Miracle-dz/UniFAR-FTUs huggingface dataset:
Material Evidence of Sediments Recovered from Ancient Amphorae Found at the Potaissa Roman Fortress

Methods for material investigation are powerful tools that allow specialists to elucidate important aspects regarding ancient artifacts such as the Roman amphorae deposits discovered at Potaissa Fortress in Turda, Romania. Archeological debate states that the deposit contained olive oil and wine amphorae, but no material evidence has been presented until now. The current research is focused on the most representative large amphora fragments found in the Potaissa deposit, with a significant amount of sediment on their walls, to give archeologists the material proof to elucidate their debate. Sediment was collected from each fragment and subjected to complex analysis. XRD investigation combined with cross-polarized light microscopy demonstrated mineral particles such as quartz, clay (muscovite and traces of biotite), and calcite. Quartz and calcite particles have a rounded shape and diameters in a range of 20–200 µm, and clay particles have a lamellar shape and dimensions from 1 to 20 µm, a fact confirmed by SEM microscopy. Sample 2 presented a large amount of amorphous phase followed by Samples 1 and 3, with a low amount of organic phase. FTIR investigation confirms organic phase presence owing to strong absorption bands regarding C-H, C=O, and O-H chemical bonds related to aliphatic compounds in Sample 2, and to some decayed wine residue in Samples 1 and 3. EDS elemental analysis was used for organic particle identification in the amphora sediments and to obtain a correlation with their microstructure. GC–MS investigation showed volatile compounds related to wine residue for Samples 1 and 3 and decomposed fats for Sample 2. Tartaric and malic acid were identified by HPLC in Samples 1 and 3, which are wine biomarkers. The correlation of all experimental results concludes with no doubt that Amphora 2 contained olive oil and Amphorae 1 and 3 contained wine in ancient times.
An Investigation of the Antiviral Potential of Phytocompounds against Avian Infectious Bronchitis Virus through Template-Based Molecular Docking and Molecular Dynamics Simulation Analysis

Vaccination is widely used to control Infectious Bronchitis in poultry; however, the limited cross-protection and safety issues associated with these vaccines can lead to vaccination failures. Keeping these limitations in mind, the current study explored the antiviral potential of phytocompounds against the Infectious Bronchitis virus using in silico approaches. A total of 1300 phytocompounds derived from fourteen botanicals were screened for their potential ability to inhibit the main protease, papain-like protease or RNA-dependent RNA–polymerase of the virus. The study identified Methyl Rosmarinate, Cianidanol, Royleanone, and 6,7-Dehydroroyleanone as dual-target inhibitors against any two of the key proteins. At the same time, 7-alpha-Acetoxyroyleanone from Rosmarinus officinalis was found to be a multi-target protein inhibitor against all three proteins. The potential multi-target inhibitor was subjected to molecular dynamics simulations to assess the stability of the protein–ligand complexes along with the corresponding reference ligands. The findings specified stable interactions of 7-alpha-Acetoxyroyleanone with the protein targets. The results based on the in silico study indicate that the phytocompounds can potentially inhibit the essential proteins of the Infectious Bronchitis virus; however, in vitro and in vivo studies are required for validation. Nevertheless, this study is a significant step in exploring the use of botanicals in feed to control Infectious Bronchitis infections in poultry.
Visualization of Cutibacterium acnes with visible light using deep learning

In this study, we investigated a deep-learning method for visualizing Cutibacterium acnes (C. acnes) in facial photographs taken with visible-light irradiation. We used a dermatological device to capture photos with UV-light irradiation wherein C. acnes on the faces of people and paired these photos with visible-light images to train a deep learning model. Then, two methods were devised to increase the learning efficiency, namely a method to crop and reduce the size of the image used for learning and a method for highlighting the points where the C. acnes emitted light. These results suggested the potential application of these methods in visualizing C. acnes using only smartphones.
Data Augmentation Strategies for High-Frequency NILM Datasets

