Miracle-dz/UniFAR-FTUs huggingface dataset:
The immunoassay handbook : theory and applications of ligand binding, ELISA and related techniques

The fourth edition of The Immunoassay Handbook provides an excellent, thoroughly updated guide to the science, technology and applications of ELISA and other immunoassays, including a wealth of practical advice. It encompasses a wide range of methods and gives an insight into the latest developments and applications in clinical and veterinary practice and in pharmaceutical and life science research. Highly illustrated and clearly written, this award-winning reference work provides an excellent guide to this fast-growing field. Revised and extensively updated, with over 30% new material and 77 chapters, it reveals the underlying common principles and simplifies an abundance of innovation. The Immunoassay Handbook reviews a wide range of topics, now including lateral flow, microsphere multiplex assays, immunohistochemistry, practical ELISA development, assay interferences, pharmaceutical applications, qualitative immunoassays, antibody detection and lab-on-a-chip. This handbook is a must-read for all who use immunoassay as a tool, including clinicians, clinical and veterinary chemists, biochemists, food technologists, environmental scientists, and students and researchers in medicine, immunology and proteomics. It is an essential reference for the immunoassay industry. Provides an excellent revised guide to this commercially highly successful technology in diagnostics and research, from consumer home pregnancy kits to AIDS testing. www.immunoassayhandbook. com is a great resource that we put a lot of effort into. The content is designed to encourage purchases of single chapters or the entire book. David Wild is a healthcare industry veteran, with experience in biotechnology, pharmaceuticals, medical devices and immunodiagnostics, which remains his passion. He worked for Amersham, Eastman-Kodak, Johnson & Johnson, and Bristol-Myers Squibb, and consulted for diagnostics and biotechnology companies. He led research and development programs, design and construction of chemical and biotechnology plants, and integration of acquired companies. Director-level positions included Research and Development, Design Engineering, Operations and Strategy, for billion dollar businesses. He retired from full-time work in 2012 to focus on his role as Editor of The Immunoassay Handbook, and advises on product development, manufacturing and marketing. * Provides a unique mix of theory, practical advice and applications, with numerous examples* Offers explanations of technologies under development and practical insider tips that are sometimes omitted from scientific papers * Includes a comprehensive troubleshooting guide, useful for solving problems and improving assay performancee* Provides valuable chapter updates, now available on www.immunoassayhandbook. com
Precise Cumulative Error Calibration With Delay Effects Rejected for Incremental Encoders Used in High-Speed PMSMs

In this letter, we propose a novel cumulative error calibration method for the incremental encoders used in permanent magnet synchronous motors (PMSM) based on precise position estimation and counter value error (CVE) compensation. First, a sliding-mode observer (SMO) with analog delay compensated is innovatively developed to estimate the precise rotor position. The novel analog delay compensation method is achieved based on two sequential procedures: phase delay computation and current compensation. Then, relying on the errors between the accurate position estimated by the SMO and the measured one, the CVE can be calculated to correct the output of the counter. Finally, the experiment is conducted on a three-phase 1.5 kW PMSM to validate the proposed cumulative error calibration strategy.
Spatial Metabolism of Urban Densities: Integument of Bangalore, India

Rising population density in our cities is one of the major challenges unleashed by rampant urbanisation. In the western world the urbanisation is often linked to the phenomenon of urban sprawl. However, in the developing world, lack of transportation linkages and poor infrastructure in the peri urban areas makes sprawl an unattractive proposition. Even as cities continue to expand, the inner city neighbourhoods are being continuously intensified in terms of population and dwelling units. Density is a term which is used repeatedly in urban discourses. The paper aims at delineating the concept of density into its diverse metrics. The density metrics are evaluated and examined to reveal their inter relationships and interactions with the city structure. A spatial analysis of diverse high density metrics is carried out for Bangalore city. The study helps in enhancing our understanding of density and its interactions with the spatial structure of the city
A hybrid intelligent technique for induction motor condition monitoring

