Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 µM to 3.9 µM based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk.
A Thermosensitive, Chitosan-Based Hydrogel as Delivery System for Antibacterial Liposomes to Surgical Site Infections

Prophylaxis and the treatment of surgical site infections (SSIs) with antibiotics frequently fail due to the antibiotic resistance of bacteria and the ability of bacteria to reside in biofilms (i.e., bacterial clusters in a protective matrix). Therefore, alternative antibacterial treatments are required to combat biofilm infections. The combination of diethyldithiocarbamate (DDC−) and copper ions (Cu2+) exhibited antibiofilm activity against the staphylococci species associated with SSIs; however, the formation of a water-insoluble Cu(DDC)2 complex limits its application to SSIs. Here, we describe the development and antibiofilm activity of an injectable gel containing a liposomal formulation of Cu(DDC)2 and Cu2+ (lipogel). Lyophilized liposomes were incorporated into a mixture of chitosan (CS) and beta-glycerophosphate (βGP), and the thermosensitive gelling properties of CS-βGP and the lipogel were determined. The liposomes remained stable after lyophilization over six months at 4–6 °C and −20 °C. The sol-gel transition of the gel and lipogel occurred between 33 and 39 °C, independently of sterilization or storage at −20 °C. CS-βGP is biocompatible and the liposomes were released over time. The lipogel prevented biofilm formation over 2 days and killed 98.7% of the methicillin-resistant Staphylococcus aureus and 99.9% of the Staphylococcus epidermidis biofilms. Therefore, the lipogel is a promising new prophylaxis and treatment strategy for local application to SSIs.
Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

In this article, we present a low-cost multimodal tactile sensor capable of providing accelerometer, gyroscope, and pressure data using a seven-axis chip as a sensing element. This approach reduces the complexity of the tactile sensor design and collection of multimodal data. The tactile device is composed of a top layer (a printed circuit board (PCB) and a sensing element), a middle layer (soft rubber material), and a bottom layer (plastic base) forming a sandwich structure. This approach allows the measurement of multimodal data when force is applied to different parts of the top layer of the sensor. The multimodal tactile sensor is validated with analyses and experiments in both offline and real-time. First, the spatial impulse response and sensitivity of the sensor are analyzed with accelerometer, gyroscope, and pressure data systematically collected from the sensor. Second, the estimation of contact location from a range of sensor positions and force values is evaluated using accelerometer and gyroscope data together with a convolutional neural network (CNN) method. Third, the estimation of contact location is used to control the position of a robot arm. The results show that the proposed multimodal tactile sensor has the potential for robotic applications, such as tactile perception for robot control, human–robot interaction, and object exploration.
CRVC-GAN: combining cross-scale fusion and recursion for video colorization adversarial generative networks

Abstract. We proposed an innovative end-to-end generative adversarial network structure (CRVC-GAN) for video colorization, which is dedicated to solving the temporal consistency and coloring quality problems in the video coloring domain. For the temporal consistency problem, we utilize the network structure of recurrent neural network (RNN) because the model shape of RNN does not change with the input length and the calculation takes into account the history information to ensure the degree of correlation between successive frames. This better ensures the continuity between frames, so the consecutive frames get good temporal consistency in coloring. For the coloring quality problem, we utilize the UNet++ coding method to decode the video frames, integrating different levels of features to improve accuracy, and then enhance the object boundary information in the video frames, thus improving the coloring quality of grayscale video frames. In addition, we innovatively utilize the style transformation loss function in terms of images for model training to ensure temporal consistency and coloring quality by exploiting the slight difference in style variation from frame to frame. The experimental results show that, compared with the existing methods and through its own designed balance index, the colorization and temporary consistency balance index, CRVC-GAN obtained certain advantages on the DAVIS and Videvo public datasets with the results of 0.56176 and 0.74286, respectively, and achieved a better balance between time consistency and color quality.
Optimized Production of Second-Generation Bioethanol from a Spent C4 Grass: Vetiver (Chrysopogon zizanioides)

