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Background: Electronic health can help improve perioperative outcomes and overcome organizational constraints related to human resources and health care-related costs. In this initial report, we assess patient compliance with a digital platform including pre- and rehabilitation programs, checklists, electronic patient-reported outcomes (ePROs), and remote monitoring after surgery. Materials and Methods: Twenty-four patients undergoing major uro-oncological surgery were prospectively included and followed from the surgical decision time point to the 3-month follow-up visit by Betty.care app. The primary end-point was patient compliance with the digital tool materials, grouped into four domains: safety, education, ePRO collection, and remote monitoring. Results and Limitations: The mean age was 62 years. The mean hospital stay was 1.8 days (50% of outpatient surgeries). Four complications occurred (16.7%), including one emergency readmission. The compliance of patients for fulfilling or reading all safety, education, and ePRO questionnaires was 79.2%, 76.5%, and 86.2%, respectively. The activation of real-time notifications as predefined by Betty.care detected half of the complications. All the generated alerts were related to a confirmed postoperative grade 2 or 3 complication. The mean duration of daily remote monitoring was 18.5 days after discharge. Satisfaction with their perioperative pathway was scored 8.4 out of 10 by patients. Conclusions: In conclusion, the implementation of a mobile app that provides a holistic approach to the perioperative period, integrating prehabilitation, rehabilitation, ePRO collection, checklists, and remote monitoring is feasible in clinical practice with good patient compliance. These preliminary findings suggest that the Betty.care digital platform could improve outcomes, facilitate recovery, and secure at-home discharge.

Recent advancements in communication technologies have significantly enhanced localization techniques, improving both accuracy and operating modes. Initially, localization methods relied on global navigation satellite systems, offering high accuracy but proving inefficient in Non-Line-of-Sight scenarios. Furthermore, the absence of a passive mode, where the user can be localized without explicitly requesting it, renders these methods unsuitable for applications like passive tracking systems. Fingerprinting methods, a pattern matching techniques based on signal power estimation from target devices and distance estimation from reference points, can be seen as a valid and promising alternative. However, these methods face limitations due to extensive measurement campaigns needed to establish accurate sampling systems within specific areas and the substantial amount of data required for machine learning algorithms to achieve optimal performance. This study introduces a novel fingerprinting method capable of passive operation, involving all smartphones within a designated area, suitable for both indoor and outdoor scenarios. The proposed solution leverages Generative Adversarial Networks (GANs) to augment fingerprinting datasets, enhancing machine learning models’ capabilities. Additionally, the offline phase’s cost-effectiveness is improved by integrating a Bayesian system as a secondary machine learning component.

Monitoring rainfall is becoming increasingly important due to the impacts of climate change. In this context, opportunistic sensing based on satellite microwave links (SMLs) is gaining significant interest for its potential to provide real-time, low-cost, and valuable information. In this work, we deployed a network of 26 SML sensors over a survey territory corresponding to a catchment area of 140 km2, and the data collected from it were used to train two machine learning (ML) algorithms and a statistical method to predict wet/dry conditions. Additionally, a voting mechanism based on the majority vote of the three classifiers enhances the performance of the individual methods. The results show that ML algorithms are valuable solutions for real-time monitoring of rainfall, even in an extended sensor network. The voting mechanism improves the prediction of low precipitation at the expense of a higher false positive rate.

Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain–machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm2. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.

A low-power, low-voltage universal multi-mode Gm-C filter using a 180 nm TSMC technology node is presented in this paper. The proposed filter employs only three transconductance operational amplifiers (OTAs) operating in the sub-threshold region with a supply voltage of 0.5 V, resulting in a power consumption of 32 nW. Moreover, without additional active elements, the proposed circuit can operate various functional modes, such as voltage, current, transconductance, and trans-resistance. The filter’s frequency, centered at 462 Hz, and a compact and low-power solution showing only 93.5 µVrms input-referred noise make the proposed filter highly suitable for bio-signal processing.

