment carried out with different sizes of protein substrates, the observed anaerobic phosphate-release activity was used to obtain the rate of hydrolysis of different sizes of casein. Based on the hydrolysis rate obtained, it is seen that in an AGS reactor with a typical sludge concentration of 8g/l, up to 90% of the proteins present in domestic wastewater influent could be potentially taken up by the PAOs, provided that the proteins are completely dissolved. It was also seen that 60-80% of the particulate casein COD (>0.45um) was hydrolyzed within 24 hours of the assay. In another lab experiment, fluorescent protease assay was performed to assess the effect of granule size on the rate of hydrolysis. A significant decrease (by at least 2 times) in the specific rate of protein hydrolysis was observed when the aerobic granule size was increased from 1-2mm to 3.15-4mm. This may be significant especially when the stratification of granules and plug-flow feeding in an AGS reactor is taken into account. Further research is recommended to analyze the relative effect of substrate size and granule size on the rate of hydrolysis of proteins in AGS.Civil Engineering | Environmental Engineerin
Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression
No full textIndustrial robots can be found in automotive, food, chemical, and electronics industries. These robots are often caged and are secluded from human beings. A recent trend in a subclass of industrial robots named collaborative robots allows the humans to interact with the robots safely. The word “safety” mentioned above is of supreme importance. The safety is achieved in these robots by their lightweight and sleek design. Often, robots are operated under low stiffness conditions to achieve less impact force during an unavoidable collision. A severe damage to the environment may occur if the robot becomes unstable under any conditions. It is of paramount importance for the controller present in the robot to stabilize the system under all conditions. One such controller is the joint impedance controller, which helps the robot to interact with an unknown environment by causing no harm to humans. The thesis marks its importance, as it is closely related to ensuring safety in collaborative robots and is mainly focused on tackling the situations whenever the controller fails. The controller in these manipulators has an Inverse Dynamics Model (IDM) and a Proportional Derivative (PD) controller. Under low stiffness and damping conditions, the PD gains are low and the manipulator is entirely compensated by the inverse dynamics model. This inverse dynamics model can become problematic in the presence of the un-modeled dynamics like flexibility, friction, dynamics of hydraulic tubes, actuators and cable drives or if the IDM model is inherently inaccurate. Consequently, the in-built joint impedance and position controller will fail to work under low stiffness and damping conditions, in-turn making the robot unstable. If this robot was to be used on an industrial platform and the problem is unresolved, it might cause some danger to the humans working closely and also damage the environmentand itself.Since the robot is entirely compensated by the IDM under low stiffness and damping conditions, the thesis tries to acquire the accurate IDM of the robot for control purposes. To do so, two cases were modeled in this thesis, one with the internal IDM with correct base parameters and another one with the incorrect internal IDM by adding offset in the base parameters. But in both cases, the internal IDM model failed to compensate for the un-modeled dynamicsoccurring in the manipulator.The thesis incorporates a semi-parametric Gaussian process regression to tackle the two cases. A semi-parametric model consists of a parametric term and a non-parametric term. First, the parametric term is identified using the least squares approach. Later, the parametric term is used as mean to capture the non-parametric term using the Gaussian Process Regression. The proposed methods were tested on the PUMA 560 robot and the two-link manipulator in MATLAB. From the simulation results, the semi-parametric model was able to provide accurate feed-forward control torques to compensate for the model inaccuracies and the un-modeled dynamics at low stiffness and damping conditions. Additionally, implementing these proposed methods on a real robot will be a future scope of improvement on this topic.Mechanical Engineering | Systems and Contro
Detection of Atmospheric Gravity Waves: Two classification approach - Image classification and meteorological feature classification
No full textWith the advent of offshore wind farms, the research into the various phenomenon that affects their performance is vast and detailed. But the effect of a particular phenomenon, atmospheric gravity waves (AGWs), on wind farm performance is limited. AGWs are oscillations of the airflow due to an imbalance in the buoyancy and gravity forces, generated by topographical or meteorological obstacles in neutral or stable surface atmospheric conditions. AGWs are frequent over offshore regions and affect offshore wind farms as the event occurs over a large area. Detecting them through satellite images is easy by an eye test, but not so much when viewed digitally through meteorological data. Weather data can be obtained from reanalysis data which combines past weather forecasts with observational data assimilation. This project aims to develop machine learning models that detect AGWs in satellite images and detect AGWs from atmospheric conditions, such as temperature and wind speed profile with height. The models learn using the reanalysis data and satellite images. The same satellite images are used to label the reanalysis data so that the model is taught to pick out gravity waves in the case of having no satellite image. Thus the final objective of the project is to train a model to detect an AGW event, based solely on reanalysis data. The trained model is then used to predict the percentage of time an AGW occurs or will occur over a chosen wind farm site.Electrical Engineering | Sustainable Energy Technolog
Role of gut microbiome in gestational diabetes mellitus, in South Indian population
No full textBackground: Gestational diabetes mellitus (GDM) is one of the most common metabolic complications of pregnancy, characterized by glucose intolerance first recognized during gestation. Emerging evidence suggests that the gut microbiome an intricate community of microorganisms residing in the gastrointestinal tract plays a crucial role in metabolic health, insulin resistance, and inflammation. Alterations in gut microbiota composition have been implicated in the development of metabolic disorders, including type 2 diabetes mellitus (T2DM) and obesity.
