Dynamic Scaling Research Articles

A novel 8T SRAM cell using PFC and PPC VS-CNTFET transistor

The 8T static random-access memory (SRAM) cell using carbon nanotube technology, positive feedback, and dynamic supply voltage scaling are presented in this work. Positive feedback strengthens the feedback loop and enhances the noise margin making SRAM cells less susceptible to disturbance and improving the stabilization of the cell by improving read and write timing response. Positive feedback control (PFC) adjusts the cell’s operating condition based on its current and external condition under varying conditions. The positive power supply controlled (PPC) technique in SRAM cell design improves the stability and leakage power consumption by adjusting the voltage level during the operation mode of the cell. The experiment with carbon nanotube field-effect transistor (CNTFET) offers higher drive current and lower power consumption compared to conventional silicon-based transistors. The performance of the 8T SRAM cell incorporating PFC and PPC transistor is investigated with Synopsys HSPICE using the Stanford CNFET model. The proposed SRAM cell architecture archives a 99.99% improvement in power consumption and delay product (PDP) compared to a conventional 6T SRAM cell. The static noise margin of 300 mV ensures better noise immunity and reliable retention of data. The mean value of power consumption is 43.19 nW showing a variance of 93.16 fW and a standard deviation (σ) of 305.2 nW and the mean value of delay is 14.71 ps showing a variance of 1.010 and a standard deviation (σ) of 10.05 ps. CNTFET 8T SRAM cell with the combination of positive feedback and dynamic feedback enhances the performance and efficiency of the memory cell under varying conditions.

Violence Risk Assessment Tools Used in Forensic and Acute Psychiatry in North America: A Scoping Review

Violence in psychiatric settings presents a significant risk to patients, staff, and society at large. With over 400 risk assessment tools available globally, their applications and the risks they assess vary, allowing for diverse use in different situations. This scoping review investigated the risk management tools utilized in North America’s forensic psychiatry and acute psychiatric units, aiming to identify which ones are mainly used. A comprehensive search was conducted across PubMed, Embase, and PsycINFO databases, following PRISMA Guidelines, covering the literature from their inception date until 2023. Criteria for study inclusion required a focus on risk management tool use in forensic or acute psychiatric settings, originality (original studies, case reports, or systematic reviews), and a North American context. Out of 3059 identified studies, 40 were thoroughly analyzed. Commonly used risk assessment scales include HARM-FV, eHARM-FV, HCR-20, PCL-R, START, BVC, and DASA, with their reliability varying by the clinical context and the assessed population. The review highlights the heterogeneous application of static and dynamic scales across clinical settings, underscoring a need for more precise tools to improve risk assessments in forensic psychiatry, signaling a call for the development and validation of more sophisticated assessment instruments.

Breaking New Ground in Monocular Depth Estimation with Dynamic Iterative Refinement and Scale Consistency

Monocular depth estimation (MDE) is a critical task in computer vision with applications in autonomous driving, robotics, and augmented reality. However, predicting depth from a single image poses significant challenges, especially in dynamic scenes where moving objects introduce scale ambiguity and inaccuracies. In this paper, we propose the Dynamic Iterative Monocular Depth Estimation (DI-MDE) framework, which integrates an iterative refinement process with a novel scale-alignment module to address these issues. Our approach combines elastic depth bins that adjust dynamically based on uncertainty estimates with a scale-alignment mechanism to ensure consistency between static and dynamic regions. Leveraging self-supervised learning, DI-MDE does not require ground truth depth labels, making it scalable and applicable to real-world environments. Experimental results on standard datasets such as SUN RGB-D and KITTI demonstrate that our method achieves state-of-the-art performance, significantly improving depth prediction accuracy in dynamic scenes. This work contributes a robust and efficient solution to the challenges of monocular depth estimation, offering advancements in both depth refinement and scale consistency.

Research on Improved Dynamic Time Scale Algorithm for Time-keeping System

Research on Improved Dynamic Time Scale Algorithm for Time-keeping System

Facile Generation of Cyanoselenocysteine as a Vibrational Label for Measuring Protein Dynamics on Longer Time Scales by 2D IR Spectroscopy.

