Search Results (1,395)

The aim of this work is to develop high-precision time-of-flight (TOF) devices based on high-refractive-index solid Cherenkov radiators read out by silicon photomultipliers (SiPMs). Cherenkov light is prompt and, therefore, ideal for reaching the intrinsic timing limits of TOF systems. By utilizing a thin, high-refractive-index radiator, a nearly instantaneous signal is generated by particles exceeding the Cherenkov threshold. In order to achieve the ultimate time resolution, we carried out a rigorous optimization of the radiator material and geometry, alongside the efficiency of the optical coupling to the SiPM sensors. The key factors limiting the time resolution were characterized by comprehensive Monte Carlo simulations, subsequently validated against experimental beam test data. We assembled small-scale prototypes instrumented with various Hamamatsu SiPM array sensors with active areas ranging from 1.3 to 3 mm, coupled with various window materials, such as fused silica and MgF₂, featuring various thickness values. The prototypes were successfully tested in beam test campaigns at the CERN-PS T10 beamline. The data were collected with a complete chain of front-end and readout electronics based on either the Petiroc 2A or the Radioroc 2 interfaced to a picoTDC to measure charges and times. By comparing the time measurements from two SiPM arrays, we were able to measure a time resolution better than 33.2 ps at the full system level, with a charged-particle detection efficiency of 100%. Our results demonstrate the expected performance benchmarks for the charged-particle detection efficiency and time resolution, and they highlight the potential of the developed Cherenkov-based TOF detectors for next-generation particle identification systems. Full article

Differences in gut microbiota composition related to diet have been reported in horses, but characterization of specific microbial taxa remains limited, particularly at the species level. The objective of this study was to use long-read sequencing of the 16S rRNA gene to provide additional taxonomic insight into the intestinal microbiota in horses. Fecal samples were collected from 12 horses on pasture and from 6 of them after switching to a hay diet. Sequencing yielded low read counts per sample, and the analysis failed to detect statistical differences in alpha- and beta-diversity among dietary groups (p > 0.05). Species-level taxonomic resolution was not substantially enhanced using long-read sequencing, as only 3% of reads were assigned at the species level, and an additional 3% of reads were assigned at the genus level. The majority of reads (49%) were classified at the family level. Accordingly, in this dataset, long-read sequencing did not provide additional biological insight into diet-associated differences in the equine gut microbial community. This limited added value can be explained by the low sequencing depth obtained for several samples and the current incompleteness of reference databases for equine bacterial taxa, highlighting ongoing challenges in achieving high-resolution characterization of the equine gut microbiome. Full article

Internet of Things (IoT) nodes continuously acquire, buffer, and transmit sensor data under strict constraints on area, latency, and energy consumption. However, conventional complementary metal–oxide–semiconductor (CMOS)-based memory-access circuits face increasing power loss, parasitic effects, interconnect complexity, and sensitivity to process variations at the nanoscale. To address these limitations, this paper proposes a quantum-dot cellular automata (QCA)-based decoder-driven static random-access memory (SRAM)-access architecture for compact and energy-efficient IoT perception-layer memory. The proposed framework integrates three main components: a majority-logic RAM cell with feedback-based storage and non-destructive readout, a compact 2 × 4 decoder with enable and auxiliary asynchronous set/reset control, and a 1 × 4 SRAM array in which the decoder is embedded to reduce routing and clocking overhead. The circuit layouts were implemented and functionally verified using QCADesigner 2.0.3, while the energy behavior was evaluated using QCADesigner-E. Simulation results confirm correct write/read (W/R) and address-selection behavior. The proposed 2 × 4 decoder achieves 86 QCA cells, 0.08 µm² occupied area, and one clocking unit, reducing cell count, area, and clocking by 48.19%, 50.00%, and 20.00%, respectively, compared with the best selected decoder baseline. The integrated 1 × 4 SRAM array achieves 684 cells and 14 clocking units, improving timing by 30.00% compared with the closest SRAM-array baseline. These results demonstrate that the proposed QCA-based memory-access structure provides a compact and low-overhead solution for energy-constrained IoT communication systems. Full article

