Miracle-dz/UniFAR-FTUs huggingface dataset:
Narrative review of developing new biomarkers for decision making in advanced testis cancer

Management of testicular germ cell tumor (GCT) patients is based on clinical determinants, mainly CT scan and serum tumor markers (alpha-fetoprotein, beta subunit of HCG and LDH). Treatment decisions are usually straightforward for patients with clear evidence of metastatic disease, confirmed either by imaging tests or by unequivocal elevated tumor markers. However, there are several clinical scenarios where the assessment of metastatic disease is complicated by the limited specificity of the current imaging tests and serum tumor markers. These include patients with clinical stage IIA GCT with negative tumor markers and patients with post-chemotherapy residual disease where, in absence of clear indicators of GCT, decision making and patient treatment allocation become challenging. Therefore, more accurate biomarkers are critical to reduce the risk of under-or over-treatment and to always deliver the most optimal therapy. The objectives of this narrative review are to review the available publications about micro-RNAs in GCT s and their potential clinical applications. Two clusters of micro-RNAs, miR-371a-3p and miR-302/367, specifically expressed by both seminoma and non-seminoma GCT and easily detectable in the peripheral blood, have demonstrated to be promising in this endeavor. Large prospective trials are ongoing to define the operating characteristics of these biomarkers and their clinical utility to improve GCT patient management and reduce the error rate deriving from clinical uncertainty, therefore reducing the risk of sub-optimal treatments.
Coral reef pore recognition and feature extraction based on borehole image

Abstract The coral reef pores are the main reservoir space of oil and gas resources. Through determining the structural form and distribution characteristics of pores, it can be helpful for scientific and reasonable stratigraphic division and reservoir evaluation, and provide basic data for the efficient development of using coral reefs as oil and gas reservoir sites. The core difficulty of interpretation and evaluation of coral reef pore structure and distribution characteristics is the pore recognition and feature extraction. In view of problems of low accuracy and efficiency in the recognition and feature extraction of coral reef pore at present, this paper proposes a method of coral reef pore recognition and feature extraction based on borehole image data. First of all, based on the borehole image and combined with the pore characteristics in borehole image, this paper proposes the coral reef pore recognition method. By using the feature of color gradient difference between the pore and the non pore in the borehole image, it realizes the segmentation of coral reef pore with low noise and weak interference. On the basis of obvious coral reef pores, the contour tracking method and image filling system of coral reef pore are established. Then, on the basis of traditional eight connected region marking algorithm, a pore region feature extraction method is proposed. The equivalent relationship between the temporary mark and the final mark is established, and an evaluation criterion that can comprehensively reflect the pore region feature parameters in the borehole image is formed. Finally, a systematic analysis and a comparison of results are carried out based on the actual coral reef borehole images. The results are consistent with the real values, which verifies the correctness of method.
Quality attributes of probiotic-enriched chocolate: A preliminary study

Research on the development of foods containing probiotics is growing, such as research in the incorporation of probiotics in chocolate. Nevertheless, the quality attributes of the probiotic-enriched chocolate have not been fully explored. This present study is a preliminary study to the impact of ingredients proportion and processing method on the quality attributes of probiotic milk chocolate, more specifically on moisture content, appearance, hardness, rheological behavior, and microstructural characteristics. In this study, four different formulations and processing methods were involved. Skim milk powder was used as a freeze-dried probiotic replacer as it has high similarity in terms of physical characteristics. The result showed that chocolate containing freeze-dried probiotic powder replacer (Choc B) tended to have the comparable hardness and L*values to the chocolate reference (Choc A). However, hardness and L* values of milk chocolates enriched with freeze-dried probiotic powder replacer formulated with milk butter (Choc C and Choc D) were lower than that of chocolate reference (Choc A). Concerning the moisture level and flow parameters, Choc C exhibited higher moisture content and flow parameters values than other chocolates. Microstructural visualization showed that all chocolates exhibited a low level of agglomeration degree with particle sizes in the range of 19-32 μm.
Non-destructive measurement of rice leaf dimension in 3D point cloud

In order to try to improve the yield, knowing the expanding of a leaf by measuring its dimension can be one easy way to monitor the plant growth. While rice is considered to be one of the most important agricultural plant, their leaves are quite difficult to measure the size since they are mostly small and direct measurement could harm the plant. To do so, we deploy the non-destructive system to measure the dimension of the rice leaf from a point cloud. The proposed system takes only photos from a smartphone camera to produce the 3D model of a specific leaf using structure from motion and multi-view stereo. To measure the dimension of the rice leaf in metric units, the scale reference is placed into the scene before the photo taking process to involve it in the reconstruction result. From the reconstructed point cloud, the leaf veins can be extracted in order to find the accurate width of a leaf based on thresholding technique using combination of RGB and HSV spaces. By comparing with measured length of the leaf in point cloud with length of one block in scale reference, we can eventually obtain the distant between each leaf vein in the metric system unit.
Churn Prediction in a Pay-TV Company via Data Classification

