Transmission Scenarios Research Articles

Brief Screening for Distress among Healthcare Professionals: Psychometric Properties of the Physician Well-Being Index-Spanish Version.

Background: The Physician Well-Being Index (PWBI) is a brief, valid, reliable self-assessment instrument to identify health professionals’ distress and those in need of an intervention. Objective: to evaluate the construct, predictive validity (of depression, suicidal ideation, insomnia, and generalized anxiety), and internal consistency of the 7-item Spanish version of the PWBI (PWBI-S). Methods: out of a national population of approximately 1 million Mexican healthcare professionals, a sample of 3506 subjects (42.0% physicians, 28.7% nurses and 29.3% psychologists) completed an online survey between 17 April and 7 May 2020, at the time of the COVID-19 case cluster transmission scenario in Mexico. Results: In the three sub-samples, PWBI-S’s Confirmatory factor analyses (adding residual covariances) exhibited adequate goodness of fit indices for the PWBS original unidimensional model. Overall Cronbach’s alphas were 0.89 for physicians, 0.90 for nurses, and 0.86 for psychologists. Univariate logistic regression models showed that a cutoff point of 3 on the total score of the PWBI-S was generally related to the presence of depression, suicidal ideation, and insomnia, but not with generalized anxiety among nurses and psychologists. When trying with a cutoff point of 3, a relationship with GA was shown in psychologists, but not in nurses. Conclusions: our findings suggest that PWBI-S is a valid, reliable measure for clinical and research purposes in the field.

Arsenic contamination in groundwater and food chain with mitigation options in Bengal delta with special reference to Bangladesh.

Bangladesh, situated in Bengal delta, is one of the worst affected countries by arsenic contamination in groundwater. Most of the people in the country are dependent on groundwater for domestic and irrigation purposes. Currently, 61 districts out of 64 districts of Bangladesh are affected by arsenic contamination. Drinking arsenic contaminated groundwater is the main pathway of arsenic exposure in the population. Additionally, the use of arsenic-contaminated groundwater for irrigation purpose in crop fields in Bangladesh has elevated arsenic concentration in surface soil and in the plants. In many arsenic-affected countries, including Bangladesh, rice is reported to be one of the significant sources of arsenic contamination. This review discussed scenario of groundwater arsenic contamination and transmission of arsenic through food chain in Bangladesh. The study further highlighted the human health perspectives of arsenic exposure in Bangladesh with possible mitigation and remediation options employed in the country.

Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study.

While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.

Outage Analysis of Cooperative Satellite-Aerial-Terrestrial Networks With Spatially Random Terminals

Due to the considerable fading arising from the long-distance transmission between satellites and terrestrial terminals as well as the complex geographic environments on the earth, aerial relays have been designed to facilitate the information delivery between the satellites and terrestrial terminals when reliable direct data transmissions cannot be achieved. In this paper, a cooperative satellite-aerial-terrestrial network (CSATN) system including a satellite (S), an aerial relay (R), and a terrestrial receiver (D) is considered, while decode-and-forward relay scheme is adopted. Specifically, inspired by the beam shape of the transmission signal from S, we model the operation space of R as a circular truncated cone to accurately reflect the practical large-scale transmission scenarios, the height of which is decided by the upper and lower limitations of the operation height of R. Thus, considering the randomness of R and D and employing geometric probability theory, the exact and asymptotic outage probability (OP) of S-R, S-D, and R-D links is studied, and then the end-to-end outage performance of CSATN is investigated accordingly by using approximation method. Finally, numerical results are presented to validate our proposed analysis.

Data Collection Method of Energy Adaptive Distributed Wireless Sensor Networks Based on UAV

