Computer Science

The TensorTest2D package provides the means to fit generalized linear models on secondorder tensor type data. Functions within this package can be used for parameter estimation (e.g., estimating regression coefficients and their standard deviations) and hypothesis testing. We use two examples to illustrate the utility of our package in analyzing data from different disciplines. In the first example, a tensor regression model is used to study the effect of multi-omics predictors on a continuous outcome variable which is associated with drug sensitivity. In the second example, we draw a subset of the MNIST handwritten images and fit to them a logistic tensor regression model. A significance test characterizes the image pattern that tells the difference between two handwritten digits. We also provide a function to visualize the areas as effective classifiers based on a tensor regression model. The visualization tool can also be used together with other variable selection techniques, such as the LASSO, to inform the selection results.

This research investigates the implementation and impact of personalized learning systems underpinned by advanced recommendation algorithms in the realm of online education. The study encompasses a diverse group of participants from various educational backgrounds and explores their interactions with the personalized learning platform. The key findings of this research are noteworthy. Participants who had access to the personalized learning environment exhibited a substantial increase in engagement, satisfaction, and learning outcomes compared to those in the control group. This signifies the transformative potential of personalized learning in online education. The research emphasizes the critical role of personalization in enhancing learner engagement and satisfaction. It highlights how learners actively engaged with the system, making use of personalized recommendations to tailor their learning experiences. Moreover, the study sheds light on the positive impact of personalization on learning outcomes, indicating that learners achieved higher academic performance when their learning experiences were customized to their needs and preferences. In addition to its benefits for learners, the research underscores the advantages of personalized learning for instructors. The system provided instructors with valuable insights into each learner's progress and challenges, enabling more targeted and effective support. While the study demonstrates the effectiveness of personalized learning, it acknowledges certain limitations, including a relatively limited sample size and short duration. Future research endeavors could involve larger and more diverse samples and extend the study duration to gain a more comprehensive understanding of the long-term effects of personalized learning. In conclusion, this research contributes to the growing body of literature on personalized learning in online education. It provides compelling evidence that personalized learning, facilitated by sophisticated recommendation algorithms, can significantly enhance the online learning experience. The findings offer insights for educators and institutions looking to integrate personalized learning features into their online platforms to improve learner engagement, satisfaction, and learning outcomes.

Penelitian ini bertujuan untuk menghasilkan perangkat pembelajaran pada materi bangun segitiga dan segi empat dengan penemuan terbimbing untuk meningkatkan kemampuan pemecahan masalah pada Kompetensi Dasar “Menghitung keliling dan luas bangun segitiga dan segi empat serta menggunakannya dalam pemecahan masalah” yang layak digunakan dalam proses pembelajaran. Penelitian ini merupakan penelitian dan pengembangan menggunakan model Plomp. Pengembangan yang dilakukan dengan 5 tahap yaitu: (1) analisis permasalahan, (2) rancangan, (3) realisasi, (4) implementasi, (5) evaluasi. Subjek uji coba dalam penelitian ini adalah kelas VII SMP Negeri 1 Patuk Gunungkidul dengan 32 siswa. Instrumen pengumpulan data yang digunakan adalah lembar validasi perangkat, lembar kepraktisan perangkat, dan tes. Data yang dikumpulkan berupa data tentang kualitas produk yang dikembangkan yaitu kevalidan, kepraktisan, dan keefektifan. Penelitian ini menghasilkan perangkat pembelajaran pada Kompetensi Dasar “Mengh...

Syntactic complexity is the variety and sophistication degree of the syntactic structures conveyed in written production. The syntactic complexity of general Chinese university students’ EFL writing has been studied previously, but the performance of university students in educationally underdeveloped Southwestern China remains unclear. Taking Pu’er University as a case, this study collected 400 EFL compositions from 100 university students in Southwestern China and compared them with 200 writing samples from the Louvain Corpus of Native English Essays. Scores of 11 syntactic complexity indices were calculated using the L2 Syntactic Complexity Analyzer. The independent samples t-test was conducted to investigate whether and the extent to which the two groups differed on syntactic complexity indices. The results showed that university EFL students in Southwestern China produce a similar length of linguistic units when compared to native English writers. However, the amount of subordi...

This paper reviews more than 60 research papers, articles, or book chapters on syntactic complexity in the context of EFL/ESL writing in the past two decades. Most of the papers are from journals indexed in Social Science Citation Index, Scopus, and Chinese Social Science Citation Index. Five strands of syntactic complexity studies in the context of EFL/ESL writing are concluded: syntactic complexity measurement indices and tools, the relationship between syntactic complexity and language proficiency, syntactic complexity developmental studies, comparative studies, and variables influencing syntactic complexity. Gaps in previous studies and future research focuses are analyzed and concluded: new indices from other syntactic perspectives should be considered and research on their validity and reliability should be done. For comparative studies, more attention should be given to comparing the writing of EFL/ESL learners with different backgrounds. For research on variables influencing syntactic complexity, the interactive effect of multiple variables needs to be investigated; if only one variable is examined, other variables should be controlled. Besides, in future syntactic complexity research, theoretical interpretation and theory building should be given more attention, and the observation period for longitudinal research should be extended. Finally, more qualitative studies are needed for in-depth investigation of specific syntactic perspectives, such as syntactic errors.

Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study utilizing a dataset of 100,000 nonoverlapping image patches from hematoxylin & eosin-stained histological images of CRC and normal tissue. The dataset includes diverse tissue classes such as adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. To perform survival prediction, we employ various deep learning architectures, including convolutional neural network, DenseNet201, InceptionResNetV2, VGG16, VGG19, and Xception. These architectures are trained on the dataset using a multicenter retrospective analysis approach. Extensive preprocessing steps are undertaken, including image normalization using Macenko's method and data augmentation techniques, to optimize model performance. The experimental findings reveal promising results, demonstrating the effectiveness of deep learning models in prognostic survival prediction. Our models achieve high accuracy, precision, recall, and validation metrics, showcasing their ability to capture relevant histological patterns associated with prognosis. Visualization techniques are employed to interpret the models' decision-making process, highlighting important features and regions contributing to survival predictions. The implications of this research are manifold. The accurate prediction of survival outcomes in CRC can aid in personalized medicine and clinical decision-making, facilitating tailored treatment plans for individual patients. The identification of important histological features and biomarkers provides valuable insights into disease mechanisms and may lead to the discovery of novel prognostic indicators. The transparency and explainability of the models enhance trust and acceptance, fostering their integration into clinical practice. Research demonstrates the potential of deep learning models for prognostic survival prediction in human colorectal cancer histology. The findings contribute to the understanding of disease progression and offer practical applications in personalized medicine. By harnessing the power of deep learning and histopathological analysis, we pave the way for improved patient care, clinical decision support, and advancements in prognostic prediction in CRC.

By applying REMD simulations we have performed comparative analysis of the conformational ensembles of amino-truncated Aβ10-40 peptide produced with five force fields, which combine four protein parameterizations (CHARMM36, CHARMM22*, CHARMM22/cmap, and OPLS-AA) and two water models (standard and modified TIP3P). Aβ10-40 conformations were analyzed by computing secondary structure, backbone fluctuations, tertiary interactions, and radius of gyration. We have also calculated Aβ10-40 3 J HNHα -coupling and RDC constants and compared them with their experimental counterparts obtained for the fulllength Aβ1-40 peptide. Our study led us to several conclusions. First, all force fields predict that Aβ adopts unfolded structure dominated by turn and random coil conformations. Second, specific TIP3P water model does not dramatically affect secondary or tertiary Aβ10-40 structure, albeit standard TIP3P model favors slightly more compact states. Third, although the secondary structures observed in CHARMM36 and CHARMM22/cmap simulations are qualitatively similar, their tertiary interactions show little consistency. Fourth, two force fields, OPLS-AA and CHARMM22* have unique features setting them apart from CHARMM36 or CHARMM22/cmap. OPLS-AA reveals moderate β-structure propensity coupled with extensive, but weak long-range tertiary interactions leading to Aβ collapsed conformations. CHARMM22* exhibits moderate helix propensity and generates multiple exceptionally stable long-and short-range interactions. Our investigation suggests that among all force fields CHARMM22* differs the most from CHARMM36. Fifth, the analysis of 3 J HNHα -coupling and RDC constants based on CHARMM36 force field with standard TIP3P model led us to an unexpected finding that in silico Aβ10-40 and experimental Aβ1-40 constants are generally in better agreement than these quantities computed and measured for identical peptides, such as Aβ1-40 or Aβ1-42. This observation suggests that the differences in the conformational ensembles of Aβ10-40 and Aβ1-40 are small and the former can be used as proxy of the full-length peptide. Based on this argument, we concluded that CHARMM36 force field with standard TIP3P model produces the most accurate representation of Aβ10-40 conformational ensemble.

By applying REMD simulations we have performed comparative analysis of the conformational ensembles of amino-truncated Aβ10-40 peptide produced with five force fields, which combine four protein parameterizations (CHARMM36, CHARMM22*, CHARMM22/cmap, and OPLS-AA) and two water models (standard and modified TIP3P). Aβ10-40 conformations were analyzed by computing secondary structure, backbone fluctuations, tertiary interactions, and radius of gyration. We have also calculated Aβ10-40 3 J HNHα -coupling and RDC constants and compared them with their experimental counterparts obtained for the fulllength Aβ1-40 peptide. Our study led us to several conclusions. First, all force fields predict that Aβ adopts unfolded structure dominated by turn and random coil conformations. Second, specific TIP3P water model does not dramatically affect secondary or tertiary Aβ10-40 structure, albeit standard TIP3P model favors slightly more compact states. Third, although the secondary structures observed in CHARMM36 and CHARMM22/cmap simulations are qualitatively similar, their tertiary interactions show little consistency. Fourth, two force fields, OPLS-AA and CHARMM22* have unique features setting them apart from CHARMM36 or CHARMM22/cmap. OPLS-AA reveals moderate β-structure propensity coupled with extensive, but weak long-range tertiary interactions leading to Aβ collapsed conformations. CHARMM22* exhibits moderate helix propensity and generates multiple exceptionally stable long-and short-range interactions. Our investigation suggests that among all force fields CHARMM22* differs the most from CHARMM36. Fifth, the analysis of 3 J HNHα -coupling and RDC constants based on CHARMM36 force field with standard TIP3P model led us to an unexpected finding that in silico Aβ10-40 and experimental Aβ1-40 constants are generally in better agreement than these quantities computed and measured for identical peptides, such as Aβ1-40 or Aβ1-42. This observation suggests that the differences in the conformational ensembles of Aβ10-40 and Aβ1-40 are small and the former can be used as proxy of the full-length peptide. Based on this argument, we concluded that CHARMM36 force field with standard TIP3P model produces the most accurate representation of Aβ10-40 conformational ensemble.

