Miracle-dz/UniFAR-FTUs huggingface dataset:
Essential System Services Reform: Australian Market Design for Renewable-Dominated Grids

IN 1863, a single arc lamp on observatory Hill in Sydney, Australia, was lit to celebrate the marriage of Prince Albert of Wales and Princess Alexandra of Denmark. It was the first use of electricity anywhere in the country. It took 25 years until Australia established its first permanent 240-V electrical grid, in the small country town of Tamworth, New South Wales, in 1888. Two 18-kW, dc, coal-fired generators were supplied by the plentiful Gunnedah black coal basin nearby, and in the same year, on the other side of the continent, C.J. Otte supplied electricity to the Western Australian Government House with a small, 15-kW dynamo. By 1899, a full three-phase 240-V ac grid had been built on the east coast, establishing the foundation of the future power system across the country.
Output and Control Characteristics of an LLC DC-DC Converter Operating above Resonant Frequency based on State Plane Analysis

The paper presents an LLC resonant DC-DC converter output and control characteristics for frequency control obtained using state plane analysis. It also expands on the quantities on which the choice of the main circuit elements, used in the design procedures of the LLC converter, depends. Finally, a comparison of the results obtained through state plane analysis and those obtained using the first harmonic approximation method is made.
Lifetime Reliability Improvement of Nano-Scale Digital Circuits Using Dual Threshold Voltage Assignment

In nano-scale CMOS technology, circuit reliability is a growing concern for complicated digital circuits due to manufacturing process variation and aging effects. In this paper, a statistical circuit optimization framework is presented to analyze and improve the lifetime reliability of digital circuits in the presence of process variation and aging degradation. The proposed framework takes advantage of a process variation- and aging-aware gate-level delay degradation model to characterize and evaluate the lifetime reliability of combinational circuits. A metric called Guardband-Aware Reliability (abbreviated as GAR) is proposed for a fair evaluation of the lifetime reliability of combinational circuits considering a guardband and timing yield specified by the designer. Then, using a criticality metric, a set of statistically critical gates is selected for being optimized in the optimization framework. As the improvement procedure, the dual-threshold voltage assignment technique is applied to the identified critical gates to enable the manufactured chip to improve the lifetime reliability in terms of low timing yield loss. Experimental results on ISCAS’85 and ISCAS’89 benchmark circuits show that our proposed framework increases the circuit reliability up to 9.93% for a 6-year lifetime imposing less than 6.9% timing yield loss.
A dynamic resource management model of Internet of things based on service migration : State Grid Corporation of China headquarters science and technology project funding

With the development of Internet of things and mobile computing, users can access to the network in different places and in different scenarios. In order to make Internet of things users have better service experience, this paper proposes a distributed service migration mode (DSMM) for dynamic management of Internet of things resources. DSMM can dynamically migrate services in the whole network according to the user's access location and access scenario, so as to make the user’s service experience better. DSMM uses computing power, storage space, network bandwidth and data to define user services, and uses distributed hash table DHT to realize service migration without center, which can be used in large networks. DSMM proposed in this paper enhances the adaptability of Internet of things to provide services to users, and is an efficient dynamic resource management model for Internet of things.
Simultaneous Wireless Information and Power Transfer in Low-Latency Relaying Networks with Nonlinear Energy Harvesting

This work studies a simultaneous wireless information and power transfer (SWIPT)-enabled relaying network, where a power splitting protocol is applied at the relay before energy harvesting (EH). Due to low-latency requirements, the transmissions are operated with short blocklength codes. We aim at providing a reliability-oriented network design, while for the first time, the finite blocklength (FBL) impact and a realistic nonlinear EH process are jointly considered. In particular, we characterize the overall error probability of the considered network, and formulate the problem minimizing the overall error probability by optimally choosing the power splitting ratio. However, the formulated problem is non-convex due to the nonlinear EH process (which is also non-convex), thus making it challenging to be solved optimally. To tackle this difficulty, we propose a three-step approach to obtain an efficient solution. We first introduce two auxiliary variables, with the assistance of which we reformulate the problem. Then, we apply a convex approximation technique to tightly approximate the problem (at each local point/solution), based on which an efficient iterative algorithm (updating the local point) is finally proposed approaching a high-quality sub-optimal solution. Simulation results are provided to validate the convergence of the proposed algorithm and evaluate the performance in comparison to benchmarks with linear EH.
Case Report: Expressive Speech Disorder in a Family as a Hallmark of 7q31 Deletion Involving the FOXP2 Gene

