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Background/Objectives: Molecular docking and molecular dynamics are accurate but computationally expensive, so the compounds entering them must be chosen well. The present study proposes CADT, a confidence-gated affinity–ADME-T docking-triage cascade that decides which compounds are worth docking. Methods: The gate combines an ensemble estimate of drug–target affinity with its epistemic uncertainty and an applicability-domain check. Predicted absorption, distribution, metabolism, excretion, and toxicity (ADME-T) developability is added as a soft flag. All components were trained on openly licensed Therapeutics Data Commons data. Ranking was assessed on the DAVIS and KIBA kinase panels and on BindingDB

K_{d}

, under three split protocols over five seeds. The routing decision was then examined against molecular docking, in which 407 compound–target pairs were docked into six withheld kinases. Results: A Morgan-fingerprint gradient-boosting model reached a concordance index of

0.866 \pm 0.006

, with

0.813

for unseen targets and

0.720

for unseen drugs. Across eight ADME-T endpoints, the area under the ROC curve ranged from

0.65

0.91

. On the cold-target split the cascade reduced the compounds sent to docking by

86 %

while retaining

61 %

of the true strong binders. Docking measured that reduction at

85 %

, and at an equal budget, the gate enriched true binders more than the docking score itself. Conclusions: A transparent pre-screen can prioritise compounds ahead of structure-based calculation at a fraction of its cost. However, the uncertainty and applicability-domain terms act as an abstention mechanism rather than an accuracy gain, and that abstention is not free. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery, 2nd Edition)

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27 pages, 1302 KB

Open AccessArticle

Multi-Fault Diagnosis in Twisted-Pair Cables of Networked Control Systems Using Transferometry

by Abdel Karim Abdel Karim

Eng 2026, 7(9), 471; https://doi.org/10.3390/eng7090471 - 11 Sep 2026

Abstract

In networked control systems, power line communication technique is used to transfer data over existing energy cables. A soft fault degrades the integrity of the signal without impacting the system behaviour. This work develops a transferometry-based method for detecting, localising, and estimating the severity of two simultaneous soft faults in such cables. A soft fault is modelled as a series impedance, and the transmission coefficient (TC) is computed from the ABCD cascade model of the cable. We prove that, under unmatched terminations, the time-domain TC exhibits a five-pulse signature whose peak positions and amplitudes map directly to the two fault positions and their individual severities. A residual signal constructed from this signature yields closed-form estimators for the fault positions and their combined severities; individual fault severities require a bounded nonlinear least-square fit, valid for approximately symmetric, known terminations. We further show that the method extends to n simultaneous soft faults under a combined soft-fault condition, with the

(2 n + 1)

-pulse pattern verified in simulation for

n \in {1, 2, 3, 4}

. A Monte Carlo study using correct localisation probability as the detection criterion establishes a practical SNR threshold of 25 dB; fault-separation resolvability shows intermittent, sidelobe-driven degradation rather than a single threshold. Simulations on a measured 24 AWG cable, extrapolated beyond its characterised band, confirm reliable two-fault diagnosis under additive noise, with reliable multi-fault performance demonstrated for

n = 1, 2

, presented as a numerical proof of concept on this extrapolated cable model rather than a characterisation confirmed by measurement over the full simulated band. Full article

(This article belongs to the Section Electrical and Electronic Engineering)

24 pages, 5099 KB

Open AccessArticle

From Blast to Biological Uptake: Residual Dinitrotoluene as an Occupational Exposure Hazard in Explosive Ordnance Disposal

by Gareth Collett, Kelly Johnstone, Tim Bashford, William Proud, Mia Tazi, Mieke Van Hemelrijck, Richard T. Bryan and Bryan G. Fry

