Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytoplankton Size Classes in the Global Ocean at Different Bathymetric Depths

A three-component model is often used to invert the phytoplankton size class (PSC) concentration globally, especially in open oceans. Limited by the assumption of this three-component model, new efforts have been made to explore PSCs in different bathymetric regions. Mass global cruise datasets were gathered and classified into coastal, mixed, and open ocean datasets determined by the variation in bathymetric depth. A new three-component power model was established for coastal water samples (< 50 m), where the determination coefficients (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) were 0.95, 0.37, and 0.44 for microphytoplankton (Micro), nanophytoplankton (Nano), and picophytoplankton (Pico), respectively. We also updated the coefficients of the three-component exponential model in the open ocean (>200 m) and found that the PSC verification results performed better in the ocean north of the 40° S latitude line (<inline-formula> <tex-math notation="LaTeX">$R^{2}= 0.83$ </tex-math></inline-formula>, 0.70, and 0.64, respectively). A smooth function for the samples in mixed ocean waters (50–200 m) was designed to obtain PSCs with different weights between the three-component power and exponential models with relatively low accuracy (<inline-formula> <tex-math notation="LaTeX">$R^{2}=0.84$ </tex-math></inline-formula>, 0.37, and 0.14, respectively), which is indicative of the complex conditions in these regions. The global PSC proportion distribution is consistent with the existing knowledge in this field. Finally, we analyzed the performance and limitations of different PSC models based on the entire dataset and found that the proposed model outperformed the others, and the assumption of the three-component exponential model may fail in the Southern Ocean.
Rate and Detection Error-Exponent Tradeoffs of Joint Communication and Sensing

We consider a communication model in which a transmitter attempts to communicate with a receiver over a state-dependent channel and simultaneously estimate the state using strictly causal noisy state observations. Motivated by joint communication and sensing scenarios in which the physical phenomenon of interest for sensing evolves at a much slower rate than the rate of communication, the state is assumed to remain constant over the duration of the transmission. We derive a complete characterization of the optimal asymptotic trade-off between communication rate and detection-error exponent when coding strategies are open loop. We also show that closed-loop strategies result in strict improvements of the trade-offs.
A Semantic Guidance and Transformer-Based Matching Method for UAVs and Satellite Images for UAV Geo-Localization

It is a challenging task for unmanned aerial vehicles (UAVs) without a positioning system to locate targets by using images. Matching drone and satellite images is one of the key steps in this task. Due to the large angle and scale gap between drone and satellite views, it is very important to extract fine-grained features with strong characterization ability. Most of the published methods are based on the CNN structure, but a lot of information will be lost when using such methods. This is caused by the limitations of the convolution operation (e.g. limited receptive field and downsampling operation). To make up for this shortcoming, a transformer-based network is proposed to extract more contextual information. The network promotes feature alignment through semantic guidance module (SGM). SGM aligns the same semantic parts in the two images by classifying each pixel in the images based on the attention of pixels. In addition, this method can be easily combined with existing methods. The proposed method has been implemented with the newest UAV-based geo-localization dataset. Compared with the existing state-of-the-art (SOTA) method, the proposed method achieves almost 8% improvement in accuracy.
Reduced-Complexity FFT-Spread Multicarrier Faster-Than-Nyquist Signaling in Frequency-Selective Fading Channel

In this paper, we propose novel reduced-complexity fast Fourier transform (FFT)-spread multicarrier faster-than-Nyquist (MFTN) signaling with power allocation for a frequency-selective fading channel. The information rate of the proposed MFTN signaling is derived by relying on the circulant approximation of the FTN-specific intersymbol interference matrix and noise covariance matrix. This allows us to constitute efficient calculations of precoding and weighting matrices. The power allocation coefficients are optimized such that the approximated information rate is maximized. Our simulation results demonstrate that the proposed scheme with power allocation achieves the bit error ratio (BER) performance close to the conventional eigenvalue-decomposition (EVD)-precoded FTN signaling counterpart that is optimal in terms of an achievable information rate while significantly reducing the computational complexity as low as the order of $\mathcal {O}(N\log N)$ . While the proposed scheme exhibits a high peak-to-average-power ratio similar to the conventional EVD-precoded counterpart due to the effects of FFT-based precoding, it achieves better BER performance than the conventional open-loop single-carrier FTN signaling scheme and the Nyquist signaling scheme, employing the same root-raised cosine shaping filter. It is also confirmed that the proposed MFTN signaling scheme does not suffer from any significant bandwidth broadening.
An Self-supervised Learning & Self-attention Based Method for Defects Classification on PCB Surface Images

