Input Spectrum Research Articles

Distribution and sources of fatty acids in surface sediments of mangrove ecosystems in the Northern Kerala Coast, India

Fatty acids and bulk geochemical proxies were employed to understand the sources and transformation of sedimentary organic matter from surface sediments of major five mangrove systems in the northern Kerala coast. Texture, tidal rhythm and the proximity to the south eastern Arabian Sea were the main factors influencing distribution and accumulation of the organic matter in these systems. A total of 118 fatty acids were quantified and grouped into short-chain (SCFA), long-chain (LCFA), monounsaturated (MUFA), polyunsaturated (PUFA) and branched-chain (BrFA) fatty acids, hydroxy fatty acids, cyclic fatty acids, and dicarboxylic acids. The distribution of fatty acid groups was in the order SCFA > BrFA > MUFA > LCFA > PUFA. Total fatty acids (TFA) concentration exhibited profound seasonal variations and ranged from 6.25 to 580.49 μg/g (pre-monsoon > post-monsoon > monsoon except in Kadalundi which follows the reverse order). The sedimentary fatty acids in the study region reveal a wide spectrum of inputs from terrestrial, planktonic (preponderance of diatom followed by dinoflagellates along with brown algae, cyanobacteria, red algae) and bacterial sources (e.g., Desulfobulbus-type bacteria, sulfur-reducing bacteria, Sulfur-oxidizing bacteria). Significant concentrations of bacterial fatty acids suggest the functioning of an effective microbial loop and various biogeochemical pathways operating in these sediments (sulfate reduction, iron and manganese oxide reduction, etc.).

Leveraging Pathway-Interference to Overcome Drug-Resistance in Acute Lymphoblastic Leukemia

Background: The concept of multi-step carcinogenesis (Fearon and Vogelstein 1990) suggests that acquisition of mutations in addition to an existing set of mutations invariably accelerates tumor-progression. In colorectal cancer and many other cancer types, activation of multiple distinct oncogenic pathways is required for the development of invasive cancer. Here, we examined this paradigm for genetic lesions in B-ALL and 13 other cancer types.Bioinformatic approaches: To broadly study how oncogenic drivers across multiple signaling pathways interact, we developed a bioinformatic platform to map interactions between genetic lesions that cause oncogenic activation of eight oncogenic pathways, including PI3K, STAT5, NF-κB, Hippo, Notch, WNT, RAS-ERK and TGFβ-Smad pathways. Plotting of interaction scores between 56 pathway pairs in a matrix for 14 cancer types revealed that 12 of the 14 cancer types showed a pattern of globally synergistic pathway interactions, consistent with the Fearon and Vogelstein model of cooperation of multiple pathways to drive malignant transformation. Strikingly, B-ALL and gliomas showed the opposite behavior with largely antagonistic pathway interactions.Results. Unlike the vast majority of cancer types, B-ALL and gliomas are driven by one principal oncogenic pathway at a time. While the reasons for negative pathway interactions in glioma are unknown, we focused on functional analyses on pathway interference patterns in 1,148 cases of B-ALL. Genetic lesions leading to STAT5- or ERK-pathway activation are frequently found in B-ALL. Interestingly, activating lesions of both pathways co-occurred in only 3% of the cases studied, suggesting that co-activation of STAT5- and ERK-occurs much less frequently than expected by chance (odds ratio 0.13, P=2e-16, Figure, left). Unbiased interaction mapping analyses of mutational co-occurrence indicated strong negative selection for dual activation of both STAT5- and ERK-pathways (Figure, middle). Importantly this inter-pathway aversion is much stronger than intra-pathway effects, reflecting incompatibility rather than redundancy. Even in rare cases of co-occurrence in the same sample, single-cell mutation and phosphoprotein analyses revealed that STAT5- and ERK-activating mutations were mutually exclusive and reflected two competing clones.STAT5- and ERK-pathways engage conflicting transcriptional and biochemical programs, resulting in “friction”, when both pathways are concurrently activated. In agreement with pathway interference, we demonstrated that Cre-mediated deletion of divergent pathway components - Erk2 fl/fl in a STAT5-driven model of B-ALL and Stat5 fl/fl in an ERK-driven B-ALL model - dramatically accelerated initiation of fatal leukemia in vivo. While Cre-mediated deletion of divergent pathway components precipitated leukemia-initiation, these findings suggest that reactivation of divergent signaling pathways represents a powerful barrier against malignant transformation.Interestingly, our preclinical studies suggested that pharmacological reactivation of divergent (suppressed) pathways can be leveraged for therapeutic benefit: The DUSP6 small molecule inhibitor BCI-215 functions as powerful activator of ERK and suppresses STAT5-phosphorylation, i.e. the principal pathway in STAT5-driven B-ALL (Figure, right). Likewise, DPH, a small molecule STAT5-agonist interferes with ERK-phosphorylation, the principal oncogenic driver in RAS-pathway B-ALL (Figure, right). Both BCI-215 and DPH significantly prolonged overall survival of NSG mice transplanted with refractory STAT5- and ERK-driven B-ALL PDX, respectively.Conclusions: We propose that a diverse spectrum of signaling input reflects interactions of normal cells with their environment, while convergence on one centralized pathway is a hallmark of cancer. Tracking early stages of leukemia-initiation, we identified convergence on one principal oncogenic driver and inactivation of diverging pathways as an early critical step. Pharmacological reactivation of divergent signaling pathways to subvert transformation was achievable by STAT5- and ERK-agonists. Proof-of-concept studies in patient-derived B-ALL cells revealed that pharmacological reactivation of suppressed divergent circuits can be leveraged as a previously unrecognized strategy to overcome drug-resistance. [Display omitted] DisclosuresIzraeli: Roche: Consultancy, Speakers Bureau; Bayer: Speakers Bureau; sightDx: Consultancy. Weinstock: ASELL: Consultancy; SecuraBio: Consultancy; Bantam: Consultancy; AstraZeneca: Consultancy; Abcuro: Research Funding; Verastem: Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Travera: Other: Founder/Equity; Ajax: Other: Founder/Equity.