In data mining, if a data set is new to the literature, the study is comparing the existing algorithms and determining the most suitable algorithm. This study is an example of this by including many quantitative analysis. Real data was obtained from a Pay-TV Company in Turkey to predict the churn behavior of the customers. The attributes such as membership period, payment method, education status, and city information of customers were used in order to predict the customers' churn status. By applying attributes selection algorithms, the most important attributes are obtained. As a result, two datasets are proposed. While one of the datasets consists of all attributes, the other one just includes the selected attributes. Many different data classification algorithms were applied to these datasets by using WEKA software. The best method and the best dataset which has the best accuracy rate was proposed to the company. The company can predict the customers' churn status and contact the right group of people for a specific campaign with a proposed user-friendly prediction methodology.
Information Extraction Research Review

Information extraction is to extract the required specific entity, event, relationship and other information from a large number of texts and store it in a structured form, which occupies an important position in the field of natural language. Information extraction helps people get the information they need efficiently, avoids a lot of complicated work, and saves manpower and material resources. In this paper, named entity recognition and so on are described and summarized in detail, as well as the prospect of information extraction.
A Critical Review of Analytical Methods for Comprehensive Characterization of Produced Water

Produced water is the largest waste stream associated with oil and gas production. It has a complex matrix composed of native constituents from geologic formation, chemical additives from fracturing fluids, and ubiquitous bacteria. Characterization of produced water is critical to monitor field operation, control processes, evaluate appropriate management practices and treatment effectiveness, and assess potential risks to public health and environment during the use of treated water. There is a limited understanding of produced water composition due to the inherent complexity and lack of reliable and standardized analytical methods. A comprehensive description of current analytical techniques for produced water characterization, including both standard and research methods, is discussed in this review. Multi-tiered analytical procedures are proposed, including field sampling; sample preservation; pretreatment techniques; basic water quality measurements; organic, inorganic, and radioactive materials analysis; and biological characterization. The challenges, knowledge gaps, and research needs for developing advanced analytical methods for produced water characterization, including target and nontarget analyses of unknown chemicals, are discussed.
Tail or no Tail? Applicability of the Long Tail Theory to the German Online Media Market

The Long Tail theory serves as an explanation for market change triggered through evolving online market conditions. However, it is often referred to without empirical validation, or with inconsistent findings on its applicability. Therefore, this paper analyses the applicability of the Long Tail theory to German online media offerings as of 2014 and 2016, focusing on offerings that serve information purposes (information offerings). Based on a unique dataset of commercially oriented online media offerings (Longitudinal IntermediaPlus 2014–2016), an analysis for three Long Tail characteristics (variety increase, opposing concentration between hit and niche offerings and niche market share increase) is conducted. By additionally distinguishing between political and entertainment-oriented information offerings as boundaries of democratized online market conditions the analysis reveals that the Long Tail theory is not fully applicable to any market sample, and especially not to politically oriented offerings.
The Relationship Between Work Intensification and Employee Learning in a Cognitive Appraisal Perspective

Based on the cognitive interaction theory of stress, this paper explores the effects of work intensification on employee learning and examines the mediating role of cognitive appraisal. Finally, the study finds that work intensification can influence employee learning in the workplace through the fully mediating role of challenge appraisal. The results verify the potential value of work intensification to employee learning, supplement the research on work intensification, and provide a reference for talent incentive management in enterprises.
Current status and perspectives on the role of polyamines in plant immunity

Polyamines are low molecular weight amines, with versatile and essential functions during the lifespan of prokaryotes and eukaryotes. Modulation of polyamine metabolism under biotic stress has attracted a great deal of interest; however, the precise functions of these amines in plant defence responses to pathogen attack are still poorly understood. Herein we summarise and review important aspects of the regulation of polyamine biosynthesis, catabolism and conjugation under biotic stress that might contribute to improve present-day knowledge about these compounds in plant immunity. Particular attention is paid to the intracellular localisation of the proteins/ enzymes associated to polyamine metabolism, as well as the impact of pathogen effector proteins on the control of plant polyamine metabolism is discussed.
A SIMPLE EFFORT TO ENHANCE SOLUBILITY AND DISSOLUTION RATE OF SIMVASTATIN USING CO-CRYSTALLIZATION Original Article

