Miracle-dz/UniFAR-FTUs huggingface dataset:
A Program of Elliptic Solver Development and Implementation in Semi-Implicit Numerical Ocean Circulation Models.

Abstract : Several elliptic solver programs have been developed using the highly efficient and versatile 'error vector propagation' (EVP) method as the fundamental algorithm component. The basic new program is a modified version of Madala's SEVP method that can handle arbitrary geometry, with or without islands. There are several other versions of this new program: one allows irregular boundaries but not islands; one does not allow irregular boundaries or islands; and some versions exist with and without roundoff relaxation in the forward sweep. All versions are capable of handling Dirichlet, Neuman, and mixed boundary conditions. (Author)
How can GOCE and TerraSAR‐X contribute to the topside ionosphere and plasmasphere research?

In this study we analyze concurrent observations derived from two low‐Earth orbit satellite missions—the GOCE (Gravity field and steady state Ocean Circulation Explorer) satellite with unprecedented low orbit of 250 km and the TerraSAR‐X satellite with orbit altitude of 515 km. The science payloads of both satellites have no instruments for ionospheric research, but they include a dual‐frequency GPS (Global Positioning System) receiver. GPS measurements from the precise orbit determination GPS antenna on board these satellites can be used to determine TEC (total electron content) at the topside ionosphere‐plasmasphere system for different conditions. Results derived from these missions are presented for the first time. The comparison was done for June and December solstice conditions at low and moderate solar activity levels for quiet geomagnetic activity. We obtain the quantitative estimates of electron content and its percentage contribution to the ground‐based GPS TEC for topside ionosphere/plasmasphere at altitudes 250–500 km and above 500 km. Similarities and differences in such electron content distribution on a global scale are discussed. Distinctive features of the Weddell Sea Anomaly and equatorial ionization anomaly are evident in the topside TEC derived from GOCE GPS data. We report the Weddell Sea Anomaly presence in TEC at early morning local time.
Design and Purchase Intention Analysis of Cultural and Creative Goods Based on Deep Learning Neural Networks

With the rise of cultural and creative industries, cultural creativity has gradually become an important factor to promote the value of design in the future, and it is also a trend to integrate “cultural elements” into the design of products. At present, the cultural and creative industries in Western countries and Taiwan are the mainstay of their economic development. We should actively absorb their successful experiences and, with the support of national policies, carry out effective and lasting development of them, as well as continuously improve the quality of cultural and creative products. In today's steady economic development, the emotionalization of cultural and creative consumption has gradually formed a new consumption trend. When cultural and creative consumers buy stationery, they will inevitably have three situations: purchasing instinct, purchasing behavior, and reflection. Therefore, this paper adopts the method based on machine learning to conduct in-depth research on the users of cultural and creative products of “Forbidden Day and Night Set Gift Box. ” Through the research on the cultural and creative consumption intention of “Forbidden Day and Night Set Gift Box,” it can effectively promote the development of domestic cultural and creative enterprises, and then promote the customer satisfaction of cultural and creative enterprises. This paper makes a detailed analysis of it from the perspectives of data processing, feature engineering, classification prediction models, and future development directions. By studying the cultural and creative behavior of users, a deep learning model based on neural network is established. The feature extraction, feature preprocessing, feature selection, and asymmetric data collection in the process of data processing are discussed in depth. In order to further improve the prediction accuracy and conduct more in-depth research, this paper establishes a deep learning prediction model based on depth. This model is experimentally validated and it can be observed that the model is 10% more efficient than the traditional model, so the model can learn data better from the user's behavior in several aspects, and the proposed and practice of this model has good practical significance.
Measuring Zero-Waste City Performance of a Coal Resource-Based Area in China with MCDM Approach

The zero-waste city is one of the most visionary initiatives for solving waste problems by enhancing the life cycle solid waste management system. However, the development level of ZW city construction remains unclear. Resource-based areas tend to produce large amounts of industrial solid waste, and achieving a zero-waste city is difficult due to insufficient resource utilization capacity. This study proposed a practical integrated MCDM approach to assess the performance of the ZW city and applied the approach to a typical coal resource-based province in China. The spatiotemporal characteristics and factors influencing the performance of zero-waste cities in each city were further explored. The performance levels increased during the study period; however, the growth rate of most cities is slow. Spatially, the performance levels of zero-waste cities gradually decreased from south to north, showing a radiation pattern with Taiyuan at its core. Challenges toward zero-waste city development were further identified, including heavy industrial structure, widespread underutilization of industrial solid waste, inadequate management of hazardous solid waste, low rate of urban domestic waste classification, and ineffective treatment. The approach gives a holistic and broader picture of the zero-waste management performance, which enables us to identify the challenges in promoting zero-waste cities.
Dual-Band SIW Filter With Widely Separated Passbands Based on TE101 and TE301 Modes

