Multiple Shifts Research Articles

Effect of synchronization system errors on the reception noise immunity of amplitude-phase shift keyed signals

Objectives. An urgent task in the context of modern radio and television systems is to improve the quality and quantity of transmitted information. For example, the use of multiple amplitude-phase shift keyed (APSK) signals—16-APSK and 32-APSK—in digital satellite television systems of the Digital Video Broadcasting-Satellite2 (DVB-S2) standard made it possible to transmit 30% more data in the same frequency bands in comparison with the previous DVB-S standard. Such increases in information transmission rates impose more stringent requirements on hardware. An important role in the reception of APSK signals, as well as the signals of other coherent signal processing systems, is played by the stability of synchronization systems. The presence of operational errors can significantly reduce the quality of information reception. The aim of the present work was to analyze the effect of phase and clock synchronization errors on the reception noise immunity of APSK signals with a ring signal constellation structure.Methods. The study used statistical radio engineering methods informed by optimal signal reception theory.Results. The effect of phase and clock synchronization errors on the reception noise immunity of APSK signals having a signal constellation ring structure is analyzed. The dependencies of the bit error probability on the magnitude of the phase shift and the clock offset were characterized. The effect of synchronization errors on reception quality were compared with the known results for quadrature amplitude modulation (QAM) signals.Conclusions. At an acceptable energy loss of no more than 1 dB, the critical phase error can be considered as 2°-3°, while the critical clock error is 3-4%. A coherent receiver of APSK signals is more sensitive to the phase error of reference oscillations than a similar receiver of QAM signals, whereas clock errors have the same effect on the reception quality of these signals.

Reaching the unreachable workforce : Adopting a mobile-first communication strategy to retain employees

Ineffective communications cost companies more than just safety and productivity issues; they are a direct cause of turnover. While corporate leaders feel their communications are relevant and the way they are delivered is effective, research shows their employees do not feel the same way. In a tight labour market, businesses cannot afford to overlook this fact. As an integrated facilities management (IFM) company that distinguishes itself in self-performing more than 80 per cent of the hard and soft services it provides clients, ESFM depends on an engaged workforce to deliver on its client promises every day. As noted in its bi-annual employee engagement survey, the company recognised its internal communications were missing the mark with respect to hourly employees. Deskless workers, spread across the country and covering multiple shifts, were challenging to connect with. To address these challenges, ESFM inverted the traditional top-down communication cascade as the first IFM company to leverage the Nudge app, originally designed for retail workers, to implement a mobile-first communication strategy for frontline personnel. Adapting the app’s capabilities for the FM space was a process, but ultimately empowered ESFM to digitise critical learnings and best practices to efficiently equip its employees with the knowledge and tools necessary to execute on their clients’ highest priorities. Business outcomes following the first year of implementing this communications approach earned ESFM the George Graves Award for Facility Management Achievement from the International Facilities Management Association (IFMA) in 2020. This paper shares business outcomes from this award-winning communications programme, as well as steps any company can apply on their journey toward developing a more intentional, engaging and effective communications strategy.

Computing vibrational energy levels using a canonical polyadic tensor method with a fixed rank and a contraction tree.

In this paper, we use the previously introduced Canonical Polyadic (CP)-Multiple Shift Block Inverse Iteration (MSBII) eigensolver [S. D. Kallullathil and T. Carrington, J. Chem. Phys. 155, 234105 (2021)] in conjunction with a contraction tree to compute vibrational spectra. The CP-MSBII eigensolver uses the CP format. The memory cost scales linearly with the number of coordinates. A tensor in CP format represents a wavefunction constrained to be a sum of products (SOP). An SOP wavefunction can be made more accurate by increasing the number of terms, the rank. When the required rank is large, the runtime of a calculation in CP format is long, although the memory cost is small. To make the method more efficient, we break the full problem into pieces using a contraction tree. The required rank for each of the sub-problems is small. To demonstrate the effectiveness of the ideas, we computed vibrational energy levels of acetonitrile (12-D) and ethylene oxide (15-D).

