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Background: The need for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG (SMBG) have driven the development of non-invasive approaches. Methods: This prospective study aimed to investigate the accuracy of glucose level calculation using the near-infrared spectroscopy (NIRS) technology Glucube® system. People with Type 1 diabetes, Type 2 diabetes, prediabetes, and normal glucose metabolism were included. Over one week, individuals performed glucose measurements with the Glucube® system and capillary blood fingersticks with a standard glucometer (Ascensia Contour® Next). To assess the impact of the improvement in dexterity, the accuracy variables were compared with the point-of-care (POC) glucometer Accu-Chek® Inform II in a one-week sub-study. Results: Overall, 105 subjects (mean age 53.8 ± 13.8 years, 50.5% female) participated, resulting in 1914 paired glucose measurements between 49 and 331 mg/dL. Total mean absolute relative difference (MARD) was 20.3%, MARD for values >100 mg/dL was 18.3%, and mean absolute deviation (MAD) for values p = 0.068). The percentages within Zone A were 51.6 vs. 61.2%, Zone B 46.8 vs. 33.9%, and Zone C 1.6 vs. 4.8%, and the sum of Parkes Zones A+B was 98.4 vs. 95.2% (p = 0.311) for day 1 and day 8, respectively. Conclusions: Glucube® is a novel non-invasive system based on NIRS technology for monitoring blood glucose levels. Its promising capabilities support further research.

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Background: A study to assess the glucose levels of people with type 1 diabetes (T1D) overnight, based on the insulin type and timing. Methods: A real-world, retrospective study of T1D, using multiple daily insulin injections. Continuous glucose monitoring and insulin injection data were collected for ten hours after dinner using the Insulclock® connected cap. Meal events were identified using the ROC detection methodology. The timing of the rapid insulin, second injections, and the type of insulin analogs used, were evaluated. Results: The nocturnal profiles (n = 775, 49 subjects) were analyzed. A higher glucose AUC of over 180 mg/dL was observed in subjects with delayed injections (number; %; mg/dL × h): −45–15 min (n = 136; 17.5%, 175.9 ± 271.0); −15–0 min (n = 231; 29.8%, 164.0 ± 2 37.1); 0 + 45 min (n = 408; 52.6%, 203.6 ± 260.9), (p = 0.049). The use of ultrarapid insulin (FiAsp®) (URI) vs. rapid insulin (RI) analogs was associated with less hypoglycemia events (7.1 vs. 13.6%; p = 0.005) and TBR70 (1.7 ± 6.9 vs. 4.6 ± 13.9%; p = 0.003). Users of glargine U300 vs. degludec had a higher TIR (70.7 vs. 58.5%) (adjusted R-squared: 0.22, p < 0.001). The use of a correction injection (n = 144, 18.6%) was associated with a higher number of hypoglycemia events (18.1 vs. 9.5%; p = 0.003), TBR70 (5.5 ± 14.2 vs. 3.0 ± 11.1%; p = 0.003), a glucose AUC of over 180 mg/dL (226.1 ± 257.8 vs. 178.0 ± 255.3 mg/dL × h; p = 0.001), and a lower TIR (56.0 ± 27.4 vs. 62.7 ± 29.6 mg/dL × h; p = 0.004). Conclusion: The dinner rapid insulin timing, insulin type, and the use of correction injections affect the nocturnal glucose profile in T1D.