The nonintrusive load monitoring (NILM) process aims to monitor the different appliances connected to a power grid. It accomplishes this by analyzing one or multiple signals measured at the main breaker level of the power grid. In order for it return a response with high fidelity, it needs to be trained and tested using data pools of individual and aggregated appliance signals. Although some datasets are publicly available, they lack a sufficient quantity of labeled and time-stamped aggregated signals and a sufficient quantity of individual signals per appliance. Although recent works around this topic are available, they, however, work with low sampling frequencies, which does not allow us to extract high-frequency signatures from the signal waveform and in particular from the transient phases. In this article, we aim to solve the two problems mentioned above. We solve the first one using an algorithm that takes any number of individual appliance signals and construct a fully labeled and time-stamped aggregated signal. Next, we solve the second problem by introducing an approach that allows us to construct a large number of synthetic single appliance signals using a relatively smaller number of real single appliance signals. In addition, we perform all of this work on signals with high sampling frequencies, which will allow the extraction of the high-frequency features for later use.
Pilot study to visualise and measure skin tissue oxygenation, erythema, total haemoglobin and melanin content using index maps in healthy controls

We report on a method for analysing multispectral images of skin in vivo for the measurement and visualisation of skin characteristics. Four different indices were used to characterise skin tissue oxygenation, erythema, total haemoglobin and melanin content. Index values were calculated pixel-wise and combined to create index maps to visualise skin properties. Quantitative measurement of tissue oxygenation saturation was possible by calibrating the oxygenation index using a commercial, calibrated oximeter. Index maps were tested by arterial occlusion of the index finger with multispectral images taken before, during and after occlusion in a pilot study with 10 healthy controls.
GaN and SiC Device Characterization by a Dedicated Embedded Measurement System

This work proposes a comparison among GaN and SiC device main parameters measured with a dedicated and low-cost embedded system, employing an STM32 microcontroller designed to the purpose. The system has the advantage to avoid the use of expensive laboratory measurement equipment to test the devices, allowing to obtain their behavior in operating conditions. The following KPIs (Key Performance Indicators) are measured and critically compared: threshold voltage, on-resistance and input capacitance. All the measurements are carried out in a short time interval and on a wide range of switching frequencies, ranging from 10 kHz to 1 MHz. This investigation is focused on the deviation of the figures of merit when the switching frequency changes, since it is crucial for wide-bandgap devices. The devised, low-cost, microcontroller unit allows high flexibility and system portability, while the employed equivalent-time sampling technique overcomes some issues related to the need of high sampling frequency. It allows good performances with common microcontroller embedded AD converters. To validate the proposed system, the obtained results have been compared with the time-domain waveforms acquired with a traditional laboratory oscilloscope and a study of the system’s measurement errors has been carried out. Results show that GaN devices achieve a higher efficiency with respect to SiC devices in the considered range of switching frequencies. The on-resistance exhibited by GaN devices shows, as expected, an increase with frequency, which happens to switching losses, too. On the other hand, GaN devices are more sensitive to parasitic effects and the high dV/dt, due to the reduced switching times, can excite unwanted ringing phenomena.
Simulation Tools for Fog Computing: A Comparative Analysis

Fog Computing (FC) was introduced to offer resources closer to the users. Researchers propose different solutions to make FC mature and use simulators for evaluating their solutions at early stages. In this paper, we compare different FC simulators based on their technical and non-technical characteristics. In addition, a practical comparison is conducted to compare the three main FC simulators based on their performance such as execution time, CPU, and memory usage for running different applications. The analysis can be helpful for researchers to select the appropriate simulator and platform to evaluate their solutions on different use cases. Furthermore, open issues and challenges for FC simulators are discussed that require attention and need to be addressed in the future.
STOCHASTIC SEQUENTIAL ASSIGNMENT PROBLEM WITH THRESHOLD CRITERIA