A Workplace Environmental Scan of Employed Carers During COVID-19

The carer-employee experience has undergone multiple shifts during the COVID-19 pandemic. This study seeks to understand how changes in the workplace as a result of the pandemic have impacted employed carers with their ability to perform both care obligations and paid work responsibilities. Using an online workplace-wide survey at a large Canadian firm, we conducted an environmental scan of: the current state of workplace supports and accommodations, supervisor attitudes, and carer-employee burden and health. Our findings demonstrate that while employees are generally in good health, care burden and time spent caregiving has been higher during COVID-19. Notably, employee presenteeism is higher during the pandemic than it was previously, with carer-employees experiencing significantly reduced levels of co-worker support. The most common workplace adaptation to COVID-19, work-from-home, was preferred by all employees as it allowed greater schedule control. However, this comes at the cost of reduced communications and sense of workplace culture, especially for carer-employees. We identified several actionable changes within the workplace, including: greater visibility of existing carer resources, and standardized training of managers on carer issues.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020

BackgroundCutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters.MethodsOn the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance.ResultsIn general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.ConclusionsOur study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.Graphical

The Shape of Inspiration: Exploring the Emotional Arcs and Self-Transcendent Elicitors Within Inspirational Movies

ABSTRACT Emotions have long been regarded as a central component of the entertainment media experience. The present study combines computational analysis and quantitative content analysis to analyze the emotional arcs of inspirational media content and associated elicitors of self-transcendent emotions in order to develop a more comprehensive understanding of inspirational media. Results demonstrate that inspirational movies can be characterized by a positive shift in sentiment at the end of the narrative and multiple shifts between relatively negative and relatively positive sentiment throughout the story. Furthermore, the “peaks” and “valleys” in the emotional arcs are associated with the presence of differing inspirational elicitors, and the presence of certain elicitors (such as kindness/moral goodness) within a scene predicted positive shifts in the emotional arcs in the scenes following the elicitor.

What is the process of personal growth? Introducing the Personal Growth Process Model

Personal growth as a process remains vaguely understood. We introduce the Personal Growth Process (PGP) model based on Carl Rogers's organismic valuing process (OVP). The PGP model explains personal growth as a sociocognitive embodied process whereby an individual undergoes multiple mental shifts that make up an ongoing, fluctuating process over the long term, starting from a place of psychological safety. The mental shifts occurring throughout the growth process are broadly categorized as self-awareness, openness towards experiencing and change, existential courage, autonomy/internal locus of control, taking responsibility for the self and others, self-compassion, and compassion towards others. These shifts all represent progress toward well-being, defined here as authenticity, harmony and life-satisfaction. Importantly, the model does not include only individual psychological constructs, but ties the process to one's social environment and common social responsibility.

“Our Mission Doesn’t Stop Just Because We Don’t Have a Building”: Librarians’ and Museum Educators’ Discursive Construction of Their Shifting Roles during the Pandemic

The COVID-19 pandemic resulted in multiple shifts in the roles of youth services librarians and museum educators. Although changes in job duties, processes, and day-to-day practices were inevitable, this article suggests that these professionals viewed their service as continuous with prepandemic roles. Based on interviews with 20 professionals, the article analyzes two constructs that emerged as professionals described their pandemic roles: guiding principles and collaboration. Not only did these discursive constructs provide a sense of continuity, they also served to advocate for the role of libraries and museums. These findings indicate the importance of reflecting and drawing on guiding principles and collaborations to make decisions about services during times of crisis.

A phylogenomic analysis of Lonicera and its bearing on the evolution of organ fusion.

