Level Of Compression Research Articles

External Short-Circuit Scenarios under Different Mechanical Pressures Applied to Lithium-Ion Cells with Silicon-Dominant Anodes

Abstract The safety behavior of lithium-ion cells using high contents of silicon as new anode active material was barely investigated. This work investigates the short-circuit behavior of self-manufactured single-layered pouch cells using 70%wt microscale silicon and NCA cathodes in a novel dual reference cell setup consisting of a gold wire and a lithium metal reference electrode. Six cells were externally shortened at three different compression levels using a novel developed quasi-isothermal calorimetric shortcircuit test bench. The same current plateaus and transition zones were observed for all pressure settings, as reported in the literature for graphite-containing anodes. In addition, a direct dependency of the short-circuit intensity and the heat generation is measured for the differently compressed cells. This higher short-circuit intensity was quantitatively explained by the decreasing impedance at increased pressure levels. The built-in reference electrodes allow for monitoring of the negative terminal voltage, revealing potentials of up to 3:508 V versus Li+/Li and thus explaining the measured over-discharge in the range of ≈25% SoC. The post-mortem analysis reveals copper deposition on both electrodes and the separator, confirming the electrochemically measured over-discharge. Linear scans and potential trajectories reveal 3.379 V versus Li+/Li as oxidation potential for copper dissolution using copper||lithium-metal coin cells.

Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review

Machine Learning (ML), particularly Deep Learning (DL), has become increasingly integral to medical imaging, significantly enhancing diagnostic processes and treatment planning. By leveraging extensive datasets and advanced algorithms, ML models can analyze medical images with exceptional precision. However, their effectiveness depends on large datasets, which require extended training times for accurate predictions. With the rapid increase in data volume due to advancements in medical imaging technology, managing the data has become increasingly challenging. Consequently, irreversible compression of medical images has become essential for efficiently handling the substantial volume of data. Extensive research has established recommended compression ratios tailored to specific anatomies and imaging modalities, and these guidelines have been widely endorsed by government bodies and professional organizations globally. This work investigates the effects of irreversible compression on DL models by reviewing the relevant literature. It is crucial to understand how DL models respond to image compression degradations, particularly those introduced by JPEG and JPEG2000—both of which are the only permissible irreversible compression techniques in the most commonly used medical image format—the Digital Imaging and Communications in Medicine (DICOM) standard. This study provides insights into how DL models react to such degradations, focusing on the loss of high-frequency content and its implications for diagnostic interpretation. The findings suggest that while existing studies offer valuable insights, future research should systematically explore varying compression levels based on modality and anatomy, and consider developing strategies for integrating compressed images into DL model training for medical image analysis.

Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, improved network usage, and certain privacy improvements, split learning, where deep learning models are split away from the mobile device and computed in a distributed manner, has become an extensively explored topic. Incorporating compression-aware methods (where learning adapts to compression level of the communicated data) has made split learning even more advantageous. This method could even offer a viable alternative to traditional methods, such as federated learning techniques. In this work, we develop an adaptive compression-aware split learning method (“deprune”) to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources. This method is also extended (“prune”) to very quickly train deep learning models through a transfer learning approach, which tradesoff little accuracy for much more network-efficient inference abilities. We show that the “deprune” method can reduce network usage by 4× when compared with a split-learning approach (that does not use our method) without loss of accuracy, while also improving accuracy over compression-aware split-learning by up to 4 percent. Lastly, we show that the “prune” method can reduce the training time for certain models by up to 6× without affecting the accuracy when compared against a compression-aware split-learning approach.

Mechanical fatigue in microtubules

Mechanical failure of biological nanostructures due to sustained force application has been studied in great detail. In contrast, fatigue failure arising from repeated application of subcritical stresses has received little attention despite its prominent role in engineering and potentially biology. Here, paclitaxel-stabilized microtubules are up to 256 times bent into sinusoidal shapes of varying wavelength and the frequency of breaking events are observed. These experiments allow the calculation of fatigue life parameters for microtubules. Repeated buckling due to 12.5% compression–equal to the compression level experienced by microtubules in contracting cardiomyocytes – results in failure after in average 5 million cycles, whereas at 20.0% compression failure occurs after in average one thousand cycles. The fatigue strength (Basquin) exponent B is estimated as − 0.054±0.009.

