bluuebunny/crossref_metadata_2025_split huggingface dataset:
Therapeutic Potential of Polyphenols in Cellular Reversal of Patho‐Mechanisms of Alzheimer's Disease Using In Vitro and In Vivo Models: A Comprehensive Review
Authors: Samal Monalisha , Srivastava Varsha , Khan Muzayyana , Insaf Areeba , Penumallu Naveen Reddy , Alam Aftab , Parveen Bushra , Ansari Shahid Hussain , Ahmad Sayeed 
Published: November 2024

ABSTRACTAlzheimer's disease (AD) is considered one of the most common neurological conditions associated with memory and cognitive impairment and mainly affects people aged 65 or above. Even with tremendous progress in modern neuroscience, a permanent remedy or cure for this crippling disease is still unattainable. Polyphenols are a group of naturally occurring potent compounds that can modulate the neurodegenerative processes typical of AD. The present comprehensive study has been conducted to find out the preclinical and clinical potential of polyphenols and elucidate their possible mechanisms in managing AD. Additionally, we have reviewed different clinical studies investigating polyphenols as single compounds or cotherapies, including those currently recruiting, completed, terminated, withdrawn, or suspended in AD treatment. Natural polyphenols were systematically screened and identified through electronic databases including Google Scholar, PubMed, and Scopus based on in vitro cell line studies and preclinical data demonstrating their potential for neuroprotection. A total of 63 significant polyphenols were identified. A multimechanistic pathway for polyphenol's mode of action has been proposed in the study. Out of 63, four potent polyphenols have been identified as promising potential candidates, based on their reported clinical efficacy. Polyphenols hold tremendous scope for the development of a future drug molecule as a phytopharmaceutical that may be incorporated as an adjuvant to the therapeutic regime. However, more high‐quality studies with novel delivery methods and combinatorial approaches are required to overcome obstacles such as bioavailability and blood–brain barrier crossing to underscore the therapeutic potential of these compounds in AD management.
Split‐plot experiments with replicated runs in pharmaceutical synthesis
Authors: Otava Martin , Mylona Kalliopi 
Published: November 2024

AbstractRestricted randomised designs are essential in the pharmaceutical synthesis due to the operational restrictions and their cost‐effectiveness compared to entirely randomised designs. Specifically, the split‐plot designs are very effective in reducing the cost of an experiment in the presence of hard‐to‐change factors and/or of multi‐stage processes. In classical designs, replicated runs for pure‐error estimation are commonly employed, but they are rarely used in the more complex setting of restricted randomisation. The reason is that, in practice, experiments in industry rarely can follow all the assumptions/conditions that are included in the methodological papers. In this work, we demonstrate how a split‐plot design based on a Bayesian ‐optimality criterion built to ensure more precise pure‐error estimation of the variance components can be easily adapted to fit the additional needs, the speedy implementation and the restrictions that a real case scenario in industry often imposes. We focus on the practical aspect of how to modify the complex design in a way that keeps/improves the desired qualities and on how to assess impact of changes that are more arbitrary.
Condition‐based maintenance policy for a multi‐component system considering stochastic dependence and quality loss
Authors: Zhang Anming , Qi Faqun , Fu Peihong , Yu Luchuan , Zhou Hongming 
Published: November 2024

AbstractThe reliability evaluation of multi‐component systems has attracted increasing concern in academic and industrial circles. However, the research maintenance optimization for multi‐component systems is still underexplored due to the curse of dimensionality. This paper makes a novel contribution to the existing literature by proposing a condition‐based maintenance (CBM) policy for multi‐component systems with joint consideration of stochastic dependence and quality loss. The system is composed of critical and auxiliary components. The deterioration process of the critical component can be accelerated when the auxiliary one degrades to a certain state. Based on the states of the components, the operation cost of the system owing to quality loss is analyzed, and the operation cost is used as the threshold for the preventive maintenance (PM) decision. Semi‐regenerative technology is used to model the evolution process of the system, and the long‐run average cost is calculated. The optimal inspection period and PM threshold are derived by minimizing the long‐run average cost, and the stationary distribution of the system state under the optimal long‐run average cost is derived with a proposed algorithm. Sensitivity analysis is conducted to reveal the most sensitive parameter. Finally, an illustrative example is presented, and a comparison experiment with other maintenance policies is conducted to demonstrate the effectiveness of the proposed policy.
Mid‐Infrared Sensing Using a Hollow–Core Fiber in a Nonlinear Interferometer
Authors: Produit Thomas , Chakraborty Tanmoy , Deng Ang , Krivitsky Leonid , Chang Wonkeun , Paterova Anna V. 
Published: November 2024

