Analytical Applications Research Articles

Innovative Silica Acorn Core-Shell Nanostructures: Morphological Control and Applications in Chromatography.

This study introduces the synthesis and characterization of advanced silica core-shell nanostructures, with an emphasis on the innovative Si-ACS (Silica Acorn Core-Shell) design and its modified counterparts. Employing the classic Stöber method, SiCore particles were first produced, followed by the creation of the acorn-like Si-ACS structures. A key aspect of this research is the exploration of the effects of CTAB and TEOS concentrations on the morphology and properties of the silica shells. The study reveals that surfactant concentration influences shell morphology from corn-like to uniformly thin structures, as well as the shell thickness. Specifically, increasing the CTAB concentration from 45.8 mM to 166.9 mM increased the silica shell thickness from 160 to 280 nm, demonstrating the significant impact of surfactant concentration on shell formation. Si-ACS particles exhibited a surface area of 55.54 m2/g and a pore volume of 0.64 cm3/g, as evidenced by BET measurements, indicating successful mesopore formation critical for catalytic and adsorption applications. The materials were further modified with cholesterol and tetraethyl pentaamine (TEPA), which was confirmed by FT-IR analysis. Additionally, the study demonstrates the application of these functionalized nanostructures as chromatographic columns. In particular, the dual-mode interactions of Si-ACS-CHOL-TEPA significantly improve the separation of phthalate esters, thereby highlighting the potential of these materials in advanced analytical and biotechnological applications.

Dynamic Emerging Pathways in Entrance and Exit Detection: Integrating Deep Learning and Mathematical Modeling

Entrance and exit event detection in dynamic environments has a lot of real-world applications in security, crowd management, and retail analytics. Traditional methods used for this problem, namely Line Partition and Bounding Box Diameter methods often struggle in complex scenarios that contain less predictable movement patterns of individuals. This paper proposes a model that integrates deep learning-based object detection and tracking techniques with linear regression to enhance the overall performance of enter and exit detection in static and dynamic environments. This approach captures the movement patterns using advanced object detection and tracking algorithms, enabling the extraction of y-coordinate variations from bounding box centers which are used to calculate the tangent of the linear regression equation and determine if the event is entrance or exit. Experimentations were conducted on 132 video sequences and show the superiority of our approach over the traditional methods achieving an overall accuracy of 86.36% and an F1-score of 0.86. These results demonstrate the high efficiency of this approach to accurately detect entrance and exit events, making it highly reliable and applicable to this problem. This research contributes to computer vision by integrating object detection and tracking algorithms with linear regression offering a solution for enhancing entrance and exit events detection in dynamic environments.

Predicting Exploration Crew Medical Officer Training Needs: Applying Evidence-Based Predictive Analytics to Space Medicine Training.

Predictive analytics may be a useful adjunct to identify training needs for exploration class medical officers onboard deep space vehicles. This study used a preliminary version of NASA's newest medical predictive analytics tool, the Medical Extensible Database Probabilistic Risk Assessment Tool (MEDPRAT), to test the application of predictive analytics to exploration crew medical officer curriculum design for 5 distinct design reference mission (DRM) profiles. Partial and fully treated paradigms were explored. Curriculum elements were identified using a leave-one-out analysis and a threshold of 5% risk increase over the fully treated baseline. For the partial treatment scenario, among the 5 DRM profiles 4-32 curriculum elements met the 5% RRI increase. For the absolute treatment scenario, among the 5 DRM profiles, 13-126 curriculum elements met the 5% RRI increase. For the partial treatment paradigm, 13 capabilities are present in at least 3 of the 5 DRM profiles, and these elements may constitute a common baseline curriculum. This covers 41% of the skillsets needed for an ISS-like profile, 100% of a late Artemis-like profile, 41% of a Mars mission-like profile, 100% of a Starship orbital-like profile, and 68% of a Starship lunar flyby-like profile. This proof-of-concept study demonstrated that predictive analytics can rapidly generate generic and mission profile-specific exploration CMO curricula using an evidence-based process driven by optimizing mission risk reduction. This technique may serve as part of a human-machine team approach to medical curriculum planning for future space missions. It has significant potential to improve astronaut health and save time and effort for planners, trainers, and trainees.