The objective of this research is to advance the field of condition monitoring and fault diagnosis for induction motors. This involves processing the signals produced by induction motors, classifying the types and estimating the severity of induction motors faults. A typical process of condition monitoring and fault diagnosis for induction motors consists of four steps: data acquisition, signal analysis, fault detection and post-processing. A description of various kinds of faults that can occur in induction motors is presented. The features reflecting faults are usually embedded in transient motor signals. The signal analysis is a very important step in the motor fault diagnosis process, which is to extract features which are related to specific fault modes. The signal analysis methods available in feature extraction for motor signals are discussed. The wavelet packet decomposition results consist of the time-frequency representation of a signal in the same time, which is inherently suited to the transient events in the motor fault signals. The wavelet packet transform-based analysis method is proposed to extract the features of motor signals. Fault detection has to establish a relationship between the motor symptoms and the condition. Classifying motor condition and estimating the severity of faults from the motor signals have never been easy tasks and they are affected by many factors. AI techniques, such as expert system (ES), fuzzy logic system (FLS), artificial neural network (ANN) and support vector machine (SVM), have been applied in fault diagnosis of very complex system, where accurate mathematical models are difficult to be built. These techniques use association, reasoning and decision making processes as would the human brain in solving diagnostic problems. ANN is a computation and information processing method that mimics the process found in biological neurons. But when ANN-based methods are used for fault diagnosis, local minimums caused by the traditional training algorithms often result in large approximation error that may destroy their reliability. In this research, a novel method of condition monitoring and fault diagnosis for induction motor is proposed using hybrid intelligent techniques based on WPT. ANN is trained by improved genetic algorithm (IGA). WPT is used to decompose motor signals to extract the feature parameters. The extracted features with different frequency resolutions are used as the input of ANN for the fault diagnosis. Finally, the proposed method is tested in 1.5 kW and 3.7 kW induction motor rigs. The experimental results demonstrate that the proposed method improves the sensitivity and accuracy of the ANN-based methods of condition monitoring and fault diagnosis for induction motors.
Usage of a dosimeter spectrum evaluator for different environments

The ranges of conditions and environments of a dosimetric spectrum evaluator previously developed and employed in the evaluation of the UV source spectrum have been considered in this paper. The complete system of four dosimeter materials can be employed for a total UV exposure of up to approximately 10 J cm-2. The exposure times required were influenced by the UV environment. The exposure times ranged from 20 minutes to 3 hours for filtered solar erythemal UV, 5 to 30 minutes for solar erythemal UV, 30 to 53 minutes for quartz tungsten halogen lamp erythemal UV, 10 to 15 minutes for plant damage solar UV and 90 minutes for plant damage UV from fluorescent sun lamps. In these environments, employing the system in open and well-ventilated conditions minimises changes in the dose response of the system due to temperature. The results in this paper provide a guide for the range of conditions and exposure times required for different environments for future research employing the dosimetric spectrum evaluator.
Supporting interoperability frameworks in the UK public sector

This paper describes the design and implementation of a component based software application, which alleviates the problem of software interoperability in the UK public sector. We analyze the current interoperability frameworks across the United Kingdom (UK) and European Union (EU) and propose a software solution that enhances such interoperability initiatives. Our example scenario is placed within a UK local authority, which shares data stored within the Police databases, for making efficient and more accurate operational decisions. The prototype, implemented as a J2EE application and built upon existing databases, proves our concept that it is possible to achieve data and application interoperability without integrating data sources and without using XML formats for data sharing.
An analysis of the relationship between airport systems and operations and the impact the efficiency of this cycle can have on environmental emissions

This thesis presents the analysis of the relationship between airport systems and operations and the impact the efficiency of this cycle can have on environmental emissions. Simulations of the existing and hypothetical relationships between the buildings existing services and operations are developed using the quantitative methodology, so allowing valid comparisons across the models. Although these systems and building efficiency techniques are well discussed in the literature in the light of different reduction techniques, this research has a different approach. The simulations allow analysis using a variety of hypothetical relationship patterns. The first simulation (Phase One) is the existing service and operations relationship. Results are analysed on a quantitative analysis technique basis to produce performance metrics. The performance metrics to be compared are the kWh and CO2 emissions from the plants providing the comfort conditions. The first hypothetical relationship (Phase Two) manipulates the building service, to be provided only on operational demand. Results from the simulation are analysed on a comparative basis with the performance metrics from Phase One. The second hypothetical relationship (Phase Three) manipulates both the operation and service. The second simulation manipulates the buildings operation, and the service to be provided on the hypothetical operational demand in an attempt to reduce the CO2 emissions. Results from the second simulation are analysed on a comparative basis with the performance metrics from Phase Two.
A new-type wheel-legged lunar rover and its mobile performance analysis

Based on the principle of a double-half-revolution mechanism, a new-style wheel-legged lunar rover was proposed in this paper. Its mobile system includes rover body frame, four identical wheel-legged components and steering brackets, etc. The independently driven and steering wheel-legged components are based on a double-half-revolution mechanism, and their driving moments are transmitted by two tandem planetary gear trains. Its mobile performance was analyzed, including fluctuation of rover body, capability of anti-rolling and vertical obstacle-climbing capability. Analysis shows that the wheel-legged lunar rover with double-half-revolution mechanism is adaptive to the undulate lunar surface.
Modelling and simulation of the biological and physical processes of slow sand filtration.