The stochastic sequential assignment problem (SSAP) allocates distinct workers to sequentially arriving tasks with stochastic parameters to maximize the expected total reward. In this paper, the assignment of tasks is performed under the threshold criterion, which seeks a policy that minimizes the probability of the total reward failing to achieve a target value. A Markov-decision-process approach is employed to model the problem, and sufficient conditions for the existence of a deterministic Markov optimal policy are derived, along with fundamental properties of the optimal value function. An algorithm to approximate the optimal value function is presented, and convergence results are established.
Capillary-driven microfluidic chips with evaporation-induced flow control and dielectrophoretic microbead trapping

Abstract. This work reports our efforts on developing simple-to-use microfluidic devices for point-of-care diagnostic applications with recent extensions that include the trapping of microbeads using dielectrophoresis (DEP) and the modulation of the liquid flow using integrated microheaters. DEP serves the purpose of trapping microbeads coated with receptors and analytes for detection of a fluorescent signal. The microheater is actuated once the chip is filled by capillarity, creating an evaporation-induced flow tuned according to assay conditions. The chips are composed of a glass substrate patterned with 50-nm-thick Pd electrodes and microfluidic structures made using a 20-μm-thick dry-film resist (DFR). Chips are covered/sealed by low temperature (50°C) lamination of a 50-μm-thick DFR layer having excellent optical and mechanical properties. To separate cleaned and sealed chips from the wafer, we used an effective chip singulation technique which we informally call the “chip-olate” process. In the experimental section, we first studied dielectrophoretic trapping of 10-μm beads for flow rates ranging from 80 pL s−1 to 2.5 nL s−1 that are generated by an external syringe pump. Then, we characterized the embedded microheater in DFR-covered chips. Flow rates as high as 8 nL s−1 were generated by evaporation-induced flow when the heater was biased by 10 V, corresponding to 270-mW power. Finally, DEP-based trapping and fluorescent detection of functionalized beads were demonstrated as the flow was generated by evaporation-induced flow after the microfluidic structures were filled by capillarity.
CFD Calculations of the Air Flow along a Cold Vertical Wall with an Obstacle

In buildings with large glazed facades there may be problems with thermal discomfort due to downdraught. Convectors placed close to the facade can prevent this but they will cause an increased energy_. consumption. Recent research has shown that the risk of thermal discomfort due to downdraught can be reduced by stmctural measures. Laboratory experiments have shown that large obstacles, such as the structural system of a glazed facade, which is placed in the cold boundary layer, can reduce downdraught considerably. This paper deals with the ability of Computational Fluid Dynamics to predict downdraught at a plane wall and at a wall with large obstacles. Quite simple boundary conditions were used in this study. Predictions of the main flow characteristics and the velocity levels in the occupied zone showed reasonable results.
High-Level CNN and Machine Learning Methods for Speaker Recognition

Speaker Recognition (SR) is a common task in AI-based sound analysis, involving structurally different methodologies such as Deep Learning or “traditional” Machine Learning (ML). In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. A custom CNN is compared to several pre-trained nets using image inputs of spectrograms and Cepstral-temporal (MFCC) graphs. AML approach based on acoustic feature extraction, selection and multi-class classification by means of a Naïve Bayes model is also considered. Results show how a custom, less deep CNN trained on grayscale spectrogram images obtain the most accurate results, 90.15% on grayscale spectrograms and 83.17% on colored MFCC. AlexNet provides comparable results, reaching 89.28% on spectrograms and 83.43% on MFCC.The Naïve Bayes classifier provides a 87.09% accuracy and a 0.985 average AUC while being faster to train and more interpretable. Feature selection shows how F0, MFCC and voicing-related features are the most characterizing for this SR task. The high amount of training samples and the emotional content of the DEMoS dataset better reflect a real case scenario for speaker recognition, and account for the generalization power of the models.
Primary Cutaneous B-Cell Lymphomas with Large Cell Morphology: A Practical Review

