Search Results (38)

In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data with a custom customer relationship management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance. Full article

The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs. Full article

The totem-pole bridgeless power factor correction (PFC) circuit based on the triangular current mode (TCM) in the front-end PFC of a switching power supply has the advantage of realizing zero-voltage switching (ZVS) in the full working range. However, the TCM control based on the critical conduction mode (CRM) further increases the inductance current ripple, and the traditional input voltage AC sampling circuit increases the circuit complexity and device cost. Therefore, this paper studies the corresponding improvement technology from two dimensions. Firstly, the coordinated interleaved parallel technology is employed to design the system’s overall control-improvement strategy. This approach not only achieves full working-range ZVS but also reduces both the inductor current ripple and power device stress. Simultaneously, an optimized input voltage sampling circuit is designed to accommodate varying voltage requirements of control chip pins. This circuit demonstrates strong synchronization in both voltage and phase sampling, and the structural characteristics of the optocoupler can also suppress electrical signal interference. Finally, a 600 W totem-pole bridgeless PFC prototype is developed. The experimental results demonstrate the effectiveness of the proposed improved method. The prototype efficiency peak reaches 97.3%. Full article

White-light-emitting diodes (wLEDs) are central to next-generation lighting technologies, yet their reliance on critical raw materials (CRMs), such as rare-earth elements, raises concerns regarding sustainability and supply security. In this work, we present a simple, low-cost method to produce photoluminescent carbon-based nanostructures—known as oxidative debris (OD)—via alkaline treatment of graphene oxide (GO) using KOH solutions ranging from 0.04 M to 1.78 M. The resulting OD, isolated from the supernatant after acid precipitation, exhibits strong and tunable photoluminescence (PL) across the visible spectrum. Emission peaks shift from blue (~440 nm) to green (~500 nm) and yellow (~565 nm) as a function of treatment conditions, with excitation wavelengths between 300 and 390 nm. Optical, morphological. and compositional analyses were performed using UV-Vis, AFM, FTIR, and Raman spectroscopy, confirming the presence of highly oxidized aromatic domains. The blue-emitting (S2) and green/yellow-emitting (R2) fractions were successfully separated and characterized, demonstrating potential color tuning by adjusting KOH concentration and treatment time. This study highlights the feasibility of reusing GO-derived byproducts as sustainable phosphor alternatives in wLEDs, reducing reliance on CRMs and aligning with green chemistry principles. Full article

The increasing demand for critical raw materials (CRMs), driven by global energy transition, underscores the need for innovative approaches to identify secondary resources, such as mining residues. Mining residues, often overlooked during initial mining activities, now represent valuable sources of raw materials thanks to technological advancements, including hyperspectral remote sensing. This study investigates the potential of hyperspectral satellite imagery to detect and map CRMs in mining residues of the abandoned Sidi Bou Azzouz mine in Morocco. The proposed approach is based on the integration between satellite data, field spectroscopy, chemical, and mineralogical analyses in a strong multi-scale and interdisciplinary framework. The integration between advanced laboratory techniques, including LIBS, XRF, XRPD, and SEM-EDS, was employed to enhance hyperspectral data interpretation. The integration of remote sensing and laboratory results provided a comprehensive understanding of mineral composition, confirming the effectiveness of hyperspectral methods for characterizing heterogeneous surface deposits. This research demonstrates the potential of hyperspectral observations to identify valuable raw materials and to map them using PRISMA imagery in abandoned mining residues, offering a tool useful for planning cost-effective and sustainable solutions aimed at answering the growing demand for CRMs crucial to industrial competitiveness and sustainable growth. Full article

This review describes mass spectrometry (MS)-based approaches for the absolute quantification of therapeutic monoclonal antibodies (mAbs), focusing on technical challenges in sample treatment and calibration. Therapeutic mAbs are crucial for treating cancer and inflammatory, infectious, and autoimmune diseases. We trace their development from hybridoma technology and the first murine mAbs in 1975 to today’s chimeric and fully human mAbs. With increasing commercial relevance, the absolute quantification of mAbs, traceable to an international standard system of units (SI units), has attracted attention from science, industry, and national metrology institutes (NMIs). Quantification of proteotypic peptides after enzymatic digestion using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) has emerged as the most viable strategy, though methods targeting intact mAbs are still being explored. We review peptide-based quantification, focusing on critical experimental steps like denaturation, reduction, alkylation, choice of digestion enzyme, and selection of signature peptides. Challenges in amino acid analysis (AAA) for quantifying pure mAbs and peptide calibrators, along with software tools for targeted MS data analysis, are also discussed. Short explanations within each chapter provide newcomers with an overview of the field’s challenges. We conclude that, despite recent progress, further efforts are needed to overcome the many technical hurdles along the quantification workflow and discuss the prospects of developing standardized protocols and certified reference materials (CRMs) for this goal. We also suggest future applications of newer technologies for absolute mAb quantification. Full article

