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Magneto-optical magnetic field/current sensors are based on the Faraday effect, which involves changing the polarized state of light. Polarimetric methods are therefore used for measuring polarization characteristics. Channeled polarimetry allows polarization information to be obtained from the analysis of the spectral domain. Although this allows the characterization of Faraday materials, the method has not yet been used for detection in magneto-optical sensors. This paper reports experimental results for magnetic field/current detection using the channeled polarimetry method. It is shown that in contrast to other methods, this method allows the detection of the phase shift caused by Faraday rotation alone, making the detection independent of temperature. Although an increase in measurement accuracy is required for practical applications by refining the data processing, the experimental results obtained show that this method offers a new approach to improving the performance of magneto-optical current sensors.

The material of an object is an inherent property that can be perceived through various sensory modalities, yet the integration of multisensory information substantially improves the accuracy of these perceptions. For example, differentiating between a ceramic and a plastic cup with similar visual properties may be difficult when relying solely on visual cues. However, the integration of touch and audio feedback when interacting with these objects can significantly clarify these distinctions. Similarly, combining audio and touch exploration with visual guidance can optimize the sensory examination process. In this study, we introduce a multisensory approach for categorizing object materials by integrating visual, audio, and touch perceptions. The main contribution of this paper is the exploration of a computational model of visual attention that directs the sampling of touch and audio data. We conducted experiments using a subset of 63 household objects from a publicly available dataset, the ObjectFolder dataset. Our findings indicate that incorporating a visual attention model enhances the ability to generalize material classifications to new objects and achieves superior performance compared to a baseline approach, where data are gathered through random interactions with an object's surface.

In physics, we construct idealized mathematical models in order to explain various phenomena which we observe or create in our laboratories. In this article, I recall how sophisticated mathematical models evolved from the concept of a number created thousands of years ago, and I discuss some challenges and open questions in quantum foundations and in the Standard Model. We liberated nuclear energy, landed on the Moon and built 'quantum computers'. Encouraged by these successes, many believe that when we reconcile general relativity with quantum theory we will have the correct theory of everything. Perhaps we should be much humbler. Our perceptions of reality are biased by our senses and by our brain, bending them to meet our priors and expectations. Our abstract mathematical models describe only in an approximate way different layers of physical reality. To describe the motion of a meteorite, we can use a concept of a material point, but the point-like approximation breaks completely when the meteorite hits the Earth. Similarly, thermodynamic, chemical, molecular, atomic, nuclear and elementary particle layers of physical reality are described using specific abstract mathematical models and approximations. In my opinion, the theory of everything does not exist.

Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance in the study of H. pylori outer membrane proteins and their identified receptors. In the present study we assess the way in which outer membrane protein-cell receptor reactions are applicable in establishing a reliable diagnosis. Herein, as well as in other previous studies of ours, we explore the reliability of the binding reaction between the best characterized H. pylori adhesin BabA and its receptor, the blood antigen Le b . For the purpose we developed surface plasmon resonance (SPR) and double resonance long period grating (DR LPG) biosensors based on the BabA-Le b binding reaction for diagnosing H. pylori infection. In SPR detection, the sensitivity was estimated at 3000 CFU/mL-a much higher sensitivity than that of the RUT test. The DR LPG biosensor proved to be superior in terms of accuracy and sensitivity-concentrations as low as 10 2 CFU/mL were detected.

In his article in Science, Nicolas Gisin claimed that quantum correlations emerge from outside space-time. We explainthat they are due to space-time symmetries. This paper is a critical review of metaphysical conclusions found in many recent articles. It advocates the importance of contextuality , Einstein -causality and global symmetries. Bell tests allow only rejecting probabilistic coupling provided by a local hidden variable model, but they do not justify metaphysical speculations about quantum nonlocality and objects which know about each other's state, even when separated by large distances. The violation of Bell inequalities in physics and in cognitive science can be explained using the notion of Bohr- contextuality . If contextual variables, describing varying experimental contexts, are correctly incorporated into a probabilistic model, then the Bell-CHSH inequalities cannot be proven and nonlocal correlations may be explained in an intuitive way. We also elucidate the meaning of statistical independence assumption incorrectly called free choice , measurement independence or no- conspiracy . Since correlation does not imply causation, the violation of statistical independence should be called contextuality ; it does not restrict the experimenter's freedom of choice. Therefore, contrary to what is believed, closing the freedom-of choice loophole does not close the contextuality loophole.

