Miracle-dz/UniFAR-FTUs huggingface dataset:
Heavy Precipitation Forecasting using the Combination of Local and Global Modes with Application to Malaysian Rainfall

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d) index, we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. Traditionally, a data-driven model is built on a whole data set describing the phenomenon within the data. This type of model does not considered the seasonal processes embedded in the data. Therefore, there is a need to built a model that encompassing different time scale and localized. One of the ways of doing this is to discover the different physically embedded relationships in precipitation process at different seasonal period and to built separate localized models for each of these seasonal periods. The use of committee models such as modular and ensemble models in weather and hydrological forecasting are increasing day by day. The study uses the modular concept by separating the heavy precipitation events based on sub-processes which are the seasonal monsoon and trained the subset of seasonal data using data driven models. Besides that, the study is carried out to evaluate the influence of global climate indices on local precipitation. It is interesting to see the influence of combining global and local predictors on local precipitation events. The method used past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia using modular model trained on subset of data that represent the seasonal monsoon. The analysis started with evaluating the local and global inputs (MX5d with SOI, MJO, and DMI) in order to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four subset of data are sampled representing two major seasonal variations in Malaysia. The experimental data are focused on the east coast area of Malaysia such that the effect of northeast monsoon season causes heavy precipitation events. The results showed that the combination of local and global modes in a modular model is favourable than a single localized mode. The proposed modular model is promising an encouraging result when different subset of data are trained on separate methods with different parameter values.
Red Tide Detection Method Based on Improved U-Net Model-Taking GOCI Data in East China Sea as an Example

In the coastal areas of China, the eutrophication of seawater leads to the continuous occurrence of red tide, which has caused great damage to Marine fisheries and aquatic resources. Therefore, the detection and prediction of red tide have important research significance. The rapid development of optical remote sensing technology and deep-learning technology provides technical means for realizing large-scale and high-precision red tide detection. However, the difficulty of the accurate detection of red tide edges with complex boundaries limits the further improvement of red tide detection accuracy. In view of the above problems, this paper takes GOCI data in the East China Sea as an example and proposes an improved U-Net red tide detection method. In the improved U-Net method, NDVI was introduced to enhance the characteristic information of the red tide to improve the separability between the red tide and seawater. At the same time, the ECA channel attention mechanism was introduced to give different weights according to the influence of different bands on red tide detection, and the spectral characteristics of different channels were fully mined to further extract red tide characteristics. A shallow feature extraction module based on Atrous Spatial Pyramid Convolution (ASPC) was designed to improve the U-Net model. The red tide feature information in a multi-scale context was fused under multiple sampling rates to enhance the model’s ability to extract features at different scales. The problem of limited accuracy improvement in red tide edge detection with complex boundaries is solved via the fusion of deep and shallow features and multi-scale spatial features. Compared with other methods, the method proposed in this paper achieves better results and can detect red tide edges with complex boundaries, and the accuracy, precision, recall, and F1-score are 95.90%, 97.15%, 91.53%, and 0.94, respectively. In addition, the red tide detection experiments in other regions with relatively concentrated distribution also prove that the method has good applicability.
Spontaneous Cultural Conversion Rate of Mycobacterium avium Complex Pulmonary Disease Based on BACES Severity

Background: Only a few clinical factors can aid in predicting spontaneous culture conversion (SCC) in patients with Mycobacterium avium complex-pulmonary disease (MAC-PD). In this study, we aimed to evaluate whether the rate of SCC varies according to the severity of the disease in MAC-PD patients. Methods: We retrospectively classified 373 MAC-PD patients who had undergone watchful waiting without antibiotics based on the severity assessment using the ‘body mass index (BMI), age, cavity, erythrocyte sedimentation rate (ESR), and sex (BACES)’ criteria. We evaluated the rate of SCC in MAC-PD patients based on BACES severity and analyzed the relevant factors. Results: Of 373 patients, 153 (41%) achieved SCC without antibiotics during a median follow-up of 48.1 months. There was a trend toward a higher SCC rate in patients with lower BACES severity: 48% (87/183), 37% (58/157), and 24% (8/33) in the mild, moderate, and severe BACES groups, respectively. In addition, a favorable outcome, defined as maintaining SCC or having two consecutive negative sputum cultures until the last follow-up date, was also more common in patients with lower BACES severities of 53% (97/183), 34% (54/157), and 18% (6/33) in the mild, moderate, and severe BACES groups, respectively. In multivariate analysis, moderate BACES (hazard ratio [HR] = 0.63; 95% confidence interval [CI] 0.44–0.91; p = 0.013) and severe BACES (HR 0.37; 95% CI 0.16–0.90; p = 0.028) had a significantly negative impact on favorable outcomes compared to mild BACES. Conclusions: Lower BACES severity may be associated with SCC in MAC-PD patients.
Solitary wave-induced seepage effects on the bottom of vertical cylinder resting on porous seabed