Water pollution caused by industrial and waste-water discharges, agricultural activities, and human activities is a major concern. This paper presents a hardware implementation of data augmentation ASIC design for a low-power, low-cost Water Quality Indexing application. The design has been developed using a Multilayer Perceptron (MLP) feedforward network with backpropagation learning to predict the data of DO and EC using pH and ORP as the input vector. This eliminates the need for expensive sensor electrodes, thereby reducing the cost of the design. The Augmentation ANN achieves 98% in the prediction of DO and EC. The results have been presented and compared with standard WQI devices.

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This chapter presents the lessons and challenges in land change modeling that emerged from years of reflection and numerous panel discussions at scientific conferences concerning a collaborative cross-case comparison in which the authors have participated. We summarize the lessons as nine challenges grouped under three themes: mapping, modeling, and learning. The mapping challenges are: to prepare data appropriately, to select relevant resolutions, and to differentiate types of land change. The modeling challenges are: to separate calibration from validation, to predict small amounts of change, and to interpret the influence of quantity error. The learning challenges are: to use appropriate map comparison measurements, to learn about land change processes, and to collaborate openly. To quantify the pattern validation of predictions of change, we recommend that modelers report as a percentage R.G. Pontius Jr.

In this paper, the standard indirect field-oriented controller (IFOC) commonly used in current-fed induction motor drives is modified to achieve global exponential rotor velocity/rotor flux tracking. The modifications to the IFOC scheme, which involve the injection of nonlinear terms into the current control input and the so-called desired rotor flux angle dynamics, facilitate the construction of a standard Lyapunov stability argument. The construction of a standard Lyapunov exponential stability argument allows one to easily design adaptive controllers to compensate for parametric uncertainty associated with the mechanical load. Simulation results are included to illustrate the improvement in performance over the standard IFOC scheme.

We propose a learning-based method to extract symbolic representations of the belief state and its dynamics in order to solve planning problems in a continuous-state partially observable Markov decision processes (POMDP) problem. While existing approaches typically parameterize the continuous-state POMDP into a finite-dimensional Markovian model, they are unable to preserve fidelity of the abstracted model. The first major contribution of this paper is we propose a Neural Network based method to learn the non-Markovian transition model based on the Mori-Zwanzig (M-Z) formalism. Different from existing work in applying M-Z formalism to autonomous time-invariant systems, our approach is the first work generalizing the M-Z formalism to robotics, by addressing the non-Markovian modeling of the belief dynamics that is dependent on historical observations and actions. The second major contribution is we theoretically show that modeling the non-Markovian memory effect in the abstracted bel...

This paper presents the first swing stabilization control for indoor miniature autonomous blimps (MABs). Indoor MABs are safe to operate in close proximities to humans and can fly for multiple hours, but swing oscillation is commonly observed due to their underactuated design and unique aerodynamic shape. In this paper, we analyze the flight characteristics of indoor MABs, and describe the design of the swing-reducing flight control system in detail. Key mechatronic designs for swing-stabilization control are also presented. Experimental results show that the proposed controller can keep the blimp travel at the desired velocity while effectively stabilizing the swing oscillation. The swing-reducing velocity controller is then expanded for station keeping and waypoint navigation in 3D space.

The Advanced Quality Control class in the Industrial Engineering Department at Kettering University has taken a unique approach to practical learning by turning the course into a multidisciplinary effort with the Mechanical Engineering Polymer Processing course. Building on the knowledge developed in prerequisite courses, the IE student will use tools in Statistical Process Control (SPC), Design of Experiments (DoE), and Response Surface Methodology (RSM) to characterize and optimize the process of injection molding of plastics. The mechanical engineering students will better understand the significant effect of certain factors (parameters) on the quality of injection molded parts by using the statistical tools available. In this paper, we will discuss how this group of students interact and gain knowledge and skills through teamwork. We will also discuss how integration of the two engineering disciplines using DoE and polymer processing provides the students with the experience of the real-world work environment.

Ground tree rewrite systems (GTRS) are an extension of pushdown systems with the ability to spawn new subthreads that are hierarchically structured. In this paper, we study the following problems over GTRS: (1) model checking EF-logic, (2) weak bisimilarity checking against finite systems, and (3) strong bisimilarity checking against finite systems. While they are all known to be decidable, we show that problems (1) and ( ) have nonelementary complexity, whereas problem (3) is shown to be in coNEXP by finding a syntactic fragment of EF whose model checking complexity is complete for P NEXP . The same problems are studied over a more general but decidable extension of GTRS called regular GTRS (RGTRS), where regular rewriting is allowed. Over RGTRS we show that all three problems have nonelementary complexity. We also apply our techniques to problems over PA-processes, a well-known class of infinite systems in Mayr's PRS (Process Rewrite Systems) hierarchy. For example, strong bisimilarity checking of PAprocesses against finite systems is shown to be in coNEXP, yielding a first elementary upper bound for this problem.