Steel automotive wheel rims are subject to wear and tear, down to the end of their service life. Manufacturers use standard destructive tests to determine the probable lifetime of the car wheel rim. With this approach, to predict the remaining use time, it is necessary to know the initial parameters of the wheel rim, actual mileage, and its use characteristics, which is difficult information to obtain in the real world. Moreover, this work shows that a vehicle's technical condition can affect the rim's remaining service time. This work describes a new method of precise binary identification of the technical condition of steel car wheel rims using the dispersion of damping factors which result from experimental modal analysis. This work also proposes a new method of indicating the approaching end of wheel rim service life with limited parameters: run-out, average of damping factors, and dispersion of damping factors. The proposed procedure requires two sequential examinations of the rim in standard periods related to the average annual mileage of the vehicle. On this basis, it is possible to indicate the approaching end of the life of the steel rims about 10,000 km in advance.

We report on a study of the temperature dependence of the response of a BSO crystal based polarimetric current sensor with spectral interrogation. Two possible interrogation schemes are discussed. The spectral dependence of the optical rotation along the crystal caused by temperature and current changes is investigated, and approximate dependences for the sensitivities to current S I and temperature S T are derived. A mixed term in the response with spectral interrogation is revealed, the elimination of which is achieved by tracking wavelength shifts Δλ 1 and Δλ 2 of two distinct extrema in the polarimetric response. A temperature independent second degree equation for the current changes Δ I as a function of the measured spectral shifts is derived and tested.

Self-employed individuals with cancer face unique challenges that have not been fully explored in previous research. For example, while some studies conducted in Europe have suggested that self-employed workers with cancer experience more adverse health and work-related outcomes compared to salaried workers, the specific manners in which cancer affects the health, work, and businesses of self-employed individuals remain inadequately understood. This lack of understanding represents a crucial gap in the literature, as self-employed individuals make up a significant portion of the workforce in many countries, including Canada. To address this gap, a qualitative interpretive description study was undertaken to explore the experiences of 23 self-employed Canadians diagnosed with cancer from six provinces, with the aim of generating insights into the unique challenges faced by this population. The interviews were conducted in the language chosen by the participants from the two official languages of Canada, namely English and French. Using reflexive thematic analysis, four major themes and twelve subthemes were generated from the participants' shared accounts that reflected the impact of cancer on self-employed Canadians' physical, cognitive, and psychological function, affecting their work ability and their ability to maintain their business and financial well-being. Participants in the study also shared strategies they used to continue working and maintain their business during their cancer experience. This study sheds light on the impact of cancer on self-employed individuals and provides insight into the experiences of self-employed individuals with cancer that can inform the development of interventions to support this population.

One of the first clinical observations related to COVID-19 identified hematological dysfunctions. These were explained by theoretical modeling, which predicted that motifs from SARS-CoV-2 structural proteins could bind to porphyrin. At present, there is very little experimental data that could provide reliable information about possible interactions. The surface plasmon resonance (SPR) method and double resonance long period grating (DR LPG) were used to identify the binding of S/N protein and the receptor bind domain (RBD) to hemoglobin (Hb) and myoglobin (Mb). SPR transducers were functionalized with Hb and Mb, while LPG transducers, were only with Hb. Ligands were deposited by the matrix-assisted laser evaporation (MAPLE) method, which guarantees maximum interaction specificity. The experiments carried out showed S/N protein binding to Hb and Mb and RBD binding to Hb. Apart from that, they demonstrated that chemically-inactivated virus-like particles (VLPs) interact with Hb. The binding activity of S/N- and RBD proteins was assessed. It was found that protein binding fully inhibited heme functionality. The registered N protein binding to Hb/Mb is the first experimental fact that supports theoretical predictions. This fact suggests another function of this protein, not only binding RNA. The lower RBD binding activity reveals that other functional groups of S protein participate in the interaction. The high-affinity binding of these proteins to Hb provides an excellent opportunity for assessing the effectiveness of inhibitors targeting S/N proteins.

