Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Event Effects in Modern COTS DC-DC Buck Converter ICs and Their Influence on the Safe Operating Area

The increase of lower class satellite missions and the reduction of space launch costs increase the need for performant and affordable electronics with a short lead time. This leads to the increasing demand for Commercial Off-The-Shelf (COTS) component usage in Space. Especially the selection of DC-DC buck converters is highly important considering their sensitivity to Destructive Single Event Effects which can lead to the loss of the regulated load or even a short on the power bus. In this paper, multiple modern COTS DC-DC buck converter ICs with integrated power MOSEFTs are tested against SEEs and their usability in Space is evaluated. Devices from various manufacturers and with different maximum input voltages were chosen. First promising results were gathered and therefore the derating capability as well as the overall SEE sensitivity is evaluated for these given COTS buck converters in Space. SEE testing showed not only DSEEs but also overvoltage conditions at the load as well as multiple Single Event Functional Interrupts and for some devices Single Event Latch-ups, all of which need consideration depending on their application in Space.
Multimodal Learning with Incompleteness towards Multimodal Sentiment Analysis and Emotion Recognition Task

Multimodal machine learning tasks have gained significant popularity and demonstrated promising results in mul-timodal data analysis. However, existing multimodal approaches primarily focus on scenarios where all modalities have complete data, neglecting the challenges posed by missing data. In real-world settings, certain modalities may lack parallel sequences due to noise during data collection and preprocessing, leading to random missingness in each modality. In this paper, we propose an integrated model MM-Align+ that leverages information from three input modalities: audio, visual, and text, to address two important multimodal tasks: sentiment analysis and emotion recognition under missing data with varying rates. We conduct extensive experiments on three public datasets, namely MOSEI, MOSI, and MELD. The preliminary results demonstrate the promising performance of the MM-Align+ model and suggest potential for further improvements and novel insights.
XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (Vi$T$). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile Vi$T$ is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs.
Demystifying Template-Based Invariant Generation for Bit-Vector Programs

The template-based approach to invariant generation is a parametric and relatively complete methodology for inferring loop invariants. The relative completeness ensures the generated invariants' accuracy up to the template's form and the inductive condition. However, there has been limited in advancing the approach to bit-precise reasoning, which involves modeling integers using bit-vector arithmetic. This is unfortunate because bit-precise reasoning is crucial for faithfully and accurately modeling machine integer semantics and, thus, for ensuring sound and precise program verification. In this experience paper, we present an experimental study of bit-precise, template-based invariant generation on three fronts: the precision of different invariant templates, the performance of different constraint solvers for solving the constraints, and the effectiveness of the template-based approach compared to existing bit-precise verification techniques. Through an extensive experimental evaluation over a wide range of benchmarks, we find that (1) the choices of invariant templates and constraint solvers have varying degrees of impact on the precision and efficiency of invariant generation; (2) the template-based approach can handle benchmarks that other approaches for bit-vectors cannot handle. The results also reveal several guidelines for advancing future research on template-based invariant generation.
Automated system for simultaneous characterization of acoustic and photoacoustic thermal properties of materials

Understanding the temperature dependence of acoustic and photoacoustic (PA) properties is important for the characterization of materials and measurements in various applications. Ultrasound methods have been developed to estimate these properties, but they require careful consideration of multiple variables and steps to obtain reliable results. This study aimed to develop an automated system for simultaneous characterization of acoustic and PA properties of materials. The system was designed to minimize operator errors, ensuring robust temperature control and reproducibility for acoustic measurements. This was made possible through the integration of a commercially available PA imaging system with a custom-built platform specifically tailored for ultrasound-based acoustic characterization. This platform consisted of both hardware and software modules. The system was evaluated with NaCl solutions at different concentrations and a gelatin/agar cubic phantom prepared with uniformly distributed magnetic nanoparticles serving as optical absorbers. Results obtained from the NaCl solution samples exhibited a high Lin's concordance coefficient (above 0.9) with previously reported studies. In the ultrasound/PA experiment, temperature dependences of the speed of sound and PA intensity revealed a strong Pearson's correlation coefficient (0.99), with both measurements exhibiting a monotonic increase as anticipated for water-based materials. These findings demonstrate the accuracy and stability of the developed system for acoustic property measurements.
Palaeomagnetic secular variation studies on Holocene lake sediments