Abstract The release of extracellular vesicles (EVs) in cell cultures as well as their molecular cargo can be influenced by cell culture conditions such as the presence of foetal bovine serum (FBS). Although several studies have evaluated the effect of removing FBS‐derived EVs by ultracentrifugation (UC), less is known about the influence of FBS heat inactivation (HI) on the cell‐derived EVs. To assess this, three protocols based on different combinations of EV depletion by UC and HI were evaluated, including FBS ultracentrifuged but not heat inactivated (no‐HI FBS), FBS heat inactivated before EV depletion (HI‐before EV‐depl FBS), and FBS heat inactivated after EV depletion (HI‐after EV‐depl FBS). We isolated large (L‐EVs) and small EVs (S‐EVs) from FBS treated in the three different ways, and we found that the S‐EV pellet from HI‐after EV‐depl FBS was larger than the S‐EV pellet from no‐HI FBS and HI‐before EV‐depl FBS. Transmission electron microscopy, protein quantification, and particle number evaluation showed that HI‐after EV‐depl significantly increased the protein amount of S‐EVs but had no significant effect on L‐EVs. Consequently, the protein quantity of S‐EVs isolated from three cell lines cultured in media supplemented with HI‐after EV‐depl FBS was significantly increased. Quantitative mass spectrometry analysis of FBS‐derived S‐EVs showed that the EV protein content was different when FBS was HI after EV depletion compared to EVs isolated from no‐HI FBS and HI‐before EV‐depl FBS. Moreover, we show that several quantified proteins could be ascribed to human origin, thus demonstrating that FBS bovine proteins can mistakenly be attributed to human cell‐derived EVs. We conclude that HI of FBS performed after EV depletion results in changes in the proteome, with molecules that co‐isolate with EVs and can contaminate EVs when used in subsequent cell cultures. Our recommendation is, therefore, to always perform HI of FBS prior to EV depletion.

Owing to the geographically scattered end devices (EDs) in long-range wide area networks (LoRaWAN), devices that combat challenging cyber threats and attacks are of critical significance. In this perspective, LoRa Alliance® is continuously evolving the security of LoRaWAN and recently introduced a new version i.e., LoRaWAN 1.1x that is featured with security improvement. However, the wireless nature of LoRaWAN implementation still leaves it vulnerable to security breaches that compromise its integrity. Several problems have been pinpointed in the newer version such as one issue with key distribution in LoRaWAN 1.1 is that the keys are often pre-installed on the devices at the time of manufacturing. It can introduce security risks if the keys are not adequately protected or if the devices are compromised before they are deployed. In other words, the pre-installed keys may not be updated regularly, which can also introduce security risks. Thus, the keys need to be handled securely to maintain the security of the network and the over-the-air firmware updates feature could introduce new security challenges for the key distribution. This paper presents a key generation and distribution (KGD) mechanism that securely exchanges the root key between the ED and the application server (AS). The KGD protocol provides authentication by integrating Advanced Encryption Standard (AES-128) in addition to a secure hash function known as Argon2. The proposed protocol utilizes Elliptic-Curve Diffie-Hellman (ECDH) key exchange method that makes the protocol resilient to cyber threats. The ECDH algorithm exchanges the keys on the insecure channels and is, therefore, vulnerable to Man-in-the-Middle (MITM) attacks in the network. Therefore, to validate the key agreement and avoid adversaries, the KGD protocol considers the Elliptic Curve Digital Signature Algorithm (ECDSA) that authenticates and allows legitimate instances in the network. In last, a formal security analysis using the Scyther tool validates the security enhancement of the KGD protocol.

The rapid increase in Internet of Things (IoT) applications has raised security and privacy issues due to the huge amount of data acquired by IoT devices and transmitted through the Internet. Therefore, there is a need to understand what strategies should be applied to make IoT systems robust to security flaws and privacy weaknesses. In this paper, we first identify and discuss the best practices for IoT privacy and security, which include a set of procedures that can be taken as the guidelines to determine and solve privacy and security issues of IoT systems. Then, we follow and apply the identified best practices to two real IoT-based use cases: a crowding monitoring system and a vehicular mobility system. Finally, we computed the risk assessment score to evaluate the impact of the application of the identified best practices on the implemented IoT systems. We observe that following the proposed best practices the implemented IoT systems achieve an overall risk score of 1.3, which is from 215% to 361% lower than that achieved by comparable IoT systems proposed in the literature studies.

Abstract Pecan [Carya illinoinensis (Wangenh.) K. Koch] is a crop fruit native to the USA and Mexico currently cultivated in several countries, including Brazil, Uruguay, Argentina, Chile, Peru, China, South Africa, and Australia. Supported by the increasing consumption and market prices, the interest in the cultivation of this fruit crop is strongly growing around the world. In this study, AFLP and S-SAP markers were employed to characterize the genetic diversity of ancient accessions of pecan from southern Brazil. The evaluated plants were selected and preserved by the farmers and are remnants of the first introduction of seedlings from the U.S.A into southern Brazil aiming at developing research towards establishing commercial orchards. High levels of genetic diversity were estimated, suggesting that these plants have an important genetic background for the establishment of a germplasm collection with a wide genetic basis, for the development of breeding programs for this fruit crop. Cluster analysis of the genetic datasets revealed some correlation between the nuts’ morphometric traits and genetic markers. Such correlation should be further exploited. These ancient genotypes must be evaluated for other agronomic traits of interest and included in core collections of pecans.

The energy consumption of a heating, ventilation, and air conditioning (HVAC) system represents a large amount of the total for a commercial or civic building. In order to optimize the system performance and to increase the comfort of people living or working in a building, it is necessary to monitor the relevant parameters of the circulating air flux. To this end, an array of sensors (i.e., temperature, humidity, and CO2 percentage sensors) is usually deployed along the aeraulic ducts and/or in various rooms. Generally, these sensors are powered by wires or batteries, but both methods have some drawbacks. In this paper, a possible solution to these drawbacks is proposed. It presents a wireless sensor node powered by an Energy Harvesting (EH) device acted on by the air flux itself. The collected data are transmitted to a central unit via a LoRa radio channel. The EH device can be placed in air ducts or close to air outlets.

In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers.

The Internet of Things (IoT) has disrupted the IT landscape drastically, and Long Range Wide Area Network (LoRaWAN) is one specification that enables these IoT devices to have access to the Internet. Former security analyses have suggested that the gateways in LoRaWAN in their current state are susceptible to a wide variety of malicious attacks, which can be notoriously difficult to mitigate since gateways are seen as obedient relays by design. These attacks, if not addressed, can cause malfunctions and loss of efficiency in the network traffic. As a solution to this unique problem, this paper presents a novel certificate authentication technique that enhances the cyber security of gateways in the LoRaWAN network. The proposed technique considers a public key infrastructure (PKI) solution that considers a two-tier certificate authority (CA) setup, such as a root-CA and intermediate-CA. This solution is promising, as the simulation results validate that about 66.67% of the packets that are arriving from an illegitimate gateway (GW) are discarded in our implemented secure and reliable solution.

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.

Sensor nodes have been assigned a lot of tasks in a connected environment that is growing rapidly. The power supply remains a challenge that is not answered convincingly. Energy harvesting is an emerging solution that is being studied to integrate in low power applications such as internet of things (IoT) and wireless sensor networks (WSN). In this work an interface circuit for a novel fluttering wind energy harvester is presented. The system consists of a switching converter controlled by a low power microcontroller. Optimization techniques on the hardware and software level have been implemented, and a prototype is developed for testing. Experiments have been done with generated input signals resulting in up to 67% efficiency for a constant voltage input. Other experiments were conducted in a wind tunnel that showed a transient output that is compatible with the target applications.

Correct regulation of meteoric surface and subsurface flow waters is a fundamental goal for the sustainable development of the territories. A new system, aimed at real-time monitoring of the rainfall and of the cumulated rainfall, is introduced and discussed in the present paper. The system implements a Sensor Network based on the IoT paradigm and can cover safety-critical “hot spots” with a relatively small number of sensors, strategically placed, in areas not covered by traditional weather radars and rain gauges, and lowering the costs of deployment and maintenance with respects to these devices. A real application case, based on the implementation of the pilot plant at the Monte Scarpino landfill (Genoa, Italy), is presented and discussed. The system performances are assessed on the basis of comparisons with data provided by a polarimetric weather radar and by a traditional rain gauge.

