Single Platform Research Articles

Intelligence-based anti-doping via an Intelligence and Drug Testing Management (IDTM) system.

The Intelligence and Drug Testing Management (IDTM), a system that can enhance drug testing analytics with related horse information and intelligence in a single platform, can help identify and mitigate potential doping and other threats.

Customizing an all‐in‐one polydimethylsiloxane‐based electroanalytical platform using a 3D‐pen templated channel

AbstractThe combination of 3D‐printing technology and microfluidics has garnered widespread interest for creating new designs and has opened a large window for creativity. However, some laboratories do not have the necessary technology to explore this potential advantage. In this paper, we demonstrate how to explore target technologies to fabricate a single version of a polydimethylsiloxane (PDMS)‐based electroanalytical platform using a low‐cost protocol and disposable materials. This protocol is cost‐effective in terms of equipment and materials, allowing for the creation of a circular channel, a T‐junction system, and two designs of electrochemical detectors customized onto a single platform. The interesting new device has some important characteristics, such as fabrication following green and sustainable protocols. Additionally, it can address the limitations associated with flow‐based analysis, for which the classical three‐electrode configuration is inadequate. It has also demonstrated the feasibility of using the device in analytical applications for the determination of salicylic acid in commercial dermocosmetic samples, including aqueous samples.

The Prospects of AfCFTA in Promoting Industrialization and Economic Diversification in Africa

African Continental Free Trade Area (AfCFTA) functions as a boost for Africa's economic development by enhancing intra-African trade. This is because trade is an engine of growth and development. For the fact that most African nations that are located within the same region engage in similar and competitive production, the ensuing economic cooperation and trade exchanges not only have the promise of increasing trade but also stimulating other ancillary economic activities, creating new job opportunities, speeding up economic growth, promote the standard of living, and promote economic development. However, AfCFTA's record has not been very impressive. The main obstacles to intra- regional commerce among African countries include heavy reliance on trade in primary goods, high product and market concentration of exports, and inadequate regional manufacturing networks. This study therefore examines continental economic trends and Intra African Trade: Prospects for African Continental Free Trade Area. The study adopted qualitative methods of data collection and analysis and the comparative theory of integration. The study found that the low level of trade diversification implicated the capacity of the African Continental Free Trade Area (AfCFTA) to achieve improved intra-regional trade performances in Africa. African countries maintain as much as 77% export specialization, with higher external/world trade at 84.6 percent of their total import and export trade. The study recommended that Governments should invest in infrastructure development, including building roads, railways, ports, and airports, as well as improving access to electricity and broadband. Governments should adopt modern trade facilitation measures such as electronic customs systems, single window platforms, and risk management techniques and should align their technical regulations and norms with global best practices and standards, as those created by appropriate international standard-setting organisations like the International Organisation for Standardisation (ISO).

19F NMR Probes: Molecular Logic Material Implications for the Anion Discrimination and Chemodosimetric Approach for Selective Detection of H2O2.

Detection and discrimination of similar solvation energies of bioanalytes are vital in medical and practical applications. Currently, various advanced techniques are equipped to recognize these crucial bioanalytes. Each strategy has its own benefits and limitations. One-dimensional response, lack of discrimination power for anions, and reactive oxygen species (ROS) generally limit the utilized fluorescent probe. Therefore, a cutting-edge, refined method is expected to conquer these limitations. The use of 19F NMR spectroscopy for detecting and discriminating essential analytes in practical applications is an emerging technique. As an alternative strategy, we report two fluorinated boronic acid-appended pyridinium salts 5-F-o-BBBpy (1) and 5-CF3-o-BBBpy (2). Probe (1) acts as a chemosensor for identifying and discriminating inorganic anions with similar solvation energies with strong bidirectional 19F shifts in the lower ppm range. Probe (2) turns as a chemo dosimeter for the selective detection and precise quantification of hydrogen peroxide (H2O2) among other competing ROS. To demonstrate real-life applicability, we successfully quantified H2O2 via probe (2) in different pharmaceutical, dental, and cosmetic samples. We found that tuning the -F/-CF3 moiety to the arene boronic acid enables the π-conjugation, a crucial prerequisite for the discrimination of anions and H2O2. Characteristic 19F NMR fingerprints in the presence of anions revealed a complementary implication (IMP)/not implication (NIMP) logic function. Finally, the 16 distinct binary Boolean operations on two logic values are defined for "functional completeness" using the special property of the IMP gate. Boolean logic's ability to handle information by utilizing characteristic 19F NMR fingerprints has not been seen previously in a single chemical platform for detecting and differentiating such anions.