In sensor networks, UAVs are often introduced to assist data collection tasks. UAVs can operate as data ferry nodes, connecting distributed areas that are separated from each other. This paper proposes a data collection method for distributed wireless sensor networks based on UAV and introduces the idea of edge computing in it. In the single-hop transmission scenario, the K -means++ clustering method is used for sensor node clustering and cluster head election in the initial state. In the next rounds of data collection, UAV is used to assist in the election of new cluster heads and data collection tasks, taking into account the relative distance and the relative remaining energy relationship of the sensor nodes in their clusters. In addition, reasonable priorities are set for some nodes that have never been elected in the previous rounds and for the dead nodes. In the multihop transmission scenarios, for nodes that cannot deliver directly, the optimal relay node is selected for routing by comprehensively considering factors such as transmission angle, transmission distance, and remaining energy of the node in each cluster. The method proposed in this paper coordinates the overall energy consumption of sensor nodes in the environmental monitoring area, delays the death time of key sensor nodes, and extends the network lifetime. At the same time, an improved ACO is used to reasonably plan the data collection path of the UAV. Compared with the comparison scheme, the improved ACO can obtain a better shortest path length and has the fastest convergence speed when reaching the shortest path.

Time-Critical Data Transmission Scheme in Wireless Sensor Networks Using Machine Learning Approach

Wireless sensor network has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.

Modified frequency-domain channel-estimation based on the dual-dependent pilots for polarization-division-multiplexed coherent optical orthogonal frequency division multiplexing systems with offset-quadrature-amplitude modulation

In the past, several frequency-domain channel-estimation methods were proposed for coherent optical orthogonal frequency division multiplexing systems, which use offset-quadrature-amplitude modulation (CO-OFDM-OQAM). A common challenge for these methods is that the training sequences they use, even though they increase the magnitude of the pseudo-pilots, can make the peak-to-average power ratio (PAPR) of the signal worse. To address this problem, an innovative modified frequency-domain channel estimation method is designed and investigated for a polarization-division-multiplexed CO-OFDM-OQAM system that uses dual-dependent pilots. The operating principle of this new method is described in detail. Then, its PAPR performance and channel-estimation capability are examined using numerical simulations. The proposed method not only ensures a sufficiently large magnitude of the pseudo-pilot but also does not seriously degrade the PAPR of the signal. It is also found that the magnitude of its pseudo-pilot (2.3136) exceeds that of the interference approximation method with real pilots (1.1086), and it is only slightly worse than that of the enhanced IAM with complex pilots (2.6074). The PAPR of the new method (max 11.4 dB) is much lower than that of other frequently used methods. Finally, the channel-estimation capability of the proposed method is validated for both back-to-back and long-distance transmission scenarios. Furthermore, the BER performance of several different channel-estimation methods is also compared. The obtained results confirm the above observations.

Use of Surface Acoustic Waves for Crack Detection on Railway Track Components—Laboratory Tests

The present work investigates the technical feasibility of a condition monitoring setup aiming at the detection of gauge corner cracks (aka head checks) in pearlitic railway rails, using a wayside (i.e., stationary) setup with surface acoustic waves (SAW) as its detection principle. The experimental SAW setup consists of a pitch-catch setup using piezo transducers equipped with comb adaptors to excite and measure narrowband Rayleigh waves with a center frequency of 1 MHz. SAW experiments were performed on a rail subjected to cyclic loading in a 1:1 wheel–rail test rig yielding the specific rolling contact fatigue, i.e., head checks. Elastodynamic finite integration technique (EFIT) simulations were performed to analyze the surface and bulk wave propagation in the rail and to predict the signals at specific receiver positions. SAW transmission and reflection scenarios at cracks were analyzed numerically via modelled variations of gauge corner crack configurations according to number of cracks (0–3) and depth (0, 0.5 mm and 1 mm). The numerical and the experimental results each show a clear correlation between the appearance and intensity of head check damage and the wave attenuation in transmission mode.

Least Angle Regression-Based Modified Volterra Nonlinear Equalizer for Optical Interconnection System

Advanced digital signal processing (DSP) is viewed as an inevitable technology to deal with nonlinear distortions in cost-sensitive optical interconnection systems. Classic methods such as equalization based on Volterra series turn out to be quite effective solutions to compensate signal impairments. In practical imple-mentations, a great deal of efforts focus on reducing the com-putational complexity. Several model selection and dimensionality reduction approaches have been proposed to simplify the struc-ture, which employ backward strategies. However, the threshold settings for these pruning approaches are environment-sensitive. In this paper, we propose a least angle regression-based nonlinear equalization method (LaNLE). We extend the non-intrusive least angle regression technique to generalized linear models. Through an incremental process by stagewise feature selection and weight fitting, we obtain the low-complexity nonlinear structure with significant feature basis. Experimental verification is performed based on 120 Gbps PAM8-modulated 850 nm VCSEL plus MMF optical link. The results show that, compared to fully-connected Volterra nonlinear equalizer structure (VNLE), up to 60.4% and 70.1% reduction in complexity can be achieved with similar BER performance in back-to-back and 100 m transmission scenarios, respectively. In addition, we experimentally demonstrate 225 Gbps per lane PAM-8 signal transmission over 100 m MMF in a simple and cost-effective implementation manner based on mass-production 850 nm VCSEL and proposed LaNLE method.