Pushdown systems (PDS) naturally model sequential recursive programs. Numeric data types also often arise in real-world programs. We study the extension of PDS with unbounded counters, which naturally model numeric data types. Although this extension is Turingpowerful, reachability is known to be decidable when the number of reversals between incrementing and decrementing modes is bounded. In this paper, we (1) pinpoint the decidability/complexity of reachability and linear/branching time model checking over PDS with reversal-bounded counters (PCo), and (2) experimentally demonstrate the effectiveness of our approach in analysing software. We show reachability over PCo is NP-complete, while LTL is coNEXP-complete (coNP-complete for fixed formulas). In contrast, we prove that EF-logic over PCo is undecidable. Our NP upper bounds are by a direct poly-time reduction to satisfaction over existential Presburger formulas, allowing us to tap into highly optimized solvers like Z3. Although reversal-bounded analysis is incomplete for PDS with unbounded counters in general, our experiments suggest that some intricate bugs (e.g. from Linux device drivers) can be discovered with a small number of reversals. We also pinpoint the decidability/complexity of various extensions of PCo, e.g., with discrete clocks.

In his seminal paper, Mayr introduced the well-known Process Rewrite Systems (PRS) hierarchy, which contains many wellstudied classes of infinite-state systems including pushdown systems, Petri nets and PA-processes. A seperate development in the term rewriting community introduced the notion of Ground Tree Rewrite Systems (GTRS), which is a model that strictly extends pushdown systems while still enjoying desirable decidable properties. There have been striking similarities between the verification problems that have been shown decidable (and undecidable) over GTRS and over models in the PRS hierarchy such as PA and PAD processes. It is open to what extent PRS and GTRS are connected in terms of their expressive power. In this paper we pinpoint the exact connection between GTRS and models in the PRS hierarchy in terms of their expressive power with respect to strong, weak, and branching bisimulation. Among others, this connection allows us to give new insights into the decidability results for subclasses of PRS, e.g., simpler proofs of known decidability results of verifications problems on PAD.

In this paper we introduce an unsupervised online clustering algorithm to detect abnormal activities using mobile devices. This algorithm constantly monitors a user's daily routine and builds his/her personal behavior model through online clustering. When the system observes activities that do not belong to any known normal activities, it immediately generates alert signals so that incidents can be handled in time. In the proposed algorithm, activities are characterized by users' postures, movements, and their indoor location. Experimental results show that the behavior models are indeed user-specific. Our current system achieves 90% precision and 40% recall for anomalous activity detection.

In this paper, we present a hierarchical multi-label classification system for visual concepts detection and image annotation. Hierarchical multi-label classification (HMLC) is a variant of classification where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. The system is composed of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images. These descriptions are then used to learn a classifier and to annotate an image with the corresponding concepts. To this end, we use predictive clustering trees (PCTs), which are able to classify target concepts that are organized in a hierarchy. Our approach to HMLC exploits the annotation hierarchy by building a single predictive clustering tree that can simultaneously predict all of the labels used to annotate an image. Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance. We tested our system on the image database from the ImageCLEF@ICPR 2010 photo annotation task. The extensive experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to large number of visual concepts and large amounts of data.

Occurring in transmission or distribution networks, voltage sags (transient reductions of voltage magnitude) can cause serious damage to end-use equipment (domestic appliances, precision instruments, etc.) and industrial processes (PLC and controller resets, time life reduction, etc.) resulting in important economic losses. We present a statistical method to determine whether these disturbances originate upstream (transmission system) or downstream (distribution system) of the registering point located in distribution substations. The method uses only information from the recorded disturbances and exploits their statistical properties of them in terms of covariance. Multiway Principal Component Analysis (MPCA) is proposed to model classes of sags according to their origin upstream/downstream using the RMS values of voltages and currents. New, not-yet-seen sags are projected to these models and classified based on statistical criteria measuring their consistency with the models. the successful classification of real sags recorded in electric substations is compared with other methodologies.

In the wireless communication systems, the objective of using array antennas is to extract the desired signal while filtering out the unwanted interferences. New methods are developed for the optimization and synthesis, in terms of the directivity and the side lobe level. For the optimization of the array pattern, it is proposed to adjust both the excitation and the spacing of the antenna elements. The determination of the optimal excitation and spacing is shown to be a polynomial problem; this is described in terms of a unified mathematical approach to nonlinear optimization of multidimensional array geometries. The approach utilizes a class of limiting properties of Legendre functions that are dictated by the array geometry addressed. The objective of this paper is to describe a unified mathematical approach to nonlinear optimization of multidimensional array geometries.

Text-to-speech (TTS) conversion is a crucial technology for various applications, including accessibility, education, and entertainment. With the rapid growth of big data, TTS conversion systems face new challenges in terms of data size and diversity. In this paper, we propose to use the state-of-the-art language model ChatGPT to enhance TTS conversion for big data. We first introduce the background of TTS conversion and big data, and then review the existing TTS conversion systems and their limitations. Next, we describe the architecture and training of ChatGPT, and how it can be applied to TTS conversion. Finally, we evaluate the performance of the ChatGPT-based TTS conversion system on a large-scale real-world big data dataset, and compare it with the existing TTS systems. Our experimental results demonstrate that ChatGPT can significantly improve the quality and efficiency of TTS conversion for big data.