Unmanned aerial vehicle (UAV) thermal imagery is a powerful tool for monitoring coastal waters, but achieving high-accuracy temperature retrieval remains a major challenge due to complex, non-linear biases introduced by dynamic environmental conditions. This study presents a novel spatio-temporal machine learning framework to overcome this limitation. The core innovation is a multi-layer perceptron (MLP) model that decouples sensor readings from environmental effects by integrating UAV-retrieved temperatures with crucial contextual features: geographical coordinates (spatial) and tidal status (temporal). Applied to the challenging scenario of a nuclear power plant’s thermal plume, the framework demonstrated exceptional performance. On the test set, the model achieved a coefficient of determination (R²) of 0.998 and a mean absolute error (MAE) of only 0.15 °C, significantly outperforming traditional regression approaches. Ultimately, this method successfully generated high-resolution, high-accuracy sea surface temperature (SST) maps under 24 distinct tidal conditions spanning both summer and winter seasons. This work provides a robust and transferable framework for the quantitative thermal mapping of any complex and dynamic aquatic environment using UAVs. Full article

Introduction: Artificial intelligence (AI)-based chatbots are becoming an increasingly popular source of health information, particularly for common dermatological conditions such as scabies. However, concerns remain about the accuracy, reliability, quality and readability of the information they provide. Objectives: The aim of this study was to evaluate the accuracy, reliability, quality and readability of responses generated by different AI chatbots in answer to patient questions about scabies. Methods: Scabies-related questions were collected from Quora, a publicly accessible question-and-answer platform, and screened for relevance. Following expert review, 20 representative questions were selected. Responses were generated by three large language models: ChatGPT-5.2, DeepSeek and Claude Sonnet 4.5. The outputs were evaluated by expert reviewers using the hallucination rate, modified DISCERN (mDISCERN), Global Quality Score (GQS), Flesch Reading Ease Score (FRES), and an accuracy assessment based on a 5-point Likert scale. Results: In this study, it was found that ChatGPT-5.2 demonstrated the highest information quality (mDISCERN: 33.6 ± 1.8) and readability (FRES: 63.25 ± 11.5). DeepSeek achieved the highest global quality score (GQS: 5.00 ± 0.00) and accuracy score (5.00 ± 0.00). Claude Sonnet 4.5 had lower scores across most metrics. There were significant differences in hallucination rates among the models (p = 0.003), with DeepSeek exhibiting higher rates. Overall, statistically significant differences were observed among the models in terms of quality, readability and accuracy. Conclusions: AI chatbots provide generally informative but variable-quality responses to scabies-related questions. While DeepSeek demonstrated higher accuracy and overall quality, it also showed higher hallucination rates, whereas ChatGPT-5.2 provided more readable and reliable responses. These findings highlight variability across models and the need for cautious use. AI tools should be considered supportive resources rather than substitutes for professional medical advice. Full article

