Oxygen functionalization-assisted anionic exchange toward unique construction of flower-like transition metal chalcogenide embedded carbon fabric for ultra-long life flexible energy storage and conversion

The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storage and conversion application.The NiCCZ was then oxygen functionalized,facilitating the next step of stoichiometric sulfur anion diffusion during hydrothermal sulfurization,generating a flower-like metal hydroxysulfide structure(NiCCZOS)with strong partial implantation inside CC.Thus obtained NiCCZOS shows an excellent capacity when tested as a supercapacitor electrode in a three-electrode configuration.Moreover,when paired with the biomass-derived nitrogen-rich activated carbon,the asymmetric supercapacitor device shows almost 100%capacity retention even after 45,000 charge–discharge cycles with remarkable energy density(59.4 Wh kg^(-1)/263.8μWh cm^(–2))owing to a uniquely designed cathode.Furthermore,the same electrode performed as an excellent bifunctional water-splitting electrocatalyst with an overpotential of 271 mV for oxygen evolution reaction(OER)and 168.4 mV for hydrogen evolution reaction(HER)at 10 mA cm−2 current density along with 30 h of unhinged chronopotentiometric stability performance for both HER and OER.Hence,a unique metal chalcogenide composite electrode/substrate configuration has been proposed as a highly stable electrode material for flexible energy storage and conversion applications.

Hydrogeochemical Characterization of Groundwater from Fissured Aquifers in the Angovia Mine Operating Permit Area (Central-West Côte d’Ivoire)

The main objective of this study is to determine the hydrogeochemical specificities of the groundwater of the Angovia mine operating permit, located in the Yaouré mountains in the center-west of Côte d’Ivoire. To do so, descriptive and multivariate statistical analysis methods with the SOM (Self Organizing Maps) algorithm were applied to the physicochemical parameters of 17 boreholes using the calcite (ISC) and dolomite (ISD) saturation indices. The results obtained have shown that the groundwater in the Angovia mine operating permit area has an average temperature of 27.52°C (long rainy season) and 27.87&degC (long dry season) and has an average pH of 7.09 ± 0.35 during the main rainy season and 7.32 ± 0.35 during the main dry season. They are mineralized with an average electrical conductivity of 505.98 ± 302.85 μS/cm during the long rainy season and with 450.33 ± 233.74 μS/cm as average during the long dry season. The main phenomena at the origin of groundwater mineralization are water residence time, oxidation-reduction and surface inflow. The study of the relative age of the water shows that the groundwater in the Angovia mine operating permit area is mainly undersaturated with respect to calcite and dolomite. They are therefore very old in the aquifer with a slow circulation speed during the long rainy season and the long dry season.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Enhancing Patient Safety in the Operating Room: A Comprehensive Guide from a Nurse’s Perspective

Ensuring patient safety within the operating room is a paramount concern in contemporary healthcare, and this guide aims to provide an in-depth exploration of this crucial aspect from the perspective of nurses. Nurses play a pivotal role in supporting surgeons and maintaining a safe environment for patients undergoing various medical procedures.

Finite Element Method Simulation of Wellbore Stability under Different Operating and Geomechanical Conditions

The variation of the principal stress of formations with the working and geo-mechanical conditions can trigger wellbore instabilities and adversely affect the well completion.A finite element model,based on the theory of poro-elasticity and the Mohr-Coulomb rock damage criterion,is used here to analyze such a risk.The changes in wellbore stability before and after reservoir acidification are simulated for different pressure differences.The results indicate that the risk of wellbore instability grows with an increase in the production-pressure difference regardless of whether acidification is completed or not;the same is true for the instability area.After acidizing,the changes in the main geomechanical parameters(i.e.,elastic modulus,Poisson’s ratio,and rock strength)cause the maximum wellbore instability coefficient to increase.