SpikeFlow: automated and flexible analysis of ChIP-Seq data with spike-in control.

ChIP with reference exogenous genome (ChIP-Rx) is widely used to study histone modification changes across different biological conditions. A key step in the bioinformatics analysis of this data is calculating the normalization factors, which vary from the standard ChIP-seq pipelines. Choosing and applying the appropriate normalization method is crucial for interpreting the biological results. However, a comprehensive pipeline for complete ChIP-Rx data analysis is lacking. To address these challenges, we introduce SpikeFlow, an integrated Snakemake workflow that combines features from various existing tools to streamline ChIP-Rx data processing and enhance usability. SpikeFlow automates spike-in data scaling and provides multiple normalization options. It also performs peak calling and differential analysis with distinct modalities, enabling the detection of enrichment regions for histone modifications and transcription factor binding. Our workflow runs in-depth quality control at all the processing steps and generates an analysis report with tables and graphs to facilitate results interpretation. We validated the pipeline by performing a comparative analysis with DiffBind and SpikChIP, demonstrating robust performances in various biological models. By combining diverse functionalities into a single platform, SpikeFlow aims to simplify ChIP-Rx data analysis for the research community.

Endotypes of severe neutrophilic and eosinophilic asthma from multi-omics integration of U-BIOPRED sputum samples.

Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development. To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma. We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics. Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy. Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes. Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22.

A Compact Hygiene and Sterilization Prototype to Curb the Outbreak of Covid-19 and Other Contagious Diseases

With the spread of deadly virus such as Covid-19, which is so far incurable and mainly transferrable through touch and contact, only precautionary measures are considered to prevent it. As per the guidelines of WHO, provided for Corona Virus, washing hands and sanitizing them frequently is one of the prime effective solutions. There are hundreds of other contagious diseases that spread through the contact of hands. In addition to that, sterilization of accessories such as mobile phone, keys, wallet etc. with least manual intervention is also need of the time. There are separate systems available in the market but there is no compact system that contains all the necessary features on single platform. Therefore, taking all these factors into consideration, this project proposes a novel design of one-stop compact touch-less station for hand washing, sanitization, and manual sterilization. The hand hygiene prototype works on a very low power of 12v. It avoids contact of people with system to get Water, Soap and Sanitizer. This prototype based on the range of 4cm to 18cm from which 4 to 8cm is for soap, 12 to 14cm for sanitizer and 16 to 18cm for water according to defined ranges the prototype is properly working and it is also showing a 20 second timer after the user takes soap. This prototype may prove to be helpful in controlling the hygiene standards in private as well as public places like Educational Institutes, Hospitals, Malls, Offices, Mosque, Railway Stations and all other places where human intervention is involved. This device is helpful for controlling the blowout of transmittable infections.

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

BackgroundShotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration.ResultsIn this study, we have developed a Java-based statistical framework (https://github.com/KociOrges/cviewer) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case–control relationships.ConclusionsCViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn’s disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential.EDLr-6-tH-9CdVCxoa6z_8Video

Maximizing Rewards: An Evaluation of an Integrated App for Efficient Reward Manegement,Currency Transfer

Abstract– In an era marked by digital innovation and evolving consumer behaviors, the management of rewards, currency transfers, and travel bookings has become increasingly complex yet integral to modern lifestyles. This research paper examines the functionality and efficiency of an integrated app designed to streamline these processes, offering users a comprehensive solution for maximizing rewards and facilitating seamless transactions. Through a combination of literature review, methodology description, and functionality analysis, this study provides insights into the app’s capabilities in managing rewards, transferring currencies, booking flights, and handling vouchers within a single platform. Utilizing a mixed-methods approach, including user surveys and interface evaluations, the research evaluates the app’s usability, effectiveness, and potential impact on user behaviors. Findings suggest that the app presents a promising solution for efficient reward management and travel planning, offering users tangible benefits such as cost savings and convenience. However, areas for improvement are identified, particularly in enhancing user experience and optimizing currency transfer mechanisms. This research contributes to the understanding of digital reward management systems and informs future developments in this rapidly evolving field. Keyword: Magnify , TypeScript , reactNative, Prisma

Using Human Intestinal Organoids to Understand the Small Intestine Epithelium at the Single Cell Transcriptional Level.

