Selection Issues Research Articles

Pd-Catalyzed Site-Selective Defluorinative Etherification between Unactivated Perfluoroarenes and Hydrobenzoxazoles.

Polyfluoroaryl ethers represent an important framework of biologically active molecules and materials. Owing to the strong bond dissociation energy of C-F bond, selectivity and other issues, transition metal-catalyzed synthesis of polyfluoroaryl ethers from perfluoroarenes via the activation of C-F bond is challenging and underdeveloped, as compared to the well-documented C-O bond formation starting from aryl iodides, aryl bromides or aryl chlorides. Herein, an unprecedented Pd-catalyzed defluorinative etherification for the synthesis of polyfluoroaryl ether skeletons using hydrobenzoxazoles as phenol surrogate, has been reported. The substrate scope for this protocol is broad, with respect to hydrobenzoxazoles and perfluoroarenes, under mild reaction conditions. More importantly, challenging alkenyl and alkynyl substituted polyfluoroarenes could be successfully used as the coupling component for Pd-catalyzed etherification reaction. Density functional theory (DFT) calculations were employed to investigate the reaction mechanism, which suggested that oxidative addition between polyfluorobenzene and Pd(0) constituted the ratedetermining step.

A portfolio optimization model under uncertain random environment

<p>Uncertain events frequently occur in today’s financial markets. Consequently, the issue of portfolio selection is becoming increasingly significant. This paper thoroughly considers the complexities of stock returns in real-world scenarios and employs uncertain differential equations (UDE), uncertain time series analysis (UTSA), stochastic differential equations (SDE), and random time series analysis (RTSA) to predict stock returns, thereby enhancing the accuracy of these predictions. Furthermore, this paper addresses investors’ preferences and the limitations of using variance as a measure of investment risk. It introduces a risk preference factor and proposes an uncertain random mean-lower variance model. Finally, a genetic algorithm is utilized to solve the model, and numerical simulations are conducted to demonstrate the model’s practicality.<strong></strong></p>

Computational analysis of the Logistics Supply Chain from a global trade perspective

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset

The modern industries of today demand the classification of satellite images, and to use the information obtained from it for their advantage and growth. The extracted information also plays a crucial role in national security and the mapping of geographical locations. The conventional methods often fail to handle the complexities of this process. so, an effective method is required with high accuracy and stability. In this paper, a new methodology named RankEnsembleFS is proposed that addresses the crucial issues of stability and feature aggregation in the context of the SAT-6 dataset. RankEnsembleFS makes use of a two-step process that consists of ranking the features and then selecting the optimal feature subset from the top-ranked features. RankEnsembleFS achieved comparable accuracy results to state-of-the-art models for the SAT-6 dataset while significantly reducing the feature space. This reduction in feature space is important because it reduces computational complexity and enhances the interpretability of the model. Moreover, the proposed method demonstrated good stability in handling changes in data characteristics, which is critical for reliable performance over time and surpasses existing ML ensemble methods in terms of stability, threshold setting, and feature aggregation. In summary, this paper provides compelling evidence that this RankEnsembleFS methodology presents excellent performance and overcomes key issues in feature selection and image classification for the SAT-6 dataset.

Three-Dimensional Printing Technology in Drug Design and Development: Feasibility, Challenges, and Potential Applications

Background/Objectives: The present investigation evaluates the impact of 3D-printing technology on the design of pharmaceutical drugs, considering the feasibility issues and problems concerning technological, pharmaceutical, and clinical matters. This paper aims to review how 3D printing can modify the traditional manufacturing of drugs with personalized medicine-therapy outcomes being individualized and optimized, hence improving patients’ compliance. Methods: The historical development of 3D printing from rapid prototyping to advanced pharmaceutical applications is discussed. A comparison is then made between traditional drug manufacturing approaches and the different techniques of 3D printing, including stereolithography, material extrusion, and binder jetting. Feasibility is assessed based on clinical trials and studies evaluating the efficacy, safety, bioavailability, and cost-effectiveness of 3D-printed drugs. Results: Current evidence indicates that material selection, regulatory barriers, and scalability issues are some of the major challenges to be overcome for wider acceptance. Other matters, such as ethical issues concerning patient data privacy, the misuse of 3D-printing technology, and technical complexities related to pharmaceutical 3D printing, are discussed further. Future applications also include bioprinting and in situ printing together with their implications for personalized drug delivery, which will also be discussed. Conclusions: This review stresses that intersectoral collaboration and the updating of regulatory frameworks are a must to overcome the barriers that confront 3D-printing applications in drug development. can could be an opportunity for innovative licensing and manufacturing techniques in pharmaceutical product development that can change the paradigm of personalized medicine through modern printing techniques.