The ~140 species of Lonicera are characterized by variously fused leaves, bracteoles, and ovaries, making it a model system for studying the evolution and development of organ fusion. However, previous phylogenetic analyses, based mainly on chloroplast DNA markers, have yielded uncertain and conflicting results. A well-supported phylogeny of Lonicera will allow us to trace the evolutionary history of organ fusion. We inferred the phylogeny of Lonicera using restriction site-associated DNA sequencing (RADSeq), sampling all major clades and 18 of the 23 subsections. This provided the basis for inferring the evolution of five fusion-related traits. RADSeq data yielded a well-resolved and well-supported phylogeny. The two traditionally recognized subgenera (Periclymenum and Chamaecerasus), three of the four sections (Isoxylosteum, Coeloxylosteum, and Nintooa), and half of the subsections sampled were recovered as monophyletic. However, the large and heterogeneous section Isika was strongly supported as paraphyletic. Nintooa, a clade of ~22 mostly vine-forming species, including L. japonica, was recovered in a novel position, raising the possibility of cytonuclear discordance. We document the parallel evolution of fused leaves, bracteoles, and ovaries, with rare reversals. Most strikingly, complete cupules, in which four fused bracteoles completely enclose two unfused ovaries, arose at least three times. Surprisingly, these appear to have evolved directly from ancestors with free bracteoles instead of partial cupules. We provide the most comprehensive and well-supported phylogeny of Lonicera to date. Our inference of multiple evolutionary shifts in organ fusion provides a solid foundation for in depth developmental and functional analyses.

A 3D Mathematical Breast Texture Model With Parameters Automatically Inferred From Clinical Breast CT Images.

A numerical realistic 3D anthropomorphic breast model is useful for evaluating breast imaging applications. A method is proposed to model small and medium-scale fibroglandular and intra-glandular adipose tissues observed in the center part of clinical breast CT images. The method builds upon a previously proposed model formulated as stochastic geometric processes with mathematically tractable parameters. In this work, the medium-scale parameters were automatically and objectively inferred from breast CT images. We hypothesized that a set of random ellipsoids exhibiting cluster interaction is representative to model the medium-scale intra-glandular adipose compartments. The ellipsoids were reconstructed using a multiple birth, death and shift algorithm. Then, a Matérn cluster process was used to fit the reconstructed ellipsoid centers. Finally, distributions of the ellipsoid shapes and orientations were estimated using maximum likelihood estimators. Feasibility was demonstrated on 16 volumes of interests (VOI). To assess the realism of the 3D breast texture model, β and LFE metrics computed in simulated projection images of simulated texture realizations and clinical images were compared. Visual realism was illustrated. For 12 out of 16 VOIs, our hypothesis on clustering interaction process is confirmed. The average β values from simulated texture images (3.7 to 4.2) of the 12 different VOIs are higher than the average β value from 2D clinical images (2.87). LFE of simulated texture images and clinical mammograms are similar. Compared to our previous model, whereby simulation parameters were based upon empirical observations, our inference method substantially augments the ability to generate textures with higher visual realism and larger morphological variety.

Testing for multiple level shifts with an integrated or stationary noise component

SummaryThe paper analyzes the detection and estimation of multiple level shifts regardless of the order of integration of the time series. We show that it is possible to extend the Bai‐Perron methodology (1998) to the I(1) and NI(1) nonstationary cases so that a unified framework to test for the presence of multiple level shifts in a robust way is designed. The finite sample performance of the proposed statistics is carried out, establishing a comparison with other existing approaches in the literature. The paper illustrates the implementation of the statistics focusing on the real exchange rate with time series that either cover a long time period or provide a worldwide analysis. Robust detection of multiple level shifts is of great importance to define the statistical approach that is used to test the purchasing power parity hypothesis.

Multiple facets of cutting-edge mycorrhizal research: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, August 2022: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, 1-5 August 2022.

Multiple facets of cutting-edge mycorrhizal research: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, August 2022: 11th International Conference on Mycorrhiza (ICOM 11), Beijing, China, 1-5 August 2022.

