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Growing evidence has demonstrated the role of mutations of tumor biomarkers in diagnosing and treating epithelial ovarian cancer. This review aims to analyze recent literature on the correlation between tumor biomarkers and chemotherapy in nonmucinous ovarian cancer, providing suggestions for personalized treatment approaches. An extensive literature search was conducted to identify relevant studies and trials. BRCA1/2 mutations are central in homologous recombination repair deficiency (HRD) in ovarian cancer, but several other genetic mutations also contribute to varying cancer risks. While the role of MMR testing in ovarian cancer is debated, it is more commonly linked to non-serous ovarian cancer, often associated with Lynch syndrome. A significant proportion of ovarian cancer patients have HRD, affecting treatment decisions in both first-line (especially in advanced stages) and second-line therapy due to HRD's connection with platinum-based therapy and PARP inhibitors' response. However, validated genetic tests to identify HRD have not yet been universally implemented. There is no definitive therapeutic algorithm for advanced ovarian cancer, despite ongoing efforts and multiple proposed tools. Future research should focus on expanding the utility of biomarkers, reducing resistance, and increasing the actionable biomarker pool. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings. [ABSTRACT FROM AUTHOR]

Norbormide (NRB) is a Rattus-selective toxicant, which was serendipitously discovered in 1964 and formerly marketed as an eco-friendly rodenticide that was deemed harmless to non-Rattus species. However, due to inconsistent efficacy and the emergence of second-generation anticoagulants, its usage declined, with registration lapsing in 2003. NRBs' lethal action in rats entails irreversible vasoconstriction of peripheral arteries, likely inducing cardiac damage: however, the precise chain of events leading to fatality and the target organs involved remain elusive. This unique contractile effect is exclusive to rat arteries and is induced solely by the endo isomers of NRB, hinting at a specific receptor involvement. Understanding NRB's mechanism of action is crucial for developing species-selective toxicants as alternatives to the broad-spectrum ones currently in use. Recent research efforts have focused on elucidating its cellular mechanisms and sites of action using novel NRB derivatives. The key findings are as follows: NRB selectively opens the rat mitochondrial permeability transition pore, which may be a factor that contributes to its lethal effect; it inhibits rat vascular KATP channels, which potentially controls its Rattus-selective vasoconstricting activity; and it possesses intracellular binding sites in both sensitive and insensitive cells, as revealed by fluorescent derivatives. These studies have led to the development of a prodrug with enhanced pharmacokinetic and toxicological profiles, which is currently undergoing registration as a novel efficacious eco-sustainable Rattus-selective toxicant. The NRB-fluorescent derivatives also show promise as non-toxic probes for intracellular organelle labelling. This review documents in more detail these developments and their implications. [ABSTRACT FROM AUTHOR]

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios. [ABSTRACT FROM AUTHOR]

Among all the functions that electronics currently perform, clock synthesis has a backbone role. Charge pump phase-locked loops (CP-PLL) are widely used to accomplish clock synthesis thanks to their versatility. One of the most critical parts of CP-PLLs is the charge pump, which greatly influences the system's performance. Even though several high-performance charge pumps have been proposed in the past, with the quick spread of electronics in all the engineering fields, the design of such electronic devices has encountered several additional challenges dictated by external environmental conditions. Examples of these engineering sectors are space, aerospace, industrial, and automotive applications, where the charge pump has to face high environmental temperatures and radiation effects. As a consequence, its design and experimental characterization have to be performed to ensure reliability when operating in harsh conditions. However, to the best of the authors' knowledge, no works in the literature have ever presented a complete charge pump design and characterization in such harsh environments. Therefore, to fill this gap, this paper presents a charge pump for PLL applications specifically designed to reach operating temperatures up to 200 °C and total ionizing dose levels up to 100 Mrad. All design choices have been experimentally verified and are discussed throughout the paper in detail. With the proposed design, we obtained an output current variation of less than 8% at 200 °C and less than 2.5% at 100 Mrad. As opposed to the CPs that can be found in the literature, these results were measured on silicon. The performed measurements confirm that the current variation at 200 °C is better than that of the state-of-the-art CPs operating at lower temperatures, which, moreover, were only simulated. [ABSTRACT FROM AUTHOR]

