Compression Rate Research Articles

Underwater point cloud transmission framework: hybrid encoder implementation based on CNN and transformer

Abstract In underwater multi-robot systems, 3D point cloud data generated by sonar and depth sensors facilitates the execution of more complex collaborative tasks among robots, which partly rely on efficient data transmission. In this work, we propose a hybrid encoder framework based on convolutional neural network and Transformer for underwater point cloud transmission, aimed at dealing with large-scale point cloud data. Our encoder allows setting lower compression rate on regions or objects of interest for semantic point clouds, preserving crucial information in the point cloud. For underwater acoustic communication, we employ orthogonal frequency division multiplexing combined with deep joint source-channel coding for transmission to enhance the system’s error-resilience. Compared to state-of-the-art methods in the simulation experiments, our end-to-end framework achieves considerable compression performance while eliminating certain cliff and leveling effects, also demonstrating robustness even with changing channels.

72 Hospital level of service, rural-urban hospital location, and neonatal resuscitation interventions at birth: A population-based study in Alberta, Canada from 2000-2020

Abstract Background Advanced neonatal resuscitation interventions (ANRIs: endotracheal intubation, chest compressions, and epinephrine) are rarely needed for infants born at ≥34 weeks gestational age (GA). However, healthcare providers (HCPs) in community hospitals can encounter the need for ANRIs, while having less experience and resources than HCPs in centers with neonatal intensive care units (NICUs). Understanding practice differences between hospitals of different levels of service (LofS) and rural/urban location can inform quality improvement to reduce health disparities. Objectives To examine how hospital level of service and rural/urban location relate to ANRI rates in Alberta, Canada, a universal public health system with standardized Neonatal Resuscitation Program (NRP) training. Design/Methods All live births ≥34 weeks GA in Alberta from 2000-2020 inclusive were examined using retrospective, population-level administrative data from the Alberta Perinatal Health Program (APHP). Hospitals (n=97) were contacted to determine annual LofS during the study period, with levels (0,1A/B/C,2,3) based on provincial obstetrical triage for availability of delivery support, cesarean-sections, obstetricians, paediatricians/neonatologists, and NICUs. Hospitals were further subcategorized by population and proximity to metropolitan centers.(Table 1) Rates of individual interventions or any ANRI were compared. Results There were 971,403 live births during the study period with a mean GA of 38.9 weeks; 1.6% were missing ANRI data, 955,835 births were included. Most occurred in hospitals with level 2 (57.4%) or level 3 (21.8%) NICUs; few occurred at sites with no delivery support (Level 0, 0.1%). ANRI rates were intubation (0.8%), chest compression (0.2%), and epinephrine (0.02%), any ANRI (0.96%, ~1/1000 live births). However, chest compression rates progressively decrease, and intubation rates progressively increase as LofS increases.(Table 1, Figure 1) When considering rural/urban location, there were outliers in chest compression rates in remote 0 (25.4/1000 live births) and metro-influenced 1A (30.2/1000 live births), intubation in remote 1C (33.3/1000 live births) and metro 0 (193.5/1000 live births) and epinephrine in metro 0 (64.5/1000 live births). Conclusion In this population-based cohort study of ≥34 weeks births, there were higher chest compressions rates and lower intubations rates at hospitals without NICUs despite standardized training. Reasons for this difference require further investigation; potential solutions such as targeted outreach education and introduction of laryngeal mask airways could be explored.

GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model’s parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model’s adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset’s Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model’s parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.

High-Quality Image Compression Algorithm Design Based on Unsupervised Learning.

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression.