Vetiver grass (Chrysopogon zizanioides) is well-known for its contaminant phytoextraction potential and its capacity to reduce soil erosion, owing to its massive, dense root system. However, the shoots are not major contributors to either of these processes, and are either not utilized at all or they become part of the waste stream. It is well-recognized that lignocellulosic biomass can serve as a source of raw material to produce second-generation bioethanol. This study investigated the simultaneous saccharification and fermentation (SSF) of acid–alkali pretreated vetiver (VG) shoots by Saccharomyces cerevisiae. Vetiver shoots were obtained from three sources: (1) shoots from VG grown in clean potting soil, (2) shoots from VG used for antibiotics phytoextraction from a constructed wetland setup, and (3) shoots from VG used for lead phytoextraction during soil remediation. Bioethanol yield from the shoots from clean soil was the highest (19.58 g/L), followed by the one used for lead phytoextraction (19.50 g/L) and the one used for antibiotics phytoextraction (19.17 g/L). Bioethanol yield and quality obtained from these three VG shoots was superior or similar to other C4 grasses used for bioethanol generation. This study successfully demonstrated that spent vetiver biomass after phytoextraction applications can be repurposed to generate high-quality bioethanol.
Examining Psychotherapeutic Processes with Depressed Adolescents: A Comparative Study of Two Psychodynamic Therapies

To understand processes associated with better or poorer psychotherapy outcomes is vital. This study examined and contrasted interaction patterns between one therapist and two depressed 17-year-old girls, Johanna (good outcome) and Sonja (poor outcome), in short-term psychoanalytic therapies selected from an RCT. Outcome data were collected regarding level of inter- and intra-personal functioning and symptoms of depression. Process data were obtained using the Adolescent Psychotherapy Q-Set on all available sessions. Analyses yielded five relational patterns or “interaction structures” in the two therapy processes; Three explained most of the variance in sessions with Johanna (i.e., ‘positive working alliance’, ‘therapist’s active use of psychodynamic techniques’, and ‘a receptive patient’) and two explained more of the variance in sessions with Sonja (i.e., ‘therapist using a more problem-solving and symptom-oriented approach’ and ‘patient displaying limited capacity for mentalization’). The processes in the two cases presented differences related to mentalization, psychological mindedness, and attachment style of the patients. The therapist used different therapeutic approaches, favouring more psychodynamic interventions in the good outcome case and a more problem-solving and symptom-oriented approach with the poor outcome case. In the latter case, the relationship seemed to be more of a struggle.
IDEvelopAR: A Programming Interface to enhance Code Understanding in Augmented Reality

During software maintenance developers spend a considerable amount of time on tasks like navigating, identifying required code locations or tracing various call hierarchies. The classical tabbed interfaces, as found in modern IDEs, are not ideal for such tasks, leading to an inefficient workflow containing many context switches. Therefore, several programming environments, like Code Bubbles, were proposed to overcome these issues by allowing users to freely arrange code fragments on a canvas to make relations more explicit and better understand the codebase. Relations are made explicit using visual links or extra space between groups of code fragments. As a consequence, these approach quickly run out of screen space. In this paper, we present IDEVELOPAR, a tool to enhance code understanding in augmented reality. Due to the use of AR, a user is not restricted anymore by limited display sizes and can use the entire physical space as a workspace for placing and grouping code fragments as well as making changes to the codebase. First, we introduce the views and interactive functionalities of our tool. Next, we illustrate the usefulness of the tool by navigating an example program to locate and fix a bug. Finally, we briefly discuss the results of a cognitive walk-through using the cognitive dimension framework as well as a formative user study to identify potential usability problems. Moreover, in this study the participants also mentioned several advantages of our approach over the classical one. Furthermore, we found that over time the participants developed their own placement strategies.
CHARGE STATE EVOLUTION IN THE SOLAR WIND. II. PLASMA CHARGE STATE COMPOSITION IN THE INNER CORONA AND ACCELERATING FAST SOLAR WIND