Pig feeding prior to the extensive fattening phase might affect the final lipid profile and product quality. This study evaluates how maternal supplementation with vitamin E (VITE) (100 mg/kg), hydroxytyrosol (HXT) (1.5 mg/kg), or combined administration (VE + HXT) affects the piglet’s plasma and tissues’ fatty acid profiles and lipid stability according to the sow’s parity number (PN), as well as the possible changes to the lipid profile after extensive feeding. The sows’ PN affected the total fatty acid profile of plasma, muscle, and liver of piglets, with lower Δ-9 and Δ-6 desaturase indices but higher Δ-5 in those from primiparous (P) than multiparous (M) sows. Dietary VITE was more effective at decreasing C16:0 and saturated fatty acids in the muscle of piglets born from M than P sows, and modified the liver phospholipids in a different way. Sows’ supplementation with HXT increased C18:2n-6 in triglycerides and polyunsaturated fatty acids (PUFA) in muscle phospholipids. In the liver, HXT supplementation also increased free-PUFA and free-n-3 fatty acids. However, lipid oxidation of piglets’ tissues was not affected by the antioxidant supplementation, and it was higher in the livers of piglets born from M sows. The fatty acid profile in the muscle of pigs after extensive feeding was not affected by the PN, but it was by the sows’ antioxidant supplementation, with positive effects on quality by both compounds.

This article introduces a control architecture designed for the development of Hybrid Remotely Operated Underwater Vehicles. The term ”Hybrid” characterizes Remotely Operated systems capable of autonomously executing specific operations. The presented architecture maintains teleoperation capabilities while enabling two fully autonomous applications. The approach emphasizes the implementation of reactive navigation by exclusively utilizing data from a Mechanical Scanned Imaging Sonar for control decisions. This mandates the control system to solely react to data derived from the vehicle’s environment, without considering other positioning information or state estimation. The study involves transforming a small-scale commercial Remotely Operated Underwater Vehicle into a hybrid system without structural modifications, and details the development of an intermediate Operational Control Layer responsible for sensor data processing and task execution control. Two practical applications, inspired by tasks common in natural or open-water aquaculture farms, are explored: one for conducting transects, facilitating monitoring and maintenance operations, and another for navigating toward an object for inspection purposes. Experimental results validate the feasibility and effectiveness of the authors’ hypotheses. This approach expands the potential applications of underwater vehicles and facilitates the development of Hybrid Remotely Operated Underwater Vehicles, enabling the execution of autonomous reactive tasks.

Performance-based seismic codes ensure proper inelastic behaviour of reinforced concrete frames through capacity design, among others. This strategy relies not only on avoiding brittle failures and providing ductility to plastic hinges but also in their distribution within the frame aimed at a greater number of storeys involved in the eventual collapse mechanism. Although codes are generally in agreement to some basic principles in order to ensure capacity design, they show some discrepancies regarding the specific strategies. In this paper, capacity design provisions proposed by some European current codes—Eurocode 8, Italian NTC, and Spanish NCSE-02—are compared, and their effectiveness is discussed. The alternative formulation proposed by Italian code for “strong column–weak beam” turns out to be not suitable under specific circumstances, such as with large gravity loads or significant cantilever deformation in lower storeys. Regarding the value of axial load in columns to be considered for the calculation of shear and moment capacities, provisions in the three codes could eventually cause unconservative design for perimeter columns. The Spanish whole set of provisions is proved to not be effective due to their different fundamentals—they are based on overstrength instead of capacity. For all the three cases, some alternative procedures are suggested in this work.

Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human–computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain–computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.

Current formulations proposed by Eurocode 8 part 3 for the inelastic deformations of existing reinforced concrete members are assessed separately for wide beams (WB) and conventional deep beams (DB). The current approach, based on a large experimental database of members, predicts larger ultimate chord rotation but lower chord rotation ductility for WB rather than for DB despite the similar curvature ductility, due to lower plastic hinge lengths in WB. However, if the data are disaggregated into DB and WB, predicted chord rotations are consistently conservative for DB and not conservative for WB if compared with experimental values, especially at ultimate deformation. Thus, plastic hinge length may be even greater for DB in comparison to WB. Therefore, some feasible corrections of the formulations for chord rotations are proposed, in order to reduce the bias and thus increase the robustness of the model for cross-section shape variability.