Experimental investigation of the polarityswitching process with different bipolar ionic liquid thruster operating frequencies

The bipolar ionic liquid thruster employs ionic liquid as a propellant to discharge positively and negatively charged high-energy particles under an alternating current(AC)power source,effectively suppressing electrochemical reaction and ensuring charge neutrality.Determining an optimal AC supply power source frequency is critical for sustained stable thruster operation.This study focuses on the emission characteristics of the ionic liquid thruster under varied AC conditions.The AC power supply was set within the frequency range of 0.5-64 Hz,with eight specific frequency conditions selected for experimentation.The experimental results indicate that the thruster operates steadily within a voltage range of±1470 to±1920 V,with corresponding positive polarity current ranging from 0.41 to 4.91μA and negative polarity current ranging from−0.49 to−4.10μA.During voltage polarity switching,an emission delay occurs,manifested as a prominent peak signal caused by circuit capacitance characteristics and a minor peak signal resulting from liquid droplets.Extended emission test was conducted at 16 Hz,demonstrating approximately 1 h and 50 min of consistent emission before intermittent discharge.These findings underscore the favorable impact of AC conditions within the 8-16 Hz range on the self-neutralization capability of the ionic liquid thruster.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Prospective randomized study to evaluate and compare the post-procedural sexual function in patients undergoing semi-rigid ureterorenoscopy for distal ureteric stones in three different operating room settings

Objective:To evaluate and compare the effect of semi-rigid ureterorenoscopy on post-procedural sexual function with three different operating room settings.Methods:In this prospective randomized study,consecutive sexually active patients with normal pre-operative sexual function undergoing uncomplicated semi-rigid ureterorenoscopy for distal ureteric stones were randomized into three groups,with three different operating room settings.Procedure-related anxiety and sexual function were assessed pre-operatively using Amsterdam Preoperative Anxiety and Information Scale and Brief Sexual Function Inventory(in males)and Female Sexual Function Index-6(in females),respectively.All the participants were stented following the procedure,and the stent was removed after 3 weeks.Post-procedural sexual function and general discomfort were assessed and compared between three groups at 1 week,3 weeks,and 12 weeks.The effect of surgery-related anxiety,preoperative sexual function,age,and general discomfort(including stent-related discomfort)on post-procedural sexual function were analyzed using multiple regression analysis.A p-value of less than 0.05 was considered statistically significant.Results:Totally,327 eligible patients were randomized into three groups.The group of patients who underwent the procedural with a screen separating the operating area from the patient vision,while the patient could watch the endoscopy through a separate monitor,had better post-procedural sexual function compared to those who had total vision of the operating area as well as to those whose eyes were blocked.This difference was statistically significant.This post-procedural reduction in sexual function could not be attributed to in situ stent alone.Conclusions:Our study showed that semi-rigid ureterorenoscopy can have significant negative effect on sexual function,which can be reduced with proper preoperative counseling and an ideal operating room settings.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

The Impact of Optimizing Details in the Operating Room on the Level of Knowledge, Attitude, and Practice of Hospital Infection Prevention and Control by Surgeons, as Well as the Effectiveness of Infection Control

Objective: This paper aims to explore the impact of optimizing details in the operating room on the level of knowledge, attitude, and practice of hospital infection prevention and control by surgeons, as well as the effectiveness of infection control. Methods: From January 2022 to June 2023, a total of 120 patients were screened and randomly divided into a control group (routine care and hospital infection management) and a study group (optimizing details in the operating room). Results: Significant differences were found between the two groups in the data of surgeons’ level of knowledge, attitude, and practice in hospital infection prevention and control, infection rates, and nursing satisfaction, with the study group showing better results (P Conclusion: The use of optimizing details in the operating room among surgeons can effectively improve surgeons’ level of knowledge, attitude, and practice in hospital infection prevention and control, reduce infection occurrence, and is worth promoting.

Aero-Hydro-Elastic-Servo Modeling and Dynamic Response Analysis of A Monopile Offshore Wind Turbine Under Different Operating Scenarios

This paper constructs a coupled aero-hydro-elastic-servo simulation framework for a monopile offshore wind turbine(OWT).In this framework,a detailed multi-body dynamics model of the monopile OWT including the gearbox,blades,tower and other components(nacelle,hub,bedplate,etc.)has been explicitly established.The effects of pile−soil interaction,controller and operational conditions on the turbine dynamic responses are studied systematically in time domain and frequency domain.The results show that(1)a comprehensive drivetrain model has the capability to provide a more precise representation of the complex dynamic characteristics exhibited by drivetrain components,which can be used as the basis for further study on the dynamic characteristics of the drivetrain.(2)The pile−soil interaction can influence the wind turbine dynamic responses,particularly under the parked condition.(3)The effect of the pile−soil interaction on tower responses is more significant than that on blade responses.(4)The use of the controller can substantially affect the rotor characteristics,which in turn influences the turbine dynamic responses.(5)The tower and blade displacements under the operational condition are much larger than those under the parked condition.The model and methodology presented in this study demonstrate potential for examining complex dynamic behaviors of the monopile OWTs.To ensure accuracy and precision,it is imperative to construct a detailed model of the wind turbine system,while also taking into account simulation efficiency.