A Qualitative Examination of Instructor Experiences Regarding Pedagogical Challenges in Classical Guitar Education

This study investigates the pedagogical challenges faced by classical guitar instructors at Linyi University’s Music Department, with a focus on the integration of traditional Chinese musical elements into Western classical guitar education. Employing a qualitative research design, the study explores the lived experiences of 20 instructors through interviews, observations, and document analysis. Key findings reveal significant technical challenges, issues in repertoire selection, and difficulties in maintaining student engagement. The study also examines the strategies adopted by instructors, the influence of institutional support, and the impact of cultural factors on teaching practices. The results contribute to the broader discourse on cross-cultural music education and offer practical recommendations for instructors and institutions aiming to enhance classical guitar pedagogy in diverse cultural settings.

Doxorubicin-loaded methoxy-intercalated kaolinite as a repackaging of doxorubicin for an enhanced breast cancer treatment: in vitro and in vivo investigation

Doxorubicin is one of the most common wide-spectrum chemotherapeutics. However, its efficacy is limited due to off-target accumulation and selectivity issues. In this study, we compared the anti-cancer effect and biocompatibility of KaoliniteMeOH-Dox, a doxorubicin repackaging, to doxorubicin monotherapy. The formulation was extensively tested using transmission electron microscopy, dynamic light scattering, zeta potential, Fourier transform infrared, x-ray diffraction, and in vitro drug release. The MTT assay measured MCF-7 cell growth inhibition in vitro. In vivo testing involved 20 naïve mice and 40 Ehrlich solid tumor-inoculated mice. The tumor size was monitored for 18 days. In all experimental groups, tumor and cardiac tissues were evaluated for cytotoxicity and genotoxicity by addressing oxidative stress, histopathology, and comet assay. We found that kaoliniteMeOH-Dox has many advantages in terms of size, charge, shape, high loading efficiency (90.16%), and pH-dependent release. The MTT assay showed that the formulation outperformed doxorubicin in growth inhibition and selectivity. In vivo, research showed that kaoliniteMeOH-Dox suppressed tumors by 86.075% compared to 60.379% for free doxorubicin. Histological analysis showed that kaoliniteMeOH-Dox reduced tumor size, metastasis, and carcinogenic oxidative stress and inflammation in mice without harming naive mice. Based on the obtained data, the kaoliniteMeOH-Dox formulation holds promise for breast cancer treatment and warrants further investigation.

Modern Aspects of Endovascular Thrombectomy of Acute Ischemic Stroke. Selection Criteria for Endovascular Thrombectomy. Prediction Of Treatment Outcomes

Endovascular thrombectomy (ET) effectively and safely recanalizes the occluded artery and restores the ischemic area in patients with acute ischemic stroke (IS), improving the clinical prognosis of stroke in the anterior and posterior circulation system, expanding the time therapeutic window from no more than 6 to 24 hours, greatly increasing the chances of functional independence and survival. However, some patients develop an unfavorable postoperative outcome, complications and “ineffectiveness” of revascularization. The thrombectomy result depends not only on the patient selection criteria, timing and success of the procedure, but on many other factors as well. Despite the advances in stroke treatment, the issues of neuroimaging and patient selection for ET remain relevant; the pathophysiological mechanisms of the influence of some factors on the effectiveness of the procedure are not completely clear; the causes of “uneffective” revascularization, unfavorable outcome and mortality after ET are unclear. An analysis of global experience in treating ischemic stroke with ET showed the heterogeneity of the patient selection criteria, clinical and neuroimaging variables, prognostic factors and treatment outcomes, which makes it difficult to draw a general conclusion and requires further targeted research. The article discusses the issues of patient selection, pathophysiological mechanisms of the influence of some risk factors on the outcome of ischemic stroke and the causes of unfavorable outcome and death after ET.