Recognition method for the bionic camouflage cetacean whistle modulated by CPMFSK signals

Bionic covert camouflage underwater acoustic communication has drawn great interests in recent years as a promising way for covert underwater acoustic communication. However, there is little relevant literature to research the recognition of bionic camouflage communication signals (BCCS). Concentrating on the countermeasures for the BCCS, this paper proposes a recognition method for a bionic cetacean whistle signal modulated by continuous phase multiple frequency shift keying (CPMFSK) signal. Based on the features that CPMFSK modulated whistle signals carry the information by imitating the tremble phenomenon of the real whistles, a method is designed to extract a tremble signal which vibrates among the time–frequency contour (TFC) of cetacean whistles. As the tremble signal of the real cetacean whistle and bionic whistle has obvious time, and frequency distinction, focusing on these distinctions, characteristics like normalized power spectrum mean, wiener entropy, etc. are extracted as the characteristic vectors for SVM classification to achieve recognition. Simulations demonstrate the accuracy of the recognition method in different conditions, including modulation parameters, SNR, and underwater acoustic channels.

Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality. To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images. An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. Speed-resolved perfusion was estimated in the three speed intervals 0 to , 1 to , and , with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.

Optimizing Data Placement for Hybrid SRAM+Racetrack Memory SPM in Embedded Systems

Nonvolatile memory (NVM) has the potential as the medium for scratchpad memory (SPM) in embedded devices. Racetrack memory (RM), in particular, is a developing memory technology that possesses high density and read latency comparable to SRAM. The RM’s access operations, however, are based on shift operations. Multiple shift operations will lead to long access latency and high energy. In this article, SRAM is borrowed to help the shifts reduction. Thus, a novel hybrid SRAM+RM SPM is presented to make use of SRAM’s random access and RM’s high density. But, there are some challenges to the proposed architecture: 1) the large capacity of SRAM is not available due to its low density and 2) due to the drawbacks of RM mentioned above, data that are randomly accessed are not expected to be stored on RM. Therefore, a data placement scheme and an instruction scheduling strategy are presented for the proposed architecture. First, an access instruction scheduling strategy is introduced to obtain a relatively sequential access sequence to help with the shifts and SRAM size reduction; second, to help with data placement, a metric for representing the data access cost is proposed; third, a data placement strategy based on the metric is proposed; and finally, a solution for decreasing SRAM size is suggested to maximize the capacity of SPM (or minimize the size of SPM). Experiments show that the suggested scheme can significantly improve the performance of the hybrid SPM while also reducing the shifts on RM with minimal SRAM.

Wheel Defect Detection with Advanced Machine Learning

Abstract: Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway network. Our methods automatically learn different types of wheel defects and predict during normal operation if a wheel has a defect or not. The first method is based on novel features for classifying time series data and it is used for classification with a support vector machine. To evaluate the performance of our method we construct multiple data sets for the following defect types: flat spot, shelling, and non-roundness. We outperform classical defect detection methods for flat spots and demonstrate prediction for the other two defect types for the first time. Motivated by the recent success of artificial neural networks for image classification, we train custom artificial neural networks with convolutional layers on 2-D representations of the measurement time series. The neural network approach improves the performance on wheels with flat spots and non-roundness by explicitly modelling the multi sensor structure of the measurement system through multiple instances learning and shift invariant networks.

Governing Wildfire Risk in Canada: The Rise of an Apparatus of Security

This article argues that the governance of wildfire risk in Canada is increasingly oriented toward governance through a security apparatus. As climate change complicates wildfire “problems” in fast-expanding wildland–urban interface areas, fire managers and other actors increasingly seek a shift toward a fire-permitting, risk-based fire management style, even as the balance between private and public responsibility for wildfire protection gets renegotiated. This approach, typified by FireSmart, is characterized by a gradual, geographically uneven shift from state-centered fire suppression toward a multiplicity assembled around an expectation of security and the promise of economic freedom. These multiple shifts, we argue, reflect a characteristic approach to governing through the Foucauldian “apparatus of security,” a mechanism of power that seeks security through economic freedom and indirect governmental intervention. Central to the emerging apparatus of wildfire security are three core rationalizing discourses focused on the valorization of the individual’s capacity for wildfire management and protection, the negotiation of limits of state and public institutions in wildfire management, and the invitation to live resiliently with wildfires by embracing biophysical contingency. At stake is the complex politics through which the very ideas of wildfire risk, responsibility, and security are being, and can be, reconstituted. Our analysis furthers the poststructural geographies of wildfires and climate risk governance in Canada and beyond.