Objective: The main objective of this study was to explore co-crystallization as an effort to enhance the solubility of simvastatin (SV) using tartaric acid (TA) as co-former. Methods: The simulation of molecular modeling of TA against SV has been done by in silico using auto dock 4.2. A preparation of co-crystal carried out by using solvent drops grinding (SGD) with an equimolar ratio. A co-crystal formed was confirmed by scanning electron microscopy (SEM), saturated solubility test, in vitro dissolution test, infrared spectrophotometry (FTIR), powder X-ray diffraction (PXRD), and differential scanning calorimetry (DSC). Results: The in silico studies showed that the interaction of TA and SV has synthon molecular by hydrogen bonding. An increasing of solubility and in vitro dissolution profile of co-crystal resulted as compared to the value of pure SV and its physical mixer. Characterizations of a co-crystal SV: TA (1: 1) including SEM, FTIR, PXRD, and DSC have indicated the formation of new solid crystal phase that different compared to SV, TA, and its physical mixture. Conclusion: The co-crystallization has been used to enhance the solubility and dissolution of simvastatin. All characterization either in silico and in vitro has shown the formation of co-crystal SV: TA (1:1).
COMPUTATIONAL INVESTIGATION OF NANOSCALE SEMICONDUCTOR DEVICES AND OPTOELECTRONIC DEVICES FROM THE ELECTROMAGNETICS AND QUANTUM PERSPECTIVES BY THE FINITE DIFFERENCE TIME DOMAIN METHOD (INVITED REVIEW)

In the simulation of high frequency nanoscale semiconductor devices in which electromagnetic (EM) fields and carrier transport are coupled, and optoelectronic devices in which strong interactions between EM fields and charged particles exist, both the Maxwell’s equations and the time-dependent Schrödinger equation (TDSE) need to be solved to capture the interactions between EM and quantum mechanics (QM). One of the numerical simulation methods for solving these equations is the finite difference time domain (FDTD) method. In this review paper, the development of FDTD method applied in EM and QM simulation is discussed. Several widely used FDTD techniques, i.e., explicit, implicit, explicit staggered-time, and Chebyshev methods, for solving the TDSE are introduced and compared. The hybrid approaches based on FDTD method, which are used to solve the PoissonTDSE and Maxwell-TDSE coupled equations for EM-QM simulation, are also discussed. Furthermore, the applications of these simulation methods for nanoscale semiconductor devices and optoelectronic devices are introduced. Finally, a conclusion is given.
Multisource Remote Sensing Data Classification With Graph Fusion Network

The land cover classification has been an important task in remote sensing. With the development of various sensors technologies, carrying out classification work with multisource remote sensing (MSRS) data has shown an advantage over using a single type of data. Hyperspectral images (HSIs) are able to represent the spectral properties of land cover, which is quite common for land cover understanding. Light detection and ranging (LiDAR) images contain altitude information of the ground, which is greatly helpful with urban scene analysis. Current HSI and LiDAR fusion methods perform feature extraction and feature fusion separately, which cannot well exploit the correlation of data sources. In order to make full use of the correlation of multisource data, an unsupervised feature extraction-fusion network for HSI and LiDAR, which utilizes feature fusion to guide the feature extraction procedure, is proposed in this article. More specifically, the network takes multisource data as input and directly output the unified fused feature. A multimodal graph is constructed for feature fusion, and graph-based loss functions including Laplacian loss and t-distributed stochastic neighbor embedding (t-SNE) loss are utilized to constrain the feature extraction network. Experimental results on several data sets demonstrate the proposed network can achieve more excellent classification performance than some state-of-the-art methods.
A High-Efficiency Fast Transient COT Control DC–DC Buck Converter With Current Reused Current Sensor

This article presents a high-efficiency fast transient constant on-time (COT) control dc–dc buck converter for the Internet of Things applications. The current reused current sensor is proposed to enhance the loop stability and improve the power conversion efficiency at light load. Input current is sensed and added to the feedback voltage of the dc–dc converter to increase the sensed output ripple voltage. Fast transient dc-offset cancelation technique is introduced to achieve a fast transient recovery time and to cancel the output dc offset. This chip is fabricated with a 0.13-μm CMOS process. A standard supply voltage of 7–15 V is applied to produce the output voltage of 5–5.33 V. The total die area is 2 mm × 1.5 mm. The settling time is 17 μs. The transient recovery time from light to heavy load and from heavy load to light load are 3 μs and 2.7 μs, respectively, with a maximum dc offset of 3.5 mV. The overshoot voltage is 85 mV and the undershoot voltage is 72 mV. The peak efficiency of the proposed design is 95.56% at 0.5 A and efficiency at the light load of 10 mA is 84.60%.
Efficient caching on parity chunks in RAID-enabled SSDs