Anthropogenic noise from navigation is a major contributor to the disturbance of the acoustic soundscape in underwater environments containing noise-sensitive life forms. While previous studies mostly developed protocols for the empirical determination of noise source levels associated with the world's commercial fleet, this work explores the radiated noise emitted by small recreational vessels that thrive in many coastal waters, such as in the St. Lawrence Estuary beluga population's summer habitat. Hydrophone-based measurements in the Saguenay River (QC, Canada) were carried out during the summers of 2021 and 2022. Shore-based observations identified 45 isolated transits of small, motorized vessels and were able to track their displacement during their passage near the hydrophone. Received noise levels at the hydrophone typically fell below the hearing audiogram of the endangered St. Lawrence Estuary beluga. Monopole source levels at low frequencies (0.1-≲2 kHz) held on average twice the acoustic power compared to their mid-frequency (≳2-30 kHz) counterparts. The speed over ground of recreational vessel showed a positive correlation with the back-propagated monopole source levels. Estimations of the mid-frequency noise levels based on low-frequency measurements should be used moderately.

Taking account of sex and gender in occupational health studies poses statistical challenges. Other sociodemographic variables, such as racialization, class, and age, also affect the relations between workplace exposures and health and interact with sex and gender. Our objective was to perform a critical review of conventional and emerging statistical tools, examining whether each analysis takes account of sociodemographic variables (1) in a way that contributes to identification of critical occupational determinants of health (2) while taking account of relevant population characteristics to reflect intersectional approaches to health and (3) using sample sizes and population characteristics available to researchers. A two-step search was conducted: (1) a scientific watch concerning the statistical tools most commonly used in occupational health over the past 20 years; (2) a screening of the 1980-2022 literature with a focus on emerging tools. Our examination shows that regressions with adjustment for confounders and stratification fail to reveal the sociodemographic mechanisms that interact with occupational health problems, endangering the identification of occupational risks. Multilevel (notably MAIHDA) analyses, decision tree, cluster, and latent analyses are useful methods to consider when seeking to orientate prevention. Researchers should consider methods that adequately reveal the mechanisms connecting sociodemographic variables and occupational health outcomes., Competing Interests: The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Diesel fuel stability can be considered from many points of view, of which the two considered most important are stability in contact with the environment and internal stability. Fuel stability in touch with the environment is often defined as oxidation stability, of which measurement procedures are well developed. The presented paper shows that fuel's internal stability can also be important. The internal stability of diesel fuel with the local use of thermal and ultraviolet radiation (UV) as degradation factors and fluorescence signals as a probe is presented in this paper. We show that the internal degradation of fuel with temperature use differs from that with UV and simultaneous both factors use. Our study shows that using temperature as a degradation factor introduces significant fluorescence fading. Moreover, the fluorescence signal restores significantly later than the sample stabilizes at room temperature. The novelty proposed based on examination is hybrid degradation and an examination cycle that enables the simultaneous use of degradation factors and fluorescence reading. For this purpose, a dedicated measurement setup of signal control and processing was constructed and programmed. The measurement procedure of the data series for specific wavelength enables calculation of signal shifts that allow the internal stability classification of diesel fuel samples in less than 30 min with the cost of a single disposable capillary probe and one polymer plug. Premium and regular fuel examination results show that internal fuel stability can be related to polycyclic aromatic hydrocarbons (PAH) concentrations and can be modified with dedicated additives.

Studies using transcranial magnetic stimulation (TMS) have demonstrated the importance of direction and intensity of the applied current when the primary motor cortex (M1) is targeted. By varying these, it is possible to stimulate different subsets of neural elements, as demonstrated by modulation of motor evoked potentials (MEPs) and motor behaviour. The latter involves premotor areas as well, and among them, the presupplementary motor area (pre-SMA) has recently received significant attention in the study of motor inhibition. It is possible that, similar to M1, different neuronal populations can be activated by varying the direction and intensity of TMS; however, the absence of a direct electrophysiological outcome has limited this investigation. The problem can be solved by quantifying direct cortical responses by means of combined TMS and electroencephalography (TMS-EEG). We investigated the effect of variable coil orientations (0°, 90°, 180° and 270°) and stimulation intensities (100%, 120% and 140% of resting motor threshold) on local mean field potential (LMFP), transcranial evoked potential (TEP) peaks and TMS-related spectral perturbation (TRSP) from pre-SMA stimulation. As a result, early and late LMFP and peaks were larger, with the coil handle pointing posteriorly (0°) and laterally (90°). This was true also for TRSP in the β-γ range, but, surprisingly, θ-α TRSP was larger with the coil pointing at 180°. A 90° orientation activated the right M1, as shown by MEPs elicitation, thus limiting the spatial specificity of the stimulation. These results suggest that coil orientation and stimulation intensity are critical when stimulating the pre-SMA.