In this paper, a novel dual-band substrate integrated waveguide (SIW) filter with widely separated passbands is presented, which is realized by dual-mode TE101 and TE301 resonances. To make the TE301 mode create the second passband, two novel techniques to suppress the TE201 mode are proposed and discussed. The proposed techniques excite the TE201 mode but either block its coupling path or cancel its effect at the output port, which can be identified as mode blocking and mode cancelling, respectively. They show a merit of introducing an extra transmission zero, which is beneficial to enhance the out-of-band rejection. To verify the concept, a dual-band filter prototype operating at 6.5 and 11 GHz is designed and fabricated, which shows a good electrical performance and a compact size.
In-Needle Pre-Column Derivatization for Amino Acid Quantification (iPDAQ) Using HPLC

Pre-column fluorescent derivatization has been used for the fast quantification of amino acids using high-performance liquid chromatography (HPLC) systems. However, it generally requires an offline in-vial derivatization process with multiple derivatization reagents. The offline derivatization requires the same number of reaction vials as the number of sample vials for use as a reaction chamber for the derivatization reaction in an autosampler. Therefore, the number of samples analyzed per batch using the pre-column derivatization method is halved. To benefit from the pre-column derivatization method, we transformed the derivatization process from an offline chamber process to an online in-needle process (in-needle Pre-column Derivatization for Amino acids Quantification; iPDAQ). Fluorescent derivatization in the injection needle obviated the need for vacant vials as reaction chambers. Consequently, the throughput per batch improved up to two times, and the consumption of derivatization reagents was reduced to less than one-tenth of that in the conventional vial method. We demonstrated to separate and quantify the amino acids in various biological samples. Herein, we presented a novel HPLC-based amino acid quantification method that enables the continuous analysis of a large number of samples. The iPDAQ facilitates accurate amino acid quantification due to the automation of derivatization and achieves improvement in the throughput and reduction of analysis labor.
Extrinsic Motivation and Organizational Commitment : A Literarure Review

Increasing employee commitment at work is one of the most important points for the company. Without organizational commitment from an employee, the employee will tend to work less than optimally. This study aims to find the answers to the research question of whether extrinsic motivation influence organizational commitment. Metode penelitian yang digunakan dalam penelitian ini adalah article review dari beberapa jurnal yang telah publish. The results of the study proved that extrinsic motivation influence organizational commitment. The results of this research have implications for policymakers, that employee must be given support and aslo increasing the employee extrinsic motivation such as providing fair promotion, reward, and also providing a good income. The limitations of this study are that the variables used as independent variables to test commitments are still few. In the future, it will be expected to increase variables and conduct research empirically. Keywords: Extrinsic Motivation; Organizational Commitment
Cryogenic Compact mm-Wave Broadband SPST Switch in 22nm FDSOI CMOS for Monolithic Quantum Processors

This paper reports the experimental characterization at the cryogenic temperature of a compact mm-wave broadband single-pole single-throw (SPST) switch in 22nm FDSOI CMOS technology. The switch consists of two n-MOSFETs with a special device option to reduce the substrate parasitic effects, and a third n-MOSFET to improve isolation. Unlike prior wideband mm-wave switches, it does not require any large passive components, allowing a very compact design, low loss and high isolation performance. The cryogenic measurements at 2 K show an insertion loss lower than 2.3 dB, an isolation better than 25.3 dB, and the return loss better than −11.5 dB, over the entire frequency range from DC to 70 GHz.
1D-Convnet Model for Detection of Antidepressant Drugs

A drug is a substance or mixture of materials to be used in determining the diagnosis, preventing, reducing, eliminating, curing disease or symptoms of disease, bodily or spiritual injury or disorder in humans or animals, including to beautify the body or parts of the human body. Problems begin to arise when a patient is wrong in consuming the target drug used, which is not by the type of disease suffered. For example, suppose a person suffers from a psychological disorder that requires taking different types of drugs, if it turns out that the type of drug consumed is not by the disease, it is very dangerous. This problem is certainly very dangerous because it can cause death for those who consume it. Currently, many researchers are using the deep learning Convolutional Neural Network (CNN) model for drug detection problems. The CNN model has a higher level, namely 1D-Convolutional Neural Network (1D-Convnet) which is still rarely used for drug detection problems. So, the purpose of this study was to detect the classification of atypical antidepressants and SSRIs antidepressants using a deep learning model of the 1D-Convolutional Network (1D-Convnet) type. The results obtained using this model are 98.3% with the most influential parameter, namely dropout. The proposed research model also produces higher accuracy than the Naive Bayes supervised learning model.
Real-Time Forestry Pest Detection Method Based on Enhanced Feature Fusion with Deep Learning

