Frequency Scaling Research Articles

The Post-Operative Handoff: Perceptions and Preferences of Pediatric Hospitalists and Surgeons.

Postoperative communication errors contribute to patient harm and excess costs. There are no existing standards for postoperative handoff to the acute care inpatient unit. We aimed to compare the experiences and preferences of pediatric hospitalists and surgeons about the content and timing of this handoff. We conducted a cross-sectional multisite survey of pediatric hospitalists and surgeons at 4 hospitals using a novel survey tool developed through a systematic 7-step process. We collected data on the perceived frequency of communication for 37 handoff elements and how essential each element was for an ideal handoff. We used 5-point Likert scales of communication frequency and essentialness. Respondents identified perceived and preferred handoff timing. Mention frequency and timing data were analyzed with the Mann-Whitney U test and Fisher's exact test, respectively. Seventy hospitalists (61%) and 27 surgeons (25%) responded to the survey. Over half of both hospitalist and surgeon respondents rated 13 handoff elements a 5 on the essentialness Likert scale. Surgeons perceived that 33 handoff elements were mentioned significantly more frequently than perceived by hospitalists (P < .05). Of hospitalists, 58% preferred that handoff occur immediately before the patient leaves the postanesthesia care unit. Of surgeons, 60% preferred that handoff occur immediately postoperatively. The 13 core elements we identified may facilitate the development of a standardized handoff checklist for postoperative communication between surgeons and hospitalists on acute care units. Areas of future study could include checklist validation, audits of handoff practice, and qualitative research on handoff preferences.

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

The forecasting of surface displacement for tunnel slopes utilizing the WD-IPSO-GRU model

To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time–frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c1, and social factor c2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units hN, HN, LN and dropout rates D1 and D2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

A Extensive Review of Recent Technologies and Trends in Low-Power VLSI Design

The demand for energy-efficient electronic devices has driven significant advancements in low-power Very-Large-Scale Integration (VLSI) design. This paper presents a Extensive review of the recent technologies and trends that have emerged to address the challenges associated with power utilization in VLSI circuits. The review covers a broad spectrum of innovations, including advanced transistor technologies such as FinFETs, Gate-All-Around (GAA) FETs, and Silicon-on-Insulator (SOI), which offer improved power efficiency. It also explores cutting-edge power enhance techniques like Dynamic Voltage and Frequency Scaling (DVFS), power gating, and Multi-Threshold CMOS (MTCMOS), highlighting their impact on reducing both dynamic and leakage power. Further, the paper examines energy-efficient architectures, including Near-Threshold Computing (NTC), approximate computing, and asynchronous circuits, which promise substantial power savings. The fusing of machine learning and AI in power optimization and the development of advanced Electronic Design Automation tools are also discussed as emerging trends. Finally, the paper considers future directions, including the potential of 3D Integrated Circuits (3D ICs), quantum and neuromorphic computing, and post-CMOS technologies, to revolutionize low-power VLSI design. This review provides a critical exploration of the actual state of the art and offers comprehensions into the future trajectory of low-power VLSI, making it a valuable resource for researchers and practitioners in the field.

A general framework for generative self-supervised learning in non-invasive estimation of physiological parameters using photoplethysmography

Aligning physiological parameter labels with large-scale photoplethysmographic (PPG) data for deep learning is challenging and resource-intensive. While self-supervised representation learning (SSRL) can handle limited annotated data, the challenge lies in learning robust shared representations from vast unlabeled data and integrating various contextual cues to learn distinctive representations. To alleviate these challenges, a generative SSRL framework TS2TC is proposed to collaboratively utilize the temporal, spectrogram, and temporal-spectrogram mixed domains to explore and incorporate the unique features of PPG for universal and non-invasive physiological parameter estimation. Initially, a pretext task named Cross-Temporal Fusion Generative Anchor (CTFGA) is designed, modeling temporal dependencies and reconstructing independent segments at a coarse level to provide robust global feature extraction and local semantic contextual representation. The framework also includes sub-signals from PPG with diverse frequency scales and order derivatives reflecting hemodynamics to facilitate learning shared representations at varying semantic levels. Secondly, an advanced cognitive-inspired dual-process transfer (DPT) strategy is formulated, consisting of prior-dependent autonomous processes and posterior observation reasoning processes, to leverage the independent and integrated advantages of shared and specific representations. Furthermore, TS2TC introduces a novel bilinear temporal-spectrogram fusion method in the mixed domain, aligning latent representations from different domains, and establishing fine-grained contextual interactions at the feature level across multiple sources of information. Extensive experiments on physiological parameter estimation tasks showed that the joint performance of CTFGA and DPT outperforms standard generative learning significantly. TS2TC achieved an average 2.49% improvement in RMSE over the current state-of-the-art estimation methods with only 10% training data.

Linkage equilibrium between rare mutations.

Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination, and discuss their implications for the rates of horizontal gene transfer in bacteria.

Propagation Dynamics from Meteorological to Agricultural Drought in Northwestern China: Key Influencing Factors

Meteorological and agricultural droughts are inherently correlated, whereas the propagation mechanism between them remains unclear in Northwestern China. Investigating the linkages between these drought types and identifying the potential influencing factors is crucial for effective water resource management and drought mitigation. This study adopted the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI) to characterize the meteorological and agricultural droughts from 1960 to 2018. The propagation time between these droughts was detected using the Pearson correlation analysis, and the cross-wavelet transform and wavelet cross-correlation were utilized to describe their linkages across the time–frequency scales. The grey relational analysis was applied to explore the potential factors influencing the propagation time. The results revealed that the agricultural drought typically lagged behind the meteorological drought by an average of 6 months in Northwestern China, with distinct seasonal and regional characteristics. The shortest propagation time occurred in the summer (3 months), followed by the autumn (4 months), and the propagation time was longer in the winter (8 months) and spring (9 months). Additionally, the average propagation time was longer in the plateau climate zone (8 months) than in the southeastern climate zone (6 months) and the westerly climate zone (4 months). There was a multi-timescale response between the meteorological and agricultural droughts, with a relatively stable and significant positive correlation over long timescales, whereas the correlation was less clear over short timescales. The key factors influencing the propagation time were soil moisture, elevation, precipitation, and potential evapotranspiration. Furthermore, the wavelet cross-correlation between agricultural and meteorological droughts was relatively high, with a lag of 0 to 3 months; as the timescale increased, the fluctuation period of their cross-correlation also increased.

An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

Harmonic Scale Factors of Fundamental Transitions for Dispersion-corrected Quantum Chemical Methods.

This work provides a procedure and database for obtaining the vibrational frequency scale factors that align quantum chemically computed harmonic frequencies with experimental vibrational spectroscopic data. The database comprises 441 molecules of various sizes, from diatomics to the buckminsterfullerene C60. We provide scale factors for 27 dispersion-corrected methods, 24 of which are DF-Dn/B with DF=BLYP,PBE,B3LYP,PBE0, Dn=D3(BJ),D4, and B=6-31G,def2-SVP,def2-TZVP, and three of them are the 3c-family composite methods (HF-3c, PBEh-3c, and r2SCAN-3c). The two scale factors are derived for each method: the absolute scaling, minimizing the absolute deviation of the scaled harmonic frequency from the experimental value, and the relative scaling, which minimizes an analogous relative deviation. The absolute type of scaling is recommended for frequencies above 2000 cm-1, while the relative scaling is optimal for frequencies below 2000 cm-1.

A Cyclic Spectroscopy Scintillation Study of PSR B1937+21. I. Demonstration of Improved Scintillometry

We use cyclic spectroscopy to perform high-frequency resolution analyses of multihour baseband Arecibo observations of the millisecond pulsar PSR B1937+21. This technique allows for the examination of scintillation features in far greater detail than is otherwise possible under most pulsar timing array observing setups. We measure scintillation bandwidths and timescales in each of eight subbands across a 200 MHz observing band in each observation. Through these measurements we obtain intra-epoch estimates of the frequency scalings for scintillation bandwidth and timescale. Thanks to our high-frequency resolution and the narrow scintles of this pulsar, we resolve scintillation arcs in the secondary spectra due to the increased Nyquist limit, which would not have been resolved at the same observing frequency with a traditional filterbank spectrum using NANOGrav’s current time and frequency resolutions, and the frequency-dependent evolution of scintillation arc features within individual observations. We observe the dimming of prominent arc features at higher frequencies, possibly due to a combination of decreasing flux density and the frequency dependence of the plasma refractive index of the interstellar medium. We also find agreement with arc curvature frequency dependence predicted by Stinebring et al. in some epochs. Thanks to the frequency-resolution improvement provided by cyclic spectroscopy, these results show strong promise for future such analyses with millisecond pulsars, particularly for pulsar timing arrays, where such techniques can allow for detailed studies of the interstellar medium in highly scattered pulsars without sacrificing the timing resolution that is crucial to their gravitational-wave detection efforts.

Constrained motion of self-propelling eccentric disks linked by a spring.

It has been supposed that the interplay of elasticity and activity plays a key role in triggering the non-equilibrium behaviors in biological systems. However, the experimental model system is missing to investigate the spatiotemporally dynamical phenomena. Here, a model system of an active chain, where active eccentric-disks are linked by a spring, is designed to study the interplay of activity, elasticity, and friction. Individual active chain exhibits longitudinal and transverse motions; however, it starts to self-rotate when pinning one end and self-beat when clamping one end. In addition, our eccentric-disk model can qualitatively reproduce such behaviors and explain the unusual self-rotation of the first disk around its geometric center. Furthermore, the structure and dynamics of long chains were studied via simulations without steric interactions. It was found that a hairpin conformation emerges in free motion, while in the constrained motions, the rotational and beating frequencies scale with the flexure number (the ratio of self-propelling force to bending rigidity), χ, as ∼(χ)4/3. Scaling analysis suggests that it results from the balance between activity and energy dissipation. Our findings show that topological constraints play a vital role in non-equilibrium synergy behaviors.