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales ( Delphinapterus leucas ) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.

A stacked multi-layer substrate integrated waveguide (SIW) microstrip patch antenna with broadband operating bandwidth and low cross-polarization radiation is provided. A complete study on the propagating element bandwidth and cross polarization level is presented to demonstrate the importance of the design. The proposed antenna includes three stacked printed circuit board (PCB) layers, including one layer for the radiating 2 × 2 rectangular patch elements and two SIW PCB layers for the feeding network. There are two common methods for excitation in cavity-backed patch antennas: probe feeding (PF) and aperture coupling (AC). PF can be used to increase the bandwidth of the antenna. Although this method increases the antenna’s bandwidth, it produces a strong cross-polarized field. The AC method can be used to suppress cross-polarized fields in microstrip patch antennas. As microstrip patch antennas are inherently narrowband, the AC method has little effect on their bandwidth. This paper proposes an antenna that is simultaneously fed by AC and PF. As a result of this innovation, the operating bandwidth of the antenna has increased, and cross-polarization has been reduced. Actually, the combination of probe feeding and aperture coupling schemes leads to achieving a broadband operating bandwidth. The arrangement of radiator elements and cavities implements a mirrored excitation technique while maintaining a low cross-polarization level. In both numerical and experimental solutions, a less than −30 dB cross-polarization level has been achieved for all of the main directions. A fractional impedance bandwidth of 29.8% (10.55−14.25 GHz) for S11 < −10-dB is measured for the proposed array. Simulated and measured results illustrate good agreement. Having features like low cost, light weight, compactness, broadband, integration capabilities, and low cross-polarization level makes the designed antenna suitable for remote-sensing and satellite applications.

Background: Philosophy for children (P4C) was initially developed in the 1970s and served as an educational program to promote critical thinking, caring, creative reasoning and inquiry in the educational environment. Quasi-experimental research on P4C, a school-based approach that aims to develop children's capacity to think by and for themselves, has suggested it could be an interesting intervention to foster greater basic psychological need satisfaction in children in school settings., Objective: The goal of the present study was to evaluate the impact of P4C on basic psychological need satisfaction and mental health in elementary school students., Method: Students from grades one to three ( N = 57) took part in this study and completed pre- and post-intervention questionnaires. A randomized cluster trial with a wait-list control group was implemented to compare the effects of P4C on students' mental health., Results: Analyses of covariance (ANCOVAs) revealed a significant effect of group condition on levels of autonomy and anxiety, after controlling for baseline levels. Participants in the experimental group showed higher scores in autonomy, when compared to participants in the control group, and participants in the experimental group showed lower anxiety scores, when compared to participants in the control group., Conclusion: Overall, results from this study show that P4C may be a promising intervention to foster greater autonomy in elementary school children, while also improving mental health.

Contextuality and entanglement are valuable resources for quantum computing and quantum information. Bell inequalities are used to certify entanglement; thus, it is important to understand why and how they are violated. Quantum mechanics and behavioural sciences teach us that random variables 'measuring' the same content (the answer to the same Yes or No question) may vary, if 'measured' jointly with other random variables. Alice's and BoB's raw data confirm Einsteinian non-signaling, but setting dependent experimental protocols are used to create samples of coupled pairs of distant ±1 outcomes and to estimate correlations. Marginal expectations, estimated using these final samples, depend on distant settings. Therefore, a system of random variables 'measured' in Bell tests is inconsistently connected and it should be analyzed using a Contextuality-by-Default approach, what is done for the first time in this paper. The violation of Bell inequalities and inconsistent connectedness may be explained using a contextual locally causal probabilistic model in which setting dependent variables describing measuring instruments are correctly incorporated. We prove that this model does not restrict experimenters' freedom of choice which is a prerequisite of science. Contextuality seems to be the rule and not an exception; thus, it should be carefully tested.