Toxics 2026, 14(9), 812; https://doi.org/10.3390/toxics14090812 - 11 Sep 2026

Abstract

Repeated mixed-munition demolition may require explosive ordnance disposal personnel to re-enter a common demolition pit to inspect effects, recover debris and prepare subsequent stacks. Dinitrotoluene (DNT) is a recognised explosive ordnance occupational toxicant absorbed by inhalation, skin and ingestion. Urinary-tract malignancies and other adverse outcomes have been reported in highly exposed nitroaromatic-explosives workers. Recently, bladder-cancer incidence was documented as elevated among former British Army ammunition technicians. While these observations do not establish a causal relationship with DNT, they however provide a strong rationale for further investigation of credible exposure pathways. This study developed a scenario-based source-pathway-receptor model to examine whether residual DNT could constitute an occupational exposure source during these tasks. This model was applied to an explosive ordnance disposal operation involving 20 sequential demolitions and a documented aggregate TNT-equivalent basis of 1000 kg, used as a screening proxy because the operational inventory was predominantly TNT-filled. The model identified plausible primary inhalation from a blast-generated plume and secondary inhalation from resuspended residues, together with dermal and incidental-ingestion pathways. An important caveat is that it does not reconstruct individual dose or establish disease causation. Validation requires time-resolved post-blast and task-based personal air sampling, surface assessment, biomonitoring and detailed exposure reconstruction during representative demolition operations. As such, this study establishes crucial foundational hypotheses for future occupational hazard research into DNT exposure to explosive ordnance disposal personnel. Full article

(This article belongs to the Special Issue Health Risk Assessment of Emerging Pollutants—an Integration of Exposure Estimation and Mechanism-Based Toxicity Study)

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19 pages, 900 KB

Open AccessArticle

Multimodal Physiological Detection of Passive Fatigue in SAE Level 3 Automated Driving Using Eye-Movement and ECG Features

by Jiangtian Li and Chenghui Lan

Appl. Sci. 2026, 16(18), 9049; https://doi.org/10.3390/app16189049 - 11 Sep 2026

Abstract

In SAE Level 3 automated driving, drivers are required to supervise vehicle operation and respond to overtaking requests. Owing to task monotony and insufficient workload, drivers are prone to passive fatigue, which may impair vigilance and safety. This study investigated passive fatigue development during automated driving and proposed a multimodal detection method. Thirty licensed participants completed one automated driving task and one manual driving task in a driving simulator. Eye-movement and ECG/heart rate variability indicators were synchronously collected, and fatigue states were assessed using the Karolinska Sleepiness Scale. Results show that passive fatigue during automated driving developed differently from active fatigue during manual driving. Based on PERCLOS, pupil diameter, pupil diameter variation, SDNN, and LF/HF, an SVM-based passive fatigue detection model was developed to classify alert and passive fatigue states. Across repeated subject-wise validation, the model achieved an accuracy of 89.19%, sensitivity of 91.83%, specificity of 86.54%, balanced accuracy of 89.19%, F1 score of 89.48%, and precision of 87.28%, outperforming the model trained on manual driving active fatigue data. These findings demonstrate the need for scenario-specific driver-state monitoring models in automated driving systems and provide an applied physiological sensing approach for passive fatigue detection and warning design. Full article

(This article belongs to the Section Transportation and Future Mobility)

20 pages, 1596 KB

Open AccessArticle

Anthranilate Reduces Pseudomonas aeruginosa Infection Severity through an Antibiotic-Independent Anti-Virulence Mechanism

by Huiyan Li, Min-Hee Kang, Wen-Xin Niu, Eunsoo Kim and Joon-Hee Lee

Antibiotics 2026, 15(9), 898; https://doi.org/10.3390/antibiotics15090898 - 11 Sep 2026