AbstractIncreasing the interaction path length is a well‐known method for enhancing the sensitivity of the optical detection system. Hollow–core fibers (HCFs) represent a viable alternative to the traditional multi‐path cells offering low optical losses and strong confinement of the optical field. Here, the incorporation of an Antiresonant Hollow–core Fiber (AR‐HCF) section into a nonlinear interferometer, where the AR‐HCF section serves as a gas‐sensing cell operating in the IR range is presented. By exploiting the effect of nonlinear interference, the detection is brought into the more operation‐friendly visible range. The detection of methane (CH4) gas at mid‐IR wavelengths within a half‐meter section of AR‐HCF, with an estimated concentration accuracy of 200 ppm·m is demonstrated. These results represent the combination of two research fields within a single instrument and pave the way for further advancement of quantum‐inspired gas sensing techniques.
Sense‐Making Through Hybrid Talk: High‐Achieving Secondary Students' Language Use during Practical Work
Authors: Kolstø Stein Dankert , Stadler Matthias G. 
Published: November 2024

ABSTRACTThis study contributes to discussions on facilitating students' sense‐making in science by analyzing the utterances of high‐achieving students in dialogues during practical work and identifying characteristics of their language use and learning processes. The context of the study is a general science course at an upper secondary school in Norway. During four lessons, students from two classes conducted three exercises in electrochemistry. Data consist of video recordings from group work and whole‐class dialogues, students' written explanations, and their grades in science. Using the Bakhtinian concept of hybrid constructions (i.e., utterances including both vernacular and scientific elements), we analyze dialogues about scientific concepts and possible explanations for observations made during practical work. The analysis focuses on mixed‐ability groups that include one or two high‐achieving students. These students' successful learning processes enable the identification of language use that is part of sense‐making dialogues. We found that all students participating in dialogues used a mixture of vernacular talk, hybrid constructions, and scientific language during their learning processes. The content of scientific explanations proposed by high‐achieving students initially tended to be correct but superficial. The content then became more complex and hybrid and displayed errors and inaccuracies when students discussed detailed explanatory mechanisms, and finally, it became complex and largely correct. The results indicate that the high‐achieving students' use of hybrid constructions during practical work was beneficial for their learning. We discuss challenges related to creating situations in science teaching that encourage all students to engage in hybrid talk during sense‐making dialogues.
Metallo‐Surfactant Complex‐Assisted Biomimetic Synthesis of Silver Nanoparticles: Exploring Relativistic Effects in Catalytic and Sensing Applications
Authors: Nagaraj Karuppiah , Velmurugan Gunasekaran , Thangamuniyandi Pilavadi , Govindasamy Chandramohan , Kamalesu Subramaniam , Vasava Mahesh , Prajapathi Prajesh , Kaliyaperumal Raja , Vaishnavi Ellappan 
Published: November 2024

