Search Results (3,543)

In many monitoring scenarios, repeated and operator-independent assessments are needed. Wearable ultrasound technology has the potential to continuously provide the vital information traditionally obtained from conventional ultrasound scanners, such as in fetal monitoring for high-risk pregnancies. This work is an engineering study motivated by that setting. A 144-element annular capacitive micromachined ultrasonic transducer (CMUT) is hereby proposed for 3-D ultrasound imaging. The array is characterized by its compact size and cost-effectiveness, with a geometry and low-voltage operation that make it a candidate for future wearable integration. To enhance the imaging performance, we propose the utilization of a Fermat’s spiral virtual source (VS) pattern for diverging wave transmission and conduct a performance comparison with other VS patterns and standard techniques, such as focused and plane waves. To facilitate this analysis, a simplified and versatile simulation framework, enhanced by GPU acceleration, has been developed. The validation of the simulation framework aligned closely with expected values (0.002 ≤ MAE ≤ 0.089). VSs following a Fermat’s spiral led to a balanced outcome across metrics, outperforming focused wave transmissions for this specific aperture. The proposed transducer presents imaging limitations that could be improved in future developments, but it establishes a foundational framework for the design and fabrication of cost-effective, compact 2-D transducers suitable for 3-D ultrasound imaging, with potential for future integration into wearable devices. Full article

Batteries’ state-of-health (SOH) estimation has attracted appealing attention in energy industrial systems. In conventional data-driven methods, the lack of target data and different source data can also lead to poor model training effect. To tackle this problem, this paper combines the instance-based transfer (ITL) and interpretable self-attention mechanism (SAM) to integrate the fitting ability of long short-term memory (LSTM), which can improve the SOH estimation performance. ITL re-weights the temporal instance of a training set to give more impact of target-like data, which can relax the independent and identical distribution (IID) assumption. SAM method can enhance the estimation performance by re-weighting the spatial features, and be interpreted by detailed visualization. During the model training, the pre-trained multi-layer LSTM model is fine-tuned by target data to make full use of target information. The proposed method has outperformed other compared algorithms in transfer tasks, and has tested in real-world cross-domain conditions datasets. Full article

Background/Objectives: To evaluate the modality-specific impact of cataracts on the detection of hydroxychloroquine retinopathy. Methods: In this retrospective cohort study, 202 eyes (101 patients) with confirmed HCQ retinopathy were included; analyses focused on 141 cataractous eyes from 72 patients. At each visit, the severity of cataracts in 141 eyes was graded using the Lens Opacities Classification System III (LOCS III), with clinically significant cataracts defined as a LOCS III grade ≥ 3. Screening was performed using swept source optical coherence tomography (OCT), ultrawide field fundus autofluorescence (FAF), and Humphrey visual field (HVF) tests. The detection rates of abnormalities on OCT, FAF, and HVF were compared between minimal (at the time of diagnosis or after cataract surgery) and maximal cataract severity as well as between eyes with clinically significant cataracts and others. Multivariate logistic regression was performed to identify the factors associated with the detection of retinopathy-associated abnormalities across each screening modality. Results: Of the 141 eyes with cataracts, 52 (36.9%) developed clinically significant opacities during the monitoring period, and 23 (16.3%) underwent cataract surgery. OCT detected ellipsoid zone disruptions in 100% of cataractous eyes, while visual fields revealed characteristic paracentral scotomas with comparable sensitivity regardless of cataract severity. In contrast, FAF sensitivity was significantly lower in eyes with clinically significant cataracts (61.5%) compared to those with mild cataracts (92.1%, p < 0.001). Sensitivities were also reduced at maximal versus minimal severity in eyes with clinically significant cortical opacities and nuclear opalescence (both p < 0.05). Multivariate analysis demonstrated that higher cortical opacity (odds ratio [OR] 0.43 per grade increase, 95% CI 0.22–0.85) and nuclear opalescence (OR 0.21, 95% CI 0.07–0.66) independently decreased FAF detection, whereas greater retinopathy severity was positively associated with detection on both FAF (OR 4.85, 95% CI 1.40–16.9) and HVF (OR 3.37, 95% CI 1.17–9.71). Conclusions: Cataracts impaired the FAF-based detection of hydroxychloroquine retinopathy, while OCT and HVF remained reliable despite significant lens opacities. Therefore, clinicians should consider cataract severity when interpreting FAF results and prioritize OCT and HVF assessments in patients with clinically significant cataracts. Full article

