Ultraviolet‑Irradiated All‑Organic Nanocomposites with Polymer Dots for High‑Temperature Capacitive Energy Storage

Polymer dielectrics capable of operating efficiently at high electric fields and elevated temperatures are urgently demanded by next-generation electronics and electrical power systems.While inorganic fillers have been extensively utilized to improved high-temperature capacitive performance of dielectric polymers,the presence of thermodynamically incompatible organic and inorganic components may lead to concern about the long-term stability and also complicate film processing.Herein,zero-dimensional polymer dots with high electron affinity are introduced into photoactive allyl-containing poly(aryl ether sulfone)to form the all-organic polymer composites for hightemperature capacitive energy storage.Upon ultraviolet irradiation,the crosslinked polymer composites with polymer dots are efficient in suppressing electrical conduction at high electric fields and elevated temperatures,which significantly reduces the high-field energy loss of the composites at 200℃.Accordingly,the ultraviolet-irradiated composite film exhibits a discharged energy density of 4.2 J cm^(−3)at 200℃.Along with outstanding cyclic stability of capacitive performance at 200℃,this work provides a promising class of dielectric materials for robust high-performance all-organic dielectric nanocomposites.

Gouge stability controlled by temperature elevation and obsidian addition in basaltic faults and implications for moonquakes

Basalt is a major component of the earth and moon crust.Mineral composition and temperature influence frictional instability and thus the potential for seismicity on basaltic faults.We performed velocitystepping shear experiments on basalt gouges at a confining pressure of 100 MPa,temperatures in the range of 100-400℃ and with varied obsidian mass fractions of 0-100%under wet/dry conditions to investigate the frictional strength and stability of basaltic faults.We observe a transition from velocity-neutral to velocity-weakening behaviors with increasing obsidian content.The frictional stability response of the mixed obsidian/basalt gouges is characterized by a transition from velocitystrengthening to velocity-weakening at 200℃ and another transition to velocity-strengthening at temperatures>300℃.Conversely,frictional strengths of the obsidian-bearing gouges are insensitive to temperature and wet/dry conditions.These results suggest that obsidian content dominates the potential seismic response of basaltic faults with the effect of temperature controlling the range of seismogenic depths.Thus,shallow moonquakes tend to occur in the lower lunar crust due to the corresponding anticipated higher glass content and a projected temperature range conducive to velocity-weakening behavior.These observations contribute to a better understanding of the nucleation mechanism of shallow seismicity in basaltic faults.

Sustainable land management in Mali

This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustain-able land management(SLM)practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages.The SLMs considered include the applica-tion of organic fertilizers,the application of inorganic fertilizers,the use of improved seeds,and the practice of intercropping.On average the application of organic fertilizers(39.2%),and inorganic fertilizers(28.7%)are the most frequent SLM practices among Malian farmers,and between 2014 and 2017,we observe a decline in the practice of intercropping.The regression results show that farmers’adoption of different SLMs is significantly associated with biophysical factors(average temperature,climate type,plot size,plot shape,and location),de-mographic factors(age,gender,education,household size),and socioeconomic factors(number of cultivated plots,livelihood diversification,type of crop grown,market access,credit access,economic shocks,and social capital).Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.

A multi-mechanism numerical simulation model for CO_(2)-EOR and storage in fractured shale oil reservoirs

Under the policy background and advocacy of carbon capture,utilization,and storage(CCUS),CO_(2)-EOR has become a promising direction in the shale oil reservoir industry.The multi-scale pore structure distribution and fracture structure lead to complex multiphase flow,comprehensively considering multiple mechanisms is crucial for development and CO_(2) storage in fractured shale reservoirs.In this paper,a multi-mechanism coupled model is developed by MATLAB.Compared to the traditional Eclipse300 and MATLAB Reservoir Simulation Toolbox(MRST),this model considers the impact of pore structure on fluid phase behavior by the modified Peng—Robinson equation of state(PR-EOS),and the effect simultaneously radiate to Maxwell—Stefan(M—S)diffusion,stress sensitivity,the nano-confinement(NC)effect.Moreover,a modified embedded discrete fracture model(EDFM)is used to model the complex fractures,which optimizes connection types and half-transmissibility calculation approaches between non-neighboring connections(NNCs).The full implicit equation adopts the finite volume method(FVM)and Newton—Raphson iteration for discretization and solution.The model verification with the Eclipse300 and MRST is satisfactory.The results show that the interaction between the mechanisms significantly affects the production performance and storage characteristics.The effect of molecular diffusion may be overestimated in oil-dominated(liquid-dominated)shale reservoirs.The well spacing and injection gas rate are the most crucial factors affecting the production by sensitivity analysis.Moreover,the potential gas invasion risk is mentioned.This model provides a reliable theoretical basis for CO_(2)-EOR and sequestration in shale oil reservoirs.