In this paper, a practicable contraction approach is proposed for solving the sum of the generalized polynomial ratios problem (P) with generalized polynomial constraints. Due to the intrinsic difficulty of problem (P), less work has been devoted to solving this problem. The proposed approach is based on reducing the original nonconvex problem (P) as a standard geometric programming (GP) problem by utilizing simple transformation and contraction strategies. The resulting optimization problem can be solved effectively by utilizing the solutions of a series of GP problems. The tractability and effectiveness of the proposed successive contraction approach are demonstrated by several numerical examples, and the performance comparison of the proposed approach and other methods published is also presented in terms of solution quality.

Pediatric heart transplantation has become a mainstream in the treatment of end-stage heart disease in infants and children. Heart rate variability is a measure of autonomic nervous influence on the heart. After heart transplantation, the donor heart loses its nervous control. Nonlinear analysis of heart rate is a method to study the physiologic mechanisms responsible for the control of heart rate fluctuations, in which the autonomic nervous system appears to play a primary role. This work evaluated the heart rate entropy in children who has undergone heart transplantation. Patients who underwent heart transplantation showed significant decrease in entropy as assessed by sample entropy (0.63 ± 0.31 vs. 0.90 ± 0.33, p < 0.05). System complexity as can be assessed by entropy methods decreases in transplanted patients and this may be related to loss of the neural modulation of heart rate.

We present a novel method for the classification and identification of electrocardiograms (ECGs) of various heart rhythm disturbances. This is an essential step in the automatic analysis of heart rhythm disturbances. Dynamic time warping (DTW) is used for this purpose. DTW is utilized successfully in speech recognition. Wavelet analysis is used in some implantable cardioverter defibrillators currently for ECG waveform recognition and classification purpose. The simulations of time-series ECG data of various rhythm disturbances are produced. Normal sinus rhythm ECG templates are compared to the simulated rhythms by both methods. DTW analysis successfully differentiates the ECGs of various arrhythmias. Of note, DTW is able to differentiate ventricular tachycardia from supraventricular tachycardia unlike wavelet analysis. Differentiation of these two rhythm types has significant clinical implications. DTW can potentially be used for automatic pattern recognition of ECG changes representative of various rhythm disturbances.

Background: In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring structure of Gene Regulatory Networks (GRNs). Bayesian network is one of the popular methods in this field, however, still has many drawbacks and there is still a great space to be improved. For example, the Path Consistency (PC)-based algorithms as Bayesian network methods are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. Results: We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the l...

Researchers have, for a long time, tried to, and are today highly successful at, demystifying the human brain through brain signals. In the field of computer science, early attempts were more algorithmic, focused on artificial neural networks in order to solve complex software engineering problems. The advent of possibilities offered by Brain Computer Interface (BCI) has opened up avenues for a plethora of research and development projects. The latest published advancement in the field is the famous "thought-to-text" application, whereby an individual thought could be transformed into textual information. One may guess the potentials for such an advancement in the medicine field. The mathematical armada to allow digital processing of brain signals, coupled with progression in sensors and electrodes has prompted innovative products on the market. Capturing raw data from one's brain with in-built software for analysis could be produced as a bundle on the market at affordable prices. With more computational power and visualisation techniques, future generations of neuroscientists will undoubtedly be in a more knowledgeable situation, probably the notion of computer interface with the brain might look more natural that it is now. Nonetheless, BCI remains an ocean to be explored and this book aims at capturing the ongoing activities globally. The chapters comprise deep research with solid mathematical foundations supplemented with experimental results. Moreover readers will find extensive lists of references pertaining to BCI and original contributions of the chapters from the authors. The manuscript is therefore a trusted primary source of information on BCI. Interfaces is a compilation of recent achievements ranging from optical technologies, mental task-based BCI, speech enhancement, brain dynamics, and brain-computer modelling. Authors across the globe have formulated the chapters to be accessible to students, lecturers, and researchers, as well as readers passionate about the topic. This book aims to disseminate the latest findings and research work in BCI. There is potential for future research for PhD students as well as food for thought about the next generation of BCI technologies. We wish to thank all the authors for their efforts and good will to make this book project possible. Without their contributions, recent knowledge about BCI would remain hidden. A vote of thanks to the IntechOpen Author Service Manager Ms Dolores Kuzelj, the technical board, and the commissioning editor. Providing open access to knowledge is undoubtedly a noble cause.

Ordinary Hermitian λϕ 4 theory is known to exist in d < 4 dimensions when λ > 0. For negative values of the coupling, it has been suggested that a physical meaningful definition of the interacting theory can be given in terms of PT-symmetric field theory. In this work, we critically reexamine the relation between analytically continued Hermitian field theory with quartic interaction and PT-symmetric field theory, including OðNÞ models. We find that in general PT-symmetric field theory does not correspond to the analytic continuation of the Hermitian theory to negative coupling, except at high temperature where the instanton contribution present in the analytically continued theory can be neglected.

This article comments upon some new developments in the field of Queuing Theory and Markov Processes. Matrix-analytic methods are used to solve particular kinds of a Markov Processes. The level dependent quasi-birthdeath process (LDQBD) is an example of a matrix-analytic model, and in this article we give the background on how queues are modelled with Markov Processes which leads to some new mathematics to overcome problems in modelling particular situations. We conclude by outlining new methods used to numerically calculate results for a LDQBD process.