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article

This article provides a review of modern technologies for processing chromite ores and beneficiation wastes, with a focus on the recovery of magnesium and platinum-group metals (PGMs). It reveals that the traditional use of chromites solely as a source of chromium limits the potential of this raw material, whereas comprehensive processing enables the recovery of associated components, including serpentine minerals, which are widely present in chromite ores and tailings. Pyrometallurgical, hydrometallurgical, plasma-arc, and biotechnological methods are examined, as well as their integration into combined flowsheets. Particular attention is given to sulfation, chloridization, and carbochlorination processes, which ensure a high degree of PGM recovery. Economic and environmental aspects of comprehensive processing are discussed, including carbon footprint reduction, waste minimization, and prospects for the development of “green metallurgy.” It is concluded that the further advancement of resource-efficient and environmentally safe technologies for chromite processing will increase production efficiency, ensure resource independence, and support compliance with global carbon neutrality requirements. Full article

This study introduces a modular, behaviorally curated malware dataset suite consisting of eight independent sets, each specifically designed to represent a single malware class: Trojan, Mirai (botnet), ransomware, rootkit, worm, spyware, keylogger, and virus. In contrast to earlier approaches that aggregate all malware into large, monolithic collections, this work emphasizes the selection of features unique to each malware type. Feature selection was guided by established domain knowledge and detailed behavioral telemetry obtained through sandbox execution and a subsequent report analysis on the AnyRun platform. The datasets were compiled from two primary sources: (i) the AnyRun platform, which hosts more than two million samples and provides controlled, instrumented sandbox execution for malware, and (ii) publicly available GitHub repositories. To ensure data integrity and prevent cross-contamination of behavioral logs, each sample was executed in complete isolation, allowing for the precise capture of both static attributes and dynamic runtime behavior. Feature construction was informed by operational signatures characteristic of each malware category, ensuring that the datasets accurately represent the tactics, techniques, and procedures distinguishing one class from another. This targeted design enabled the identification of subtle but significant behavioral markers that are frequently overlooked in aggregated datasets. Each dataset was balanced to include benign, suspicious, and malicious samples, thereby supporting the training and evaluation of machine learning models while minimizing bias from disproportionate class representation. Across the full suite, 10,000 samples and 171 carefully curated features were included. This constitutes one of the first dataset collections intentionally developed to capture the behavioral diversity of multiple malware categories within the context of Internet of Things (IoT) security, representing a deliberate effort to bridge the gap between generalized malware corpora and class-specific behavioral modeling. Full article

Event cameras provide microsecond temporal resolution, high dynamic range, and low latency by asynchronously capturing per-pixel luminance changes, thereby introducing a novel sensing paradigm. These advantages render them well-suited for high-speed applications such as autonomous vehicles and dynamic environments. Nevertheless, the sparsity of event data and the absence of dense annotations are significant obstacles to supervised learning for motion segmentation from event streams. Domain adaptation is also challenging due to the considerable domain shift in intensity images. To address these challenges, we propose a two-phase cross-modality adaptation framework that translates motion segmentation knowledge from labeled RGB-flow data to unlabeled event streams. A dual-branch encoder extracts modality-specific motion and appearance features from RGB and optical flow in the source domain. Using reconstruction networks, event voxel grids are converted into pseudo-image and pseudo-flow modalities in the target domain. These modalities are subsequently re-encoded using frozen RGB-trained encoders. Multi-level consistency losses are implemented on features, predictions, and outputs to enforce domain alignment. Our design enables the model to acquire domain-invariant, semantically rich features through the use of shallow architectures, thereby reducing training costs and facilitating real-time inference with a lightweight prediction path. The proposed architecture, alongside the utilized hybrid loss function, effectively bridges the domain and modality gap. We evaluate our method on two challenging benchmarks: EVIMO2, which incorporates real-world dynamics, high-speed motion, illumination variation, and multiple independently moving objects; and MOD++, which features complex object dynamics, collisions, and dense 1kHz supervision in synthetic scenes. The proposed UDA framework achieves 83.1% and 79.4% accuracy on EVIMO2 and MOD++, respectively, outperforming existing state-of-the-art approaches, such as EV-Transfer and SHOT, by up to 3.6%. Additionally, it is lighter and faster and also delivers enhanced mIoU and F1 Score. Full article