Utility of cervical dynamic magnetic resonance imaging for evaluating patients with cervical myelopathy: a retrospective study.

Retrospective observational study. This study aimed to evaluate the utility of cervical dynamic magnetic resonance imaging (dMRI) in the assessment of cervical canal stenosis. Cervical spondylotic myelopathy has been intricately linked to both static and dynamic narrowing of the cervical spinal canal. Traditional MRI with the neck in a neutral position fails to identify the dynamic changes and may lead to misdiagnosis. Cervical dMRI is a promising tool for evaluating cervical myelopathy, enabling clinicians to assess spinal cord compression, segmental instability, and alterations in range of motion, often missed on conventional imaging. A retrospective analysis was conducted on 369 patients with symptoms of cervical myelopathy assessed using cervical dMRI. After assessing the subaxial cervical spine at each disc level (C3-T1), significant changes in the degree of central canal stenosis were determined. The appearance and extent of hyperintense lesions on T2-weighted sequences were also noted. Overall, 653/1,845 (35.39%) disc levels showed an increase in stenosis grade on extension MRI, with 168/653 (25.72%) and 180/653 (27.56%) disc levels changing from grades 0/1 to grades 2 and 3, respectively. Moreover, 120/369 (32.52%) patients showed a mean increase of 1.55±0.75 levels of compression on extension MRI when compared to neutral MRI. A fresh-appearing hyperintense lesion was observed in 79 (4.28%) disc levels on flexion MRI, which was not visualized on neutral MRI. Cervical dMRI may help surgeons plan for surgery, discuss the prognosis with the patient, and safeguard themselves from medico-legal issues arising from improper or missed diagnosis and treatment.

Unlocking theoretical strength and ductility in sapphire nanopillars: Insights into size-dependent deformation mechanisms

Developing engineering structures that blend exceptional strength with superior ductility poses a formidable task, given that these attributes typically oppose each other. This is notably evident in brittle materials such as sapphire, where balancing strength and ductility becomes particularly challenging. In our investigation, we illustrate that single-crystal sapphire nanopillars can concurrently achieve both theoretical strength and notable ductility. Through meticulous experimental scrutiny, we have determined that these sapphire pillars exhibit compressive strength levels of up to 22 GPa, in line with the theoretical strength cap of E/10, where E denotes the elastic modulus. This discovery challenges the longstanding belief that ceramics cannot attain their E/10 ideal strength. Furthermore, our research unveils exceptional deformability in these structures, with observed strains reaching up to 20 %. Analyses using Transmission Electron Microscopy (TEM) have unveiled the deformation mechanisms, characterized by dislocation generation and substantial lattice distortions. Additionally, we delve into mechanical modeling to delineate the critical dimensions required to achieve maximum strength and facilitate a transition from brittle to ductile behavior. These revelations demonstrate the feasibility of combining significant strength with remarkable ductility in sapphire at the nanoscale, thereby paving the way for novel applications of brittle materials in precision engineering and nanotechnology.

Are research articles becoming more syntactically complex? Corpus-based evidence from research articles in applied linguistics and biology (1965–2015)

Research on the diachronic changes of syntactic complexity in research articles (RAs) in recent decades has been scant. As one of the first studies addressing this gap, the present study investigated the diachronic changes of eight indices of syntactic complexity in the RAs of applied linguistics and biology from 1965 to 2015 based on a corpus of 720 RAs (totalling 4.14 million tokens) from prestigious journals. The study revealed several common patterns of diachronic changes in syntactic complexity in two disciplines. First, RA writers in both disciplines tended to produce longer production units and employed more coordinate phrases and complex nominals over time, reflecting increased levels of syntactic complexity in two disciplines in recent five decades. In addition, both disciplines displayed an overall decline in the reliance on clausal elaboration (e.g. clausal coordination and clausal subordination), but an increase in phrasal complexity (e.g. phrasal coordination and complex nominals), which demonstrated an increased level of information compression in RA writing over time. The study also discussed changes in publication norms and practices in the two academic fields in recent decades that may be related to the diachronic changes of syntactic complexity features.