Advances in Liquid Crystal Epoxy:Molecular Structures,Thermal Conductivity,and Promising Applications in Thermal Management

Traditional heat conductive epoxy composites often fall short in meeting the escalating heat dissipation demands of large-power,high-frequency,and highvoltage insulating packaging applications,due to the challenge of achieving high thermal conductivity(k),desirable dielectric performance,and robust thermomechanical properties simultaneously.Liquid crystal epoxy(LCE)emerges as a unique epoxy,exhibiting inherently high k achieved through the self-assembly of mesogenic units into ordered structures.This characteristic enables liquid crystal epoxy to retain all the beneficial physical properties of pristine epoxy,while demonstrating a prominently enhanced k.As such,liquid crystal epoxy materials represent a promising solution for thermal management,with potential to tackle the critical issues and technical bottlenecks impeding the increasing miniaturization of microelectronic devices and electrical equipment.This article provides a comprehensive review on recent advances in liquid crystal epoxy,emphasizing the correlation between liquid crystal epoxy’s microscopic arrangement,organized mesoscopic domain,k,and relevant physical properties.The impacts of LC units and curing agents on the development of ordered structure are discussed,alongside the consequent effects on the k,dielectric,thermal,and other properties.External processing factors such as temperature and pressure and their influence on the formation and organization of structured domains are also evaluated.Finally,potential applications that could benefit from the emergence of liquid crystal epoxy are reviewed.

Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Effect of lateral stress on frictional properties of a fracture in sandstone

The injection of large volumes of natural gas into geological formations,as is required for underground gas storage,leads to alterations in the effective stress exerted on adjacent faults.This increases the potential for their reactivation and subsequent earthquake triggering.Most measurements of the frictional properties of rock fractures have been conducted under normal and shear stresses.However,faults in gas storage facilities exist within a true three-dimensional(3D)stress state.A double-direct shear experiment on rock fractures under both lateral and normal stresses was conducted using a true triaxial loading system.It was observed that the friction coefficient increases with increasing lateral stress,but decreases with increasing normal stress.The impact of lateral and normal stresses on the response is primarily mediated through their influence on the initial friction coefficient.This allows for an empirical modification of the rate-state friction model that considers the influence of lateral and normal stresses.The impact of lateral and normal stresses on observed friction coefficients is related to the propensity for the production of wear products on the fracture surfaces.Lateral stresses enhance the shear strength of rock(e.g.Mogi criterion).This reduces asperity breakage and the generation of wear products,and consequently augments the friction coefficient of the surface.Conversely,increased normal stresses inhibit dilatancy on the fracture surface,increasing the breakage of asperities and the concomitant production of wear products that promote rolling deformation.This ultimately reduces the friction coefficient.

Bifurcation Analysis Reveals Solution Structures of Phase Field Models

The phase field method is playing an increasingly important role in understanding and predicting morphological evolution in materials and biological systems.Here,we develop a new analytical approach based on the bifurcation analysis to explore the mathematical solution structure of phase field models.Revealing such solution structures not only is of great mathematical interest but also may provide guidance to experimentally or computationally uncover new morphological evolution phenomena in materials undergoing electronic and structural phase transitions.To elucidate the idea,we apply this analytical approach to three representative phase field equations:the Allen-Cahn equation,the Cahn-Hilliard equation,and the Allen-Cahn-Ohta-Kawasaki system.The solution structures of these three phase field equations are also verified numerically by the homotopy continuation method.

