Miracle-dz/UniFAR-FTUs huggingface dataset:
Creative and Experimental Design of Artwork Based on Artificial Neural Network Algorithm and Internet of Things

Art creativity and experimental design are fields full of creativity and exploration, and traditional design methods are often limited by the personal abilities and experiences of designers. Therefore, introducing artificial intelligence (AI) algorithms can help designers better explore creativity and improve design efficiency. This article investigates the application of artificial neural networks (ANN) and the Internet of Things (IoT) in computer-aided artwork creativity and experimental design. By analyzing and learning a large amount of artwork data, the creative patterns and features of artwork are extracted, providing creative suggestions and optimization solutions for artists. The results indicate that the art creation computer-aided design (CAD) system constructed in this article has strong interactive capabilities and a good user experience. Meanwhile, ANN has significant advantages in image processing time compared to Support Vector Machine (SVM). This system can be applied to creative design, experimental creation, and image processing of artworks, providing more efficient and intelligent auxiliary tools for artists and designers.
Transcriptional regulatory cascade of LcMYB71 and LcNAC73 affects low-temperature and drought stress response in Lonicera caerulea

The development of stress tolerance is regulated via the transcriptional regulatory networks involving regulatory homeostasis mediated by protein–DNA interactions. LcNAC73 from Lonicera caerulea was characterized to understand the underlying mechanism of low-temperature and drought stress response in L. caerulea. To better understand the transcription pathway of LcNAC73, we cloned the promoter and screened proteins that could interact with the promoter. Using Yeast one-hybrid, electrophoretic mobility shift, and chromatin immunoprecipitation assays, we found that the LcMYB71 protein specifically bound to the promoter of LcNAC73. The transient transformation and stable transgenic system were used to produce transgenic L. caerulea plants with overexpressed and silenced LcNAC73, elucidating the effect of LcNAC73 on low-temperature and drought stress tolerance. LcNAC73 positively regulated the proline content and enhanced the scavenging of reactive oxygen species, thus improving tolerance to low-temperature and drought stress. Further studies revealed that LcMYB71 and LcNAC73 had similar functions and could improve plant low-temperature and drought tolerance. It is necessary to identify the upstream regulators of a specific gene to characterize gene functions and the associated transcriptional pathways.
Glucuronidation Pathways of 5- and 7-Hydroxypropranolol: Determination of Glucuronide Structures and Enzyme Selectivity

Propranolol, a non-selective beta-blocker medication, has been utilized in the treatment of cardiovascular diseases for several decades. Its hydroxynaphthyl metabolites have been recognized to possess varying degrees of beta-blocker activity due to the unaltered side-chain. This study achieved the successful separation and identification of diastereomeric glucuronic metabolites derived from 4-, 5-, and 7-hydroxypropranolol (4-OHP, 5-OHP, and 7-OHP) in human urine. Subsequently, reaction phenotyping of 5- and 7-hydroxypropranolol by different uridine 5’-diphospho-glucuronosyltransferases (UGTs) was carried out, with a comparison to the glucuronidation of 4-hydroxypropranolol (4-OHP). Among the 19 UGT enzymes examined, UGT1A1, UGT1A3, UGT1A7, UGT1A8, UGT1A9, UGT1A10, UGT2A1, and UGT2A2 were found to be involved in the glucuronidation of 5-OHP. Furthermore, UGT1A6 exhibited glucuronidation activity towards 7-OHP, along with the aforementioned eight UGTs. Results obtained by glucuronidation of corresponding methoxypropranolols and MS/MS analysis of 1,2-dimethylimidazole-4-sulfonyl (DMIS) derivatives of hydroxypropranolol glucuronides suggest that both the aromatic and aliphatic hydroxy groups of the hydroxypropranolols may be glucuronidated in vitro. However, the analysis of human urine samples collected after the administration of propranolol leads us to conclude that aromatic-linked glucuronidation is the preferred pathway under physiological conditions.
Parenting Practices, Communication about Adoption and Children’s Behavior Problems in Adoptive Families

Aspects relating to adoption have not been much in focus of psychology research in Latvia. The aim of the study was to investigate associations between parenting practices, attitude and communication about adoption and child behavior problems in adoptive families raising school-age children, as well as to investigate to what extent parenting practices, attitude and communication about adoption, as well as parent’s perception of adopted child’s adverse childhood experiences explains the internalized and externalized child behavior problems. Participants were 130 adoptive parents (94.6% women and 5.4% men) who are currently raising at least one adopted child between the ages of 7 and 18 (50.8% girls, 49.2% boys). Alabama Parenting Questionnaire, psychological control scale from Block’s Child Rearing Practices Report, parent report form of the Child Behavior Checklist were used in the study, as well as Kirk’s Adoption Questionnaire, which was adapted as part of the study. Results of the study revealed that adverse childhood experience, empathy for the child’s understanding and feelings about adoption, maladaptive parenting practices – inconsistent discipline and low involvement predict internalized behavior problems. On the other hand, poor monitoring/ supervision, psychological control, as well as adverse childhood experience and acknowledgement of differences related to adoption by adoptive parents predict externalized behavior problems. Implications for practice include recommendation to empathize the importance of positive parenting practices during pre and post-adoption trainings for adoptive parents.
Root Cause Analysis of Communication Network Based on Deep Fuzzy Neural Network