Optimizing marine vehicles industry: a hybrid analytical hierarchy process and additive ratio assessment approach for evaluating and selecting IoT-based marine vehicles

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Impact of heterogenous capabilities on export performance amid the digital transformation

We investigate the effects of adopting digital technology on export performance of Korean manufacturing firms amidst the digital transformation. We recognize firm-level capabilities should be closely associated with adoption of rapidly progressing digital technology but also with export performance. And our data indicates that the most common purpose of digital technology adoption is to launch new products to the market. Hence, we consider that technology adoption is a strategic and purposeful decision to gain competitive edge mostly by producing new product, and treating technology adoption exogenous can be misleading. Due to the endogeneity and selection issues in technology adoption, the endogenous switching regression is applied to this study as Coad et al. (2020). On technology adoption decision, we find external innovative resources from strategic alliance in addition to internal innovative capabilities stand out. Given technology adoption decision, internal capabilities like patent rights and international affiliation are complementary factors to export growth. The treatment effect analysis has implications as follows: the result on contribution of technology adoption to export growth for actually adopting firms is rather small, and shows heterogenous innovation, organizational, and external capabilities are still critical factors as much as new products embedding high-end digital technology; the result on non-adopting firms indicates potential of advanced digital technology to improve export performance by helping to produce such new products if they were actually capable to do it. In sum, our findings provide another evidence that structurally positive interaction between innovative activities and export performance as Aw et al. (2011), since adoption of digital technology to products itself is innovative in the digital transformation. Furthermore, the result is consistent with the capability theory in that heterogenous innovative and complementary capabilities determine strategic technology adoption and export performance simultaneously. Finally, our findings indicate that the digital transformation might be still at the early stage. The fact that Korean firms have adopted advanced digital technology mainly for new products can be interpreted as an indicator of early development stage of transformation, since firms concentrate on product innovation than process innovation to gain competitive edge at the early stage (Utterback and Abernathy, 1975). Thus, we expect that further evolution of digital transformation can facilitate process innovation, then contribute to firm performance by improving cost efficiency that should be tackled in the upcoming study.

Sampling Confined Fission Tracks for Constraining Geological Thermal Histories

Fission-track modeling rests on etching, counting and measuring the lattice damage trails from uranium fission. The tools for interpreting fission-track data are advanced but the results are never better than the data. Confined-track samples must be an adequate size for statistical analysis, representative of the track population and consistent with the model assumptions and with the calibration data. Geometrical and measurement biases are understood and can be dealt with up to a point. However, the interrelated issues of etching protocol and track selection are more difficult to untangle. Our investigation favors a two-step protocol. The duration of the first step is inversely proportional to the apatite etch rate so that different apatites etch to the same Dpar. A long immersion reveals many more confined tracks, terminated by basal and prism faces. This allows consistent length measurements and permits orienting each track relative to the c-axis. Long immersion times combined with deep ion irradiation reveal confined tracks deep inside the grains. Provided it is long enough, the precise immersion time is not important if the effective etch times of the selected tracks are calculated from their measured widths. Then, whether the sample is mono- or multi-compositional, we can, post hoc, select tracks with the desired properties. The second part of the protocol has to do with the fact that fossil tracks in geological samples appear to be under-etched compared to induced tracks etched under the same conditions. This should be assumed if the semi-axes of a fitted ellipse plot above the induced-track line. In that case, an additional etch can increase the track lengths to a point where they are consistent with the model based on lab-annealing of induced tracks, a condition for valid thermal histories. Here too, it is possible to select a subset of tracks with effective etch times consistent with the model if the widths of confined tracks are measured along with their lengths and orientations.