Automatic detection of PCB defects become a difficult work in electronics industry, with the rapid development of Integrated Circuit. A good detection method can effectively improve production efficiency and reduce the costs of quality inspection. The traditional image processing methods have the backwards of complicated system structure, difficult image segmentation, and low detection accuracy, etc. In order to solve the above problems, this paper proposes a method of extracting features by self-supervised learning with supervised learning to complete the classification task. This method uses a deep neural network model based on self-supervised learning technology, uses multi-head self-attention mechanism; automatically recognize single or multiple defect areas in PCB, to extract defects features of PCB, and uses full connection classifier to classify features. The open-source (DeepPCB) is selected as the dataset, including 6 defect categories and non-defect categories. In the training process, data enhancement technology is used to increase the training samples, train the model and make it converge, and realize low error recognition on the verification dataset. The experimental results show that the features extracted by the self-supervised learning method are richer than those extracted by the supervised learning. The composite model formed by combining the self-attention mechanism has high accuracy and good robustness, reaches the classification accuracy of 94.33%, and has high accuracy and good generalization ability.
An intrabody signal propagation study for human body hydration

Human body composition refers to the relative proportions of fat, bone, water, muscle and minerals in the body. Adequate proportions of these are a primary requirement for healthy living. Measurement of body composition is important for medical diagnosis and for understanding the physiological proportions of body tissues for physical fitness and exercise performance. Studies in human body hydration, as an example, provides the information necessary to understand the desired fluid levels for optimal performance of the body's physiological and metabolic processes during exercise and activities of daily living. It can help identify, or quantify issues of ill-health or wellbeing, e.g. lymphoedema and risk of heart attack. This thesis proposes a new system for assessing human body hydration which measures changes in body fluid level in real time.
Adaptive Spectrum Monitoring Algorithm for Cognitive Radio Systems

At present, the communications industry is one of the fastest-growing sectors, as well as an important component of modern society, which has led to rapid growth in demand for the radio frequency spectrum (RF). Since virtually all RF is distributed among corporations providing various communication services, it is becoming increasingly difficult to introduce new communication systems and technologies. The deficit of RF has led to the development of new communication systems aimed at the efficient use of radio frequency resources. These include the Cognitive Radio System (CRS), which makes it possible to use the RF of the primary user efficiently and without reducing the quality of service for all subscribers. In the previous article, we proposed an algorithm for implementing CRS based on models of spectral occupancy. One stage of the algorithm is spectrum monitoring. Spectrum monitoring is used to obtain actual information about the spectrum occupation. There are many scientific works aimed at detecting the signals of primary users. Due to its implementation simplicity and low complexity, the main method of signal detection is the use of an energy detector, which was implemented in our previous article. However, this approach is sensitive to various types of interference and fluctuations signal-to-noise ratio (SNR), due to the dynamics of the radio environment. A new detection algorithm using an adaptive threshold is therefore proposed. The value of the adaptive threshold is selected taking into account the noise power fluctuations, thus avoiding the disadvantages of the classical energy detector. Simulation results show that an adaptive threshold algorithm is more resistant to the dynamic changes of SNR than a classical algorithm. The new algorithm also makes it possible to increase the performance of the algorithm proposed by us for the implementation of CRS based on models of spectral occupancy.
Security Classification Of Bank Data Based On NLP

With the rapid development of modern information technology, the world has entered a new era of big data. In order to effectively protect the security of bank data assets and safeguard the legitimate rights and interests of customers, commercial banks need to carry out security grading of the data they manage and need to strictly regulate the data security grading elements, data security impact assessment and data security level change within the objectives, principles and scope of data security grading. The process of data security classification is continuously improved in all aspects. This project is based on natural language processing technology, using Python language to implement data security grading, reducing the tediousness of manual grading, but also ensuring data security.
Multi-Band Superresolution Multipath Channel Path Delay Estimation for CIR-Based Localization

The channel path delay estimation (PDE) plays an essential role in channel impulse response (CIR)-based localization techniques. Several state-of-the-art superresolution path delay estimation (SPDE) approaches have been proposed to provide more precise PDE based on eigendecomposition. Although superresolution techniques reduce the bandwidth requirement on the sensor, the achievable resolution is still limited. The resolution can be further improved when measurements on different frequency bands are combined. In this paper, we propose an approach to combine the frequency-domain channel estimates on different nonconsecutive frequency bands to provide a better resolution for the PDE. The performance is evaluated with numerical simulations.
Investigation of SAR Resolution at Translational and Rotational Motion of Interlinked Antenna Phase Centers