This research introduces a model-based approach for the analysis and control of an onboard charger (OBC) system for contemporary electrified vehicles. The primary objective is to integrate the modeling of SiC/GaN MOSFETs electrothermal behaviors into a unified simulation framework. The motivation behind this project stems from the fact that existing literature often relies on finite element method (FEM) software to examine thermal dynamics, necessitating the development of complex models through partial derivative equations. Such intricate models are computationally demanding, making it difficult to integrate them with circuit equations in the same virtual environment. As a result, lengthy wait periods and a lack of communication between the electrothermal models limit the thorough study that can be conducted during the design stage. The selected case study for examination is a modular 1 ϕ (single phase) onboard computer (OBC). This system comprises a dual active bridge (DAB) type DC/DC converter, which is positioned after a totem pole power factor correction (PFC) AC/DC converter. Specifically, the focus is directed toward a 7 kW onboard computer (OBC) utilizing high-voltage SiC/GaN MOSFETs to ensure optimal efficiency and performance. A systematic approach is presented for the assessment and selection of electronic components, employing circuit models for the totem pole power factor correction (PFC) and dual active bridge (DAB) converter. These models are employed in simulations closely mimicking real-world scenarios. Furthermore, rigorous testing of the generated models is conducted across a spectrum of real-world operating conditions to validate the stability of the implemented control algorithms. The validation process is bolstered by a comprehensive exploration of parametric variations relative to the nominal case. Notably, each simulation adheres to the recommended operational limits of the selected components and devices. Detailed data sheets encompassing electrothermal properties are provided for contextual reference. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose a configurable hardware device in order to build a coherent data log unit. We address the need for analyzing mixed-criticality systems, thus guaranteeing the best performances without introducing additional sources of interference. Log data are essential to inspect the behavior of running applications when safety analyses or worst-case execution time measurements are performed. Furthermore, performance and timing investigations are useful for solving scheduling issues to balance resource budgets and investigate misbehavior and failure causes. We additionally present a performance evaluation and log capabilities by means of simulations on a RISC-V use case. The simulations highlight that such a data log unit can trace the execution from a single- to an octa-core microcontroller. Such an analysis allows a silicon developer to obtain the right sizings and timings of devices during the development phase. Finally, we present an analysis of a real RISC-V implementation for a Xilinx UltraScale+ FPGA, which was obtained with Vivado 2018. The results show that our data log unit implementation does not introduce a significant area overhead if compared to the RISC-V core targeted for tests, and that the timing constraints are not violated. [ABSTRACT FROM AUTHOR]

Cyberattacks and cybercriminal activities constitute one of the biggest threats in the modern digital era, and the frequency, efficiency, and severity of attacks have grown over the years. Designers and producers of digital systems try to counteract such issues by exploiting increasingly robust and advanced security mechanisms to provide secure execution environments aimed at preventing cyberattacks or, in the worst case, at containing intrusions by isolation. One of the most significative examples comes from General Purpose Processor (GPP) manufacturers such as Intel, AMD, and ARM, which in the last years adopted the integration of dedicated resources to provide Trusted Execution Environments (TEEs) or secure zones. TEEs are built layer by layer on top of an implicitly trusted component, the Root-of-Trust (RoT). Since each security chain is only as strong as its weakest link, each element involved in the construction of a TEE starting from the RoT must be bulletproof as much as possible. In this work, we revise and propose a design methodology to implement in both hardware (HW) and software (SW) highly featured and robust security blocks by highlighting the key points that designers should take care of, and the key metrics that should be used to evaluate the security level of the developed modules. We also include an analysis of the state of the art concerning RoT-based TEEs, and we illustrate a case study that documents the implementation of a cryptographic coprocessor for the secure subsystem of the Rhea GPP from the European Processor Initiative (EPI) project, according to the presented methodology. This work can be used by HW/SW security module designers as a cutting-edge guideline. [ABSTRACT FROM AUTHOR]

In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm's resilience under varying temperatures. [ABSTRACT FROM AUTHOR]

Catalyzed by the increasing interest in bi-directional electric vehicles, this paper delves into their significance and the challenges they encounter. Bi-directional electric vehicles not only serve as transportation but also function as essential electricity resources. Central to this energy revolution are On-Board Chargers (OBCs), which are pivotal in converting alternating (AC) energy into direct (DC) energy and vice versa. In this context, we explore the various circuit architectures of OBCs employed in bi-directional electric vehicles. We delve into the intricacies of rectifiers, switching converters, and the application of advanced control and filtering technologies. Our analysis extends to the implications of these circuit architectures on aspects such as voltage regulation capability, energy efficiency, and thermal management. Furthermore, we address the broader significance of these developments in the integration of bidirectional systems, which are driving advances in circuit architectures to better harness the energy flexibility of electric vehicles. We emphasize the critical role of bi-directional electric vehicles in the transition toward a smart and sustainable energy grid. To enhance accessibility for a diverse readership, we will provide concise definitions or explanations for technical terms used throughout the paper, ensuring that our work is approachable even for those who may not be experts in the field. [ABSTRACT FROM AUTHOR]

At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks. [ABSTRACT FROM AUTHOR]

The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to ensure the suitability of the model construction for IDS usage. The evaluation was conducted for both binary and multi-class classification to ensure the consistency of the selected ML algorithms for the given dataset. The experimental results demonstrated accuracy rates of 100% for binary classification and 99.4In conclusion, it can be stated that supervised machine learning algorithms exhibit high and promising classification performance based on the study of three popular datasets. [ABSTRACT FROM AUTHOR]