Energy management in hybrid organic-silicon nanostructured solar cells by downshifting using CdZnS/ZnS and CdZnSe/ZnS quantum dots

Management of the solar spectrum by downshifting has been a key theme in broadband solar cell research over the last few years. The absorption of high-energy photons that exceed the bandgap edge of Si causes thermalization of charge carriers, which is a major loss mechanism by which collected energy is underutilized in silicon solar cells. The spectral response of Si also falls in the UV region. Progress in overcoming these effects achieved by modifying the input spectrum will dramatically enhance power conversion efficiency (PCE). Herein we report the application of highly luminescent CdZnS/ZnS and CdZnSe/ZnS quantum dots (QDs) as downshifting materials for nanostructured silicon. The applied QDs show major potential for shifting the high energy in the ultraviolet region to low energy, which is then transferred to the underlying silicon nanostructures via radiative and nonradiative energy transfer. This energy transfer dramatically improves the cell efficiency from 10.4% to 13.5%, showing this approach to be a reliable solution for improving solar cell properties. A Novel hybrid organic/inorganic silicon nanotips solar cell employing CdZnS/ZnS QDs as a downshifting layer which exhibits a drastic enhancement in the cell characteristics due to the radiative and nonradiative energy transfer (RET & NRET) from the QDs to underlying silicon nanostructure. • Highly emissive CdZnS/ZnS and CdZnSe/ZnS core/shell QDs were prepared by hot injection in two growth steps. • Silicon nanowires were fabricated by metal-catalyzed chemical etching and modified to tapered nanotip structure. • The current QDs show high potential for downshifting in hybrid silicon solar cells and improve the PCE to 13.49%.

Optimization of GEM-based detector readout electrode structure for SXR imaging of tokamak plasma

The paper presents an optimization of a readout structure of the GEM-based detector designed for X-ray imaging for DTT tokamak in the energy range of 2–15 keV. The readout electrode of approximately 100 cm2 surface is composed of hexagonal pixels connected in a way that allows reducing the actual number of signal pixels (electronics channels). At the same time, based on time coincidence analysis, it makes possible to unambiguously identify the position of the recorded X-ray photon. For the input spectrum, the Detective Quantum Efficiency (DQE) of the detector was calculated using the Geant4 program and the spatial distributions of electron avalanches at the readout electrode were simulated using the Garfield++ program. These were conducted for a given energy range of radiation and a statistical distribution consistent with the shape of the spectrum considering the DQE of the detector. As a result, the size of a single hexagonal pixel was proposed to capture the position of the recorded radiation quanta in an optimal and effective way.