The paper presents a study of assessing angular resolution of radar systems with a synthetic aperture created via translational and rotational motion of an interlinked set of the phase centers of real antennae. The research also deals with an overview of relevant studies regarding the ways to enhancing the angular resolution evaluation of synthetic-aperture radar systems via creation of a synthetic aperture in circumstances where the rotational motion of the phase center of a real antenna or the set of interlinked antennae. The performed analytical studies yielded mathematical equations describing the law of variation of distance between a stationary ground-based target and an interlinked set of receiving and transmitting radar antennae with the radar system being in an arbitrary position. In addition, for cases where a point source target being detected in the front-looking and side-looking modes, the analysis yielded equations describing potential linear resolving power of radar systems for a chosen method of synthetic aperture creation. The simulation modeling has been used for proving mathematical models of generation and processing of a trajectory signal with selected positions of a point-source target relative to the airborne radar carrier.
Tumor infiltrating lymphocytes in head and neck squamous cell carcinoma – Evaluation and clinical impact

Background: Head-and-neck squamous cell carcinomas (HNSCCs) are one of the most common malignancies worldwide. Emerging evidence reveals that tumor-infiltrating lymphocytes (TIL) provide valuable prognostic information in various malignancies, including HNSCC. Aims and Objectives: The present study aimed to evaluate a simple yet accurate method of counting TILs in HNSCCs. It was also done to determine if there is any correlation between different clinicopathological parameters of HNSCC and TIL counts. Materials and Methods: A retrospective institutional-based, observational study was undertaken for 1 year. Patients with histologically proven HNSCCs were included. All clinicopathological parameters were noted, and evaluation of TIL was meticulously done both in the stromal and intratumoral regions for each case. Standard statistical methods were employed for data analysis. Results: The present study included 81 patients, among which 51 (63%) were male and the rest (30, 37%) were female. The mean age of the patients was 55.3 ± 10.5 years. Most tumors were located in the oral cavity (73, 90.1%). The most common histologic type was moderately differentiated (MD) squamous cell carcinoma (SCC) (50 cases, 61.7%). Stromal TIL score was high in 45 cases (55.6%). In 69 cases (85.2%), the intratumoral TIL level was low. A statistically significant correlation was found between TIL levels (both stromal and intratumoral TIL) and tumor differentiation, pT stage, and lymphovascular invasion. The correlation between intratumoral TIL level, age of patients, and pN stage was also statistically significant. Conclusion: TIL evaluation is an inexpensive, simple, and reproducible method, which furnishes relevant prognostic data. It has the potential for consideration of its inclusion in routine histopathological reports of HNSCCs.
Comparing Two Forecast Models of Ports' Handling Capacity

The forecast of ports' handling capacity is one important work before planning total arrangement of ports. The influence of the work is profound and far-reaching.Taking a port as an example, the paper simply introduces the forecast process of its total handling capacity with second-index flatness, puts forward logarithm second-index flatness for the forecast process of ports' total handling capacity,analyses its excellence and reaches a conclusion that logarithm second-index flatness is fit for the forecast of ports' total handling capacity which is developing rapidly. It provides the scientific gist for ports' planning. It can be referenced by some relational departments.
Using Mobile Tools to Support Meaningful Work-based Learning in Vocational Education

This case study focused on meaningful work-based learning (WBL) and the pedagogical use of mobile information and communication technologies (ICTs) in vocational tourism education. The aim was to reveal how teaching/tutoring and learning are realized and how the use of smartphones supports the realization of meaningful learning characteristics during WBL periods in highly versatile environments. Within a design-based research framework, the data was collected through learning journals written by students and qualitative interviews. The results of thematic analysis were used to develop a practice-oriented pedagogical model for meaningful WBL. The model visualizes the roles of students, teachers, and companies involved in WBL, the meaningful learning characteristics that can be amplified through the use of mobile ICTs, and the outcomes for each stakeholder. The model suggests structuring WBL through four negotiations involving a student, a teacher, and a company to assure that each student has clearly formulated learning goals and possibilities to pursue those goals regardless of the mobility of their work or facilities during their WBL period.
Cardinal Sins? Conspicuous Consumption, Cardinal Status and Inequality

This paper analyzes the social dilemma arising when a large population of individuals with differing incomes have concerns over relative deprivation in terms of visible or conspicuous consumption. These relative concerns are cardinal - people care about the size of the gap between own and others’ consumption - and include inequity aversion, where negative comparisons are more important than positive, rivalrous preferences, and comparison with mean consumption. The resulting Nash equilibrium is inefficient, with consumption generally exceeding the socially efficient level. In this model, the income distribution has a direct effect on behavior and under rivalrous preferences, an increase in incomes for the rich can raise consumption at all income levels and make almost everyone worse off.
Training in machine translation post-editing for foreign language students