Two-dimensional infrared (2D IR) spectroscopy is a powerful technique for measuring molecular heterogeneity and dynamics with a high spatiotemporal resolution. The methods can be applied to characterize specific residues of proteins by incorporating frequency-resolved vibrational labels. However, the time scale of dynamics that 2D IR spectroscopy can measure is limited by the vibrational label's excited-state lifetime due to the decay of 2D IR absorption bands. To extend this time scale, vibrational labels with longer lifetimes are sought. An effective approach to inhibiting intramolecular energy relaxation is to isolate the vibration from the rest of the molecule by inserting a heavy atom bridge. Although this strategy has been demonstrated through the generation of functionalized amino acids, a straightforward route to their selective incorporation into proteins is often unclear. A facile approach for the attachment of a cyano group at cysteine to generate a thiocyanate has contributed to its adoption as a vibrational label of proteins. We demonstrate that an analogous route can be used for introducing cyanoselenocysteine to generate a selenocyanate vibrational label containing a heavier bridge atom. We confirm by infrared pump-probe and 2D IR spectroscopy longer vibrational lifetimes of 100-250 ps, depending on the solvent, which enable the collection of 2D IR spectra to measure frequency dynamics on longer time scales.

The Study of Machine Learning Inference Tasks Based on Serverless Computing Platforms

This study proposes a distributed inference method based on the ResNet50 model, aiming to improve inference efficiency and resource utilization by dividing the model into multiple sub-models. Specifically, the model is divided into the initial convolutional layer, four stages of residual blocks, and the subsequent global average pooling and fully connected layers. Each sub-model independently handles specific tasks, allowing for parallel execution on different computing devices, thereby accelerating the overall inference process. This partitioning strategy effectively addresses high-concurrency requests, enhancing the system‘s response speed. Additionally, it enables dynamic scaling of resources based on workload demands, which is crucial for real-time applications. The implementation of distributed inference also makes the model more flexible, adapting to various computing resources and application scenarios. Experimental results indicate that this method significantly enhances inference efficiency while maintaining model performance, providing new ideas and solutions for the practical application of deep learning models. These findings underscore the potential of distributed architectures in advancing the deployment of complex neural networks across diverse environments.

Dynamic adaptive scaling network for camouflaged object detection

Dynamic adaptive scaling network for camouflaged object detection

Дистанционная калибровка динамических вагонных весов

Metrological maintenance of measuring instruments is aimed at ensuring the uniformity of measurements. It allows you to achieve the required accuracy of the measured physical quantities, determine corrections to the readings of measuring instruments and estimate the error. With the development of scientific and technological progress, the achievement of accuracy is ensured by more advanced and simple methods. Remote calibration of dynamic wagon scales examines the current issues of the application of remote calibration methods in the field of measurements in transport. The analysis of the advantages of remote calibration in comparison with traditional metrological services emphasizes economic efficiency and reduces time costs. The article describes the measurement process using remote control methods, the use of which will allow the introduction of remote calibration of dynamic wagon scales in the railway industry. Purpose: to describe the possibility of remote calibration of dynamic wagon scales. Methods: conducting an experiment of remote calibration of dynamic wagon scales using the global Internet and special software, a weighing program and communication programs. Results: the possibility of remote calibration of dynamic wagon scales was determined, the necessary programs for operation were described, advantages and disadvantages were presented. Practical importance: the possibility of introducing remote calibration of dynamic wagon scales has been determined, which will reduce personnel time and financial costs for metrological maintenance and periodic calibration.

Generalized Langevin equation for a tagged monomer in a Gaussian semiflexible polymer.

In this study, we present a comprehensive analysis of the motion of a tagged monomer within a Gaussian semiflexible polymer model. We carefully derived the generalized Langevin equation (GLE) that governs the motion of a tagged central monomer. This derivation involves integrating out all the other degrees of freedom within the polymer chain, thereby yielding an effective description of the viscoelastic motion of the tagged monomer. A critical component of our analysis is the memory kernel that appears in the GLE. By examining this kernel, we characterized the impact of bending rigidity on the non-Markovian diffusion dynamics of the tagged monomer. Furthermore, we calculated the mean-squared displacement of the tagged monomer using the derived GLE. Our theoretical findings were corroborated by the Langevin dynamics simulation and scaling theory. Our results not only show remarkable agreement with previously known results in certain limiting cases but also provide dynamic features over the entire timescale.