Single cell transcriptomics has revolutionized our understanding of the cell biology of the human body. State-of-the-art human small intestinal organoid cultures provide ex vivo model systems that bridge the gap between animal models and clinical studies. The application of single cell transcriptomics to human intestinal organoid (HIO) models is revealing previously unrecognized cell biology, biochemistry, and physiology of the GI tract. The advanced single cell transcriptomics platforms use microfluidic partitioning and barcoding to generate cDNA libraries. These barcoded cDNAs can be easily sequenced by next generation sequencing platforms and used by various visualization tools to generate maps. Here, we describe methods to culture and differentiate human small intestinal HIOs in different formats and procedures for isolating viable cells from these formats that are suitable for use in single-cell transcriptional profiling platforms. These protocols and procedures facilitate the use of small intestinal HIOs to obtain an increased understanding of the cellular response of human intestinal epithelium at the transcriptional level in the context of a variety of different environments.

Shear strengthening of damaged reinforced concrete beams with iron-based shape memory alloy (Fe-SMA) strips: numerical and parametric analysis

Shape memory alloys (SMAs) are metallic materials that are characterized by their ability to restore their original shape after large deformation when activated by heating. This unique property renders SMAs appealing for various civil engineering applications. Iron-based SMAs (Fe-SMAs), including alloys like Fe–Mn–Si, stand out due to their cost-effectiveness and high strength. The primary focus of this research lies in the computational modeling of Fe-SMA strips utilized to reinforce damaged concrete structures. To achieve this, details from an experimental test are leveraged for the computational simulation of real-scale reinforced concrete beams that were first loaded to some level of damage, then released and strengthened, and subsequently retested. The strengthening approach involves the application of external Fe-SMA strips wrapping around the beams. This paper presents an original computational modeling setup that incorporates a switch option for the Fe-SMA material. This feature enables one to use a single simulation platform for the whole process. The significance of this method originates from its capacity to ensure a robust analysis that includes all simulation steps-testing unstrengthened beams, installing and heating Fe-SMA strips, and testing both damaged and strengthened beams—in a single, multi-step analysis. The computational simulation results were compared with the outcomes of the experimental test, revealing an acceptable level of agreement. The findings indicate a substantial increase in both shear strength and ductility as a result of the application of Fe-SMA strips. Additionally, parametric and mesh sensitivity studies were conducted. These aimed to investigate the mesh dependency of the model and to identify the optimal mesh size. Furthermore, variations in the details of the Fe-SMA strips, including thickness, width, quantity, and effect of applied temperature were explored to compare the outcomes of different applications of these strips.

MitoPADdb: A database of mitochondrial proteins associated with diseases

Mitochondrial protein/gene mutations and expression variations contribute to the pathogenesis of various diseases, such as neurodegenerative and metabolic diseases. Detailed studies on mitochondrial protein-encoding (MPE) genes across diseases can provide clues for novel therapeutic strategies. Here, we collected, compiled, and manually curated the MPE gene mutation and expression variations data and their association with diseases in a single platform named mitoPADdb. The database contains 810 genes with 18,356 mutations and 1284 qualitative expression variations associated with 1793 diseases, grouped into 15 categories. It allows users to perform a comparative quantitative gene expression analysis for 317 transcriptomic studies across disease categories. Further, it provides information on MPE genes-associated molecular pathways. The mitoPADdb is a valuable resource for investigating mitochondrial dysfunction-related diseases. It can be accessed via http://bicresources.jcbose.ac.in/ssaha4/mitopaddb/index.html.