Compression rates of microbial genomes are associated with genome size and base composition

BackgroundTo what degree a string of symbols can be compressed reveals important details about its complexity. For instance, strings that are not compressible are random and carry a low information potential while the opposite is true for highly compressible strings. We explore to what extent microbial genomes are amenable to compression as they vary considerably both with respect to size and base composition. For instance, microbial genome sizes vary from less than 100,000 base pairs in symbionts to more than 10 million in soil-dwellers. Genomic base composition, often summarized as genomic AT or GC content due to the similar frequencies of adenine and thymine on one hand and cytosine and guanine on the other, also vary substantially; the most extreme microbes can have genomes with AT content below 25% or above 85% AT. Base composition determines the frequency of DNA words, consisting of multiple nucleotides or oligonucleotides, and may therefore also influence compressibility. Using 4,713 RefSeq genomes, we examined the association between compressibility, using both a DNA based- (MBGC) and a general purpose (ZPAQ) compression algorithm, and genome size, AT content as well as genomic oligonucleotide usage variance (OUV) using generalized additive models.ResultsWe find that genome size (p < 0.001) and OUV (p < 0.001) are both strongly associated with genome redundancy for both type of file compressors. The DNA-based MBGC compressor managed to improve compression with approximately 3% on average with respect to ZPAQ. Moreover, MBGC detected a significant (p < 0.001) compression ratio difference between AT poor and AT rich genomes which was not detected with ZPAQ.ConclusionAs lack of compressibility is equivalent to randomness, our findings suggest that smaller and AT rich genomes may have accumulated more random mutations on average than larger and AT poor genomes which, in turn, were significantly more redundant. Moreover, we find that OUV is a strong proxy for genome compressibility in microbial genomes. The ZPAQ compressor was found to agree with the MBGC compressor, albeit with a poorer performance, except for the compressibility of AT-rich and AT-poor/GC-rich genomes.

Safety management within the scope of teaching practical clinical skills: framing errors for cardiopulmonary resuscitation training – a multi-arm randomized controlled equivalence trial

Introduction Cardiopulmonary resuscitation (CPR) is among the most important skills in clinical practice. Errors can happen here, just like everywhere, and potentially have severe consequences. Two common error handling strategies known from practice are Error Management (EM) and Error Avoidance (EA). However, its effects on medical performance outcomes remain unclear. This study aimed to examine the role of error framing in basic life support (BLS) training for future healthcare professionals. Materials and Methods In an equivalence trial (N = 430), first-year medical, dentistry, physiotherapy, and midwifery students underwent BLS training. In the three study arms, participants received either (1) instructions framing errors positively (EM), (2) instructions framing errors to be avoided (EA), or (3) no further instructions (Control). CPR performance was assessed using a resuscitation manikin measuring compression depth (CD) and compression rate (CR). The self-confidence ratings were assessed using a questionnaire. Equivalence margins for the outcome parameters and sample size calculations were based on previous standard BLS studies, using two-sided 95% confidence intervals to determine significance of equivalence. Results The results regarding CD revealed equivalence with a trend toward superiority of EM over EA (proportional difference 23.3%-points; 95% CI 11.4%–34.2%) and EM over control (proportional difference 23.4%-points; 95% CI 11.5%–34.2%.) and significant equivalence of EA and control (proportional difference 0.1%-points; 95% CI −11.6%–11.7%). Significant equivalence was determined for all study arms with respect to CR and self-confidence. Conclusion Our study revealed that EM was not detrimental to learners’ CPR performance. Given existing research on long-term beneficial effects of EM on patient safety, coupled with the proven equivalence of EM and EA concerning short-term performance, we argue that EM is a promising approach for future medical education purposes. Raising awareness of error framing and teaching error-handling strategies is expected to benefit ongoing safety management efforts in medical education and beyond.

Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.

Effectiveness of Adult Chest Compressions during Resuscitation Performed by Children Aged 10–14 Years under Simulated Conditions

