Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Physics-Informed Machine-Learning-Based Predictive Maintenance for Power Converters—A Review

Predictive maintenance for power electronic converters has emerged as a critical area of research and development. With the rapid advancements in deep-learning techniques, new possibilities have emerged for enhancing the performance and reliability of power converters. However, addressing challenges related to data resources, physical consistency, and generalizability has become crucial in achieving optimal strategies. This comprehensive review article presents an insightful overview of the recent advancements in the field of predictive maintenance for power converters. It explores three paradigms: model-based approaches, data-driven techniques, and the emerging concept of physics-informed machine learning (PIML). By leveraging the integration of physical knowledge into machine-learning architectures, PIML holds great promise for overcoming the aforementioned concerns. Drawing upon the current state-of-art, this review identifies common trends, practical challenges, and significant research opportunities in the domain of predictive maintenance for power converters. The analysis covers a broad spectrum of approaches used for parameter identification, feature engineering, fault detection, and remaining useful life estimation. This article not only provides a comprehensive survey of recent methodologies but also highlights future trends, serving as a resource for researchers and practitioners involved in the development of predictive maintenance strategies for power converters.
Dynamics of on-farm management of rice (Oryza sativa L.) landrace populations in Jeypore tract and other parts of Odisha

In the present study, Interand intra-population diversity of five named rice landraces from parts of Odisha state of India representing static and dynamic management were examined using 14 Sequence-tagged microsatellite site primer pairs. A total of 64 alleles were detected in 10 populations of the five named landraces. The number of alleles ranged from 2 to 7 with an average of 4.57 alleles per locus. Of the 64 alleles, 60 were common and four were rare. Moderate to low diversity in landrace populations was observed with number of alleles per population ranging from 16 to 25 and per cent polymorphism ranging from 14.29 to 64.29, respectively. The AMOVA indicated highest variation of 75.7% among populations within groups (static vs dynamic). A model based (structure analysis) method was also used for analyzing genetic structure of individuals showing the diversity pattern. Pair-wise estimates of FST revealed very high significant population differentiation which ranged from 0.68 to 0.89 indicating that the populations share limited genetic diversity among them. However, not many variations were observed in the phenotypes of populations representing static and dynamic management. This shows that adaptations of a population are apparently persisting over generations but the underlying genotypes have changed and new alleles or combinations may arise and increase in frequency at the expense of other alleles that have disappeared. The importance of population biology research for in situ conservation, requires both descriptive and hypothesis testing, in order to guide technical improvement and management of landrace populations was emphasized.
Wave Climate Change and Coastal Erosion in the US Pacific Northwest

Wave-buoy data collected off the coast of the US Pacific Northwest (Washington and Oregon) document that significant wave heights and periods have progressively increased during recent decades. The average deep-water significant wave heights during the "winter" (October through March) have increased at a rate 0.032 m/yr from 1978 through 2001, a 0.77-m change, while the increase in wave heights associated with the strongest storms has been even greater. Calculated swash runup levels on beaches, which depend on both deep-water wave heights and periods, show parallel increases; the rate of increase has been 0.010 m/yr, which would have produced more than a 6-m landward shift in the mean shoreline during the winter since the late 1970's. Such decadal trends, together with extreme-value analyses of deep-water wave parameters and swash runup levels, are being used to assess the potential for future beach and property erosion along the Pacific Northwest coast.
Face and number plate recognition for car anti-theft

