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Adsorption of Malachite Green Using Activated Carbon from Mangosteen Peel: Optimization Using Box-Behnken Design

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Journal of Renewable Materials 2024年第5期12卷 981-992页

作者： Nabila Eka Yuningsih Latifa Ariani Suprapto Suprapto Ita Ulfin Harmami Harmami Hendro Juwono Yatim Lailun Ni’mah Department of Chemistry Faculty of Science and Data AnalyticsInstitute Technology of Sepuluh NopemberSurabaya60111Indonesia

In this research,activated carbon from mangosteen peel has been synthesized using sulfuric acid as an *** adsorption performance of the activated carbon was optimized using malachite green dye as ***-chite green dye waste is a toxic and non-biodegradable material that damages the *** of adsorption processes was carried out using Response Surface Methodology(RSM)with a Box-Behnken Design(BBD).The synthesized activated carbon was characterized using FTIR and SEM *** FTIR spectra conﬁrmed the presence of a sulfonate group(-SO_(3)H)in the activated carbon,indicating that the activation pro-cess using sulfuric acid was *** characterization shows that activated carbon has porous *** was carried out for three adsorption parameters,namely contact time(20,60,and 120 min),adsor-bent mass(0.005,0.025,and 0.05 g),and initial concentration of malachite green solution(5,50,and 100 mg·L^(-1)).The concentration of the malachite green solution was determined using a UV-Vis spectrophotometer at the max-imum wavelength of malachite green,618 *** optimum of malachite green adsorption using mangosteen peel activated carbon was obtained at a contact time of 80 min,an adsorbent mass of 0.032 g,and malachite green initial concentration of 25 mg·L^(-1),with a maximum removal percentage and maximum adsorption capacity of 93.66%and 19.345 mg·g^(-1),respectively.

关键词： Response surface methodology dyes activated carbon experimental design

Abaca Fiber as a Potential Reinforcer for Acoustic Absorption Material at Middle-High Frequencies

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Journal of Renewable Materials 2024年第5期12卷 909-921页

作者： Susilo Indrawati Lila Yuwana Suyatno Mochamad Zainuri Darminto Department of Physics Faculty of Science and Data AnalyticsInstitut Teknologi Sepuluh NopemberSurabaya60111Indonesia

Recently,abacaﬁbers have become the focus of specialized research due to their intriguing characteristics,with their outstanding mechanical properties being a particularly *** the conducted study,the abacaﬁbers underwent a preliminary treatment process involving an alkaline solution,which was composed of 0.5%sodium hydroxide(NaOH)and 50%acetic acid(CH3COOH).This process entailed immersing eachﬁber in the solution for a period of one *** treatment led to a 52.36%reduction in lignin content compared to the levels before treatment,resulting in a dramatic decrease in the full width at half maximum(FWHM)in the XRD spectra from 1.13 to *** change indicates that theﬁbers became more crystalline following the *** abacaﬁbers were also characterized using BET(Brunauer Emmett Teller)measurements,which revealed that the aver-age pore length ranged from 33–49 nm and the surface area was between 13–28 m^(2)·g^(-1).The morphology of the abacaﬁber after alkali an hydrolisis treatment(AFAH)appeared rougher and more *** measurements revealed a signiﬁcant rise in the storage modulus of the singleﬁber post-treatment,with dependencies on both frequency and *** exhibited an optimal absorption coefﬁcient ofα=0.9 for frequencies above 2500 *** combined effect of alkalization and hydrolyzation treatments,while resulting in an enhancement in the mechanical properties of theﬁbers,also reduced high-intensity noise produced by sources such as machin-ery,aircraft takeoffs and landings,etc.,across a broader working frequency range.

关键词： Abacaﬁber alkalization mechanical property acoustic characteristics middle frequency

Multifaceted Disparities Associated with Translator Earnings: A Quantitative Study of Upwork Profiles

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Journal of Social Computing 2024年第1期5卷 88-100页

作者： Tyler Horan Data analytics and Computational Social Science Program University of Massachusetts AmherstAmherstMA 01003USA

This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation *** a dataset comprising 45000 translator profiles,the study focusses on delineating disparities correlated with demographic variables such as age,gender,home country wealth,and language *** study uses a random forest regression model to delineate the complex interaction between gender,the economic standing of a translator’s domicile country,age,and linguistic proficiency,as they relate to *** findings substantiate and,in many ways,extend existing qualitative and anecdotal evidence that has shaped the discourse in this *** rigorous empirical framework employed here can be replicated or adapted to study other sectors within the gig economy,thus contributing to a more comprehensive understanding of labour dynamics in the digital age.