The development of novel materials with improved radiation shielding capability is a fundamental step towards the optimization of passive radiation countermeasures. Polyethylene (PE) nanocomposites filled with carbon nanotubes (CNT) or graphene nanoplatelets (GNP) can be a good compromise for maintaining the radiation shielding properties of the hydrogen-rich polymer while endowing the material with multifunctional properties. In this work, nanocomposite materials based on medium-density polyethylene (MDPE) loaded with different amounts of multi-walled carbon nanotubes (MWCNT), GNPs, and hybrid MWCNT/GNP nanofillers were fabricated, and their properties were examined before and after proton exposure. The effects of irradiation were evaluated in terms of modifications in the chemical and physical structure, wettability, and surface morphology of the nanocomposites. The aim of this work was to define and compare the MDPE-based nanocomposite behavior under proton irradiation in order to establish the best system for applications as space shielding materials. [ABSTRACT FROM AUTHOR]

An ultrasound-assisted extraction method, employing ethanol and water as solvents at low temperature (30 °C) and reduced time (15 min), was proposed to extract bioactive molecules from different cultivars (Magliocco Canino, Magliocco Rosato, Gaglioppo, and Nocera Rosso) of wine lees. All the extract yields were evaluated and their contents of phenolic acids, flavonoids, and total polyphenols were determined by means of colorimetric assays and high-performance liquid chromatography coupled with diode-array detection (HPLC-DAD) and Fourier transform infrared (FTIR) techniques. Radical scavenging assays were performed and the Magliocco Canino extracted with a hydroalcoholic mixture returned the best results both against ABTS (0.451 mg mL−1) and DPPH (0.395 mg mL−1) radicals. The chemometric algorithms principal component analysis (PCA) and partial least square regression (PLS) were used to process the data obtained from all qualitative–quantitative sample determinations with the aim of highlighting data patterns and finding possible correlations between composition and antioxidant features of the different wine lees cultivars and the extraction procedures. Wine lees from Magliocco Canino and Magliocco Rosato were found to be the best vegetable matrices in terms of metabolite content and antioxidant properties. The components extracted with alcoholic or hydroalcoholic solvents, specifically (−)-epigallocatechin gallate, chlorogenic acid, and trans-caftaric acid, were found to be correlated with the antioxidant capacity of the extracts. Multivariate data processing was able to identify the compounds related to the antioxidant features. Two PLS models were optimized by using their concentration levels to predict the IC50 values of the extracts in terms of DPPH and ABTS with high values of correlation coefficient R2, 0.932 and 0.824, respectively, and a prediction error lower than 0.07. Finally, cellular (SH-SY5Y cells) antioxidant assays were performed on the best extract (the hydroalcoholic extract of Magliocco Canino cv) to confirm its biological performance against radical species. All these recorded data strongly outline the aptness of valorizing wine lees as a valuable source of antioxidants. [ABSTRACT FROM AUTHOR]

Ultrasound (US) is acoustic energy that interacts with human tissues, thus, producing bioeffects that may be hazardous, especially in sensitive organs (i.e., brain, eye, heart, lung, and digestive tract) and embryos/fetuses. Two basic mechanisms of US interaction with biological systems have been identified: thermal and non-thermal. As a result, thermal and mechanical indexes have been developed to provide a means of assessing the potential for biological effects from exposure to diagnostic US. The main aims of this paper were to describe the models and assumptions used to estimate the "safety" of acoustic outputs and indices and to summarize the current state of knowledge about US-induced effects on living systems deriving from in vitro models and in vivo experiments on animals. This review work has made it possible to highlight the limits associated with the use of the estimated safety values of thermal and mechanical indices relating above all to the use of new US technologies, such as contrast-enhanced ultrasound (CEUS) and acoustic radiation force impulse (ARFI) shear wave elastography (SWE). US for diagnostic and research purposes has been officially declared safe, and no harmful biological effects in humans have yet been demonstrated with new imaging modalities; however, physicians should be adequately informed on the potential risks of biological effects. US exposure, according to the ALARA (As Low As Reasonably Achievable) principle, should be as low as reasonably possible. [ABSTRACT FROM AUTHOR]

The current trend of increasing the complexity of hardware accelerators to improve their functionality is highlighting the problem of sharing a high-frequency clock signal for all integrated modules. As the clock itself is becoming the main limitation to the performance of accelerators, in this manuscript, we present the design of an asymmetric Ring Oscillator-Voltage-Controlled Oscillator (RO-VCO) based on the Current Mode Logic architecture. The RO-VCO was designed on commercial-grade 65 nm CMOS technology, and it is capable of driving large capacitance loads, avoiding the need for additional buffers for clock-trees, reducing the silicon area and power consumption. The proposed RO-VCO is composed of three closed-loop differential and asymmetrical stages, and it is able to tune the working frequency in the range from 4.72 GHz to 6.12 GHz. The phase noise and a figure of merit of −103.2 dBc/Hz and −186 dBc/Hz were obtained at 1 MHz offset from the 5.5 GHz carrier. In this article, the analytical model, full custom schematic, and layout of the proposed RO-VCO are presented and discussed in detail together with the experimental electrical and thermal characterization of the fabricated device. [ABSTRACT FROM AUTHOR]