Smart parking systems along with that is continuous development of new technologies, are widely applied to improve our lives. It can also add new technologies with advanced functions making it a multi-functional management system. Thanks to the anti-theft technologies that are installed, modern cars are significantly more difficult to steal than they once were. However, electrical systems can still experience issues, though and malfunction at some point. This paper suggests using video image recognition technology, at car parks and parking lots as an anti-theft solution, alerting the presence of the non-owner of vehicle in the driver’s seat. The system automatically predicts whether the driver is valid with the registration number plate. The image of the car is captured by the camera at the entrance gates of the parking lot. The proposed algorithm includes face recognition in images, building a deep learning convolutional network that classifies faces (subscriber’s images); using Cascade trainer to train number plate object recognition, vehicle number recognition through character recognition technique. The system can recognize reality through a personal computer connected to the camera at the scene or through photos and video files. Result, the model can face recognition and match to license plate in at a moment.
EHB Gear-Drive Symmetric Dead-Zone Finite-Time Adaptive Control

Intelligent driving vehicles require more accurate and stable braking control. Electrohydraulic braking (EHB) systems can better adapt to the development of autonomous driving technology. The gear transmission system plays a crucial role in EHB deceleration and torque increase mechanisms. However, its dead-zone nonlinearity poses challenges for EHB control. To address the position-control problem in the EHB gear transmission system, we propose a finite-time adaptive control method for the symmetrical dead zone. This approach combines adaptive control theory with finite-time control theory and designs parameter-updating laws for the unknown parameters in the system. Boundary estimates are introduced into the parameter-update laws and control laws to compensate for unknown disturbances. By adjusting the relevant parameters, the convergence rate can be improved, ensuring that errors converge within a specified range within a limited time. After modifying the parameter-updating laws and control laws, all closed-loop signals remain bounded. Finally, we validate the proposed control strategy through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate that the control strategy developed in this study achieves high tracking accuracy and stability even in the presence of dead zones, unknown parameters, and unknown interferences in the EHB gear-drive servo system.
Secondary metabolites of Bacillus subtilis L2 show antiviral activity against pseudorabies virus

Bacillus subtilis (B. subtilis) is a commercially important probiotic known to produce secondary metabolites with antibacterial, antifungal and anti-inflammatory activities. However, the potential ability of B. subtilis to combat viruses, especially DNA viruses, has not been extensively investigated. In this study, we identified two distinct B. subtilis strains and examined the efficiency of their secondary metabolites against pseudorabies virus (PRV), a swine herpesvirus resulting in economic losses worldwide. We found that treatment with the secondary metabolites of B. subtilis L2, but not the metabolites of B. subtilis V11, significantly inhibited PRV replication in multiple cells. Notably, the antiviral activity of the metabolites of B. subtilis L2 was thermal stable, resistant to protease digestion. Moreover, these metabolites effectively impeded PRV binding, entry and replication. Importantly, oral administration of the metabolites of B. subtilis L2 protected mice from lethal PRV infection, rescuing weight loss and reducing the viral load in vivo. In summary, our results reveal that the metabolites of B. subtilis L2 exhibit anti-PRV activity both in vitro and in vivo, providing a potential candidate for novel antiviral drugs.
On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma, USA

Drought early warning systems are a vital component of drought monitoring and require information at submonthly time scales because of the rapidly evolving nature of drought. This study evaluates the utility of in situ soil moisture observations for drought early warning in Oklahoma. Soil moisture was used to identify drought events, and the results were compared with the U.S. Drought Monitor with respect to the identification of drought onset. Soil moisture observations consistently identify rapid‐onset (flash) drought events earlier than the U.S. Drought Monitor. Our results show that soil moisture percentiles provide a 2–3 week lead time over the U.S. Drought Monitor based on five flash drought events that occurred in Oklahoma between 2000 and 2013. We conclude that in situ soil moisture observations are an important source of information for early warning of flash drought events in the Oklahoma.
O-16 Multiplexed SubCellular Scale Microarrays from direct DNA Nanolithography

