Search Results (47)

This paper proposes a dual-mode CMOS analog front-end (AFE) circuit for short-wave infrared (SWIR) image sensors, which integrates a hybrid readout circuit (ROIC) and a 12-bit two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC dynamically switches between buffered-direct-injection (BDI) and direct-injection (DI) modes, thus balancing injection efficiency against power consumption. While the DI structure offers simplicity and low power, it suffers from unstable biasing and reduced injection efficiency under high background currents. Conversely, the BDI structure enhances injection efficiency and bias stability via an input buffer but incurs higher power consumption. To address this trade-off, a dual-mode injection architecture with mode-switching transistors is implemented. Mode selection is executed in-pixel via a low-leakage transmission gate and coordinated by the column timing controller, enabling low-current pixels to operate in low-noise BDI mode, whereas high-current pixels revert to the low-power DI mode. The TS-SS ADC employs a four-terminal comparator and dynamic reference voltage compensation to mitigate charge leakage and offset, which improves signal-to-noise ratio (SNR) and linearity. The prototype occupies 2.1 mm × 2.88 mm in a 0.18 µm CMOS process and serves a 64 × 64 array. The AFE achieves a dynamic range of 75.58 dB, noise of 249.42 μV, and 81.04 mW power consumption. Full article

This paper presents a simulation-based investigation of passive frequency tunability in frequency-selective surfaces (FSSs) enabled by Moiré pattern interference. By overlapping two identical hexagonal FSS layers and introducing rotational misalignment between them, we demonstrate that the resulting Moiré patterns induce significant shifts in the resonance frequency without any external bias or active components. Using full-wave simulations in HFSS, we show that rotating the second layer from 0° to 30° can shift the resonant frequency from 4.4 GHz down to 1.2 GHz. This tunable behavior emerges solely from geometrical manipulation, offering a low-complexity alternative to active tuning methods that rely on varactors or micro-electromechanical systems (MEMSs). We discuss the theoretical basis for this tuning mechanism based on effective periodicity modulation via rotational interference and highlight potential applications in passive reconfigurable filters and refractive index sensors. The proposed approach provides a promising route for implementing tunable electromagnetic structures without compromising simplicity, power efficiency, or integration compatibility. Full article

Hip flexion range of motion (ROM) during gait is an important surgery outcome for patients undergoing total hip arthroplasty (THA) that could help patient monitoring and rehabilitation. To allow systematic measurements during patients’ clinical pathways, hip ROM measurement should be as simple and cheap as possible to ensure patient and clinician acceptance. Single IMU options can match these requirements and offer measurements both during daily living conditions and standardized clinical tests (e.g., 10 m walk, timed up-and-go). However, single-IMU approaches to measure hip ROM have been limited. Thus, the objective of this study was to explore the accuracy of one IMU in measuring hip ROM during gait and to determine whether a single-IMU approach can provide results comparable to those of multi-IMU systems. To assess this, machine learning models were employed, ranging from the simplest (linear regression) to more complex approaches (artificial neural networks). Eighteen patients undergoing THA and seven controls were measured using a 3D opto-electronic motion capture system and one thigh-mounted IMU. Hip ROM was predicted from thigh ROM using regression and classification models and was compared to the reference hip ROM. Multiple regression was the best-performing model, with limits of agreement (LoA) of ±13° and a systematic bias of 0. Random forest, RNN, GRU and LSTM models yielded LoA ranges > 27.8°, exceeding the threshold of acceptable error. These results showed that one IMU can measure hip ROM with errors comparable to those of two-IMU methods, with potential for improvement. Using multiple linear regression was sufficient and more appropriate than employing complex ANN models. This approach offers simplicity and acceptance to users in clinical settings. Full article

Link prediction in bipartite networks is a challenging task due to their distinct structural characteristics, where edges only exist between nodes of different types. Most existing methods are based on structural similarity, assigning similarity scores to node pairs under the assumption that a higher similarity corresponds to a higher likelihood of connection. Local structural methods, in particular, are widely favored for their simplicity, interpretability, and computational efficiency. However, real-world bipartite networks often exhibit highly heterogeneous node degree distributions, which introduce biases and undermine the effectiveness of traditional local structure-based methods. To address this issue, we propose a novel link prediction framework that explicitly adjusts for the degree heterogeneity of intermediate nodes between unconnected node pairs and incorporates their influence within local connection patterns formed around these pairs. Furthermore, our framework differentiates between the roles of same-type and cross-type nodes by leveraging quadrangle graphs between unconnected nodes. This approach allows for a more nuanced capture of unique properties of bipartite networks and effectively mitigates the inherent degree bias commonly observed in such networks, resulting in considerable improvements in prediction accuracy. Experimental results on ten diverse bipartite networks demonstrate that our framework achieves competitive and robust performance compared to nineteen state-of-the-art link prediction methods. Full article