AbstractA biological reduction method for silver nanoparticles was employed using Cassia alata extract from plant leaves, which functions as a reducing agent and the metallo‐surfactant [Co(dqn)2(C12H27N)2]3+ (dqn = dipyrido[3,2‐f:2′,3′‐h]‐quinoxaline; C12H27N)2 = dodecylamine) acting as both stabilizing and capping agent. The ratio of Ag nanoparticles (AgNPs) formation was adjusted to be equal to the amount of AgNO3, along with variations in the amount of plant leaf extract, the metallo‐surfactant, pH, surrounding temperature, and the length of interaction periods. High‐resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FE‐SEM), energy‐dispersive spectroscopy (EDS), and energy‐dispersive X‐ray analysis (EDAX) were used to confirm the formation of AgNPs. The infrared results indicate the presence of hydroxyl, amine, and carboxylate groups in the extract plays a crucial role in the reduction process. Additionally, the metallo‐surfactant acts as a capping agent for the silver nanoparticles, preventing agglomeration. By adjusting the acidity of the solution and the quantity of the additive metallo‐surface active agent utilized, the size of AgNPs can be precisely regulated. The relativistic effects observed in this metallo‐surfactant‐assisted silver nanoparticle demonstrate excellent reduction capabilities for nitro compounds, effective dye degradation, and mercury sensing applications.
On the nature, predictors, and outcomes of work passion profiles: A generalisability study across distinct types of employees
Authors: Gillet Nicolas , Morin Alexandre J. S. , Brault Stéphanie , Becker Margaux , Verbeke Iria 
Published: November 2024

AbstractThus far, little research has adopted a person‐centred approach to investigate the nature of work passion profiles. As a result, our understanding of the most commonly occurring combinations of harmonious passion (HP) and obsessive passion (OP) in the workplace remains limited. To achieve a more refined understanding of the nature of these work passion profiles, our first aim was thus to identify the configurations of HP and OP for work observed among five samples, including 11 subsamples, of employees (N = 7258). Then, we also considered the extent to which these profiles and their associations with theoretically‐relevant predictors (work‐home segmentation and organisational support) and outcomes (work engagement, work‐family conflict, turnover intentions, presenteeism, and counterproductive work behaviours) generalised across all subsamples. We identified a total of five profiles with a structure that differed slightly across samples: High OP Dominant, High HP Dominant, Average HP Dominant, Low HP Dominant (i.e., low levels of passion dominated by higher levels of HP relative to OP), and Moderately Low Passion. The High OP Dominant profile was systematically the most prevalent (37.5%–54.1% of the sample), whereas the High HP Dominant was the least prevalent (2.1%–7.7%). Across all samples, work‐home segmentation was related to a higher likelihood of membership into the profiles characterised by higher, relative to lower or moderate, levels of passion (HP and OP), whereas organisational support also helped employees to stay away from the High OP Dominant profile. Lastly, the least desirable outcomes were observed in the High OP Dominant profile, whereas the most desirable outcomes were observed in the High HP Dominant profile. Interestingly, work engagement levels where comparable in these two profiles. Beyond their theoretical implications for research on work passion, these results highlight how work passion has highly similar implications across contexts.
Phage‐Templated Synthesis of Targeted Photoactive 1D‐Thiophene Nanoparticles
Authors: Costantini Paolo Emidio , Saporetti Roberto , Iencharelli Marika , Flammini Soraia , Montrone Maria , Sanità Gennaro , De Felice Vittorio , Mattioli Edoardo Jun , Zangoli Mattia , Ulfo Luca , Nigro Michela , Rossi Tommaso , Di Giosia Matteo , Esposito Emanuela , Di Maria Francesca , Tino Angela , Tortiglione Claudia , Danielli Alberto , Calvaresi Matteo 
Published: November 2024

AbstractThiophene‐based nanoparticles (TNPs) are promising therapeutic and imaging agents. Here, using an innovative phage‐templated synthesis, a strategy able to bypass the current limitations of TNPs in nanomedicine applications is proposed. The phage capsid is decorated with oligothiophene derivatives, transforming the virus in a 1D‐thiophene nanoparticle (1D‐TNP). A precise control of the shape/size of the nanoparticles is obtained exploiting the well‐defined morphology of a refactored filamentous M13 phage, engineered by phage display to selectively recognize the Epidermal Growth Factor Receptor (EGFR). The tropism of the phage is maintained also after the bioconjugation of the thiophene molecules on its capsid. Moreover, the 1D‐TNP proved highly fluorescent and photoactive, generating reactive oxygen species through both type I and type II mechanisms. The phototheranostic properties of this platform are investigated on biosystems presenting increasing complexity levels, from in vitro cancer cells in 2D and 3D architectures, to the in vivo tissue‐like model organism Hydra vulgaris. The phage‐templated 1D‐TNP showed photocytotoxicity at picomolar concentrations, and the ability to deeply penetrate 3D spheroids and Hydra tissues. Collectively the results indicate that phage‐templated synthesis of organic nanoparticles represents a general strategy, exploitable in many diagnostic and therapeutic fields based on targeted imaging and light mediated cell ablation.
CAPP: Context‐Aware Privacy‐Preserving Continuous Authentication in Smartphones
Authors: Jacob Susmi , Vinod P. , Menon Varun G. 
Published: November 2024