The main purpose of this study is to evaluate the effectiveness of Machine Translation Post-Editing (MTPE) training for FL students. Our hypothesis was that with specific MTPE training, students will able to detect and correct machine translation mistakes in their FL. Training materials were developed to detect six typical mistakes from Machine Translation (MT) raw output: Accuracy, Word Order, Official Name, Preposition, Omission, and Formal Style. The training materials include three levels of difficulty: Initial - ability to spot a mistake, Intermediate - ability to classify the type of mistake, and Advanced - ability to correct the mistake. A pretest-posttest design with a control group and a trained experimental group was chosen to test the effectiveness of the training programme. In the posttest, the experimental group could identify and correct more mistakes successfully. and in less time than the control group, especially for omission , official name and preposition . Accuracy , formal style , and word order errors were more difficult to correct. Results suggest that specific MTPE training is not only useful to identify and correct MT mistakes but also a way to incorporate a critical view on machine translation in FL classes.
A theory for the relationship between lake surface area and maximum depth

Maximum depth is crucial for many lake processes and biota, but attempts to explain its variation have achieved little predictive power. In this paper, we describe the probability distribution of maximum depths based on recent developments in the theory of fractal Brownian motions. The theoretical distribution is right‐tailed and adequately captures variations in maximum depth in a dataset of 8164 lakes (maximum depths 0.1–135 m) from the northeastern United States. Maximum depth increases with surface area, but with substantial random variation—the 95% prediction interval spans more than an order of magnitude for lakes with any specific surface area. Our results explain the observed variability in lake maximum depths, capture the link between topographic characteristics and lake bathymetry, and provide a means to upscale maximum depth‐dependent processes, which we illustrate by upscaling the diffusive flux of methane from northern lakes to the atmosphere.
Aryl hydrocarbon receptor-dependent and -independent pathways 1 mediate curcumin anti-aging effects 2

29 The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor whose activity 30 can be modulated by polyphenols such as curcumin. AhR and curcumin have evolutionarily 31 conserved effects on aging. Here, we investigated whether and how the AhR mediates the 32 anti-aging effects of curcumin across species. Using a combination of in vivo, in vitro , and in 33 silico analyses, we demonstrated that curcumin has AhR-dependent or -independent effects 34 in a context-specific manner. We found that in Caenorhabditis elegans , AhR mediates 35 curcumin-induced lifespan extension, most likely through a ligand-independent inhibitory 36 mechanism related to its antioxidant activity. Curcumin also showed AhR-independent anti- 37 aging activities such as protection against aggregation-prone proteins and oxidative stress in 38 C. elegans and promotion of the migratory capacity of human primary endothelial cells. 39 These AhR-independent effects are largely mediated by the Nrf2/SKN-1 pathway. 40 vs. DMSO, Bonferroni p-value < 0.05. No statistical significance was observed in ugt-45 vs. 277 control RNAi-treated worms. inducible Secondary structures attributed
Communication in the shadow of catastrophe*

We study the role of risk in strategic information transmission. We show that an increased likelihood of extreme states – heavier tails – decreases the amount of information transmission and makes it optimal to alter the mode of decision-making from communication to simple delegation. Moreover, the worst-case losses under communication increase relative to the worst-case losses under delegation when the tails get heavier. JEL: D83, D82
Thinned Nectarines, an Agro-Food Waste with Antidiabetic Potential: HPLC-HESI-MS/MS Phenolic Characterization and In Vitro Evaluation of Their Beneficial Activities

Due to the side effects of synthetic drugs, the interest in the beneficial role of natural products in the management of diabetic conditions is growing over time. In the context of agro-food waste products, a screening of different fruit thinning by-products identified thinned nectarines (TN) as the richest matrices of abscisic acid (ABA), a phytohormone with well-documented hypoglycemic potential. These waste-food matrices may represent not only precious sources of ABA but also other bioactive molecules with potential health benefits, such as polyphenols. Therefore, we aimed to perform a qualitative and quantitative characterization of a polyphenolic profile of a TN-based nutraceutical formulation through HPLC-HESI-MS/MS and HPLC-DAD-FLD analyses. Additionally, the in vitro antioxidant and antidiabetic potential of TN was investigated. HPLC analyses allowed us to identify forty-eight polyphenolic compounds, nineteen of which were quantified. Moreover, the results obtained through different in vitro assays showed the antioxidant and antidiabetic potential exerted by the tested nutraceutical formulation. In conclusion, the concomitant presence of different bioactive compounds in TN-based nutraceutical formulation, such as ABA and polyphenols, would reasonably support TN as an innovative nutraceutical formulation useful for the management of glucose homeostasis. Further in-depth animal-based studies and clinical trials are needed to deepen these aspects.