On the coupling instability of a gas jet impinging on a liquid film

We investigate the dynamics of a gas jet impinging perpendicular to a thin liquid film dragged by a rising vertical substrate. This configuration is relevant to the jet-wiping process in hot-dip galvanization and it is unstable. Previous studies analysed the dynamics of the instability in the case of liquids with low Kapitza numbers (highly viscous liquids), more amenable to experimental and numerical investigations. This work extends the previous investigations by focusing on the wiping at much higher Kapitza numbers, which are more relevant to the galvanizing process. The simulations are carried out by combining volume of fluid and large-eddy simulations, and the dynamics of the gas–liquid interaction is analysed using extended multiscale proper orthogonal decomposition. The simulations allowed for analysing the jet-wiping instability in new flow conditions. Despite the largely different conditions, the results show that the interaction between the gas jet and the liquid film is qualitatively similar, featuring two-dimensional waves in the liquid correlated with oscillations and deflections of the gas jet in all cases. The wave characteristics (e.g. frequency and propagation speed) scale remarkably well using the Shkadov-like scaling based on the liquid, suggesting a dominant role of the liquid film in the coupling, and potentially enabling extrapolation of the results to a broader range of wiping conditions. Finally, we use the numerical results to discuss the limitations of liquid-film models, which constitute currently the only possible approach to study the jet-wiping process in industrial conditions.

Conceptualizing problematic use of medicinal Cannabis: Development and preliminary validation of a brief screening questionnaire.

The use of Medicinal Cannabis (MC) for treating chronic pain is increasing, globally, yet the definition of problematic MC use remains unclear. Defining problematic use of cannabis in medical and non-medical contexts may be fundamentally different since individuals prescribed MC often experience physical dependence, which do not necessarily imply pathology. We aimed to conceptualize problematic use of MC and develop a brief questionnaire for identifying and quantifying problematic MC use. Content validation used a broad definition of problematic substance use, adapting and compiling an initial list of 36 items from various sources: (a) screening tools for assessing problematic prescription opioid medication use; (b) screening tools for problematic recreational cannabis use and (c) qualitative interviews with MC patients. 390 American self-identified chronic pain patients holding a MC card rated each item from the initial list on a 5-point frequency scale and filled out questionnaires assessing various clinical outcomes. Following initial item titration, a multi-group measurement invariance comparison strategy, using two external indicators: alcohol-related problems and depression, resulted a final eight-item list that met fit quality in a baseline model, presented excellent internal consistency reliability (α=0.929), and significantly correlated with anxiety and low quality of life. Items in the final list related predominantly to negative consequences of MC use. Problematic use of MC is characterized by negative physiological, social, emotional and functional consequences. The final eight-item list was named the Medicinal Cannabis Negative Consequences Scale (MCNCS), emerging as a brief measure for problematic MC use and demonstrating preliminary reliability and validity, which could aid clinicians and researchers.

Effectiveness of greater occipital nerve blockade in chronic cluster headache.

Greater occipital nerve (GON) blockade injections can be used to prevent episodic and chronic cluster headaches. In recent studies, prophylactic treatment has been used in addition to the GON blockade. In this study, we aimed to elucidate the effect of GON blockade on the attack frequency, pain intensity, and duration in patients diagnosed with chronic cluster headaches. The demographic characteristics of 30 patients who received GON blockade along with acute attack treatment, short- and long-term prophylactic treatment for cluster headache, and 24 patients who received only acute attack treatment, short- and long-term prophylactic treatment, before blockade treatment, in the 1st week and 1st month after blockade were investigated. Attack frequency, attack duration, and visual analog scale (VAS) variables were compared. We evaluated the VAS score, daily attack frequency, and duration of pain attacks after repeated GON blockade and found a statistically significant difference in the VAS score, daily attack frequency, duration of pain attacks, average values of the treatment, and time interaction of pain intensity in the group in which GON blockade was applied in the 1st week and 1st month compared to the pre-treatment period (p<0.01), (p<0.01), (p=0.044). Regarding the outcomes of this research, GON blockade provided significant improvement in pain frequency, attack duration, and VAS score in the period from attack treatment to the start of long-term prophylaxis treatment and one month after treatment, without the need to switch to different prophylaxis treatments. Therefore, GON blockade may be a preferable and reliable treatment option.

Advanced computer architecture optimization for machine learning/deep learning

Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.

