Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive One-Step Model Predictive Control Using Lyapunov Theory-Based Deep Neural Networks

Neural network model predictive control has been widely used in the field of automatic control. However, the off-line training of the neural predictor and the appearance of disturbances and parameter variations, during the control phase, affect the system output by inducing steady state error. This paper tries to overcome this problem by proposing an adaptive deep neural network model predictive controller. To ensure the convergence of the adaptive process, we use Lyapunov stability theory, which guides the updating of the weights of the deep neural network. Likewise, a Lyapunov based algorithm guides the updating of the control signal of the one-step model predictive controller. Simulation results for two cases demonstrate the effectiveness of the proposed control scheme.
Canola Responses to Drought, Heat, and Combined Stress: Shared and Specific Effects on Carbon Assimilation, Seed Yield, and Oil Composition

Photosynthetic assimilation is remarkably altered by heat and drought, and this depends on the individual or combined occurrence of stressors and their respective intensities and durations. Abiotic stressors may also alter the nutritional quality and economic value of crops. In this controlled greenhouse study, we evaluated the response of Brassica napus L. , from flowering to seed development, to two temperature and water treatments and a combination of these treatments. The diffusional limitations of stomatal conductance and mesophyll conductance on photosynthesis, as well as resource-use efficiency (particularly water and nitrogen), were assessed. In addition, the effects of stressors on the seed fatty acid content and composition and the total protein content were examined. The results showed that the reduction in the net photosynthetic assimilation rate was caused by combinations of heat and drought (heat + drought) treatments, by drought alone, and, to a lesser extent, by heat alone. The stomatal conductance decreased under drought and heat + drought treatments but not under heat. Conversely, the mesophyll conductance was reduced significantly in the plants exposed to heat and heat + drought but not in the plants exposed to drought alone. The carboxylation efficiency rate and the electron transport rate were reduced under the heat treatment. The seed yield was reduced by 85.3% under the heat treatment and, to a lesser extent, under the drought treatment (31%). This emphasizes the devastating effects of hotter weather on seed formation and development. Seed oil content decreased by 52% in the plants exposed to heat, the protein content increased under all the stress treatments. Heat treatment had a more deleterious effect than drought on the seed oil composition, leading to enhanced levels of saturated fatty oils and, consequently, desaturation efficiency, a measure of oil frying ability. Overall, this study showed that except for the photosynthetic assimilation rate and stomatal conductance, heat, rather than drought, negatively affected the photosynthetic capacity, yield, and oil quality attributes when imposed during the flowering and silique-filling stages. This result highlights the necessity for a better understanding of heat tolerance mechanisms in crops to help to create germplasms that are adapted to rapid climate warming.
Verifying the Enforcement and Effectiveness of Network Lateral Movement Resistance Techniques

As the sophistication of cyber-attacks is ever increasing, cyber breaches become inevitable and their consequences are often highly damaging. Isolation and diversity are key techniques of cyber resilience for creating built-in resistance in cyber networks against the lateral movement of multi-step Advanced Persistent Threats (APTs) and epidemic attacks. However, the key unaddressed challenges are (1) how to ensure that specific isolation and diversity configurations are sufficient to prevent the lateral movement of attacks and (2) how to verify that such configurations are enforced safely despite the complex inter-dependency between cyber components. In this paper, we address these challenges by developing formal models and properties to verify the effectiveness and enforceability of proactive cyber resistance techniques. We present a bounded model checking approach based on satisfiability Modulo theories (SMT) for OpenFlow software defined networks (SDNs). We verify that given resistance techniques are enforced in a way that does not violate the cyber mission requirements and we evaluate the configuration resistance based on user-defined resistance properties.
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.
Evaluation of Embedded Systems for Automotive Image Processing

With the emergence of industry 4.0, autonomous driving vehicles have become an exciting research topic in the science technology community. The driving system requires many complex algorithms that provide both accurate results and fast running times. However, the performance of these algorithms is usually limited to standard personal computer (PC) systems. Currently, the computation power of available embedded systems lags far behind that of a standard PC, even when compared to PCs with moderate capabilities. Hence, we present some benchmark results of several systems, including standard PCs, laptops, and embedded systems, for performing computer vision algorithms. We collected a set of algorithms that are commonly used in autonomous driving systems and then ran each of these on our selected systems. In this evaluation, we focused only on the processing time without concerning about the precision of the algorithm. The details of the testing algorithms and systems are provided in this report also. We believe that our experiment can provide practical information to people who aim to transfer their algorithm to an embedded system.
Molecular markers and cytogenetics to characterize a wheat-Dasypyrum villosum 3V (3D) substitution line conferring resistance to stripe rust