ABSTRACTThe pervasive use of smart devices in everyday life has increased the risk of theft and cyberattacks, necessitating robust and continuous behavioral authentication techniques during every interactive session. Traditional authentication methods are insufficient to address these evolving threats, as they often fail to maintain security throughout the session. To address this challenge, we propose a continuous authentication scheme that leverages touch and motion sensors, incorporating user context to enhance security. However, building an effective authentication model requires creating a behavioral template by aggregating data from multiple users. Moreover, outsourcing sensitive sensor data to third‐party servers for processing introduces the risk of privacy breaches, such as behavioral profiling or the theft of personal information. To mitigate these risks, we designed CAPP—a context‐aware privacy‐preserving data outsourcing scheme for continuous authentication during live sessions. CAPP employs Format Preserving Encryption (FPE) to encrypt the sensor data, ensuring that the data's format remains intact, which allows the machine learning model to function with minimal loss of accuracy. Our solution enables secure and efficient authentication by creating a model on the server side using data from motion sensors like the accelerometer, gyroscope, and magnetometer. Our experimental results demonstrate the effectiveness of the proposed scheme, achieving an accuracy of 87.57% and an Equal Error Rate (EER) of 12.15%, while requiring only 1.69 ms to authenticate a user. These results show that CAPP preserves user privacy and provides a rapid and reliable authentication process, outperforming existing state‐of‐the‐art methods.
Spatial Extremal Modelling: A Case Study on the Interplay Between Margins and Dependence
Authors: Kakampakou Lydia , Simpson Emma S. , Wadsworth Jennifer L. 
Published: November 2024

ABSTRACTIt is no secret that statistical modelling often involves making simplifying assumptions when attempting to study complex stochastic phenomena. Spatial modelling of extreme values is no exception, with one of the most common such assumptions being stationarity in the marginal and/or dependence features. If non‐stationarity has been detected in the marginal distributions, it is tempting to try to model this while assuming stationarity in the dependence, without necessarily putting this latter assumption through thorough testing. However, margins and dependence are often intricately connected and the detection of non‐stationarity in one feature might affect the detection of non‐stationarity in the other. This work is an in‐depth case study of this interrelationship, with a particular focus on a spatio‐temporal environmental application exhibiting well‐documented marginal non‐stationarity. Specifically, we compare and contrast four different marginal detrending approaches in terms of our post‐detrending ability to detect temporal non‐stationarity in the spatial extremal dependence structure of a sea surface temperature dataset from the Red Sea.
Petri net modeling and analysis of an IoT‐enabled system for real‐time monitoring of eggplants
Authors: Yang Cheng‐Ying , Lin Yi‐Nan , Shen Victor R. L. , Shen Frank H. C. , Lin Yan‐Cheng 
Published: November 2024

AbstractAs the current agricultural industry is facing several challenges such as climate changes and lack of qualified farmers, it is extremely necessary to ensure sustainable agriculture and food supply by smart farming (SF). The SF can assist farmers and the associated stakeholders in making correct decisions on improving the yield and quality of agricultural products. In this study, an SF system based on low‐cost Raspberry Pi and Internet of Things (IoT) technologies has been successfully implemented. Through the data items detected by sensors to deeply manage the planting process, the IoT‐enabled communication protocol under ISO standards of Message Queuing Telemetry Transport (MQTT) was used. The bar charts of real‐time environmental parameters are presented on ThingsBoard to achieve the goal of data visualization. Meanwhile, a web server is built for the customized requirements, and the historical datasets are stored in the SQLite database system. Furthermore, a Petri net (PN) model was employed to detect all possible abnormal processes and to verify the feasibility and soundness of an IoT‐enabled system by using a software tool, WoPeD. Finally, the experimental results show that the plants cultivated by the proposed IoT‐enabled system are superior to those cultivated by the existing systems in many perspectives, including the monitoring distance, power consumption, precision, deadlock detection, and running time.
Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction
Authors: Tâmega Giovanna Scalli , Costa Mateus Oliveira , de Araujo Pereira Ariel , Barbosa Ferreira Marco Antonio 
Published: November 2024