关键词： translation online employment gig economy intercultural communication

An artificial intelligence framework for explainable drift detection in energy forecasting

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Energy and AI 2024年第3期17卷 368-379页

作者： Chamod Samarajeewa Daswin De Silva Milos Manic Nishan Mills Harsha Moraliyage Damminda Alahakoon Andrew Jennings Centre for Data analytics and Cognition La Trobe UniversityAustralia Department of Computer Science Virginia Commonwealth UniversityRichmondUSA

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

关键词： Building energy consumption forecasting Explainable drift detection Energy embedding Dimensional modeling

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1944-1953页

作者： Kailong Liu Qiao Peng Yuhang Liu Naxin Cui Chenghui Zhang School of Control Science and Engineering Shandong UniversityJinan 250061China Information Technology Analytics&Operations groupQueen’s University BelfastBelfast BT71NNUK

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery *** battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently *** paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven *** be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and *** experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating *** addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural *** to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

关键词： Battery management battery manufacturing data science explainable artificial intelligence sensitivity analysis

Davidson base as an appropriate base for the shell model in the case of 1p- and 1h- nuclei

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Communications in Theoretical Physics 2024年第10期76卷 94-100页

作者： Hadi Sobhani Yan-An Luo Amir Jalili Hassan Hassanabadi School of Physics Nankai University Department of Physics Zhejiang Sci-Tech University Institute of Theoretical Physics Faculty of Physics University of Warsaw Institute of Actuarial Science and Data analytics UCSI University

This study endeavors to develop an improved set of basis functions for nuclear shell-model calculations. The shell model, a well-established approach to understanding nuclear structure,typically employs the harmonic oscillator potential within the mean-field approximation. Given the extensive validation and consistency of harmonic oscillator results with experimental data,we propose a modification by incorporating a centrifugal term that converges toward the Davidson potential. This potential has been extensively studied, and we aim to extract relevant experimental data for the simplest cases in the shell model, namely one-particle and one-hole nuclei. A comprehensive comparison between the Davidson-based results and the harmonic oscillator calculations is presented to demonstrate the effectiveness of the new basis functions.

关键词： beta decay gamma transition nuclear structure shell-model calculations

Oligopeptide Self-Assembly:Mechanisms,Stimuli-Responsiveness,and Biomedical Applications

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CCS Chemistry 2024年第1期6卷 69-90页

作者： Lei Ge Hanyan Xu Xin Jiang Jing Yu School of Materials Science and Engineering Nanyang Technological UniversitySingapore 637553 Institute for Digital Molecular analytics and Science Nanyang Technological UniversitySingapore 637553

Oligopeptide self-assembly materials have emerged as a promising class of biomaterials with diverse applications in *** review highlights the recent progress in comprehending the selfassembly mechanisms intrinsic to oligopeptides and their behavior in response to specific *** methodically structuring the amino acid sequence and managing external stimuli such as pH levels,redox conditions,or enzymatic activity,we can exercise unprecedented control over the self-assembly *** controlling the self-assembly process of oligopeptides,various structures with extraordinary versatility can be obtained,including micelles,nanofibers,and coacervate droplets,each possessing modifiable mechanical and chemical ***,these self-assembled constructs demonstrate immense potential within varied biomedical *** stimuli-sensitive nature of oligopeptide assembly materials facilitates timely encapsulation and release of therapeutic cargos,consequently eliciting desired cellular *** approach paves the way for more precise tumor targeting,personalized medicinal treatments,and well-regulated drug *** innate biocompatibility and proficiency in replicating the extracellular matrix(ECM)render them ideally suited for applications such as tissue engineering,wound remediation,and regenerative *** summary,oligopeptide self-assembling materials show tremendous potential as adaptable platforms for cutting-edge biomedical applications,thereby bridging the divide between fundamental research and practical clinical application.

关键词： oligopeptides self-assembly nonequilibrium stimuli-responsive behavior biomedical applications

SOLVING OPTIMIZATION PROBLEMS OVER THE STIEFEL MANIFOLD BY SMOOTH EXACT PENALTY FUNCTIONS

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Journal of Computational Mathematics 2024年第5期42卷 1246-1276页

作者： Nachuan Xiao Xin Liu The Institute of Operations Research and analytics National University of SingaporeSingapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of Sciencesand University of Chinese Academy of SciencesChina

In this paper,we present a novel penalty model called ExPen for optimization over the Stiefel *** from existing penalty functions for orthogonality constraints,ExPen adopts a smooth penalty function without using any first-order derivative of the objective *** show that all the first-order stationary points of ExPen with a sufficiently large penalty parameter are either feasible,namely,are the first-order stationary points of the original optimization problem,or far from the Stiefel ***,the original problem and ExPen share the same second-order stationary ***,the exact gradient and Hessian of ExPen are easy to *** a consequence,abundant algorithm resources in unconstrained optimization can be applied straightforwardly to solve ExPen.

关键词： Orthogonality constraint Stiefel manifold Penalty function