Slow sand filtration (SSF) is the earliest form of engineered potable water treatment and remains one of the most efficient processes for improving the physical, biological and chemical quality of water. However, whilst widely used throughout the world, knowledge of the filtration mechanisms remains limited. This is important in understanding and managing the processes that are responsible for gradually blocking the filter reducing its operational life and filtration efficiency. The objective of this thesis was to develop a mechanistic simulation model of the fundamental physico-chemical and biological processes responsible for the filtration mechanisms operating in slow sand filters. The model solves a set of equations describing schmutzdecke development above the sand and microbial biomass growth within the sand. The model assumes that the schmutzdecke layer contributes to the water purification process and its growth is described as linear function in relation to time. The dynamic interactions between the principal groups of microorganisms including: algae, bacteria and protozoa, were modelled using Monod-type kinetic equations. The filtration performance of the filter media was defined in the model by the removal of particulate material from water and was represented by a combination of headloss and filtration coefficient functions. The model was calibrated and verified using data from full and pilot plant-scale SSF operated by Thames Water Utilities Ltd. Simulation results showed that interstitial biomass was the smallest part of the bulk specific deposit in both covered and uncovered filters. However, microbial dynamics played an important role in the filtration performance. Schmutzdecke development had a major influence on the operation of uncovered filters and was responsible for the significant increase of headloss observed during operation. The model provides a representation of the fundamental nature of SSF processes and could form the basis of an operational management system to optimise SSF.
Autonomous Shopping Cart Platform for People with Mobility Impairments

Providing a platform able to interact with a spe- cific user is a challenging problem for assistance technologies. Among the many platforms accomplishing this task, we address the problem of designing an autonomous shopping cart. We assume that the shopping cart is set-up on a unicycle-like robot endowed with two sensors: an RGB-D camera and a planar laser range finder. To combine the information from these two sensors, a data fusion algorithm has been developed using a particle filter, augmented with a k-clustering step to extract person estimations. The problem of stabilizing the robot's position at a fixed distance from the user has been solved through classical control design. Results on a real mobile platform verify the effectiveness of the approach here proposed.
Thermal Profiling in CMOS/Memristor Hybrid Architectures

CMOS/memristor hybrid architectures combine conventional CMOS processing elements with thin-film memristor-based crossbar circuits for high-density reconfigurable systems. These architectures have received an explosive growth in research over the past few years due to the first practical demonstration of a thin-film memristor in 2008. The reliability and lifetimes of both the CMOS and memristor partitions of these architectures are severely affected by temperature variations across the chip. Therefore, it is expected that dynamic thermal management (DTM) mechanisms will be needed to improve their reliability and lifetime. This thesis explores one aspect of DTM–thermal profiling–in a CMOS/memristor memory architecture. A temperature sensing resistive random access memory (TSRRAM) was designed. Temperature information is extracted from the TSRRAM by measuring the write time of thin-film memristors. Active and passive sensing mechanisms are also introduced as means for DTM algorithms to determine the thermal profile of the chip. Crosstherm, a simulation framework, was developed to analyze the effects of temperature variations in CMOS/memristor architectures. The TSRRAM design was simulated using the Crosstherm framework for four CMOS processor benchmarks. Passive sensing produced a mean absolute sensor error across all benchmarks of 2.14 K. The size of the DTM unit’s memory was also shown to have a significant impact on the accuracy of extracted thermal data during passive sensing. Active sensing was also demonstrated to show the effect of dynamic adjustment of sensor resolution on the accuracy of hotspot temperature estimations.
Knowledge sharing in schools with data teams