Utilizing incineration bottom ash-infused steel fiber-reinforced concrete: An analysis of compressive strength and carbonation durability

This study has developed concrete with moderate strength, and excellent carbonation resistance, by blending incineration bottom ash with steel fibers into the concrete mixture. Through single-factor experiments, investigate the effects of bottom ash content and steel fiber volume fraction on the mechanical properties and carbonation resistance of concrete. The results indicate that the addition of steel fibers effectively reduces the adverse effects of incineration bottom ash content on the compressive strength and carbonation level of concrete. When the bottom ash content is 20 % and the steel fiber content is 2 %, the compressive strength of concrete reaches 56.1 MPa, and the carbonation depth drops to 4.19 mm after 28 days. In addition, a carbonation depth prediction model was established based on the compressive performance of concrete, accurately predicting the carbonation depth at different carbonation stages.

Development of a novel 3D-printed dynamic anthropomorphic thorax phantom for evaluation of four-dimensional computed tomography

Background and purposeIn radiotherapy, the image quality of four-dimensional computed tomography (4DCT) is often degraded by artifacts resulting from breathing irregularities. Quality assurance mostly employ simplistic phantoms, not fully representing complexities and dynamics in patients. 3D-printing allows for design of highly customized phantoms. This study aims to validate the proof-of-concept of a realistic dynamic thorax phantom and its 4DCT application. Materials and methodsUsing 3D-printing, a realistic thorax phantom was produced with tissue-equivalent materials for soft tissue, bone, and compressible lungs, including bronchi and tumors. Lung compression was facilitated by motors simulating customized breathing curves with an added platform for application of monitoring systems. The phantom contained three tumors which were assessed in terms of tumor motion amplitude. Three 4DCT sequences and repeated static images for different lung compression levels were acquired to evaluate the reproducibility. Moreover, more complex patient-specific breathing patterns with irregularities were simulated. ResultsThe phantom showed a reproducibility of ±0.2 mm and ±0.4 mm in all directions for static 3DCT images and 4DCT images, respectively. Furthermore, the tumor close to the diaphragm showed higher amplitudes in the inferior/superior direction (13.9 mm) than lesions higher in the lungs (8.1 mm) as observed in patients. The more complex breathing patterns demonstrated commonly seen 4DCT artifacts. ConclusionThis study developed a dynamic 3D-printed thorax phantom, which simulated customized breathing patterns. The phantom represented a realistic anatomy and 4DCT scanning of it could create realistic artifacts, making it beneficial for 4DCT quality assurance or protocol optimization.

A METHOD FOR FORMING A TRUNCATED POSITIONAL CODE SYSTEM FOR TRANSFORMED VIDEO IMAGES

The article substantiates the requirements for the quality characteristics of video information services. It is shown that it is necessary to improve the quality of video information by the following indicators: time delays for video data delivery in terms of ensuring the required completeness; integrity of video information in accordance with the requirements of the application. The consequence of this fact is an imbalance between meeting the requirements for different groups of quality characteristics of video services. A set of measures is used to reduce the load on information and communication systems. One of the key ones is the use of compression technologies to reduce the bit volume. Therefore, the development of new coding technologies in terms of localising the balance between the level of video data compression and its integrity is an urgent scientific and applied problem. The article describes the main stages of creating a method for determining the informative and positional weight for a coding system in a truncated positional basis. For this purpose, a system of mathematical relations is developed to determine the number of admissible sequences. The scientific novelty is as follows. For the first time, a system of correlations has been created to determine the informative and positional weight of the components of the diagonal sequence of a transformant based on the combinatorial configurations. On the basis of experimental studies, it is shown that the developed method has advantages in terms of ensuring the level of video data integrity under conditions of a given compression rate.

Elastocaloric effect of NiTi shape memory alloys manufactured by laser powder bed fusion

To address the challenges posed by the complex processing of NiTi alloys, the additive manufacturing technology has been employed for the preparation of NiTi alloys samples. Up to now, there is limited research on the elastocaloric effects of NiTi alloys manufactured by LPBF. This study focused on the utilization of laser powder bed fusion (LPBF) to fabricate NiTi samples, with a particular emphasis on the influence of laser power, scanning speed and hatch spacing on the microstructure and properties of NiTi alloys. The results indicated that the process parameters affect the stress hysteresis of superelasticity by influencing the Ni content in the NiTi alloys. The higher Ni content, the lower the stress hysteresis. In addition, the critical stress for plastic deformation of the texture in the <001> direction in NiTi alloys is the smallest. When only the P was changed, the proportion of the texture in the <001> direction in the sample increased with the increase of P, the proportion of plastic deformation increased, and the degree of recovery also gradually decreased. Furthermore, at a specific temperature, 10 K higher than the end temperature of austenite transformation, each sample exhibited good superelasticity. In particular, the 1st cooling capacity of the sample with the best elastocaloric effect reached 11.0 K, which was superior to other results obtained by LPBF under similar compressive strain levels.