TU Dortmund, Fakultät für Chemie und Chemische Biologie Biologisch-Chemische Mikrostrukturtechnik, Otto-Hahn Str. 6, 44227, Dortmund, Germany (current address: Department of Electronic Engineering, University of Tor Vergata, Via del Politecnico 1, 00133, Roma, Italy) Leibnitz-Institut für Analytische Wissenschaften – ISAS Otto Hahn Str. 6b, 44227 Dortmund, Germany Max Planck Institute of Molecular Physiology, Department of Systemic Cell Biology, OttoHahn-Str. 11, 44227, Dortmund, Germany Dipartimento di Fisica e Chimica, Università di Palermo, V.le delle Scienze, Parco d’Orleans II 90128 Palermo, Italy. Karlsruhe Institute of Technology (KIT), Institute for Biological Interfaces, Hermann-vonHelmholtz-Platz, D-76344 Eggenstein-Leopoldshafen, Germany (niemeyer@kit.edu).
A OpenThread WSN Prototype for the Monitoring of Crops

Wireless sensor networks can be an low-cost and efficient solution for monitoring environmental parameters in agriculture. In this work, we analyze the potentialities of using a network based on a general purpose internet of things protocol as OpenThread. The aim of this paper is to test and verify the platform functionalities during the monitoring of environmental parameters such as: temperature, relative humidity, visible and infrared irradiance in a real environment.
Sub-mmWave Transmission Lines on Silicon-Based Technologies

In modern complementary metal-oxide semiconductor (CMOS) process, the quality of the transmission line is very crucial to the integrated circuit (IC) design. However, with the continuous scaling of the advanced CMOS node, the compact back-end-of-line (BEOL) drastically influence the performance of all passive circuits, which cannot be completely overcome due to the physical limit. After a comprehensive comparison with the various transmission lines from the state-of the-art, three optimum transmission lines (stripline, microstrip, and coplanar waveguides) based on GlobalFoundries (GF)22FDX® are designed and measured. Then, these proposed transmission lines are benchmarked with silicon-filled waveguide to evaluate the potential of the later when moving to the sub-mmWave domain. It shows that the low loss (0.25, 0.46, and 0.6 dB/mm reduction at, respectively, W-band, D-band, and H-band compared with planar TLs) of silicon-filled waveguide opens a new window for the IC designers, especially above 300 GHz.
Method for establishing high resolution digital elevation model

The interpolation software ANUDEM is applied to establish the high resolution digital model for a typical small gully river basin in loess plateau based on the map with scale 1∶10? 000.The determination of major parameters in application of the ANUDEM is investigated.The quality of the established DEM is also assessed.It is found that the optimal resolution of the DEM is one half of the vertical-interval of contour lines,generally it should be 2.5m or 5m,the second roughness penalty is 0.8,and the number of iterations is 20-30 times.The river systems and contours extracted from the DEM by using this method are well accord with general configuration of the land surface and suitable for hydrological analysis.
Research on Machine Learning Application for the Fault Localization in the Medium Voltage Power Distribution Grid

Nowadays the problem of fault localization in power distribution grids still remains important and actual. Whereas classic approaches face several difficulties, machine learning based methods are assumed to be prospective. The following study proposes a variety of a neural network fault locator for phase-to-ground fault and phase-to-phase fault types occurred typically in grids, although this method has not yet been researched sufficiently. The ability of method to handle uncertainty of power distribution grid parameters and fault parameters is demonstrated. The study also analyzes which data should be collected to achieve better results for different problem statements and different conditions of uncertainty and shows how to preprocess data and how to adjust correctly and train neural network.
Adaptation languages as vehicles of explicit intelligence in Adaptive Hypermedia

This paper deals with a new challenge in Adaptive Hypermedia (AH) and web-based systems: finding the adaptation language to express, independently from the domain model or platform, the intelligent, adaptive behaviour of personalised web courseware. The major requirements for the ideal language are: reuse, flexibility, high level semantics, and ease of use. To draw closer to this ideal language, we compare two such language proposals: LAG, a generic adaptation language, and a new XML adaptation language for Learning Styles (LS) in AHA! , LAG-XLS.
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation

Rewards serve as a measure of user satisfaction and act as a limiting factor in interactive recommender systems. In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning. Previous approaches either introduce additional procedures for learning to reward, thereby increasing the complexity of optimization, or assume that user-agent interactions provide perfect demonstrations, which is not feasible in practice. Ideally, we aim to employ a unified approach that optimizes both the reward and policy using compositional demonstrations. However, this requirement presents a challenge since rewards inherently quantify user feedback on-policy, while recommender agents approximate off-policy future cumulative valuation. To tackle this challenge, we propose a novel batch inverse reinforcement learning paradigm that achieves the desired properties. Our method utilizes discounted stationary distribution correction to combine LTR and recommender agent evaluation. To fulfill the compositional requirement, we incorporate the concept of pessimism through conservation. Specifically, we modify the vanilla correction using Bellman transformation and enforce KL regularization to constrain consecutive policy updates. We use two real-world datasets which represent two compositional coverage to conduct empirical studies, the results also show that the proposed method relatively improves both effectiveness (2.3\%) and efficiency (11.53\%)
Beyond the Gini Coefficient : Accounting for Spatial Income Curdling in Resource Allocation Maps ( I )

A spatial curdling index (C) is presented in order to study income concentration in a two dimensional field originated by a cascade process resembling resource allocation maps. Computational simulations of spatial distributions show the relation between C and measures of income inequality such as the Gini coefficient (G) form a concave area. Acknowledgement of this fact, brings additional information to formulate geopolitical strategies for income inequality reduction, and for comparison purposes in a set of countries or across time in a single country.
Distributed multi-UAV shield formation based on virtual surface constraints

This paper proposes a method for the deployment of a multi-agent system of unmanned aerial vehicles (UAVs) as a shield with potential applications in the protection of infrastructures. The shield shape is modeled as a quadric surface in the 3D space. To design the desired formation (target distances between agents and interconnections), an algorithm is proposed where the input parameters are just the parametrization of the quadric and the number of agents of the system. This algorithm guarantees that the agents are almost uniformly distributed over the virtual surface and that the topology is a Delaunay triangulation. Moreover, a new method is proposed to check if the resulting triangulation meets that condition and is executed locally. Because this topology ensures that the formation is rigid, a distributed control law based on the gradient of a potential function is proposed to acquire the desired shield shape and proofs of stability are provided. Finally, simulation and experimental results illustrate the effectiveness of the proposed approach.
Evaluation of Antiproliferative activity of Taxol, Proanthocyanidin and Atorvastatin against human hepatocellular carcinoma cell line (HepG2).

Keywords Liver cancer is the sixth most frequent cancer in the world, with a significant morbidity and mortality rate, is liver cancer. Discovery of a suitable therapeutic alternative for this disease is of greatest importance because various types of cancer annually result in thousands of fatalities worldwide. Atorvastatin is a competitive inhibitor of hydroxy methylglutaryl CoA reductase, which plays a role in anticancer activities in a range of malignancies, including human liver cancer. Proanthocynadin is a phytochemical that has a variety of pharmacological effects and chemotherapeutic properties. Taxol, an anti-microtubule chemotherapeutic medication, has been shown to be effective in the treatment of several HepG2 (liver carcinoma cell line). In this study, the antiproliferative and proapoptotic properties of Atorvastatin, Proanthocynadin and Taxol against HepG2 liver cancer cell line was evaluated in vitro. Cell survival and apoptosis in liver cancer cell lines were analyzed using MTT assay. The results showed that Taxol, Atorvastatin and Proanthcyanidin inhibited the viability of HepG2 cells in a time-and dose-dependent manner, induced a significant enhancement of treated cancer cells compared to untreated cells. Hepatocellular
Middle Eocene-Early Miocene larger foraminifera from Dhofar (Oman) and Socotra Island (Yemen)