Pathogenic variants of FOXP2 gene were identified first as a monogenic cause of childhood apraxia of speech (CAS), a complex disease that is associated with an impairment of the precision and consistency of movements underlying speech, due to deficits in speech motor planning and programming. FOXP2 variants are heterogenous; single nucleotide variants and small insertions/deletions, intragenic and large-scale deletions, as well as disruptions by structural chromosomal aberrations and uniparental disomy of chromosome 7 are the most common types of mutations. FOXP2-related speech and language disorders can be classified as “FOXP2-only,” wherein intragenic mutations result in haploinsufficiency of the FOXP2 gene, or “FOXP2-plus” generated by structural genomic variants (i.e., translocation, microdeletion, etc.) and having more likely developmental and behavioral disturbances adjacent to speech and language impairment. The additional phenotypes are usually related to the disruption/deletion of multiple genes neighboring FOXP2 in the affected chromosomal region. We report the clinical and genetic findings in a family with four affected individuals having expressive speech impairment as the dominant symptom and additional mild dysmorphic features in three. A 7.87 Mb interstitial deletion of the 7q31.1q31. 31 region was revealed by whole genome diagnostic microarray analysis in the proband. The FOXP2 gene deletion was confirmed by multiplex ligation-dependent probe amplification (MLPA), and all family members were screened by this targeted method. The FOXP2 deletion was detected in the mother and two siblings of the proband using MLPA. Higher resolution microarray was performed in all the affected individuals to refine the extent and breakpoints of the 7q31 deletion and to exclude other pathogenic copy number variants. To the best of our knowledge, there are only two family-studies reported to date with interstitial 7q31 deletion and showing the core phenotype of FOXP2 haploinsufficiency. Our study may contribute to a better understanding of the behavioral phenotype of FOXP2 disruptions and aid in the identification of such patients. We illustrate the importance of a targeted MLPA analysis suitable for the detection of FOXP2 deletion in selected cases with a specific phenotype of expressive speech disorder. The “phenotype first” and targeted diagnostic strategy can improve the diagnostic yield of speech disorders in the routine clinical practice.
Spatially patterned bi-electrode epiretinal stimulation for axon avoidance at cellular resolution

Objective. Epiretinal prostheses are designed to restore vision to people blinded by photoreceptor degenerative diseases by stimulating surviving retinal ganglion cells (RGCs), which carry visual signals to the brain. However, inadvertent stimulation of RGCs at their axons can result in non-focal visual percepts, limiting the quality of artificial vision. Theoretical work has suggested that axon activation can be avoided with current stimulation designed to minimize the second spatial derivative of the induced extracellular voltage along the axon. However, this approach has not been verified experimentally at the resolution of single cells. Approach. In this work, a custom multi-electrode array (512 electrodes, 10 μm diameter, 60 μm pitch) was used to stimulate and record RGCs in macaque retina ex vivo at single-cell, single-spike resolution. RGC activation thresholds resulting from bi-electrode stimulation, which consisted of bipolar currents simultaneously delivered through two electrodes straddling an axon, were compared to activation thresholds from traditional single-electrode stimulation. Main results. On average, across three retinal preparations, the bi-electrode stimulation strategy reduced somatic activation thresholds (∼21%) while increasing axonal activation thresholds (∼14%), thus favoring selective somatic activation. Furthermore, individual examples revealed rescued selective activation of somas that was not possible with any individual electrode. Significance. This work suggests that a bi-electrode epiretinal stimulation strategy can reduce inadvertent axonal activation at cellular resolution, for high-fidelity artificial vision.
Hardware-Based Spiking Neural Networks Using Capacitor-Less Positive Feedback Neuron Devices

In this article, hardware-based spiking neural networks (SNNs) using capacitor-less positive feedback (PF) neuron devices are designed. It was reported that the PF device can simultaneously process the excitatory and inhibitory signals. The PF device shows very steep subthreshold slope (SS < 1 mV/dec) due to the PF opertaion, leading to low-power and reliable neuron device. The PF devices also show the behavior of leaky integrate and fire (LIF) neuron, which is the most popular neuron model in SNNs. For hardware configuration, the neuron characteristics of PF device are investigated with the transient behavior of the anode current. Based on the PF neuron devices, the SNN shows the accuracy of 98.19% for the Modified National Institute of Standards and Technology (MNIST) database classification in four-hidden layer, fully-connected neural network, which is near the accuracy (98.46%) of the artificial neural networks using rectified linear unit (ReLU) activation function.
Efeito do balanço pH/carbono inorgânico em parâmetros ecofisiológicos de Cylindrospermopsis raciborskii (Cyanobacteria) sob condições experimentais

