Interfacial friction effects on sealing performances of elastomer packer

Elastomer sealing performance is of critical importance for downhole tools application including the use of fracturing(Frac)plugs during multi-stage hydraulic fracking.In practice sealing performances of such plugs are normally evaluated through pressure tests,and in numerical simulation studies,maximum contact stress,average contact stress and contact length data are used to determine sealing quality between a packer and casing.In previous studies,the impact of friction forces on sealing performance is often overlooked.This work aims to fill this knowledge gap in determining the influence of friction forces on elastomer packer sealing performances.We first determined the most appropriate constitutive hyperelastic model for the elastomers used in frac plug.Then we compared analytical calculation results with Finite Element Analysis simulation using a simplified tubular geometry and showed the significant influences on interfacial friction on elastomer packer stress distribution,deformation,and contact stress after setting.With the demonstration of validity of FEA method,we conducted systematic numerical simulation studies to show how the interfacial friction coefficients can affect the maximum contact stress,average contact stress,contact stress distribution,and maximum mises stress for an actual packer used in plug products.In addition,we also demonstrated how the groove in a packer can affect packer deformation and evolvement during setting with the consideration of interfacial stress.This study underscores the critical role that friction forces play in Frac plug performance and provides a new dimension for optimizing packer design by controlling interfacial interactions at the packer contact surfaces.

Defining failure of endoluminal biliary drainage in the era of endoscopic ultrasound and lumen apposing metal stents

The role of endoscopy in pathologies of the bile duct and gallbladder has seen notable advancements over the past two decades.With advancements in stent technology,such as the development of lumen-apposing metal stents,and adoption of endoscopic ultrasound and electrosurgical principles in therapeutic endoscopy,what was once considered endoscopic failure has transformed into failure of an approach that could be salvaged by a second-or third-line endoscopic strategy.Incorporation of these advancements in routine patient care will require formal training and multidisciplinary acceptance of established techniques and collaboration for advancement of experimental techniques to generate robust evidence that can be utilized to serve patients to the best of our ability.

Improvement of hepatic fibrosis after tenofovir disoproxil fumarate switching to tenofovir alafenamide for three years

BACKGROUND Both tenofovir alafenamide(TAF)and tenofovir disoproxil fumarate(TDF)are the first-line treatments for chronic hepatitis B(CHB).We have showed switching from TDF to TAF for 96 weeks resulted in further alanine aminotransferase(ALT)improvement,but data remain lacking on the long-term benefits of TDF switching to TAF on hepatic fibrosis.AIM To assess the benefits of TDF switching to TAF for 3 years on ALT,aspartate aminotransferase(AST),and hepatic fibrosis improvement in patients with CHB.METHODS A single center retrospective study on 53 patients with CHB who were initially treated with TDF,then switched to TAF to determine dynamic patterns of ALT,AST,AST to platelet ratio index(APRI),fibrosis-4(FIB-4)scores,and shear wave elastography(SWE)reading improvement at switching week 144,and the associated factors.RESULTS The mean age was 55(28-80);45.3%,males;15.1%,clinical cirrhosis;mean baseline ALT,24.8;AST,25.7 U/L;APRI,0.37;and FIB-4,1.66.After 144 weeks TDF switching to TAF,mean ALT and AST were reduced to 19.7 and 21,respectively.From baseline to switching week 144,the rates of ALT and AST<35(male)/25(female)and<30(male)/19(female)were persistently increased;hepatic fibrosis was also improved by APRI<0.5,from 79.2%to 96.2%;FIB-4<1.45,from 52.8%to 58.5%,respectively;mean APRI was reduced to 0.27;FIB-4,to 1.38;and mean SWE reading,from 7.05 to 6.30 kPa after a mean of 109 weeks switching.The renal function was stable and the frequency of patients with glomerular filtration rate>60 mL/min was increased from 86.5%at baseline to 88.2%at switching week 144.CONCLUSION Our data confirmed that switching from TDF to TAF for 3 years results in not only persistent ALT/AST improvement,but also hepatic fibrosis improvement by APRI,FIB-4 scores,as well as SWE reading,the important clinical benefits of long-term hepatitis B virus antiviral treatment with TAF.