Although associative interference has traditionally been attributed to retrieval competition, emerging evidence suggests that interference may also arise from encoding-based representational processes. The present study examined whether retroactive interference (RI) and proactive interference (PI) can occur in the absence of explicit retrieval competition and whether they reflect distinct underlying mechanisms. Participants studied two lists of word–picture pairs in an AB/AC associative learning paradigm, followed by a non-competitive two-alternative forced-choice (2AFC) associative recognition test and a source memory task. Across both frequentist and Bayesian analyses, recognition accuracy revealed a significant RI effect—lower accuracy for earlier A-B pairs relative to non-overlapping controls—whereas PI manifested as longer reaction times (RTs) for later A-C pairs, despite comparable accuracy. Source judgments showed faster correct responses for overlapping than for non-overlapping pairs, suggesting that cue overlap facilitated more fluent retrieval rather than confusion. These findings indicate that interference can emerge independently of retrieval competition and that RI and PI are supported by dissociable mechanisms: RI reflects encoding-related reorganization that weakens earlier associations, whereas PI reflects increased retrieval effort following differentiation of overlapping traces. Together, the results support a process-interaction framework in which encoding-based reactivation and reorganization shape later retrieval dynamics, demonstrating that associative interference arises from the interplay between encoding and retrieval processes rather than retrieval competition alone. Full article

Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

Background/Objectives: Artificial intelligence (AI) is gaining importance in clinical pharmacology, supporting therapeutic decisions and the prediction of drug interactions, although its applications have significant limitations. The aim of the study was to evaluate the accuracy of the responses of four large language models (LLMs), namely ChatGPT-4o, ChatGPT-3.5, Gemini Advanced 2.0, and DeepSeek, in the field of clinical pharmacology and drug interactions, as well as to analyze the impact of prompting and questions from the National Specialization Examination for Pharmacists (PESF) on the results. Methods: In the analysis, three datasets were used: 20 case reports of successful pharmacotherapy, 20 reports of drug–drug interactions, and 240 test questions from the PESF (spring 2018 and autumn 2019 sessions). The responses generated by the models were compared with source data and the official examination key and were independently evaluated by clinical-pharmacotherapy experts. Additionally, the impact of prompting techniques was analyzed by expanding the content of the queries with detailed clinical and organizational elements to assess their influence on the accuracy of the obtained recommendations. Results: The analysis revealed differences in the accuracy of responses between the examined AI tools (p < 0.001), with ChatGPT-4o achieving the highest effectiveness and Gemini Advanced 2.0 the lowest. Responses generated by Gemini were more often imprecise and less consistent, which was reflected in their significantly lower level of substantive accuracy (p < 0.001). The analysis of more precisely formulated questions demonstrated a significant main effect of the AI tool (p < 0.001), with Gemini Advanced 2.0 performing significantly worse than all other models (p < 0.001). An additional analysis comparing responses to simple and extended questions, which incorporated additional clinical factors and the mode of source presentation, did not reveal significant differences either between AI tools or within individual models (p = 0.34). In the area of drug interactions, it was also shown that ChatGPT-4o achieved a higher level of response accuracy compared with the other tools (p < 0.001). Regarding the PESF exam questions, all models achieved similar results, ranging between 83 and 86% correct answers, and the differences between them were not statistically significant (p = 0.67). Conclusions: AI models demonstrate potential in the analysis of clinical pharmacology; however, their limitations require further refinement and cautious application in practice. Full article

Objectives: To establish normative data for anterior segment parameters in healthy pediatric and adult populations using swept-source optical coherence tomography (SS-OCT), and to evaluate the influence of age and sex on these parameters. Methods: This retrospective study included the right eyes of 390 healthy participants. Subjects were divided into three age groups: Group 1 (6–17 years, n = 97), Group 2 (18–45 years, n = 144), and Group 3 (46–77 years, n = 149). All patients were categorized according to their biological sex as female and male. Exclusion criteria were corneal pathology, prior intraocular/refractive surgery, recent contact lens use, severe dry eye, ectatic disorders, low-quality imaging, and refractive error of ±2.0 D or greater. Measurements of anterior and posterior keratometry, total corneal power (TCP), central corneal thickness (CCT), thinnest corneal thickness (TCT), pupil diameter (PD), lens thickness (LT), and white-to-white distance (WTW) were obtained using the Anterion^® SS-OCT system. Data were analyzed using SPSS software. Results: Group 1 demonstrated the highest PD and CCT values, whereas LT was lowest. In adults, LT increased with age and was significantly higher in males older than 45 years. Keratometric analysis revealed greater anterior and total steep astigmatism in the pediatric group, independent of sex. Adult females had significantly higher anterior and posterior keratometry values compared with males. In the pediatric cohort, females exhibited greater CCT, while WTW varied with age. PD decreased with age, whereas LT increased. Conclusions: Anterior segment parameters measured with SS-OCT show significant variations across different age groups and between sexes. Normative data, particularly for pediatric and adult populations, may serve as valuable reference values in keratorefractive surgical planning and corneal pathology assessment. Future studies with larger cohorts, especially in pediatric populations, are warranted. Full article