Introduction: Numerous educational programs recommend implementing the teaching principles of BLS from an early age. The aim of this study was to evaluate selected parameters of the quality of resuscitation performed by children aged 10–14 years during simulated circulatory arrest in an adult. Materials and Methods: The project involved four stages, culminating in students performing thoracic compressions on an adult simulator for 2 min. A digital analysis of the quality, depth, relaxation and rate of compressions allowed us to formulate results and conclusions. The authors’ proprietary questionnaire form allowed for the correlation of criteria such as age, gender, body mass and past experience in first aid training of the participant. Results: A total of 149 girls and 130 boys were studied. The mean age was 12 years (SD ± 1.41). A directly proportional increase in body mass with participant age was observed (p &lt; 0.000). Children as young as 10 years old achieved only 24.13% quality, while those at the age of 14 demonstrated a more than doubled value (67.61%). The minimum depth of chest compressions recommended for an adult (5–6 cm) was not reached in any age group. Girls from all grades achieved a mean quality of 44.69% (SD ± 32.57), while boys achieved a score of 60.23% (SD ± 31.74). On the other hand, in the case of evaluating thoracic relaxation, a significantly better result was achieved by girls compared to boys (66.14% vs. 56.78%; rho-Spearman test for p = 0.011). Conclusions: Age, sex and body mass play important roles in the quality of resuscitation provided by children. None of the age groups studied achieved the minimum mean depth during adult thoracic compressions under simulated conditions. It is recommended to modify school-based BLS classes to better match the exercises to students’ predispositions.

MWIR image deep denoising reconstruction based on single-pixel imaging

Single-pixel imaging enables cost-effective and highly sensitive mid-wave infrared imaging, with the added convenience facilitated by deep learning algorithms. However, most networks train separate models depending on compression rates, frequently neglecting real-world infrared noise, leading to inconveniences and suboptimal reconstruction performance. To address these issues, we propose to integrate intrinsic and external deep denoising priors for the reconstruction of compressive mid-wave infrared images from single-pixel measurements. The generalized alternating projection is unfolded into a designed network to harness the benefits of robustness and internal priors. Furthermore, to mitigate real-world noise in measurements, a Swin Transformer U-shaped network that embeds noise variance is introduced to learn external denoise prior. A comparative analysis of simulated and real infrared single-pixel imaging in experiments highlights the promising performance of the proposed method.

Degradation mechanism and strength prediction of aeolian sand concrete under sulphate dry-wet cycles

In this paper, aeolian sand (AS) with different contents (0 %, 25 %, 50 %, 75 % and 100 %) was used as fine aggregate to prepare aeolian sand concrete (ASC). The effects of AS content and dry-wet cycle (DWC) times on the durability of ASC under sulfate (5 % Na2SO4) erosion conditions were investigated by DWC tests. The mass loss rate, compressive strength loss rate and relative dynamic elastic modulus (RDEM) were used as the macroscopic performance evaluation indexes, while the damage degradation mechanism of ASC was revealed by combining the microscopic technical means such as SEM, XRD, TG-DSC and NMR. The results show that the macroscopic properties of ASC under the action of DWC of sulphate all show the first increase and then decrease, as when the dosage of AS is below 50 %, the "bead effect" and "padding effect" of AS can further improve the internal densification of ASC, thus effectively resisting external sulphate erosion. During sulphate erosion, SO42- firstly reacts with cement hydration product Ca(OH)2 to generate erosion products such as Ettringite (AFt) and fills them within the original defects, with short-term rnhancement of macroscopic properties. As the DWC continue, the excessive erosion products accumulated, resulting in the destruction of pore structure and extension through the phenomenon, the matrix compactness is reduced, and the macroscopic properties degraded. The grey entropy correlation analysis yielded that the grey entropy correlation grade of bound fluid saturation, harmless pore percentage and less harmful pore percentage with compressive strength were all above 0.8, and the GM(1,4) strength prediction model established on the basis of the above pore structure parameters has a high prediction accuracy, and the relative errors between the predicted strength values and the actual strength values are within 10 %, and the research results can provide certain theoretical support for the study of the durability of ASC in sulfate erosion environment.