Elementary econometric and strategic analysis of curling matches

PurposeWe develop a Markov model of curling matches. This enables strategic and econometric analyses to be performed alongside computer simulation work.Design/methodology/approachWe develop a Markov model of curling matches, parametrised by the probability of winning an end and the probability distribution of scoring ends. In practical applications, these end-winning probabilities can be estimated econometrically and are shown to depend on which team holds the hammer as well as the offensive and defensive strengths of the respective teams. Using a maximum entropy argument, based on the idea of characteristic scoring patterns in curling, we predict that the points distribution of scoring ends should follow a constrained geometric distribution.FindingsWe provide analytical results detailing when it is optimal to blank the end in preference to scoring one point and losing possession of the hammer. Statistical and simulation analysis of international curling matches is also performed.Originality/valueOur work adds to the theory and application of sports analytics, especially Markov models, and to the econometric and strategic analysis of curling matches.

Comparative Analysis of YOLOv5/v8/v9 for Object Detection, Tracking, and Human Action Recognition in Combat Sports

YOLO models are widely used object detectors in computer vision (CV). This study investigates the relative performance of YOLOv5, YOLOv8, and YOLOv9 for object detection, tracking, and human action recognition in combat sports. The models were evaluated using curated datasets encompassing various combat scenarios, athlete movements, and equipment configurations. Pre-processing protocols and augmentation techniques were applied to improve model accuracy and generalizability, including automated orientation correction, image dimension standardisation, contrast enhancement, and methods such as zoom, rotation, shear, and grayscale conversion. The key findings provide insight into the comparative performance of the models across various evaluation metrics, such as precision, recall, and mean average precision. Each model's ability to detect, track, and recognise human actions in dynamic combat sports environments is evaluated. Computational efficiency and real-time performance were assessed as these are important indicators for practical applications in coaching, training, and competitive scoring systems. The findings suggest that YOLOv8 offers the best balance of precision and recall, making it particularly suitable for real-time applications in combat sports analytics. This study contributes to advancing CV technologies in combat sports analytics, with potential implications for improving athletic training methods, facilitating personalised coaching interventions, and enhancing objectivity and consistency in competitive scoring processes in combat sports.

Tokenization Beyond NLP: Potential Applications in Data Analytics, Cybersecurity, and Beyond

Tokenization is the process of segmenting redundant patterns of input data, such as text, into tokens that are suitable for model training and computational analysis. Tokenization plays a foundational role in Natural Language Processing (NLP). Additionally, tokenization methods exhibit significant potential in domains outside of NLP, where combining redundant patterns in data can enhance the efficiency, scalability, analytical capabilities, and accuracy of predictions. This paper explores the potential applications of tokenization in fields beyond NLP in multiple areas, including but not limited to bioinformatics, cybersecurity, and healthcare. These applications demonstrate the ability of tokenization to simplify complex data patterns, thereby enhancing predictive accuracy. By leveraging the pattern recognition strengths of tokenization, multiple domains could receive benefits from efficient data processing and pattern recognition, which indicates a promising future for custom tokenization techniques across disciplines. Keywords: Pattern Recognition, tokenization, tokens, Machine Learning (ML), Natural Language Processing (NLP)

Sztuczna inteligencja (AI) w modyfikacjach biznesowych i niezauważone przejście od informacji do inwigilacji

Artificial Intelligence (AI) is becoming the foundation of IT systems and will gradually take over many decision-making processes. Passion for modern technologies is commonly combined with many years of experience gained in analyzing large data sets and creating analytical applications. Interdisciplinary teams specializing in the use of artificial intelligence and machine learning tools (Machine Learning), in particular classification and prediction models and data clustering, are formed. In the new development conditions, there were radical restrictions on social and economic activity and the emergence of new barriers, which resulted in an increase in the importance of innovative products and services based on modern technologies, aimed at overcoming the economic and social effects of the COVID-19 pandemic. Trends such as gamification, Big Data and artificial intelligence (AI) were visible. Thanks to this, the right vision was created, supported by the necessary technical knowledge to implement solutions that open the gate of undiscovered potential. Acceleration of digitization processes resulted in long-term organizational changes, including more frequent remote work of some employees. Uncertainty in the global economy caused by the pandemic has prom pated many companies reassess their development models. Instead of relying on global supply chains, more and more companies have invested in robots, resulting in a manufacturing renaissance in industrialized countries. The main aim of the study is to present artificial intelligence and the threats of its use.