This paper presents the experimental implementation and validation of the two-chamber method presented in Part I for the high-precision determination of the temperature coefficient of resistance (TCR) of current shunts. The two-chamber approach enables improved thermal isolation and independent temperature control of the reference and test shunts, which significantly reduces the measurement uncertainty. In this part, the complete experimental setup is described, including the thermoelectric temperature control, the current generation and the data acquisition system with synchronized high-resolution digital multimeters (DMMs). The experimental measurements were carried out for different resistance ratios ranging from 0.1 to 10. The results confirm the theoretical predictions and the uncertainty analysis from Part I. The influences of the stability of the current source, the temperature uniformity and the synchronization accuracy on the measurement results are evaluated. The two-chamber method shows high repeatability, ease of use and suitability for laboratory and interlaboratory tests, and thus represents a robust alternative to classical TCR determination methods. Full article

Sporothrix schenckii is a pathogenic fungus with both clinical and environmental origins that was traditionally described as a single species but is increasingly recognized as being genetically diverse. In this study, we analyzed multiple isolates recovered from human sporotrichosis cases and environmental sources across Latin America (Mexico, Guatemala, Colombia). We conducted a polyphasic analysis of 16 isolates, integrating morphological data with multilocus sequence analysis (MLSA) targeting the internal transcribed spacer (ITS), calmodulin (CAL), β-tubulin (BT2), and translation elongation factor 1-α (TEF) gene regions. Phylogenetic relationships were resolved via maximum likelihood, and genetic structure was corroborated via four independent clustering methods: minimum spanning tree, principal component analysis, multidimensional scaling, and self-organizing maps. MLSA reidentified six isolates as S. globosa and confirmed the absence of S. brasiliensis in the cohort. The remaining S. schenckii s. str. isolates were resolved into three clades (A, B, and C). Notably, clade B (EH748, EH194, and EH257) formed a genetically divergent cluster with the highest nucleotide diversity (π = 0.03556) and was consistently segregated by all clustering algorithms. Clinical and environmental isolates were phylogenetically intermingled, supporting an active environmental reservoir for human infections. Phenotypic data, including colony size and conidial and yeast dimensions, varied but did not clearly distinguish between clinical and environmental origins. Our study provides compelling molecular evidence for a previously unrecognized, highly divergent clade within S. schenckii s. str., indicative of ongoing cryptic speciation. These findings refine the taxonomy of medically important Sporothrix species and reveal a distinct epidemiological profile for sporotrichosis in the studied regions, separate from the S. brasiliensis-driven epizootic. This highlights the critical role of molecular surveillance for accurate diagnosis, treatment, and public health strategies. Full article

The relationship between climate and independent glacier masses is now understood, but what is not understood is how ice sheets respond during times of rapid climate change. At its maximum extent the southern Laurentide Ice Sheet (LIS) was sourced from two domes that terminated in multiple lobes across central North America. The extent and timing of the eastern lobes, which were sourced from the Labrador Dome are relatively well constrained. Although the extent of the lobes sourced from the western Keewatin Dome is better understood, there is little chronologic data on them. Twenty-six radiocarbon ages recovered from within the drift of the James Lobe from South Dakota are used to reconstruct the timing of late-glacial fluctuations of the James Lobe. Lithologic logs from 21 South Dakota counties were analyzed and provide stratigraphic context for the radiocarbon ages. Analysis of the stratigraphy reveals two distinct glacial till units with a distinct, widespread layer of silt between them. The silt is interpreted here as evidence for interstadial conditions between two separate advances of the James Lobe. Radiocarbon ages of organics from this silt layer and from within the uppermost oxidized till indicate that interstadial conditions persisted from ~15.8 to 13.7 ka, followed by an advance of the James Lobe of at least 230 km to its maximum position at the Missouri River. Comparison to other locations in Wisconsin, northern lower Michigan, and western New York reveals a similar period of interstadial conditions followed by ice margin advance. We correlate this advance across ~1000 km and suggest that the simplest explanation is reduced summer ablation caused by widespread climatic cooling. Full article