The aim of this study is to address the real-time requirements of forestry pest detection and the problem of a low detection rate caused by anchor box redundancy of existing detection methods. This paper proposes a real-time forestry pest detection method based on theanchor-free method that can balance the detection rate and detection accuracy. Based on the TTFNet method, a mobile feature extraction network is introduced, and the effective feature weights are increased by one-dimensional convolution before feature output to suppress invalid features. For pest detection, data are mostly small-scale targets. An enhanced feature fusion method is proposed to introduce an asymmetric convolution module in multi-scale feature fusion to feature-enhance the feature maps extracted by the backbone network and connect across layers to improve the detection accuracy. To address the degradation of the anchor box position regression loss in the original method, DIOULoss is introduced to optimize the position regression loss function of the anchor box. Finally, data augmentation is performed on a relatively small number of samples in the dataset, the accuracy of the model is improved by 1.94%, the FPS is improved to 1.6 times of the original one, and the training time is slightly increased compared with the preinnovation model. Ablation experiments are designed to demonstrate the effectiveness of the proposed algorithm while being more conducive to deployment on edge devices.
Haptic Devices: Wearability-Based Taxonomy and Literature Review

In the last decade, several new haptic devices have been developed, contributing to the definition of more realistic virtual environments. An overview of this topic requires a description of the various technologies employed in building such devices, and of their application domains. This survey describes the current technology underlying haptic devices, based on the concept of “wearability level”. It reviews more than 90 devices newly developed and described in scientific papers published in the period 2010-2021. The reviewed devices provide either haptic illusions or novel haptic feedback for teleoperation, entertainment, training, education, guidance and notification. They are categorized into grounded, hand-held and wearable devices; the latter are further split into exoskeletons and gloves, finger-worn devices, and arm-worn devices. For the systems in each of these categories, descriptions and tables are provided that analyze their structure, including device mass and employed actuators, their applications, and other characteristics such as type of haptic feedback and tactile illusions. The survey also provides an overview of devices worn in parts of the human body other than arms and hands, and precisely haptic vests, jackets and belts, and haptic devices for head, legs and feet. Finally, the paper discusses research gaps and challenges, and potential future directions.
Next-Generation Sequencing of Local Romanian Tomato Varieties and Bioinformatics Analysis of the Ve Locus

Genetic variability is extremely important, not only for the species’ adaptation to environmental challenges, but also for the creation of novel varieties through plant breeding. Tomato is an important vegetable crop, as well as a model species in numerous genomic studies. Its genome was fully sequenced in 2012 for the ‘Heinz 1706’ variety, and since then, resequencing efforts have revealed genetic variability data that can be used for multiple purposes, including triggering mechanisms of biotic and abiotic stress resistance. The present study focused on the analysis of the genome variation for eight Romanian local tomato varieties using next-generation sequencing technique, and as a case study, the sequence analysis of the Ve1 and Ve2 loci, to determine which genotypes might be good candidates for future breeding of tomato varieties resistant to Verticillium species. The analysis of the Ve locus identified several genotypes that could be donors of the Ve1 gene conferring resistance to Verticillium race 1. Sequencing for the first time Romanian genotypes enriched the existing data on various world tomato genetic resources, but also opened the way for the molecular breeding in Romania. Plant breeders can use these data to create novel tomato varieties adapted to the ever-changing environment.
Cyber-Security of Industrial Internet of Things in Electric Power Systems

Electric Power Systems (EPSs) are among the most critical infrastructures of any society, since they significantly impact other infrastructures. Recently, there has been a trend toward implementing modern technologies, such as Industrial Internet of Things (IIoT), in EPSs to enhance their real-time monitoring, control, situational awareness, and intelligence. This movement, however, has exposed EPSs to various cyber intrusions that originate from the IIoT ecosystem. Statistics show that 38% of reported attacks have been against power and water infrastructure, and so far at least 91% of power utilities have experienced a cyber-attack. The cyber-security problem is even more severe for IIoT applications in EPSs due to the vulnerabilities and resource limitations of such applications. Thus, based on the above statistics, it is necessary to investigate the vulnerabilities of IIoT-based applications in EPSs, identify probable attacks and their consequences, and develop intrusion prevention and detection approaches to secure IIoT systems. On this basis, this paper first elaborates on the applications of IIoT-based systems in EPSs, and evaluates their security challenges. Afterwards, it comprehensively reviews various cyber-attacks against IIoT-assisted EPSs, with a particular focus on attack entry points and adversarial methods. Finally, efforts to prevent cyber-intrusions against IIoT systems in EPSs are explained, and different attack detection techniques are discussed.
Polyelectrolyte Multilayers Composed of Polyethyleneimine-Grafted Chitosan and Polyacrylic Acid for Controlled-Drug-Delivery Applications