Swelling pressure is a key geotechnical property that influences the behaviour and stability of engineering structures built on expansive clayey soils. This pressure can be measured directly through laboratory tests or estimated using indirect methods. This paper analyses a dataset of undisturbed clay samples from southeastern Spain using advanced symbolic regression techniques, namely: deep symbolic regression (PhySO), high-performance symbolic regression (PySR), multi-objective symbolic regression (MOSR), and physics-guided symbolic regression (PGSR). These methods provide interpretable results as equations, unlike standard machine learning models. All generated equations showed high performance (R² > 0.91 and MAE < 23 kPa) and simplicity, making them suitable for practical engineering applications. PySR yielded the best overall metrics (R² = 0.933, MAE = 20.49 kPa), particularly excelling in high-pressure ranges, while PhySO demonstrated the most balanced performance, especially for low to medium pressures. MOSR minimized edge-case bias, and PGSR, despite lower overall performance, remained competitive. The plasticity index (PI) was identified as the most influential factor in all models, followed by the percentage of fines. The use of undisturbed samples enhanced the reliability of the findings, and the resulting equations enable a flexible estimation of swelling pressure based on commonly available geotechnical parameters. Full article

Aim: This systematic review evaluates the effectiveness and clinical outcomes of Atraumatic Restorative Treatment (ART) in pediatric dentistry, comparing it with other restorative techniques, analyzing material performance, assessing cost-effectiveness, and exploring the long-term success in managing dental caries. Background: ART is a minimally invasive approach that removes decayed tissue using hand instruments and restores teeth with adhesive materials like glass ionomer cement (GIC). ART is particularly valuable in pediatric dentistry due to its simplicity, reduced discomfort, and suitability for resource-limited settings. It eliminates the need for anesthesia and expensive dental equipment, making it accessible in remote and underserved areas. Studies have shown its effectiveness in providing durable restorations while improving patient comfort. Materials and Methods: This systematic review follows the PRISMA guidelines. PubMed, Web of Science, and Scopus were searched for studies published in the last ten years. The inclusion criteria included in vivo studies on children, randomized controlled trials, and case–control studies assessing ART’s effectiveness. Quality and risk of bias were evaluated using the ROBINS-I tool. Results: Eighteen studies met the inclusion criteria. ART effectively managed dental caries, especially with high-viscosity GIC. Comparisons with the Hall Technique and Papacarie showed that ART remains a viable, cost-effective option. Conclusions: ART is a reliable, minimally invasive technique for pediatric restorative dentistry. Its accessibility and cost-effectiveness make it suitable for low-resource settings. High-quality materials and technique modifications further enhance restoration longevity. Full article

Few-shot relation extraction constitutes a critical task in natural language processing. Its aim is to train a model using a limited number of labeled samples when labeled data are scarce, thereby enabling the model to rapidly learn and accurately identify relationships between entities within textual data. Prototypical networks are extensively utilized for simplicity and efficiency in few-shot relation extraction scenarios. Nevertheless, the prototypical networks derive their prototypes by averaging the feature instances within a given category. In cases where the instance size is limited, the prototype may not represent the true category centroid adequately, consequently diminishing the accuracy of classification. In this paper, we propose an innovative approach for few-shot relation extraction, leveraging instances from the query set to enhance the construction of prototypical networks based on the support set. Then, the weights are dynamically assigned by quantifying the semantic similarity between sentences. It can strengthen the emphasis on critical samples while preventing potential bias in class prototypes, which are computed using the mean value within prototype networks under small-size scenarios. Furthermore, an adaptive fusion module is introduced to integrate prototype and relational information more deeply, resulting in more accurate prototype representations. Extensive experiments have been performed on the widely used FewRel benchmark dataset. The experimental findings demonstrate that our AIRE model surpasses the existing baseline models, especially the accuracy, which can reach 91.53% and 86.36% on the 5-way 1-shot and 10-way 1-shot tasks, respectively. Full article

Objectives: Ultrafiltration (UF) is widely accepted as a method for assessing the plasma protein binding (PPB) of drugs. However, it is vulnerable to non-specific binding (NSB) to the device, which can result in inaccuracies. This study presents a straightforward, high-throughput modified UF method aimed at minimizing bias due to NSB. Methods: The modified UF method, sequential UF, features the addition of a 2 min pre-UF phase designed to saturate the NSB in the device, followed by the main 20 min UF procedure, compared to the conventional UF method. To evaluate the feasibility of this sequential UF method, we measured the PPB of nine compounds using sequential UF and compared these results to those obtained with the conventional mass balance UF method, recognized as a standard for NSB correction. Results: The PPB values determined through sequential UF were generally consistent with those derived from the mass balance UF method. The fold differences ranged from 97.9% to 113.8%, with an average of 103.5%. No significant differences were observed between the two methods for all compounds, with the exception of quercetin, which showed an unusually high PPB. Conclusions: Sequential UF was effective in correcting NSB to the device while providing advantages in terms of simplicity and efficiency. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