Random number generators are a key element for various applications, such as computer simulation, statistical sampling, and cryptography. They are used to generate/derive cryptographic keys and non-repeating values, e.g., for symmetric or public key cyphers. The strength of a data protection system against cyber attacks corresponds to the strength of the weakest point in the security chain. Therefore, from a mathematical point of view, the security chain can be compromised even if the strongest algorithm is implemented. In fact, if the system requires keys or other random values and the generation process shows a certain vulnerability, the security of the system itself can be compromised. In this article, we present the most reliable tools and methodologies and the main standardisation efforts in the field of computer security to assess the quality of random number generators and ensure that they can be applied to computer security applications by offering adequate security strength. We offer a comprehensive guide that can be used as a quick and practical reference by developers of random number generators of any type to evaluate the random bit streams generated by implemented modules and determine whether or not they can be used in cybersecurity applications. Finally, we also present some use cases to which we applied the presented approach. [ABSTRACT FROM AUTHOR]

Lysosomes are acidic Ca2+ storage organelles that actively generate local Ca2+ signaling events to regulate a plethora of cell functions. Here, we characterized lysosomal Ca2+ signals in mouse renal collecting duct (CD) cells and we assessed their putative role in aquaporin 2 (AQP2)-dependent water reabsorption. Bafilomycin A1 and ML-SA1 triggered similar Ca2+ oscillations, in the absence of extracellular Ca2+, by alkalizing the acidic lysosomal pH or activating the lysosomal cation channel mucolipin 1 (TRPML1), respectively. TRPML1-dependent Ca2+ signals were blocked either pharmacologically or by lysosomes' osmotic permeabilization, thus indicating these organelles as primary sources of Ca2+ release. Lysosome-induced Ca2+ oscillations were sustained by endoplasmic reticulum (ER) Ca2+ content, while bafilomycin A1 and ML-SA1 did not directly interfere with ER Ca2+ homeostasis per se. TRPML1 activation strongly increased AQP2 apical expression and depolymerized the actin cytoskeleton, thereby boosting water flux in response to an hypoosmotic stimulus. These effects were strictly dependent on the activation of the Ca2+/calcineurin pathway. Conversely, bafilomycin A1 led to perinuclear accumulation of AQP2 vesicles without affecting water permeability. Overall, lysosomal Ca2+ signaling events can be differently decoded to modulate Ca2+-dependent cellular functions related to the dock/fusion of AQP2-transporting vesicles in principal cells of the CD. [ABSTRACT FROM AUTHOR]

We previously reported the novel finding that β3-AR is functionally expressed in the renal tubule and shares its cellular localization with the vasopressin receptor AVPR2, whose physiological stimulation triggers antidiuresis by increasing the plasma membrane expression of the water channel AQP2 and the NKCC2 symporter in renal cells. We also showed that pharmacologic stimulation of β3-AR is capable of triggering antidiuresis and correcting polyuria, in the knockout mice for the AVPR2 receptor, the animal model of human X-linked nephrogenic diabetes insipidus (XNDI), a rare genetic disease still missing a cure. Here, to demonstrate that the same response can be evoked in humans, we evaluated the effect of treatment with the β3-AR agonist mirabegron on AQP2 and NKCC2 trafficking, by evaluating their urinary excretion in a cohort of patients with overactive bladder syndrome, for the treatment of which the drug is already approved. Compared to baseline, treatment with mirabegron significantly increased AQP2 and NKCC2 excretion for the 12 weeks of treatment. This data is a step forward in corroborating the hypothesis that in patients with XNDI, treatment with mirabegron could bypass the inactivation of AVPR2, trigger antidiuresis and correct the dramatic polyuria which is the main hallmark of this disease. [ABSTRACT FROM AUTHOR]

We numerically investigated the use of graphene nanoribbons placed on top of silicon-on-insulator (SOI) strip waveguides for light polarization control in silicon photonic-integrated waveguides. We found that two factors mainly affected the polarization control: the graphene chemical potential and the geometrical parameters of the waveguide, such as the waveguide and nanoribbon widths and distance. We show that the graphene chemical potential influences both TE and TM polarizations almost in the same way, while the waveguide width tapering enables both TE-pass and TM-pass polarizing functionalities. Overall, by increasing the oxide spacer thickness between the silicon waveguide and the top graphene layer, the device insertion losses can be reduced, while preserving a high polarization extinction ratio. [ABSTRACT FROM AUTHOR]

This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology. [ABSTRACT FROM AUTHOR]

Digital designs complexity has exponentially increased in the last decades. Heterogeneous Systems-on-Chip integrate many different hardware components which require a reliable and scalable verification environment. The effort to set up such environments has increased as well and plays a significant role in digital design projects, taking more than 50% of the total project time. Several solutions have been developed with the goal of automating this task, integrating various steps of the Very Large Scale Integration design flow, but without addressing the exploration of the design space on both the software and hardware sides. Early in the co-design phase, designers break down the system into hardware and software parts taking into account different choices to explore the design space. This work describes the use of a framework for automating the verification of such choices, considering both hardware and software development flows. The framework automates compilation of software, cycle-true simulations and analyses on synthesised netlists. It accelerates the design space exploration exploiting the GNU Make tool, and we focus on ensuring consistency of results and providing a mechanism to obtain reproducibility of the design flow. In design teams, the last feature increases cooperation and knowledge sharing from single expert to the whole team. Using flow recipes, designers can configure various third-party tools integrated into the modular structure of the framework, and make workflow execution customisable. We demonstrate how the developed framework can be used to speed up the setup of the evaluation flow of an Elliptic-Curve-Cryptography accelerator, performing post-synthesis analyses. The framework can be easily configured taking approximately 30 min, instead of few days, to build up an environment to assess the accelerator performance and its resistance to simple power analysis side-channel attacks. [ABSTRACT FROM AUTHOR]

