Development Program of Chemical Industry Based Computer Assisted Instruction for Students of Industrial Engineering Department in Indonesia

This study aims to improve the competence of students of the Department of Industrial Engineering in Indonesia in the subject of Chemical Industry, in particular through the model-based teaching materials CAI (Computer Assisted Instruction) in the form of an interactive CD. In particular, the study was carried out for the purposes of: 1) designing and developing models of devices based learning CAI (Computer Assisted Instruction) systematically in prototype form, 2) producing an interactive CD as a model learning devices Chemical Industry based CAI (Computer Assisted Instruction) to improve the competence of students of the Department of Industrial Engineering in Industrial chemistry courses. The benefits of this research are: 1) for the government, the results of this study can be used as a reference in implementing educational policies, especially to enhance the nation’s competitiveness in the era of informatics;and 2) for the Department of Industrial Engineering in Indonesia, the results of this research can be used to enhance learning that can improve the competence of students in the subject of Chemical Industry, which in turn can be passed with high achievement. Products produced in the first year are a design-based teaching materials CAI (Computer Assisted Instruction) in prototype form, with the following steps: 1) pre- production which includes needs analysis, identifying and analyzing the needs based on the content of curriculum and learning model based CAI (Computer Assisted Instruction), the development of a concept related to Chemical Industry, the development of multimedia content that includes developing materials, animation, and evaluation related to industrial chemicals, gathering material to make the recording sound, shooting, and editing with regard to the development of teaching materials chemical Industry based CAI (Computer Assisted Instruction), as well as developing the storyboard as the layout of the multimedia contents by involving experts multimedia;2) production process that includes design/design and conduct of programming a prototype which means at this stage of the design and development of teaching materials based CAI (Computer Assisted Instruction);and 3) post-production which includes the evaluation justification experts, conducted trials on stakeholders, being revised based on input from experts, and doing packing and labeling.

Evaluation of Industrial IoT Service Providers with TOPSIS Based on Circular Intuitionistic Fuzzy Sets

Industrial Internet of Things(IIoT)service providers have become increasingly important in the manufacturing industry due to their ability to gather and process vast amounts of data from connected devices,enabling manufacturers to improve operational efficiency,reduce costs,and enhance product quality.These platforms provide manufacturers with real-time visibility into their production processes and supply chains,allowing them to optimize operations and make informed decisions.In addition,IIoT service providers can help manufacturers create new revenue streams through the development of innovative products and services and enable them to leverage the benefits of emerging technologies such as Artificial Intelligence(AI)and machine learning.Overall,the implementation of IIoT platforms in the manufacturing industry is crucial for companies seeking to remain competitive and meet the ever-increasing demands of customers in the digital age.In this study,the evaluation criteria to be considered in the selection of IIoT service provider in small andmedium-sized(SME)manufacturing enterprises will be determined and IIoT service providers alternatives will be evaluated using the technique for order preference by similarity to an ideal solution(TOPSIS)method based on circular intuitionistic fuzzy sets.Based on the assessments conducted in accordance with the literature review and expert consultations,a set of 8 selection criteria has been established.These criteria encompass industry expertise,customer support,flexibility and scalability,security,cost-effectiveness,reliability,data analytics,as well as compatibility and usability.Upon evaluating these criteria,it was observed that the security criterion holds the highest significance,succeeded by cost-effectiveness,data analytics,flexibility and scalability,reliability,and customer support criteria,in descending order of importance.Following the evaluation of seven distinct alternatives against these criteria,it was deduced that the A6 alternative,a German service provider,emerged as the most favorable option.The identical issue was addressed utilizing sensitivity analysis alongside various multi-criteria decision-making(MCDM)methods,and after comprehensive evaluation,the outcomes were assessed.Spearman’s correlation coefficient was computed to ascertain the association between the rankings derived from solving the problem using diverse MCDM methods.

Impediments of Cognitive System Engineering in Machine-Human Modeling

A comprehensive understanding of human intelligence is still an ongoing process,i.e.,human and information security are not yet perfectly matched.By understanding cognitive processes,designers can design humanized cognitive information systems(CIS).The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems.The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction(HCI).Apractice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model;thereafter,amodel development paradigm was used to observe and monitor the progression of CIS during HCI.The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements.We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice.