Cores of sediment were collected from six lakes in southern and eastern Finland. Measurements of the natural remanent magnetization (NRM) and the magnetic susceptibility were made on both the unopened cores and on subsamples in order to define secular variation curves for Finland. Tests showed the remanence to be stable. Age estimates were obtained from pollen analysis since radiocarbon dating of the sediments proved to be unsatisfactory. The correlation between cores from any one lake was facilitated by use of the intensity and susceptibility logs. Swings in both the declination and inclination records obtained from the cores were correlatable within and to some extent between lakes. p Using a variety of magnetic methods it was concluded that fine grained magnetite carried the NRM of the sediments. Haematite was present in some of the sediments but did not appear to carry any of the NRN. It was found that the NRM was decreased by drying and by cooJing through -10°C. In both cases randomization of fine grains which. carried the NRM, brought about by physical changes in the sediment, was responsible for the loss in intensity. Rotation of magnetic grains alter deposition of the sediment appeared to be the main mechanism by which the lake sediments acquire an NRM. Further experiments showed that the grains were not fixed in place simply by dewatering of the sediments but that the growth of gels in the sediment stabilized the particles Some grains in the sediments were still free to rotate and to acquire a remanence. Samples of bedrock, soil, drift and stream sediment were collected from the catchment areas and magnetic measurements made on them to determine the source of the magnetic minerals in the lake sediments. The measurements indicated that the magnetic minerals were derived from the glacial drift. Haematite, was present as a secondary mineral in the drift but was broken down during erosion and transport of detritus to the lakes.
Nutricline variation in the equatorial Atlantic coincident with the Younger Dryas

Variations in nutricline depth during the last deglaciation are recorded by the phytoplankton species Florisphaera profunda. A stacked record of six deep-sea cores shows that across the entire equatorial Atlantic there was a brief shallowing of the nutricline, centered on 11 ka, coincident with the Younger Dryas. This shallowing is linked with high-latitude North Atlantic deglacial events. We invoke high albedo and cool sea surface temperatures in the northern latitudes during 11 ka which steepened an already higher-than-expected winter insolation (and hence temperature) gradient for a time of June perihelion. We postulate that the resultant increased pressure gradient caused an intensification of the northern hemisphere winter tropical easterlies which in turn caused equatorial divergence to occur in boreal winter.
VIKOR decision making method based on multi-granularity hesitant fuzzy linguistic term sets

In the existing multi-attribute decision making researches, most of the hesitant fuzzy linguistic multi-attribute decision making methods assume the decision makers use the linguistic term set under the same granularity to express their linguistic evaluation informations. However, in actual decision making, due to different cultural and educational backgrounds, decision makers may use different sets of linguistic terms at different granularities, which leads to the mismatch of decision information and inaccuracy of decision results. To address this problem, this paper proposes VIKOR decision making method based on multi-granularity hesitant furzy linguistic term sets, which consider both the overall benefits of the group and the regret values of individuals to select the optimal solution. Specifically, a VIKOR decision making method based on the multi-granularity hesitant fuzzy linguistic term set is first proposed, which employs to convert the multi-granularity hesitant fuzzy linguistic term set of each decision maker to the same granularity for aggregation, and obtains the corresponding membership level linguistic term set. Secondly, using the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked based on their proximity to the positive ideal solution. This ranking facilitates the selection of the optimal solution. Secondly, combined with the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked in order of merit according to the magnitude of the closeness of the preference value setting of each alternative to the positive ideal solution, so as to achieve the selection of the optimal solution. Finally, an arithmetic example is used to prove the accuracy, rationality and feasibility of the method, and the stability of the proposed method is further illustrated by experimentally analyzing the decision mechanism coefficients.
State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the GP. In this article, we study two novel GP estimation methods: a State-based Method (SM) which employs the phase portrait of the hip angle (similar to previous methods), but uses a stretching transformation to reduce the nonlinearity of the estimated GP; and a Time-based Method (TM) that utilizes feature recognition on the hip angle signal to update the estimated cadence twice per gait cycle. The methods were tested across various speeds and slopes, encompassing steady and transient walking conditions. The results demonstrated the ability of both methods to estimate the GP in a range of conditions. The TM outperformed the SM, exhibiting a root-mean-squared error below 3% compared to 8.5% for the SM. However, the TM exhibited diminished performance during speed transitions, whereas the SM performed consistently in steady and transient conditions. The SM displayed a better performance in inclined walking and demonstrated higher linearity at faster speeds. Through the assessment of these methods in diverse conditions, this study lays the groundwork for further advancements in GP estimation methods and their application in assistive controllers.
Disaster Image Classification Using Pre-trained Transformer and Contrastive Learning Models