Parasomnias in Post-Secondary Students: Prevalence, Distress, and Coping Strategies.

Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal, or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the current and recent post-secondary student population and to explore their coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia experienced was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced several parasomnias. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies (i.e., coping strategies that help manage distressing feelings) and physical manipulation of one's body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these. Therefore, we provide some accepted ways of dealing with these.

Synthesis of Singular Systems Walsh and Walsh-like Functions of Arbitrary Order

Functionally complete systems of Walsh functions (bases), a particular case of alternating piecewise constant sequential functions, are widely used in various scientific and technological fields. As applied to the tasks of spectral analysis of discrete signals, the most interesting are those Walsh bases that deliver linear coherence of the frequency scales of fast Fourier transform (FFT) processors. By the frequency scales of an FFT processor, we mean the scale on which the normalized frequencies of the input signal are arranged (input scale) and the scale on which the signal's spectral components are arranged (output scale). The frequency scales of the FFT processor are considered linearly coherent if the processor responses with maximum amplitudes and phases of the same sign are located on the bisector of the Cartesian coordinate system formed by the frequency scales of the processor. None of the known Walsh bases ordered by Hadamard, Kaczmage, or Paley provide linear coherence of the frequency scales of the FFT processor. In this study, we develop algorithms to synthesize two systems, called Walsh-Cooley and Walsh-Tukey systems, which turn out to be the only ones in the set of classical Walsh systems and sequents Walsh-like systems, respectively, that deliver linear coherence to the frequency scales of FFT processors.

Hints from Hertz: Dynamic Frequency Scaling Side-Channel Analysis of Number Theoretic Transform in Lattice-Based KEMs

Hints from Hertz: Dynamic Frequency Scaling Side-Channel Analysis of Number Theoretic Transform in Lattice-Based KEMs

Muscle Excitability Scale for the assessment of spastic reflexes in spinal cord injury: development and evaluation

Study designA psychometric study.ObjectivesTo introduce a novel simple tool designed to evaluate the intensity of the phasic (dynamic) component of spastic motor behavior in spinal cord injury (SCI) people and to assess its reliability and validity.SettingThe study was developed in the Spinal Cord Unit at University Hospital Motol and Paraple Centre in Prague, Czech Republic.MethodsThe Muscle Excitability Scale (MES) is designed to rate muscle motor response to exteroceptive and proprioceptive stimuli. The impairment rating ranges from zero muscle/muscle group spasm or clonus to generalized spastic response. The selected 0 to 4 scale allows for comparing the MES results with those of the Modified Ashworth Scale (MAS). After long-term use and repeated revisions, a psychometric analysis was conducted. According to the algorithm, two physiotherapists examined 50 individuals in the chronic stage after SCI.ResultsThe inter-rater reliability of MES for both legs showed κ = 0.52. The intra-rater reliability of MES for both legs showed κ = 0.50. The inter-rater reliability of simultaneously assessed MAS for both legs was higher, with κ = 0.69. The intra-rater reliability of MAS for both legs showed κ = 0.72. Spearman’s rank correlation coefficient between MES and spasm frequency of Penn Spasm Frequency Scale (PSFS) was low, while the correlation coefficient between MES and the severity part of PSFS was moderate.ConclusionsThe MES is a complementary tool for assessing the dynamic component of spastic motor behavior in SCI people. It allows a more comprehensive clinical characterization of spastic reflexes when used along with the MAS.

Density Functional Theory Calculations for Interpretation of Infra-Red Spectra of Liquid Crystalline Chiral Compound

The experimental IR spectra of (S)-4′-(1-methylheptyloxycarbonyl) biphenyl-4-yl 4-[2-(2,2,3,3,4,4,4-heptafluorobutoxy) ethyl-1-oxy]-2-fluorobenzoate in the crystal phase are analyzed with the help of dispersion-corrected density functional theory (DFT+D3) calculations for isolated molecular monomer and dimer models, and a periodic model computed at the extended density functional tight-binding (xTB) level of theory. It is found that the frequency scaling coefficients obtained with the results of the molecular calculations are good matches for the crystal phase, being close to 1. The molecular and periodic models both confirm that varied intra- and intermolecular interactions are crucial in order to reproduce the broadened shape of the experimental band related to C=O stretching; the key factors are the conjugation of the ester groups with the aromatic rings and the varied intermolecular chemical environments, wherein the C=O group that bridges the biphenyl and F-substituted phenyl groups seems particularly sensitive. The C=O stretching vibrations are investigated as a function of temperature, covering the range of the crystal, smectic CA*, smectic C* and isotropic liquid phases. The structure changes are followed based on the X-ray diffraction patterns collected in the same temperatures as the IR spectra. The experimental and computational results taken together indicate that the amount of weak C=O…H-C hydrogen bonds between the molecules in the smectic layers decreases with increasing temperature.