Using Linear and Non-Linear Techniques to Characterize Gait Coordination Patterns of Two Individuals with NGLY1 Deficiency

Individuals with NGLY1 Deficiency, an inherited autosomal recessive disorder, exhibit hyperkinetic movements including athetoid, myoclonic, dysmetric, and dystonic movements impacting both upper and lower limb motion. This report provides the first set of laboratory-based measures characterizing the gait patterns of two individuals with NGLY1 Deficiency, using both linear and non-linear measures, during treadmill walking, and compares them to neurotypical controls. Lower limb kinematics were obtained with a camera-based motion analysis system and bilateral time normalized lower limb joint time series waveforms were developed. Linear measures of joint range of motion, stride times and peak angular velocity were obtained, and confidence intervals were used to determine if there were differences between the patients and control. Correlations between participant and control mean joint waveforms were calculated and used to evaluate the similarities between patients and controls. Non-linear measures included: joint angle-angle diagrams, phase-portrait areas, and continuous relative phase (CRP) measures. These measures were used to assess joint coordination and control features of the lower limb motion. Participants displayed high correlations with their control counterparts for the hip and knee joint waveforms, but joint motion was restricted. Peak angular velocities were also significantly less than those of the controls. Both angle-angle and phase-portrait areas were less than the controls although the general shapes of those diagrams were similar to those of the controls. The NGLY1 Deficient participants’ CRP measures displayed disrupted coordination patterns with the knee-ankle patterns displaying more disruption than the hip-knee measures. Overall, the participants displayed a functional walking pattern that differed in many quantitative ways from those of the neurotypical controls. Using both linear and non-linear measures to characterize gait provides a more comprehensive and nuanced characterization of NGLY1 gait and can be used to develop interventions targeted toward specific aspects of disordered gait.

Auscultation transformed:the ingenious invention of the stethoscope by René Théophile Hyacinthe Laennec

This study delves into the life and significant contributions of René Théophile Hyacinthe Laennec,a prominent French physician of the 19^(th) century,and thoroughly examines his revolutionary creation,the stethoscope.Laennec’s innovative spirit not only revolutionized medical diagnosis during his time but also left a lasting imprint on the broader field of medicine,influencing healthcare for generations.This extensive inquiry covers various aspects,including his historical context,the development of the stethoscope,its profound implications for medical diagnosis,and its enduring impact on the history of medicine.

3-Dimensional Kinematic Comparison of Arm Movements between an Individual with NGLY1 Deficiency and a Neurotypical Individual

NGLY1 Deficiency is an ultra-rare autosomal recessively inherited disorder. Characteristic symptoms include among others, developmental delays, movement disorders, liver function abnormalities, seizures, and problems with tear formation. Movements are hyperkinetic and may include dysmetric, choreo-athetoid, myoclonic and dystonic movement elements. To date, there have been no quantitative reports describing arm movements of individuals with NGLY1 Deficiency. This report provides quantitative information about a series of arm movements performed by an individual with NGLY1 Deficiency and an aged-matched neurotypical participant. Three categories of arm movements were tested: 1) open ended reaches without specific end point targets;2) goal-directed reaches that included grasping an object;3) picking up small objects from a table placed in front of the participants. Arm movement kinematics were obtained with a camera-based motion analysis system and “initiation” and “maintenance” phases were identified for each movement. The combination of the two phases was labeled as a “complete” movement. Three-dimensional analysis techniques were used to quantify the movements and included hand trajectory pathlength, joint motion area, as well as hand trajectory and joint jerk cost. These techniques were required to fully characterize the movements because the NGLY1 individual was unable to perform movements only in the primary plane of progression instead producing motion across all three planes of movement. The individual with NGLY1 Deficiency was unable to pick up objects from a table or effectively complete movements requiring crossing the midline. The successfully completed movements were analyzed using the above techniques and the results of the two participants were compared statistically. Almost all comparisons revealed significant differences between the two participants, with a notable exception of the 3D initiation area as a percentage of the complete movement. The statistical tests of these measures revealed no significant differences between the two participants, possibly suggesting a common underlying motor control strategy. The 3D techniques used in this report effectively characterized arm movements of an individual with NGLY1 deficiency and can be used to provide information to evaluate the effectiveness of genetic, pharmacological, or physical rehabilitation therapies.