Performance research on ZigBee wireless sensor network self-organizing network for 220 kV four-circuit transmission lines on the same tower

With the integration of massive power transmission equipment and the Internet of Things (IoT), it is particularly important to choose a suitable low-power sensor networking structure, when facing a large amount of on-site sensing data. To select a suitable ZigBee network structure with a large number of embedded sensing nodes, for 220 kV four-circuit transmission lines on the same tower, this paper uses OPNET simulation software to conduct three different ZigBee wireless sensor network (WSN) models, respectively with star, tree and mesh topologies. Then, the performance of the Media Access Control (MAC) layer (delay and throughput performance), application layer (end-to-end delay and traffic received performance), and network layer (number of hops and packets dropped performance) is further investigated and compared for these three different ZigBee WSNs under 220 kV transmission scenario. Considering that quantity of sensing nodes would influence the ZigBee WSN performance greatly, thus, the simulation results under two application scenarios, 220 kV four-circuit transmission lines on the same tower and 500 kV double-circuit transmission lines on the same tower, were thoroughly compared and analyzed in this paper.

A road map for in vivo evolution experiments with blood-borne parasitic microbes.

Laboratory experiments in which blood-borne parasitic microbes evolve in their animal hosts offer an opportunity to study parasite evolution and adaptation in real time and under natural settings. The main challenge of these experiments is to establish a protocol that is both practical over multiple passages and accurately reflects natural transmission scenarios and mechanisms. We provide a guide to the steps that should be considered when designing such a protocol, and we demonstrate its use via a case study. We highlight the importance of choosing suitable ancestral genotypes, treatments, number of replicates per treatment, types of negative controls, dependent variables, covariates, and the timing of checkpoints for the experimental design. We also recommend specific preliminary experiments to determine effective methods for parasite quantification, transmission, and preservation. Although these methodological considerations are technical, they also often have conceptual implications. To this end, we encourage other researchers to design and conduct in vivo evolution experiments with blood-borne parasitic microbes, despite the challenges that the work entails.

Transparent vs Translucent Multi-Band Optical Networking: Capacity and Energy Analyses

Multi-band optical fiber transmission is generally proposed for capacity upgrades in optical transport networks. To comprehensively assess the potential of multi-band transmission, key metrics such as the potential capacity increase, energy consumption, and the number of required interfaces must be evaluated for different transmission scenarios. We consider progressive spectral exploitation, starting from the C-band only and up to C+L+S+U-band transmission, for both transparent and translucent solutions that exploit optical signal regeneration. By considering accurate state-of-the-art physical layer models, we derive a networking performance metric that enables the comparison of different solutions in terms of capacity allocation and energy consumption. For a translucent network design, different regenerator placement algorithms are compared, with the aim of minimizing energy consumption. The proposed network-wide numerical analysis shows that, for spectral occupations exceeding the C+L-band, translucent solutions can significantly increase network capacity, while leading to a similar energy consumption per transmitted bit as in the transparent design case, but they require the deployment of additional line interfaces. Significantly, these results provide evidence that the transparent exploitation of an additional transmission band produces a capacity increment that is at least comparable to that of a translucent solution based on already-in-use bands. Since this is attained at the expense of fewer line interfaces, it is a key finding suggesting that extending the number of bands supported is a cost-effective approach to scaling the capacity of existing fiber infrastructures.