Effect of particle size distribution and content of limestone powder on compressive response of high-early-strength cement mortars

This study examined the effect of particle size distribution and content of limestone powder on the compressive response of high-early-strength portland cement mortars cured either in water or under ambient conditions. Nineteen mortar mixtures were classified into three groups based on the particle size distribution of the pulverized limestone powders: L (blaine = 3467 cm2/g), M (blaine = 4710 cm2/g), and H (blaine = 5764 cm2/g). The design equations for the compressive strength gain rates and stress–strain relationship of ordinary portland cement (OPC) concrete were adapted for the present mortars. The test results revealed that the 28-d compressive strength (fc′) of the mortars tended to decrease with an increase in limestone powder content, even for mortars containing 5 % limestone powder with small particle sizes. This contrasts with the common trend of the positive effect of limestone powder on the compressive strength of OPC concrete up to a certain substitution level. Furthermore, mortars containing finer limestone powders exhibited a greater rate of decrease in fc′, regardless of the curing conditions. The modified equations show promise for reliably assessing both the compressive strength gain rates at different ages and the stress–strain behavior of high-early-strength portland cement mortars containing limestone powders.

The effect of hand and body position on chest compression quality and rescuer fatigue in prone cardiopulmonary resuscitation

AimThis study aimed to compare the quality of compressions in supine cardiopulmonary resuscitation (CPR) and prone CPR by performing chest compressions on a manikin. Evaluating the effect of prone CPR using different hand and body position on the quality of manual chest compressions and fatigue of participants. MethodsAfter completing 2 min of chest compression in the supine position (Supine Group), 25 participants randomly performed three sets of 2-minutes chest compressions on a prone position manikin. Stand + hands overlapped Group: participants stood beside the patient bed with their hands overlapped and placed on the posterior segment of the thoracic spine between the scapulae, Straddle + hands separated Group: participants straddled the patient bed with their hands placed above the scapulae on both sides at the mid-chest level, and Straddle + hands overlapped Group: participants straddled the patient bed with their hands overlapping on the posterior segment of the thoracic spine between the scapulae. Subsequently, the quality of chest compressions and participants fatigue were assessed. ResultsChest compression depth ratio and mean chest compression depth (MCCD) were worse in the three prone CPR groups (Stand + hands overlapped Group: 0.0(0.0,15.6) %, 39.8 ± 1.3 mm; Straddle + hands separated Group: 1.4(0.0,11.7) %, 42.4 ± 1.2 mm; Straddle + hands overlapped Group: 0.0(0.0,9.2) %, 40.9 ± 1.2 mm) than in the Supine group (87.1(68.1,94.0) %; p < 0.001, 52.4 ± 0.4 mm; p < 0.001). In the three prone CPR groups, Straddle + hands separated Group had the greatest MCCD, lowest changes in heart rate (p = 0.018) and lowest changes in RPE scores (p < 0.001). There were no significant differences between the four groups in terms of mean chest compression rate, accurate chest compression rate ratio, or correct recoil ratio. ConclusionThis simulation-based study showed that the quality of chest compressions was worse in the prone position than in the supine position. When prone chest compressions were performed using different hand and body position, prone CPR performed by a participant straddling a hospital bed with hands placed above the scapula on either side at the mid-chest level provided higher-quality chest compressions and lower rescuer fatigue.

RFCSC: Communication efficient reinforcement federated learning with dynamic client selection and adaptive gradient compression

In the field of public safety, high-quality data is often owned by governments, companies, and organizations, making it difficult to train effective models through centralized datasets. This paper leverages federated learning as a mechanism to address data privacy concerns. Within the federated learning framework, the data across different clients is typically Non-IID (non-Independently and Identically Distributed). Furthermore, The complexity of sensitive image recognition tasks in public safety, along with the large number of model parameters, can lead to communication congestion issues in federated learning. To solve these challenges, this paper proposes a Reinforcement Federated Client Selection and Gradient Compression Method(RFCSC). By integrating client data prototypes with accuracy metrics, we dynamically assess the contribution of each client in federated learning. Through an intelligent dynamic incentive mechanism based on reinforcement learning, high-quality client nodes are dynamically selected to participate in federated learning, achieving dynamic adaptive aggregation, reducing the influence of Non-IID data, reducing communication cost, enhancing model accuracy, and achieving a balance between quality and efficiency. To address the issue of high communication cost for parameter transmission, we propose an adaptive model compression strategy in reinforcement federated learning which make local clients find an appropriate compression rate. This approach not only reduces gradient communication overhead but also minimizes the impact of compression on model accuracy. The effectiveness of our proposed approach are corroborated through comprehensive experiments conducted on one private dataset and two public datasets.