CRISPR/Cas12a-mediated fluorescent aptasensor based on DNA walker amplification for oxytetracycline detection

Nowadays, although most oxytetracycline (OTC) are used only in animals, their leaching into the environment and residues in food lead to noticeable health problems. Fortunately, by studying the molecular dynamics of OTC aptamers and the mutations of key bases, aptamer’s base fragments involved in identifying OTC was confirmed for the first time, which provided theoretical guidance for the ingenious design of aptamer detection. Thereafter, benefiting from the amplification impact by DNA walker, the disadvantage of low efficiency caused by CRISPR/Cas12a was effectively avoided. In the established delicate aptasensing platform, small quantity of OTC promoted DNA walker to produce a massive of activators, which stimulated the trans-cleavage activity of CRISPR/Cas12a. By implying nanoclusters (NCs) with covalent organic framework (COF), the sensitive fluorescence “turn-on” method for OTC detection was realized. Under the optimum conditions, the linear range of aptasensor was 0.05-50 μM, and the detection limit was 0.041 μM. In addition, the aptasensing method can be successfully used for the quantitative detection of OTC in milk and honey samples with a recovery rate of 95.67%-104.10% and 95.33%-109.70%, implying excellent analytical performance and practical application prospect.

4(N)-ARYL/ALKYL SUBSTITUTED THIOSEMICARBAZONE DERIVED METAL COMPLEXES, THEIR SPECTRAL AND BIOCHEMICAL STUDIES: A REVIEW

N(4)-alkyl/aryl substituted thiosemicarbazones and their metal complexes show significantly enhanced biochemical properties than well-known standard drugs. In this review, we extensively focused on the synthesis, biological applications, and structural characterization of the aforementioned compounds over the past decade. A detailed description of the structures of thiosemicarbazone derivatives substituted at the N(4)-position and their metal complexes is presented here. The structures of the compounds were elucidated with respect to their bonding interactions with metal ions and their geometric configurations. Additionally, the review emphasizes the importance of ligands and complexes by reviewing their biological and analytical applications, such as the newly discovered N(4)-substituted thiosemicarbazones and metal chelates throughout the previous decade. It highlights their potential impact on future research and practical applications.

AI for Optimizing Supply Chain Management

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

BRIDGING THE GAP: A CONCEPTUAL REVIEW OF BIG DATA ANALYTICS KNOWLEDGE BY BRAND COMMUNICATION CAMPAIGN MANAGERS IN NIGERIA

Big data analytics has revolutionized various industries, including advertising, by enabling more precise targeting, enhanced customer insights, and improved campaign performance. Yet, there is a noticeable gap in the knowledge and application of big data analytics among brand communication campaign managers in Nigeria. This review aims to bridge this gap by examining the state of big data analytics knowledge within the Nigerian advertising industry. Key challenges faced by brand communication campaign managers were identified through a comprehensive literature review and analysis of existing studies premised on the Technology Acceptance Theory. This review highlights the disparity between global advancements in advertising analytics and the slower adoption rate in Nigeria. The implications of the knowledge gap on the effectiveness of brand campaigns and the overall industry growth were discussed. Furthermore, strategies were proposed to enhance big data analytics proficiency among Nigerian brand communication campaign professionals. Findings reveal that the Nigerian advertising industry can leverage big data to achieve more effective and efficient marketing outcomes by addressing these challenges. The review underscores the importance of equipping brand communication campaign managers with the necessary skills and knowledge to harness the power of big data, ultimately contributing to the industry's competitiveness in the global market.

Developing a data-driven predictive model for substance abuse prevention among youth using behavioral analytics

Substance abuse among youth presents a critical public health challenge, necessitating innovative approaches for early intervention and prevention. This study proposes the development of a data-driven predictive model that leverages behavioral analytics to identify youth at risk of substance abuse. The model utilizes data from multiple sources, including social media activity, school performance records, and mental health screenings, to analyze behavioral patterns that may indicate a predisposition toward substance misuse. The core methodology integrates machine learning algorithms to process and analyze large datasets, uncovering correlations between specific behaviors and the likelihood of substance abuse. Predictive features such as changes in social engagement, academic performance fluctuations, and indicators of emotional distress are identified and incorporated into the model to enhance its accuracy. By applying supervised learning techniques, the model is trained to recognize patterns in historical data, allowing it to make predictions about future substance use risks. Furthermore, the model's design emphasizes real-time monitoring and adaptability, enabling health professionals and educators to receive timely alerts and intervene early when behavioral warning signs are detected. The application of behavioral analytics in this context offers a more proactive, personalized approach to prevention, targeting at-risk individuals before they develop harmful substance use habits. In addition to its predictive capabilities, the model also provides actionable insights into effective intervention strategies. By identifying the most influential behavioral factors, it informs tailored prevention programs that address specific risk behaviors among youth. These findings can support policymakers and healthcare providers in developing data-driven, evidence-based prevention initiatives that better allocate resources to high-risk populations.