Abstract

Background/Objectives: Antibiotic resistance is a growing global health challenge, especially with tricky bacteria like Pseudomonas aeruginosa. Anthranilate, previously identified as a signaling molecule in P. aeruginosa, modulates various pathogenicity-related phenotypes by reducing virulence factor production, inhibiting biofilm formation, and increasing antibiotic susceptibility. Based on these good properties as an anti-virulence agent, this study evaluated the actual therapeutic potential of anthranilate using mouse skin infection and lung infection models. Specifically, the efficacy of anthranilate in a standalone treatment was investigated without reliance on conventional antibiotics. Methods: The therapeutic efficacy of anthranilate was evaluated in murine skin and lung infection models of P. aeruginosa. Anthranilate was administered as a standalone treatment, and its effects on bacterial burden, inflammatory responses, and tissue lesion progression were assessed and compared with those of gentamicin treatment. Results: Anthranilate effectively reduced bacterial suppuration, mitigated inflammatory responses, and suppressed lesion progression in infected skin and lung tissues. Notably, anthranilate alone produced therapeutic effects comparable to those of gentamicin. Conclusions: These findings highlight its potential of standalone treatment as an alternative to traditional antibiotics and offer a novel anti-virulence strategy that minimizes selective pressure for resistance development. Full article

(This article belongs to the Special Issue New Insights Toward the Development of Novel Inhibitors of S. aureus and P. aeruginosa Factors)

35 pages, 9197 KB

Open AccessArticle

Data-Driven Position Control of a McKibben Pneumatic Artificial Muscle: Simulation and Experimental Validation of PID and LQI Controllers

by Tomislav Bazina, Luka Kopajtić, Ervin Kamenar and Goran Gregov

Actuators 2026, 15(9), 484; https://doi.org/10.3390/act15090484 - 11 Sep 2026

Abstract

Pneumatic artificial muscles, including McKibben-type actuators, offer high power-to-weight ratio, compliance, and inherent safety, but their nonlinear pressure–contraction behavior, hysteresis, saturation, and load-dependent dynamics make accurate position control challenging. This study develops a practical data-driven workflow that derives a branchwise feedforward compensator and an LQI or PID controller from one open-loop characterization experiment. Quasi-static characterization first identifies a conservative control-ready voltage window. A bounded random excitation within this window is replayed with

4 s

holds to expose terminal and transient behavior. The same experiment supplies branchwise discrete plant models and a feedforward lookup. Two open-loop-derived transient layers, voltage creep compensation and dynamic pressure referencing, are applied to the raw lookup before simulation. Four controller variants are compared on a common simulated closed-loop benchmark built from the identified plant: a feedforward-only baseline, a branchwise proportional–integral–derivative (PID) baseline, a base linear quadratic integral (LQI) controller with displacement and pressure feedback, and a velocity-state LQI extension with a filtered velocity estimate. A multi-metric optimization score balances tracking RMS, settled oscillation, command activity, saturation, and gain magnitude. The score selects the base LQI within the LQI family. The selected gains and transient layers are deployed in a real-time implementation with manually reduced position gains. The controllers are then evaluated on a common reference stream against the physical actuator. Although simulation metrics cannot be transferred directly to the real system, the combined-metric ranking of the controllers remains unchanged. Full article

(This article belongs to the Special Issue Human-Centric Intelligent Actuation Systems: Innovations in Control, Sensing, and Multidisciplinary Applications)

27 pages, 8011 KB

Open AccessArticle

Learning Thermospheric State Evolution: An Adaptive Neural Operator Framework Based on TIE-GCM Simulations

by Shuyang Zhou, Changyong He, Dunyong Zheng and Dongfang Lin

Remote Sens. 2026, 18(18), 3134; https://doi.org/10.3390/rs18183134 - 11 Sep 2026

Abstract

Reliable short-term prediction of thermospheric states is important for satellite drag applications but remains difficult because of nonlinear, multiscale variability. We developed a multivariable Adaptive Fourier Neural Operator (AFNO) surrogate using 24 years (2000–2023) of Thermosphere–Ionosphere Electrodynamics General Circulation Model (TIE-GCM) simulations. The model predicts neutral density, temperature, winds, and geopotential height over 24-h autoregressive forecasts. We compared direct state prediction with increment-based flow prediction and tested logarithmic density scaling and one-hour historical inputs. Both formulations preserved dominant large-scale density structures and the equatorial mass density anomaly, with anomaly correlation coefficients above 0.94 for all variables. The Direct formulation maintained lower errors and greater stability at longer lead times, whereas Flow performed better only at early steps. Logarithmic density scaling produced variable- and altitude-dependent trade-offs, and historical inputs yielded no overall benefit. During a representative geomagnetic storm, the baseline reproduced broad density morphology but increasingly underestimated enhancement magnitude with lead time. Because evaluation used the independent 2023 TIE-GCM test year with prescribed forecast-time forcing and no observational validation, the framework should be interpreted as a TIE-GCM-consistent surrogate rather than a validated predictor of the observed thermosphere. Full article