Screening the characteristic hyperspectral wavelength variables of peanut leaves based on coupled algorithms to predict SPAD value in peanut

ABSTRACT The accurate acquisition and prediction of the chlorophyll content in peanuts can provide reference for scientifically sound planting and management of peanut crops. In this study, peanut canopy leaves analysed during the flowering and pegging period were taken as the research object, and the spectral data collected by a portable hyperspectral spectrometer were used as the data source to study the inversion model of peanut leaf soil and plant analyser development (SPAD). To address the issues of randomness, the need for multiple iterations, and strong subjectivity in threshold selection in the initial variable set of the random frog (RF) algorithm, an improved RF algorithm was developed based on the sparse group lasso tool. By analysing and comparing the results of variable extraction of the improved RF algorithm and six characteristic wavelength screening algorithms, three algorithms were selected for pairwise coupling. These six algorithms were the correlation coefficient method (CC), successive projections algorithm, iteratively retains informative variables (IRIV), uninformative variable elimination (UVE), competitive adaptive reweighed sampling, and a genetic algorithm. The results show that: (1) the results of CC, UVE, and IRIV algorithms are better in a single algorithm experiment; and (2) in the coupled algorithms experiment, CC-UVE, CC-IRIV, and UVE-IRIV methods can effectively reduce dimensions, and the model has a certain level of stability. Among them, the UVE-IRIV-Extreme gradient boosting model had the highest accuracy, R 2 = 0.652, RMSE = 1.867, and the variable compression rate was as high as 97.84%. The research results can provide a theoretical basis for rapid and accurate monitoring of the SPAD values of peanut leaves.

Quantum data compression under localized features

In this paper, we first introduce the local quantum information processing task by compressing the density operator based on local quantum Bernoulli noises. Then, the local quantum compression theorem is given, that is, the local quantum entropy is the minimum achievable rate of local compression. Finally, we prove the theorem with the proof of the direct coding theorem and the inverse theorem. The direct coding theorem shows that a scheme with such a local compression rate exists, and that this local compression rate converges to the local quantum entropy. The inverse theorem shows that the compression scheme with the rate below the local entropy is unachievable. With the continuous development of quantum technology, local quantum data compression technology will have broad application prospects and development space.

Real-Time Chest Compression Depth, Rate, Posture, and Person Detections Using Binocular Vision for CPR Training

Real-Time Chest Compression Depth, Rate, Posture, and Person Detections Using Binocular Vision for CPR Training

DNS of laboratory-scale turbulent premixed counterflow flames under elevated gravity conditions

In the present work, direct numerical simulations (DNSs) of laboratory-scale turbulent premixed counterflow flames are performed to understand the effect of elevated gravity on flame structures. In the DNS, a turbulent jet of CH4/N2/O2 mixture is injected in opposition to a stream of combustion product. Three cases were considered, i.e., one case with normal gravity and two cases with elevated gravity in different directions. The DNS results of the case under normal gravity conditions were found to be in good agreement with the experimental data. The turbulent flame speed is the highest in the case with elevated gravity aligned with the mean flame propagating direction, which is due to the increase in flame surface area and stretch factor. The gravity levels have a substantial influence on the flame extinction characteristics. It was observed that the flame front is pushed toward the direction of the gravity. When the elevated gravity antialigns with the mean flame propagating direction, the flame front travels across the stagnation plane toward the combustion product stream, leading to significant reactant dilution and flame extinction. The flame normal vector preferentially aligns with the most compressive strain rate for various gravity conditions. The tangential strain rate is the greatest (smallest) when elevated gravity antialigns (aligns) with the mean flame propagating direction. The impact of elevated gravity on the tangential strain rate at the flame fronts ultimately leads to a displacement in the flame front location relative to the stagnation plane, thereby influencing flame extinction.