Q-matrix construction is a foundational yet challenging step in cognitive diagnostic assessment (CDA), which is traditionally reliant on labor-intensive and subjective methods like expert judgment and verbal report analysis. This study explores the potential of generative artificial intelligence (GenAI) to optimize this critical process within the domain of EFL reading. By applying three GenAI models (DeepSeek-V3.2, Kimi 2.5, and Doubao 2.0), three purely GenAI-informed Q-matrices (Qmat-DS, Qmat-K, and Qmat-DB) were generated, and through expert revision, a human–AI collaborative Q-matrix (Qmat-DS-H) was obtained. These were compared with an expert-constructed Q-matrix (Qmat-E) and a student-derived Q-matrix (Qmat-S). Using a simulated dataset (N = 1000) and empirical response data from 1083 EFL learners on a diagnostic reading test, the psychometric performance of the six Q-matrices was estimated via the G-DINA model, ACDM model, and RRUM model. Results demonstrated that the human–AI collaborative Q-matrix consistently outperformed the other five Q-matrices, achieving the best absolute model-data fit, the highest classification accuracy, the most stable item parameters, and the most balanced attribute correlation structure. The purely GenAI-informed Q-matrices showed mixed results: there were some improvements in relative fit and slip stability compared to manually constructed Q-matrices, but variable absolute fit and attribute correlation patterns. The findings substantiate GenAI as a feasible pathway for enhancing the efficiency, consistency, and psychometric quality of Q-matrix construction. This study offers a preliminary framework for advancing CDA development, addressing a key methodological bottleneck in language assessment. Full article

Single-species hedges can help mitigate a range of urban and climate change-related issues, such as slowing stormwater flow and reducing rainfall runoff, particularly during the growing season. There is, however, little information on the service delivery of mixed hedges and their comparison to single-species, year-round, as well as on the practicality of functional rather than ornamental plant mixing. Here, we report on an initial case study to address this. Chosen hedge taxa (Crataegus monogyna, Elaeagnus × submacrophylla ‘Gilt Edge’, Ligustrum ovalifolium, Thuja plicata ‘Atrovirens’) represented a range of plant characteristics. These were trialled outdoors in Reading (SE England, UK) as treatment groupings of either single-species or mixed-species (‘evergreen’ and ‘broadleaf’ mix), along with a bare soil control, in 110 L troughs. We applied 5 min simulated rainfall onto each treatment twice in every meteorological season and assessed canopy throughfall. We also monitored substrate moisture content change as a proxy for evapotranspiration and substrate storage capacity of subsequent rainfall. During summer, the deciduous taxa and mixed hedges had the highest evapotranspiration rates, suggesting their potential to influence soil water storage, but in our experimental setup, that did not translate into significant differences in substrate moisture between treatments. During autumn and winter, the single-species Thuja treatment had the highest rainfall interception rate, followed by both mixed species treatments. In winter, canopy and leaf characteristics rather than physiological activity correlated with increased rainfall attenuation. However, by the end of the experiment (spring 2023), Crataegus, Thuja and both mixed hedge treatments had significantly lower throughfall (higher interception) compared to bare soil. We are continuing to test these treatments in a longer-term field experiment. Management of mixed-species hedges for rainfall attenuation is practically achievable, despite some differences in individual species’ growth rates and plant habits. Full article

The purpose of this research is to determine which factors contribute to extending the read range of transponders equipped with different coupling-circuit topologies operating within selected RFID frequency bands. The analysis covered transponders that varied in both the configuration of their coupling circuits and their geometric dimensions. To accomplish this, transponder models were created using the EMCoS Studio electromagnetic simulation environment. Each model was subjected to simulations that yielded the mutual inductance and the voltage induced at the chip terminals. This study examines how the impedance of the embroidered antenna, the impedance of the chip’s coupling circuit, and the magnetic flux density affect the resulting chip voltage. In several of the investigated configurations, the peak chip voltage appeared outside the frequency range normally associated with RFID systems. The frequency at which this maximum occurred was dependent on the mutual inductance value. Understanding how individual parameters influence mutual inductance makes it possible to shift the voltage peak into a target operating band. Numerical simulation results, combined with the transponder’s mathematical model, enabled the calculation of the mutual inductance and the terminal voltage—quantities that directly determine the achievable read range. This study focuses on factors such as the resonant frequencies of the antenna and coupling circuit, their impedances, and the characteristics of the magnetic field. The findings show that tuning these parameters can affect not only the location of the voltage maximum, but also its amplitude. This effect introduces additional complexity in designing and selecting suitable transponder configurations. Full article