(This article belongs to the Special Issue Satellite Observations and Numerical Studies for Ionosphere, Plasma Dynamics, and Space Weather Prediction)

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19 pages, 1831 KB

Open AccessArticle

Pairwise Growth Dynamics of Environmental Escherichia coli and Enterococcus Isolates with Contrasting Antibiotic Susceptibility Phenotypes: Influence of Temperature

by Athanasios Tsiartsafis and Foteini F. Parlapani

Antibiotics 2026, 15(9), 897; https://doi.org/10.3390/antibiotics15090897 - 11 Sep 2026

Abstract

Background/Objectives: Environmental bacterial isolates with contrasting, previously established antimicrobial-susceptibility phenotypes may differ in growth under antibiotic-free conditions, but such differences can reflect strain background and temperature. This study characterized the monoculture population development and growth kinetics of predefined pairs of naturally occurring, genetically distinct environmental Escherichia coli, Enterococcus faecalis, and Enterococcus faecium isolates selected from a broader previously characterized collection. Methods: Nine isolates were grown separately in a controlled nutrient-enriched seawater model at 12, 22, and 30 °C, representing low, intermediate, and high water-temperature conditions relevant to the Thermaic Gulf. Growth was described using the Baranyi–Roberts model. Within each predefined strain pair and temperature, population development over time was assessed by two-way mixed-design ANOVA with Greenhouse–Geisser correction and Šídák-adjusted time-point comparisons. Temperature dependence of μ_max was summarized using Ratkowsky and Arrhenius models. Results: Population-development patterns differed between paired isolates in all pair–temperature comparisons except MA60–T640 at 30 °C, but the direction and degree of the differences varied among pairs and sampling times. Fitted μ_max did not always correspond directly to observed population development, as illustrated by SB10–T280. SB10 had a higher μ_max at all three temperatures while remaining at lower absolute population levels during early-to-mid growth. Conclusions: The selected environmental isolate pairs did not show a uniform direction of difference associated with antimicrobial-susceptibility phenotype. Population-development patterns were pair- and time-specific and differed among the tested temperature conditions, while fitted μ_max did not always reflect observed population development. These findings provide a temperature-resolved basis for interpreting the growth behaviour of selected environmental isolates with contrasting antimicrobial-susceptibility phenotypes under controlled nutrient-enriched marine conditions across temperatures relevant to warming shellfish-production environments. Full article

(This article belongs to the Special Issue Monitoring the Dissemination of Antibiotic Resistance in Environments)

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18 pages, 1515 KB

Open AccessArticle

Intelligent Synchronization of Machine Learning Models Using Graph Neural Networks: Application to Flood Prediction

by Boban Temelkovski, Rexhep Mustafovski, Jugoslav Achkoski, Georgi Dimirovski and Mile Stankovski

Future Internet 2026, 18(9), 474; https://doi.org/10.3390/fi18090474 - 11 Sep 2026

Abstract

Flood prediction remains a critical challenge in environmental risk management and disaster preparedness. Accurate river-level forecasting is essential for the development of reliable early warning systems and the mitigation of flood-related risks. However, conventional ensemble approaches, such as averaging and majority voting, often exhibit limited adaptability when individual models respond differently to anomalies or incomplete data. To address this limitation, this study proposes a graph-based synchronization framework that integrates XGBoost and Random Forest models using a Graph Convolutional Network (GCN). The proposed framework represents the outputs of the base prediction models as graph nodes and employs graph message passing to learn context-dependent relationships between their predictions. The framework is evaluated using real-world hydrological observations from the Lepenec River Basin in North Macedonia together with meteorological data obtained from the OpenWeatherMap API. Experimental results demonstrate that the proposed GCN-based synchronization framework outperforms both the standalone prediction models and the previously proposed linear synchronization method, achieving an R² value of 0.91 and a Mean Absolute Error (MAE) of 0.21. The obtained results indicate that graph-based synchronization provides an adaptive approach for integrating heterogeneous machine-learning models and has the potential to support future flood early-warning systems and intelligent environmental monitoring applications. Full article