Purpose To compare surgical parameters among eyes undergoing laser-assisted cataract surgery (LACS) using different lens fragmentation patterns (LFP). Methods Prospective, randomized, unmasked clinical trial. One-hundred eyes underwent LACS and were randomly assigned to 1 of 3 LFP treatment groups: (1) laser capsulotomy only; no lens fragmentation (NLF) (n = 34); (2) three-plane chop (TPC) (n = 33); and, (3) pie-cut pattern (PCP) fragmentation (n = 33). Prechop phacoemulsification (PHACO) was performed on all eyes using the same femtosecond (FS) laser and active-fluidics PHACO machine. Main outcome measures: FS laser dock time (s), PHACO time (s), PHACO power (%), cumulative dissipated energy (CDE) (%-s), irrigating fluid volume, and operative time. The 3 treatment groups were comparable in terms of patient age (P = 0.164) and nuclear density (P = 0.669). FS dock time was higher in the PCP group (184.18 ± 25.86) compared to the TPC (145.09 ± 14.15) group (P \ 0.001). PHACO time was significantly shorter in the PCP (23.19 ± 17.20 s) compared to TPC (35.27 ± 17.70) and NLF (46.15 ± 23.72) groups (P \ 0.001). PHACO power was lower in the PCP (11.81 ± 3.71) compared to the NLF (14.41 ± 1.88) and TPC (14.04 ± 2.46) groups (P \ 0.001). CDE was lower in the PCP (2.85 ± 2.32) compared to NLF (6.55 ± 3.32) and TPC (6.55 ± 5.45) groups (P \ 0.001). Fluid volumes and operative times were similar. Conclusion LFP can influence PHACO surgical parameters. Extensive fragmentation patterns such as PCP appear to lower PHACO time, power, and CDE and may potentially reduce the risk of PHACO related complications.

Morphogenesis relies on the active generation of forces, and the transmission of these forces to surrounding cells and tissues. Hence measuring forces directly in developing embryos is an essential task to study the mechanics of development. Among the experimental techniques that have emerged to measure forces in epithelial tissues, force inference is particularly appealing. Indeed it only requires a snapshot of the tissue, as it relies on the topology and geometry of cell contacts, assuming that forces are balanced at each vertex. However, establishing force inference as a reliable technique requires thorough validation in multiple conditions. Here we performed systematic comparisons of force inference with laser ablation experiments in four epithelial tissues from two animals, the fruit fly and the quail. We show that force inference accurately predicts single junction tension, tension patterns in stereotyped groups of cells, and tissue-scale stress patterns, in wild type and muta...

In this chapter we demonstrate the feasibility of digital computation in cells by building several operational in vivo digital logic circuits, each composed of three gates that have been optimized by genetic process engineering. We have built and characterized an initial cellular gate library with biochemical gates that implement the NOT, IMPLIES, andANDlogic functions in E. coli cells. The logic gates perform computation using DNA-binding proteins, small molecules that interact with these proteins, and segments of DNA that regulate the expression of the proteins. We also demonstrate engineered intercellular communications with programmed enzymatic activity and chemical diffusions to carry messages, using DNA from the Vibrio fischeri lux operon. The programmed communications is essential for obtaining coordinated behavior from cell aggregates. This chapter is structured as follows: the first section describes experimental measurements of the device physics of in vivo logic gates, as...

Many synthetic gene circuits are restricted to single-use applications or require iterative refinement for incorporation into complex systems. One example is the recombinase-based digitizer circuit, which has been used to improve weak or leaky biological signals. Here we present a workflow to quantitatively define digitizer performance and predict responses to different input signals. Using a combination of signal-to-noise ratio (SNR), area under a receiver operating characteristic curve (AUC), and fold change (FC), we evaluate three small-molecule inducible digitizer designs demonstrating FC up to 508x and SNR up to 3.77 dB. To study their behavior further and improve modularity, we develop a mixed phenotypic/mechanistic model capable of predicting digitizer configurations that amplify a synNotch cell-to-cell communication signal (Δ SNR up to 2.8 dB). We hope the metrics and modeling approaches here will facilitate incorporation of these digitizers into other systems while providin...