The contamination of water by heavy metals poses an escalating risk to human health and the environment, underscoring the critical need for efficient removal methods to secure safe water resources. This study evaluated the performance of four cationic exchange materials (labeled “PS—DVB”, “PA—DVB”, “TFSA”, and “OGL”) in removing or harvesting metals such as copper, silver, lead, cobalt, and nickel from aqueous solutions, several of which are precious and/or classified as Critical Raw Materials (CRMs) due to their economic importance and supply risk. The objective was to screen and benchmark the four ion exchange materials for water treatment applications by investigating their metal sequestration capacities. Experiments were conducted using synthetic solutions with controlled metal concentrations, analyzed through ICP-OES, and supported by kinetic modeling. The adsorption capacities (qe) obtained experimentally were compared with those predicted by pseudo-first-order and pseudo-second-order models. This methodology enables high precision and reproducibility, validating its applicability for assessing ion exchange performance. The results indicated that PS—DVB and PA—DVB resins proved to be of “wide range”, exhibiting high efficacy for most of the metals tested, including CRM-designated ones, and suggesting their suitability for water purification. Additionally, the second-life Nafion-based “TFSA” material demonstrated commendable performance, highlighting its potential as a viable and technologically advanced alternative in water treatment. Lastly, the lignin-based material, “OGL”, representing the most innovative and sustainability apt option, offered relevant performance only in selected cases. The significant differences in performance among the resins underscore the impact of structural and compositional factors on adsorption efficiency. This study offers valuable insights for investigating and selecting new sustainable materials for treating contaminated water, opening new pathways for targeted and optimized solutions in environmental remediation. Full article

Adopting advanced e-commerce practices is essential for enhancing user engagement and business performance, particularly in tourism. This study evaluates the e-commerce adoption of Kazakhstan’s tourism websites using an innovative Integrated Multi-Criteria Decision Analysis (IMCDA) methodology. Traditional evaluation methods overlook the interplay between website functionality, user experience, and strategic objectives. To address this gap, the IMCDA framework integrates qualitative and quantitative approaches by combining advanced Multi-Criteria Decision-Making (MCDM) techniques, including SPOTIS, ESP-COMET, RANCOM, and SITW, with content analysis and logistic regression. The study assessed 77 tourism websites, categorized into Online Travel Agencies (OTAs), Official Tourism Websites (OTWs), and Attraction Websites (AWs), based on 34 e-commerce features grouped into dimensions such as product information, functionality, reservations, payment systems, and customer relationship management (CRM). The findings reveal that OTAs significantly outperform OTWs and AWs in most dimensions, especially in online booking and CRM functionalities. At the same time, AWs lag in key e-commerce features like reservations and payment systems. This research highlights critical gaps in Kazakhstan’s tourism e-commerce ecosystem. It provides actionable recommendations, including enhancing CRM tools, integrating advanced booking systems, and leveraging collaborations with local financial technology providers like Kaspi Pay. The IMCDA framework offers a robust, adaptable evaluation model with practical implications for digital transformation and competitiveness in the tourism industry. This study contributes to advancing digital maturity in Kazakhstan’s tourism sector by addressing these gaps. It sets the foundation for future research to explore innovative strategies in e-commerce adoption across various regions and industries. Full article

Both the mineralogy and geochemistry of coal mine waste presents environmental and social challenges while simultaneously offering the potential source for recovery of metals, including critical raw materials (CRMs). Assessing these challenges and opportunities requires effective waste management strategies and comprehensive material characterization. This study deals with the integration of analytical data obtained from various portable sensor technologies. Infrared reflection spectroscopy (covering a wide wavelength range of 0.4 to 15 µm), and geochemical x-ray fluorescence (XRF) were utilized to differentiate between samples belonging to various geological lithologies and quantify elements of interest. Therefore, we developed a methodological framework that encompasses data integration and machine learning techniques. The model developed using the infrared data predicts the Sr concentration with a model accuracy of R² = 0.77 for the testing dataset; however, the model performances decreased for predicting other elements such as Pb, Zn, Y, and Th. Despite these limitations, the approach demonstrates better performance in discriminating materials based on both mineralogical and geochemical compositions. Overall, the developed methodology, enables rapid and in-situ determination of coal mine waste composition, providing insights into waste composition that are directly linked to potential environmental impact, and the possible recovery of economically valuable metals. Full article

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature’s focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems. Full article

This study investigates the transformative effects of incorporating Trinidad Lake asphalt (TLA), crumb rubber modifier (CRM), and the warm mix additive leadcap (LC) into petroleum-based asphalt binder PG 64-22. Our results show that LC significantly reduces binder viscosity, leading to easier application and lower energy consumption, especially when combined with TLA and CRM. The addition of TLA and CRM enhances rutting resistance, with notable improvements in both pre- and post-aging conditions, particularly in formulations combining PG 64-22, 20% TLA, and 10% CRM. These formulations exhibit superior performance metrics, such as increased percentage recovery (% rec) and reduced non-recoverable creep compliance (J_nr), indicating improved flexibility and deformation resistance. Furthermore, LC balances increased rigidity and susceptibility to fatigue cracking from higher TLA and CRM levels, respectively. These modifications also promote environmental sustainability by reducing energy usage and emissions during production and paving. This study highlights LC’s critical role in advancing high-performance, eco-friendly warm mix asphalt binders, offering valuable insights for sustainable pavement engineering and setting a new benchmark for advanced asphalt technologies. Full article