In certain shallow water condition,shallow water wave may emerge in solitary wave form,exerting wave loads on offshore structures.Based on solitary wave diffraction theory and Biot seepage consolidation theory,the problem of solitary wave-induced seepage pressure inside porous elastic seabed is analyzed,the mathematical solution to seepage pressure is deduced,and seepage loads on the bottom of vertical cylinder resting on porous seabed are evaluated.The results show that variations of ocean condition and structure geometry condition may have some influence on solitary wave-induced seepage uplift force and moment,especially on seepage moment.In certain condition,solitary wave-induced seepage loads may have the same order of magnitude as the direct horizontal wave load
Genetic diversity and breed-informative SNPs identification in domestic pig populations using coding SNPs

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products. Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations. Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei’s GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures. Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome.
Mid-Infrared Gas Sensing Based on Electromagnetically Induced Transparency in Coupled Plasmonic Resonators

The existence of surface plasmon polaritons in doped silicon micro-scale structures has opened up new and innovative possibilities for applications, such as sensing, imaging, and photonics. A CMOS-compatible doped Si plasmonic sensor is proposed and investigated. The plasmon resonance can be tuned by controlling the carrier density and dopant concentration. In this paper, we demonstrate that using silicon doped with phosphorus at a concentration of 5 × 1020 cm−3 can induce surface plasmon resonance in the mid-infrared region. Two ring resonators of two different radii based on metal–insulator–metal waveguide structures are studied individually. Then, the two ring resonators are integrated in the same device. When the two ring resonators are coupled and resonate at the same frequency; two distinct resonance spectral lines are generated with striking features that improve its potential use for sensing and modulation applications. The propagating plasmonic mode is studied, including its mode profile and bend loss. We evaluate the effectiveness of a microstructure gas sensor with dimensions of 15 μm × 15 μm by measuring its sensitivity and selectivity towards methane and ethane gases. Small alterations in the surrounding refractive index led to noticeable shifts in the resonance peak. The sensor achieved a sensitivity of 7539.9 nm/RIU at the mid-infrared spectral range around the 7.7 μm wavelength. Furthermore, by combining the resonators, we can achieve a smaller full width at half maximum (FWHM), which will ultimately result in greater sensitivity than using a single-ring resonator or other plasmonic resonator configurations. Once the sensitivity and selectivity of the sensor are measured, the FOM can be calculated by dividing the sensitivity by the selectivity of the sensor, resulting in an FOM of 6732.
Design, fabrication and measurement of metal-semiconductor field effect transistor based on zinc oxide material

The paper mainly focuses on the design, fabrication and measurement on Zinc Oxide (ZnO)-based Metal-Semiconductor Field Effect Transistor. The research problem in this study is difficulty on observing the electronic properties of ZnO materials to fabricate the high performance transistor design with non-toxic semiconductor materials. Even though the wide band gap materials of Group III and V possess high performance properties for fabricating the power electronics devices, the harmful impacts could not be reduced. The research solution for the problem statement in this study is emphasized on the non-toxic materials of Group II and VI-based high performance power electronics devices fabrication. The experimental studies of the device fabrication were conducted by Pulse Laser Deposition (PLD) process in standard laboratory. The step-by-step procedures for MSFET device fabrication were discussed and the confirmation of developed device fabrication was completed. The approaches on all measurement were completed based on band diagram condition, quantum interference on metal-semiconductor materials, and current-voltage characteristics. The step by step measurement for fabricated device for the proposed structure could be confirmed by standard measurement techniques. The proposed design has been validated for the utilization of high performance applications. The physical properties and physical characteristics for measurement results were confirmed by the theoretical analyses. The numerical analyses have been completed with the help of MATLAB. All results have been proved by recent research works.
Fault-Tolerant Time-Varying Formation Tracking Control for Multi-Agent Systems With Actuator Faults and Switching Topologies