Methods: This study was conducted to investigate the association between gut microbiome composition and GDM among pregnant women. A total of 124 pregnant women were enrolled, comprising 53 diagnosed with GDM and 71 healthy controls.
Results: This study revealed significant gut microbiome dysbiosis in women with GDM, characterized by reduced microbial diversity (lower Shannon, Chaol, and Simpson indices; p<0.01) and distinct taxonomic shifts compared to healthy controls. Pro-inflammatory genera like Bacteroides and Parabacteroides were enriched in GDM (p<0.001), while beneficial taxa such as Akkermansia and Ruminococcaceae were depleted (p<0.001). These microbial alterations strongly correlated with elevated fasting glucose and CRP levels (r>0.39, p≤0.002), suggesting a link between dysbiosis, hyperglycemia, and inflammation. Longitudinal analysis further showed worsening dysbiosis in late gestation, with Bacteroides increasing and Akkermansia declining by 36 weeks (p<0.01). The findings highlight the gut microbiome’s potential role in GDM pathogenesis in this population and support future interventions targeting microbial restoration.
Conclusions: Evidence from the study findings underscores the significant role of gut microbiota in GDM pathogenesis
Trust the process : Role of trust in creative problem solving: A guidebook to empower individuals to build trustful relationships by establishing the role of trust in creative problem solving used in design consultancy - client projects
No full textTraditionally, conventional consultancies provide operational guidance to customers. However, market demands have shifted towards a human centred approach, leading to an increase in design consultancies. Design consultancies deliver not only great results but also introduce innovative ways of thinking through a process-driven approach differing from the result-driven process that clients were used to. The new ways of working poses a new challenge of rising uncertainty. Building trust with client organization reduces the inherent risk of working together, thereby building a stronger relationship. This helps consultancies gain credibility and create mutual reliability, improving brand loyalty and customer retention.Similarities with the creative problem solving process and trust building process were drawn to form a template. Further the research was scoped to see trust building from an individual standpoint, i.e. design consultant being the facilitator in enabling clients build trust towards them. The project was set with the research question of “How do individuals in design consultancies help build trustful relationships with clients amid the constant shift in design perceptions? “From empirical and theoretical analysis, eight driving factors and many trust building actions were identified. Apart from this, the research identified a lack of trust building awareness, how it is important to build trust around your context and with yourself. Further a trust building journey was developed that establishes how trust is built in a step wise approach and how it can be empowered for skill development for individuals seeking to work in a consultancy environment. The design solution is a guidebook developed to raise awareness and help surface the factors necessitated towards trust building through narrative-based learning. Narratives of working professionals experiences highlighting suitable actions taken for trust development helps individuals reflect on their ways of working and embed new actions that improves trust building. Strategic Product Desig
Incorporating crystallographic orientation in the development of resonant ultrasound spectroscopy
No full textArtificial Neural Network Based Method for Classification of Gene Expression Data of Human Diseases along with Privacy Preserving
No full textIn this paper, the author introduces a classification approach using Artificial Neural Network(ANN) with Back-Propagation learning technique for human diseases like Cancer and heart problems from clinical diagnosis data. Clinical diagnosis is done mostly by experienced doctors with expertise in this field. In many cases, the test results are not effective towards the diagnosis of the disease. The author is particular about the wrong diagnosis which leads to a wrong treatment. The author is using Artificial Neural Network technique to classify the disease with reduced number of DNA sequence. The accuracy is differing based on the training data set and validation data set. The other major issue is the privacy preserving of the patients. As we are sharing the critical data from clinical diagnostic centers, there is good chance of patient’s anonymity is revealed. To avoid this, the author is using a simple Privacy Preserving in Data Mining (PPDM) technique to crypt the identity of the patients as well as the critical data and discloses only the required data like DNA sequence to the research team, as they are not much interested in the identity or the owner of the diagnosis report.</jats:p
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