Natural disasters can have devastating consequences for communities, causing loss of life and significant economic damage. To mitigate these impacts, it is crucial to quickly and accurately identify situational awareness and actionable information useful for disaster relief and response organizations. In this paper, we study the use of advanced transformer and contrastive learning models for disaster image classification in a humanitarian context, with focus on state-of-the-art pre-trained vision transformers such as ViT, CSWin and a state-of-the-art pre-trained contrastive learning model, CLIP. We evaluate the performance of these models across various disaster scenarios, including in-domain and cross-domain settings, as well as few-shot learning and zero-shot learning settings. Our results show that the CLIP model outperforms the two transformer models (ViT and CSWin) and also ConvNeXts, a competitive CNN-based model resembling transformers, in all the settings. By improving the performance of disaster image classification, our work can contribute to the goal of reducing the number of deaths and economic losses caused by disasters, as well as helping to decrease the number of people affected by these events.
Risk assessment of chlorpyrifos (organophosphate pesticide) associated with dermal exposure in chilli-growing farmers at Ubonrachatani province, Thailand

ณัฏฐา ฐานีพานิชสกุล: การประเมินความเสี่ยงจากการไดรับสัมผัสคลอรไพรีฟอส (สารกําจัดศัตรูพืช กลุมออรแกนโนฟอสเฟต) ผานทางผิวหนังของเกษตรกรผูปลูกพริก จังหวัดอุบลราชธานี ประเทศไทย (RISK ASSESSMENT OF CHLORPYRIFOS (ORGANOPHOSPHATE PESTICIDE) ASSOCIATED WITH DERMAL EXPOSURE IN CHILLI-GROWING FARMERS AT UBONRACHATANI PROVINCE, THAILAND) อ. ที่ปรึกษาวิทยานิพนธหลัก: ดร. วัฒนสิทธิ์ ศิริวงศ อ. ที่ปรึกษาวิทยานิพนธ รวม: ศ. ดร. มารค เกรกอรี รอบสัน, 62 หนา
Image Fusion Approach Based on Heterogeneous Dense Network

In this research, we propose a novel algorithm for fusing visible and infrared images using a heterogeneous dense network and CBAM, generating fused images with more texture details while preserving salient targets. Firstly, the encoder and decoder are constructed based on the heterogeneous dense network to achieve deep feature extraction and reconstruction of the source image. Secondly, to guide network optimization, we design a loss function using the salient target mask. Under the constraint of this loss function, the encoder calculates pixel and gradient losses for background areas and salient target areas, respectively, with the decoder then fusing the features of corresponding areas to reconstruct desired results. Additionally, CBAM is added to the fusion part to process infrared image features by focusing more on salient targets and reducing redundant information introduction. Finally, experiments demonstrate that our algorithm enhances detailed features while preserving target features consistent with human visual characteristics. The proposed algorithm achieves a superior fusion effect.
Evaluating Explanation Methods of Multivariate Time Series Classification through Causal Lenses

Explainable machine learning techniques (XAI) aim to provide a solid descriptive approach to Deep Neural Networks (NN). In Multi-Variate Time Series (MTS) analysis, the most recurrent techniques use relevance attribution, where importance scores are assigned to each TS variable over time according to their importance in classification or forecasting. Despite their popularity, post-hoc explanation methods do not account for causal relationships between the model outcome and its predictors. In our work, we conduct a thorough empirical evaluation of model-agnostic and model-specific relevance attribution methods proposed for TCNN, LSTM, and Transformers classification models of MTS. The contribution of our empirical study is threefold: (i) evaluate the capability of existing post-hoc methods to provide consistent explanations for high-dimensional MTS (ii) quantify how post-hoc explanations are related to sufficient explanations (i.e., the direct causes of the target TS variable) underlying the datasets, and (iii) rank the performance of surrogate models built over post-hoc and causal explanations w.r. t. the full MTS models. To the best of our knowledge, this is the first work that evaluates the reliability and effectiveness of existing XAI methods from a temporal causal model perspective.
Assessment of histotripsy and thrombolytic in a porcine model of venous thrombosis