Renaissance of Chlorine Evolution Reaction: Emerging Theory and Catalytic Materials

Chlorine (Cl2) is one of the most important commodity chemicals that has found widespread utility in chemical industry. Most Cl2 is currently produced via the chlorine evolution reaction (CER) at the anode of chlor–alkali electrolyzers, for which precious group‐metal‐based mixed metal oxides (MMOs) have been used for more than half a century. However, MMOs suffer from the use of platinum‐group metals, which are costly and scarce, and the selectivity issue arises from the parasitic oxygen evolution reaction. Over the last decade, the field of CER catalysis has seen dramatic advances in both the theory and discovery of new catalysts. Theoretical approaches have enabled a fundamental understanding of CER mechanisms and provided catalyst design principles. The exploration of new materials has led to the discovery of CER catalysts other than MMOs, including non‐PGM‐based oxides, atomically dispersed single‐site catalysts, and organic molecules, with some of which following novel reaction pathways. This minireview provides an overview of the recent advances in CER electrocatalyst research and suggests future directions for this revitalized field.

Renaissance of Chlorine Evolution Reaction: Emerging Theory and Catalytic Materials.

Chlorine (Cl2) is one of the most important commodity chemicals that has found widespread utility in chemical industry. Most Cl2 is currently produced via the chlorine evolution reaction (CER) at the anode of chlor-alkali electrolyzers, for which precious group-metal-based mixed metal oxides (MMOs) have been used for more than half a century. However, MMOs suffer from the use of platinum-group metals, which are costly and scarce, and the selectivity issue arises from the parasitic oxygen evolution reaction. Over the last decade, the field of CER catalysis has seen dramatic advances in both the theory and discovery of new catalysts. Theoretical approaches have enabled a fundamental understanding of CER mechanisms and provided catalyst design principles. The exploration of new materials has led to the discovery of CER catalysts other than MMOs, including non-PGM-based oxides, atomically dispersed single-site catalysts, and organic molecules, with some of which following novel reaction pathways. This minireview provides an overview of the recent advances in CER electrocatalyst research and suggests future directions for this revitalized field.

Wasserstein regression with empirical measures and density estimation for sparse data.

The problem of modeling the relationship between univariate distributions and one or more explanatory variables lately has found increasing interest. Existing approaches proceed by substituting proxy estimated distributions for the typically unknown response distributions. These estimates are obtained from available data but are problematic when for some of the distributions only few data are available. Such situations are common in practice and cannot be addressed with currently available approaches, especially when one aims at density estimates. We show how this and other problems associated with density estimation such as tuning parameter selection and bias issues can be side-stepped when covariates are available. We also introduce a novel version of distribution-response regression that is based on empirical measures. By avoiding the preprocessing step of recovering complete individual response distributions, the proposed approach is applicable when the sample size available for each distribution varies and especially when it is small for some of the distributions but large for others. In this case, one can still obtain consistent distribution estimates even for distributions with only few data by gaining strength across the entire sample of distributions, while traditional approaches where distributions or densities are estimated individually fail, since sparsely sampled densities cannot be consistently estimated. The proposed model is demonstrated to outperform existing approaches through simulations and Environmental Influences on Child Health Outcomes data.

Pipette-tip microextraction using carboxymethylated wooden-based sawdust sorbent for the determination of antidepressants in oral fluid by direct infusion mass spectrometry

Matrix complexity is a challenging scenario in sample preparation and the scientific community is in the search of novel materials capable of replacing the classical sorbents to overcome the sensitivity and selectivity issues it presents. In this article, the use of a carboxymethylated wooden-based sawdust sorbent pipette-tip microextraction is introduced for the determination of four antidepressants (imipramine, amitriptyline, fluoxetine, and clomipramine) in human oral fluids. The different variables affecting the extraction and elution procedures, including sample pH, ionic strength, number of pushing cycles, dilution of the sample, eluent, and volume of the eluent have been evaluated by liquid chromatography – ultraviolet detection. Moreover, the proposed method was validated by direct infusion coupled to tandem mass spectrometry with limits of detection of 0.2 μg/L, limits of quantification of 0.6 μg/L, linear range spanning from 0.6 to 320 μg/L and intra-day precision (expressed as relative standard deviation) better than 14.2 %. Intra-day accuracy (expressed as relative recovery) ranged from 85.4 to 102.9 %, while inter-day accuracy was between 97.0 and 103.9 %. Finally, the analysis of two oral fluids from a patient under amitriptyline treatment were analyzed, providing values within the linear range of the proposed method.