In this paper, the fault-tolerant time-varying formation tracking control problem for high-order linear multi-agent systems with multiple leaders is studied in the case of actuator faults and switching topologies, where the control inputs of the leaders are unknown. Followers form a predefined formation while tracking the convex combination of the states of the multiple leaders. Based on the adjacent relative information of agents, a fault-tolerant time-varying formation control protocol is constructed to compensate for the bias fault, loss of effectiveness fault, and unknown control inputs of the leaders. The feasible conditions of formation are given. In addition, a new algorithm is proposed to determine the parameters of the formation control protocol. The stability of the closed-loop system is proved based on Lyapunov theory. Finally, the effectiveness of the theory is verified by simulations.
Deep Source Semi-Supervised Transfer Learning (DS3TL) for Cross-Subject EEG Classification

Objective: An electroencephalogram (EEG) based brain-computer interface (BCI) maps the user's EEG signals into commands for external device control. Usually a large amount of labeled EEG trials are required to train a reliable EEG recognition model. However, acquiring labeled EEG data is time-consuming and user-unfriendly. Semi-supervised learning (SSL) and transfer learning can be used to exploit the unlabeled data and the auxiliary data, respectively, to reduce the amount of labeled data for a new subject. Methods: This paper proposes deep source semi-supervised transfer learning (DS3TL) for EEG-based BCIs, which assumes the source subject has a small number of labeled EEG trials and a large number of unlabeled ones, whereas all EEG trials from the target subject are unlabeled. DS3TL mainly includes a hybrid SSL module, a weakly-supervised contrastive module, and a domain adaptation module. The hybrid SSL module integrates pseudo-labeling and consistency regularization for SSL. The weakly-supervised contrastive module performs contrastive learning by using the true labels of the labeled data and the pseudo-labels of the unlabeled data. The domain adaptation module reduces the individual differences by uncertainty reduction. Results: Experiments on three EEG datasets from different tasks demonstrated that DS3TL outperformed a supervised learning baseline with many more labeled training data, and multiple state-of-the-art SSL approaches with the same number of labeled data. Significance: To our knowledge, this is the first approach in EEG-based BCIs that exploits the unlabeled source data for more accurate target classifier training.
High cell-free DNA is associated with disease progression, inflammasome activation and elevated levels of inflammasome-related cytokine IL-18 in patients with myelofibrosis

Myelofibrosis (MF) is a clonal hematopoietic stem cell disorder classified among chronic myeloproliferative neoplasms, characterized by exacerbated myeloid and megakaryocytic proliferation and bone marrow fibrosis. It is induced by driver (JAK2/CALR/MPL) and high molecular risk mutations coupled to a sustained inflammatory state that contributes to disease pathogenesis. Patient outcome is determined by stratification into risk groups and refinement of current prognostic systems may help individualize treatment decisions. Circulating cell-free (cf)DNA comprises short fragments of double-stranded DNA, which promotes inflammation by stimulating several pathways, including inflammasome activation, which is responsible for IL-1β and IL-18 maturation and release. In this work, we assessed the contribution of cfDNA as a marker of disease progression and mediator of inflammation in MF. cfDNA was increased in MF patients and higher levels were associated with adverse clinical outcome, a high-risk molecular profile, advanced disease stages and inferior overall survival, indicating its potential value as a prognostic marker. Cell-free DNA levels correlated with tumor burden parameters and markers of systemic inflammation. To mimic the effects of cfDNA, monocytes were stimulated with poly(dA:dT), a synthetic double-stranded DNA. Following stimulation, patient monocytes released higher amounts of inflammasome-processed cytokine, IL-18 to the culture supernatant, reflecting enhanced inflammasome function. Despite overexpression of cytosolic DNA inflammasome sensor AIM2, IL-18 release from MF monocytes was shown to rely mainly on the NLRP3 inflammasome, as it was prevented by NLRP3-specific inhibitor MCC950. Circulating IL-18 levels were increased in MF plasma, reflecting in vivo inflammasome activation, and highlighting the previously unrecognized involvement of this cytokine in MF cytokine network. Monocyte counts were higher in patients and showed a trend towards correlation with IL-18 levels, suggesting monocytes represent a source of circulating IL-18. The close correlation shown between IL-18 and cfDNA levels, together with the finding of enhanced DNA-triggered IL-18 release from monocytes, suggest that cfDNA promotes inflammation, at least in part, through inflammasome activation. This work highlights cfDNA, the inflammasome and IL-18 as additional players in the complex inflammatory circuit that fosters MF progression, potentially providing new therapeutic targets.
Identification of tumor-suppressor genes in lung squamous cell carcinoma through integrated bioinformatics analyses