Exploring mechanical assonance for impact energy harvesting using acoustic metamaterials

Acoustic metamaterials are engineered to possess unique dynamic properties that are not commonly found in nature. It has been demonstrated that customizing the characteristics of their local features can help optimize their dynamic performance under specific loading conditions. Drawing inspiration from the literary device called “assonance,” the term “mechanical assonance” may be ascribed to the dynamic phenomenon realized by sequencing oscillators with tuned responses within a waveguide to engineer a prescribed wave transformation across it. In this context, assonance provides a framework to utilize resonant local features within a host structure or material and interactive mechanisms thereof as building blocks to create enriched functionalities for acoustic metamaterials. Using a discrete element representation for an acoustic metamaterial barrier (AMB), a numerical study is conducted to ascertain parametric dependence for assonant mechanisms related to resonator frequencies, their sequencing, and host material stiffness. Normalized metrics are extracted to estimate transmitted pulse mitigation under impact-type loading. It is found that resonator sets with octave spacing having the number of resonators of a specific frequency proportional to that frequency’s amplitude in the input spectrum is desirable for lower transmissibility. Further, sequencing the lowest frequency resonator set closest to the incident-side gives better performance. Engineering a high degree of impedance mismatch between host material sections is also preferable. The energy sequestered by the local resonators can be harvested utilizing the resonator’s mass as the multifunctional kernel for a linear electromagnetic generator. A multiphysical model is developed to predict the harvested electric voltage and power from the AMB and validated using proof-of-concept experiments. Finally, various coil placement and voltage rectification schemes are also studied using simulations to ascertain preferable design configurations.

Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients

We propose a method for the blind separation of sounds of musical instruments in audio signals. We describe the individual tones via a parametric model, training a dictionary to capture the relative amplitudes of the harmonics. The model parameters are predicted via a U-Net, which is a type of deep neural network. The network is trained without ground truth information, based on the difference between the model prediction and the individual time frames of the short-time Fourier transform. Since some of the model parameters do not yield a useful backpropagation gradient, we model them stochastically and employ the policy gradient instead. To provide phase information and account for inaccuracies in the dictionary-based representation, we also let the network output a direct prediction, which we then use to resynthesize the audio signals for the individual instruments. Due to the flexibility of the neural network, inharmonicity can be incorporated seamlessly and no preprocessing of the input spectra is required. Our algorithm yields high-quality separation results with particularly low interference on a variety of different audio samples, both acoustic and synthetic, provided that the sample contains enough data for the training and that the spectral characteristics of the musical instruments are sufficiently stable to be approximated by the dictionary.

An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy.

Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.

A Process-Oriented Model of Decision-Making toward Landscape-Scale Prescribed Fire Implementation in the Southern Great Plains, USA

In this investigation, we developed a model of the psychological drivers of landowners' decisions to implement prescribed fire on their properties. The Southern Great Plains in the USA evolved with fire and prescribed fire is an important management tool aimed at maintaining and enhancing ecological and economic resilience in the region. The conceptualized model is reflective of a decision-making paradigm that considers decision making to be a process inclusive of a variety of factors and their inter-relationships to arrive at judgments on whether or not to utilize prescribed fire. The approach considered a spectrum of inputs, obstacles, and their associations to capture the complexity of decision making that is often lost when modeling single factors in dynamic social-ecological settings. Further, we considered the decision to use prescribed fire as a multifactor process that incorporates not only individual barriers to fire implementation but inter-barrier associations and other inputs (e.g., sociodemographic variables). Path analysis revealed five statistically significant relationships within the hypothesized model. For prescribed fire decision making, women tended to be more analytical whereas men were more inclined to rely on heuristics. Additionally, those who indicated owning their property for non-consumptive recreation-related reasons were also more inclined to rely upon heuristics. Texans reported more experience with prescribed fire as did respondents who indicated owning property for livestock product. Alternately, those owning their property for an investment and non-consumptive recreation opportunities reported less experience with prescribed fire. Last, ownership for crop and livestock production was positively associated with past wildfire experience. Findings have implications for three issue areas: (1) the provision of an evolved conceptualization through which prescribed fire implementation decisions can be examined, (2) enhancing the approach of prescribed fire outreach to a changing landowner population, and (3) improving the content and delivery of prescribed fire education efforts.