An improved simulation based method for selection of cell in cellular network

This work discusses a novel approach to a simulation-based cell selection that does not depend on static counts of cells or simplified models used in the cell selection process. The proposed method of cell selection uses advanced simulation to analyse the actual conditions and dynamically select the optimal cell to better suit the real conditions of the network and preference of the client. This research makes important theoretical and practical contributions that increase any simulation’s performance and reliability aspects. The simulations conducted in the study demonstrate several major advantages. The method results in substantially lower call drops and higher throughputs of data. The outcome of the study is an adaptive and inherently interactive method that can be used in a plurality of networking circumstances, from 4 to 5G networks. The work more than adequately addresses the issue of cell selection and connecting, and improves the reliability and efficiency of any network that uses this method. This product can be easily implemented and can be used by network developers to ameliorate their networks’ performance. The work’s main theoretical contribution is the fact that optimal methods are made that undoubtedly make a simulation a more efficient phenomenon and that the components would interact more effectively together.

Small and medium-sized enterprises and sustainable transition: Role of FinTech in a country's banking ecosystem

During the sustainable transition, small and medium-sized enterprises (SMEs) request funding to support environmental, social, and governance (ESG) investments. In particular, these requests clash with information asymmetries typical of the bank–firm relationship, causing SMEs to experience credit crunch phenomena or increased loan costs. This aforementioned phenomenon prompted this study to examine whether FinTech increases banks’ ability to support the development of SMEs aimed at achieving ESG or, better yet, sustainable transition goals. The results suggest that FinTech in a country’s banking ecosystem improves the ESG performance of Italian and British SMEs by addressing adverse selection and moral hazard issues. This study also shows a more pronounced effect in the UK banking ecosystem, which has a higher level of FinTech development and diffusion.

The Influencing Factors of High School English Teachers Choosing Different Teaching Media

The issue of high school English teachers' selection of teaching media has garnered widespread attention. However, there remain significant gaps in understanding the influencing factors. This paper analyzes the determinants behind teachers' choices using grounded theory and three-level coding, incorporating interviews with educators from both first-tier cities and county towns in China. The findings reveal substantial disparities in resource availability, technological proficiency, and institutional support between urban and rural schools. Urban teachers benefit from abundant resources and robust technical support, which facilitate higher technological proficiency and more effective media integration with curriculum goals. Conversely, rural teachers face challenges with basic access to technology and limited professional development opportunities. Based on these findings, this paper proposes several recommendations to optimize the use of teaching media: enhancing resource availability through increased funding and community support, improving technological proficiency via targeted professional development, strengthening school support with dedicated IT staff and flexible funding policies, and aligning media with curriculum needs to enhance student engagement.

Unveiling the adverse selection problem in China's digital lending market: Evidence from CHFS

Using data from the China Household Finance Survey (CHFS) 2019, we reveals a substantial adverse selection issue within China's Internet Credit Loan (ICL) market. Empirical evidence indicates that households with higher Debt-to-Income Ratios (DIR) are more inclined to apply for Internet Credit Loans (ICLs), with an average DIR of 76.9 %, constituting 20 % of ICL applications. While Internet Credit Loans (ICLs) provide alternative financing options, their high fixed interest rates undermine the potential cost advantages associated with digital technology. Adverse selection is especially pronounced in the case of operational loans. Our findings underscore the urgency for regulatory intervention to address these issues effectively.

An active disturbance rejection stair-like generalized predictive control with time delay weakening for denitrification process in thermal power unit

The optimization control of the selective catalytic reduction (SCR) denitrification system holds significant importance in the production of the power generation industry. This paper presents an active disturbance rejection stair-like generalized predictive control with time delay weakening (WADRC-SGPC) to enhance the control performance in SCR denitrification system. Compared with classical generalized predictive control (GPC), active disturbance rejection generalized predictive control (ADRC-GPC) combines the advantages of ADRC, uses Extended State Observer (ESO) to compensate the uncertainty of the system, and regards the compensated system as a series integrator. Therefore, it has the advantages of no system identification and online calculation of Diophantine equation. In addition, a time delay weakening link is incorporated for the known part of the system model, which effectively improves the performance of the controller. The parameter selection issue of the weight function in time delay weakening is addressed using a hybrid strategy whale optimization algorithm. Finally, the superior performance of the proposed control strategy in terms of set-point tracking and disturbance rejection is verified through simulation experiments.