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

Flow Regimes in Bubble Columns with and without Internals: A Review

Hydrodynamics characterization in terms offlow regime behavior is a crucial task to enhance the design of bubble column reactors and scaling up related methodologies.This review presents recent studies on the typicalflow regimes established in bubble columns.Some effort is also provided to introduce relevant definitions pertaining to thisfield,namely,that of“void fraction”and related(local,chordal,cross-sectional and volumetric)variants.Experimental studies involving different parameters that affect design and operating conditions are also discussed in detail.In the second part of the review,the attention is shifted to cases with internals of various types(perfo-rated plates,baffles,vibrating helical springs,mixers,and heat exchanger tubes)immersed in the bubble columns.It is shown that the presence of these elements has a limited influence on the global column hydrodynamics.However,they can make the homogeneousflow regime more stable in terms of transition gas velocity and transi-tion holdup value.The last section is used to highlight gaps which have not beenfilled yet and future directions of investigation.

Grain Yield,Biomass Accumulation,and Leaf Photosynthetic Characteristics of Rice under Combined Salinity-Drought Stress

Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in saline lands,primarily due to insufficient water resources and inadequate irrigation facilities.Consequently,combined salinity-drought stress poses a major threat to rice production.In this study,two salinity levels(NS,non-salinity;HS,high salinity)along with three drought treatments(CC,control condition;DJ,drought stress imposed at jointing;DH,drought stress imposed at heading)were performed to investigate their combined influences on leaf photosynthetic characteristics,biomass accumulation,and rice yield formation.Salinity,drought,and their combination led to a shortened growth period from heading to maturity,resulting in a reduced overall growth duration.Grain yield was reduced under both salinity and drought stress,with a more substantial reduction under the combined salinity-drought stress.The combined stress imposed at heading caused greater yield losses in rice compared with the stress imposed at jointing.Additionally,the combined salinity-drought stress induced greater decreases in shoot biomass accumulation from heading to maturity,as well as in shoot biomass and nonstructural carbohydrate(NSC)content in the stem at heading and maturity.However,it increased the harvest index and NSC remobilization reserve.Salinity and drought reduced the leaf area index and SPAD value of flag leaves and weakened the leaf photosynthetic characteristics as indicated by lower photosynthetic rates,transpiration rates,and stomatal conductance.These reductions were more pronounced under the combined stress.Salinity,drought,and especially their combination,decreased the activities of ascorbate peroxidase,catalase,and superoxide dismutase,while increasing the contents of malondialdehyde,hydrogen peroxide,and superoxide radical.Our results indicated a more significant yield loss in rice when subjected to combined salinity-drought stress.The individual and combined stresses of salinity and drought diminished antioxidant enzyme activities,inhibited leaf photosynthetic functions,accelerated leaf senescence,and subsequently lowered assimilate accumulation and grain yield.

Magnetic resonance imaging of extraocular rectus muscles abnormalities in acute acquired concomitant esotropia

AIM:To investigate the difference of medial rectus(MR)and lateral rectus(LR)between acute acquired concomitant esotropia(AACE)and the healthy controls(HCs)detected by magnetic resonance imaging(MRI).METHODS:A case-control study.Eighteen subjects with AACE and eighteen HCs were enrolled.MRI scanning data were conducted in target-controlled central gaze with a 3-Tesla magnetic resonance scanner.Extraocular muscles(EOMs)were scanned in contiguous image planes 2-mm thick spanning the EOM origins to the globe equator.To form posterior partial volumes(PPVs),the LR and MR cross-sections in the image planes 8,10,12,and 14 mm posterior to the globe were summed and multiplied by the 2-mm slice thickness.The data were classified according to the right eye,left eye,dominant eye,and non-dominant eye,and the differences in mean cross-sectional area,maximum cross-sectional area,and PPVs of the MR and LR muscle in the AACE group and HCs group were compared under the above classifications respectively.RESULTS:There were no significant differences between the two groups of demographic characteristics.The mean cross-sectional area of the LR muscle was significantly greater in the AACE group than that in the HCs group in the non-dominant eyes(P=0.028).The maximum cross-sectional area of the LR muscle both in the dominant and non-dominant eye of the AACE group was significantly greater than the HCs group(P=0.009,P=0.016).For the dominant eye,the PPVs of the LR muscle were significantly greater in the AACE than that in the HCs group(P=0.013),but not in the MR muscle(P=0.698).CONCLUSION:The size and volume of muscles dominant eyes of AACE subjects change significantly to overcome binocular diplopia.The LR muscle become larger to compensate for the enhanced convergence in the AACE.