Influence of the Clamping Force and the Formation of Liquid Water Along the Channel on the Local Diffusion Processes in PEMFCs

A novel measurement setup, technique, and segmented model are introduced to spatially resolve mass transport losses along the channel in a PEMFC. The newly designed segmented cell enables not only the measurement of local current and voltage data but also the capture of local temperature and compression levels. By extending the commonly used limiting current method, the local mass transport resistance is determined as a function of local operating parameters. The local current and temperature are directly measured, while the operating conditions along the channel such as pressure, oxygen fraction, and relative humidity are obtained empirically from a 1-D differential model. Leveraging both measured and empirically-based simulation results, this comprehensive approach facilitates the characterization of mass transport resistance distribution along the gas channel. The learnings from this study provide valuable insight into the optimization of channel and materials design for enhancing large active area fuel cell performance and durability.

Influence of the Clamping Force and the Formation of Liquid Water Along the Channel on the Local Diffusion Processes in PEMFCs

A novel measurement setup, technique, and segmented model are introduced to spatially resolve mass transport losses along the channel in a PEMFC. The newly designed segmented cell enables not only the measurement of local current and voltage data but also the capture of local temperature and compression levels. By extending the commonly used limiting current method, the local mass transport resistance is determined as a function of local operating parameters. The local current and temperature are directly measured, while the operating conditions along the channel such as pressure, oxygen fraction, and relative humidity are obtained empirically from a 1-D differential model. Leveraging both measured and empirically-based simulation results, this comprehensive approach facilitates the characterization of mass transport resistance distribution along the gas channel. The learnings from this study provide valuable insight into the optimization of channel and materials design for enhancing large active area fuel cell performance and durability.

BCS-Based Encoding Schemes for Monoview and Multiview Visual Configurations in WVSN Data Gathering: A Survey

Wireless Visual Sensor Network (WVSN) has become a valuable tool in addressing the evolving needs of modern monitoring systems. Encoding in WVSNs is a multifaceted process that involves compressing visual data, optimizing energy consumption, ensuring error resilience, and adapting to various network and application requirements. The associated lightweight encoders and the demand for less storage space make block compressive sensing (BCS) techniques suitable for WVSN applications where energy, bandwidth, and storage resources are limited. Based on the number of visual perspectives or camera angles available within a network for data capture, there are two primary congurations: monoview and multiview. This paper provides a comprehensive survey of dierent BCS-based encoding schemes used for data-gathering in both monoview and multiview scenarios within WVSNs. A comparative study of these algorithms based on compression level, computational complexity, relative gain in encoder energy, and reconstruction quality is performed. A BCS-based joint encoding scheme for multiview conguration that ensures a relatively high compression level is also proposed in this paper.

Scratch-collapse test in reflex cervical-elbow syndrome

The scratch-collapse test was proposed in 2008 to detect the level of compression of the ulnar and median nerves in tunnel lesions. Further study of this phenomenon has shown that weakness of the shoulder external rotators occurs in nerve lesions at other levels as well. The scratch-collapse test was studied in 155 patients (mean age 45 years) with unilateral reflex cervical-elbow syndrome, with complaints of different localization. The scratch-collapse test was positive in all patients on the affected side. The localization of the trigger zone depended on the patient’s complaints and was established experimentally. Short-term kneading of the trapezius muscle or voluntary contraction of the forearm muscles on the side of the lesion was used to prove the reflex nature of the cervico-elbow syndrome, resulting in complete recovery of triceps or extensor strength of the first and third finger. In the course of the study, suppression of the scratch-collapse test in reflex cervico-elbow syndrome with proprioceptive stimulation was found. In confirmed carpal tunnel and cubital tunnel syndromes, the phenomenon of suppression of the scratch-collapse test was also observed in response to proprioceptive stimulation. The scratch-collapse test and reflex cervical-elbow syndrome have a common mechanism of occurrence, which is based on the protective reaction of the body in the form of a nociceptive shortening reflex. The scratch-collapse test at skin irritation over the site of nerve injury can be considered as a subthreshold physiologic nociceptive reflex. Based on the theory of prognostic action of the nociceptive system, cervicolumbar reflex syndrome is a pathologic nociceptive reflex. Scratchcollapse test suppression phenomenon and recovery of muscle strength in reflexive cervical-elbow syndrome after shortterm kneading of the trapezius muscle are the result of activation of the antinociceptive system in response to proprioceptive stimulation. This suppression phenomenon can be used for the treatment of reflex cervical lockjaw syndrome.