Cylindrospermopsis raciborskii (Wolozynska) Seenayya & Subba Raju e uma especie diazotrofica cosmopolita, potencialmente toxica e considerada invasora em diferentes latitudes. A capacidade de ocupar diferentes nichos e uma caracteristica que esta ligada as suas peculiaridades fisiologicas, as quais permitem que possua uma elevada amplitude ecologica para diferentes variaveis ambientais. No entanto, em condicoes de cultivo, essa especie pode apresentar limitacoes para o crescimento relacionadas ao pH, temperatura e nutrientes. Neste cenario, o presente trabalho objetivou investigar o efeito do pH e fonte de carbono inorgânico em parâmetros relacionados ao crescimento de C. raciborskii. A cepa ITEP-A1 (produtora de saxitoxinas) foi cultivada em meio de cultura ASM-1 e mantida sob condicoes controladas. O delineamento experimental consistiu do cultivo da linhagem ITEP-A1 em diferentes faixas de pH e disponibilidade de carbono inorgânico, com e sem aeracao. Os experimentos foram realizados em erlenmeyers de 2 L com 1,5 L de meio de cultura e foram analisadas curvas e taxas de crescimento, tracos morfologicos, aquisicao de biomassa, temperatura, pH, condutividade, alcalinidade e carbono inorgânico total dissolvido. Os resultados foram comparados usando ANOVA fatorial com medidas repetidas e ANOVA unifatorial com Teste de Tukey a posteriori, sendo as analises estatisticas e graficos realizados no programa Statistica 7.0 e R i386 3.1. 2, respectivamente. C. raciborskii ITEP-A1 apresentou melhor taxa de crescimento e biomassa sob pH alcalino (>8,0), em ambas condicoes de aeracao. Na condicao de pH > 8,0 e com aeracao, o ion bicarbonato (HCO3-) foi a principal fonte de carbono inorgânico disponivel ao processo fotossintetico, o que favoreceu o crescimento de C. raciborskii. Tais resultados podem ser extrapolados para uma escala global, e sugeridos como fatores que tem favorecido a distribuicao, bem como expansao dessa cianobacteria para outras latitudes.
Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-of-the-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and next-generation wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human- and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brain–machine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor.
Lossless AI: Toward Guaranteeing Consistency between Inferences Before and After Quantization via Knowledge Distillation

Deep learning model compression is necessary for real-time inference on edge devices, which have limited hardware resources. Conventional methods have only focused on suppressing degradation in terms of accuracy. Even if a compressed model has almost equivalent accuracy to its reference model, the inference results may change when we focus on individual samples or objects. Such a change is a crucial challenge for the quality assurance of embedded products because of unexpected behavior for specific applications on edge devices. Therefore, we propose a concept called “Loss-less AI” to guarantee consistency between the inference results of reference and compressed models. In this paper, we propose a training method to align inference results between reference and quantized models by applying knowledge distillation that batch normalization statistics are frozen at moving average values from the middle of training. We evaluated the proposed method on several classification datasets and network architectures. In all cases, our method suppressed the inferred class mismatch between reference and quantized models whereas conventional quantization-aware training did not.
Enhancing the heat tolerance of reef-building corals to future warming

Selective breeding increases the resilience of reef-building corals to climate warming. Reef-building corals thriving in extreme thermal environments may provide genetic variation that can assist the evolution of populations to rapid climate warming. However, the feasibility and scale of genetic improvements remain untested despite ongoing population declines from recurrent thermal stress events. Here, we show that corals from the hottest reefs in the world transfer sufficient heat tolerance to a naïve population sufficient to withstand end-of-century warming projections. Heat survival increased up to 84% when naïve mothers were selectively bred with fathers from the hottest reefs because of strong heritable genetic effects. We identified genomic loci associated with tolerance variation that were enriched for heat shock proteins, oxidative stress, and immune functions. Unexpectedly, several coral families exhibited survival rates and genomic associations deviating from origin predictions, including a few naïve purebreds with exceptionally high heat tolerance. Our findings highlight previously uncharacterized enhanced and intrinsic potential of coral populations to adapt to climate warming.
A deployable and inflatable robotic arm concept for aerospace applications