Scalable and Disaggregated GGN Approximation Applied to a C+L+S Optical Network

We investigate quality of transmission (QoT) estimation in a multi-band transmission scenario, including a wideband description of the frequency-dependent physical layer parameters and a disaggregated QoT estimation approach.In particular, we use experimentally-derived erbium- and thulium-doped fiber amplifier operational data, and simulate the generation of the nonlinear interference (NLI) noise by comparing implementations of the split-step Fourier method, the generalized Gaussian noise (GGN) model implemented in the open-source GNPy library, and an efficient approximation that provides accurate results in a limited computational time. The latter semi-analytical solution is obtained by approaching the Manakov equation from a disaggregated standpoint, including the power transfer induced by stimulated Raman scattering, along with the frequency-dependent evolution of the fiber parameters of loss, dispersion and the nonlinearity coefficient. We validate the proposed approximation within a C+L+S 400 G transmission scenario and investigate 21 channels under test that are equally spaced along the three bands.

Direct-Link Interference Cancellation Design for Backscatter Communications Over Ambient DVB Signals

Ambient backscatter (AB) communication enables the backscatter devices to modulate and transmit data by reflecting surrounding digital video broadcasting (DVB) signals, and has emerged as a promising solution to connect low-power and small-sized devices in Internet of Things (IoT). Unfortunately, the AB receiver may suffer from the strong direct-link interference (DLI) transmitted from the ambient DVB station, making it difficult to decode the backscattered signal (BS) directly. In this paper, to tackle this issue, we propose a DLI cancellation method for AB communications over ambient DVB signals. First, a general signal processing operation for DLI cancellation at the AB receiver is designed, where neither special design on waveform nor cooperation between the ambient DVB station and the backscatter transceiver is required. Then, by minimizing the residual reception power after DLI cancellation, a three-layer parameter searching algorithm is proposed to separate the BS from the ambient DVB signal, which is of low implementation complexity. Simulation results show that when the DLI-to-BS ratio (ISR) is greater than 30 dB, the proposed DLI cancellation method can effectively suppress the DLI at the AB receiver, and the demodulation and capacity performance of the ambient DVB transmission and the backscatter transmission are barely deteriorated, as compared with the scenarios of original DVB transmission without BS and pure backscatter transmission without DLI. In addition, there is a trade-off between the convergence time and the interference cancellation ratio (ICR) performance of the proposed DLI cancellation scheme.

Distribution Grid Resource Business Center Integrating Dynamic Migration Algorithm and Lightweight System

The existing power system does not have a unified grid resource management model. The basic data of equipment assets cannot be stored, managed, and maintained in a unified manner. The production systems of different departments are developed by different manufacturers at different times, which leads to the problem of production data silos and high maintenance costs. To solve this problem, a grid resource business middle platform based on State grid-corporation public data model (SG-CIM, CIM) model is designed in this paper. Firstly, the concept and core technology of the middle platform in the power grid resource business are introduced. Then a dynamic data migration strategy based on the FA algorithm is proposed. Finally, the model based on SG-CIM and the application scenario of power transmission and distribution are built and its effectiveness is analyzed.

Performance Evaluation of Magnetic Resonance Coupling Method for Intra-Body Network (IBNet).

Effective management of emerging medical devices can lead to new insights in healthcare. Thus, human body communication (HBC) is becoming increasingly important. In this paper, we present magnetic resonance (MR) coupling as a promising method for the intra-body network (IBNet). The study reveals that MR coupling can effectively send or receive signals in biological tissue, with a maximum path loss of PL ≤ 33 dB (i.e. at 13.56 MHz), which is lower than other methods (e.g., galvanic, capacitive, or RF) for the same distance (d = 100 cm). The angular orientation of the transmitter and receiver coils at short and long distances also show a minor variation of the path loss (0.19 ≤ ∆PL ≤ 0.62 dB), but more dependency on the distance (0.0547 dB/cm). Additionally, different postures during the MR coupling essentially does not affect path loss ( ∆PL ≤ ± 0.21 dB). In the multi-nodal transmission scenario, the MR coupling demonstrates that two nodes can simultaneously receive signals with -16.77 dBm loss at 60 cm and 100 cm distances, respectively. Such multi-node MR transmission can be utilized for communication, sensing, and powering wearable and implantable devices.