The rise of the field of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact synthetic genetic device performance at multiple scales, including the genetic, cellular and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context-dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider the application of cell fate programming as a case study to explore the potential impact of context-aware devices for regenerative medicine applications. Cellular decision-making thus depends on the concentration and modification status of key molecular players, such as DNA, RNA, and proteins, together determining cell state. It has been shown that the binding pattern of TFs differs between cell types, suggesting that changes to the cellular epigenome can change regulatory processes in the cell, allowing these processes to evolve over time as a function of accessibility of DNA binding domains and regulator concentrations . TF and coactivator binding throughout the genome is a function of accessible binding sites, where the relative binding affinity and concentration of competing binding partners determines the dominant regulatory interactions (Hosokawa and Rothenberg 2020). Indeed, systems biology has demonstrated the utility of modeling to better understand the impact of cell state on cellular decisions Davidson and Peter 2015;). These efforts have aimed to predict the phenotypic behaviors of cells, including mammalian stem cells , by computationally modeling the cell's processor and its initial state. Through the addition of microenvironmental inputs, cellular outcomes have been predicted using models. These models can be augmented to yield probabilistic predictions of cellular outcomes by including different sources of cellular noise . In these stochastic models, fluctuations in biochemical reactions involved in the dynamical processes and regulatory interactions within the cellular processor (Raser and O'Shea 2005) serve as an additional stochastic input that influences cellular decision-making . To this end, combined experimental and computational techniques have helped to improve our understanding of the molecular players in the cell's regulatory network. In this review, we summarize the progress made by the field of mammalian synthetic biology, which adopts the systems biology view of the cell as a dynamical system, to program novel functions into the cellular processor (Khalil and Collins 2010). Synthetic biology applies genetic engineering, mathematical modeling and computational approaches to design and construct genetic circuits that produce predictable cellular outcomes. Many early genetic circuits were developed in bacteria, including the toggle switch and oscillator . Given that cell state and the inner regulatory network are key drivers of cellular decision-making, the behaviors of synthetic genetic circuits that are transplanted into cells are inevitably shaped by these drivers. Here, we specifically focus on challenges that the mammalian cell context imposes, providing an overview of context effects that have important implications for synthetic genetic device design. We then explore strategies involving control systems approaches towards context-aware device design, with a particular emphasis on applications in cell fate programming. Owing to two cardinal properties -the ability to self-renew and to give rise to all of the cell types of the bodypluripotent stem cells (PSCs) have generated excitement as a powerful substrate for regenerative medicine. Stem cell potency has been conceptually visualized through the classic Waddington landscape . As cells roll down the hills on the landscape, they lose their potential and commit to specialized cell types, which are epigenetically stabilized in valleys that represent their endpoint fate (Figure ). The ability to reliably control the differentiation, growth and death dynamics of stem cells and their progeny has been a key focus of stem cell bioengineers . Cell fate programming of PSCs to clinically relevant cell types has opened the door to new classes of off-the-shelf cell therapies, in which viable cells are implanted into a patient in order to effectuate a medicinal effect. Cellular therapies are a booming biotechnology industry, valued at over $6 billion USD in 2020 and projected to reach a global market share of $9 billion by 2027 (Grand View Research report . They offer an exciting paradigm shift towards the treatment of chronic and acute diseases through the transplantation of living cells and are a compelling example of the clinical implications for mammalian synthetic biology.

Microfluidic devices have the potential to automate and miniaturize biological experiments, but open-source sharing of device designs has lagged behind sharing of other resources such as software. Synthetic biologists have used microfluidics for DNA assembly, cell-free expression, and cell culture, but a combination of expense, device complexity, and reliance on custom set-ups hampers their widespread adoption. We present Metafluidics, an open-source, community-driven repository that hosts digital design files, assembly specifications, and open-source software to enable users to build, configure, and operate a microfluidic device. We use Metafluidics to share designs and fabrication instructions for both a microfluidic ring-mixer device and a 32-channel tabletop microfluidic controller. This device and controller are applied to build genetic circuits using standard DNA assembly methods including ligation, Gateway, Gibson, and Golden Gate. Metafluidics is intended to enable a broad c...

The RNA-guided bacterial nuclease Cas9 can be reengineered as a programmable transcription factor by a series of changes to the Cas9 protein in addition to the fusion of a transcriptional activation domain (AD) 1-5 . However, the modest levels of gene activation achieved by current Cas9 activators have limited their potential applications. Here we describe the development of an improved transcriptional regulator through the rational design of a tripartite activator, VP64-p65-Rta (VPR), fused to Cas9. We demonstrate its utility in activating expression of endogenous coding and non-coding genes, targeting several genes simultaneously and stimulating neuronal differentiation of induced pluripotent stem cells (iPSCs).

Multicellular organisms create complex patterned structures from identical, unreliable components. Learning how to engineer such robust behavior is important to both an improved understanding of computer science and to a better understanding of the natural developmental process. Earlier work by our colleagues and ourselves on amorphous computing demonstrates in simulation how one might build complex patterned behavior in this way. This work reports on our first efforts to engineer microbial cells to exhibit this kind of multicellular pattern directed behavior. We describe a specific natural system, the Lux operon of Vibrio fischeri, which exhibits density dependent behavior using a well characterized set of genetic components. We have isolated, sequenced, and used these components to engineer intercellular communication mechanisms between living bacterial cells. In combination with digitally controlled intracellular genetic circuits, we believe this work allows us to begin the more difficult process of using these communication mechanisms to perform directed engineering of multicellular structures, using techniques such as chemical diffusion dependent behavior. These same techniques form an essential part of our toolkit for engineering with life, and are widely applicable in the field of microbial robotics, with potential applications in medicine, environmental monitoring and control, engineered crop cultivation, and molecular scale fabrication.

The behavior of gene modules in complex synthetic circuits is often unpredictable. After joining modules to create a circuit, downstream elements (such as binding sites for a regulatory protein) apply a load to upstream modules that can negatively affect circuit function. Here we devised a genetic device named a load driver that mitigates the impact of load on circuit function, and we demonstrate its behavior in Saccharomyces cerevisiae. The load driver implements the design principle of timescale separation: inclusion of the load driver&#39;s fast phosphotransfer processes restores the capability of a slower transcriptional circuit to respond to time-varying input signals even in the presence of substantial load. Without the load driver, we observed circuit behavior that suffered from a 76% delay in response time and a 25% decrease in system bandwidth due to load. With the addition of a load driver, circuit performance was almost completely restored. Load drivers will serve as fund...