Since the discovery of antibodies by Emil Von Behring and Shibasaburo Kitasato during the 19th century, their potential for use as biotechnological reagents has been exploited in different fields, such as basic and applied research, diagnosis, and the treatment of multiple diseases. Antibodies are relatively easy to obtain from any species with an adaptive immune system, but birds are animals characterized by relatively easy care and maintenance. In addition, the antibodies they produce can be purified from the egg yolk, allowing a system for obtaining them without performing invasive practices, which favors the three “rs” of animal care in experimentation, i.e., replacing, reducing, and refining. In this work, we carry out a brief descriptive review of the most outstanding characteristics of so-called “IgY technology” and the use of IgY antibodies from birds for basic experimentation, diagnosis, and treatment of human beings and animals.

Helicobacter pylori (H. pylori) has been proposed as the foremost risk factor for the development of gastric cancer. We found that H. pylori express the enzyme glucose-6-phosphate dehydrogenase (HpG6PD), which participates in glucose metabolism via the pentose phosphate pathway. Thus, we hypothesized that if the biochemical and physicochemical characteristics of HpG6PD contrast with the host G6PD (human G6PD, HsG6PD), HpG6PD becomes a potential target for novel drugs against H. pylori. In this work, we characterized the biochemical properties of the HpG6PD from the H.pylori strain 29CaP and expressed the active recombinant protein, to analyze its steady-state kinetics, thermostability, and biophysical aspects. In addition, we analyzed the HpG6PD in silico structural properties to compare them with those of the HsG6PD. The optimal pH for enzyme activity was 7.5, with a T1/2 of 46.6 °C, at an optimum stability temperature of 37 °C. The apparent Km values calculated for G6P and NADP+ were 75.0 and 12.8 µM, respectively. G6P does not protect HpG6PD from trypsin digestion, but NADP+ does protect the enzyme from trypsin and guanidine hydrochloride (Gdn-HCl). The biochemical characterization of HpG6PD contributes to knowledge regarding H. pylori metabolism and opens up the possibility of using this enzyme as a potential target for specific and efficient treatment against this pathogen; structural alignment indicates that the three-dimensional (3D) homodimer model of the G6PD protein from H. pylori is different from the 3D G6PD of Homo sapiens.

Genetic kidney diseases (GKDs) are a group of rare diseases, affecting approximately about 60 to 80 per 100,000 individuals, for which there is currently no treatment that can cure them (in many cases). GKDs usually leads to early-onset chronic kidney disease, which results in patients having to undergo dialysis or kidney transplant. Here, we briefly describe genetic causes and phenotypic effects of six GKDs representative of different ranges of prevalence and renal involvement (ciliopathy, glomerulopathy, and tubulopathy). One of the shared characteristics of GKDs is that most of them are monogenic. This characteristic makes it possible to use site-specific nuclease systems to edit the genes that cause GKDs and generate in vitro and in vivo models that reflect the genetic abnormalities of GKDs. We describe and compare these site-specific nuclease systems (zinc finger nucleases (ZFNs), transcription activator-like effect nucleases (TALENs) and regularly clustered short palindromic repeat-associated protein (CRISPR-Cas9)) and review how these systems have allowed the generation of cellular and animal GKDs models and how they have contributed to shed light on many still unknown fields in GKDs. We also indicate the main obstacles limiting the application of these systems in a more efficient way. The information provided here will be useful to gain an accurate understanding of the technological advances in the field of genome editing for GKDs, as well as to serve as a guide for the selection of both the genome editing tool and the gene delivery method most suitable for the successful development of GKDs models.