Dasypyrum villosum has been used as a valuable gene resource for disease resistances, yield increase and quality improvement in wheat. A novel wheat-D. villosum alien introgression line CD-3 was generated through hybridization between the common wheat Chinese Spring (CS) and a CS- D. villosum 3V addition line having considerably high stripe rust resistance, which enable the characterization of a potential new stripe rust resistance gene (s) derived from D. villosum. The results of non-denaturing fluorescent in situ hybridization (ND-FISH) showed that CD-3 contained 42 chromosomes, including a 3V chromosome pair, and the absence of both of the 3D chromosomes. PCR-based Landmark Unique Gene (PLUG) molecular marker analysis supported results from the FISH analysis, revealing CD-3 was a wheat-D. villosum 3V (3D) disomic substitution line. Resistant test of stripe rust on 52 plants of F2 generation (CD-3/CS), CD-3, CS and D.villosum have been conducted at seedling stage. 7 plants of F2 generation possessing two 3V chromosomes exhibited high resistance to stripe rust as CD-3 and D.villosum, 10 plants carrying one 3V chromosome and 35 plants without 3V chromosome were susceptive to stripe rust as CS. The result implied the high stripe rust resistance of CD-3 should be controlled by recessive gene(s) originating from D.villosum. To rapidly detect chromosome 3V in the genetic background of wheat, we developed a novel Sequence Characterized Amplified Region (SCAR) marker specific for 3V chromosome based on the sequence of a grain size-related gene DvGS5 in D. villosum, an orthologue of TaGS5 from wheat. The SCAR marker was designated DvGS5-1443, which could successfully amplify a unique 3V-specific fragment in CD-3 and D. villosum, suggesting that this SCAR marker could facilitate targeting the chromosome 3V in the genetic background of wheat for wheat improvement.
Augmenting Health Care System Using Internet of Things

Internet has encouraged the world to become a better place and internet of things has made the world a smarter place. Internet of thing is a buzzing word in the area of information technologies. In this paper we will see how Internet of things can be used in health care centres. We will see how we can environment around a patient can be monitored using IOT devices like sensors, Arduino uno ATmega 328P, HLK-RM04 WIFI module and a data base server using SQL. The proposed model includes very cheap and easily available components. It will boost the use of internet of things in a great way. This model will help us to deal with many limitations like reduction of staff or expensive traditional units used in hospitals. Moreover this helps in promotion of internet of things using very simple components.
Play it again

Play It Again is a section of the journal where we re-publish quotes, gaVes, and immortal lines from friends and foes of tobacco control. It is compiled by Gene Borio, the webmaster of Tobacco BBS, which is the premier tobacco news-gathering site on the internet. A resident of New York City, he has been involved in tobacco issues since the late 1980s. Send contributions (including an original version or photocopy of the sourced item) to him at Tobacco BBS, PO Box 359, Village Station, New York 10014-0359; fax +1 212 260 6825. Send quotes from online stories (including the full article) or scanned documents (in GIF or JPEG format), to gborio@mindspring.com.
Three-Parameter Models for Conservative Relay Feedback Autotuning*

Relay feedback method is one of the standard methods for PID controller autotuning in field. Relay feedback oscillations provide the ultimate gain and period of processes to design PID controllers with Ziegler-Nichols type tuning rules. Since the original relay feedback method uses only two process data of ultimate gain and period, its performances can be limited. For wider applications of the relay feedback autotuning method, methods using additional process data such as the steady state gain have been available. They often use the first order plus time delay (FOPTD) models and the FOPTD model-based tuning rules. Here it is shown that FOPTD model-based methods can provide closed-loop systems with very oscillatory responses for some processes. To relieve such drawbacks, the critically damped second order plus time delay (C2PTD) model that uses the same process data of previous FOPTD model-based methods is proposed. When three process data are extracted from the relay feedback oscillation, the C2PTD model-based method instead of the usual FOPTD model-based methods is shown to be better for wider applications of the relay feedback autotuning.
A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning

This paper presents a control strategy for Cyber-Physical System defense developed in the framework of the European Project ATENA, that concerns Critical Infrastructure (CI) protection. The aim of the controller is to find the optimal security configuration, in terms of countermeasures to implement, in order to address the system vulnerabilities. The attack/defense problem is modeled as a multi-agent general sum game, where the aim of the defender is to prevent the most damage possible by finding an optimal trade-off between prevention actions and their costs. The problem is solved utilizing Reinforcement Learning and simulation results provide a proof of the proposed concept, showing how the defender of the protected CI is able to minimize the damage caused by his her opponents by finding the Nash equilibrium of the game in the zero-sum variant, and, in a more general scenario, by driving the attacker in the position where the damage she/he can cause to the infrastructure is lower than the cost it has to sustain to enforce her/his attack strategy.
Different types of white matter hyperintensities in CADASIL: Insights from 7-Tesla MRI

In Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), by contrast to sporadic cerebral small vessel disease related to age and hypertension, white matter hyperintensities (WMH) are frequently observed in the white matter of anterior temporal poles, external capsules, and superior frontal regions. Whether these WMH (specific WMH) differ from those observed in other white matter areas (nonspecific WMH) remains unknown. Twenty patients were scanned to compare specific and nonspecific WMH using high-resolution images and analyses of relaxation times (T1R: longitudinal relaxation time and T2*R: effective transversal relaxation time). Specific WMH were characterized by significantly longer T1R and T2*R (T1R: 2309 ± 120 ms versus 2145 ± 138 ms; T2*R: 40 ± 5 ms versus 35 ± 5 ms, p < 0.001). These results were not explained by the presence of dilated perivascular spaces found in the close vicinity of specific WMH. They were not either explained by the normal regional variability of T1R and T2*R in the white matter nor by systematic imaging artifacts as shown by the study of 17 age- and sex-matched healthy controls. Our results suggest large differences in water content between specific and nonspecific WMH in CADASIL, supporting that mechanisms underlying WMH may differ according to their location.
Distributed formation tracking for high-order multi-agent systems with a leader of unknown input and switching topologies via edge-based adaptive protocol

This paper studies the distributed time-varying formation tracking problems for high-order multi-agent systems with a leader of unknown input and switching topologies. The followers can realize the given formation configuration and track the trajectory of the leader at the same time. By assigning the adaptive gains and the sliding mode terms to each edge of the graph, a novel edge-based adaptive formation tracking protocol is presented to compensate for the leader’s unknown input. The proposed approach is fully distributed with no need for the global information of the topology and the upper bound of the leader’s input. Based on the common Lyapunov theory, it is proved that the expected formation tracking can be realized by high-order multi-agent systems with arbitrary switching graphs. A simulation example is given to verify the effectiveness of the theoretical results.
An Artificial Neural Network based MPPT Algorithm For Solar PV System

The need of renewable energy integration with power system is shooting up day by day. Solar PV generation has an important role for battery charging, grid tied applications etc. In order to intensify output power of a solar photovoltaic arrangement, it is imperative to find the maximum possible energy harvest from photovoltaic panel. In this paper Maximum Power Point Tracking (MPPT) controller for solar photovoltaic system is developed by practicing artificial neural network (ANN). Also the performance of an ANN based MPPT controller is compared with Conventional MPPT methods. In particular hill climbing method (perturb & observe), Incremental Conductance method and fractional open circuit voltage method. Simulations are done by using MATLAB/SIMULINK to analyze results.
Designed for life: biocompatible de novo designed proteins and components

A principal goal of synthetic biology is the de novo design or redesign of biomolecular components. In addition to revealing fundamentally important information regarding natural biomolecular engineering and biochemistry, functional building blocks will ultimately be provided for applications including the manufacture of valuable products and therapeutics. To fully realize this ambitious goal, the designed components must be biocompatible, working in concert with natural biochemical processes and pathways, while not adversely affecting cellular function. For example, de novo protein design has provided us with a wide repertoire of structures and functions, including those that can be assembled and function in vivo. Here we discuss such biocompatible designs, as well as others that have the potential to become biocompatible, including non-protein molecules, and routes to achieving full biological integration.
Koopman Analysis Based Wide-Area Back-Up Protection and Faulted Line Identification for Series-Compensated Power Network