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture.

Damages of different kinds that can be inflicted to a parked car. Among them, loosening of the car wheel bolts is difficult to detect during normal use of the car and is at the same time very dangerous to the health and life of the driver. Moreover, in patents and publications, only little information is presented about electronic sensors available for activation from inside of the car to inform the driver about the mentioned dangerous situation. Thus, the main aim of this work is the proposition and examination of a sensing device using of a wireless accelerometer head to detect loosening of wheel fixing bolts before ride has been started. The proposed sensing device consists of a wireless accelerometer head, an assembly interface and a receiver unit. The assembly interface between the head and the inner part of the rim enables the correct operation of the system. The data processing algorithm developed for the receiver unit enables the proper detection of the unscrewing of bolts. Moreover, the tested algorithm is resistant to the interference signals generated in the accelerometer head by cars and men passing in close distance.

Population aging is the 21st century's predominant demographic event. The old-age dependency ratio is projected to rise sharply in the next decades. Variables of health-related quality of life can be useful in designing interventions for promoting active aging to prevent dependency and save governments' budgets. This study aims to find a model capable of explaining how psychosocial variables are related to improved quality of life during active aging, and if this relationship varies with age. Structural equation modelling (SEM) was used to examine the relationships among the availability of social resources, memory, depression, and perception of quality of life from three community senior centers in Madrid (Spain) in a sample of 128 older adult volunteers. The results suggest a psychosocial model where the availability of social support improves quality of life and explicit memory, reduces depression in active older adults, and where there are two main elements for understanding quality of life: perception of health and satisfaction. Importantly, age does not modify the interactions between variables, suggesting that their behavior is constant across aging. We concluded that the availability of social resources, understood not only as the people we interact with daily but also other family members, close friends, or institutions that could help in case of an emergency, allows people to avoid isolation and loneliness, increasing satisfaction and well-being in older adults. Professionals and policymakers should promote well-being by incorporating psychosocial variables related to personal satisfaction in the existential project, not only health, functional activity, or a friendly environment. Older adults need to feel that they are not alone, and in this sense, the availability of social resources is key.

Selected optical fiber sensors offer extraordinary sensitivity to changes in external refractive (RI), which make them promising for label-free biosensing. In this work the most sensitive ones, namely long-period gratings working at (DTP-LPG) and micro-cavity in-line Mach-Zehnder interferometers (µIMZI) are discussed for application in bacteria sensing. We describe their working principles and RI sensitivity when operating in water environments, which is as high as 20,000 nm/RIU (Refractive index unit) for DTP-LPGs and 27,000 nm/RIU for µIMZIs. Special attention is paid to the methods to enhance the sensitivity by etching and nano-coatings. While the DTP-LPGs offer a greater interaction length and sensitivity to changes taking place at their surface, the µIMZIs are best suited for investigations of sub-nanoliter and picoliter volumes. The capabilities of both the platforms for bacteria sensing are presented and compared for strains of Escherichia coli , lipopolysaccharide E. coli , outer membrane proteins of E. coli, and Staphylococcus aureus . While DTP-LPGs have been more explored for bacteria detection in 10 2 -10 6 Colony Forming Unit (CFU)/mL for S. aureus and 10 3 -10 9 CFU/mL for E. coli , the µIMZIs reached 10 2 -10 8 CFU/mL for E. coli and have a potential for becoming picoliter bacteria sensors.

This work discusses sensing properties of a long-period grating (LPG) and microcavity in-line Mach-Zehnder interferometer (µIMZI) when both are induced in the same single-mode optical fiber. LPGs were either etched or nanocoated with aluminum oxide (Al 2 O 3 ) to increase its refractive index (RI) sensitivity up to ≈2000 and 9000 nm/RIU, respectively. The µIMZI was machined using a femtosecond laser as a cylindrical cavity (d = 60 μm) in the center of the LPG. In transmission measurements for various RI in the cavity and around the LPG we observed two effects coming from the two independently working sensors. This dual operation had no significant impact on either of the devices in terms of their functional properties, especially in a lower RI range. Moreover, due to the properties of combined sensors two major effects can be distinguished-sensitivity to the RI of the volume and sensitivity to the RI at the surface. Considering also the negligible temperature sensitivity of the µIMZI, it makes the combination of LPG and µIMZI sensors a promising approach to limit cross-sensitivity or tackle simultaneous measurements of multiple effects with high efficiency and reliability.