Efficient Electromagnetic Wave Absorption and Thermal Infrared Stealth in PVTMS@MWCNT Nano‑Aerogel via Abundant Nano‑Sized Cavities and Attenuation Interfaces

Pre-polymerized vinyl trimethoxy silane(PVTMS)@MWCNT nano-aerogel system was constructed via radical polymerization,sol-gel transition and supercritical CO_(2)drying.The fabricated organic-inorganic hybrid PVTMS@MWCNT aerogel structure shows nano-pore size(30-40 nm),high specific surface area(559 m^(2)g^(−1)),high void fraction(91.7%)and enhanced mechanical property:(1)the nano-pore size is beneficial for efficiently blocking thermal conduction and thermal convection via Knudsen effect(beneficial for infrared(IR)stealth);(2)the heterogeneous interface was beneficial for IR reflection(beneficial for IR stealth)and MWCNT polarization loss(beneficial for electromagnetic wave(EMW)attenuation);(3)the high void fraction was beneficial for enhancing thermal insulation(beneficial for IR stealth)and EMW impedance match(beneficial for EMW attenuation).Guided by the above theoretical design strategy,PVTMS@MWCNT nano-aerogel shows superior EMW absorption property(cover all Ku-band)and thermal IR stealth property(ΔT reached 60.7℃).Followed by a facial combination of the above nano-aerogel with graphene film of high electrical conductivity,an extremely high electromagnetic interference shielding material(66.5 dB,2.06 mm thickness)with superior absorption performance of an average absorption-to-reflection(A/R)coefficient ratio of 25.4 and a low reflection bandwidth of 4.1 GHz(A/R ratio more than 10)was experimentally obtained in this work.

Dealuminated Hβ zeolite for selective conversion of fructose to furfural and formic acid

The fructose-to-furfural transformation is facing major challenges in the selectivity and high efficiency. Herein, we have developed a simple and effective approach for the selective conversion of fructose to furfural using Hβ zeolite modified by organic acids for dealuminization to regulate its textural and acidic properties. It was found that citric acid-dealuminized Hβ zeolite possessed high specific surface areas, wide channels and high Brønsted acid amount, which facilitated the selective conversion of fructose to furfural with a maximum yield of 76.2% at433 K for 1 h in the γ-butyrolactone(GBL)-H_(2)O system, as well as the concomitant formation of 83.0% formic acid. The^(13)C-isotope labelling experiments and the mechanism revealed that the selective cleavage of C1–C2 or C5–C6 bond on fructose was firstly occurred to form pentose or C5 intermediate by weak Brønsted acid, which was then dehydrated to furfural by strong Brønsted acid. Also this dealuminized Hβ catalyst showed the great recycling performance and was active for the conversion of glucose and mannose.

The application of cellulosic-based materials on interfacial solar steam generation for highly efficient wastewater purification: A review

The world's population is growing,leading to an increasing demand for freshwater resources for drinking,sanitation,agriculture,and industry.Interfacial solar steam generation(ISSG)can solve many problems,such as mitigating the power crisis,minimizing water pollution,and improving the purification and desalination of seawater,rivers/lakes,and wastewater.Cellulosic materials are a viable and ecologically sound technique for capturing solar energy that is adaptable to a range of applications.This review paper aims to provide an overview of current advancements in the field of cellulose-based materials ISSG devices,specifically focusing on their applications in water purification and desalination.This paper examines the cellulose-based materials ISSG system and evaluates the effectiveness of various cellulosic materials,such as cellulose nanofibers derived from different sources,carbonized wood materials,and two-dimensional(2D)and 3D cellulosic-based materials from various sources,as well as advanced cellulosic materials,including bacterial cellulose and cellulose membranes obtained from agricultural and industrial cellulose wastes.The focus is on exploring the potential applications of these materials in ISSG devices for water desalination,purification,and treatment.The function,advantages,and disadvantages of cellulosic materials in the performance of ISSG devices were also deliberated throughout our discussion.In addition,the potential and suggested methods for enhancing the utilization of cellulose-based materials in the field of ISSG systems for water desalination,purification,and treatment were also emphasized.