Towards the prediction of molecular parameters from astronomical emission lines using Neural Networks

Molecular astronomy is a field that is blooming in the era of large observatories such as the Atacama Large Millimeter/Submillimeter Array (ALMA). With modern, sensitive, and high spectral resolution radio telescopes like ALMA and the Square Kilometer Array, the size of the data cubes is rapidly escalating, generating a need for powerful automatic analysis tools. This work introduces MolPred, a pilot study to perform predictions of molecular parameters such as excitation temperature (Tex) and column density (log(N)) from input spectra by the use of neural networks. We used as test cases the spectra of CO, HCO+, SiO and CH3CN between 80 and 400 GHz. Training spectra were generated with MADCUBA, a state-of-the-art spectral analysis tool. Our algorithm was designed to allow the generation of predictions for multiple molecules in parallel. Using neural networks, we can predict the column density and excitation temperature of these molecules with a mean absolute error of 8.5% for CO, 4.1% for HCO+, 1.5% for SiO and 1.6% for CH3CN. The prediction accuracy depends on the noise level, line saturation, and number of transitions. We performed predictions upon real ALMA data. The values predicted by our neural network for this real data differ by 13% from the MADCUBA values on average. Current limitations of our tool include not considering linewidth, source size, multiple velocity components, and line blending.

Simulation of shock test for an AUV propulsion motor based on DDAM

The paper studies the simulation of shock test for a propulsion motor equipped on an AUV. According to DDAM, the modal parameters are calculated using finite element analysis. Then the input spectrum of impacting acceleration is given based on modal analysis. The response after shocking is analyzed at last. Distribution of the stress in different directions are given, showing that the shock resistance performance of the propulsion motor satisfied the shock test.

Reconstructive spectrometer using a photonic crystal cavity.

Optical spectrometers have propelled scientific and technological advancements in a wide range of fields. While sophisticated systems with excellent performance metrics are serving well in controlled laboratory environments, many applications require systems that are portable, economical, and robust to optical misalignment. Here, we propose and demonstrate a spectrometer that uses a planar one-dimensional photonic crystal cavity as a dispersive element and a reconstructive computational algorithm to extract spectral information from spatial patterns. The simple fabrication and planar architecture of the photonic crystal cavity render our spectrometry platform economical and robust to optical misalignment. The reconstructive algorithm allows miniaturization and portability. The intensity transmitted by the photonic crystal cavity has a wavelength-dependent spatial profile. We generate the spatial transmittance function of the system using finite-difference time-domain method and also estimate the dispersion relation. The transmittance function serves as a transfer function in our reconstructive algorithm. We show accurate estimation of various kinds of input spectra. We also show that the spectral resolution of the system depends on the cavity linewidth that can be improved by increasing the number of periodic layers in distributed Bragg mirrors. Finally, we experimentally estimate the center wavelength and linewidth of the spectrum of an unknown light emitting diode. The estimated values are in good agreement with the values measured using a commercial spectrometer.

Modelling mangrove forest structure and species composition over tidal inundation gradients: The feedback between plant water use and porewater salinity in an arid mangrove ecosystem

Conceptual models have described mangrove forest structure in response to tidal inundation, porewater salinity and mangrove water use, however, the importance of feedbacks have not been assessed. We assessed the processes underlying the feedback between water availability and plant water use in mangrove ecosystems by applying the process- and individual-based mangrove model MANGA, evaluating individual tree growth behavior, emerging forest structure, and mangrove species composition. We compared simulation data with field data collected at a field site with a monospecific mangrove forest that showed a strong gradient in tree height and biomass across the intertidal zone. While tidal inundation and the resulting porewater salinity gradient caused variation in mangrove forest structure within the MANGA model, the inclusion of tree water use in the model led to high similarity between model results and field observations. We further show the importance of the feedback between tree water use and porewater salinity for emergence of species zonation patterns. In accordance with particular site observations, we simulated the response to a broad spectrum of modelled porewater salinity inputs of an artificial forest. The modelled forest consists of two distinct species differing in their water use efficiency and salt tolerance. The modelled feedback mechanism produced a strong correlation between mangrove species zonation along the intertidal gradient and porewater salinity inputs at the system boundaries. The results of this study describe and demonstrate the absolute and relative importance of the implemented interacting processes on mangrove vegetation patterns. A better understanding of the role of tidal inundation and the feedback loops between plant water use and porewater salinity in determining mangrove forest structure will help to predict and manage the fate of mangrove ecosystems in a changing climate.