In the present work, we calculate the evolution of the charge state distribution within the fast solar wind. We use the temperature, density, and velocity profiles predicted by Cranmer et al. to calculate the ionization history of the most important heavy elements in the solar corona and solar wind: C, N, O, Ne, Mg, Si, S, and Fe. The evolution of each charge state is calculated from the source region in the lower chromosphere to the final freeze-in point. We show that the solar wind velocity causes the plasma to experience significant departures from equilibrium at very low heights, well inside the field of view (within 0.6 Rsun from the solar limb) of nearly all the available remote-sensing instrumentation, significantly affecting observed spectral line intensities. We also study the evolution of charge state ratios with distance from the source region, and the temperature they indicate if ionization equilibrium is assumed. We find that virtually every charge state from every element freezes in at a different height, so that the definition of freeze-in height is ambiguous. We also find that calculated freeze-in temperatures indicated by charge state ratios from in situ measurements have little relation to the local coronal temperature of the wind source region, and stop evolving much earlier than their correspondent charge state ratio. We discuss the implication of our results on plasma diagnostics of coronal holes from spectroscopic measurements as well as on theoretical solar wind models relying on coronal temperatures.
Digital Financial Inclusion, Household Financial Participation and Well-Being: Micro-Evidence from China

ABSTRACT Though financial inclusion has drawn a lot of attention lately, especially in emerging markets, it remains unclear how it affects household well-being. This study investigates the connection between digital financial inclusion (DFI) and household well-being using two databases in China. The findings suggest that DFI is positively associated with household well-being. Mechanism analysis reveals that a rise in DFI facilitates household financial participation, thereby increasing the probability of household well-being. Our further empirical analysis demonstrates that groups with lower education and income levels are more significantly affected by DFI regarding household well-being. Overall, the research provides empirical evidence for the assertion that expanding financial inclusion in the digital economy era can promote social fairness and provide a basis for a vigorous expansion of financial inclusion in emerging economies.
Semantic underwater world modeling by using Probabilistic Particle Filter Anchoring

Creating an accurate world model of the scenario where an Autonomous Underwater Vehicle (AUV) is navigating can be considered a crucial stage for understanding the surrounding environment. As a result, the targets detected by a cutting-edge Automatic Target Recognition (ATR) architecture alongside their localized positions, must be handled, selected and filtered to get a symbolic representation of the underwater context. Even though the specific World Modeling (WM) architecture may vary, current WM methodologies usually rely on the 3D localization knowledge of the detected target by introducing a not-negligible constraint. Motivated by the aforementioned considerations, a novel Probabilistic Particle Filter Anchoring (PPFA) approach has been developed. Starting from ATR 2D results, the PPFA methodology aims at providing a semantic 3D representation of the subsea environment by merging the upsides of both Data Association (DA) and object tracking, handled by a custom designed Particle Filter (PF) with resampling.
Reduction of threading dislocation densities in AlN∕sapphire epilayers driven by growth mode modification

A strategy to reduce the density of threading dislocations (TDs) in AlN epilayers grown on sapphire substrates is reported. The TDs experience a redirection of their line orientation which is found to coincide with imposed increases in both of V/III ratio and overall flux rate leading to the formation of an internal subinterface delineated by the changes in dislocation orientation. Threading dislocations either experience large kinks and then redirect into threading orientation or form dipole half loops via annihilation of redirected threading segments of opposite sign with the latter leading to a significant dislocation density reduction. These phenomena can be accounted for by a transition of growth mode from atomic step flow to two-dimensional layer-by-layer growth which accompanies the imposed changes in V/III ratio and flux. As this occurs, macrosteps (several atomic layers thick) laterally overgrow pre-existing dislocation outcrops. Image forces initiate the redirection processes and create trailing.
Characterization of AMF-diversity of endosphere versus rhizosphere of tea (Camellia sinensis) crops