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

Advances in quantitative analysis of astrocytes using machine learning

Astrocytes,a subtype of glial cells,are star-shaped cells that are involved in the homeostasis and blood flow control of the central nervous system(CNS).They are known to provide structural and functional support to neurons,including the regulation of neuronal activation through extracellular ion concentrations,the regulation of energy dynamics in the brain through the transfer of lactate to neurons,and the modulation of synaptic transmission via the release of neurotransmitters such as glutamate and adenosine triphosphate.In addition,astrocytes play a critical role in neuronal reconstruction after brain injury,including neurogenesis,synaptogenesis,angiogenesis,repair of the blood-brain barrier,and glial scar formation after traumatic brain injury(Zhou et al.,2020).

Three-dimensional computational characterization of grain size and texture effects in magnesium alloys

This work systematically investigates the microstructure-property relationship in Mg alloys. Emphasis is placed on understanding, through high resolution crystal plasticity modeling, how grain size and texture collectively impact material strengthening and hardening, net plastic anisotropy, and tension-compression asymmetry. To achieve this, 528 fully three-dimensional finite element calculations are performed, which comprise eleven textures, four grain sizes, six loading orientations, and two uniaxial loading states(tension and compression). The grain size effect follows Hall-Petch relation that depends on both, loading orientation and initial texture. The reduction in extension twinning with grain size refinement is influenced by texture as well. Below a threshold textural strength, grain size refinement leads to an appreciable reduction in the net plastic anisotropy at yield, quantified using Hill anisotropy, and reduced tension-compression asymmetry. Using a micromechanical basis, the effect of grain size and texture on material ductility is predicted to be non-monotonic. The computational predictions serve as synthetic data sets for experimental validation and reduced-order modeling.

Unpredictability of Digital Twin for Connected Vehicles

Digital twin is an essential enabling technology for 6G connected vehicles.Through highfidelity mobility simulation,digital twin is expected to make accurate prediction about the vehicle trajectory,and then support intelligent applications such as safety monitoring and self-driving for connected vehicles.However,it is observed that even if a digital twin model is perfectly derived,it might still fail to predict the trajectory due to tiny measurement noise or delay in the initial vehicle locations.This paper aims at investigating the sources of unpredictability of digital twin.Take the car-following behaviors in connected vehicles for case study.The theoretical analysis and experimental results indicate that the predictability of digital twin naturally depends on its system complexity.Once a system enters a complex pattern,its longterm states are unpredictable.Furthermore,our study discloses that the complexity is determined,on the one hand,by the intrinsic factors of the target physical system such as the driver’s response sensitivity and delay,and on the other hand,by the crucial parameters of the digital twin system such as the sampling interval and twining latency.

Robust monitoring machine:a machine learning solution for out‑of‑sample R_(2)‑hacking in return predictability monitoring

The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.

Theoretical Prediction of Superconductivity in Boron Kagome Monolayer:MB3(M=Be,Ca,Sr)and the Hydrogenated CaB3

Using first-principles calculations, we predict a new type of two-dimensional(2D) boride MB3(M = Be,Ca, Sr), constituted by boron kagome monolayer and the metal atoms adsorbed above the center of the boron hexagons. The band structures show that the three MB3compounds are metallic, thus the possible phononmediated superconductivity is explored. Based on the Eliashberg equation, for BeB3, CaB3, and SrB3, the calculated electron–phonon coupling constants λ are 0.46, 1.09, and 1.33, and the corresponding superconducting transition temperatures Tc are 3.2, 22.4, and 20.9 K, respectively. To explore superconductivity with higher transition temperature, hydrogenation and charge doping are further considered. The hydrogenated CaB3, i.e.,HCaB3, is stable, with the enhanced λ of 1.39 and a higher Tc of 39.3 K. Moreover, with further hole doping at the concentration of 5.8 × 1011hole/cm2, the Tc of HCaB3can be further increased to 44.2 K, exceeding the Mc Millan limit. The predicted MB3and HCaB3provide new platforms for investigating 2D superconductivity in boron kagome lattice since superconductivity based on monolayer boron kagome lattice has not been studied before.