Correlation between critical thinking and emotional intelligence:a national crosssectional study on operating room nursing students in Iran

Objective:According to the World Federation of Medical Education,critical thinking should be part of the training of medical and paramedical students.Professionals can improve the quality of care of patients after surgery by having or acquiring this skill in health care.Also,Emotional intelligence is introduced as an impor tant and effective factor on the professional performance and mental health of healthcare professionals.Thus,the present study was designed and implemented to determine the relationship between emotional intelligence and critical thinking among operating room nursing students of medical sciences universities in Iran.Methods:This cross-sectional study was done on 420 operating room students in 10 top medical sciences universities of Iran in 2022.The sampling method in this research was multistage sampling.The data collection instruments included demographic characteristics,Rickett's critical thinking,and Bradberry-Greaves'emotional intelligence questionnaires.After receiving the ethics code,data collection was done for 2 months.For data analysis,descriptive and inferential analyses including independent t-tests,analysis of variance,and Pearson correlation were used.The collected data were analyzed by SPSS 18(IBM Corporation,Armonk,New York,United States).P-value<0.05 was considered significant.Results:The mean age of the students participating in this study was 23.02±3.70 years,with women constituting 67.4%of them.The results of data analysis indicated that the mean total score of critical thinking and emotional intelligence was 124.10±37.52 and 114.12±43.63,respectively.A direct significant correlation between critical thinking and emotional intelligence(r=0.459,P-value<0.001)and a significant relationship between gender and emotional intelligence(P-value=0.028)were found.Conclusions:Based on the present study results,educational managers in the Ministry of Health are suggested to consider suitable educational programs for improving critical thinking and emotional intelligence to enhance the quality of care provided by students in operating rooms.

Operating Parameters of the District Heating Substation Cooperating With the Installation of Technological Air Conditioning With High Efficiency of Heat Recovery

The article analyses the problem of determining the operating parameters of the district heating substation cooperating with the air heating system in technological air conditioning systems equipped with heat exchangers with high efficiency of heat recovery.Attention was paid to the correct selection of heat exchangers for the heat output balance depending on the heat recovery protection algorithms against a drop in the temperature of the heat transfer surface below 0℃.Critical parameters were determined in Polish climatic conditions,at which the operation of the heat recovery exchanger in the air conditioning system is switched off or limited.It has been proven that the proper functioning of the district heating substation cooperating with the installation of air conditioning with high heat recovery efficiency requires the use of two heat exchangers with different characteristics,equipped with properly selected temperature control systems.The optimal model of cooperation between the technological air conditioning system and the heating substation was also indicated.

Teaching Reform and Practice of Embedded System Design Based on Outcome-Based Education

Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.

Operating Room Nurses’ Role in Multidisciplinary Surgical Coordination for a Patient with a Large Abdominal Tumor and Multiple Pelvic Fractures

In this study,we have summarized the coordination of operating room nurses participating in the multidisciplinary team in diagnosing and treating a patient with a large abdominal tumor and multiple pelvic fractures.To perform surgical treatment on patients with various conditions,it is crucial to consider the patients from a holistic perspective.Thus,the existing medical model has shifted from a“disease-centered”approach focusing on single-disciplinary diagnosis and treatment,to a“patient-centered”approach that involves multiple disciplines in diagnosis and therapy.Operating room nurses,as crucial collaborators of surgeons,should make necessary adjustments to enhance their comprehension of patients,improving the overall quality of surgical coordination.