Most primary cutaneous lymphomas consist of T-cell lymphomas or small cell lymphomas; however, the skin may also be affected by lymphomas with large cell morphology, as a primary or secondary localization. A minority of cases consist of primary cutaneous B-cell lymphomas (PCBCLs). PCBCLs are a heterogeneous group of rare neoplasms with an overlapping morphological and immunohistochemical picture of the different subtypes. Nevertheless, differential diagnosis in the setting of this group of neoplasms is mandatory to identify the correct therapy and prognosis, but it may be challenging since, due to the rarity of these neoplasms, they may not always be familiar to pathologists. Indeed, immunohistochemistry may not be enough to distinguish the different histotypes, which overlap in immunohistochemical features. Furthermore, the ever-increasing knowledge of the molecular features of systemic B-cell lymphomas, such as gene rearrangements with clinical significance, has led in recent years to further investigation into the molecular landscape of PCBCLs with large cell morphology. This work aimed to provide a practical diagnostic guide for pathologists dealing with primary cutaneous large B-cell lymphomas.
Humor as a Political Act: Study of Indonesian Presidents’ Humor

This study aims to explain the use of humor by Indonesian presidents for political communication. Presidents’ use of humor is an interesting phenomenon, given that they are social subjects who wield great power within a country. Their social position leads to different characteristics and social impacts due to the humor used. This research uses a qualitative descriptive approach. Data was collected from books, news sites, and YouTube channels documenting the humor of the three Indonesian presidents. Data were then analyzed using pragmatic analysis. The results show that presidential humor varies according to personal preferences, speech objectives, and contexts. Presidents use joke, conversational humor, and pun to joke, tease, praise, satirize, criticize, and delegate power. Joking, teasing, and praising are used on friends and political allies. Meanwhile, satirizing and criticizing are applied to political opponents. Humor is a middle ground for presidents, enabling them to achieve speech objectives without explicitly expressing their intention. Politically, humor functions to (1) represent oneself positively and represent opponents negatively, (2) build relationships and positioning with opponents and allies, and (3) exercise control and discipline. However, the relationship between form, style, and illocutionary of presidential humor is often ambiguous. Such conditions seem related to political relations' dynamic and equal nature, where friends and foes are not always crystal clear.
Saturated Pseudoadiabats—A Noniterative Approximation

AbstractTwo noniterative approximations are presented for saturated pseudoadiabats (also known as moist adiabats). One approximation determines which moist adiabat passes through a point of known pressure and temperature, such as through the lifting condensation level on a skew T or tephigram. The other approximation determines the air temperature at any pressure along a known moist adiabat, such as the final temperature of a rising cloudy air parcel. The method used to create these statistical regressions is a relatively new variant of genetic programming called gene-expression programming. The correlation coefficient between the resulting noniterative approximations and the iterated data such as plotted on thermodynamic diagrams is over 99.97%. The mean absolute error is 0.28°C, and the root mean square error is 0.44 within a thermodynamic domain bounded by −30° 20 kPa, and −60° ≤ T ≤ 40°C, where θw, P, and T are wet-bulb potential temperature, pressure, and air temperature.
Surface Curvatures Computation from Equidistance Contours

The subject of our research is on the 3D shape representation problem for a special class of range image, one where the natural mode of the acquired range data is in the form of equidistance contours, as exemplified by a moire interferometry range system. In this paper we present a novel surface curvature computation scheme that directly computes the surface curvatures (the principal curvatures, Gaussian curvature and mean curvature) from the equidistance contours without any explicit computations or implicit estimates of partial derivatives. We show how the special nature of the equidistance contours, specifically, the dense information of the surface curves in the 2D contour plane, turns into an advantage for the computation of the surface curvatures. The approach is based on using simple geometric construction to obtain the normal sections and the normal curvatures. This method is general and can be extended to any dense range image data. We show in details how this computation is formulated and give an analysis on the error bounds of the computation steps showing that the method is stable. Computation results on real equidistance range contours are also shown.
Contested by the State: Institutional Offloading in the Case of Crossover Youth