Effect of non-uniform swelling on coal multiphysics during gas injection: The triangle approach

In current dual porosity/permeability models,there exists a fundamental assumption that the adsorption-induced swelling is distributed uniformly within the representative elementary volume (REV),irrespective of its internal structures and transient processes.However,both internal structures and transient processes can lead to the non-uniform swelling.In this study,we hypothesize that the non-uniform swelling is responsible for why coal permeability in experimental measurements is not only controlled by the effective stress but also is affected by the adsorption-induced swelling.We propose a concept of the swelling triangle composed of swelling paths to characterize the evolution of the non-uniform swelling and serve as a core link in coupled multiphysics.A swelling path is determined by a dimensionless volumetric ratio and a dimensionless swelling ratio.Different swelling paths have the same start and end point,and each swelling path represents a unique swelling case.The swelling path as the diagonal of the triangle represents the case of the uniform swelling while that as the two perpendicular boundaries represents the case of the localized swelling.The paths of all intermediate cases populate inside the triangle.The corresponding relations between the swelling path and the response of coal multiphysics are established by a non-uniform swelling coefficient.We define this method as the triangle approach and corresponding models as swelling path-based ones.The proposed concept and models are verified against a long-term experimental measurement of permeability and strains under constant effective stress.Our results demonstrate that during gas injection,coal multiphysics responses have a close dependence on the swelling path,and that in both future experiments and field predictions,this dependence must be considered.

Evolution of pore systems in low-maturity oil shales during thermal upgrading--Quantified by dynamic SEM and machine learning

In-situ upgrading by heating is feasible for low-maturity shale oil,where the pore space dynamically evolves.We characterize this response for a heated substrate concurrently imaged by SEM.We systematically follow the evolution of pore quantity,size(length,width and cross-sectional area),orientation,shape(aspect ratio,roundness and solidity)and their anisotropy—interpreted by machine learning.Results indicate that heating generates new pores in both organic matter and inorganic minerals.However,the newly formed pores are smaller than the original pores and thus reduce average lengths and widths of the bedding-parallel pore system.Conversely,the average pore lengths and widths are increased in the bedding-perpendicular direction.Besides,heating increases the cross-sectional area of pores in low-maturity oil shales,where this growth tendency fluctuates at<300℃ but becomes steady at>300℃.In addition,the orientation and shape of the newly-formed heating-induced pores follow the habit of the original pores and follow the initial probability distributions of pore orientation and shape.Herein,limited anisotropy is detected in pore direction and shape,indicating similar modes of evolution both bedding-parallel and bedding-normal.We propose a straightforward but robust model to describe evolution of pore system in low-maturity oil shales during heating.

Constrained re-calibration of two-equation Reynolds-averaged Navier–Stokes models

Machine-learned augmentations to turbulence models can be advantageous for flows within the training dataset but can often cause harm outside.This lack of generalizability arises because the constants(as well as the functions)in a Reynolds-averaged Navier–Stokes(RANS)model are coupled,and un-constrained re-calibration of these constants(and functions)can disrupt the calibrations of the baseline model,the preservation of which is critical to the model's generalizability.To safeguard the behaviors of the baseline model beyond the training dataset,machine learning must be constrained such that basic calibrations like the law of the wall are kept intact.This letter aims to identify such constraints in two-equation RANS models so that future machine learning work can be performed without violating these constraints.We demonstrate that the identified constraints are not limiting.Furthermore,they help preserve the generalizability of the baseline model.

Enhanced performance of solution-processed carbon nanotube transparent electrodes in foldable perovskite solar cells through vertical separation of binders by using eco-friendly parylene substrate

The successful utilization of an eco-friendly and biocompatible parylene-C substrate for high-performance solution-processed double-walled carbon nanotube(CNT)electrode-based perovskite solar cells(PSCs)was demonstrated.Through the use of a novel inversion transfer technique,vertical separation of the binders from the CNTs was induced,rendering a stronger p-doping effect and thereby a higher conductivity of the CNTs.The resulting foldable devices exhibited a power conversion efficiency of 18.11%,which is the highest reported among CNT transparent electrode-based PSCs to date,and withstood more than 10,000 folding cycles at a radius of 0.5 mm,demonstrating unprecedented mechanical stability.Furthermore,solar modules were fabricated using entirely laser scribing processes to assess the potential of the solution-processable nanocarbon electrode.Notably,this is the only one to be processed entirely by the laser scribing process and to be biocompatible as well as eco-friendly among the previously reported nonindium tin oxide-based perovskite solar modules.