Single-cell transcriptional analysis of irradiated skin reveals changes in fibroblast subpopulations and variability in caveolin expression

BackgroundRadiation-induced fibrosis (RIF) is an important late complication of radiation therapy, and the resulting damaging effects of RIF can significantly impact reconstructive outcomes. There is currently a paucity of effective treatment options available, likely due to the continuing knowledge gap surrounding the cellular mechanisms involved. In this study, detailed analyses of irradiated and non-irradiated human skin samples were performed incorporating histological and single-cell transcriptional analysis to identify novel features guiding development of skin fibrosis following radiation injury.MethodsPaired irradiated and contralateral non-irradiated skin samples were obtained from six female patients undergoing post-oncologic breast reconstruction. Skin samples underwent histological evaluation, immunohistochemistry, and biomechanical testing. Single-cell RNA sequencing was performed using the 10X single cell platform. Cells were separated into clusters using Seurat in R. The SingleR classifier was applied to ascribe cell type identities to each cluster. Differentially expressed genes characteristic to each cluster were then determined using non-parametric testing.ResultsComparing irradiated and non-irradiated skin, epidermal atrophy, dermal thickening, and evidence of thick, disorganized collagen deposition within the extracellular matrix of irradiated skin were readily appreciated on histology. These histologic features were associated with stiffness that was higher in irradiated skin. Single-cell RNA sequencing revealed six predominant cell types. Focusing on fibroblasts/stromal lineage cells, five distinct transcriptional clusters (Clusters 0–4) were identified. Interestingly, while all clusters were noted to express Cav1, Cluster 2 was the only one to also express Cav2. Immunohistochemistry demonstrated increased expression of Cav2 in irradiated skin, whereas Cav1 was more readily identified in non-irradiated skin, suggesting Cav1 and Cav2 may act antagonistically to modulate fibrotic cellular responses.ConclusionIn response to radiation therapy, specific changes to fibroblast subpopulations and enhanced Cav2 expression may contribute to fibrosis. Altogether, this study introduces a novel pathway of caveolin involvement which may contribute to fibrotic development following radiation injury.

Platforms for Integration of Open Access Resources of Research and Educational Institutions

In order to study the involvement of Russian academic and educational institutions in joint projects to create open access (OA) archives, we have researched the websites of 106 Russian HEIs participating in the program “Priority 2030” and 453 research institutes (RIs) of the first three categories. As a result of the work carried out, it has been found out that 38% of educational institutions’ websites contain institutional repositories (IRs), but half of them are remotely inaccessible. 20% of repositories of educational organizations participate in joint projects to ensure OA. Only 6% of the research organizations’ websites contain IRs that are presented mainly as part of an electronic library (EL), but not as independent repositories. In addition, 10.5% of the total number of RIs do not maintain their own repositories or ELs, but provide information about research results of their employees, the most important academic achievements of researchers as well as give access to digitized texts of the most significant works published within the framework of joint projects. At the same time, information about participation in these projects is missing on the websites of academic institutions. The examination of the websites of Russian educational and research institutions has shown that their participation in projects to develop cooperative OA archives is not uniform: while some institutions are indifferent, others take an active part in several projects at once and provide information about themselves in foreign aggregators. As a result of the research, we have found 7 projects aimed at integrating OA information resources on a single platform. These resources are implemented mainly in ELs. They are different in number of participants, quantity and chronological depth of the content offered. The identified projects can be conditionally divided into multidisciplinary and thematic, national and regional. It has been found out that some ELs provide open access to all content only for project participants, i.e. remote users have the opportunity to familiarize themselves only with public pages or only a small part of the content is available to them.