How do people become the responsibility of one state institution over another? Prevailing theory suggests that marginalized groups are funneled toward increasingly coercive control over the life course, yet more coercive institutions may not always assume responsibility for people sent their way. This article uses the unique case of crossover youth—children at the junction of child welfare and juvenile justice systems—to illustrate how state institutions negotiate and contest responsibility for marginalized groups. To explain this process, I advance a conceptual framework of institutional offloading, which contends that institutional actors seek to offload responsibility for eligible tasks or clients they perceive to unduly strain the resources at their disposal and expose them to blame. Drawing on ethnographic data from a California juvenile court and interviews with court actors, the analysis demonstrates how actors from Social Services, on one side, and Probation, on the other, attempt to offload responsibility for crossover youth. In this process, institutional actors construct and contest crossover youths’ status as dependent or delinquent. The findings highlight the importance of analyzing governance decisions as interlocking state processes and illuminate mechanisms by which the pipeline to prison for marginalized groups may be perpetuated and potentially disrupted.
Spectral Efficiency of Wireless Systems in Correlated Channels with Receive Antenna Selection

The multiple input-output (MIMO) wireless technology increases data capacity, especially for many transmitting and receiving antennas. The concept of antenna selection is considered a tradeoff between performance and complexity to achieve better performance. First, this work presents three-antenna selection algorithms to maximize channel capacity in correlated MIMO systems with antenna selections. In our scheme, we assume that the receiver picks the best subset antenna for given Rr receive antennas that can guarantee capacity maximization. The receiver has access to the channel state information (CSI), while the transmitter does not. Finally, the simulation outcomes prove that our scheme is practical, accurate, and achieves higher performance than other antenna selection schemes.
Solution of the Blasius and Sakiadis equation by generalized iterative differential quadrature method

The Blasius and Sakiadis equation was solved earlier with different numerical methods. In this study, it was solved by using the generalized iterative differential quadrature method (GIDQM). And more than one condition are imposed at the same point without using any higher‐order polynomial or δ‐point approximation in GIDQM although it is one of the most important drawbacks in the differential quadrature method (DQM). Procedure is started with an initial guess value and true results are obtained by iterations. More grid points are used. Hence, the solution of the Blasius equation is calculated precisely and showed good agreements when compared with other works. Copyright © 2009 John Wiley & Sons, Ltd.
Antioxidant and Protective Effects of the Polyphenolic Glycoconjugate from Agrimonia eupatoria L. Herb in the Prevention of Inflammation in Human Cells

Herein, structural and biological studies of a complex biopolymer (polyphenolic glycoconjugate) isolated from the flowering parts of Agrimonia eupatoria L. (AE) are presented. Spectroscopic analyses (UV–Vis and 1H NMR) of the aglycone component of AE confirmed that it consists mainly of aromatic and aliphatic structures characteristic of polyphenols. AE showed significant free radical elimination activity, i.e., ABTS+ and DPPH·, and was an effective copper reducing agent in the CUPRAC test, eventually proving that AE is a powerful antioxidant. AE was nontoxic to human lung adenocarcinoma cells (A549) and mouse fibroblasts (L929) and was nongenotoxic to S. typhimurium bacterial strains TA98 and TA100. Moreover, AE did not induce the release of proinflammatory cytokines such as interleukin 6 (IL-6) and tumor necrosis factor (TNF-α) by human pulmonary vein (HPVE-26) endothelial cells or human peripheral blood mononuclear cells (PBMCs). These findings correlated with the low activation of the transcription factor NF-κB in these cells, which plays an important role in the regulation of the expression of genes responsible for inflammatory mediator synthesis. The AE properties described here suggest that it may be useful for protecting cells from the adverse consequences of oxidative stress and could be valuable as a biomaterial for surface functionalization.