Here, the larger foraminifera found in Middle Eocene-Early Miocene rocks from Dhofar (Oman) and Socotra Island (Yemen) are studied in detail. The architectural analysis leads to the description of four new genera and nine new species: five agglutinated foraminifera, Pseudolituonella roberti n. sp. , Socotraella ashawqi n. gen. n. sp. , Pseudochrysalidina ayaki n. sp. , Barattolites andhuri n. sp. and Dhofarella aydimi n. gen. n. sp. ; and four porcellaneous foraminifera, Idalina grelaudae n. sp. , Idalina pignattii n. sp. , Macetadiscus incolumnatus n. gen. n. sp. and Omanodiscus tenuissimus n. gen. n. sp. The larger foraminifera identified in a Composite section located in Western Dhofar, in the Shuwaymiyah section located in Eastern Dhofar and in the Wadi Ayak section located on Socotra Island, has facilitated the identification of the following larger foraminifera zones: SBZ 14-SBZ 15 (Middle Lutetian), SBZ 16 (Late Lutetian), SBZ 17 (Bartonian), SBZ 18 (latest Bartonian-earliest Priabonian), SBZ 19-SBZ 20 (Priabonian), SB 21-SB 22A (Rupelian), SB 22B-SB 23 (Chattian) and SB 24 (Aquitanian). All these data permit stablished the age of the following lithostratigraphic units: Dammam Fm. Andhur Mb Early Lutetian? -Middle Lutetian age (SBZ 13? -SBZ 14 partim), Qara Mb Middle Lutetian (SBZ 14-SBZ 15), Uyun Mb Late Lutetian (SBZ 16). Aydim Fm. Heiron Mb Bartonian (SBZ 17), Moosak Mb upper Bartonian-Priabonian (SBZ 18-SBZ 20), Tagut Mb Priabonian (SBZ 19-SBZ 20), Haluf Mb Priabonian (SBZ 19-SBZ 20) till SBZ 21 on Socotra Island. Ashawq Fm Rupelian (SB 21-SB 22A). Mughsayl Fm Chattian-Aquitanian (SB 23-SB 24).
Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders

Vision Transformers (ViTs) have become ubiquitous in computer vision. Despite their success, ViTs lack inductive biases, which can make it difficult to train them with limited data. To address this challenge, prior studies suggest training ViTs with self-supervised learning (SSL) and fine-tuning sequentially. However, we observe that jointly optimizing ViTs for the primary task and a Self-Supervised Auxiliary Task (SSAT) is surprisingly beneficial when the amount of training data is limited. We explore the appropriate SSL tasks that can be optimized alongside the primary task, the training schemes for these tasks, and the data scale at which they can be most effective. Our findings reveal that SSAT is a powerful technique that enables ViTs to leverage the unique characteristics of both the self-supervised and primary tasks, achieving better performance than typical ViTs pre-training with SSL and fine-tuning sequentially. Our experiments, conducted on 10 datasets, demonstrate that SSAT significantly improves ViT performance while reducing carbon footprint. We also confirm the effectiveness of SSAT in the video domain for deepfake detection, showcasing its generalizability. Our code is available at https://github.com/dominickrei/Limited-data-vits.
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion

Query expansion, pivotal in search engines, enhances the representation of user information needs with additional terms. While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations. Retrieval-based methods often fail to accurately capture search intent, particularly with brief or ambiguous queries. Generation-based methods, utilizing large language models (LLMs), generally lack corpus-specific knowledge and entail high fine-tuning costs. To address these gaps, we propose a novel zero-shot query expansion framework utilizing LLMs for mutual verification. Specifically, we first design a query-query-document generation method, leveraging LLMs' zero-shot reasoning ability to produce diverse sub-queries and corresponding documents. Then, a mutual verification process synergizes generated and retrieved documents for optimal expansion. Our proposed method is fully zero-shot, and extensive experiments on three public benchmark datasets are conducted to demonstrate its effectiveness over existing methods. Our code is available online at https://github.com/Applied-Machine-Learning-Lab/MILL to ease reproduction.