Evaluation of compression behaviour of 316L SS Gyroid and Diamond structures using SLM process – Experimental programme under static and dynamic compression loadings

The relative comparison in terms of energy absorption efficiency for a set of 4 structures made of various Triply Periodic Minimal Surfaces (TPMS) topologies is experimentally investigated. These TPMS structures are printed by Selective Laser Melting AM process using 316L SS. The study is carried out in consideration of the effect of parameters such as relative density, compressive loading directions and loading rates, number of unit cells for Diamond and Gyroids TPMS both declined for Sheet and Skeletal topologies. The objective is to quantify their structural responses in terms of apparent stress and strain, dynamic enhancement and Specific Energy Absorbed (SEA) and to evaluate their structural integrity in terms of collapse stability. The results reveal that the Sheet pattern of TPMS structures with its constant wall thickness and uniform geometry exhibits better energy absorption capabilities than the Skeletal pattern. The Diamond family shows greater interest rather than the Gyroid family only in the case of the Sheet pattern. The increase in relative density from 20 to 30% is characterized by improved manufacturing quality, an increase in energy absorption capacity and more homogeneous progressive deformations during compression. On the whole, the set of TPMS geometries exhibits energy absorption capacities prior to those of other conventional cellular materials currently used for impact engineering applications. Finally, in a first approach, an original design methodology using charts can be developed to establish a link between the energy absorption capabilities and the design geometric parameters of TPMS structures.

A novel cocrystal approach celecoxib with piperine: Simultaneously enhance dissolution rate and compressibility

Celecoxib, a selective COX-2 inhibitor non-steroidal anti-inflammatory drug (NSAID), exhibits analgesic and anti-inflammatory properties similar to piperine, the secondary metabolite of Piper nigrum L. Unfortunately, celecoxib has a low compressibility and low dissolution rate in aqueous medium. This study aimed to prepare a cocrystal of celecoxib and piperine to enhance the dissolution rate and compressibility properties of celecoxib. The cocrystal was synthesized using the seeding method and thoroughly characterized using Powder X-ray diffraction (XRD), differential scanning calorimetry (DSC), infrared spectrophotometry, and single-crystal X-ray diffraction techniques. The complete change in PXRD, decrease in melting point in DSC measurements, and shift in the NH stretching band in the FT-IR spectrum suggested the formation of cocrystals phase. Single-crystal XRD confirmed the formation of an equimolar ratio of cocrystals of celecoxib and piperine. The intrinsic dissolution test was conducted to confirm the impact on the cocrystal to dissolution, and it showed a slight increase compared to intact celecoxib. To assess the physico-mechanical properties, the cocrystal powders were compressed into tablets with varying forces. The results demonstrated a significant improvement in compressibility compared with intact celecoxib owing to the slip plane in the crystal lattice of the cocrystal. In conclusion, our novel celecoxib-piperine cocrystal exhibited distinct physicochemical characteristics compared to intact celecoxib, showing enhanced dissolution rate and compressibility.

Facilely prepared extreme environments tolerable PIF /c-CNTx composites for piezoresistive sensor based on powder foaming strategy

Polyimide foams (PIF) are widely used in piezoresistive sensors due to their light weight and weather resistance to weathering at extreme temperatures. Nevertheless, the mechanical strength of PIF prepared by freeze-drying method is poor, with only tens to hundreds of kPa ranging from 50 % to 80 % compression. Herein, a polyimide composite reinforced with –COOH functionalized multi-walled carbon nanotubes (PIF/c-CNTx) was produced via a thermal foaming process using a powder-based method. The PIF/c-CNTx composites exhibit ultra-low density (0.165 g/cm3), excellent mechanical properties (compressive strength of up to 2.5 MPa at 60 % compressive strain, tens to hundreds of times higher than similar materials) as well as stable compressive properties and recoverability in liquid nitrogen and at high temperatures up to 200 ℃. The PIF/c-CNTx piezoresistive sensor achieves excellent sensing capability over a wide strain range high sensitivity (GF = 12.9) and exhibit good stability over 5000 s of cyclic compression at a compression rate of 10 mm/min. The stability and reproducibility of the strong piezoresistive signal at extreme temperatures shows the great potential of PIF/c-CNTx composites as structural components under extreme conditions for applications in aerospace and polar exploration.