Revolutionizing Knowledge Management in Transportation Agencies: The Role of Generative AI and Scalable Frameworks

The increased interconnectedness of the globe has led to a fast-paced expansion of transportation systems and, consequently, to the creation of effective Knowledge Management Strategies. By simply put, the ways of decision making, operations management, and solving problems related to the complex infrastructure systems have all improved. Knowledge management is made easier by the use of Generative Artificial Intelligence (AI) which enables all the processes of data generation, data synthesis and even gives room for a real-time insight within a transport department. There is also the availability of Structure and Generative Theory which allows agencies to cope with needs that keep changing without losing the quality and the speed of the flow of the knowledge. In this sense, the present contribution focuses on the perspective of generative AI and structural frameworks in transportation agencies understanding knowledge management and its future prospects in terms of technology usefulness (Chen & Li, 2021). In these external environments, transportation agencies manage vast and complex datasets that change rapidly and are often dispersed. For instance, Generative AI has applications in natural language processing, predictive analytics and intelligent reporting, which assist agencies in solving hurdles posed by conventional knowledge management. When these AI systems are given, scalable frameworks guarantee that the knowledge management systems will address the challenges that come with increased complexity in operations. This research examines how generative artificial intelligence can be applied in various knowledge management situations, with particular focus on how it can be used to deal with mundane processes, enhance responses and help to mitigate problems in the field of transportation (Nguyen et al., 2023). The results underscore the possible benefits of using generative AI within efficient KM systems, such as lower costs, better accuracy, and improved accessibility to the users. On the other hand, the concerns such as data quality, costs of implementation, and other factors like the presence of easy to use interface for non-technical personnel, are also brought up by the authors. These perspectives will be useful for transportation agencies that seek to transform their KM activities. Overcoming these obstacles, generative AI and scalable frameworks can promote radical transformation and development of dynamic and sustainable knowledge systems that meet the needs of transport management in the case of Rahman and Smith, 2023.

Vulnerability and Malware Detection

The increasingly linked digital world oftoday has made cybersecurity a top priority. The rise in cyber dangers, notably vulnerabilities and malware, poses major risks to individuals, organizations, and governments. This review article offers a thorough analysis of the approachesand instruments currently in use for malware and vulnerability detection. It examines and contrasts several detection methods, such as machine learning techniques, static and dynamic analysis, and signature-based detection. The study also examines new developments in the sector, such as the application of big data analytics and artificial intelligence (AI) to the detection of complex threats. It also covers the need for real-time detection methods, changing threat environments, and the difficulties posed by false positives.

A Case Study on the Data Mining-Based Prediction of Students’ Performance for Effective and Sustainable E-Learning

The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Technological study programs in universities often experience high dropout rates, which makes it essential to analyze and predict potential risks to reduce dropout percentages. Student performance prediction (SPP) offers potential benefits, including personalized learning and early interventions. However, challenges such as (1) data quality and availability and (2) incomplete and inconsistent data complicate this process. Moreover, to support the fourth Sustainable Development Goal (SDG), we focus on the quality of education. A case study approach is used using data mining techniques, particularly classification, regression, and clustering, to predict student performance. The case presented aims to predict risks and ensure academic success and quality. The cross-industry standard process for data mining (CRISP-DM) methodology is used to structure and guide the prediction process. The study shows that using data from student learning processes within an academic success prediction model and data mining can identify at-risk students.

A brief review on recent high-performance platforms for electrochemical sensing of azo dye Allura Red (E129): food safety and pharmaceutical applications

Artificial dyes are increasingly widespread, especially in foodstuffs and pharmaceutical products as an industry marketing strategy to attract consumers. Consumption of the synthetic azo dye Allura Red (AR) has potential risks to human health and can cause several adverse health effects such as genotoxicity, carcinogenicity, attention deficit hyperactivity disorder, allergic and asthmatic diseases in children. The limited number of electrochemical sensing platforms for AR successfully applied for safety assessment of foods, beverages, and drug formulations testifies that the food and pharmaceutical matrices present significant analytical challenges and there is still a need to amplify the analytical performances of these systems. The authors intensively reviewed recent papers (mainly since 2019) emphasizing electrode engineering, strategies for electrochemical signal amplification, and analytical applications in food and drug control. A critical discussion on the latest interesting innovations and perspectives of the most promising electrochemical tools for AR electro­analysis is presented. The challenges and limitations in the design of electrochemical sensors for AR analysis are also discussed with a view to providing new directions for future research and development of sustainable sensing devices, paving the way for advancements in this field.