AbstractAdvancements in synthetic organic chemistry are closely related to understanding substrate and catalyst reactivities through detailed mechanistic studies. Traditional mechanistic investigations are labor‐intensive and rely on experimental kinetic, thermodynamic, and spectroscopic data. Linear free energy relationships (LFERs), exemplified by Hammett relationships, have long facilitated reactivity prediction despite their inherent limitations when using experimental constants or incorporating comprehensive experimental data. Data‐driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting and interpreting mechanisms and effectively handling complex reactivities through multiparameter strategies. This review explores selected examples of data‐driven strategies for investigating organic reaction mechanisms. It highlights the evolution and application of computational descriptors for mechanistic inference.
Nanomotors as Therapeutic Agents: Advancing Treatment Strategies for Inflammation‐Related Diseases
Authors: Luo Min , Zhao Fu‐kun , Wang Yuan‐min , Luo Yong 
Published: November 2024

AbstractInflammation is a physiological response of the body to harmful stimuli such as pathogens, damaged cells, or irritants, involving a series of cellular and molecular events. It is associated with various diseases including neurodegenerative disorders, cancer, and atherosclerosis, and is a leading cause of global mortality. Key inflammatory factors, such as Tumor Necrosis Factor‐alpha (TNF‐α), Interleukin‐1 (IL‐1), Interleukin‐6 (IL‐6), Monocyte Chemoattractant Protein‐1 (MCP‐1/CCL2), RANTES (CCL5), and prostaglandins, play central roles in inflammation and disease progression. Traditional treatments such as NSAIDs, steroids, biologic agents, and antioxidants have limitations. Recent advancements in nanomaterials present promising solutions for treating inflammation‐related diseases. Unlike nanomaterials that rely on passive targeting and face challenges in precise drug delivery, nanomotors, driven by chemical or optical stimuli, offer a more dynamic approach by actively navigating to inflammation sites, thereby enhancing drug delivery efficiency and therapeutic outcomes. Nanomotors allow for controlled drug release in response to specific environmental changes, such as pH and inflammatory factors, ensuring effective drug concentrations at disease sites. This active targeting capability enables the use of smaller drug doses, which reduces overall drug usage, costs, and potential side effects compared to traditional treatments. By improving precision and efficiency, nanomotors address the limitations of conventional therapies and represent a significant advancement in the treatment of inflammation‐related diseases. This review summarizes the latest research on nanomotor‐mediated treatment of inflammation‐related diseases and discusses the challenges and future directions for optimizing their clinical translation.
Unlocking Opportunities: How Women Navigate Informal Networks in Saudi Arabia's Evolving Workplace
Authors: Ali Sa'ad , AlQahtani Munirah , Mrabet Mansour 
Published: November 2024

ABSTRACTIn the context of the Arab countries of the Middle East, Wasta informal networks predominate in the business environment. While historically women in the Kingdom of Saudi Arabia had limited access to informal networks, recent socio‐economic changes have had a positive impact on women's participation in the workplace. This research explores the use of Wasta informal networks by women in the context of the new Saudi workplace based on 26 interviews conducted with Saudi professional women. The findings highlight that, due to the changes in the formal institutions, which in turn impact informal institutions, women are generally afforded more direct access to networks, enabling them to attain jobs and progress their careers. The research also contributes to informal network literature by distinguishing between Wasta (negative practice) and other forms of informal networks (positive/beneficial practice). Suggestions are offered to policymakers, managers, and women practitioners to navigate the use of informal networks and Wasta.