The interest in soft systems for space missions represents a growing trend in recent years. The development of inflatable robots, combined with the improvement of deployment mechanisms, allows to build novel lightweight and deployable robotic manipulators. In several space applications, the use of soft robots could minimize bulk and mass, reducing space mission costs. The main challenges in soft robotics are the control of the system and the exertion of high forces. In this manuscript, the concept of an inflatable manipulator with two inflatable links and three degrees of freedom is proposed. After a review about the possible materials to be used for the inflatable parts, the robot mechanical structure, the deploying strategy and the pneumatic line are presented. Then, an elastostatic approach is proposed to model the robot with the aim of developing its control. The last section shows preliminary experimental tests performed on the link prototype with the purpose to evaluate a static characterization in relation to the supplied pressure. Results suggest the validity of the adopted approach to model the system and clarify the pressure influence about the system performances. The study puts the basis for the development of the first prototype of the robotic system.
Fast Bayesian Model Selection in Imaging Inverse Problems Using Residuals

This paper presents a fast heuristic for comparing Bayesian models to solve inverse problems related to signal processing. We focus on problems that are convex w.r. t. the unknown signal and where no ground truth is available. The proposed heuristic is very computationally efficient and does not require the estimation of the model evidence. Instead, the model evidence is used indirectly to set the regularisation parameters that define each competing model by maximum marginal likelihood estimation, followed by a simple likelihood-based or residual-based comparison of the models based on their empirical Bayesian maximum-a-posteriori solutions. The proposed methodology is illustrated with a total-variation image deblurring experiment, where it performs remarkably well.
Classification of Breast Ultrasound Images Based on Convolutional Neural Networks - A Comparative Study

Computer aided diagnosis (CAD) helps physicians towards an early characterization of tumors in different biomedical tissues, including Breast. Deep learning (DL) based image classification, especially convolutional neural networks (CNNs), has achieved a noticeable success in automatic differentiation of breast ultrasound (BUS) images through the last few years. In this paper: ten well-known pretrained CNNs classification models (ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, GoogleNet, MobilenetV2, SqueezeNet, DenseNet201, and Xception) have been utilized to classify BUS images by transfer learning (TL). A dataset of 780 BUS images (133 normal, 437 benign, and 210 malignant) has been utilized in training and validation. A selected 375 BUS images from the dataset have been utilized to evaluate the classification's accuracy of each CNN model after TL process. Ultrasound's speckle noise effect on classification has been studied by applying a simulated multiplicative speckle noise to the mentioned 375 BUS images before being classified by the ten CNN models. Three restoration approaches have been applied to treat speckled BUS images before being classified. Accuracy evaluation results have represented different values for each CNN model over all 375 BUS images through different studied circumstances. The best accuracy's value was for: ResNet101 when the input images were clear with high resolution, SqueezeNet in case of speckled input images, and InceptionResNetV2 in case of restored images using the three applied restoration schemes. So, in case of speckled BUS images, it is recommended to utilize a proper preprocessing step for image enhancement before applying a CNN based classification model (InceptionResNetV2 is preferred). Ten modified and trained CNNs models for BUS images' classification (input image size: 128 by 128 by 3) utilized in this study are available to researchers at: https://www.kaggle. com/mohammedtgadallah/ten-cnns-for-breast-us-images-classification
Creation of a High-Interaction Honeypot System based-on Docker containers

This article proposes the creation of a high interaction honeypot based on Docker for the detection of attacks at both network-level and host-level. Open source tools have been used to make it scalable, dynamic and secure for its implementation. It has been tested in a real environment showing its effectiveness in capturing malicious data for subsequent analysis at network-level and host-level and using tools such as VirusTotal.
Boost Converter of Maximum Power Point Tracking (MPPT) Using Particle Swarm Optimization (PSO) Method

The Maximum Power Point Tracking (MPPT) method is one of the most popular and widely used methods to extract the maximum power from the PV system and it has been applied in Solar Photovoltaic (PV) energy conversion. This method, however, exhibits fluctuations between the maximum power point due to the nature of unpredicted and environmental parameter changes. Therefore, it is important to include an intelligent controller that can track the maximum peak irrespective of variations in parameters such as temperature and irradiation. This paper describes the simulation of the PV energy conversion method based MPPT algorithm based on particle swarm optimization (PSO). The proposed MPPT is fast, adjustable, accurate, and efficient for full tracking of PV power. In this work, the simulation package MATLAB/Simulink helps to simulate the circuits to evaluate the performance of the proposed MPPT algorithm. By comparing the output value in difference irradiance, the performance of the proposed PSO algorithm is evaluated. It is observed that maximum power from the PV array is achieved at the input and output side of the dc-dc converter. The result of the simulation shows that this design can be realized in real-time application.