This article reports results of the life trajectories from 92 Inuit who died by suicide, matched for age and gender with 92 living-controls. A proxy-based procedure and semi-structured interviews with informants were conducted to obtain trajectories of developmental events occurring over the life course for suicide and community-matched controls. Results from this research indicate two different trajectories that differentiate the control-group from the suicide-group throughout the life course. Even though the number of suicide attempts are similar between both groups, the suicide-group had a more important burden of adversity, which seemed to create a cascading effect, leading to suicide.

Since the norovirus is the main cause of acute gastroenteritis all over the world, its fast detection is crucial in medical diagnostics. In this work, a rapid, sensitive, and selective optical fiber biosensor for the detection of norovirus virus-like particles (VLPs) is reported. The sensor is based on highly sensitive long-period fiber gratings (LPFGs) coated with antibodies against the main coat protein of the norovirus. Several modification methods were verified to obtain reliable immobilization of protein receptors on the LPFG surface. We were able to detect 1 ng/mL norovirus VLPs in a 40-min assay in a label-free manner. Thanks to the application of an optical fiber as the sensor, there is a possibility to increase the user's safety by separating the measurement point from the signal processing setup. Moreover, our sensor is small and light, and the proposed assay is straightforward. The designed LPFG-based biosensor could be applied in both fast norovirus detection and in vaccine testing.

Diesel fuel quality can be considered from many different points of view. Fuel producers, fuel consumers, and ecologists have their own ideas. In this paper, a sensor of diesel fuel quality type, and fuel condition that is oriented to the fuel's consumers, is presented. The fuel quality types include premium, standard, and full bio-diesel classes. The fuel conditions include fuel fit for use and fuel degraded classes. The classes of fuel are connected with characteristics of engine operation. The presented sensor uses signal processing of an optoelectronic device monitoring fuel samples that are locally heated to the first step of boiling. Compared to previous works which consider diesel fuel quality sensing with disposable optrodes which use a more complex construction, the sensor now consists only of a capillary probe and advanced signal processing. The signal processing addresses automatic conversion of the data series to form a data pattern, estimates the measurement uncertainty, eliminates outlier data, and determines the fuel quality with an intelligent artificial neural network classifier. The sensor allows the quality classification of different unknown diesel fuel samples in less than a few minutes with the measurement costs of a single disposable capillary probe and two plugs.

Bell-type inequalities are proven using oversimplified probabilistic models and/or counterfactual definiteness (CFD). If setting-dependent variables describing measuring instruments are correctly introduced, none of these inequalities may be proven. In spite of this, a belief in a mysterious quantum nonlocality is not fading. Computer simulations of Bell tests allow people to study the different ways in which the experimental data might have been created. They also allow for the generation of various counterfactual experiments' outcomes, such as repeated or simultaneous measurements performed in different settings on the same "photon-pair", and so forth. They allow for the reinforcing or relaxing of CFD compliance and/or for studying the impact of various "photon identification procedures", mimicking those used in real experiments. Data samples consistent with quantum predictions may be generated by using a specific setting-dependent identification procedure. It reflects the active role of instruments during the measurement process. Each of the setting-dependent data samples are consistent with specific setting-dependent probabilistic models which may not be deduced using non-contextual local realistic or stochastic hidden variables. In this paper, we will be discussing the results of these simulations. Since the data samples are generated in a locally causal way, these simulations provide additional strong arguments for closing the door on quantum nonlocality.