Spectral Shape Recovery and Analysis Via Data-driven Connections

We introduce a novel learning-based method to recover shapes from their Laplacian spectra, based on establishing and exploring connections in a learned latent space. The core of our approach consists in a cycle-consistent module that maps between a learned latent space and sequences of eigenvalues. This module provides an efficient and effective link between the shape geometry, encoded in a latent vector, and its Laplacian spectrum. Our proposed data-driven approach replaces the need for ad-hoc regularizers required by prior methods, while providing more accurate results at a fraction of the computational cost. Moreover, these latent space connections enable novel applications for both analyzing and controlling the spectral properties of deformable shapes, especially in the context of a shape collection. Our learning model and the associated analysis apply without modifications across different dimensions (2D and 3D shapes alike), representations (meshes, contours and point clouds), nature of the latent space (generated by an auto-encoder or a parametric model), as well as across different shape classes, and admits arbitrary resolution of the input spectrum without affecting complexity. The increased flexibility allows us to address notoriously difficult tasks in 3D vision and geometry processing within a unified framework, including shape generation from spectrum, latent space exploration and analysis, mesh super-resolution, shape exploration, style transfer, spectrum estimation for point clouds, segmentation transfer and non-rigid shape matching.

Abstract 572: The liquid biopsy Guardant360 CDx has robust performance at low inputs allowing for high rate of returning patient results

Abstract Background Liquid biopsy tests capable of tumor profiling with NGS have recently received FDA approval for companion diagnostic treatment selection and tumor profiling, demonstrating the clinical validity of the identification of druggable variants with such information. As the amount of cfDNA per volume of plasma varies greatly from patient to patient, and whole blood/plasma volumes from advanced cancer patients are often limited, it would be advantageous for a liquid biopsy to be able to operate with minimal input, providing robust results across a broad spectrum of blood sample volumes and resultant cfDNA input. In this study, we evaluate the performance of Guardant360 CDx with as little as 3 mL whole blood and 5 ng cfDNA input. Methods The primary concern with low input like 3 mL whole blood is excess preservative in a blood collection tube (BCT) relative to whole blood. In a simulated short draw experiment, the same advanced cancer patient provided whole blood in BCTs with standard, elevated (2X), and high (3.3X) preservatives. We evaluated the molecule coverage across the panel at key exons and positions of interest, across genes evaluated for copy number amplifications, and at frequent fusion gene breakpoint positions. Precision and limit of detection (LoD) have been demonstrated at 5 ng input (P200010B) the lowest allowable input. For clinical utility of a liquid biopsy with low cfDNA input, we also assessed the cfDNA input of 87,305 samples tested with Guardant360 CDx or LDT from a single 10mL BCT. Results and Discussion Differences in molecule coverage between the high preservative and standard preservative were found to fall within expected technical variation (the median of log10 differences in exon-level DNA molecule coverage between short draw and reference conditions fell within +/- 0.125), indicating no meaningful loss of sensitivity for detecting somatic variants. Among the 87,305 clinical samples tested by Guardant360 CDx or LDT, median cfDNA yield was 25ng, with 98.5% yielding at least 5 ng, 88.4% at least 10ng, 73.5% at least 15 ng, and 17.9% at least 60ng. Clinically actionable biomarkers are detectable in low input samples. Of the Guardant360 CDx samples yielding 5-15ng cfDNA input, an average of 0.64 clinically significant variants was reported per sample. With the robust analytical performance demonstrated at low inputs, Guardant360 CDx (a 5ng cutoff) may provide tumor profiling results to guide therapy decisions for about 34% more patients compared to some liquid biopsies that use 2 BCTs for minimal 30ng cfDNA yield (a 15 ng cutoff). Retrospective studies for pharma research are often performed with as little as 2mL of plasma, meaning Guardant360 CDx can return results on 88.4% of such samples whereas other technologies may only be able to address 17.9% (extrapolated from the cfDNA yield distribution for 10mL whole blood yielding 4mL plasma). Citation Format: Aaron O. Richardson, Aaron Hardin, Soni Shukla, Jing Zhao, Catalin Barbacioru, Hilda Beas, Carin Espenschied, Adrian Bubie, Jamie Hutchins, Han-Yu Chuang, Tingting Jiang, Darya Chudova. The liquid biopsy Guardant360 CDx has robust performance at low inputs allowing for high rate of returning patient results [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 572.

