Search Results (63)

This article presents a gamified digital twin in Minecraft designed to support practical exercises in digital logic in the Computer Engineering I course at the University of Salamanca. Implemented as a Spigot/Paper server plugin based on the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA) multi-agent architecture, the system orchestrates four virtual organizations and employs a world cloning strategy (via Multiverse and WorldGuard) to ensure individual and isolated workspaces, while also enabling collaborative work. The central contribution is a multi-agent system with an integrated ‘black box’ verification engine that mitigates redstone asynchrony and latency through controlled signal injection and software clock synchronization, enabling real-time deterministic validation of both basic logic gates and more complex sequential circuits. Additionally, the ecosystem includes a specialized suite of logic scenarios and a web-based dashboard for real-time teacher monitoring. In a pilot study ($N=30$), the system achieved an average task completion rate of 89.1%, and an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) analysis indicated that technical stability is positively associated with student performance. Full article

This research simulates Aswan City’s urban transportation dynamics utilizing the Multi-Agent Transport Simulation (MATSim) framework. As a fast-expanding urban center, Aswan has many transportation difficulties that require extensive modeling toward sustainable mobility solutions. MATSim, recognized for its agent-based methodology, offers a detailed portrayal and analysis of individual travel behaviors and their interactions within the metropolitan transportation system. This study compiled and combined many databases, including demographic data, road infrastructure, public transit plans, and travel demand trends. These data are altered to produce a realistic digital clone of Aswan’s transportation system. Simulated scenarios analyze the consequences of several actions, such as increased public transit scheduling, traffic flow management, and the adoption of alternative transport modes, on minimizing congestion and boosting accessibility. Pilot findings show that MATSim effectively captures the distinct features of Aswan’s transportation network and offers practical insights for decision-makers. The results identified some opportunities to improve mobility and promote sustainable urban growth in developing cities. This study emphasized the importance of agent-based simulations in designing future transportation systems and urban infrastructure. Full article

Digital images play an increasingly central role in journalism, legal investigations, and cybersecurity. However, modern editing tools make image manipulation difficult to detect with traditional forensic methods. This research addresses the challenge of improving the accuracy and stability of deep-learning-based forgery detection by developing a convolutional neural network enhanced through automated hyperparameter optimisation. The framework integrates a Firefly-based search strategy to optimise key network settings such as learning rate, filter size, depth, dropout, and batch configuration, reducing reliance on manual tuning and the risk of suboptimal model performance. The model is trained and evaluated on a large raster dataset of tampered and authentic images, as well as a custom vector-based dataset containing manipulations involving geometric distortion, object removal, and gradient editing. The Firefly-optimised model achieves higher accuracy, faster convergence, and improved robustness than baseline networks and traditional machine-learning classifiers. Cross-domain evaluation demonstrates that these gains extend across both raster and vector image types, even when vector files are rasterised for deep-learning analysis. The findings highlight the value of metaheuristic optimisation for enhancing the reliability of deep forensic systems and underscore the potential of combining deep learning with nature-inspired search methods to support more trustworthy image authentication in real-world environments. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

C. canephora exhibits high genetic variability, and to estimate this variability, morphological descriptors associated with coffee quality are used. Bean size is a physical trait of great importance for coffee classification. Manual classification is known to be inaccurate and time-consuming, which is why researchers have adopted digital imaging techniques to improve classification efficiency. The objective of this study was to quantify the genetic diversity in 43 C. canephora clones using the Ward-MLM strategy and to estimate genetic parameters and correlations from digital phenotyping of beans and cherries. The experiment was conducted on a crop consisting of 43 C. canephora genotypes, where the cherries were manually pulped and dried until they reached 12% moisture content. Using GroundEye^® equipment, four replicates of 50 beans and cherries were evaluated for each treatment, and the software generated spreadsheets with the results of the geometric traits. To determine the existence of genetic variability among the genotypes, the data obtained were subjected to analysis of variance, estimation of genetic parameters, Ward-MLM analysis, and Pearson correlation. The genotypic variance was higher than the environmental variance for all variables analyzed, both for beans and cherries, indicating that the genotypes evaluated have high genetic variability. The greatest genetic distance was observed between groups I and IV, suggesting favorable conditions for crosses between the genotypes of these groups. Phenotypic correlation analysis revealed significant positive and negative correlations between the variables. Digital seed analysis successfully detected genetic divergence among the 43 C. canephora clones. The variables ‘area’, ‘maximum diameter’, and ‘minimum diameter’ are the most suitable for selecting genotypes with larger beans. Full article