In the realm of communication networks, root cause analysis plays a vital role in maintaining efficient and reliable operation. However, existing root cause analysis methods face limitations and drawbacks, including their inability to handle complex data and disturbances, as well as inaccuracies in identifying root causes. To this end, the paper presents the Deep Fuzzy Neural Network approach as an innovative solution. Integrating the strengths of deep learning and fuzzy logic inference, where the deep learning technique utilizes the parallel computing fusion of convolutional neural network and long short-term memory to extract the spatial-temporal features from sophisticated fault data of communication network. By leveraging this parallel computing fusion module, the proposed framework effectively addresses the flaws of traditional root cause analysis methods. Furthermore, the incorporation of fuzzy logic enables the proposed model to manage disturbances such as uncertainty and noise inherent in the data, ensuring robust performance. Experimental results also demonstrate our proposed deep fuzzy neural network approach is an effective method for network root cause analysis in overcoming limitations inherent in existing methods and providing superior accuracy and resilience.
A location-based approach to the classification of mesoscale oceanic structures in SeaWiFS and Aqua-MODIS images of Northwest Africa

This study presents a different approach to the classification of Mesoscale Oceanic Structures (MOS) present in the Northwest African area, based on their location. The main improvement stems from the partition of this area in four large zones perfectly differentiated by their morphological characteristics, with attention to seafloor topography and coastal relief. This decomposition makes it easier to recognize structures under adverse conditions, basically the presence of clouds partly hiding them. This is observed particularly well in upwellings, which are usually very large structures with a different morphology and genesis in each zone. This approach not only improves the classification of the upwellings, but also makes it possible to analyse changes in the MOS over time, thereby improving the prediction of its morphological evolution. To identify and label the MOS classified in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) chlorophyll-a and temperature images, we used a tool specifically designed by our group for this purpose and which has again shown its validity in this new proposal.
High speed laser-assisted copper microstructures synthesis from deep eutectic solutions

Laser assisted metallization from deep eutectic solutions is a method that allows metal (Cu, Ni, Ag.) to be deposited locally from a film of deep eutectic solution using picosecond laser radiation. As a result, it is possible to create metals or oxides structures on the surface of almost any dielectric without template. The method allows creating conductive structures on curved surfaces with high resolution. This paper shows the creation of electronic components from copper on the surface of glass. We created RFID tag and other devices with a maximum deposition rate of 18mm/sec to show the opportunities of the method. The results demonstrate the advantages of the method, and the low cost of precursors, the simplicity of the method, and its environmental friendliness make it attractive for further implementation in the industry.
Surface Air Temperature Trends during the Last 20 Years in Iran

In order to observed climate changes and global warming in recent years over the world, it is necessary to investigate meteorological trends specially air temperature. Hydrological variables might be used as indicators to detect and monitor climate change. This paper presents trends computed for the 20-year period of mean annual air temperature obtained from 119 synoptic stations over Iran. Spearman and Mann-Kendall non-parametric trend tests which are known as appropriate tools in detecting linear trends of a hydrological time series are adapted in this study. As results showed stations of Iran except Shahre Kord exhibit upward trend. In almost 85.7% of stations upward trend detected significantly at the 0.05 or lower level. It is concluded that global warming and increasing air temperature maybe cause to climate changes in recent years over most regions of Iran.
Some results on strongly coupled elliptic systems

This work focuses on qualitative properties for nonnega-tive solutions to elliptic systems driven by a Gross–Pitaevskii nonlinearity on a punctured domain. We aim to present some classification results and a description of the local behavior near an isolated (non-removable) singularity for second and fourth order systems of this class
A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks

The consideration of millimeter wave (mmWave) bands and ultra-dense deployment have emerged as essential enabling solutions in Fifth Generation (5G) networks. However, these solutions result in a significant surge in the number of handovers (HOs), leading to an increased occurrence of unnecessary handovers and a higher likelihood of dropped connectivity. Hence, designing a robust mobility technique that facilitates seamless connections to the user equipment (UE) has become crucial and challenging. One of the essential HO optimisation techniques is referred to as Mobility Robustness Optimisation (MRO), which focuses on adapting HO Control Parameters (HCPs) such as Hysteresis Margin (HM) and Time-to-Trigger (TTT). While it was originally designed for 4G and subsequently enhanced for 5G, the effectiveness of this functionality requires further refinement to meet the demands of future mobile networks. Existing MROs approaches primarily focus on UE mobility and heavily rely on metrics such as Received Signal Reference Power (RSRP) and Received Signal Reference Quality (RSRQ). However, these approaches tend to overlook critical handover challenges, including the widespread deployment of small cells, which is crucial in 5G mmWave networks, as well as issues related to interference and channel intermittency. This research proposes a Smart Handover Strategy (SHS) mechanism to autonomously fine-tune handover control parameters, including HM and TTT, by utilising real-time channel conditions assessed through the Signal-to-Interference-Noise Ratio (SINR), thereby ensuring precise parameter adjustments. The utilisation of SINR in this proposed mechanism aims to address handover challenges arising from interference, high intermittent, and unpredictable behaviours encountered in 5G mmWave wireless channels. The primary goal of this mechanism is to facilitate seamless handovers as UE transitions from one Base Station (BS) to another in a dual connectivity multi-radio network. The developed algorithm underwent testing using a 5G mmWave statistical channel model that emulates a dynamic channel matrix, including fading and the Doppler effect. The Network Simulator 3 (NS3) software was utilised to analyse and evaluate the performance of the SHS mechanism, specifically in terms of latency and throughput. The simulation results demonstrate the remarkable effectiveness of the SHS mechanism, showcasing a substantial improvement. In comparison to other handover enhancement techniques, the SHS mechanism achieves a notable increase of over 7% (38 Mbps) in PDCP throughput and a significant reduction of more than 56% (1.87 ms) in latency. Notably, when compared to fixed or traditional handover techniques in digital beamforming configurations, the SHS mechanism exhibits an impressive enhancement, with over 15% (80 Mbps) increase in PDCP throughput and a substantial reduction of more than 77% (4.85 ms) in latency.
Estimating Microbial Protein Synthesis in the Rumen—Can ‘Omics’ Methods Provide New Insights into a Long-Standing Question?

Simple Summary Microbial protein is a valuable resource within the global food chain. It is produced by microbes inhabiting the rumen (i.e., the rumen microbiota), and provides at least half of the building blocks for the synthesis of milk and meat protein in ruminants. Our paper reviews experimental techniques previously used to estimate the quantity of microbial protein produced, and mathematical prediction models developed based on those previous studies. Earlier work involved direct sampling from the gut, whilst more recently the use of proxies such as urine purine derivatives to estimate microbial protein synthesis has been explored. Whilst the theory about microbial protein synthesis is well understood, predictions are not accurate. We show examples of newer lab techniques that identify relationships between the rumen microbiota and their genes (‘who is there? ’ and ‘what are they doing? ’) and host traits, e.g., methane emissions. We suggest that these techniques will enable better estimates and lead to more accurate predictions of microbial protein synthesis. We urge for a renewed programme of research using these techniques to describe and model protein degradation and synthesis in the rumen. These questions are fundamental to global food protein security and reduction in the environmental effects of ruminant livestock production. Abstract Rumen microbial protein synthesis (MPS) provides at least half of the amino acids for the synthesis of milk and meat protein in ruminants. As such, it is fundamental to global food protein security. Estimating microbial protein is central to diet formulation, maximising nitrogen (N)-use efficiency and reducing N losses to the environment. Whilst factors influencing MPS are well established in vitro, techniques for in vivo estimates, including older techniques with cannulated animals and the more recent technique based on urinary purine derivative (UPD) excretion, are subject to large experimental errors. Consequently, models of MPS used in protein rationing are imprecise, resulting in wasted feed protein and unnecessary N losses to the environment. Newer ‘omics’ techniques are used to characterise microbial communities, their genes and resultant proteins and metabolites. An analysis of microbial communities and genes has recently been used successfully to model complex rumen-related traits, including feed conversion efficiency and methane emissions. Since microbial proteins are more directly related to microbial genes, we expect a strong relationship between rumen metataxonomics/metagenomics and MPS. The main aims of this review are to gauge the understanding of factors affecting MPS, including the use of the UPD technique, and explore whether omics-focused studies could improve the predictability of MPS, with a focus on beef cattle.
Eikonal equation on the unstructured grids using quadratic interpolation