(This article belongs to the Section Smart System Infrastructure and Applications)

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32 pages, 31502 KB

Open AccessArticle

Study on Nonlinear Driving Mechanisms of Spatiotemporal Evolution in Sanjiang Plain Wetlands Based on Explainable Learning Methods

by Nan Lin, Yanan Lu, Ruifei Zhu, Menghong Wu, Hao Yu, Zeyue Jing, Chenglong Xu, Botao Zhang and Ranzhe Jiang

Remote Sens. 2026, 18(18), 3132; https://doi.org/10.3390/rs18183132 - 11 Sep 2026

Abstract

Against a backdrop of global climate fluctuations and intensifying human activities, wetlands are undergoing severe degradation. Understanding the mechanisms that govern wetland evolution is essential for the sustainable development of wetland ecosystems. However, wetland evolution is highly heterogeneous across space and time, and existing studies have generally paid insufficient attention to nonlinear effects and interactions among driving mechanisms, limiting a comprehensive understanding of its intrinsic processes. We integrated the Light Gradient Boosting Machine model with SHapley Additive exPlanations to explain the nonlinear driving mechanisms of spatiotemporal wetland evolution across the Sanjiang Plain using multitemporal remote sensing data from 1990 to 2023. Wetland dynamics exhibited a stage-dependent pattern characterized by substantial natural-wetland loss in the early period, followed by partial marsh-wetland recovery and continued artificial-wetland expansion. Artificial wetlands expanded continuously, whereas marsh wetlands declined markedly before 2005 and showed partial recovery thereafter. From 1990 to 2005, wetland evolution was mainly controlled by topographic and climatic factors. From 2005 to 2023, the influence of socioeconomic development and proximity to the road network on wetland change intensified. Nonlinear interactions among driving factors shifted from synergistic promotion by natural factors in the early stage to inhibitory effects among natural, socioeconomic, and locational factors in the later stage. This stage-specific change in driving mechanisms was crucial to the shift in dominant drivers of wetland evolution. By characterizing single-factor nonlinear effects and multifactor interactions, this study reveals the stage-specific driving mechanisms of wetland evolution in the Sanjiang Plain and provides scientific support for regional wetland management and remote sensing monitoring. Full article

19 pages, 9697 KB

Open AccessArticle

System-Level Dynamic Modeling and Cross-Domain Disturbance Propagation of an Electricity–Hydrogen–Heat Coupling Subsystem for Integrated Transportation Hubs

by Dengrui Zhu, Xueqin Zhang, Junhao Liang, Guoqiang Gao, Song Xiao, Yujun Guo, Hanbing Yang, Aoxu Feng, Aihong Tang and Guangning Wu

Energies 2026, 19(18), 4313; https://doi.org/10.3390/en19184313 - 11 Sep 2026

Abstract

Integrated transportation hubs are characterized by fast-varying and strongly coupled electricity, hydrogen-refueling, and thermal demands driven by traffic activities. To characterize their short-term dynamic interactions, this paper develops a compact system-level model of a core electricity–hydrogen–heat coupling subsystem comprising a PEM electrolyzer, a hydrogen storage tank, a fuel cell, and a thermal side. Power- and temperature-dependent off-design models are established for the PEM electrolyzer and fuel cell, while a lumped-parameter thermodynamic model with real-gas correction is developed for the hydrogen storage tank. The electrolyzer and fuel-cell models achieve calibration MAPEs of 0.39% and approximately 0.81%, respectively, against published reference data. Two typical disturbance scenarios are then investigated. Under a 30 kW electrical-load step, the grid-power deviation is reduced from a peak of approximately 29.4 kW to about 9.1 kW, while the hydrogen-refueling-demand disturbance produces only a minor thermal-side temperature variation. The results reveal distinct propagation magnitudes and time-scale characteristics across the electrical, hydrogen, and thermal domains. The proposed framework provides a compact and physically interpretable tool for short-term cross-domain dynamic analysis of integrated transportation hubs. Full article