In this work, polyethyleneimine (PEI)-grafted chitosan (Chi-g-PEI) was prepared for the fabrication of layer-by-layer (LBL) films for use in sustained-drug-delivery applications. Chi-g-PEI and polyacrylic acid (PAA) multilayer films were formed using the LBL technique. Methylene blue (MB) was used as a model drug for the investigation of loading and release capabilities of the LBL films. Characterizations of the synthesized copolymer were performed using Fourier-transform infrared spectroscopy (FTIR), Nuclear magnetic resonance spectroscopy (NMR), Thermogravimetric analysis (TGA), and X-ray Powder Diffraction (XRD) techniques, and the thickness of the LBL films was measured using Atomic force microscopy (AFM). The drug-loading and -release behaviors of the LBL films were assessed using a UV–visible spectrophotometer. The results showed that the loading capacity and release rate of MB were affected by ionic strength and pH. In addition, it was demonstrated that PEI-grafted chitosan is a good candidate for the assembling of LBL films for drug-delivery applications.
Influence of Homogenization Time and Speed on Rheological and Volatile Composition in Olive-Based Pâtés

The influence of the homogenization time and speed on rheological and volatile composition in olive-based pâtés was studied. Five experimental trials were performed applying different combinations of time and speed homogenization: 1, 3, and 5 min at 12,000 rpm and 4000, 8000, and 12,000 rpm at 5 min. The obtained results showed that the processing parameters of the homogenization step significantly influenced the rheological and sensory properties of olive-based pâtés. Both time and speed influenced the rheological properties of the product. The increase of homogenization time and speed determined a significant reduction of hardness and syneresis. As regards color indices, significantly higher L* values were obtained when intermediate time and speed conditions were applied, whereas a* and b* indices showed a not univocal behavior. Both time and speed variables also influenced the volatile fraction of the pâtés (higher homogenization speed and time corresponded to higher terpenes and aldehydes).
Ophthalmic Solutions with a Broad Antiviral Action: Evaluation of Their Potential against Ocular Herpetic Infections

HSV-1 can be associated with severe and recurrent eye infections characterized by a strong inflammatory response that leads to blepharoconjunctivitis, epithelial and stromal keratitis, and retinal necrosis. The incidence of HSV-1 keratitis is 1.5 million every year worldwide, including more than 40,000 new cases exhibiting serious visual failures. Generally, the therapy uses antiviral drugs to promote healing; however, there are currently no compounds that are able to completely eradicate the virus. In addition, the phenomenon of resistance is rapidly spreading among HSV-1 strains, creating mutants developing resistance to the common antiviral drugs; therefore, deep research on this issue is warranted. The efficacy of different ophthalmic solutions already on the market was evaluated for reducing HSV-1 infection. Different plaque assays were set up on epithelial cells, revealing that two ophthalmic solutions were able to inhibit viral replication in the early stages of infection. The data were further confirmed by molecular tests analyzing the expression levels of the principal genes involved in HSV-1 infection, and a strong reduction was observed after only 1 min of eye-drop treatment. Collectively, these results suggested the use of ophthalmic solutions as potential antiviral options for the treatment of ocular herpetic infection.
Dithering strategies for WFC3

In this report we describe different dithering strategies for WFC3. This includes moving the telescope by steps large enough to step over detector artifacts and the use of subinteger pixel steps to recover some of the spatial information lost due to the undersampling of the point spread function by both WFC3 detectors. For both the UVIS and IR channels, we describe the different detector characteristics that have to be taken into account in order to derive an optimal dithering strategy. A number of dither patterns for different scenarios are presented.
Design and Implementation of a 3.9-to-5.3 GHz 65 nm Cryo-CMOS LNA with an Average Noise Temperature of 10.2K

Realization of CMOS SoC readout solutions for future quantum computers will require demonstrating cryo-CMOS LNAs with cutting-edge noise and power. However, a lack of design models makes the systematic implementation of these devices challenging. Here, we propose a method for the first-pass design of cryo-CMOS LNAs by complementing room temperature PDK models with additional noise sources and report a cryo-CMOS LNA in 65nm bulk CMOS achieving > 38 dB gain and an average noise of 10.2K from 3.9-5.3 GHz while dissipating 23.1mW. The amplifier can be operated at < 10mW at a cost of 1K additional noise. Using a FOM representing the number of added noise photons, bandwidth, and power, the amplifier is shown to have a > 2x better FOM than other state-of-the-art cryo-CMOS LNAs. Additionally, we extract cryogenic noise models and find that the Fano factor for this technology does not appear to change with cryogenic cooling.