Trichomoniasis is a sexually transmitted disease with a high incidence worldwide, affecting 270 million people. Despite the existence of a catalog of available drugs to combat this infection, their extensive use promotes the appearance of resistant Trichomonas vaginalis (T. vaginalis), and some side effects in treated people, which are reasons why it is necessary to find new alternatives to combat this infection. In this study, we investigated the impact of an in-house library comprising 55 compounds on the activity of the fused T. vaginalis G6PD::6PGL (TvG6PD::6PGL) protein, a protein mediating the first reaction step of the pentose phosphate pathway (PPP), a crucial pathway involved in the parasite’s energy production. We found four compounds: JMM-3, CNZ-3, CNZ-17, and MCC-7, which inhibited the TvG6PD::6PGL protein by more than 50%. Furthermore, we determined the IC50, the inactivation constants, and the type of inhibition. Our results showed that these inhibitors induced catalytic function loss of the TvG6PD::6PGL enzyme by altering its secondary and tertiary structures. Finally, molecular docking was performed for the best inhibitors, JMM-3 and MCC-7. All our findings demonstrate the potential role of these selected hit compounds as TvG6PD::6PGL enzyme selective inhibitors.

Giardia lamblia is a single-celled eukaryotic parasite with a small genome and is considered an early divergent eukaryote. The pentose phosphate pathway (PPP) plays an essential role in the oxidative stress defense of the parasite and the production of ribose-5-phosphate. In this parasite, the glucose-6-phosphate dehydrogenase (G6PD) is fused with the 6-phosphogluconolactonase (6PGL) enzyme, generating the enzyme named G6PD::6PGL that catalyzes the first two steps of the PPP. Here, we report that the G6PD::6PGL is a bifunctional enzyme with two catalytically active sites. We performed the kinetic characterization of both domains in the fused G6PD::6PGL enzyme, as well as the individual cloned G6PD. The results suggest that the catalytic activity of G6PD and 6PGL domains in the G6PD::6PGL enzyme are more efficient than the individual proteins. Additionally, using enzymatic and mass spectrometry assays, we found that the final metabolites of the catalytic reaction of the G6PD::6PGL are 6-phosphoglucono-δ-lactone and 6-phosphogluconate. Finally, we propose the reaction mechanism in which the G6PD domain performs the catalysis, releasing 6-phosphoglucono-δ-lactone to the reaction medium. Then, this metabolite binds to the 6PGL domain catalyzing the hydrolysis reaction and generating 6-phosphogluconate. The structural difference between the G. lamblia fused enzyme G6PD::6PGL with the human G6PD indicate that the G6PD::6PGL is a potential drug target for the rational synthesis of novels anti-Giardia drugs.

Helicobacter pylori (H. pylori) is a pathogen that can remain in the stomach of an infected person for their entire life. As a result, this leads to the development of severe gastric diseases such as gastric cancer. In addition, current therapies have several problems including antibiotics resistance. Therefore, new practical options to eliminate this bacterium, and its induced affections, are required to avoid morbidity and mortality worldwide. One strategy in the search for new drugs is to detect compounds that inhibit a limiting step in a central metabolic pathway of the pathogen of interest. In this work, we tested 55 compounds to gain insights into their possible use as new inhibitory drugs of H. pylori glucose-6-phosphate dehydrogenase (HpG6PD) activity. The compounds YGC-1; MGD-1, MGD-2; TDA-1; and JMM-3 with their respective scaffold 1,3-thiazolidine-2,4-dione; 1H-benzimidazole; 1,3-benzoxazole, morpholine, and biphenylcarbonitrile showed the best inhibitory activity (IC50 = 310, 465, 340, 204 and 304 μM, respectively). We then modeled the HpG6PD protein by homology modeling to conduct an in silico study of the chemical compounds and discovers its possible interactions with the HpG6PD enzyme. We found that compounds can be internalized at the NADP+ catalytic binding site. Hence, they probably exert a competitive inhibitory effect with NADP+ and a non-competitive or uncompetitive effect with G6P, that of the compounds binding far from the enzyme’s active site. Based on these findings, the tested compounds inhibiting HpG6PD represent promising novel drug candidates against H. pylori.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

Audit reports represent the only information stakeholders have about conducted audits and they are a key instrument used in economic and financial decisions. Improving audit reports should be a priority of regulators and auditors. The authors solicited perceptions from 212 experienced auditors and financial report users about the value of audit reports and ways to improve their format and content. An analysis of the responses suggests that adding information on audits (such as auditor’s responsibility about fraud) and on annual accounts and client’s information systems, without significant changes in the format, would improve the decision usefulness of audit reports. The growing sophistication of markets and reporting standards requires new information in audit reports, such as auditors’ conclusions about management’s estimates in annual accounts. The study is useful to regulators, auditors’ corporations, academics, and users and contributes to the current audit literature by providing evidence on consensus between auditors and users with regard to the format and content of audit reports.