A bonding process was developed for glass-to-glass fusion bonding using Borofloat 33 wafers, resulting in high bonding yield and high flexural strength. The Borofloat 33 wafers went through a two-step process with a pre-bond and high-temperature bond in a furnace. The pre-bond process included surface activation bonding using O2 plasma and N2 microwave (MW) radical activation, where the glass wafers were brought into contact in a vacuum environment in an EVG 501 Wafer Bonder. The optimal hold time in the EVG 501 Wafer bonder was investigated and concluded to be a 3 h hold time. The bonding parameters in the furnace were investigated for hold time, applied force, and high bonding temperature. It was concluded that the optimal parameters for glass-to-glass Borofloat 33 wafer bonding were at 550 °C with a hold time of 1 h with 550 N of applied force. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) applies IoT technologies on industrial automation systems with the aims of providing remote sensing, remote control, self-organization and self-maintenance. Since IIoT systems often constitute a critical infrastructure, cybersecurity risks have rapidly increased over the last years. To address cybersecurity requirements, we need to deploy cryptographic processing components which are particularly efficient, considering also that many IIoT systems have real-time constraints. Hardware acceleration can greatly improve the efficiency of cryptographic functions, but the speed-up could be jeopardized by a bad hardware–software integration, which is an aspect often underrated by the literature. Considering that modern IIoT devices often mount an operating system to fulfill their complex tasks, software influence on efficiency cannot be neglected. In this paper, we develop a software–hardware integration of various cryptographic accelerators with a Linux operating system, and we test its performance with two typical IIoT reference applications. We also discuss our design choices and the lessons learned during the development process. [ABSTRACT FROM AUTHOR]

Sesquiterpenes such as leucodin and the labdane-type diterpene manool are natural compounds endowed with remarkably in vitro vasorelaxant and in vivo hypotensive activities. Given their structural similarity with the sesquiterpene lactone (+)-sclareolide, this molecule was selected as a scaffold to develop novel vasoactive agents. Functional, electrophysiology, and molecular dynamics studies were performed. The opening of the five-member lactone ring in the (+)-sclareolide provided a series of labdane-based small molecules, promoting a significant in vitro vasorelaxant effect. Electrophysiology data identified 7 as a CaV1.2 channel blocker and a KCa1.1 channel stimulator. These activities were also confirmed in the intact vascular tissue. The significant antagonism caused by the CaV1.2 channel agonist Bay K 8644 suggested that 7 might interact with the dihydropyridine binding site. Docking and molecular dynamic simulations provided the molecular basis of the CaV1.2 channel blockade and KCa1.1 channel stimulation produced by 7. Finally, 7 reduced coronary perfusion pressure and heart rate, while prolonging conduction and refractoriness of the atrioventricular node, likely because of its Ca2+ antagonism. Taken together, these data indicate that the labdane scaffold represents a valuable starting point for the development of new vasorelaxant agents endowed with negative chronotropic properties and targeting key pathways involved in the pathophysiology of hypertension and ischemic cardiomyopathy. [ABSTRACT FROM AUTHOR]

Hypertension is a risk factor for cardiovascular diseases, which are the main cause of morbidity and mortality in the world. In the search for new molecules capable of targeting KCa1.1 and CaV1.2 channels, the expression of which is altered in hypertension, the in vitro vascular effects of a series of flavonoids extracted from the heartwoods, roots, and leaves of Dalbergia tonkinensis Prain, widely used in traditional medicine, were assessed. Rat aorta rings, tail artery myocytes, and docking and molecular dynamics simulations were used to analyse their effect on these channels. Formononetin, orobol, pinocembrin, and biochanin A showed a marked myorelaxant activity, particularly in rings stimulated by moderate rather than high KCl concentrations. Ba2+ currents through CaV1.2 channels (IBa1.2) were blocked in a concentration-dependent manner by sativanone, 3′-O-methylviolanone, pinocembrin, and biochanin A, while it was stimulated by ambocin. Sativanone, dalsissooside, and eriodictyol inhibited, while tectorigenin 7-O-[β-D-apiofuranosyl-(1→6)-β-D-glucopyranoside], ambocin, butin, and biochanin A increased IKCa1.1. In silico analyses showed that biochanin A, sativanone, and pinocembrin bound with high affinity in target-sensing regions of both channels, providing insight into their potential mechanism of action. In conclusion, Dalbergia tonkinensis is a valuable source of mono- and bifunctional, vasoactive scaffolds for the development of novel antihypertensive drugs. [ABSTRACT FROM AUTHOR]