A collinear holographic data storage system stores two-dimensional information in the three-dimensional spatial domain of the medium, offering features such as high speed, high density, and long lifespan, making it a promising technology for the future of data storage. However, a collinear holographic data storage system is limited by the alignment error of the optical system and is also sensitive to environmental noise and external interference, which increases the reading error. When recording and reading holographic storage materials, synchronous marks are used for positioning to correct data misalignment. Therefore, optimizing synchronous mark design of data pages is crucial for improving storage stability and reading accuracy. In this paper, we propose a star-shaped synchronous mark to replace the square-shaped synchronous mark, which improves the holographic grating coupling efficiency. Experimental results show that this method enhances reconstruction strength and reduces reading errors caused by external factors. The star-shaped synchronous mark achieves a better spectral match with the reference pattern, yielding a stronger diffracted signal. Experimental results show that this method reduces the bit error rate by approximately 25% compared to square-shaped synchronous marks under displacement multiplexing. Full article

Background: Nanopore sequencing produces ionic current signals that are sensitive to chemical modifications in DNA and RNA molecules. However, accurate modification detection remains challenging due to limited labeled data and variability across experimental conditions. Methods: We present a scalable unsupervised framework for modification discovery that learns reference signal distributions from unmodified sequences using a CNN–Transformer variational autoencoder (VAE). The model is trained on large-scale data via streaming sampling and k-mer-aware soft balancing to ensure robust signal representation. At inference, candidate nucleotides are scored using the VAE reconstruction error, and read-level signals are aggregated to produce site-level modification evidence. Results: On controlled DNA oligonucleotide datasets, models trained on unmodified sequences achieve strong discrimination when evaluated on modified oligos. In contrast, performance decreases in cell line samples when models trained on unmodified whole-genome-amplified (WGA) DNA and in vitro-transcribed (IVT) RNA are evaluated on natively modified (5mC/m6A) data, reflecting the impacts of biological noise and heterogeneity. Despite reduced classification accuracy, site-level anomaly score profiles exhibit peak-like patterns that correspond to known modification-enriched regions. Conclusions: These findings demonstrate the feasibility of large-scale unsupervised reference modeling for de novo modification detection, while underscoring the challenges in translating models built from synthetic oligo datasets into robust genome-wide modification detection. Full article

This study presents a systematic empirical evaluation of monolithic and distributed database architectures deployed in Kubernetes environments. As containerized and cloud-native infrastructures become increasingly prevalent, understanding the performance implications of running stateful data systems under orchestration platforms has become critical. We evaluate five widely used database systems—PostgreSQL, MySQL, MongoDB, Redis, and Cassandra—using standardized workload generation frameworks, including pgbench, sysbench, YCSB, redis-benchmark, and cassandra-stress. Controlled experiments were conducted across varying concurrency levels and workload types to measure throughput, latency, and scalability in both single-node and distributed deployments. Redis achieves a maximum throughput of 4.2 million operations per second with sub-millisecond latency. In contrast, Cassandra delivers 214,743 distributed read operations per second at ONE consistency, approaching Redis’s non-pipelined baseline throughput (257,732–262,467 ops/sec) within a Kubernetes cluster. The write throughput of Cassandra decreases by 45.2% when the consistency level is elevated to QUORUM, accompanied by an elevenfold increase in run-to-run variability (CV from 7.1% to 84.7%), indicating that the consistency level is the primary performance determinant in distributed systems. PostgreSQL experiences a 72% decrease in write throughput in Kubernetes (74,072 → 20,805 TPS). In contrast, MySQL PXC anomalously attains a 37.3% increase in write throughput in Kubernetes compared to its monolithic deployment—the sole reversal noted among the five systems. These findings underscore a critical trade-off between vertical efficiency and horizontal scalability, illustrating that hybrid database architecture can be an effective solution for contemporary cloud-native applications compared to either paradigm independently. Full article