RETRACTED ARTICLE: Implementation of efficient reconfigurable FIR filter with control logic for 5G applications

Filters are used to achieve frequency selectivity on the spectrum of input signal. Due to the stability of finite impulse response (FIR) filters, they are used in most of the applications. In the conventional FIR filters, the frequency band is fixed and cannot be changed once it is designed. Hence, there is a necessity of an FIR filter with auto-adjustment of band width in modern day communication systems like 5G. The design of FIR filter requires more number of filter coefficients to get the desired bandwidth specification. This results in a large slice for field programmable gate array implementation. Here, it proposed a state machine to select different FIR filters with the designated set of coefficients. Each FIR filter is having different set of coefficients, and based on the frequency of the clock signal, the FIR filter is selected and thereby frequency selectivity can be achieved. The proposed method is to implement reconfigurable FIR (RFIR) filter with control logic for auto-adjustment of frequency selections to achieve better band width requirements. The filter order is initially selected as 4 and presented the simulation results. The order of the filter(n) increased to 24 for verifying the bandwidth selection. The proposed architecture is compared with the existing architecture with 16 bits and 11 taps. The proposed method saves 33.5% of look-up-tables (LUTs) compared to the existing methods. Simulation results presented are verified using Xilinx ISE design suite 14.7. The total number of four-input LUTs utilized are 630 for order(n) of the filter 24. Power consumed by the overall design is 195 mW.

How to strengthen basic competencies in self-care – a pre-post interventional study with postgraduate trainees in family medicine in Germany

ABSTRACT Introduction: Self-care includes taking care of our psychosocial health. Many experts agree that self-care should be included in training for Family Medicine (FM) residents, though it is unclear to what extent and by what means. Objective: The aim of this study was to evaluate competencies of FM residents in self-care and changes in knowledge, skills and attitudes after an educational compact intervention. Methods: The authors performed a pre-post comparison on residents who were registered in the FM residency program KWBW VerbundweiterbildungPLUS© (n = 401). FM residents were offered participation in a self-care training (270 minutes featuring a broad spectrum of input and practical experience). The intervention group (IG) completed a questionnaire directly before the seminar (T1) and 10 weeks afterwards (T2). Non-attendees (=control group; CG) were asked to fill out a basic questionnaire once. Basic questionnaires for IG and CG covered previous experiences and skills while the follow-up questionnaire at T2 also focused on change of competencies and attitudes. All questionnaires contained free-text questions to capture qualitative impressions. Results: 287 FM residents (IG: n = 212; CG: n = 75) participated in the study. Generally, 86.4% of FM residents had worried that their profession might endanger their personal health (T1: n = 180, CG: n = 68). At T2, 59.5% of IG (n = 66/111) declared that they would not worry about their personal health anymore (T1:T2, p < .01). IG learned to facilitate time for recreation (T1:T2, p = .04) and to use relaxation techniques (T1:T2, p = .01). Gain in competence was described in awareness of stress, self-reward, activation of personal resources, and time management. A total of 85.6% of IG reflected their attitudes towards psychosocial health of physicians in general (n = 95/111) and 22.5% of IG changed the views on their own health (n = 25/111) in terms of improved risk-awareness, increased intention for self-care and change of behavior. Conclusion : A compact intervention in self-care strengthens competencies, increases awareness, and helps FM residents identify their psychosocial health risks. Further research is necessary to specify the effectiveness of similar compact interventions in self-care and their long-term results.