Implementation of Distributed Machine Learning in Medicine Industry

Abstract. As a matter of fact, machine learning (ML) has become a cornerstone in modern science and engineering, evolving from early algorithms like the perceptron to sophisticated deep learning models. The rise of distributed machine learning (DML) has enabled scalable, privacy-preserving analysis of large healthcare datasets in recent years. With this in mind, this study summarizes the principles of DML, contrasting it with traditional as well as parallel computing approaches. At the same time, this research discusses its applications in medical imaging, predictive analytics, and real-time monitoring. According to the analysis, despite significant advancements, challenges related to data privacy, heterogeneity, and scalability persist. Based on the evaluations, future research is needed to overcome these limitations and further integrate DML into healthcare. Overall, these results underscore the transformative potential of DML in enhancing healthcare delivery and outcomes and pave a path as well as offer a guideline for implementations of machine leaning in medic industry.

Hydrogen-Bond-Induced Melem Assemblies to Resist Aggregation-Caused Quenching for Ultrasensitive ECL Detection of COVID-19 Antigen.

Nowadays, aggregation-caused quenching (ACQ) of organic molecules in aqueous media seriously restricts their analytical and biomedical applications. In this work, hydrogen bond (H-bond) was utilized to resist the ACQ effect of 2,5,8-triamino-1,3,4,6,7,9,9b-heptaazaphenalene (Melem) as an advanced electrochemiluminescence (ECL) luminophore, whose ECL process was carefully studied in an aqueous K2S2O8 system coupled with electron paramagnetic resonance (EPR) measurements. Notably, the H-bond-induced Melem assemblies (Melem-H) showed 16.6-fold enhancement in the ECL signals as compared to the Melem aggregates (Melem-A), combined by elaborating the enhanced mechanism. On such basis, the effective ECL signal transduction was in situ achieved through the specific recognition of the double-stranded DNA embedded in Melem-H assemblies (Me-dsDNA) with spike protein (SP) of coronavirus disease 2019 (COVID-19). For that, such an ECL biosensor showed a wider linear range (1.0-125.0 pg mL-1) with a lower limit of detection (LOD) down to 0.45 pg mL-1, which also displayed acceptable results in analysis of human nasal swab samples. Therefore, the work provides a distinctive insight on addressing the ACQ effect and broadening the application scope of the organic emitter and offers a simple platform for biomedical detection.

Tutorial: Addressing Underexposed Components in Operations Research Literature

This tutorial paper discusses underrepresented practical perspectives in the operations research literature, which primarily focuses on theoretical advancements rather than documenting successful real-world applications. Drawing on experience in academia, business, and humanitarian organizations, the authors identify seven crucial—yet often overlooked—components essential for a successful analytics implementation. These components are change management, data management, model selection and validation, project management, promotion and advocacy, scoping, and software. Despite the prevalence of analytics research, these components are seldom discussed in academic literature, leaving a gap between theory and practice in analytics. We emphasize the importance of these components to achieve impactful analytics interventions and call for a more comprehensive approach to analytics research. By bridging the gap between hard analytics (mathematical modeling) and soft analytics (practical implementation), the tutorial highlights the need for scholars and practitioners to collaborate and exchange insights for more effective real-world applications of analytics. History: This paper was refereed.

Photothermal and colorimetric detection of cholesterol using hollow ruthenium nanoparticles

Nanozyme, an artificial enzyme with some advantages that compensate for the inherent shortcomings of natural enzymes have been explored. Among the various nanoparticles, those with a hollow structure have the advantage of enabling facile transfer of reactants and exhibiting a high surface-to-volume ratio. Herein, we synthesized hollow ruthenium nanoparticles (HRNs) by the galvanic replacement reaction. Transmission electron microscopy confirmed an average size of 32 nm and a shell thickness of about 2.5 nm. HRNs showed superior catalytic activity compared to natural enzymes due to the advantages of their hollow structure. Based on enhanced catalytic activity, HRNs were applied to cholesterol detection using 2,2′-azino-bis (3-ethylbenzo-thiazoline-6-sulfonic acid) (ABTS) and 3,3′,5,5′-tetramethylbenzidine (TMB). These common chromogenic substrates in colorimetric methods have strong absorbance in the UV–visible and near-infrared (NIR) regions in their oxidized forms. Therefore, both substrates can serve as probes in photothermal and colorimetric assays. Two oxidized substrates absorbed the NIR laser (808 nm) and released energy in the form of heat. Consequently, the temperature of the reaction solution increased in proportion to the cholesterol concentration. In the cholesterol detection assay using ABTS, linear ranges were ranged from 0.08 to 10 mM (R2 = 0.998) for the colorimetric assay and from 0.08 to 2.5 mM (R2 = 0.996) for the photothermal assay, respectively. The limits of detection were determined to be 12.3 μM in the colorimetric method and 43.5 μM in the photothermal method. These results demonstrate that HRN is an excellent substitute for natural enzymes and a promising material for analytical applications.