Customer relationship management (CRM) has become increasingly important as a result of the pressure organizations are under to remain competitive. CRM has been and is widely promoted as essential to a company’s ability to survive. According to this study, CRM is more than just a computer program or software package. We believe that for organizations to use CRM effectively, it must be viewed from a strategic point of view. Therefore, this study focuses on the consequences of digital marketing on business performance, specifically, the consequences of customer relationship orientation and the use of CRM technologies as a support for analysis, data integration, and access on business performance. This study addresses a contemporary and relevant research problem that has national and international relevance. The research is based on quantitative methods to test hypotheses. Data were gathered from 73 organizations. The findings show the relationships between CRM and customer satisfaction, market effectiveness, and profitability. Customer relationship orientation positively affects customer satisfaction, market effectiveness, and market profitability. CRM technologies significantly improve business performance. This research contributes to the existing knowledge by shedding light on the complex relationships among CRM, customer relationship orientation, market effectiveness, market profitability, and customer satisfaction. Based on the research results, we provide practical recommendations for managers and decision makers. Full article

Changes and uncertainty in the customs operating environment and the growth of trade and travel volumes have affected how customs administrations manage and approach their tasks. As a result of technological development, the role of customs in border control has changed dramatically. Thus, the massive volume of goods, the way they are traded worldwide, and the speed of such transactions create additional fiscal, security, financial, and safety risks, affecting the resources available to customs services. The current geopolitical situation has significantly impacted the role of customs services. The topic is relevant to simultaneously assure both the quality of the services provided by the customs and compliance with the requirements set in the framework of limited resources. This study focuses on customs risk management (CRM) issues. It acknowledges that the customs services must continuously improve their operational methods, including promoting a more structured, integrated, and systematic way to manage customs risks. Based on the literature review, we examine the CRM-related challenges and how scholars address them in the scientific literature. This study aims to identify and analyse the contemporary challenges in CRM from its effectiveness point of view. We employ a systematic literature review, searching in most recognised databases and covering the period of 2005–2024. We follow this with a qualitative content analysis and synthesis, summarising and discussing the study results. We identify and discuss relevant key factors contributing to effective CRM. Finally, we conclude with the implications of the findings for CRM practice and policy, as well as with various potential developments in CRM that we suggest for further work. Full article

Microbial fuel cells (MFCs) are sustainable energy recovery systems because they use organic waste as biofuel. Using critical raw materials (CRMs), like platinum-group metals, at the cathode side threatens MFC technology’s sustainability and raises costs. By developing an efficient electrode design for MFC performance enhancement, CRM-based cathodic catalysts should be replaced with CRM-free materials. This work proposes developing and optimizing iron-based air cathodes for enhancing oxygen reduction in MFCs. By subjecting iron phthalocyanine and carbon black pearls to controlled thermal treatments, we obtained Fe-based electrocatalysts combining high surface area (628 m² g⁻¹) and catalytic activity for O₂ reduction at near-neutral pH. The electrocatalysts were integrated on carbon cloth and carbon paper to obtain gas diffusion electrodes whose architecture was optimized to maximize MFC performance. Excellent cell performance was achieved with the carbon-paper-based cathode modified with the Fe-based electrocatalysts (maximum power density-PD_max = 1028 mWm⁻²) compared to a traditional electrode design based on carbon cloth (619 mWm⁻²), indicating the optimized cathodes as promising electrodes for energy recovery in an MFC application. Full article

The availability of resources is vital when rapid changes and updated medical information in the provision of care are needed, such as in the fight against COVID-19, which is not a conventional disease. Continuing medical education plays an essential role in preparing for and responding to such emergencies. Workflow has improved based on the virtual meetings, online trainings, and remote detailing conducted by medical representatives in order to deliver educational content instantly through digital tools, such as salesforce automation (SFA), webinars, etc. In terms of its regulatory barriers, the pharmaceutical industry mainly targets healthcare professionals, unlike most businesses that reach end users directly. Medical representatives are equipped with an SFA to enhance customer relationship management (CRM) and closed loop marketing (CLM) capabilities in pharmaceutical companies. This study aimed to fill a gap in the literature by investigating the use of SFA in work patterns, such as health professionals’ loyalty and involvement in their medical knowledge in Turkey, and how it allows for differentiating training from marketing. This study intended to compare the data on internists and medical products gathered from a well-known pharmaceutical company’s SFA. The data covered the first three months of the year 2020, when medical representatives had a normal daily routine, and that of 2021, when Turkey experienced the most powerful surge of the COVID-19 pandemic. The analysis was based on simple correspondence analysis (SCA) and multiple correspondence analysis (MCA) for 11 variables. Monitoring product, physician’s segment, and medical representatives’ behaviors with SFA had a significant influence on the pharma-physician relationship strategy, as expected. The findings supported the view that SFA technologies can be deployed to advance the medical knowledge of physicians, in addition to managing and designing superior CRM and CLM capabilities. Full article