In this work, we report the experimental results on optimizing the optical structure for ambient refractive index measuring with temperature changes monitoring. The presented optical structure is based on a dual-resonance long-period grating embedded inside a fiber loop mirror, where the long-period grating acts as the head of the refractive-index sensor, whereas the section of polarization maintaining fiber in the loop mirror ensures suitable temperature sensing. The optimization process was comprised of tuning the resonance and interferometric peaks by changing the state of polarization of propagating beams. Experimental results establish that the response of the proposed sensor structure is linear and goes in opposite directions: an increase in the ambient refractive index reduces the signal response, whereas a temperature increase produces an increased response. This enables us to distinguish between the signals from changes in the refractive index and temperature. Due to the filtering properties of the interferometric structure, it is possible to monitor variation in these physical parameters by observing optical power changes instead of wavelength shifts. Hence, the refractive index sensitivity has been established up to 2375.8 dB/RIU in the narrow RI range (1.333⁻1.341 RIU) and temperature sensitivities up to 1.1 dBm/°C in the range of 23⁻41 °C. The proposed sensor is dedicated to advanced chemical and biological sensor applications.

Epidemiological data shows an alarming prevalence of suicide in Aboriginal populations around the world. In Canada, the highest rates are found in Inuit communities. In this article, we present the findings of a secondary analysis conducted with data previously collected as part of a larger study of psychological autopsies conducted in Nunavut, Canada. The objective of this secondary analysis was to identify protective factors in the Inuit population of Nunavut by comparing people who died by suicide, people from the general population who attempted suicide, and people from the general population who never attempted suicide. This case-control study included 90 participants, with 30 participants in each group who were paired by birth date, sex, and community. Content analysis was first conducted on the clinical vignettes from the initial study in order to codify the presence of protective variables. Then, inferential analyses were conducted to highlight differences between each group in regards to protection. Findings demonstrated that (a) people with no suicide attempt have more protective variables throughout their lifespan than people who died by suicide and those with suicide attempts within the environmental, social, and individual dimensions; (b) people with suicide attempts significantly differ from the two other groups in regards to the use of services; and (c) protective factors that stem from the environmental dimension show the greatest difference between the three groups, being significantly more present in the group with no suicide attempt. Considering these findings, interventions could focus on enhancing environmental stability in Inuit communities as a suicide prevention strategy., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset., Competing Interests: The authors declare no conflict of interest.

The realistic representation of deformations is still an active area of research, especially for deformable objects whose behavior cannot be simply described in terms of elasticity parameters. This paper proposes a data-driven neural-network-based approach for capturing implicitly and predicting the deformations of an object subject to external forces. Visual data, in the form of 3D point clouds gathered by a Kinect sensor, is collected over an object while forces are exerted by means of the probing tip of a force-torque sensor. A novel approach based on neural gas fitting is proposed to describe the particularities of a deformation over the selectively simplified 3D surface of the object, without requiring knowledge of the object material. An alignment procedure, a distance-based clustering, and inspiration from stratified sampling support this process. The resulting representation is denser in the region of the deformation (an average of 96.6% perceptual similarity with the collected data in the deformed area), while still preserving the object's overall shape (86% similarity over the entire surface) and only using on average of 40% of the number of vertices in the mesh. A series of feedforward neural networks is then trained to predict the mapping between the force parameters characterizing the interaction with the object and the change in the object shape, as captured by the fitted neural gas nodes. This series of networks allows for the prediction of the deformation of an object when subject to unknown interactions.

The paper presents a novel pressure sensor based on a silicon nitride (SiNx) nanocoated long-period grating (LPG). The high-temperature, radio-frequency plasma-enhanced chemical-vapor-deposited (RF PECVD) SiNx nanocoating was applied to tune the sensitivity of the LPG to the external refractive index. The technique allows for deposition of good quality, hard and wear-resistant nanofilms as required for optical sensors. Thanks to the SiNx nanocoating it is possible to overcome a limitation of working in the external-refractive-index range, which for a bare fiber cannot be close to that of the cladding. The nanocoated LPG-based sensing structure we developed is functional in high-refractive-index liquids (nD>1.46) such as oil or gasoline, with pressure sensitivity as high as when water is used as a working liquid. The nanocoating developed for this experiment not only has the highest refractive index ever achieved in LPGs (n>2.2 at λ=1,550 nm), but is also the thinnest (<100 nm) able to tune the external-refractive-index sensitivity of the gratings. To the best of our knowledge, this is the first time a nanocoating has been applied on LPGs that is able to simultaneously tune the refractive-index sensitivity and to enable measurements of other parameters.