Lung cancer is a prevalent malignancy, and fatalities of the disease exceed 400,000 cases worldwide. Lung squamous cell carcinoma (LUSC) has been recognized as the most common pathological form of lung cancer. The comprehensive understanding of molecular features related to LUSC progression has great significance in LUSC prognosis assessment and clinical management. In this study, we aim to identify a panel of signature genes closely associated with LUSC, which can provide novel insights into the progression of LUSC. Gene expression profiles were retrieved from public resources including gene expression omnibus (GEO) and the cancer genome atlas (TCGA) database. Differentially expressed genes (DEGs) between LUSC specimens and normal lung tissues were identified by bioinformatics analyses. A total of 66 DEGs were identified based on two cohorts of data. CytoHubba plugin of Cytoscape software was utilized for the further analyses of the top 10 candidate hub genes including OGN, ABI3BP, MAMDC2, FGF7, FAM107A, SPARCL1, DCN, COL14A1, and MFAP4 and CHRDL1, which showed significant downregulation in LUSC. Two LUSC cell lines were used to validate the functions of CHRDL1 and FAM107A through overexpression experiment. Together, our data revealed novel candidate tumor-suppressor genes in LUSC, suggesting previously unappreciated mechanisms in the progression of LUSC.
A New Approach to Orphan Blocks in the Nakamoto Consensus Blockchain

One of the main problems in Nakamoto consensus is the creation of orphan blocks and sub-chains which reduce throughput and can lead to unfairness and network abuse. This paper proposes a new approach to change the behavior in dealing with these blocks. In the proposed method, almost all correct mined blocks at any height are made final by adding a small database to the blocks. This acts as distributed supervision to improve system security and encourage honest miner behavior. By preventing conflicting transactions from being finalized and detecting exceptional circumstances, the proposed method also preserves the important blockchain features of safety and liveness. To demonstrate the practicality of this approach, its main parameter is obtained using real network data for Bitcoin. In addition, simulation results are given for Bitcoin and other scenarios with different block sizes and block times which indicate that the proposed method improves the throughput.
Intermittent Intelligent Camera with LEO sensor-to-satellite Connectivity

IoT systems can operate efficiently in scenarios with limited or sporadic power availability by utilizing intermittent power sources, such as energy harvesting. Typical implementations are based on fixed-size super-capacitors as energy storage. However, this limits the possible implementations. Large capacitors take more time to charge, resulting in extended off-time following a power failure. Small capacitors charge faster but provide a shorter active time, leading to more frequent power failures. This paper presents a fully intermittent, machine learning-based, low-power smart camera for monitoring applications integrating a two-stage energy harvester. A small first stage supports data acquisition and analysis. The second bigger energy storage is activated only for data streaming to support the integrated sub-GHz Low Earth Orbit transmission radio. This dual-stage energy storage strategy ensures both system reactivity and the capacity to sustain energy-intensive data transmission. The simulation highlights how the fully intermittent pipeline (i.e., for both the neural accelerator and the ARM core) makes the system resilient to power fluctuation and increases the throughput of processed images in ultra-low light conditions by up to 13%.