Dendritic spine degeneration:a primary mechanism in the aging process

Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).

Central role of altered phosphodiesterase 2-dependent signaling in the pathophysiology of cognition-based brain disorders

The second messengers 3',5'-cyclic adenosine monophosphate(cAMP)and 3',5'-cyclic guanosine monophosphate(cGMP)modulate molecular pathways that are involved in a large variety of cellular processes.In the brain,these processes include neurogenesis,neuronal differentiation,activation and function of microglia,and synaptic plasticity,finally resulting in memory formation.

Advances in Graphene‑Based Electrode for Triboelectric Nanogenerator

With the continuous development of wearable electronics,wireless sensor networks and other micro-electronic devices,there is an increasingly urgent need for miniature,flexible and efficient nanopower generation technology.Triboelectric nanogenerator(TENG)technology can convert small mechanical energy into electricity,which is expected to address this problem.As the core component of TENG,the choice of electrode materials significantly affects its performance.Traditional metal electrode materials often suffer from problems such as durability,which limits the further application of TENG.Graphene,as a novel electrode material,shows excellent prospects for application in TENG owing to its unique structure and excellent electrical properties.This review systematically summarizes the recent research progress and application prospects of TENGs based on graphene electrodes.Various precision processing methods of graphene electrodes are introduced,and the applications of graphene electrode-based TENGs in various scenarios as well as the enhancement of graphene electrodes for TENG performance are discussed.In addition,the future development of graphene electrode-based TENGs is also prospectively discussed,aiming to promote the continuous advancement of graphene electrode-based TENGs.

An Unprecedented Efficiency with Approaching 21%Enabled by Additive‑Assisted Layer‑by‑Layer Processing in Organic Solar Cells

Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network active layer morphology,featuring a bulk p-in structure and proper vertical segregation achieved through additive-assisted layer-by-layer deposition.This optimized hierarchical gradient fibrillar morphology and optical management synergistically facilitates exciton diffusion,reduces recombination losses,and enhances light capture capability.This approach not only offers a solution to achieving high-efficiency devices but also demonstrates the potential for commercial applications of OSCs.

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

Preparation of Co/S co-doped carbon catalysts for excellent methylene blue degradation

S and Co co-doped carbon catalysts were prepared via pyrolysis of MOF-71 and thiourea mixtures at 800℃at a mass ratio of MOF-71 to thiourea of 1:0.1 to effectively activate peroxymonosulfate(PMS)for methylene blue(MB)degradation.The effects of two different mixing routes were identified on the MB degradation performance.Particularly,the catalyst obtained by the alcohol solvent evaporation(MOF-AEP)mixing route could degrade 95.60%MB(50 mg/L)within 4 min(degradation rate:K=0.78 min^(-1)),which was faster than that derived from the direct grinding method(MOF-DGP,80.97%,K=0.39 min^(-1)).X-ray photoelectron spectroscopy revealed that the Co-S content of MOF-AEP(43.39at%)was less than that of MOF-DGP(54.73at%),and the proportion of C-S-C in MOF-AEP(13.56at%)was higher than that of MOF-DGP(10.67at%).Density functional theory calculations revealed that the adsorption energy of Co for PMS was -2.94 eV when sulfur was doped as C-S-C on the carbon skeleton,which was higher than that when sulfur was doped next to cobalt in the form of Co-S bond(-2.86 eV).Thus,the C-S-C sites might provide more contributions to activate PMS compared with Co-S.Furthermore,the degradation parameters,including pH and MOF-AEP dosage,were investigated.Finally,radical quenching experiments and electron paramagnetic resonance(EPR)measurements revealed that ^(1)O_(2)might be the primary catalytic species,whereas·O~(2-)might be the secondary one in degrading MB.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

Life-Cycle Bearing Capacity for Pre-Stressed T-beams Based on Full-Scale Destructive Test

To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams,destructive tests were conducted on full-scale pre-stressed concrete beams.Based on the measurement and ana-lysis of beam deflection,strain,and crack development under various loading levels during the research tests,combined with the verification coefficient indicators specified in the codes,the verification coefficients of bridges at different stages of damage can be examined.The results indicate that the T-beams experience complete,incom-plete linear,and non-linear stages during the destructive test process.In the complete linear elastic stage,both the deflection and bottom strain verification coefficients comply with the specifications,indicating a good structural load-bearing capacity no longer adheres to the code’s requirements.In the non-linear stage,both coefficients exhi-bit a sharp increase,resulting in a further decrease in the structure’s load-bearing capacity.According to the pro-visions of the current code,the beam can be in the incomplete linear stage when both values fall within the code’s specified range.The strain verification coefficient sourced from the compression zone at the bottom of theflange is not recommended for assessing the bridge’s load-bearing capacity.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

自动化生产线工业机器人的装调工艺仿真

在自动化生产线安装调试工作中,工业机器人的装调费时费力且难以迅速满足生产线工艺要求,提出了使用Process Simulate软件对自动化生产线中的工业机器人进行工艺仿真的解决方案。文章介绍了使用Process Simulate软件对自动化生产线中的工业机器人进行工艺仿真的基本方法和过程,从而实现工业机器人装调效率和自动化生产线生产效率的提高。

基于Arduino与Processing的心率检测计设计研究

随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。

Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.