Deep vein thrombosis (DVT) is a significant source of morbidity and mortality worldwide. Catheter-directed thrombolytics are the standard-of-care treatment for critical obstructions, but have limitations that motivate the development of adjuvant strategies. Histotripsy is a focused ultrasound therapy that generates spontaneous bubbles in tissue at depth. In vitro studies have demonstrated histotripsy enhances the action of thrombolytic drugs to promote robust clot degradation. The goal of this study was to assess outcomes for this combination therapy in a porcine model of DVT. Thrombi were formed in the femoral vein were exposed to thrombolytic drug using a multi-side port infusion catheter. During the infusion, histotripsy bubble activity was generated using a focused transducer designed specifically for iliofemoral targets. A venous filter was placed in the inferior vena cava to capture treatment-induced emboli. Fluoroscopy and ultrasound imaging were used to assess outcomes for thrombi exposed to thrombolytic drugs with or without histotripsy. For thrombi exposed to thrombolytic alone, partial flow was observed in one of four cases on Doppler ultrasound. Thrombolytic alone resulted in no apparent changes in the thrombus burden on B-mode ultrasound or contrast perfusion on fluoroscopy. For histotripsy and thrombolytic, flow was increased on Doppler for 8 of 9 cases, and the thrombus burden was reduced ~ 55%. Semi-quantitative markers of fluoroscopy indicated strong perfusion within targeted regions. Further, no emboli were found in venous filters. Overall, these ongoing studies suggest histotripsy combined with catheter-directed thrombolytic is a promising approach to treat venous clots.
Pattern of Admission and Factors Associated with Treatment Outcome of Neonates Admitted in Teaching Hospital in Eastern Ethiopia

Background: Neonatal period is the most vulnerable time for the survival of newborns. Despite many efforts done to improve the outcome of neonates admitted to hospitals; it doesn ’ t show satisfactory progress. The study aimed to assess the pattern, causes of admission, and factors associated with treatment outcomes. Methods: facility - based cross - sectional study design was conducted among 707 randomly selected neonates from March 1 to 31, 2020. Data were extracted from medical records using a checklist adapted from the national neonatal registration book. The data were inserted into Epi - data version 3.1 and then exported into SPSS window version 22 for analysis. Bivariate and multivariate analyses were done to identify the association between independent variables and the outcome variable. Results: From 698 admitted neonates during the 2 years period, 594 of them were improved and 104 of them were not improved. Neonatal sepsis, hypothermia, and low birth weight were the leading causes of admission to the hospital. Res-idency [ AOR=2.30, 95%CI: (1.3, 4.12)], low birth weight [AOR=2.52,95%CI:(1.24,5.13))], respiratory distress syndrome [AOR =2.86, 95%CI:(1.11.7.35)], neonatal sepsis [AOR= 2.48, 95%CI: (1.40, 4.38)], and neonates treated with phototherapy and oxygen [AOR=0.22, 95%CI:(0.088,0.54)], [AOR=0.47,95%CI:(0.22,0.99)] were factors associated with poor treat-ment outcome. Conclusion: The causes of admission in the study setting were mainly neonatal sepsis, hypothermia, and low birth weight. Residency, low birth weight, respiratory distress syndrome, having neonatal sepsis, treatment with phototherapy, and treatment with oxygen were independent factors. The hospital administrators and staff should strengthen the quality of care provided at the unit and strengthening early detection and prevention of neonatal problems during post-partum periods are means of decreasing poor treatment outcomes.
A 3D geological model of the 1963 Vajont landslide

non-planar geometry is affected by the interference pattern of two regional-scale fold systems. The landslide is partitioned into two distinct and internally continuous rock masses with a distinct kinematics, which are characterised by a very limited internal deformation during the slide. The continuity of these two large blocks points to a very localized deformation, occurring along a thin, continuous and weak cataclastic horizon.
Pore-Scale Modeling of Fine-Particle Migration in Granular Filters

AbstractFine-particle migration and associated internal erosion are major concerns for dam safety. Granular filters are used to prevent fine-particle migration, and several empirical models have been introduced for the design of these filters. Few computational techniques have tracked particle transport through the filters. This paper presents a three-dimensional transient fully coupled pore-scale model used to study the mechanism of fine-particle migration in granular filters. A pore-scale idealization of the fluid was achieved by using the lattice Boltzmann method, and the solid phase was modeled at a microscale using a discrete element method. The fluid forces applied on the particles were calculated on the basis of the momentum exchange between the fluid and particles. The proposed numerical technique was used to model the migration of base-soil particles through granular filters of different particle sizes. Results of conducted simulations provided the erosion percentages and flow rates during the si.