This paper describes a higher-order method for gradient approximation in the solution of the eikonal equation on unstructured grids. At the element level, two approaches are discussed: a gradient approximation with quadratic shape functions for the entire element, and an application of local Fermat’s principle for each of the unknown nodes separately. The Fast Marching Dijkstra-type approach is used to propagate the solution on the unstructured grid. The error in the solution associated with the quality of the grid and error-reduction strategies for the problem related to the relative position of the normal of the wavefront and triangle orientation are discussed.
Usability Evaluation of an Educational Website for Mexican Patients with Type 2 Diabetes: Satisfaction, Facility, and Utility

Abstract Diabetes education constitutes a pivotal component of treatment aimed at achieving and sustaining metabolic control. Health information technologies have demonstrated their effectiveness as tools for delivering health education. Nonetheless, only a limited number of reports have been published illustrating the usability of educational websites concerning type 2 diabetes (T2D). In this context, a cross-sectional study was carried out to assess the usability of an educational website across four primary care clinics in Mexico City. The study included ninety patients diagnosed with T2D, 71% of whom were women, and with a median diabetes diagnosis duration of 5 years. Parameters such as glucose levels, glycated hemoglobin (HbA1c), and anthropometric measurements were recorded. Additionally, the level of usability was gauged using a previously validated instrument, revealing that 78% of participants found the educational website highly usable.
Social robots as effective language tutors for children: empirical evidence from neuroscience

The aim of the current study was to investigate children's brain responses to robot-assisted language learning. EEG brain signals were collected from 41 Japanese children who learned French vocabularies in two groups; half of the children learned new words from a social robot that narrated a story in French using animations on a computer screen (Robot group) and the other half watched the same animated story on the screen but only with a voiceover narration and without the robot (Display group). To examine brain activation during the learning phase, we extracted EEG functional connectivity (FC) which is defined as the rhythmic synchronization of signals recorded from different brain areas. The results indicated significantly higher global synchronization of brain signals in the theta frequency band in the Robot group during the learning phase. Closer inspection of intra-hemispheric and inter-hemispheric connections revealed that children who learned a new language from the robot experienced a stronger theta-band EEG synchronization in inter-hemispheric connections, which has been previously associated with success in second language learning in the neuroscientific literature. Additionally, using a multiple linear regression analysis, it was found that theta-band FC and group assignment were significant predictors of children's language learning with the Robot group scoring higher in the post-interaction word recognition test. These findings provide novel neuroscientific evidence for the effectiveness of social robots as second language tutors for children.
Point spread function engineering based on wavefront phase modulation

Point spread function (PSF) is the response of an optical system to a point source. The ideal PSF of traditional optics is Airy pattern corresponding to images without aberration. However, the PSF shape is considered to be flexible in computational imaging territory, which can be artificially manipulated, and the target image is restored by reconstruction algorithm. It is called PSF engineering and can improve the imaging quality of optical system. The method can break through traditional optical limitations and even endow optical systems with new imaging capabilities. In this paper, we will discuss PSF engineering method based on wavefront phase modulation, which is more commonly used for its advantage of high throughput. Its application, the depth of field extension of an in-situ plankton imaging system is discussed based on end-to-end joint optimization, demonstrating a defocus invariant imaging quality.
Development of Ozone-Based Processes for Decontamination of Fresh Produce to Enhance Safety and Extend Shelflife

The aim of this study is to enhance the safety of fresh produce by integrating ozone-based sanitization step into existing practices, while targeting Escherichia coli O157:H7 as the pathogen of concern. A process has been developed where fresh produce (baby spinach, Spinacia oleracea L) is treated with vacuum to induce cooling and repressurized with an ozone-containing gaseous mixture. The process of sanitizing fresh produce during vacuum cooling will be referred to as “SanVac”. Inoculated baby spinach (~10 CFU E. coli O157:H7/g) was treated in a pilot-scale SanVac system. Surviving E. coli O157:H7 and natural microbiota in spinach were enumerated before and after treatments. The SanVac processes successfully decreased E. coli O157:H7 population on the product by up to 2.4 log. Contribution of important treatment variables (ozone
A Review of Recent Innovations in Remote Health Monitoring

The development of remote health monitoring systems has focused on enhancing healthcare services’ efficiency and quality, particularly in chronic disease management and elderly care. These systems employ a range of sensors and wearable devices to track patients’ health status and offer real-time feedback to healthcare providers. This facilitates prompt interventions and reduces hospitalization rates. The aim of this study is to explore the latest developments in the realm of remote health monitoring systems. In this paper, we explore a wide range of domains, spanning antenna designs, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research also delves into the methodological approaches used in monitoring systems, including the analysis of channel characteristics, advancements in wireless capsule endoscopy, and insightful investigations into sensing and imaging techniques. These advancements hold the potential to improve the accuracy and efficiency of monitoring, ultimately contributing to enhanced health outcomes for patients.