Control system theory provides convenient tools and concepts for describing and analyzing complex cell functions. In this paper we demonstrate the use of control theory to forward-engineer a complex synthetic gene network constructed from several modular components. Specifically, we present the design and simulation of a synthetic multicellular maze-solving system. Here, bacterial cells are programmed to use artificial cell-to-cell communication and regulatory feedback in order to illuminate the correct path in a user-defined maze of cells arranged on a surface. Simulations were used to analyze the system's spatiotemporal dynamics and sensitivity to various kinetic parameters. Experiments with Escherichia coli were carried out to characterize the diffusion properties of artificial cell-to-cell communication based on bacterial quorum sensing systems. The rational design process and simulation tools employed in this study provide an example for future engineering of complex synthetic gene networks comprising multiple control system motifs.

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of b-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for 'synthetic cellular heterogeneity' that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a 'phenotypic sensitivity analysis' method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.

Recent studies have demonstrated that intracellular variations in the rate of gene expression are of fundamental importance to cellular function and development. While such 'noise' is often considered detrimental in the context of perturbing genetic systems, it can be beneficial in processes such as species diversification and facilitation of evolution. A major difficulty in exploring such effects is that the magnitude and spectral properties of the induced variations arise from some intrinsic cellular process that is difficult to manipulate. Here, we present two designs of a molecular noise generator that allow for the flexible modulation of the noise profile of a target gene. The first design uses a dual-signal mechanism that enables independent tuning of the mean and variability of an output protein. This is achieved through the combinatorial control of two signals that regulate transcription and translation separately. We then extend the design to allow for DNA copy-number regulation, which leads to a wider tuning spectrum for the output molecule. To gain a deeper understanding of the circuit's functionality in a realistic environment, we introduce variability in the input signals in order to ascertain the degree of noise induced by the control process itself. We conclude by illustrating potential applications of the noise generator, demonstrating how it could be used to ascertain the robust or fragile properties of a genetic circuit.

Synthetic gene circuits are designed to program new biological behaviour, dynamics and logic control. For all but the simplest synthetic phenotypes, this requires a structured approach to map the desired functionality to available molecular and cellular parts and processes. In other engineering disciplines, a formalized design process has greatly enhanced the scope and rate of success of projects. When engineering biological systems, a desired function must be achieved in a context that is incompletely known, is influenced by stochastic fluctuations and is capable of rich nonlinear interactions with the engineered circuitry. Here, we review progress in the provision and engineering of libraries of parts and devices, their composition into large systems and the emergence of a formal design process for synthetic biology.

We seek to couple protein-ligand interactions with synthetic gene networks in order to equip cells with the ability to process internal and environmental information in novel ways. In this paper, we propose and analyze a new genetic signal processing circuit that can be configured to detect various chemical concentration ranges of ligand molecules. These molecules freely diffuse from the environment into the cell. The circuit detects acylhomoserine lactone ligand molecules, determines if the molecular concentration falls within two prespecified thresholds, and reports the outcome with a fluorescent protein. In the analysis of the circuit and the description of preliminary experimental results, we demonstrate how to adjust the concentration band thresholds by altering the kinetic properties of specific genetic elements, such as ribosome binding site efficiencies or dna-binding protein affinities to their operators.

Synthetic biology combines knowledge from various disciplines including molecular biology, engineering, mathematics and physics to design and build novel proteins, genetic circuits and metabolic networks. Early efforts aimed at altering the behavior of individual elements have now evolved to focus on the construction of complex networks in single-cell and multicellular systems. Recent achievements include the development of sophisticated non-native behaviors such as bistability, oscillations, proteins customized for biosensing, optimized drug synthesis and programmed spatial pattern formation. The de novo construction of such systems offers valuable quantitative insight into naturally occurring information processing activities. Furthermore, as the techniques for system design, synthesis and optimization mature, we will witness a rapid growth in the capabilities of synthetic systems with a wide-range of applications.

Synthetic genetic circuits are artificial networks of transcriptional control elements inserted into living cells in order to &#39;program&#39; cellular behavior. We can extend this application to programming population behavior by incorporating cell-cell communications capabilities. By designing and building such networks, cellular circuit engineers expect to gain insight into how natural genetic networks function with remarkable robustness, stability, and adaptability to changing environments. Programmed cells also have promising applications in biotechnology and medicine. A major challenge that biological circuit engineers face is the difficulty of predicting circuit performance at the design stage, with the consequence that actual construction requires significant experimental effort, even for very simple circuits. To address this fundamental obstacle we propose the use of laboratory evolution methods to create new circuit components and optimize circuit performance inside livin...

Abstract—The search for lightweight authentication protocols suitable for low-cost RFID tags constitutes an active and challenging research area. In this context, a family of protocols based on the LPN problem has been proposed: the so-called HB-family. Despite the rich literature regarding the cryptanalysis of these protocols, there are no published results about the impact of fault analysis over them. The purpose of this paper is to fill this gap by presenting a fault analytic method against a prominent member of the HB-family: HB + protocol. We demonstrate that the fault analysis model can lead to a flexible and effective attack against HB-like protocols, posing a serious threat over them. Index Terms—Fault analysis, authentication protocols, HB + protocol, RFID systems. I.