(This article belongs to the Section F: Electrical Engineering)

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25 pages, 2425 KB

Open AccessArticle

From Buildings to the Environment: Flows, Exposure Pathways, and Toxicological Relevance of Tris(2-Chloro-1-Methylethyl) Phosphate (TCPP), Diuron, and Bis(2-(Perfluorohexyl)Ethyl) Phosphate (6:2 diPAP)

by Jolita Kruopienė, Edgaras Stunžėnas, Jenny Fäldt and Riikka K. Vainio

Toxics 2026, 14(9), 811; https://doi.org/10.3390/toxics14090811 - 11 Sep 2026

Abstract

The use of hazardous chemicals in construction materials raises concerns regarding their release into the environment and their potential impact on ecosystems and human health. This study investigated the flows of tris(2-chloro-1-methylethyl) phosphate (TCPP), diuron, and bis(2-(perfluorohexyl)ethyl) phosphate (6:2 diPAP) from buildings to environmental compartments and evaluated their toxicological relevance through environmental occurrence and exposure pathways. A quantitative substance flow analysis was performed for TCPP, while conceptual flow models were developed for diuron and 6:2 diPAP. TCPP flow analysis showed that approximately 70,804 t/year accumulated in products, primarily rigid polyurethane insulation materials, alongside continuous environmental releases into indoor dust, soils, and aquatic systems. It resulted in annual accumulation of nearly 444 t of TCPP in soils and 64 t in aquatic systems. Diuron was identified as a source of aquatic exposure through rainfall-driven wash-off, whereas 6:2 diPAP exhibited indoor and outdoor emission pathways and transformation into persistent perfluoroalkyl carboxylic acids. Field measurements of Interreg NonHazCity3 project confirmed the occurrence of organophosphate esters, biocides, and PFAS in indoor dust, stormwater, and wastewater. The results demonstrate that buildings act as continuous sources of toxicologically relevant substances, pointing to the necessity of upstream source control, the prevention of regrettable chemical substitution, and enhanced transparency in construction materials to mitigate long-term environmental and health risks. Full article

23 pages, 8190 KB

Open AccessArticle

Development of a Scenario-Guided, VR-Ready Ambulance Model for EMT Training Using Reality Capture Methods

by Nándor Bakai, Olivér Rák, Patrik Márk Máder, Dóra Erika Simon, Bálint Bachmann, Tünde Jászberényi, Gergő Szeledi, Miklós Halada, József Etlinger and Márk Balázs Zagorácz

Technologies 2026, 14(9), 578; https://doi.org/10.3390/technologies14090578 - 11 Sep 2026

Abstract

Emergency Medical Services (EMS) personnel require exceptional spatial awareness and rapid decision-making within the confined environment of an ambulance. While Virtual Reality (VR) offers a safe alternative to traditional training, the lack of high-fidelity, regionally accurate, and VR-optimized 3D ambulance models limits its application. This study presents a scenario-driven methodology for developing a VR-ready 3D ambulance environment prototype tailored for Emergency Medical Technician (EMT) training. Utilizing reality-capture techniques, terrestrial laser scanning was performed to accurately document the interior of a standard Hungarian ambulance simulator. The resulting point cloud underwent systematic processing, manual retopology, PBR shading, and the implementation of a custom dual-rigging animation system to optimize complex mechanical movements—such as stretcher operations—for standalone VR platforms. The workflow successfully reduced the vertex count to 25,373 while maintaining millimeter-level spatial fidelity. Technical evaluation confirmed that geometrical, functional, and material objectives were fulfilled, whereas pedagogical implementation remains incomplete. Structural accuracy and animation readiness were verified through preliminary inspection within Blender’s VR viewport inspector. However, interactive game-engine integration remains future work, and educational effectiveness has not yet been tested with EMT learners. Overall, this workflow delivers a 3D asset foundation that establishes the necessary technical basis for subsequent software implementation and clinical evaluation. Full article