Tracing participants for longitudinal environmental health research using social networking sites: a pilot study

Aim: Longitudinal cohort study designs are considered the gold standard for investigating associations between environmental exposures and human health yet they are characterized by limitations including participant attrition, and the resource implications associated with cohort recruitment and follow-up. Attrition compromises the integrity of research by threatening both the internal and external validity of empirical results, weakening the accuracy of statistical inferences and the generalizability of findings. This pilot study aimed to trace participants from a historical cohort study, the Hamilton Child Cohort Study (HCC) (n = 3,202), (1976–1986, 2003–2008) which was originally designed to examine the relative contribution of indoor and outdoor exposure to air pollution on respiratory health. Methods: Original participants were traced through social networking sites (SNS) by leveraging personal identifying data (name, age, sex, educational affiliation, and geographical locations) from the HCC entered into SNS search engines. Results: Of the original cohort (n = 3,166), 21% (n = 665) were identified as having social media presence (SMP) on a single social media platform, with 15% (n = 479) found on Facebook, 6% (n = 185) on LinkedIn, < 1% (n = 9) on Instagram, and n = 1 participant on Twitter. However, 68% (n = 2,168) of the cohort were associated with multiple SNS with the same features (matching names, ages, and locations), making conclusive identification challenging. The remaining 11% (n = 334) of the cohort had no SMP (NSMP). Statistical differences in sample characteristics of each cohort were analyzed using the Pearson chi-square test. Significant differences between the SMP and NSMP cohorts were found in relation to sex (p < 0.001), and childhood neighborhood of residence (p < 0.05). Conclusions: This study underscores social media’s potential for tracing participants in longitudinal studies while advising a multi-faceted approach to overcome inherent limitations and biases. A full-scale study is necessary to determine whether utilizing SNS to trace participants for longitudinal research is an effective tool for re-engaging research participants lost to attrition.

Hybrid Model for Conducting Multiple Citation Analysis Operations Using the Same Citation Data

This study proposes a hybrid model, which can be used for conducting various citation analysis operations over a set of research works/papers interconnected with each other through their citations and references. Citation analysis of papers becomes important while evaluating their role and influence in a particular field and understanding their relation and connectivity of content with other important documents in the same field. The citation analysis methods can further be used for tracing the relationship between domains, keywords, and many other relative factors, based on the criteria considered for citation analysis operation. The research in the field of citation analysis, traditionally focussed on developing new citation analysis methods for better evaluation or ranking of the papers. This paper proposes a model, where multiple existing citation analysis operations can be implemented in a single platform. The proposed model calculates the result for co-citation analysis, bibliographic coupling, and citation density analysis. The process involved in the development of this model is discussed in detail in the later part of the manuscript. The process is based on computational coding, and hence special attention needs to be paid to the proper arrangement of papers in tabular form, based on the information of their year of publication and their citation network data. An algorithm for the model has been proposed in the later part of the manuscript that is based on the code formulated to find the relative data for conducting the operations.

Driving user adoption of mobile apps through platform multihoming: The effects of multihoming delay and multihoming customization

Developers release their mobile apps through multiple app store ecosystems to extend their reach and reduce their dependency on a single distribution platform and its governance mechanisms. While this topic has piqued the interest of a growing number of studies, key multihoming decisions faced by developers including when to multihome mobile apps and whether and how much to customize them for the multihomed platform have not yet been jointly examined. In addition, the potential role that the user value of mobile apps (hedonic or utilitarian) plays in developers’ multihoming decisions has largely been overlooked. To address these gaps, the present study jointly investigates the effects of multihoming delay and multihoming customization on the user adoption of mobile apps on the multihomed platform. It further investigates the moderating effects of the user value of mobile apps. The study employed a large dataset of mobile apps that were initially released through Apple's App Store and subsequently multihomed into Google's Play Store (N = 24,906). By combining traditional count regression model with a machine learning approach, the study derives a range of theoretical and predictive insights that significantly contribute to the mobile app multihoming literature.

Pipeline for identification of genome-wide microsatellite markers and its application in assessing the genetic diversity and structure of the tropical sea cucumber Holothuria leucospilota

Microsatellite DNA (SSR) is extensively used as molecular markers in diverse fields, including molecular ecology, conservation biology, and molecular breeding. Nevertheless, the efficient and easy acquisition of high-quality SSR molecular markers for economically cultured marine animals remains a challenge. Holothuria leucospilota is an economically and ecologically important tropical sea cucumber. Recently, artificial breeding and cultivation of this species have been reported. However, there is still a lack of highly polymorphic SSR molecular markers for geographical population differentiation and genetic breeding. This study aims to develop and utilize SSR markers from genomic data to assess the genetic variation and population structure of H. leucospilota in the South China Sea. A comprehensive pipeline was created, integrating microsatellite discovery, primer design, and assessment into a single platform. Through this, 15 highly polymorphic microsatellite loci were identified and validated. The results revealed high genetic diversity and low genetic differentiation among H. leucospilota populations. Larval dispersal and ocean currents in the South China Sea were identified as the primary factors influencing genetic connectivity between populations. In addition, the artificial culture and releasing of H. leucospilota have influenced the genetic structure of their wild populations in the South China Sea. This study provides a pipeline for identifying SSR markers within genome data, and applies it to assess the genetic diversity and structure of H. leucospilota. The outcomes of this research are expected to be beneficial and applicable to the future aquaculture of tropical sea cucumbers.

Unravelling tourism destination's competitiveness using big data analytics: a comparative analysis

PurposeThe competitiveness of destinations is crucial for tourism. In this context, the study aims to uncover customer satisfaction, experiences, feelings, and thoughts by conducting a comparative analysis of social media comments from various competitive tourism destinations.Design/methodology/approachBig data research was conducted to answer the research questions. The data was collected on a social media platform focusing on three destinations in the Mediterranean region. Three methods were employed to analyse the data: sentiment analysis, topic modelling, and named-entity recognition.FindingsThis study addressed traveller satisfaction levels. It identified the topics concerning each destination, examined the emotions expressed by travellers about these topics, explored the potential impact on future behaviour, and investigated the features of the destinations and satisfaction levels about these features. It also identified the prominent food and beverage names in destinations and explored tourists’ preferences regarding these foods and beverages.Research limitations/implicationsThe limitations of this study relate to the sample. The data used in this study was solely obtained from a single social media platform and focused on English-only comments. Further research that includes different social media platforms for hotel categories and considers reviews in local languages could capture a broader range of customer opinions and experiences.Practical implicationsPolicymakers can gain insight into a destination’s position in the competitive landscape. This study has numerous implications for policymakers in the relevant destinations and managers in the design and implementation of services.Social implicationsThe findings of this study can have broader societal implications if considered and implemented by decision-makers and tourism businesses in the context of competitiveness.Originality/valueThe study’s originality lies in integrating multiple disciplines and comparing tourism destinations using big data. This study improves the understanding of competitiveness in three specific Mediterranean destinations. Previous research has focused on different contexts in these Mediterranean destinations. Therefore, the study fills this gap by focusing simultaneously on all three destinations in the context of competitiveness.

Rapid sequential determination of the explosives 2,4,6-trinitrotoluene and cyclotrimethylenetrinitramine in forensic samples employing a graphite sheet sensor and cyclic square-wave stripping voltammetry.

The development of a portable analytical procedure is described for rapid sequential detection and quantification of the explosives 2,4,6-trinitrotoluene (TNT) and cyclotrimethylenetrinitramine (RDX) in forensic samples using a graphite sheet (GS). A single GS platform works as a collector of explosive residues and detector after its assembly into a 3D-printed cell. The detection strategy is based on cyclic square-wave stripping voltammetry. The cathodic scan from + 0.1 to -1.0V with accumulation at 0.0V enables the TNT detection (three reduction peaks), and the anodic scan from + 0.2 to + 1.55V with accumulation at -0.9V provides the RDX detection (two oxidation processes). Low detection limit values (0.1 µmol L-1 for TNT and 2.4 µmol L-1 for RDX) and wide linear ranges (from 1 to 150 µmol L-1 for TNT and from 20 to 300 µmol L-1 for RDX) were obtained. The sensor did not respond to pentaerythritol tetranitrate (PETN), which was evaluated as a potential interferent, because plastic explosives contain mixtures of TNT, RDX, and PETN. The GS electrode was also evaluated as a collector of TNT and RDX residues spread on different surfaces to simulate forensic scenarios. After swiping over different surfaces (metal, granite, wood, cloths, hands, money bills, and cellphone), the GS electrode was assembled in the 3D-printed cell ready to measure both explosives by the proposed method. In all cases, the presence of TNT and RDX was confirmed, attesting the reliability of the proposed device to act as collector and sensor.