Exploring the synergy of the eyebrain connection:neuromodulation approaches for neurodegenerative disorders through transcorneal electrical stimulation

The connection and interaction between the eye and the brain are crucial to understanding brain disorders(Marchesi et al.,2021).Both the eye and the brain have a limited regenerative capacity as there are few progenitor cells,and nerve cells do not replicate.Hence,neurodegeneration implicates irreversible damage to the central nervous system,as observed in several neurodegenerative diseases(Marchesi et al.,2021).

Harvesting Technologies and Costs for Switchgrass Production

As today’s society searches for renewable energy sources that could be an alternative to fossil fuels, biomass and biofuels provide a promising solution. Switchgrass is one of feedstocks that can be utilized as a renewable energy source. When farming, one of the most important considerations is efficiency. This consists of several factors, including time, fuel use, economic and power efficiencies of equipment. Inefficient field operations could increase harvesting costs and in turn could cause hesitation when a farmer decides to participate in biomass production. This literature review will cover the main elements of biomass and biomass handling relating to determining harvesting efficiency and biomass quality for switchgrass round bales. Specifically, the following sections include past research activities relating to biomass harvesting, biomass bale quality during outdoor storage, logistics models, and data collection methods during biomass harvesting. The objective of this review is to examine status and needs for switchgrass round bale harvesting operations and the expenses that come with it.

Temperature field calculation of rail flash welding

The forging stage of rail flash welding has a decisive influence on joint strength,and the study of the temperature distribution in the process has an important role in further improving joint strength.In this paper,three calculation methods for the temperature field are given.First,the finite element model of the temperature field before forging rail flash welding is established by using the transient heat module of Ansys software and verified by infrared temperature measurement.Second,the temperature distribution of different parts of the rail before flash welding is obtained by using infrared thermal imaging equipment.Third,Matlab software is used to calculate the temperature of the non-measured part.Finally,the temperature distribution function along the rail axis is fitted through the temperature measurement data.The temperature distribution before the top forging of the rail flash welding can be used to analyze the joint and heat-affected zone organization and properties effectively and to guide the parameter setting and industrial production.

Nonparametric Feature Screening via the Variance of the Regression Function

This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their respective marginal regression functions (RV-SIS). We show that, under some mild technical conditions, the RV-SIS possesses a sure screening property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedures, and outperforms them in many different model settings.

Enhancing Cybersecurity through AI and ML: Strategies, Challenges, and Future Directions

The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed. The paper emphasizes a balanced approach that leverages technological innovation alongside rigorous ethical standards and robust cybersecurity practices. This approach facilitates collaboration among various stakeholders to develop guidelines that ensure responsible and effective use of AI in cybersecurity, aiming to enhance system integrity and privacy without compromising security.

Revealing the Hidden Mathematical Beauties of the Cayley-Hamilton Method

The inversion of a non-singular square matrix applying a Computer Algebra System (CAS) is straightforward. The CASs make the numeric computation efficient but mock the mathematical characteristics. The algorithms conducive to the output are sealed and inaccessible. In practice, other than the CPU timing, the applied inversion method is irrelevant. This research-oriented article discusses one such process, the Cayley-Hamilton (C.H.) [1]. Pursuing the process symbolically reveals its unpublished hidden mathematical characteristics even in the original article [1]. This article expands the general vision of the original named method without altering its practical applications. We have used the famous CAS Mathematica [2]. We have briefed the theory behind the method and applied it to different-sized symbolic and numeric matrices. The results are compared to the named CAS’s sealed, packaged library commands. The codes are given, and the algorithms are unsealed.

Enhancing Mobile Security through Comprehensive Penetration Testing

In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.