Towards a global perspective for Salvia L.: Phylogeny, diversification and floral evolution.

Salvia is the most species-rich genus in Lamiaceae, encompassing approximately 1000 species distributed all over the world. We sought a new evolutionary perspective for Salvia by employing macroevolutionary analyses to address the tempo and mode of diversification. To study the association of floral traits with speciation and extinction, we modelled and explored the evolution of corolla length and the lever-mechanism pollination system across our Salvia phylogeny. We reconstructed a multigene phylogeny for 366 species of Salvia in the broad sense including all major recognized lineages and 50 species from Iran, a region previously overlooked in studies of the genus. Our comprehensive sampling of Iranian species of Salvia provides higher phylogenetic resolution for southwestern Asian species than obtained in previous studies. Our phylogenetic data in combination with divergence time estimates were used to examine the evolution of corolla length, woody versus herbaceous habit, and presence versus absence of a lever mechanism. We investigated the timing and dependence of Salvia diversification related to corolla length evolution through a disparity test and BAMM analysis. A HiSSE model was used to evaluate the dependency of diversification on the lever-mechanism pollination system in Salvia. A medium corolla length (15-18 mm) was reconstructed as the ancestral state for Salvia with multiple shifts to shorter and longer corollas. Macroevolutionary model analyses indicate that corolla length disparity is high throughout Salvia evolution, significantly different from expectations under a Brownian motion model during the last 28 million years of evolution. Our analyses show evidence of a higher diversification rate of corolla length for some Andean species of Salvia compared to other members of the genus. Based on our tests of diversification models, we reject the hypothesis of a direct effect of the lever mechanism on Salvia diversification. Therefore, we suggest caution in considering the lever-mechanism pollination system as one of the main drivers of speciation in Salvia.

Optimal reception of multiple phase shift keying and quadrature amplitude modulation signals with non-coherent processing of harmonic interference

Objectives . Analysis of the reception noise immunity of multiple phase shift keying (M-PSK) and quadrature amplitude modulation (M-QAM) signals has demonstrated a significant reduction in the quality of reception of discrete information due to the presence of various types of non-fluctuating interference in a radio communication channel including targeted harmonic interference. Therefore, the development of algorithms for compensating the influence of such forms of interference is an urgent task. While various methods for combatting this kind of interference, these vary in terms of their effectiveness. The aim of the present work is to synthesize and analyze the optimal algorithm for the reception of M-PSK and M-QAM signals with incoherent processing of harmonic interference. Methods . Various statistical radio engineering and computer simulation methods were used in accordance with optimal signal reception theory. Results . Synthesis and analysis of the optimal algorithm for receiving M-PSK and M-QAM signals with incoherent processing of harmonic interference were carried out. In addition to calculating the correlation integrals in the receiver, it is necessary to form weight coefficients, whose value depends on the correlation of the interference oscillation (extracted from the received mixture) with a sample of the interference stored in the receiver. The dependences of the bit error probability on the signal-to-noise ratio, interference detuning, and inaccuracy in setting the frequency and level of the interference sample in the receiver were obtained. It is shown that the higher the gain in the noise immunity of reception, the greater the intensity of the harmonic interference. Conclusions . The synthesized receiver circuit effectively compensates for harmonic interference. However, the efficiency of its operation depends on the detuning of the harmonic interference relative to the center frequency of the spectrum of the useful signal. The scheme for incoherent processing of harmonic interference remains operational even with small (within ±10%) inaccuracies in setting the frequency and the level of the interference copy in the receiver.

DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model’s capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods.