National Institute of Astrophysics, Optics and Electronics

Context Sulfur dichloride (SCl 2 ) molecules form a harmful substance; however, it is widely used in the industry as insecticide and in organic synthesis. In contact with water, these molecules produce other toxic and corrosive gases. Therefore, it is important to remove them from the environment. In this work, we have studied the boron phosphide (BP) monolayer (ML) doped with metal atoms to be considered as a sensor material for the detection of sulfur dichloride (SCl 2 ) molecules. Studies are done by applying the density functional theory (DFT) according to the PWscf code of the Quantum ESPRESSO, using the projector-augmented-wave (PAW) method within the framework of the generalized gradient approximation (GGA) with the PBE parameterization. The results obtained indicate weak interactions between the SCl 2 molecule and the pristine BP monolayer. However, after metal-doping (with atoms of: Ga, In, N and As) the interactions between the SCl 2 molecule and the ML was increased, as expected. Parameters such as the adsorption energy (E ad ), work function (Ф), Bandgaps (E g ), recovery time (τ), electronegativity (χ) and chemical potential (μ) have been analyzed. The results suggest that the metal-doped BP monolayer may be a promising sensing material for gas sensor devices to detect SCl 2 molecules. Methods The SCl 2 -metal-doped BP ML has been investigated using DFT calculations as implemented in the PWscf code of the Quantum ESPRESSO, and using PAW pseudopotential within the framework of the GGA-PBE and energy cutoff of 40Ry. The force components were smaller than 0.05 eV/Å and the Grimme-D2 scheme was considered. The Brillouin zone was sampled using a Monkhorst–Pack grid of 5 × 5 × 1 and 17 × 17 × 1 k-points for structural relaxations and electronic-properties calculations.

In this investigation, we report the fabrication of heterostructures based on porous silicon (PS) obtained by Metal-Assisted Chemical Etching and titanium dioxide synthesized by the solvothermal method decorated with Au or Ag nanoparticles obtained by chemical reduction of metallic salts. Four different heterostructures were obtained, which were labeled as PS, PS/TiO2, PS/TiO2-Ag, and PS/TiO2-Au, and their morphological, structural, and optical characteristics were analyzed, as well as the interaction with dexamethasone (adsorption and photodecomposition). The morphological characterization of PS showed that the pore size is around 95 nm, 20 μm in length with cylindrical form. The titanium dioxide was synthesized and deposited on PS using the solvothermal method, resulting in a conformal deposit on the surface area. The structural analysis demonstrated the vibration modes of porous silicon and titanium dioxide. This analysis determined the predominance phase, and no evidence of the metallic particles was found. Diffuse reflectance was used to obtain the bandgap (BG) of the heterostructures by using the Kubelka–Munk method. These energies were 1.54 eV for PS and 3.2, 2.88, and 2.71 eV for PS/TiO2, PS/TiO2-Ag, and PS/TiO2-Au, respectively. The decoration with Ag and Au nanoparticles did not exert a considerable effect on the optical properties of the materials. The heterostructure with Ag showed the highest degradation percentage compared to the others. This could be due to the BG (2.88 eV) and the distribution of the Ag nanoparticles. The PL spectra displayed the emission light above 2.4 eV of the heterostructures. All heterostructures showed adsorption of dexamethasone, but only three heterostructures displayed photodegradation (the samples with TiO2) with percentages of 5, 18, and 7% for PS/TiO2, PS/TiO2-Ag, PS/TiO2-Au, respectively. The photodegradation tests were performed using a UV light source of 390 nm separated 20 cm between the source and the heterostructure and 40 ml of aqueous dexamethasone with an initial concentration of 1 mM. The UV source was used because the excitation of the material is in the UV range.

Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from population centers. Sounds from the human respiratory system have displayed potential in autonomously detecting these diseases using artificial intelligence (AI). This article outlines the development of an audio-based edge computing system that automatically detects chronic respiratory diseases (CRDs). The system utilizes machine learning (ML) techniques to analyze audio recordings of respiratory sounds (cough and breath) and classify the presence or absence of these diseases, using features such as Mel frequency cepstral coefficients (MFCC) and chromatic attributes (chromagram) to capture the relevant acoustic features of breath sounds. The system was trained and tested using a dataset of respiratory sounds collected from 86 individuals. Among them, 53 had chronic respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD), while the remaining 33 were healthy. The system’s final evaluation was conducted with a group of 13 patients and 22 healthy individuals. Our approach demonstrated high sensitivity and specificity in the classification of sounds on edge devices, including smartphone and Raspberry Pi. Our best results for CRDs reached a sensitivity of 90.0%, a specificity of 93.55%, and a balanced accuracy of 91.75% for accurately identifying individuals with both healthy and diseased. These results showcase the potential of edge computing and machine learning systems in respiratory disease detection. We believe this system can contribute to developing efficient and cost-effective screening tools.

This work shows the development of an electrocardiogram (ECG) data masking system based on double‐scroll synchronized chaotic oscillators. The contribution is devoted to the introduction of a CMOS implementation of a double‐scroll chaotic oscillator, which is designed by taking advantage of the intrinsic hyperbolic tangent‐type characteristic of the operational transconductance amplifier (OTA). The chaotic behavior of the CMOS oscillator is guaranteed by plotting the bifurcation diagram and evaluating the Lyapunov exponents. In this manner, a masking system based on CMOS chaotic systems is designed to protect privacy while transmitting ECG signals effectively. Basically, the chaotic time series is processed to generate pseudorandom signals in a continuous‐time domain. Mathematical modeling and simulation results under a UMC 180‐nm CMOS fabrication process demonstrate that the proposed masking system is well suited to provide hardware‐level security in the chaotic encryption of biomedical signals.

The performance of machine learning algorithms can be optimized through the implementation of methodologies that facilitate the development of autonomous and adaptive behaviors. In this context, the incorporation of goal-oriented analysis is proposed as a means of effecting a transformation in the behavior of traditionally “passive" algorithms, such as Random Forest, through the endowment of proactivity. The aforementioned analysis, represented using the i* modeling language, identifies strategies that increase the diversity of generated trees and optimize their total number while preserving the original model’s effectiveness. In addition to the outcomes achieved, it is crucial to highlight that the goal-oriented methodology plays a pivotal role in the development and comprehension of novel algorithmic variants. Based on this analysis, two proactive variants were designed: the Proactive Forest and the Progressive Forest. These variants balance simplicity and effectiveness, maintaining the original algorithm’s performance while exploring more efficient configurations. This work introduces new variants of the Random Forest algorithm and demonstrates the potential of goal-oriented analysis as a methodology for guiding the design of more adaptive and effective algorithms.

One of the main challenges of the rising field of the Internet of Things (IoT) is the self‐sustainable supply of energy to the sensors. Among the available environmental sources, heat can be harvested by means of thermoelectric devices. This work presents a new generation of densely packaged all‐silicon micro‐thermoelectric generators (µTEGs) with planar architecture. Optimized boron‐doped Si nanowires with 80 ± 30nm in diameter are epitaxially integrated as dense arrays into these generators for an improved performance. A procedure to reliably place a heat sink on top of the devices, enlarging the fraction of external thermal gradient captured by the thermoelectrically active nanowires, is described. These improvements enhance the generated voltage up to eight times with respect to that of a bare µTEG, leading to output powers well within the range of IoT needs (10 – 100 µW cm⁻²). Specifically, the µTEG on top of a heat source above 200°C and under still air convection conditions generates more than 14 µW cm⁻². When exposed to the same temperatures and to an airflow of 1.3 m s⁻¹ (equivalent to a light breeze) the power density increases above 150 µW cm⁻². Moreover, a long‐term stability study running the device in load matching conditions for a period of 1000 h does not show degradation below 200°C. Finally, the suitability of connecting the µTEG with the current state of the art DC–DC converters is discussed, showing how eventual transients in real operation conditions can allow the device to reach the required cold start‐up voltages. Overall, these results demonstrate the readiness of the presented µTEG as a reliable power source for miniaturized IoT applications.

Foodborne diseases are a significant cause of morbidity (600 million cases) and mortality (420,000 deaths) worldwide every year and are mainly associated with pathogens. Besides the direct effects on human health, they have relevant concerns related to financial, logistics, and infrastructure for the food and medical industries. The standard pathogen identification techniques usually require a sample enrichment step, plating, isolation, and biochemical tests. This process involves specific facilities, a long-time analysis procedures, and skilled personnel. Conversely, biosensors are an emerging innovative approach to detecting pathogens in real time due to their portability, specificity, sensitivity, and low fabrication costs. These advantages can be achieved from the synergistic work between nanotechnology, materials science, and biotechnology for coupling biomolecules in nano-matrices to enhance biosensing performance. This review highlights recent advancements in electrochemical and optical biosensing techniques for detecting bacteria and viruses. Key properties, such as detection limits, are examined, as they depend on factors like the design of the biorecognition molecule, the type of transducer, the target's characteristics, and matrix interferences. Sensitivity levels reported range from 1 to 1 × 10⁸ CFU/mL, with detection times spanning 10 min to 8 h. Additionally, the review explores innovative approaches, including biosensors capable of distinguishing between live and dead bacteria, multimodal sensing, and the simultaneous detection of multiple foodborne pathogens — emerging trends in biosensor development. Graphical abstract

This work describes MexSIC, a data acquisition channel designed for Silicon Photomultipliers (SiPMs), composed of a mixed-mode application specific integrated circuit (ASIC) front-end, an FPGA-based processing stage, and a user interface. The ASIC provides a 1-bit sigma-delta modulated (ΣΔ – M ) digital equivalent of the input SiPM current, a flag indicating the start/end of the SiPM pulse, and a clock reference generated by an internal Phase Locked Loop (PLL). At the ASIC input stage, the SiPM current is converted to voltage by means of a 1.57 GHz bandwidth transimpedance amplifier (TIA), the gain of which can be switched between 21 dB and 48 dB, allowing for an input current range between 20 μ A and 20 mA. The generated voltage signal is then fed to a Triggering Unit (TU) implemented to discriminate between desired signals and the spurious ones, and in parallel, also to a second-order ΣΔ modulator providing 6.1 Equivalent Number Of Bits (ENOB). The TU circuit sends a start/end bit flag by comparing the SiPM voltage signal with an 8-bit programmable voltage reference. The ΣΔ was selected to have a single output line instead of using a data bus with many lines, which is important in applications where the number of SiPM channels being read out is very large. The 10 MHz bandwidth ΣΔ – M uses an Over Sampling Ratio (OSR) of 50, and a 1 GHz sampling clock that is generated by a PLL using an off-chip 100 MHz reference. The FPGA receives the ASIC ΣΔ modulated output signal and performs a decimation process by means of a Cascade Integrator Comb (CIC) filter to complete the data recovery. The recovered signal is visualized in a Matlab programmed Graphical User Interface (GUI). The MexSIC ASIC was designed in a 180 nm CMOS standard process using Cadence ^© software, and the processing stage was implemented in a Kintex-7 FPGA.

Pseudorandom number generators (PRNGs) are fundamental components in cryptographic algorithms. The new concept of multi-PRNG introduced in this article consists of a unique generator capable of producing multiple streams of pseudorandom numbers. Multiscroll chaotic systems are known for generating multiple scrolls within a single attractor. With the aforementioned this article introduces a novel field-programmable gate array (FPGA) implementation of a multi-PRNG based on a multiscroll chaotic memristive Hopfield neural network (MHNN). The main contribution of this work is the generation of multiple spatially dependent PRNG streams from a chaotic multiscroll system by dividing the phase space of the attractor into sub-phase spaces. Each scroll in the multiscroll attractor functions as an independent PRNG. This innovative approach to generating multiple PRNGs from multiscroll chaotic systems is unprecedented in the existing literature. The 5-D MHNN chaotic model used in this work employs hyperbolic tangent and sine functions, which were implemented through a hardware-efficient CORDIC approach. Besides, The FPGA implementation to produce the chaotic time series leverages the Euler method with 32-bit fixed-point arithmetic, selected for its simplicity and low resource utilization. Finally, The randomness of the binary sequences produced by the multi-PRNG is rigorously validated using the NIST SP 800-22a and TestU01 suites, confirming their potential for cryptographic applications.