The Next Generation Gravity Mission (NGGM), currently in a feasibility study phase as a candidate Mission of Opportunity for ESA-NASA cooperation in the frame of the Mass Change and Geo-Sciences International Constellation (MAGIC), is designed to monitor mass transport in the Earth system by its variable gravity signature with increased spatial and temporal resolution. The NGGM will be composed by a constellation of two pairs of satellites, each providing the measurement of two quantities from which the map of Earth's gravity field will be obtained: the variation of the distance between two satellites of each pair, measured by a laser interferometer with nanometer precision; and the relative non-gravitational acceleration between the centers of mass of each satellite pair, measured by ultra-sensitive accelerometers. This article highlights the importance of the second "observable" in the reconstruction of the lower harmonics of Earth's gravity field, by highlighting the tight control requirements in linear and angular accelerations and angular rates, and the expectable performances from the drag-free, attitude, and orbit control system (DFAOCS) obtained through an end-to-end (E2E) simulator. The errors resulting from different mission scenarios with varying levels of drag-free control and pointing accuracy are then presented, demonstrating that a high-performance accelerometer alone is not sufficient to achieve the measurement quality necessary to achieve the mission objectives, if the spacecraft does not provide to this sensor a suitable drag-free environment and a precise and stable pointing. The consequences of these different mission scenarios on the gravity field retrieval accuracy, especially for the lower spherical harmonic degrees, are computed in order to quantitatively justify the rationale for these capabilities on the NGGM spacecraft. [ABSTRACT FROM AUTHOR]

Random numbers are widely employed in cryptography and security applications. If the generation process is weak, the whole chain of security can be compromised: these weaknesses could be exploited by an attacker to retrieve the information, breaking even the most robust implementation of a cipher. Due to their intrinsic close relationship with analogue parameters of the circuit, True Random Number Generators are usually tailored on specific silicon technology and are not easily scalable on programmable hardware, without affecting their entropy. On the other hand, programmable hardware and programmable System on Chip are gaining large adoption rate, also in security critical application, where high quality random number generation is mandatory. The work presented herein describes the design and the validation of a digital True Random Number Generator for cryptographically secure applications on Field Programmable Gate Array. After a preliminary study of literature and standards specifying requirements for random number generation, the design flow is illustrated, from specifications definition to the synthesis phase. Several solutions have been studied to assess their performances on a Field Programmable Gate Array device, with the aim to select the highest performance architecture. The proposed designs have been tested and validated, employing official test suites released by NIST standardization body, assessing the independence from the place and route and the randomness degree of the generated output. An architecture derived from the Fibonacci-Galois Ring Oscillator has been selected and synthesized on Intel Stratix IV, supporting throughput up to 400 Mbps. The achieved entropy in the best configuration is greater than 0.995. [ABSTRACT FROM AUTHOR]

Featured Application: Hybrid microgrids for residential applications with connections to renewable energy sources, electric vehicle recharging stations and the AC energy grid. This editorial manuscript reviews the papers accepted for publication in the Special Issue "DC & Hybrid Microgrids" of Applied Sciences. This Special Issue, co-organized by the University of Pisa, Italy and Østfold University College in Norway, has collected nine papers from 25 submitted, with authors from Asia, North America and Europe. The published articles provide an overview of the most recent research advances in direct current (DC) and hybrid microgrids, exploiting the opportunities offered by the use of renewable energy sources, battery energy storage systems, power converters, innovative control and energy management strategies. [ABSTRACT FROM AUTHOR]

Featured Application: Hybrid micro-grids for residential applications with connections to renewable energy sources, electric vehicle recharging stations and the AC energy grid. This paper analyzes trends in renewable-energy-sources (RES), power converters, and control strategies, as well as battery energy storage and the relevant issues in battery charging and monitoring, with reference to a new and improved energy grid. An alternative micro-grid architecture that overcomes the lack of flexibility of the classic energy grid is then described. By mixing DC and AC sources, the hybrid micro-grid proposes an alternative architecture where the use of bi-directional electric vehicle chargers creates a micro-grid that directly interconnects all the partner nodes with bi-directional energy flows. The micro-grid nodes are the main grid, the RES and the energy storage systems, both, on-board the vehicle and inside the micro-grid structure. This model is further sustained by the new products emerging in the market, since new solar inverters are appearing, where a local energy storage for the RES is available. Therefore, the power flow from/towards the RES becomes bi-directional with improved flexibility and efficiency. [ABSTRACT FROM AUTHOR]