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

基于拍卖理论的动态多代理同类机调度算法

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents.All agents are self-interested and rational with the aim of maximizing their own objectives,resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information.Within the context,a centralized scheduling approach is unfeasible,and a decentralized approach is considered to deal with the targeted problem.This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’preferences under incomplete information.For this purpose,a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed.In the proposed approach,a dynamic auction procedure is established for dynamic jobs participating in a realtime auction,and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination.In addition,an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently.A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids.Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on largescale problems with numerous consumer agents and jobs.A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Dynamic Forecasting of Traffic Event Duration in Istanbul:A Classification Approach with Real-Time Data Integration

Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Optimized tillage methods increase mechanically transplanted rice yield and reduce the greenhouse gas emissions

Biaxia lrotary tillage in dryland(DBRT)can complete biaxial rotary tillage with straw incorporation,secondary suppression,and ditching,and it has been previously studied in direct-seeded rice and wheat.However,the effects of DBRT on the mechanically transplanted rice yield and greenhouse gas emissions remain unclear.To evaluate the effects of DBRT on improving the food security of mechanically transplanted rice and reducing the greenhouse gas emissions,we conducted an experiment for two years with wheat straw incorporation.Three tillage methods were set up:DBRT,uniaxial rotary tillage in dryland and paddy(DPURT),and uniaxial rotary tillage in paddy(PURT).The results showed that compared with DPURT and PURT,DBRT increased the yield of machine-transplanted rice by 7.5-11.0%and 13.3-26.7%,respectively,while the seasonal cumulative CH_(4) emissions were reduced by 13.9-21.2%and 30.2-37.0%,respectively,and the seasonal cumulative N_(2)O emissions were increased by 13.5-28.6%and 50.0-73.1%,respectively.Consequently,DBRT reduced the global warming potential by 10.7-15.5%and 23.7-28.6%,respectively,andtheyield-scaledglobalwarmingpotentialby18.2-21.8%and36.4-39.3%,respectively,compared to DPURT and PURT.These results were mainly related to the fact that DBRT significantly reduced soil bulk density and increased soil redox potential(Eh).Therefore,implementing DBRT in machine-transplanted rice fields is feasible,which cannot only increase the rice yield,but also reduce the greenhouse gas emissions.

Cryogenic and conventional milling of AZ91 magnesium alloy

Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc.This study focuses on the effectiveness of liquid nitrogen(LN_(2))assisted cryogenic machining on the surface integrity(SI)characteristics of AZ91 magnesium alloy.Face milling using uncoated carbide inserts have been performed under liquid nitrogen(LN_(2))assisted cryogenic condition and compared with conventional(dry)milling.Experiments are performed using machining parameters in terms of cutting speeds of 325,475,625 m/min,feed rates of 0.05,0.1,0.15 mm/teeth and depth of cuts of 0.5,1,1.5 mm respectively.Most significant surface integrity characteristics such as surface roughness,microhardness,microstructure,and residual stresses have been investigated.Behaviour of SI characteristics with respect to milling parameters have been identified using statistical technique such as ANOVA and signal-to-noise(S/N)ratio plots.Additionally,the multi criteria decision making(MCDM)techniques such as additive ratio assessment method(ARAS)and complex proportional assessment(COPRAS)have been utilized to identify the optimal conditions for milling AZ91 magnesium alloy under both dry and cryogenic conditions.Use of LN_(2)during machining,resulted in reduction in machining temperature by upto 29%with a temperature drop from 251.2℃under dry condition to 178.5℃in cryogenic condition.Results showed the advantage of performing cryogenic milling in improving the surface integrity to a significant extent.Cryogenic machining considerably minimized the roughness by upto 28%and maximised the microhardness by upto 23%,when compared to dry machining.Cutting speed has caused significant impact on surface roughness(95.33%-dry,92.92%-cryogenic)and surface microhardness(80.33%-dry,82.15%-cryogenic).Due to the reduction in machining temperature,cryogenic condition resulted in compressive residual stresses(maximumσ║=-113 MPa)on the alloy surface.Results indicate no harm to alloy microstructure in both conditions,with no alterations to grain integrity and minimal reduction in the average grain sizes in the near machined area,when compared to before machined(base material)surface.The MCDM approach namely ARAS and COPRAS resulted in identical results,with the optimal condition being cutting speed of 625 m/min,a feed rate of 0.05 mm/teeth,and a depth of cut of 0.5 mm for both dry and cryogenic environments.