Language modeling has evolved from simple rule-based systems into complex assistants capable of tackling a multitude of tasks. State-of-the-art large language models (LLMs) are capable of scoring highly on proficiency benchmarks, and as a result have been deployed across industries to increase productivity and convenience. However, the prolific nature of such tools has provided threat actors with the ability to leverage them for attack development. Our paper describes the current state of LLMs, their availability, and their role in benevolent and malicious applications. In addition, we propose how an LLM can be combined with text-to-speech (TTS) voice cloning to create a framework capable of carrying out social engineering attacks. Our case study analyzes the realism of two different open-source TTS models, Tortoise TTS and Coqui XTTS-v2, by calculating similarity scores between generated and real audio samples from four participants. Our results demonstrate that Tortoise is able to generate realistic voice clone audios for native English speaking males, which indicates that easily accessible resources can be leveraged to create deceptive social engineering attacks. As such tools become more advanced, defenses such as awareness, detection, and red teaming may not be able to keep up with dangerously equipped adversaries. Full article

Assessment of Programmed Death-Ligand 1 (PD-L1) immunohistochemical (IHC) expression on tumor cells (TCs) and immune cells (ICs) in bladder cancer (BC) is challenging. Artificial Intelligence (AI) has potential for accurate PD-L1 IHC scoring, but its efficiency remains debatable. Our aim was to compare two AI protocols provided by the free QuPath software (v0.5.1) (Selected Area Interpretation (AI-SAI) and Whole Slide Imaging (AI-WSI)) with manual PD-L1 IHC scoring. A total of 43 BCs were included. PD-L1 IHC was performed using the SP263 clone. The IHC slides were digitized and further imported into QuPath. The PD-L1 positivity threshold was set at 25%. Statistically significant correlations were observed between AI-SAI and manual interpretation for both TCs (r = 0.85) and ICs (r = 0.57). AI-WSI yielded comparable results, with correlation coefficients of r = 0.82 for TCs and r = 0.56 for ICs. However, AI-SAI demonstrated stronger agreement with manual assessment (κ = 0.86) compared to AI-WSI (κ = 0.65). Receiver Operating Characteristic (ROC) analysis further supported the superiority of AI-SAI, with higher AUC values for both TCs (0.96 vs. 0.92) and ICs (0.92 vs. 0.90). Our findings indicate that AI-SAI is preferable to AI-WSI, particularly in BC cases with high PD-L1-positive TC content. Nevertheless, supervision by an experienced pathologist is mandatory. Full article

A subset of ALK+ non-small cell lung cancer (NSCLC) patients relapse on ALK inhibitor (ALKi) treatment due to on-target resistance mutations affecting the tyrosine kinase domain. Objective: In this study, we investigated the presence of minor resistant clones in pre-treatment tissue samples and assessed their predictive value for subsequent resistance mechanisms. Methods: Using the highly sensitive digital droplet (dd)PCR technique, we analyzed 40 tissue samples obtained from 17 patients who had developed on-target resistance mutations after receiving ALKi between 2013 and 2022. We focused on 10 on-target ALKi resistant mutations identified in our patient cohort. Results: Fifteen ALKi resistance mutations were detected in 13 samples from 11/17 patients. Among these, four mutations were observed as resistance mutations in follow-up biopsies taken after first or subsequent lines of ALKi. Comparison of the test results from two subsequent biopsies, before and directly after therapy, revealed presence of the resistance mutation identified upon relapse in the pre-treatment sample of three cases that were all taken from the same tumor location. In six cases taken from different tumor locations, the resistant mutations were not found in the pre-treatment sample. Conclusions: By using the highly sensitive ddPCR approach, we detected minor clones with on-target resistant mutations in both treatment-naive and relapse biopsies from ALK-positive NSCLC patients. The predictive value of these mutations as the potential resistance-causing mechanism was limited to relapses occurring at the same tumor location as the pre-treatment sample. Full article