Comparison of response transmissibility and power spectral density transmissibility on operational modal analysis

Due to the independence of input spectra, the transmissibility-based operational modal analysis (OMA) methods have been developed rapidly over the last two decades. The response transmissibility (RT) methods can extract the modal parameters from multiple different load cases while the power spectral density transmissibility (PSDT) methods just use one single load case by combining multiple transfer outputs. Their ability to identify modal parameters under non-white excitations has been claimed, verified and applied by many researches. However, some problems, such as the robustness under different load conditions, the extent of noise influence and the application conditions, still arise when they are applied in the real field. Moreover, the comparison between different transmissibility-based methods on the identification accuracy and robustness is rare, which is significant for users to select the very method for their tasks.Four representative transmissibility-based OMA methods are considered and compared in this paper. The influence factors of RT and PSDT functions are analyzed first and the results reveal the requirements of the load case for four OMA methods. A numerical example is used to assess and compare the robustness of four methods to measurement noises, damping ratio of non-white noise excitations, non-fully-excited load cases and harmonic excitations, and their identification ability is further evaluated through a real field example. The results show that the transmissibility-based OMA methods are independent of input spectra only under certain conditions and some user-defined parameters play important roles in their identification ability.

Retracted: A hybrid‐supervised learning model for compressive spectrum sensing in fifth generation(5G) cognitive Radio enabled internet of things (IoT) networks

Summary5G Internet of Things (IoT) networks are characterized by wideband radio frequency spectrum utility and are therefore of primary importance for efficient means of sensing the wideband spectrum characterized by high bandwidth. A cognitive radio network (CRN) intelligently does the sensing of authorized users ideal spectrum and allocates the same to the demanded unauthorized user. Conventional energy detection and k‐means schemes associated with CRN perform well for narrowband applications, whereas they are not quite suitable for wideband applications. Hence, a compressive collaborative sensing scheme together with deep neural network learning model (CCS‐DLNN) has been proposed to sense the information from the compressed and reconstructed information signal. Based on the extracted features, decision on presence or absence of primary user (PU) in the received signal has been observed. This paper proposes a deep learning neural network model for learning the dynamic change in the input spectra. Accordingly, this paper also updates the weights associated with the neurons to converge upon the target objective. The performance of the proposed sensing scheme has been evaluated pertaining to probability of detection, sensing error, and accuracy of detection of idle channels. The proposed work will be very useful for the upcoming generation departing to be implemented with 5G IoT networks.

Faculty Development for Milestones and Clinical Competency Committees.

Faculty Development for Milestones and Clinical Competency Committees.

Numerical simulation of long-period grating sensors (LPGS) transmission spectrum behavior under strain and temperature effects

PurposeThe purpose of this study aims to simulate the long-period fiber grating sensor pulse peak position against the transmission range. The long-period fiber grating sensor pulse peak position against the transmission range is simulated clearly where the pulse peak value at zero position is 0.972655 with the ripple factor of unity. It is demonstrated that the long-period fiber grating sensor bandwidth can be estimated to be 50 µm. Wavelength shift of the long-period grating sensor (LPGS) is reported against grating wavelength, applied temperatures and applied micro strain.Design/methodology/approachThis work has reported the numerical simulation of LPGS transmission spectrum behavior characteristics under the strain and temperature effects by using OptiGrating simulation software. The sensor fabrication material is silica-doped germanium. The transmittivity/reflectivity and input spectrum pulse intensity of long-period Bragg sensor variations are simulated against the grating wavelength variations. Input/output pulse intensity of LPGS variations is simulated against the timespan variations with the Gaussian input pulse from 100 to 500 km link length.FindingsTemperature variation and strain variation of the LPGS are outlined against both applied temperatures and micro-strain variations at the central grating wavelength of 1,550 nm.Originality/valueIt is demonstrated that the long period fiber grating sensor bandwidth can be estimated to be 50 µm. Wavelength shift of the long period grating sensor is reported against both grating wavelength, applied temperatures and applied micro strain. Temperature variation and strain variation of the long period grating sensor are outlined against both applied temperatures and micro strain variations at the central grating wavelength of 1550 nm.