Background/Objectives: The rapid development of Large Language Models (LLMs) presents promising applications in healthcare, including patient education. In anesthesia, where patient anxiety is common due to misunderstandings and fears, LLMs could alleviate perioperative anxiety by providing accessible and accurate information. This study explores the potential of LLMs to enhance patient education on anesthetic and perioperative care, addressing time constraints faced by anesthetists. Methods: Three language models—ChatGPT-4, Claude 3, and Gemini—were evaluated using three common patient prompts. To minimize bias, incognito mode was used. Readability was assessed with the Flesch–Kincaid, Flesch Reading Ease, and Coleman–Liau indices. Response quality was rated for clarity, comprehension, and informativeness using the DISCERN score and Likert Scale. Results: Claude 3 required the highest reading level, delivering detailed responses but lacking citations. ChatGPT-4o offered accessible and concise answers but missed key details. Gemini provided reliable and comprehensive information and emphasized professional guidance but lacked citations. According to DISCERN and Likert scores, Gemini had the highest rank for reliability and patient friendliness. Conclusions: This study found that Gemini provided the most reliable information, followed by Claude 3, although no significant differences were observed. All models showed limitations in bias and lacked sufficient citations. While ChatGPT-4o was the most comprehensible, it lacked clinical depth. Further research is needed to balance simplicity with clinical accuracy, explore Artificial Intelligence (AI)–physician collaboration, and assess AI’s impact on patient safety and medical education. Full article

Spermatozoa are highly specialized male cells that are characterized by a unique ability to move, which is a critical factor for successful fertilization. The relative simplicity of motility assessment, especially in livestock, has made it a widely used parameter for evaluating ejaculate quality or cryopreserved semen in the clinical field, and an advanced tool in reproductive physiology and toxicology research. Technological advances in image analysis and computational methods have substantially increased its accuracy through the use of computer-assisted sperm analysis (CASA) to minimize subjective bias in motility assessments. Nevertheless, this more objective method still presents some significant challenges, including variability in the sample preparation, imaging conditions, and analytical parameters. These issues contribute to inconsistency and impair the reproducibility and comparability of data between laboratories. The implementation of standardized protocols, combined with comprehensive training and rigorous evaluation, can serve to mitigate some of the emerging inconsistencies. In addition, the in vitro conditions under which CASA analyses are performed often differ significantly from the natural environment of the female reproductive tract in vivo. This review discusses the methodologies, critical issues, and limitations of sperm motility analyses using CASA, with a particular focus on the boar as an important agricultural and biomedical model species in which this system is widely used. Full article

Neonicotinoids (NEOs), used as insecticides against aphids, whiteflies, lepidopterans, and beetles, have numerous detrimental impacts on human health, including chronic illnesses, cancer, infertility, and birth anomalies. Monitoring the residues in food products is necessary to guarantee public health and ecological balance. The present work validated a new method to measure seven neonicotinoid insecticides (acetamiprid ACT, clothianidin CLT, dinotefuran DNT, imidacloprid IMD, nitenpyram NTP, thiacloprid TCP, and thiamethoxan THT) in wheat. The analytical procedure was based on simple and fast wheat sample cleanup using solid-phase extraction (SPE) to remove interferents and enrich the NEOs, alongside the NEOs’ separation and quantification by reverse-phase chromatography coupled with a diode array detector (DAD). The validation process was validated using the accuracy profile strategy, a straightforward decision tool based on the measure of the total error (bias plus standard deviation) of the method. Our results proved that, in the future, at least 95% of the results obtained with the proposed method would fall within the ±15% acceptance limits. The test’s cost-effectiveness, rapidity, and simplicity suggest its use for determining the levels of acetamiprid, clothianidin, dinotefuran, imidacloprid, nitenpyram, thiacloprid, and thiamethoxam in routine analyses of wheat. Full article

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input–output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on the output pattern probability is used to make a priori predictions regarding the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability–complexity relationships may be a useful tool when studying patterns in dynamical systems. Full article

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data. Full article

Various discrete lifetime distributions have been observed in real data analysis. Numerous discrete models have been derived from a continuous distribution using the survival discretization method, owing to its simplicity and appealing formulation. This study focuses on the discrete analog of the newly generalized Rayleigh distribution. Both classical and Bayesian statistical inferences are performed to evaluate the efficacy of the new discrete model, particularly in terms of relative bias, mean square error, and coverage probability. Additionally, the study explores different important submodels and limiting behavior for the new discrete distribution. Various statistical functions have been examined, including moments, stress–strength, mean residual lifetime, mean past time, and order statistics. Finally, two real data examples are employed to evaluate the new discrete model. Simulations and numerical analyses play a pivotal role in facilitating statistical estimation and data modeling. The study concludes that the discrete generalized Rayleigh distribution presents a notably appealing alternative to other competing discrete distributions. Full article