Application of Nursing Cooperation Path in Operating Room and Analysis of Complication Rate During Laparoscopic Hysterectomy

Objective:To explore the clinical benefits achieved by implementing the operating room nursing cooperation path for patients undergoing laparoscopic hysterectomy.Methods:64 laparoscopic hysterectomy cases were divided into groups according to the order of admission.The control group received routine perioperative care.The observation group implemented the nursing cooperation path in the operating room on the same basis as the control group.The two groups’physiological responses,stimulus indicators,anxiety,and complication rates were compared.Results:The heart rate,blood pressure 0.5 hours after surgery,anxiety scores 1 day before and 3 days after surgery,and the total number of complications in the observation group were all lower than those in the control group(P<0.05).Conclusion:The development of a nursing cooperation path in the operating room can help patients undergoing laparoscopic hysterectomy reduce heart rate,blood pressure,stress responses,and the risk of complications,and is worthy of promotion.

Evaluation of the Predicted Particle Properties (P3) Microphysics Scheme in Simulations of Stratiform Clouds with Embedded Convection

To evaluate the ability of the Predicted Particle Properties(P3)scheme in the Weather Research and Forecasting(WRF)model,we simulated a stratiform rainfall event over northern China on 22 May 2017.WRF simulations with two P3 versions,P3-nc and P3-2ice,were evaluated against rain gauge,radar,and aircraft observations.A series of sensitivity experiments were conducted with different collection efficiencies between ice and cloud droplets.The comparison of the precipitation evolution between P3-nc and P3-2ice suggested that both P3 versions overpredicted surface precipitation along the Taihang Mountains but underpredicted precipitation in the localized region on the leeward side.P3-2ice had slightly lower peak precipitation rates and smaller total precipitation amounts than P3-nc,which were closer to the observations.P3-2ice also more realistically reproduced the overall reflectivity structures than P3-nc.A comparison of ice concentrations with observations indicated that P3-nc underestimated aggregation,whereas P3-2ice produced more active aggregation from the self-collection of ice and ice-ice collisions between categories.Efficient aggregation in P3-2ice resulted in lower ice concentrations at heights between 4 and 6 km,which was closer to the observations.In this case,the total precipitation and precipitation pattern were not sensitive to riming.Riming was important in reproducing the location and strength of the embedded convective region through its impact on ice mass flux above the melting level.

A Discussion on the Establishment That a Fibre Metric on the Positive Definite Real Inner-Product of a Properly Embedded Smooth Submanifold Be Always Extended to a Riemannian Metric on the Positive Definite Real Inner-Product

Let M be a smooth manifold and S ⊆ M a properly embedded smooth submanifold. Suppose that we have a fibre metric on TM|s i.e. a positive definite real inner-product on TpM for all p ∈ S, which depends smoothly on p ∈ S. The purpose of this article is to figure out that the fibre metric on TM|s can always be extended to a Riemannian metric on TM from a special perspective.

ZnO-Embedded Expanded Graphite Composite Anodes with Controlled Charge Storage Mechanism Enabling Operation of Lithium-Ion Batteries at Ultra-Low Temperatures

As lithium(Li)-ion batteries expand their applications,operating over a wide temperature range becomes increasingly important.However,the lowtemperature performance of conventional graphite anodes is severely hampered by the poor diffusion kinetics of Li ions(Li^(+)).Here,zinc oxide(ZnO) nanoparticles are incorporated into the expanded graphite to improve Li^(+)diffusion kinetics,resulting in a significant improvement in lowtemperature performance.The ZnO-embedded expanded graphite anodes are investigated with different amounts of ZnO to establish the structurecharge storage mechanism-performance relationship with a focus on lowtemperature applications.Electrochemical analysis reveals that the ZnOembedded expanded graphite anode with nano-sized ZnO maintains a large portion of the diffusion-controlled charge storage mechanism at an ultra-low temperature of-50℃ Due to this significantly enhanced Li^(+)diffusion rate,a full cell with the ZnO-embedded expanded graphite anode and a LiNi_(0.88)Co_(0.09)Al_(0.03)O_(2)cathode delivers high capacities of 176 mAh g^(-1)at20℃ and 86 mAh g^(-1)at-50℃ at a high rate of 1 C.The outstanding low-temperature performance of the composite anode by improving the Li^(+)diffusion kinetics provides important scientific insights into the fundamental design principles of anodes for low-temperature Li-ion battery operation.