Rapid report of the December 18,2023 M_(S)6.2 Jishishan earthquake,Gansu,China

On December 18,2023,the Jishishan area in Gansu Province was jolted by a M_(S) 6.2 earthquake,which is the most powerful seismic event that occurred throughout the year in China.The earthquake occurred along the NWtrending Lajishan fault(LJSF),a large tectonic transformation zone.After this event,China Earthquake Networks Center(CENC)has timely published several reports about seismic sources for emergency responses.The earthquake early warning system issued the first alert 4.9 s after the earthquake occurrence,providing prompt notification that effectively mitigated panics,injuries,and deaths of residents.The near real-time focal mechanism solution indicates that this earthquake is associated with a thrust fault.The distribution of aftershocks,the rupture process,and the recorded amplitudes from seismic monitoring and GNSS stations,all suggest that the mainshock rupture predominately propagates to the northwest direction.The duration of the rupture process is~12 s,and the largest slip is located at approximately 6.3 km to the NNW from the epicenter,with a peak slip of 0.12 m at~8 km depth.Seismic station N0028 recorded the highest instrumental intensity,which is 9.4 on the Mercalli scale.The estimated intensity map shows a seismic intensity reaching up to IX near the rupture area,consistent with field survey results.The aftershocks(up to December 22,2023)are mostly distributed in the northwest direction within~20 km of the epicenter.This earthquake caused serious casualties and house collapses,which requires further investigations into the impact of this earthquake.

Constraints on Characteristics and Distribution of Gas Hydrate and Free Gas Using Broad-Band Processing of Three-Dimensional Seismic Data

Gas hydrate drilling expeditions in the Pearl River Mouth Basin,South China Sea,have identified concentrated gas hydrates with variable thickness.Moreover,free gas and the coexistence of gas hydrate and free gas have been confirmed by logging,coring,and production tests in the foraminifera-rich silty sediments with complex bottom-simulating reflectors(BSRs).The broad-band processing is conducted on conventional three-dimensional(3D)seismic data to improve the image and detection accuracy of gas hydratebearing layers and delineate the saturation and thickness of gas hydrate-and free gas-bearing sediments.Several geophysical attributes extracted along the base of the gas hydrate stability zone are used to demonstrate the variable distribution and the controlling factors for the differential enrichment of gas hydrate.The inverted gas hydrate saturation at the production zone is over 40% with a thickness of 90 m,showing the interbedded distribution with different boundaries between gas hydrate-and free gas-bearing layers.However,the gas hydrate saturation value at the adjacent canyon is 70%,with 30-m-thick patches and linear features.The lithological and fault controls on gas hydrate and free gas distributions are demonstrated by tracing each gas hydrate-bearing layer.Moreover,the BSR depths based on broad-band reprocessed 3D seismic data not only exhibit variations due to small-scale topographic changes caused by seafloor sedimentation and erosion but also show the upward shift of BSR and the blocky distribution of the coexistence of gas hydrate and free gas in the Pearl River Mouth Basin.

Influence of layer thickness on formation quality,microstructure,mechanical properties,and corrosion resistance of WE43 magnesium alloy fabricated by laser powder bed fusion

Laser powder bed fusion(L-PBF)of Mg alloys has provided tremendous opportunities for customized production of aeronautical and medical parts.Layer thickness(LT)is of great significance to the L-PBF process but has not been studied for Mg alloys.In this study,WE43 Mg alloy bulk cubes,porous scaffolds,and thin walls with layer thicknesses of 10,20,30,and 40μm were fabricated.The required laser energy input increased with increasing layer thickness and was different for the bulk cubes and porous scaffolds.Porosity tended to occur at the connection joints in porous scaffolds for LT40 and could be eliminated by reducing the laser energy input.For thin wall parts,a large overhang angle or a small wall thickness resulted in porosity when a large layer thicknesses was used,and the porosity disappeared by reducing the layer thickness or laser energy input.A deeper keyhole penetration was found in all occasions with porosity,explaining the influence of layer thickness,geometrical structure,and laser energy input on the porosity.All the samples achieved a high fusion quality with a relative density of over 99.5%using the optimized laser energy input.The increased layer thickness resulted to more precipitation phases,finer grain sizes and decreased grain texture.With the similar high fusion quality,the tensile strength and elongation of bulk samples were significantly improved from 257 MPa and 1.41%with the 10μm layer to 287 MPa and 15.12%with the 40μm layer,in accordance with the microstructural change.The effect of layer thickness on the compressive properties of porous scaffolds was limited.However,the corrosion rate of bulk samples accelerated with increasing the layer thickness,mainly attributed to the increased number of precipitation phases.