This work presents a method for the characterization of symmetrical test fixtures, based on the creation of a virtual calibration standard of the Reflect type. The standard is generated by taking advantage of the symmetry of the fixture, which produces a virtual short or open circuit along the axis of symmetry. Furthermore, the proposed method eliminates the need to calculate the propagation constant and the characteristic impedance, thus facilitating the de-embedding of the DUT. The accuracy of the proposed method has been evaluated by simulations and by experimental measurements using a vector network analyzer. These results demonstrate the effectiveness of the method even in cases where the test fixtures have reduced symmetry. The effectiveness of the method is evaluated using the coefficient of determination (R²) and the mean absolute error (MAE), which showed an average R² value of more than 0.9900 and an MAE of less than 0.0250 for the S-parameters of the de-embedded DUT. Finally, the results obtained with the TRL and L-L methods are compared for two embedded devices: a hairpin filter and a 70.71 Ω transmission line; this comparison demonstrates the effectiveness of the proposed method.

Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features. We evaluated the method across multiple retinal image datasets, including Retinal-Lesions, Fine-Grained Annotated Diabetic Retinopathy (FGADR), Indian Diabetic Retinopathy Image Dataset (IDRiD), and the Kaggle Diabetic Retinopathy dataset. The proposed method outperformed traditional generative models, such as conditional GANs and PathoGAN, achieving the best performance on key metrics: a Fréchet Inception Distance (FID) of 15.21, a Mean Squared Error (MSE) of 0.002025, and a Structural Similarity Index (SSIM) of 0.89 in the Kaggle dataset. Additionally, expert evaluations revealed that only 56.66% of synthetic images could be distinguished from real ones, demonstrating the high fidelity and clinical relevance of the generated data. These results highlight the effectiveness of our approach in improving medical image classification by generating realistic and diverse synthetic datasets.

Artificial Intelligence (AI) is a relatively new field of science that has recently had a great boom due to the development of technologies derived from scientific advances in its different areas. AI has impacted practically all the economic sectors, including the health sector (medicine) and health care for older people. This chapter presents some fields of Artificial Intelligence that have had a considerable impact on the health care of the older people as well as their applications in some areas related to medical care such as monitoring, therapies and diagnosis among others, with specific examples of works and studies that present AI-based solutions.

It is crucial for organizations to ensure that their business processes are executed accurately and comply with internal policies and requirements. Process mining is a discipline of data science that exploits business process execution data to analyze and improve business processes. It provides a data-driven approach to understanding how processes actually work in practice. Conformance checking is one of the three most relevant process mining tasks. It consists of determining the degree of correspondence or deviation between the expected (or modeled) behavior of a process vs the real one observed and revealed from the historical events recorded in an event log during the execution of each instance of the process. Under a big data scenario, traditional conformance checking methods struggle to analyzing the instances or traces in large event logs, increasing the associated computational cost. In this article, we study and address the conformance-checking task supported by a traces selection approach that uses representative sample data of the event log and thus reduces the processing time and computational cost without losing confidence in the obtained conformance value. As main contributions, we present a novel conformance checking method that (i) takes into account the data dispersion that exists in the event log data using a statistic measure, (ii) determines the size of the representative sample of the event log for the conformance checking task, and (iii) establishes selection criteria of traces based on the dispersion level. The method was validated and evaluated using fitness, precision, generalization, and processing time metrics by experiments on three actual event logs in the health domain and two synthetic event logs. The experimental evaluation and results revealed the effectiveness of our method in coping with the problem of conformance between a process model and its corresponding large event log.