We proposed a novel technique for detecting optical filter shift in ROADMs in optical transmission lines by applying machine learning on DP-16QAM constellation data captured just after Analogue-to-Digital Converters (ADCs) in a digital coherent receiver. For this purpose, we implemented Docker container applications in WhiteBox Cassini to acquire the real-time raw digital data. By using the captured data, we generated CNN models for the detections in off-line processing and used them for real-time detections. As preliminary results, we confirmed the successful detection of optical filter shift in real-time with an accuracy of 51 GHz. To enhance the detection accuracy, we challenged ourselves to reproduce digital coherent constellation data by using a Generative Adversarial Network (GAN) for real-time optical filter shift detection for the first time. By utilizing a GAN, we successfully generated clone data based on actual constellation data. By adding the cloned data onto the actually captured data, we successfully enhanced detection sensitivity to as high as 26 GHz. As a result, we reduced the amount of required data for the high detection accuracy by 68% with the help of GAN-supported data augmentation. Furthermore, we confirmed our augmentation method enables the prediction of faults before they occur by enabling high enough detection sensitivity to detect an optical filter shift before degradation of Bit Error Rates (BERs) appears. This demonstrates the potential of GAN-based data augmentation in optimizing the efficiency and precision of optical network impairment sensing by using captured digital coherent optical signal. Full article

Acute lymphoblastic leukaemia is the most common childhood malignancy that remains a leading cause of death in childhood. It may be characterised by multiple known recurrent genetic aberrations that inform prognosis, the most common being hyperdiploidy and t(12;21) ETV6::RUNX1. We aimed to assess the applicability of a new imaging flow cytometry methodology that incorporates cell morphology, immunophenotype, and fluorescence in situ hybridisation (FISH) to identify aneuploidy of chromosomes 4 and 21 and the translocation ETV6::RUNX1. We evaluated this new “immuno-flowFISH” platform on 39 cases of paediatric ALL of B-lineage known to have aneuploidy of chromosomes 4 and 21 and the translocation ETV6::RUNX1. After identifying the leukaemic population based on immunophenotype (i.e., expression of CD34, CD10, and CD19 antigens), we assessed for copy numbers of loci for the centromeres of chromosomes 4 and 21 and the ETV6 and RUNX1 regions using fluorophore-labelled DNA probes in more than 1000 cells per sample. Trisomy 4 and 21, tetrasomy 21, and translocations of ETV6::RUNX1, as well as gains and losses of ETV6 and RUNX1, could all be identified based on FISH spot counts and digital imagery. There was variability in clonal makeup in individual cases, suggesting the presence of sub-clones. Copy number alterations and translocations could be detected even when the cell population comprised less than 1% of cells and included cells with a mature B-cell phenotype, i.e., CD19-positive, lacking CD34 and CD10. In this proof-of-principle study of 39 cases, this sensitive and specific semi-automated high-throughput imaging flow cytometric immuno-flowFISH method has been able to show that alterations in ploidy and ETV6::RUNX1 could be detected in the 39 cases of paediatric ALL. This imaging flow cytometric FISH method has potential applications for diagnosis and monitoring disease and marrow regeneration (i.e., distinguishing residual ALL from regenerating haematogones) following chemotherapy. Full article

Background/objectives: Mycosis fungoides (MF) is a rare malignancy, with an indolent course in the early stages of the disease. However, due to major molecular and clinical heterogeneity, patients at an advanced stage of the disease have variable responses to treatment and considerably reduced life expectancy. Today, there is a lack of specific markers for the progression from early to advanced stages of the disease. To address these challenges, the non-interventional BIO-MUSE trial was initiated. Here, we report on a case study involving one patient, where combined omics analysis of tissue and blood was used to reveal the unique molecular features associated with the progression of the disease. Methods: We applied 10× genomics-based single-cell RNA sequencing to CD3+ peripheral T-cells, combined with T-cell receptor sequencing, to samples collected at multiple timepoints during the progression of the disease. In addition, GeoMx-based digital spatial profiling of T-helper (CD3+/CD8−), T-cytotoxic (CD3+/CD8+), and CD163+ cells was performed on skin biopsies. Results. The results pinpoint targets, such as transforming growth factor β1, as some of the mechanisms underlying disease progression, which may have the potential to improve patient prognostication and the development of precision medicine efforts. Conclusions: We propose that in patients with MF, the evolution of the malignant clone and the associated immune response need to be studied jointly to define relevant strategies for intervention. Full article

Multiselfing is a form of musicianship where one person digitally clones themself into several single selves, creating layers and a musical collective that would otherwise be impossible without the mediation of technology. There are various kinds of multiselfers. This article categorizes them as the following: singers, instrumentalists, loopers, live performers, and hybrids. While these five categories are presented distinctly here, they may often overlap. This article explores the notion of multiselfing and its implicit potential when situated in music education to develop comprehensive music skills. Comprehensive musicianship is important because it enables students to grow in broad musical knowledge and skills at all levels of instruction by synthesizing the musical materials they are working with and by making conceptual connections through performance, analysis, and composition. In addition to including many examples, this article also includes lists of resources and applications to help schoolteachers better understand how to integrate multiselfing into their pedagogic practices. Full article