The special issue, “Sensors, Wireless Connectivity and Systems for Autonomous Vehicles and Smart Mobility” on MDPI Sensors presents 12 accepted papers, with authors from North America, Asia, Europe and Australia, related to the emerging trends in sensing and navigation systems (i.e., sensors plus related signal processing and understanding techniques in multi-agent and cooperating scenarios) for autonomous vehicles, including also unmanned aerial and underwater ones. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumors (GIST) carrying the D842V activating mutation in the platelet-derived growth factor receptor alpha (PDGFRA) gene are a very rare subgroup of GIST (about 10%) known to be resistant to conventional tyrosine kinase inhibitors (TKIs) and to show an indolent behavior. In this study, we performed an integrated molecular characterization of D842V mutant GIST by whole-transcriptome and whole-exome sequencing coupled with protein-ligand interaction modelling to identify the molecular signature and any additional recurrent genomic event related to their clinical course. We found a very specific gene expression profile of D842V mutant tumors showing the activation of G-protein-coupled receptor (GPCR) signaling and a relative downregulation of cell cycle processes. Beyond D842V, no recurrently mutated genes were found in our cohort. Nevertheless, many private, clinically relevant alterations were found in each tumor (TP53, IDH1, FBXW7, SDH-complex). Molecular modeling of PDGFRA D842V suggests that the mutant protein binds imatinib with lower affinity with respect to wild-type structure, showing higher stability during the interaction with other type I TKIs (like crenolanib). D842V mutant GIST do not show any actionable recurrent molecular events of therapeutic significance, therefore this study supports the rationale of novel TKIs development that are currently being evaluated in clinical studies for the treatment of D842V mutant GIST. [ABSTRACT FROM AUTHOR]

This paper presents an improved VLSI (Very Large Scale of Integration) architecture for real-time and high-accuracy computation of trigonometric functions with fixed-point arithmetic, particularly arctangent using CORDIC (Coordinate Rotation Digital Computer) and fast magnitude estimation. The standard CORDIC implementation suffers of a loss of accuracy when the magnitude of the input vector becomes small. Using a fast magnitude estimator before running the standard algorithm, a pre-processing magnification is implemented, shifting the input coordinates by a proper factor. The entire architecture does not use a multiplier, it uses only shift and add primitives as the original CORDIC, and it does not change the data path precision of the CORDIC core. A bit-true case study is presented showing a reduction of the maximum phase error from 414 LSB (angle error of 0.6355 rad) to 4 LSB (angle error of 0.0061 rad), with small overheads of complexity and speed. Implementation of the new architecture in 0.18 µm CMOS technology allows for real-time and low-power processing of CORDIC and arctangent, which are key functions in many embedded DSP systems. The proposed macrocell has been verified by integration in a system-on-chip, called SENSASIP (Sensor Application Specific Instruction-set Processor), for position sensor signal processing in automotive measurement applications. [ABSTRACT FROM AUTHOR]

As a result of the ring-into-ring conversion of nitrosoimidazole derivatives, we obtained a molecular scaffold that, when properly decorated, is able to decrease inotropy by blocking L-type calcium channels. Previously, we used this scaffold to develop a quantitative structure-activity relationship (QSAR) model, and we used the most potent oxadiazolothiazinone as a template for ligand-based virtual screening. Here, we enlarge the diversity of chemical decorations, present the synthesis and in vitro data for 11 new derivatives, and develop a new 3D-QSAR model with recent in silico techniques. We observed a key role played by the oxadiazolone moiety: given the presence of positively charged calcium ions in the transmembrane channel protein, we hypothesize the formation of a ternary complex between the oxadiazolothiazinone, the Ca2+ ion and the protein. We have supported this hypothesis by means of pharmacophore generation and through the docking of the pharmacophore into a homology model of the protein. We also studied with docking experiments the interaction with a homology model of P-glycoprotein, which is inhibited by this series of molecules, and provided further evidence toward the relevance of this scaffold in biological interactions. [ABSTRACT FROM AUTHOR]

In the cybersecurity field, the generation of random numbers is extremely important because they are employed in different applications such as the generation/derivation of cryptographic keys, nonces, and initialization vectors. The more unpredictable the random sequence, the higher its quality and the lower the probability of recovering the value of those random numbers for an adversary. Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs) are random number generators (RNGs) with specific properties and whose output sequence has such a degree of randomness that it cannot be distinguished from an ideal random sequence. In this work, we designed an all-digital RNG, which includes a Deterministic Random Bit Generator (DRBG) that meets the security requirements for cryptographic applications as CSPRNG, plus an entropy source that showed high portability and a high level of entropy. The proposed design has been intensively tested against both NIST and BSI suites to assess its entropy and randomness, and it is ready to be integrated into the European Processor Initiative (EPI) chip. [ABSTRACT FROM AUTHOR]

In this paper, a model-based approach for the design of a bidirectional onboard charger (OBC) device for modern hybrid and fully electrified vehicles is proposed. The main objective and contribution of our study is to incorporate in the same simulation environment both modelling of electrical and thermal behaviour of switching devices. This is because most (if not all) of the studies in the literature present analyses of thermal behaviour based on the use of FEM (Finite Element Method) SWs, which in fact require the definition of complicated models based on partial derivative equations. The simulation of such accurate models is computationally expensive and, therefore, cannot be incorporated into the same virtual environment in which the circuit equations are solved. This requires long waiting times and also means that electrical and thermal models do not interact with each other, limiting the completeness of the analysis in the design phase. As a case study, we take as reference the architecture of a modular bidirectional single-phase OBC, consisting of a Totem Pole-type AC/DC converter with Power Factor Correction (PFC) followed by a Dual Active Bridge (DAB) type DC/DC converter. Specifically, we consider a 7 kW OBC, for which its modules consist of switching devices made with modern 900 V GaN (Gallium Nitrade) and 1200 V SiC (Silicon Carbide) technologies, to achieve maximum performance and efficiency. We present a procedure for sizing and selecting electronic devices based on the analysis of behaviour through circuit models of the Totem Pole PFC and DAB converter in order to perform validation by using simulations that are as realistic as possible. The developed models are tested under various operating conditions of practical interest in order to validate the robustness of the implemented control algorithms under varying operating conditions. The validation of the models and control loops is also enhanced by an exhaustive robustness analysis of the parametric variations of the model with respect to the nominal case. All simulations obtained respect the operating limits of the selected devices and components, for which its characteristics are reported in data sheets both in terms of electrical and thermal behaviour. [ABSTRACT FROM AUTHOR]