Nowadays, cities can be perceived as increasingly dangerous places. Usually, CCTV is one of the main technologies used in a modern security system. However, poor light situations or bad weather conditions (rain, fog, etc.) limit the detection capabilities of image-based systems. Microwave radar detection systems can be an answer to this limitation and take advantage of the results obtained by low-cost technologies for the automotive market. Transportation by car may be dangerous, and every year car accidents lead to the fatalities of many individuals. Humans require automated assistance when driving through detecting and correctly classifying approaching vehicles and, more importantly, pedestrians. In this paper, we present the application of machine learning to data collected by a 24 GHz short-range radar for urban classification. The training and testing take place on a Raspberry Pi as an edge computing node operating in a client/server arrangement. The software of choice is Rulex, a high-performance machine learning package controlled through a remote interface. Forecasts with a varying number of classes were performed with one, two, or three classes for vehicles and one for humans. Furthermore, we applied a single forecast for all four classes, as well as cascading forecasts in a tree-like structure while varying algorithms, cascading the block order, setting class weights, and varying the data splitting ratio for each forecast to improve prediction accuracy. In the experiments carried out for the validation of the presented approach, an accuracy of up to 100% for human classification and 96.67% for vehicles, in general, was obtained. Vehicle sub-classes were predicted with 90.63% accuracy for motorcycles and 77.34% accuracy for both cars and trucks.

Since growth promoters ban in Europe, enteritis of different aetiologies (virus, bacteria and protozoa) are increasingly becoming the main cause of economic loss in commercial turkeys production. This study is focused on typing of Clostridium spp. isolated from samples of jejunum and ileum of 82 birds out of 17 turkeys flocks. The birds were 6-day to 104-day old, both male and female, with enteric disorders. The presence of toxin NetB was investigated. Multiplex PCR to detect cpa, cpb1, cpetx, cp1, cpb2 and cpe toxin genes were used for Clostridium typing. No lesions of necrotic enteritis were observed. Clostridium perfringens type A was isolated from 25 enteric samples, Clostridium difficile was found in 4 cases and Clostridium sordelli in one case. Clostridium perfringens was present from 6 to 104 days of age indicating its possible role in the enteric disorders of commercial turkeys. NetB toxin was found in no sample. 3 out of 4 isolates of Clostridium difficile were characterized by the presence of toxin genes.

The development of the Internet of Things infrastructure requires the deployment of millions of heterogeneous sensors embedded in the environment. The powering of these sensors cannot be done with wired connections, and the use of batteries is often impracticable. Energy harvesting is the common proposed solution, and many devices have been developed for this purpose, using light, mechanical vibrations, and temperature differences as energetic sources. In this paper we present a novel energy-harvester device able to capture the kinetic energy from a fluid in motion and transform it in electrical energy. This device, named FLEHAP (FLuttering Energy Harvester for Autonomous Powering), is based on an aeroelastic effect, named fluttering, in which a totally passive airfoil shows large and regular self-sustained motions (limit cycle oscillations) even in extreme conditions (low Reynolds numbers), thanks to its peculiar mechanical configuration. This system shows, in some centimeter-sized configurations, an electrical conversion efficiency that exceeds 8% at low wind speed (3.5 m/s). By using a specialized electronic circuit, it is possible to store the electrical energy in a super capacitor, and so guarantee self-powering in such environmental conditions.

Slow motion replay and spatial zooming are special effects used in digital video rendering. At present, most techniques to perform digital spatial zoom and slow motion are based on interpolation for both enlarging the size of the original pictures and generating additional intermediate frames. Mainly, interpolation is done either by linear or cubic spline functions or by motion estimation/compensation which both can be applied pixel by pixel, or by partitioning frames into blocks. Purpose of this paper is to present an alternative technique combining fractals theory and wavelet decomposition to achieve spatial zoom and slow motion replay of HD digital color video sequences. Fast scene change detection, active scene detection, wavelet subband analysis, and color fractal coding based on Earth Mover's Distance (EMD) measure are used to reduce computational load and to improve visual quality. Experiments show that the proposed scheme achieves better results in terms of overall visual quality compared to the state-of-the-art techniques.

The Distributed Research Infrastructure for Hydro-Meteorological Study (DRIHMS) is a coordinatedaction co-funded by the European Commission. DRIHMS analyzes the main issuesthat arise when designing and setting up a pan-European Grid-based e-Infrastructure for researchactivities in the hydrologic and meteorological fields. The main outcome of the projectis represented first by a set of Grid usage patterns to support innovative hydro-meteorologicalresearch activities, and second by the implications that such patterns define for a dedicatedGrid infrastructure and the respective Grid architecture.