Data-based decision making has become more and more important for schools. Schools are increasingly held responsible for the education they provide and their pupils’ learning progress, and one way in which they can take this responsibility is by basing their decisions on data. This study focused on the data team procedure, which is one of the methods of data-based decision making. The data team procedure learns teachers to collect, interpret and analyse data and to develop improvement measures for the educational problems within the school (Schildkamp, Poortman, & Handelzalts, submitted). The group of teachers who use the data team procedure, the data team members, will not be able to solve the educational problem by themselves. The data team members need the cooperation of the non-data team members to implement the improvement measures in the school, because mostly these concern school wide problems. Therefore, knowledge sharing between the data team and the non-data team members is important. Data team members can share two types of knowledge: knowledge on the data team procedure and data use in general, and knowledge about the educational problem they are studying. This study focused on whether these two types are shared with the non-data team members, how this knowledge is shared, and what factors promote and hinder this knowledge sharing. The results showed that data team members shared both knowledge about the data team procedure and about the educational problem, although more knowledge was shared about the educational problem. With regard to the way knowledge was shared, the results showed that knowledge was mostly shared verbally at the team level and during formal moments. Knowledge sharing could be promoted by a stimulating school culture and the presence of formal opportunities to share knowledge (e.g., team meetings). A factor that seemed to hinder knowledge sharing was the organisational structure of the school, when there is not much communication between the different locations and departments within the school. This study may be a helpful overview of knowledge sharing in schools for school leaders. They could use the results to promote knowledge sharing in their schools, for example by creating a stimulating school culture and by facilitating the data team members with enough formal opportunities to share their knowledge. Future research about knowledge sharing in the context of data teams is needed in order to gain more insight in which factors have a promoting and a hindering influence on knowledge sharing within schools.
Effects of Workload on Communication Processes in Decision Making Teams: An Empirical Study with Implications for Training

Recent empirical studies of decision making in teams demonstrate that team structure and workload significantly influence team performance. In many operational environments, however, it is impossible to change these factors, even as a mechanism for enhancing team performance. Therefore, it is necessary to create training interventions that will optimize performance within existing team structures and workload levels. Several studies suggest that team processes are the most likely target for this type of intervention. The current investigation sought to develop a laboratory analogue of a common team structure (i.e., the “product team”) and to assess the effects of high and low workload on team performance processes within this structure. The results suggest that different communication behaviors facilitate effective performance under low and high workload.
POLYMORPHISMS OF BETA CASEIN (CSN2) CDS AND INFERENCE OF ITS VARIANTS IN RIVER AND SWAMP WATER BUFFALO (BUBALUS BUBALIS)

β-casein (β-CN) has an important effect on surface properties of casein micelles and milk-clotting properties. However, current understanding of buffalo CSN2 gene polymorphisms is not sufficient. In this study, the polymorphisms in the complete coding sequence (CDS) of buffalo CSN2 were detected using PCR product direct sequencing. The CDS lengths of CSN2 gene in river and swamp buffalo were the same, which was 675 nucleotides and encoded a peptide with 224 amino acid residues. A total of 15 single nucleotide polymorphisms (SNPs) were identified in two types of buffalo. Among them, c.72C>T, c.161A>G, c.167C>T, c.168A>G, c.236G>C, c.249C>G, c.350T>C and c.391G>T were nonsynonymous, which leads to the changes of p.Gly10Ser, p.Gln39Arg, p.Thr41Met, p.Gly64Ala, p.Asn68Lys, p.Met102Thr and p.Val116Phe in the mature peptide of buffalo β-CN, respectively. However, it was predicted that these substitutions had no effect on the function of buffalo β-CN. Fourteen haplotypes were defined based on the SNPs found in buffalo, and accordingly, 4 protein variants and 6 synonymous variants were added in the genetic variants of buffalo β-CN, named variant B2, B3, B4, C, C1, C2, C3, D, E and F, respectively. The main variant in river buffalo was variant B, whereas in swamp buffalo was variant C. All the variants determined in buffalo did not exist in the animals of Bos genus. In addition, there were 11 amino acid differential sites of β-CN between buffalo and Bos genus, of which Thr at residue 41 was located at the phosphorylation site. Furthermore, the results revealed that both types of buffalo β-CN were “A2like”, which indicates that the β-CN in buffalo milk is beneficial to human health.
Adaptive Filters

We know the optimum and suboptimum receiver for ISI in the transmission through band-limited non ideal channels. The optimum employ maximum likelihood detection. The sub optimum employ linear equalizer. In our design of the equalizer we assume that we know at the receiver the impulse response of the channel or the frequency response. In most communication channels this response is varying as a function of time. In this case we use an equalizer to adapt for this change. In this paper we will automatically adjust the equalizer coefficient to adapt for channel changes. We also analyze the performance characteristics including convergence rate and computational complexity. I. Adaptive Linear Equalizer n the case of linear equalizer we will consider two methods to adjust the coefficient values ck. One method is to minimize the peak distortion at the output. The second method is to minimize the mean square error at the output. Now we will talk about each method. II. The Zero-Forcing Algorithm The peak distortion D(c) is minimized by selecting the appropriate ck. In general it is not easy computation. Except for the case D0 < 1. In this case D(c) is minimized by forcing qn = 0 and q0 = 1. We will use the zero forcing algorithm to achieve this condition. The zero forcing algorithm is achieved by equating the cross correlation between the error and the desired sequence to zero. We have