A laboratory-scale analysis using coal from an underground mine was carried out, emulating a mixture from the gob area in an actual mine, consisting of waste, coal, and free space for the flow of air. Experimental tests and computational fluid dynamics modelling were done to define and verify the behavior of the collapsed region in a time-dependent analysis. In addition, the characteristics of coal were defined, regarding the self-combustion, combustion rate, and pollutants generated in each stage of the fire. The results achieved are useful for determining the behavior of the collapsed area in full-scale conditions and to provide valuable information to study different scenarios of a potential fire in a real sublevel coal mine regarding how the heat is spread in the gob and how pollutants are generated.

A fire has been analyzed in a real underground coal mine, using a sublevel method, during an entire year. The study was focused on the collapsed area, reproducing a real mixture formed by coal, waste, and air gap. The analysis was done by means of an experimental analysis, a computational fuid dynamic model (CFD), and simulations using a mine ventilation software. Three scenarios were determined and studied regarding their influence on the evolution of the fire: (a) development of the fire without taking any action, (b) sealing off the affected areas, and (c) sealing and reducing the ventilation in the affected area and surrounding drifts. The study revealed the behavior of the fire in a real mine and the effectiveness of the main fire-fighting measures over time, verifying that none of the measures taken could eliminate the fire-induced in the collapsed area.

Fires in underground spaces are especially relevant due to their potential mortality. However, there is not much research in real-scale spaces done so far. In this study, several fire scenarios were analyzed in an underground drift, taking into account the main environmental variables: airflow, temperature, oxygen, and pollutants. The behavior before and after the fire load was determined, as well as the evolution of the fire over time throughout the drift and its cross-section, finding important trends of the fire based on the airflow–fuel load ratio. Furthermore, the five most representative scenarios were modeled using the fire dynamics simulator (FDS). Results obtained in the simulations, with the adjusted parameters, display a good correlation between simulated and experimental values, being able to extrapolate these values to know the performance of potential fires in other underground spaces or mines. The outcomes could also be a very useful tool to study the effectiveness of possible emergency measures or the potential impact of a fire in this type of environments.

Many civilian applications of commercial unmanned aircraft are being planned to operate in the years ahead. Several countries have developed their own framework to design the operation of unmanned aircraft and the different services that demand safe operation. This paper focuses on the European framework denoted as U-space which concludes with the joint integration of manned and unmanned aircraft in the airspace. U-space is a set of novel services and specific procedures designed to provide safe and efficient access into the airspace to the airspace users. U-space constitutes a management system to organise unmanned operations and provides relevant information to drone operators as well as manned aircraft, air navigation service providers and authorities. The understanding of associated hazards and risks to unmanned aircraft is a critical issue for their operation in complex and non-segregated airspaces. The safety assessment developed herein is crucial to identify safety indicators for U-space. In addition, the identification of safety indicators was used to identify gaps in U-spaces services that are not correctly covered by the U-space framework. Particularly, several safety indicators are identified that currently U-space services do not consider and can imply an increase in the operational risk of unmanned operations.

In this paper, We present how we combined visualization and machine learning techniques to provide an analytic tool for web log data.We designed a visualization where advertisers can observe the visits to their different pages on a site, common web analytic measures and individual user navigation on the site. In this visualization, the users can get insights of the data by looking at key elements of the graph. Additionally, we applied pattern mining techniques to observe common trends in user segments of interest.