Most microwave sensors establish a relationship between electrical parameters or dielectric properties with the property of interest of a sample using simple linear regression to make prediction. These do not implement the assumptions of linear regression and evaluate their quality in different ways, making fair comparison between regressions impossible. In this paper, a methodology is proposed to evaluate the assumptions. The assumption of anomalies is implemented with standardized and studentized residuals; the assumption of normality, with the Shapiro-Wilk test; the assumption of homoscedasticity, with the Breusch-Pagan test; the assumption of independence, with the Durbin-Watson test; and linearity, with the F test. This methodology includes the evaluation of the quality of the linear regression. The dynamic range is considered, such as the difference between the highest and the lowest value of the property of interest, the sensitivity using ordinary least squares, the resolution with analysis of variance and the accuracy with root mean squared error of cross-validation. The sl-regression-quality package is provided to perform the methodology using Python software. As an example, a resonator sensor is considered to determine the moisture content of meat. This methodology can be used for the fairest comparison between simple linear regressions of microwave sensors.

This article presents a new planar microwave sensor for displacement measurements. The sensor is based on a modified triangular shaped quarter-wavelength resonator that has the advantage of maintaining its linearity throughout the required displacement range due to its reduced ground capacitance when compared to conventional resonators. Designs of three different sensors are shown for displacement ranges of 4mm, 14mm and 24mm. Their figure of merit (FoM) is FoM= 0.5 for the 4mm sensor, 0.7 for the 14mm sensor and 0.97 for the 24mm sensor. Moreover, the mid-range sensor (14mm) was manufactured to operate in a frequency range from 700MHz to 2GHz having an FoM= 0.7. This sensor is operated using a VNA and an alternative reflectometer board for onsite applications. The experimental values show errors of less than 4% when comparing the VNA and the reflectometer results.

Breast thermography may be used for the early detection of breast diseases in women younger than 50 years. Performed breast thermography on a woman in her 20s, revealing an average temperature difference of about 1°C. Ultrasound imaging further identified a simple cyst and enlarged, vascularized lymph nodes in both axillae.

We present examples where expressions for exp⁡(A^+B^) can be derived even though operators (or superoperators) A^ and B^ do not commute in a manner that leads to known factorizations. We apply our factorization to the case of a Lindblad operator modeling single-photon decay and to a binary Glauber–Fock photonic lattice.

White electroluminescence (EL) at room temperature was observed using a metal-insulator-semiconductor (MIS) capacitor with the silicon oxicarbide (SiOxCy) film as an active layer. SiOxCy films were deposited by the hot wire chemical vapor deposition (HW-CVD) system from vinyl silane (VS) precursor. The active film was thermally annealed at 450 °C in nitrogen (N2). Blue-red photoluminescence (PL) was observed from the as-deposited film, while the annealed film displayed a blue-green emission band. The emission was attributed to defects such as weak-oxygen bond (WOB), the neutral oxygen vacancy (NOV), and the non-bridging oxygen hole center (NBOHC). Fourier transform infrared (FTIR) spectroscopy showed an increase in the absorption band related to the rocking mode in Si-O-Si bonds after the thermal annealing. This band could be related to the changes in the PL. White electroluminescence (EL) was observed at voltages greater than 25 V and a current density of × ⁄ on the entire device area. The EL and PL emission mechanisms at 409 nm were related to NOV defects. Based on EL and current-voltage measurements, space charge limited conduction (SCLC) was proposed as the charge transport mechanism. These results highlight that the SiOxCy obtained from vinyl silane is a promising candidate for developing silicon-based white light-emitting devices.

In this work, ZnO thin films were deposited by RF magnetron sputtering at substrate temperatures ranging from 100ºC to 700ºC. The Williamson-Hall's approximation through the uniform stress deformation model (USDM) allowed the estimation of the stress and the size of the wurtzite crystal phase in these films. The sample deposited at 700 ºC exhibited the best crystallinity; then, it was subjected to thermal treatment at 900 ºC and 1000 ºC. This annealing process allowed to observe a relationship between the intensities of (103)/(002) XRD peaks with the visible/UV ratio photoluminescence peaks. This observation allows to estimate that the intensity of non-basal plane (103) is related to the generation of oxygen vacancies (VO) defects in the ZnO films.