Three novel dendritic chitosan derivatives for inhibiting acid corrosion of petroleum pipelines

In the process of exploration and development of oil and gas fields, the acidic environment of oil reservoir, production and transport processes cause corrosion of pipelines and equipment, resulting in huge economic losses and production safety risks. Corrosion inhibitors were widely used in oil industry because of simple operation process and economical. In this study, three environmentally friendly corrosion inhibitors were synthesized based on the natural polysaccharide chitosan. Corrosion inhibition of three dendritic chitosan derivatives (We name them BH, CH and DH) on mild steel in 1 mol/L HCl solution with natural ventilation system was evaluated by weight loss experiment, electrochemical analysis and surface morphology characterization. The experimental results showed that when the three dendritic chitosan derivatives added in the corrosive medium were 500 mg L^(−1), the corrosion inhibition efficiencies were all more than 80%. Based on quantum chemical calculation, inhibition mechanisms of three dendritic chitosan derivatives were investigated according to molecular structures. The results showed that the benzene ring, Schiff base and N atom contained in the molecule were the active centers of electron exchange, which were more likely to form a film on the carbon steel surface, thereby slowing or inhibiting corrosion. The results also predicted the corrosion inhibition effect BH > DH > CH, which was consistent with the experimental conclusion.

Recent advances in petrophysical and geophysical characterization of unconventional resources:Introduction

As a bridge between the reservoir performance and the geophysical well logging data,petrophysical measurements and its characterization are very important for the exploration and development of the unconventional resources in tight sandstone,carbonate and shale reservoirs.In this special issue of Energy Geoscience,“Recent advances in petrophysical and geophysical characterization of unconventional resources”,we organized and invited authors to present recent advances in various subjects addressing new petrophysical characterizations and geophysical models in unconventional reservoirs.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

Enhancing I^(0)/I^(-)Conversion Efficiency by Starch Confinement in Zinc-lodine Battery

The redox couple of I^(0)/I^(-)in aqueous rechargeable iodine–zinc(I^(2)-Zn)batteries is a promising energy storage resource since it is safe and cost-effective,and provides steady output voltage.However,the cycle life and efficiency of these batteries remain unsatisfactory due to the uncontrolled shuttling of polyiodide(I_(3)^(-)and I_(5)^(-))and side reactions on the Zn anode.Starch is a very low-cost and widely sourced food used daily around the world.“Starch turns blue when it encounters iodine”is a classic chemical reaction,which results from the unique structure of the helix starch molecule–iodine complex.Inspired by this,we employ starch to confine the shuttling of polyiodide,and thus,the I^(0)/I^(-)conversion efficiency of an I^(2)-Zn battery is clearly enhanced.According to the detailed characterizations and theoretical DFT calculation results,the enhancement of I^(0)/I^(-)conversion efficiency is mainly originated from the strong bonding between the charged products of I_(3)^(-)and I_(5)^(-)and the rich hydroxyl groups in starch.This work provides inspiration for the rational design of high-performance and low-cost I^(2)-Zn in AZIBs.

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

Multi-Organ Pathogenesis and Therapeutic Strategies for Cystic Fibrosis

Cystic Fibrosis (CF) is the most common lethal autosomal inherited disorder that affects all races and ethnicities in the United States. However, it is mostly predominant in the Caucasian populace accounting for about 80% of all CF cases. CF most severe complication can be referred to as pulmonary bronchiectasis and infections of the airways, nonetheless, the devastating effects of the disease have far-reaching consequences beyond lung damage. CF is a heterogeneous disease that is caused by mutations in Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. The impairment or absence of this gene can affect multiple organs and systems and is characterized not only by chronic lung blockage, infections, and inflammation but also by exocrine gland dysfunction, intestinal obstruction, liver pathology, elevated sweat chloride concentration, and in males, infertility due to the congenital bilateral absence of the vas deferens. To this end, we briefly explore the pathological effects of CF and how CF mediates the destruction of several critical organs in the body and some of the gene therapeutical approaches such as gene editing and viral-based strategies available for the treatment of this multi-organ disease.

A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.