Tea [Camellia sinensis (L.) Kuntze] crops are globally one of the important commercial crops grown predominantly on acidic soils, a natural ally in form of soil arbuscular mycorrhizal fungi (AMF). The present study was carried out to characterize AMF-diversity in roots and rhizosphere of 20-yr-old Camellia sinensis cv. Yichang Dayecha grown in Xingshan, Hubei, China. Small subunit ribosomal RNA (SSU rRNA) was used to identify the diversity. Tea roots were colonized by native AMF species, with 32. 71Â±3.50% of root mycorrhizal colonization. As many 163 and 177 operational taxonomic units (OTUs) were observed in the clone library of rhizosphere soils and roots respectively, suggesting that roots inhabited more AMF species than rhizosphere soils, with as many 111 OUTs overlapped between them. There were only Glomus species and Claroideoglomus / Glomus predominantly observed in roots and rhizosphere soils, respectively, with Glomus Glo20 having highest relative abundance in rhizosphere (>75%) and roots (>25%). Soil and root samples were grouped with the high taxonomic similarity, indicating good group diversity in AMF associated with tea crops.
Protective Role of Natural Compounds under Radiation-Induced Injury

In recent years, evidence has shown the potential therapeutic effects of different natural compounds for the prevention and treatment of radiotherapy-induced mucositis (RIOM). RIOM represents one of the most frequent side effects associated with anti-neoplastic treatments affecting patients’ quality of life and treatment response due to radiation therapy discontinuation. The innate radio-protective ability of natural products obtained from plants is in part due to the numerous antioxidants possessed as a part of their normal secondary metabolic processes. However, oxygen presence is a key point for radiation efficacy on cancer cells. The aim of this review is to describe the most recent evidence on radiation-induced injury and the emerging protective role of natural compounds in preventing and treating this specific damage without compromising treatment efficacy.
Fast fault estimation and accommodation for dynamical systems

The problem of fast active fault-tolerant control is studied using adaptive fault diagnosis observer (AFDO). Existence conditions for linear time-invariant system are first introduced to verify whether or not the adaptive observer for fault diagnosis exists. Then a novel fast adaptive fault estimation (FAFE) algorithm is proposed to enhance the performance of fault estimation. Using the on-line obtained fault information, the observer-based fault tolerant controller based on the separation property is designed to compensate for the loss of actuator effectiveness by stabilising the closed-loop system. Furthermore, an extension to a class of nonlinear systems is extensively investigated. Finally, simulation results are presented to illustrate the efficiency of the proposed techniques.
A Spectral Line Data Processing Pipeline for SKA Using DALiuGE

With the construction of the Square Kilometre Array (SKA), the world's largest radio telescope, a new era in the detection of 21 cm neutral hydrogen (HI) spectral line is about to be ushered in, and the SKA spectral line sciences present tremendous opportunities and unprecedented data processing challenges. In this paper, we propose a fast processing pipeline for spectral line data based on the distribution execution framework Data Activated Liu Graph Engine (DALiuGE), and compare it with the Message Passing Interface (MPI) based method. The proposed pipeline is proved to be more stable and efficient on several comparison experiments. The scalability and execution efficiency performance of SKA spectral line data fast processing pipeline using DALiuGE for data volume changes have been verified, and it is believed that the DALiuGE based pipeline can be a solution for more scientific tasks facing massive data processing on SKA Regional Centre (SRC).
Decisions within Human-Machine Teaming: The Introduction of Decision Strings

We are increasingly confronted with teams that are a mix of both humans and machines, where collaboration is necessary in order to achieve an optimized solution. While Artificial Intelligence may be used to complete monotonous and complex specific tasks the human may concentrate on maintaining wider tactical awareness. In some instances the machine may even present the human with a decision to be approved or to select from a number of options created for them. The nature of decision making is therefore a critical component of this evolving interaction between human and machine. Within this paradigm a decision may be generated or owned by human or machine, and often shared. In many instances the complex interaction between both human and machine is required to step through a number of steps prior to a single presentation of what constitutes a decision. In this paper we will introduce the nature of how these decisions are constructed through the concept of decision strings. Through a number of examples we will demonstrate the complexity of how a human-machine team collaboration follows a pattern that allows us to better understand how the human can work closely with the machine in order to achieve effective decision-making.
Total cross sections for positron and electron collisions with NH3 and H2O molecules

The total cross sections for 1-400 eV positrons and electrons colliding with NH3 and H2O molecules were measured using a TOF method. The cross sections were obtained by a normalisation method. The results for e+-NH3 and e+-H2O at low energy were much lower than the theoretical values of Jain (1986). The cross sections of positronium formation in these gases were measured.