Caching the updated parity chunks in the RAID buﬀer can absorb update operations on parity chunks of data stripes in RAID-enable SSDs. Thus, the negative eﬀects of write penalty can be minimized. However, caching integrated parity chunks fails to eﬃciently use the capacity-limited RAID buﬀer, as sometimes a major parts of parity chunk are not modiﬁed comparing with the parity chunk saved in SSD blocks. This paper presents a patch-based cache management scheme on parity chunks, for improving cache use eﬃciency of RAID-enabled SSDs. Speciﬁcally, it only caches the modiﬁed portions of parity chunks in the buﬀer (called parity patches ),whicharecorrespondingtotheupdatedpartsofdatachunks in the same stripe. Through a series of simulation tests on several disk traces, we illustrate our proposal can noticeably reduce the I/O latency by between 12.2% and 17.1% and the number of block erases by 20.8% on average, in contrast to state-of-the-art approaches. In brief, our proposed cache management scheme can work in RAID-enabled SSDs to achieve better I/O performance and extend the lifespan of SSDs.
The Effects of FDI on Domestic Investments in Western Balkans

Research background: In order to contribute to economic growth, inward foreign direct investments (FDI) need to meet certain economic and social criteria. Besides the contribution to the level of education, technological level, financial development, tax system, trade and investment policies, and market size, FDI should also encourage domestic investments (crowding in-effect). Purpose of the article: This paper examines the importance and effects of the inward and outward direct investments, gross savings as well as real growth on domestic investments in the case of Western Balkan countries (North Macedonia, Serbia, Albania, Kosovo, Montenegro, and Bosnia & Herzegovina). Thus, the logic behind this research is to determine whether and in which direction are aimed effects of FDI. Methods: The relation between FDI and domestic investments has been analyzed by employing panel data approach with and without constrains on cross-sections. The study is based on a panel data of six countries for the period between 2007-2018, (i.e., in total, we have 66 observations). Findings & Value added: The general conclusion from this analysis confirm that inward foreign direct investments in the Western Balkans, as well as real economic growth both significantly and positively affect the domestic investments.
Nonlinear MPC of a Heavy-Duty Diesel Engine With Learning Gaussian Process Regression

This contribution presents a method for modeling and controlling a heavy-duty biturbocharged diesel engine. The modeling scheme can incorporate expert knowledge of the control relevant combustion quantities into Gaussian process models. A nonlinear model predictive controller (MPC) is used to control the engine outputs subject to the gas path dynamics and nonlinear constraints for the emissions and for the sake of engine protection. In addition, an online learning scheme based on Gaussian process regression is used to compensate for model uncertainties due to aging effects and manufacturing tolerances. A consistent model smoothing strategy is derived to preserve the given expert knowledge and to avoid abrupt reactions of the MPC due to the online learning of the models. All parts of the controller are implemented with respect to real-time feasibility and small memory footprint. Experimental results for a real-world heavy-duty engine demonstrate the performance and the online learning ability of the presented nonlinear MPC scheme that may be transferred to various diesel engine applications.
Rigorous Model-Based Mask Data Preparation Algorithm Applied to Grayscale Lithography for the Patterning at the Micrometer Scale

Grayscale mask creation has for the most part been restricted to over-simplified optical and resist models usually based on a contrast curve approach. While this technique has proven to work for microstructures of large dimensions (ten to hundreds of micrometers), its capability has not been assessed for microstructures with smaller dimensions. In this paper, a rigorous lithographic model has been developed in Python to simulate the process of imaging, exposure and development of an i-line photoresist. Using this model, a mask data preparation algorithm capable of optimizing simultaneously both the size and position of the dots on a grayscale mask has been implemented. Experimental results after development of the photoresist confirm the capability of our mask data preparation algorithm to achieve microstructures with dimensions ranging between 1 to $3~\mu \text{m}$ . [2021-0018]
IS incident recovery and service value: a service-dominant logic view

ABSTRACT IS service delivery failures inside companies are value diminishing events. Information systems service providers seek to limit this value destruction but lack guidance on IS incident resolution actions that satisfy users and preserve IS service value. We apply Service-Dominant Logic (SDL) to explore what is relevant to users’ recovery service experiences while interacting with IS service providers. We use an integrated research approach including qualitative and quantitative data. We uncover actions that lead to a satisfying incident recovery and categorise them into three recovery components (responsive handling interactions, supportive communicating interactions, and effective resolving actions) that reflect IS provider resource exchanges of information, knowledge, and service skills with users in resolving incidents. We integrate these three recovery components as factors in our research model and test direct and moderating effects on value outcomes. We find that users’ recovery satisfaction results from both a “fix it fast and fully” perspective and a sense of effort and fairness conveyed. Results point to managers preserving overall IS service satisfaction and service quality by facilitating proper resource exchanges during an incident recovery.