The paper presents wide-area back-up protection (WABP) and faulted line identification (FLI) scheme for series-compensated power transmission network. The proposed scheme uses the Koopman mode technique on fault current data obtained from phasor measurement units for WABP and FLI. The main advantage of the proposed scheme is that it is based on spatiotemporal information system (STIS). As we know that in real-world applications, often time and space exist together, and hence, dealing with spatial aspect without considering temporal aspect is of limited use. Similarly, the introduction of series compensation in a power transmission network is common nowadays due to many technical benefits. The FLI in a power network with series-compensation is more complicated due to complex transients produced by series capacitor and the metal–oxide varistor. The proposed scheme is developed using STIS for WABP and FLI under stressed conditions including power swing and load encroachment. The New England 39 bus system is used as the test system, which is developed on Dig-Silent power factory commercial software (PF4C) platform for evaluating the performance of the proposed technique. Further, the efficacy of the proposed scheme is also tested for other critical situations such as symmetrical fault during power swing and multievent detection and found to work effectively.
Change Detection in Presence of Registration Error Using LTP Histograms

This paper deals with detection of significant changes between two images of the same indoor scene acquired from different positions. The nonstatic camera position leads to the need of a registration technique which may be inaccurate. Moreover in case of non-planar scenes accurate registration is not possible. To deal with such inaccuracy a method based on Local Ternary Patterns (LTP) is proposed. Tests performed on real data show that the proposed method is able to detect the changed region in the scene while neglecting the registration error.
The Relationship Between Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness and a New Index Using a Neural Network

Purpose We evaluate the relationship between Bruch's membrane opening minimum rim width (BMO-MRW) and peripapillary retinal nerve fiber layer thickness (pRNFLT) and develop a new parameter combining BMO-MRW and pRNFLT using a neural network to maximize their compensatory values. Methods A total of 402 subjects were divided into two groups: 273 (validation group) and 129 (neural net training) subjects. Linear quadratic and broken-stick regression models were used to explore the relationship between BMO-MRW and pRNFLT. A multilayer neural network was used to create a combined parameter, and diagnostic performances were compared using area under the receiver operating characteristic curves (AUROCs). Results Regression analyses between BMO-MRW and pRNFLT revealed that the broken-stick model afforded the best fit. Globally, the tipping point was a BMO-MRW of 226.5 μm. BMO-MRW and pRNFLT were correlated significantly with visual field. When differentiating normal from glaucoma subjects, the neural network exhibited the largest AUROC. When differentiating normal from early glaucoma subjects, the overall diagnostic performance decreased, but the neural network still exhibited the largest AUROC. Conclusions The optimal relationship between BMO-MRW and pRNFLT was revealed using the broken-stick model. Considerable BMO-MRW thinning preceded pRNFLT thinning. The neural network significantly improved diagnostic power by combining BMO-MRW and pRNFLT. Translational Relevance A combined index featuring BMO-MRW and pRNFLT data can aid clinical decision-making, particularly when individual parameters yield confusing results. Our neural network effectively combines information from separate parameters.
Demand-Response-Based Distributed Preventive Control to Improve Short-Term Voltage Stability

Short-term voltage stability (STVS) issues pose a significant threat to modern power systems. Based on extensive observations of STVS events, their primary predictors can be summarized as summer peak load levels with a high percentage of air conditioners, and occurrence of faults caused by thunderstorms or other emergencies. The predictors can be used as the triggering signals for preventive voltage control (PVC). Previous design of PVC dominantly focuses on the improvement of long-term voltage stability, and demand response (DR) resources are not fully utilized. In this paper, a DR-based PVC method is proposed to improve STVS if the day-ahead forecast indicates that the system will experience high STVS risk. We formulate PVC as an optimal power flow problem and solve it in a distributed manner, where the central controller (CC) and the local controllers jointly compute an optimal schedule. Thus, the computational burden of the CC is reduced compared with the existing centralized algorithms, and customer privacy is well protected. The simulation results not only verify the advantage of the improved index, but also demonstrate the fast convergence and effectiveness of the distributed PVC, which, therefore, offers an alternative approach to improve STVS.
The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

Summary Objective: The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology. Methods: In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome. Results: Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player. Conclusions: The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.