This paper presents the design flow of an advanced non-linear control strategy, able to absorb the effects that the main causes of torque oscillations, concerning synchronous electrical drives, cause on the positioning of the end-effector of a manipulator robot. The control technique used requires an exhaustive modelling of the physical phenomena that cause the electromagnetic torque oscillations. In particular, the Cogging and Stribeck effects are taken into account, whose mathematical model is incorporated in the whole system of dynamic equations representing the complex mechatronic system, formed by the mechanics of the robot links and the dynamics of the actuators. Both the modelling procedure of the robot, directly incorporating the dynamics of the actuators and the electrical drive, consisting of the modulation system and inverter, and the systematic procedure necessary to obtain the equations of the components of the control vector are described in detail. Using the Processor-In-the-Loop (PIL) paradigm for a Cortex-A53 based embedded system, the beneficial effect of the proposed advanced control strategy is validated in terms of end-effector position control, in which we compare classic control system with the proposed algorithm, in order to highlight the better performance in precision and in reducing oscillations. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the diagnosis of particle-induced failures in harsh environments such as space and high-energy physics. To address these effects, simulation-before-test and simulation-after-test can be the key points in choosing which radiation hardening by design (RHBD) techniques can be implemented to mitigate or prevent failures. Despite the fact that total ionising dose (TID) has slow but destructive effects overtime on silicon devices, single-event effect (SEE) impulsively disrupts the typical operation of a circuit with temporary or permanent effects. The recently released SpaceFibre protocol drives the current requirements for space applications, and the future upgrade of the LHC experiment scheduled by CERN will require a redesign of the electronic front-end to sustain a radiation level up to the 1 Grad TID level. The effects that these two environments have on two different architectures for high-radiation and high-frequency data transmission are reported, and the efficiency of the mitigation techniques implemented, based on simulations and measurement tests, in the commercial 65 nm technology, are exploited. [ABSTRACT FROM AUTHOR]

Quercetin and ferulic acid are two phytochemicals extensively represented in the plant kingdom and daily consumed in considerable amounts through diets. Due to a common phenolic structure, these two molecules share several pharmacological properties, e.g., antioxidant and free radical scavenging, anti-cancer, anti-inflammatory, anti-arrhythmic, and vasorelaxant. The aim of the present work was the combination of the two molecules in a single chemical entity, conceivably endowed with more efficacious vasorelaxant activity. Preliminary in silico studies herein described suggested that the new hybrid compound bound spontaneously and with high affinity on the KCa1.1 channel. Thus, the synthesis of the 3′-ferulic ester derivative of quercetin was achieved and its structure confirmed by 1H- and 13C-NMR spectra, HSQC and HMBC experiments, mass spectrometry, and elementary analysis. The effect of the new hybrid compound on vascular KCa1.1 and CaV1.2 channels revealed a partial loss of the stimulatory activity that characterizes the parent compound quercetin. Therefore, further studies are necessary to identify a better strategy to improve the vascular properties of this flavonoid. [ABSTRACT FROM AUTHOR]

Congenital heart disease, the most frequent malformation at birth, is usually not fatal but leads to multiple hospitalisations and outpatient visits, with negative impact on the quality of life and psychological profile not only of children but also of their families. In this paper, we describe the entire architecture of a system for remotely monitoring paediatric/neonatal patients with congenital heart disease, with the final aim of improving quality of life of the whole family and reducing hospital admissions. The interesting vital parameters for the disease are ECG, heart rate, oxygen saturation, body temperature and body weight. They are collected at home using some biomedical sensors specifically selected and calibrated for the paediatric field. These data are then sent to the smart hub, which proceeds with the synchronisation to the remote e-Health care center. Here, the doctors can log and evaluate the patient's parameters. Preliminary results underline the sensor suitability for children and infants and good usability and data management of the smart-hub technology (E@syCare). In the clinical trial, some patients from the U.O.C. Paediatric and Adult Congenital Cardiology- Monasterio Foundation are enrolled. They receive a home monitoring kit according to the group they belong to. The trial aims to evaluate the effects of the system on quality of life. Psychological data are collected through questionnaires filled in by parents/caregivers in self-administration via the gateway at the beginning and at the end of the study. Results highlight an overall improvement in well-being and sleep quality, with a consequent reduction in anxious and stressful situations during daily life thanks to telemonitoring. At the same time, users reported a good level of usability, ease of data transmission and management of the devices. [ABSTRACT FROM AUTHOR]