The influence of inter-band rock on rib spalling in longwall panel with large mining height

In order to improve rib stability,failure criteria and instability mode of a thick coal seam with inter-band rock layer are analysed in this study.A three-dimensional mechanical model is established for the rib by considering the rock layer.A safety factor is defined foy the rib,and it is observed that the safety factor exhibits a positive correlation with the thickness and strength of the inter-band rock.A calculation method for determining critical parameters of the rock layer is presented to ensure the rib stability.It is revealed that incomplete propagation of the fracture at the hard rock constitutes a fundamental prerequisite for ensuring the rib stability.The influence of the position of the inter-band rock in the coal seam on failure mechanism of the rib was thoroughly investigated by developing a series of physical models for the rib at the face area.The best position for the inter-band rock in the coal seam is at a height of 1.5 m away from the roof line,which tends to provide a good stability state for the rib.For different inter-band rock positions,two ways of controlling rib by increasing supports stiffness and flexible grouting reinforcement are proposed.

未热解厚热障涂层的隔热能力和黏接强度

对比研究使用未热解粉末制备的厚热障涂层(UPTTBC)和传统TBC、厚TBC(TTBC)及密集垂直裂纹(DVC)TTBC 4种涂层的隔热能力和黏接强度。采用降温法测试涂层的隔热能力;根据ASTM C633标准,采用拉拔试验检测涂层的黏接强度。结果显示,UPTTBC和DVC TTBC的黏接强度分别比TTBC高35%和25%。这可能是由于UPTTBC结构中存在亚微米尺度的未熔化区域,起到了阻止裂纹扩展的作用。此外,隔热测试的结果表明,DVCTTBC涂层的隔热能力最差,为0.28℃/μm,而UPTTBC涂层的隔热能力最强,为0.40℃/μm。由于UPTTBCs显微组织中存在亚微米区域,使涂层展现出优越的隔热能力。相反,DVCTTBC因其致密的结构,具有最低的隔热能力。

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.

Revolutionizing plasmonic platform via magnetic field-assisted confined ultrafast laser deposition of high-density,uniform,and ultrafine nanoparticle arrays

The remarkable capabilities of 2D plasmonic surfaces in controlling optical waves havegarnered significant attention.However,the challenge of large-scale manufacturing of uniform,well-aligned,and tunable plasmonic surfaces has hindered their industrialization.To address this,we present a groundbreaking tunable plasmonic platform design achieved throughmagnetic field(MF)assisted ultrafast laser direct deposition in air.Through precise control of metal nanoparticles(NPs),with cobalt(Co)serving as the model material,employing an MF,and fine-tuning ultrafast laser parameters,we have effectively converted coarse and non-uniform NPs into densely packed,uniform,and ultrafine NPs(~3 nm).This revolutionary advancement results in the creation of customizable plasmonic‘hot spots,’which play a pivotal role insurface-enhanced Raman spectroscopy(SERS)sensors.The profound impact of this designable plasmonic platform lies in its close association with plasmonic resonance and energyenhancement.When the plasmonic nanostructures resonate with incident light,they generate intense local electromagnetic fields,thus vastly increasing the Raman scattering signal.This enhancement leads to an outstanding 2–18 fold boost in SERS performance and unparalleled sensing sensitivity down to 10^(-10)M.Notably,the plasmonic platform also demonstratesrobustness,retaining its sensing capability even after undergoing 50 cycles of rinsing andre-loading of chemicals.Moreover,this work adheres to green manufacturing standards,making it an efficient and environmentally friendly method for customizing plasmonic‘hot spots’inSERS devices.Our study not only achieves the formation of high-density,uniform,and ultrafine NP arrays on a tunable plasmonic platform but also showcases the profound relation betweenplasmonic resonance and energy enhancement.The outstanding results observed in SERS sensors further emphasize the immense potential of this technology for energy-relatedapplications,including photocatalysis,photovoltaics,and clean water,propelling us closer to a sustainable and cleaner future.