A new digital countermeasure against attacks related to the clock frequency is presented. This countermeasure, known as frequency sensor, consists of a local oscillator, a transition detector, a measurement element and an output block. The countermeasure has been designed using a full-custom technique implemented in an Application-Specific Integrated Circuit (ASIC), and the implementation has been verified and characterized with an integrated design using a 0.35 mm standard Complementary Metal Oxide Semiconductor (CMOS) technology (Very Large Scale Implementation—VLSI implementation). The proposed solution is configurable in resolution time and allowed range of period, achieving a minimum resolution time of only 1.91 ns and an initialization time of 5.84 ns. The proposed VLSI implementation shows better results than other solutions, such as digital ones based on semi-custom techniques and analog ones based on band pass filters, all design parameters considered. Finally, a counter has been used to verify the good performance of the countermeasure in avoiding the success of an attack.

This paper presents the design of a wireless flood sensor to detect the presence of water on home floors, providing early warning of water leaks. A wireless sensor network has been deployed to gather the measurements from the sensor nodes. A control central coordinates the network and processes the data. Users can remotely inquire for the presence of water, status of the batteries for a specific node, the type of liquid and information about its functionality and alarms, thanks to a proprietary software application. The alerts are also communicated to the user within the home through an audible siren. The designed device is optimized in terms of costs, ease of deployment and maintenance, thus making it widely acceptable to end users.

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance.

In this study, the amount of phenol adsorbed between 0 and 10 mmol∙L−1 during an immersion calorimetric experiment is compared with the adsorption that takes place once phenol adsorption reaches the solid–solution equilibrium. The activated carbons used were prepared by impregnation of one obtained from coconut shell with solutions of nitric and phosphoric acid, nitric acid with subsequent reflux in ammonium hydroxide solution, and carbonisation in a nitrogen atmosphere at 1073, 1173, and 1273 K. The phenol/activated carbon interactions during the calorimetric experiment and in equilibrium were studied using a modified Langmuir model. The values of the BET surface area for the samples were between 469 and 1113 m2∙g−1, micropore volumes were between 0.18 and 0.43 cm3∙g−1, and the distribution of pore size was between 0.3 and 1.4 nm. The interactions between water and activated carbons determined by immersion enthalpies were between −11.0 and −24.8 J∙g−1, which showed that the treatments modified the surface chemistry of activated carbon. The results show that the presence of nitrogen as a different heteroatom to oxygen on the activated carbon surface favors the phenol adsorption rate, and this process is 97% complete during the calorimetric experiment, indicating that it is an enthalphy-driven processes.

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

This paper presents the design of a wireless sensor network particularly designed for remote monitoring and control of industrial parameters. The article describes the network components, protocol and sensor deployment, aimed to accomplish industrial constraint and to assure reliability and low power consumption. A particular case of study is presented. The system consists of a base station, gas sensing nodes, a tree-based routing scheme for the wireless sensor nodes and a real-time monitoring application that operates from a remote computer and a mobile phone. The system assures that the industrial safety quality and the measurement and monitoring system achieves an efficient industrial monitoring operations. The robustness of the developed system and the security in the communications have been guaranteed both in hardware and software level. The system is flexible and can be adapted to different environments. The testing of the system confirms the feasibility of the proposed implementation and validates the functional requirements of the developed devices, the networking solution and the power consumption management.

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology.

Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines.

This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures of merit were proposed: System Dynamic Range and System Tuning Envelope. To examine the conflict detection’s performance variability under different uncertainty levels and predictor types, simple multi-horizon trajectory predictors trained with two machine learning techniques of different characteristics are assessed at various look-ahead times: extreme gradient boosting with a discrete nature and a multi-layer perceptron regressor with a continuous nature. The results highlight the interdependence between the performances of the trajectory predictor and the conflict detector, and the quantification of this relationship can be represented through a sigmoid function. In addition, the two proposed figures of merit are effective for selecting suitable operating points in an adaptive conflict detector, based on dynamic thresholds and the performance requirements necessary for the trajectory predictors to achieve the expected detection performance at different look-ahead time.