Caregivers’ gender as a moderator in the relationship between family quality of life and children’s health-related quality of life

ABSTRACT This study investigates the moderating role of caregiver gender in the relationship between family quality of life and children’s health-related quality of life. Data was collected through an online questionnaire incorporating the Family Quality of Life Dynamics Scale, the KIDSCREEN-27 Questionnaire for Parents and a Family Information Form. The total sample consisted of n = 999 caregivers (594 mothers, 400 fathers and 5 stepfathers, Mage = 40.11, SD = 6.05) who have children aged between 8 and 18 in the northern part of Cyprus. The findings supported a positive relationship between higher levels of family quality of life and children’s health-related quality of life. However, moderation analysis did not support the second hypothesis, which proposed that the caregiver’s gender moderates the association between family life quality and children’s life quality. The results are discussed in light of cultural expectations regarding parenting responsibilities. Practical implications and suggestions for future research are provided based on these findings.

Microfluidics and Spectral Induced Polarization for Direct Observation and Petrophysical Modeling of Calcite Dissolution

AbstractWe investigate how micro‐geoelectrical monitoring is promising for studying microscale coupled processes since it facilitates the upscaling of pore‐scale observations and enhances the petrophysical interpretation of the geoelectrical measurements. Microscale geophysics using microfluidics emerges and combines direct visualization of pore scale dynamics and chemical reactivity with geoelectrical monitoring. Calcite dissolution is a usual geochemical reaction considered as an analog of water–mineral interactions involved in the critical zone. We develop a numerical workflow combining image processing and geochemical simulation as inputs for the petrophysical modeling applied to a published data set of microscale induced polarization monitoring of calcite dissolution under partially saturated conditions. The successful interpretation provides the cation exchange capacity and specific surface area evolution; essential parameters in field‐scale surveys.

Reinforcement Learning Pair Trading: A Dynamic Scaling Approach

Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n = 263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies.

Scale characteristics and growth process of shallow water delta under different lake levels— based on Delft3D numerical simulation research

Modern bays and lakes typically develop shallow deltas dominated by rivers, with lake levels playing a significant role in their formation. However, the precise effects of lake level height on the scale and growth dynamics of these deltas remain unclear. To address this, this study employs the sedimentary numerical simulation software Delft3D to model delta development under high, medium, and low lake levels. By analyzing flow velocity distribution, sediment accumulation, and sediment thickness, the study quantitatively assesses the impact of varying lake levels on shallow deltas. The results indicate that: (1) the areal extent of the delta is inversely related to the lake level, whereas sediment thickness is directly proportional to it; (2) within the same simulation period, higher lake levels tend to produce fewer breach distributary channels, while lower levels are more conducive to forming numerous breach distributary channels; however, the impact of lake level on active distributary channels is minimal; (3) deltas consist of multiple complexes. Under high lake levels, a single complex typically exhibits a bird-foot shape, characterized by active distributary channels and mouth bars, with sediment thickness decreasing from the source. In contrast, under low lake levels, a single complex tends to have a flower shape, with active distributary channels, mouth bars, and multiple breach distributary channels, resulting in a more evenly distributed sediment thickness. This research result can provide new ideas for the comparative evolution of deltas under different lake level water levels.