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) offer a promising pathway for restoring communication. Decoding tonal languages like Mandarin from EEG remains challenging due to homophones and complex temporal dynamics. This study investigates the decoding of six high-frequency Mandarin action verbs—Chi (eat), He (drink), Chuan (wear), Na (take), Kan (look), and Dai (put on)—from EEG signals. We designed a visual-cue-based overt speech production experiment and collected EEG data from 30 participants during visually guided verb reading aloud. A recurrent neural network framework incorporating dual Long Short-Term Memory (LSTM) layers was implemented to model the long-range temporal dependencies in EEG patterns. The proposed model was compared against a traditional Common Spatial Pattern combined with Support Vector Machine (CSP-SVM) baseline. Our LSTM-based model achieved an average classification accuracy of 69.93% ± 3.07% for the six-class task, significantly outperforming the CSP-SVM baseline (36.53% ± 3.17%). Accuracy exceeded 75% under specific training conditions, including more than 15 training repetitions and a training-data proportion of 38%. Furthermore, the model attained this performance level utilizing approximately 38% of the available trial data for training, demonstrating data efficiency. The results indicate that the LSTM architecture can effectively capture the neural signatures associated with Mandarin verb processing, providing a foundation for developing practical EEG-based assistive communication technologies. The inference latency of the trained model, quantified as the post-training per-trial testing time, was under 2 s, supporting near-real-time applications. Full article

With the continuous growth of data-intensive applications and artificial intelligence workloads, traditional dynamic random access memory (DRAM) is increasingly struggling to meet demands in terms of capacity scale, energy consumption constraints, and data retention after power failure. Consequently, non-volatile memory (NVM) has emerged as a crucial technology for bridging the gap between the memory and storage layers. However, due to inherent differences in write life, read–write performance variations, and consistency guarantee after failure, the systematic application of NVM still faces a series of challenges. Addressing these issues, this paper takes as its starting point the adaptation of medium characteristics and system design, and summarizes the research progress in aspects such as write optimization, consistency and security coordination mechanisms, data structure modification under hybrid memory architecture, and cross-layer resource collaboration. It also conducts an in-depth analysis of representative solutions and evaluation methods. The review results show that current research has shifted from improving a single performance bottleneck to multi-mechanism collaborative optimization. Various technical approaches have proven complementary in alleviating write amplification, enhancing persistence efficiency, and optimizing access patterns. This paper demonstrates that achieving stable and scalable application of NVM requires establishing a more systematic collaborative design concept between durability, security, and performance. As AI training workloads and big data analytics place increasing demands on memory bandwidth and persistence, the techniques surveyed here provide a foundational basis for next-generation memory-centric computing infrastructures. Full article

In this work, the design and experimental validation of passive UHF RFID tag antennas are presented with the objective of evaluating the impact of chip placement and miniaturization approaches on tag performance. Four initial antenna layouts were developed by varying the position of the RFID integrated circuit within a coupling loop. The results show that chip placement directly affects the coupling-loop efficiency, the antenna–chip matching condition, and the practical tolerance of the structure to fabrication-related variations. Simulations and measurements identified Antenna 1 as the best-performing reference configuration, exhibiting the most favorable impedance behavior around 866 MHz and a measured power sensitivity of $-16.3$ dBm. Based on this reference design, a miniaturized version (Antenna 5) was obtained by integrating meander lines and capacitive end-loading, reducing the physical size while maintaining resonance at 866 MHz. Both structures were fabricated and evaluated using a Voyantic Tagformance measurement system, with read-range measurements performed under free-space conditions and in proximity to dielectric and conductive materials. The results demonstrate a maximum read range of $8.6$ m for Antenna 1 in free space, while Antenna 5 preserved a read range of $6.3$ m. In the presence of copper, Antenna 1 maintained a read range of 3 m, whereas Antenna 5 achieved approximately $0.5$ m, highlighting the trade-off between miniaturization and robustness under conductive loading. Full article

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article