Analysis of X-Ray Beam Hardening Correction Method Relying on Cloud Computing Big Data

Regarding the correction of X-ray beam hardening in the current CT imaging system, the traditional method will cause the overlapping of images during use, which will gradually harden the beam, and the image reconstructed by the imaging system will gradually become “cup-shaped” or “striped.” “False images” seriously degrade the quality of the images, while causing more interfering diagnostic problems. In this paper, the cloud computing big data analysis algorithm is applied to the X-ray beam hard correction process. According to the transition energy and its energy absorption, the X-ray beam is used as the cut-in point, and the relationship between the attenuation coefficient of similar materials and the X-ray energy is used to remove the artifact image in the initial image reconstruction process to obtain a clear corrected image. Meanwhile, according to the thickness and gray value of the X-ray penetrable object, the result of fitting using a polynomial function is calculated, and an accurate line fitting can be completed for data with smaller coordinates. Finally, the experimental study shows that the cloud computing big data analysis proposed in this paper can detect X-ray beams in real time. This method uses optical receivers to achieve high noise sensitivity to X-rays, and in long-distance transmission scenarios, the bandwidth of communication transmission can be maximized, and different types of formats can be used to complete modulation for different X-rays. Therefore, X-ray beam hardening correction technology has better advantages and the market application scenarios compared with other technologies.

Analysis of LTE-M Adjacent Channel Interference in Rail Transit.

Long Term Evolution-Metro (LTE-M), as a special communication system for train control, has strict requirements on adjacent channel interference (ACI). According to the 3rd Generation Partnership Project (3GPP) protocol of the European Telecommunications Standards Institute (ETSI) standards, this paper presents the required isolation degree for LTE-M systems to resist ACI. Aiming at the scenario of leaky cable transmission and antenna transmission adopted by the underground LTE-M system of the subway, the isolation degree required for LTE-M system deployment is deduced by combining the channel description with the principle of ACI. For the coexistence of a LTE-M system and an adjacent cellular system in a subway ground scenario, the Monte-Carlo (MC) method is used to simulate several conceivable scenarios of the LTE-M system and the adjacent frequency cellular system. In addition, the throughput loss of the LTE-M system is estimated by considering signal to interference plus noise ratio (SINR). Simulation results demonstrate that adjacent frequency user equipment (UE) has negligible small interference with the LTE-M underground system when using the leaky cable radiation pattern, whereas for the LTE-M ground system, the main interference comes from the adjacent frequency UE to the LTE-M base station (BS). Finally, interference avoidance solutions are presented, which can be utilized as a reference in the design and deployment of LTE-M systems in the rail transit environment.

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities.

In non-pharmaceutical management of COVID-19, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients and demonstrate this approach for the United States city of Chicago. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model using daily COVID-19 ICU and hospital census data between March and August 2020. We projected various possible ICU occupancy trajectories from September 2020 to May 2021 with two possible levels of transmission increase and uncertainty in core model parameters. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

An enhanced adaptive duty cycle scheme for energy efficiency and QoS awareness in wireless sensor networks

A Wireless Sensor Network (WSN) is a small device that has batteries and radios to connect to the internet. The main problem with WSNs is a limited energy source, energy studies needed to ensure that these sensor nodes can last longer, especially since a lot of energy is wasted during idle listening, overhearing, and data collision that occurs at the medium access control (MAC) layer. The common mechanism used for saving energy in WSN, specifically at the MAC layer is the duty cycle schedule. Duty cycling coordinates sleep-wake time sensor nodes to maximize network lifetime while achieving specific application goals such as high throughput or low latency. Duty cycling of every node should be adjusted separately at any runtime depending on the network conditions to achieve desired delay guarantees and energy efficiency. Recently, a few adaptive duty cycle schemes were introduced, these schemes have reduced energy consumption by some degree, this leaves an open end to the degradation of the quality of service. In this study, adaptive duty cycles enhanced with a priority queue where packet size is the parameter to adjust the duty cycle in order to get efficient energy consumption. A variant of packet size tested to ensure optimum quality of services (QoS). These factors determine the duration of a node’s listen period for various packet transmission scenarios and requirements. As the result, the proposed Enhanced S-MAC (ESMAC) shows an improvement in the energy consumption and QoS compared to the default MAC protocol and S-MAC protocol. The success of this project will contribute to the performance improvement of sensing devices.