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The coordination of biological activities into daily cycles provides an important advantage for the fitness of diverse organisms. Thereby, an internal circadian oscillator drives gene expression in an approximate 24 hours rhythm. Circadian clocks are found in most eukaryotes. In prokaryotes only cyanobacteria are known to regulate their activities in a circadian rhythm. In vitro experiments showed that three cyanobacterial proteins KaiA, KaiB and KaiC together with ATP are sufficient to generate temperature-compensated circadian oscillations of KaiC protein phosphorylation. Thus, in contrast to eukaryotic clock models the cyanobacterial core oscillator operates independently of transcription and translation processes. Most previous models of the bacterial circadian clock used complex mathematical descriptions. Here, we suggest a minimal and manageable heuristic system. Even though only four reaction steps were assumed, our model exhibited sustained oscillations of KaiC phosphorylation. A simulation of known experimental data was successful as well as oscillations maintained even for a concerted increase of Kai protein concentration. Thus, we provided a useful minimal system of differential equations which might serve as a core module of the holistic cyanobacterial clockwork in the future.

In assessment for learning (AfL), feedback is one of the key factors to maximise the impact of student learning. This chapter examines how teacher’s use of feedback scaffolds Primary 5 students’ thinking in solving a real-world mathematics task on an online learning platform. The case study analysis revealed that teacher’s scaffolding played a significant role in students’ successful completion of a 1-week online task. As students’ thinking and discussions were made visible in the entire online discourse, the teacher was able to gather evidence of the students’ learning gaps more accurately and in a timely manner. The Advancing Children’s Thinking (ACT) framework by Fraivillig, Murphy and Fuson was modified and used by the teacher to provide timely adaptive scaffolding for students to improve on their solution. It was observed that students eventually took ownership of their learning as they engaged in the online problem solving task. This chapter offers insights to a new way of assessing student Mathematics learning beyond the boundaries of the classroom and curriculum time.

When students are asked to examine their understanding individually or in small groups, information can become part of a feedback process that supports students’ learning. As designers of technology to support learning, we are interested in supporting such feedback processes in the context of guided inquiry instruction. This paper explores the potential of automatically associating mathematical descriptions with student submissions created with interactive diagrams. The paper focuses on the feedback processes that occur when students use the descriptions provided by the technology as resources for reflection and learning. We discuss the design of personal feedback processes where students reflect on and communicate their own learning, utilizing individually-reported multi-dimensional automatic analysis of their submissions in response to example-eliciting tasks. While there is much research and development work to be done, we consider mathematical descriptions of student work as an important contribution to broader developments in learning analytics.

Atomic force microscopy (AFM) was originally developed for atomic resolution surface topography observations. Nowadays, it is also widely used for nanolithography. AFM-based lithography is an effective method compared to conventional photolithographic processes due to its simplicity, high resolution, and low cost. It can provide nanoscale stage control and the probing tip can be used as a lithographic tool. Therefore, various AFM-based nanoscale fabrication methods have been proposed using electrochemical oxidation, material transfer, mechanical lithography, and thermally induced modifications. This chapter will introduce the detailed processes and applications of AFM-based lithographic techniques.

This chapter undertakes an in-depth analysis of the interaction between OCR and information retrieval. In particular, after providing an introduction to information retrieval, we report on retrieval effectiveness from OCR generated document collections. It will be shown that, in general, average precision and recall is not affected while document term assignment, weighting and document ranking may be affected. We also point out that even though OCR errors do not affect average retrieval effectiveness, there are other consequences that should be considered when OCR text is applied.

This chapter is an introduction to complexity theory (encompassing chaos — a subset of complexity), a nascent domain, although, it possesses a historical root. Some fundamental properties of chaos/complexity (including complexity mindset, nonlinearity, interconnectedness, interdependency, far-from-equilibrium, butterfly effect, determinism/in-determinism, unpredictability, bifurcation, deterministic chaotic dynamic, complex dynamic, complex adaptive dynamic, dissipation, basin of attraction, attractor, chaotic attractor, strange attractor, phase space, rugged landscape, red queen race, holism, self-organization, self-transcending constructions, scale invariance, historical dependency, constructionist hypothesis and emergence), and its development are briefly examined. In particular, the similarities (sensitive dependence on initial conditions, unpredictability) and differences between deterministic chaotic systems (DCS) and complex adaptive systems (CAS) are analyzed. The edge of emergence (2^nd critical value, a new concept) is also conceived to provide a more comprehensive explanation of the complex adaptive dynamic (CAD) and emergence. Subsequently, a simplified system spectrum is introduced to illustrate the attributes, and summarize the relationships of the various categories of common systems.

Next, the recognition that human organizations are nonlinear living systems (high finite dimensionality CAS) with adaptive and thinking agents is examined. This new comprehension indicates that a re-calibration in thinking is essential. In the human world, high levels of human intelligence/consciousness (the latent impetus that is fundamentally stability-centric) drives a redefined human adaptive and evolution dynamic encompassing better potentials of self-organization or self-transcending constructions, autocatalysis, circular causation, localized spaces/networks, hysteresis, futuristic, and emergent of new order (involving a multi-layer structure and dynamic) — vividly indicating that intelligence/consciousness-centric is extremely vital. Simultaneously, complexity associated properties/characteristics in human organizations must be better scrutinized and exploited — that is, establishing appropriate complexity-intelligence linkages is a significant necessity. In this respect, nurturing of the intelligence mindset and developing the associated paradigmatic shift is inevitable.

A distinct attempt (the basic strategic approach) of the new intelligence mindset is to organize around human intrinsic intelligence — intense intelligence-intelligence linkages that exploits human intelligence/consciousness sources individually and collectively by focusing on intelligence/consciousness-centricity, complexity-centricity, network-centricity, complexity-intelligence linkages, collective intelligence, org-consciousness, complex networks, spaces of complexity (better risk management <=> new opportunities <=> higher sustainability) and prepares for punctuation points (better crisis management <=> collectively more intelligent <=> higher resilience/sustainability) concurrently — illustrating the significance of self-organizing capability and emergence-intelligence capacity. The conceptual development introduced will serve as the basic foundation of the intelligent organization (IO) theory.

Assume that there are p samples and q metrics, which also means the data has q dimensions. For those original accurate data, we employ the normalization method to transfer them into numbers ranging from -1 to +1; for those indicators which cannot be presented as a number, we apply the man-made scoring: -1, -0.5, 0, +0.5, +1in five levels. Then separately, we performed Analytic Hierarchy Process and Maximum Entropy Model on these secondary data. By multiplying the weight and the correlation coefficient, Level 3 data is obtained. Finally, the Principal Component Analysis is carried out, and we can derive the function between the original data and the evaluation result. However, the connection is quite unstable if any data changes significantly. So we put forward a feedback system, which evaluates the final result (as a number) to be a new original data. Apparently, this model still has some contingencies. In order to evaluate the error, we establish an implicit relationship between the original data and the final results by Artificial Neural Networks Principals. Compared the simulation without previous results, one may conclude that our model is robust and valid under most circumstances. Finally, we can work out the error percentage of our evaluation model.

A feedback control circuit is presented to solve the problem of electric isolation between the output of high voltage feedback circuit and the input of micro control unit pins in high voltage measurement system. Sampling voltage acquired by resistive divider from the secondary winding of the transformer acts as the input of voltage-to-frequency converter in the feedback control circuit, and a photoelectrical coupler is adopted in the circuit to solve the problem of isolation. Simulation and experiment results verified the validity of this design, the output of high voltage transformer and the input of the micro control unit are effectively isolated and the circuit works reliably.