(This article belongs to the Section Assistive Technologies)

23 pages, 1947 KB

Open AccessArticle

FQDA-ML: A Hierarchical Machine Learning-Powered Data Quality Framework for Evaluating Agile Sprint Performance: A Case Study in Community Engagement Projects

by Mario Pérez-Cargua, Elizabeth Salazar-Jácome, Javier De la Torre-Guzmán, Félix Chávez-Jácome and Wilson Sánchez-Ocaña

Future Internet 2026, 18(9), 473; https://doi.org/10.3390/fi18090473 - 11 Sep 2026

Abstract

Agile software development continuously generates operational data through sprint execution, task completion, and effort estimation. However, the quality of these data is rarely assessed before they are used for analytics and predictive modeling, particularly in non-industrial settings. Existing data quality approaches primarily focus on industrial big data contexts and provide limited guidance for evaluating agile sprint repositories. This study proposes FQDA-ML, a hierarchical machine learning-powered framework for assessing sprint data quality and analyzing its relationship with delivery performance. The framework adapts Cai and Zhu’s five-dimensional data quality model and operationalizes it through 13 measurable indicators aggregated into the Sprint Data Quality Index (SDQI). The framework was validated using 129 real sprints from 13 software development teams during one academic year, organized into two consecutive academic semesters (2024-S1 and 2024-S2), corresponding to two student cohorts involved in community engagement software projects. The results showed a mean SDQI of 0.603 ± 0.217 and a strong association between SDQI and sprint completion rate (ρ = 0.719. p < 0.001). The predictive evaluation achieved an AUC-ROC of 0.968 under standard five-fold cross-validation and 0.726 under Leave-One-Group-Out validation, highlighting the influence of team-level dependency on model generalization. The findings provide an empirically validated framework for data quality assessment and predictive software analytics in agile environments. Full article

(This article belongs to the Topic Data Intelligence and Computational Analytics)

28 pages, 2430 KB

Open AccessArticle

UPLOAD-HELIX: A High-Helicity Single-Mode Microwave Haloscope with Low-Noise Interferometric Readout for Ultralight Axion Dark Matter

by Robert C. Crew, Emma C. I. Paterson, Maxim Goryachev, Eugene N. Ivanov, Pashupati Dhakal, Tugrul Talha Ersoz, Michael E. Tobar and Jeremy F. Bourhill

Universe 2026, 12(9), 278; https://doi.org/10.3390/universe12090278 - 11 Sep 2026

Abstract

We propose a superconducting single-mode microwave haloscope based on chiral cavity
resonators for the detection of ultralight dark matter axions over the mass range
4 × 10−19– 4 × 10−14 eV. Building on the single-mode chiral-cavity haloscope for detecting
ultra light dark matter (ULDM) axions we develop a resonator geometry compatible with
subtractive manufacturing from high-purity bulk niobium, taking advantage of the substantially
lower surface resistance achievable relative to the additively manufactured Möbius
cavity proposed in the earlier work. An inverse-design framework is then used to maximise
a figure of merit derived to minimise the measurement time required to achieve a fixed
experimental sensitivity. The resulting optimised bulk-niobium design achieves a figure of
merit more than three orders of magnitude larger than the additively manufactured Möbius
benchmark. An experimentally informed microwave interferometric readout model incorporating
measured electronics noise and active suppression of pump amplitude noise is
used to project the sensitivity of the proposed experiment. For an acquisition time of three
months, the haloscope is projected to reach gaγγ < 10−11 GeV−1 across more than four
orders of magnitude in axion mass. The projected sensitivity extends approximately one
order of magnitude below the current exclusion limits set by CAST, providing a practical
pathway towards a high-sensitivity direct search for ultralight dark matter axions. Full article

(This article belongs to the Special Issue Ultralight Bosonic Dark Matter: Theoretical Developments and Experimental Searches)

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