Non-universality and dissipative anomaly in compressible magnetohydrodynamic turbulence

We systematically study the dissipative anomaly in compressible magnetohydrodynamic (MHD) turbulence using direct numerical simulations, and show that the total dissipation remains finite as viscosity diminishes. The dimensionless dissipation rate $\mathcal {C}_{\varepsilon }$ fits well with the model $\mathcal {C}_{\varepsilon } = \mathcal {C}_{\varepsilon,\infty } + \mathcal {D}/R_L^-$ for all levels of flow compressibility considered here, where $R_L^-$ is the generalized large-scale Reynolds number. The asymptotic value $\mathcal {C}_{\varepsilon,\infty }$ describes the total energy transfer flux, and decreases with increase of the flow compressibility, indicating non-universality of the dimensionless dissipation rate in compressible MHD turbulence. After introducing an empirically modified dissipation rate, the data from compressible cases collapse to a form similar to the incompressible MHD case depending only on the modified Reynolds number.

Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification.

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.

Compressive strength detection of tunnel lining using hyperspectral images and machine learning

Traditional tunnel lining strength detection techniques are mostly contact-based, with relatively low detection efficiency. This study innovatively proposes hyperspectral imaging method to rapidly detect tunnel concrete lining strength from a machine vision perspective. Hyperspectral cameras were employed in indoor experiments to capture hyperspectral images of concrete specimens with different compressive strength levels. The differences of concrete strength based on hyperspectral reflectance characteristics were analysed using hyperspectral images and machine learning algorithms. Firstly, the K-Nearest Neighbors (KNN) classification algorithm was used to predict the classification of the concrete hyperspectral dataset with accuracy mostly exceeding 90 %. The results indicate distinctive differences in hyperspectral reflectance characteristics among concrete specimens. Furthermore, compressive strength prediction of different concrete specimens was carried out using Principal component regression (PCR), Partial least squares regression (PLSR), and Least Squares Support Vector Machine (LSSVM) machine learning models on both original and Savitzky-Golay(S-G) processed spectral data. LSSVM and PLSR models performed excellently in the visible light spectrums(400–1000 nm), with LSSVM excelling in the near-infrared spectrums(900–1700 nm). Finally, the feasibility of using Hyperspectral imaging(HSI) technology to detect tunnel lining strength was demonstrated at the shield tunnel model site. The predicted results were validated by combining with strength values measured by the rebound hammer, and presented promoising research path for automated non-destructive detection of concrete tunnel lining strength.

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete.

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.

Mapping the Nanoscale Heterogeneous Responses in the Dynamic Acceleration of Deformed Polymer Glasses.

Understanding the evolution of local structure and mobility of disordered glassy materials induced by external stress is critical in modeling their mechanical deformation in the nonlinear regime. Several techniques have shown acceleration of molecular mobility of various amorphous glasses under macroscopic tensile deformation, but it remains a major challenge to visualize such a relationship at the nanoscale. Here, we employ a new approach based on atomic force microscopy in nanorheology mode for quantifying the local dynamic responses of a polymer glass induced by nanoscale compression. By increasing the compression level from linear elastic to plastic deformation, we observe an increase in the mechanical loss tangent (tan δ), evidencing the enhancement of polymer mobility induced by large stress. Notably, tan δ images directly reveal the preferential effect of the large compression on the dynamic acceleration of nanoscale heterogeneities with initially slow mobility, which is clearly different from that induced by increasing temperature.