Effect of safety valve types on the gas venting behavior and thermal runaway hazard severity of large-format prismatic lithium iron phosphate batteries

The safety valve is an important component to ensure the safe operation of lithium-ion batteries(LIBs).However,the effect of safety valve type on the thermal runaway(TR)and gas venting behavior of LIBs,as well as the TR hazard severity of LIBs,are not known.In this paper,the TR and gas venting behavior of three 100 A h lithium iron phosphate(LFP)batteries with different safety valves are investigated under overheating.Compared to previous studies,the main contribution of this work is in studying and evaluating the effect of gas venting behavior and TR hazard severity of LFP batteries with three safety valve types.Two significant results are obtained:(Ⅰ)the safety valve type dominates over gas venting pressure of battery during safety venting,the maximum gas venting pressure of LFP batteries with a round safety valve is 3320 Pa,which is one order of magnitude higher than other batteries with oval or cavity safety valve;(Ⅱ)the LFP battery with oval safety valve has the lowest TR hazard as shown by the TR hazard assessment model based on gray-fuzzy analytic hierarchy process.This study reveals the effect of safety valve type on TR and gas venting,providing a clear direction for the safety valve design.

Synergistic catalysis of the N-hydroxyphthalimide on flower-like bimetallic metal-organic frameworks for boosting oxidative desulfurization

Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall catalyst.Compared with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an initiator.The sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of reaction.Furthermore,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in polarity.This work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.

Optimal synthesis of heat-integrated distillation configurations using the two-column superstructure

In the realm of the synthesis of heat-integrated distillation configurations,the conventional approach for exploring more heat integration possibilities typically entails the splitting of a single column into a twocolumn configuration.However,this approach frequently necessitates tedious enumeration procedures,resulting in a considerable computational burden.To surmount this formidable challenge,the present study introduces an innovative remedy:The proposition of a superstructure that encompasses both single-column and multiple two-column configurations.Additionally,a simultaneous optimization algorithm is applied to optimize both the process parameters and heat integration structures of the twocolumn configurations.The effectiveness of this approach is demonstrated through a case study focusing on industrial organosilicon separation.The results underscore that the superstructure methodology not only substantially mitigates computational time compared to exhaustive enumeration but also furnishes solutions that exhibit comparable performance.

Recent developments in selective laser processes for wearable devices

Recently,the increasing interest in wearable technology for personal healthcare and smart virtual/augmented reality applications has led to the development of facile fabrication methods.Lasers have long been used to develop original solutions to such challenging technological problems due to their remote,sterile,rapid,and site-selective processing of materials.In this review,recent developments in relevant laser processes are summarized under two separate categories.First,transformative approaches,such as for laser-induced graphene,are introduced.In addition to design optimization and the alteration of a native substrate,the latest advances under a transformative approach now enable more complex material compositions and multilayer device configurations through the simultaneous transformation of heterogeneous precursors,or the sequential addition of functional layers coupled with other electronic elements.In addition,the more conventional laser techniques,such as ablation,sintering,and synthesis,can still be used to enhance the functionality of an entire system through the expansion of applicable materials and the adoption of new mechanisms.Later,various wearable device components developed through the corresponding laser processes are discussed,with an emphasis on chemical/physical sensors and energy devices.In addition,special attention is given to applications that use multiple laser sources or processes,which lay the foundation for the all-laser fabrication of wearable devices.

Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb

The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the processes are below 65 points.To solve these problems,a new process using anisole as the extractant was proposed.Anisole exhibits high selectivity for sinomenine and can be connected to the subsequent water-washing steps.After alkalization of the medicinal material,heating extraction,water washing,and acidification crystallization were carried out.The process was modeled and optimized.The design space was constructed.The recommended operating ranges for the critical process parameters were 3.0–4.0 h for alkalization time,60.0–80.0℃ for extraction temperature,2.0–3.0(volume ratio)for washing solution amount,and 2.0–2.4 mol·L^(-1) for hydrochloric acid concentration.The new process shows good robustness because different batches of medicinal materials did not greatly impact crystal purity or sinomenine transfer rate.The sinomenine transfer rate was about 20%higher than that of industrial processes.The greenness score increased to 90 points since the novel process proposed in this research solves the problems of long process flow,high solvent toxicity,and poor atomic economy,better aligning with the concept of green chemistry.