Spin glass behavior of single-crystalline ErFe2O4−δ

Abstract We investigated magnetic properties of ErFe2O4-δ single crystal containing oxygen vacancies with value of δ being estimated to be 0.066. The temperature dependence of dc magnetization in zero field cooling (ZFC) process reveals that ErFe2O4-δ undergoes a ferrimagnetic transition at 234 K followed by a broad peak at 224.3 K. The latter is attributable to the spin glass transition as indicated by the strong frequency dependence of temperature-dependent ac susceptibility. The frequency variation of transition temperature is described well by the dynamic scaling law and the critical exponent is similar to those reported for typical spin glasses. Additionally, the spin glass transition at 224.3 K is confirmed by the fact that the magnetic field dependence of irreversible transition temperature is coincident with the de Almeida-Thouless line and that the aging memory and rejuvenation effect is observed. Noticeably, ErFe2O4-δ single crystal exhibits the field-cooled hysteresis loop shift and training effect below 200 K, suggesting the occurrence of exchange bias effect originating from the spin glass phase.

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

Factors Affecting Romantic Relationship in Lebanon

The concepts of romance and love are rapidly evolving with new modalities of romantic relationships in Lebanon (civil marriage, cohabitation) along with a catastrophic economic crisis affecting the country since 2019, and being ranked among the top ten crises worldwide. This document represents a cross-sectional study investigating 585 Lebanese participants from different Lebanese regions randomly sampled using the snowball method. Besides, the Relationship dynamics scale (RDS) was used to evaluate vulnerability for conflicts in the context of romantic relationship and their correlates, with a very good internal consistency (Cronbach’s Alpha of 0.908) and an excellent validity (Factor analysis using Promax technique). The results are as follows: A higher vulnerability for conflicts in the context of amorous relations is associated with having a child or a partner with handicap or receiving an actual chronic treatment (personal, for children or for the partner), having 3 or more children and finally having high financial stressors. Moreover, this survey evaluated factors modulating financial burden and stress using the InCharge Financial Distress / Financial Well-Being (IFDFW) scale. The results showed that working only with day shifts schedule, being male, having physical activity of a minimum of 2.5 hours per week, having a partner working outside Lebanon, having an income in currency other than Lebanese Pound, and being married at an age above 30 years are associated with less stressful economic status. While on the other hand, having a history of chronic treatment (Personal, Partner or children) and being married before 18 years were correlated with high levels of economic stress.

From Inverse-Cascade to Subdiffusive Dynamic Scaling in Driven Disordered Bose Fluids.

We explore the emergence of universal dynamic scaling in an interacting Bose gas around the condensation transition, under the combined influence of an external driving force and spatial disorder. As time progresses, we find that the Bose gas crosses over three distinct dynamical regimes: (i)an inverse turbulent cascade where interactions dominate the drive, (ii)a stationary regime where the inverse cascade and the drive counterbalance one other, and (iii)a sub-diffusive cascade in energy space governed by the drive and disorder, a phenomenon recently observed experimentally. We show that all three dynamical regimes can be described by self-similar scaling laws.

Superdiffusive transport in quasi-particle dephasing models

Investigating the behavior of noninteracting fermions subjected to local dephasing, we reveal that quasi-particle dephasing can induce superdiffusive transport. This superdiffusion arises from nodal points within the momentum distribution of local dephasing quasi-particles, leading to asymptotic long-lived modes. By studying the dynamics of the Wigner function, we rigorously elucidate how the dynamics of these enduring modes give rise to Lévy walk processes, a renowned mechanism underlying superdiffusion phenomena. Our research demonstrates the controllability of dynamical scaling exponents by selecting quasi-particles and extends its applicability to higher dimensions, underlining the pervasive nature of superdiffusion in dephasing models.

A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

Multiscale finite element modelling of dynamic subsea power cables in bending

Abstract This paper proposes a multiscale finite element modelling strategy for dynamic offshore power cables which allows for detailed analysis of individual components. The challenge of modelling the intricate power cores within the cable is addressed by considering a separate repeated unit cell (RUC) of the power core to define its stiffness, where the power cores themselves are then discretised with beam elements in the dynamic cable model. Periodic boundary conditions allowing for geometric non-linearity are employed at both the power core and dynamic cable scale. This means the model size can be reduced to the smallest possible RUC while still permitting non-linear material behaviour. The resulting model is capable of detailed stress analysis of individual components required for fatigue life estimation. The model is validated against existing experimental data showing strong agreement. The proposed multiscale approach may therefore be used to augment existing fatigue analysis methods for dynamic offshore cables where the stress state at individual wires is required.