Primary themes in intergenerational justice are a healthy environment, the perpetuation of Earth’s biodiversity, and the sustainable management of the biosphere. However, the current rate of species declines globally, ecosystem collapses driven by accelerating and catastrophic global heating, and a plethora of other threats preclude the ability of habitat protection alone to prevent a cascade of amphibian and other species mass extinctions. Reproduction and advanced biotechnologies, biobanking of germplasm and somatic cells, and conservation breeding programs (RBCs) offer a transformative change in biodiversity management. This change can economically and reliably perpetuate species irrespective of environmental targets and extend to satisfy humanity’s future needs as the biosphere expands into space. Currently applied RBCs include the hormonal stimulation of reproduction, the collection and refrigerated storage of sperm and oocytes, sperm cryopreservation, in vitro fertilization, and biobanking of germplasm and somatic cells. The benefits of advanced biotechnologies in development, such as assisted evolution and cloning for species adaptation or restoration, have yet to be fully realized. We broaden our discussion to include genetic management, political and cultural engagement, and future applications, including the extension of the biosphere through humanity’s interplanetary and interstellar colonization. The development and application of RBCs raise intriguing ethical, theological, and philosophical issues. We address these themes with amphibian models to introduce the Multidisciplinary Digital Publishing Institute Special Issue, The Sixth Mass Extinction and Species Sustainability through Reproduction Biotechnologies, Biobanking, and Conservation Breeding Programs. Full article

Identification of contamination sources and delineation of plumes in the geological environment stand as pivotal elements in reconstructing the conceptual site model (CSM) and devising remediation strategies tailored to specific physicochemical traits. This study endeavors to showcase the capabilities of a 3D digital interface, seamlessly integrating multi-source data, to elucidate site-specific contamination dynamics and steer the implementation of remediation strategies harmoniously aligned with the ethos of remediation geology. In a site historically marred by chlorinated solvent contamination, the digitization of stratigraphic, piezometric, chemical, and membrane interface probe (MIP) data underpins geomodeling endeavors and yields a meticulously crafted, data-driven CSM. The hydrogeochemical and hydrogeophysical data were interpolated to build a volumetric, digital 3D model illustrating data-driven elements. The comprehensive 3D clone adeptly delineates secondary contamination sources and renders visible the contamination plume within a georeferenced framework, mirroring the nuanced interplay of stratigraphic nuances and groundwater path. A data-centric approach to modeling facilitates the design of the first hydraulic virtual barrier leveraging groundwater circulation well (GCW) technology, its geometry finely attuned to intercept the contamination plume originating from source dissolution and aligning with preferential groundwater flow trajectories. Conventional hydrochemical monitoring and multilevel sampling substantiate the discernible reduction in chlorinated solvent concentrations across various depths within the aquifer horizon, affirming the efficacy of GCWs in their virtual barrier configuration. The findings highlight the effectiveness and limited groundwater consumption of the virtual barrier compared to the on-site pump-and-stock system. This research underscores the potency of a multi-faceted evidence-driven puzzle in conceptualizing contamination mechanisms within the geological milieu, thereby fostering the application of cutting-edge, effective, and sustainable remediation strategies. Full article

This retrospective begins with Galvani’s experiments on frogs at the end of the 18th century and his discovery of ‘animal electricity’. It goes on to illustrate the numerous contributions to the field of physical chemistry in the second half of the 19th century (Nernst’s equilibrium potential, based on the work of Wilhelm Ostwald, Max Planck’s ion electrodiffusion, Einstein’s studies of Brownian motion) which led Bernstein to propose his membrane theory in the early 1900s as an explanation of Galvani’s findings and cell excitability. These processes were fully elucidated by Hodgkin and Huxley in 1952 who detailed the ionic basis of resting and action potentials, but without addressing the question of where these ions passed. The emerging question of the existence of ion channels, widely debated over the next two decades, was finally accepted and, a decade later, many of them began to be cloned. This led to the possibility of modelling the activity of individual neurons in the brain and then that of simple circuits. Taking advantage of the remarkable advances in computer science in the new millennium, together with a much deeper understanding of brain architecture, more ambitious scientific goals were dreamed of to understand the brain and how it works. The retrospective concludes by reviewing the main efforts in this direction, namely the construction of a digital brain, an in silico copy of the brain that would run on supercomputers and behave just like a real brain. Full article